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About

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Hi, my name is Peter. I am a independent Software Developer, mainly focussing on data related services. My experience +can be found on my LinkedIn.

+

I have created Data Caterer to help serve individuals and companies with data generation and data testing. It is a +complex area that has many edge cases or intricacies that are hard to summarise or turn into something actionable and +repeatable. Through the use of metadata, Data Caterer can help simplify your data testing, simulating production +environment data, aid in data debugging, or whatever your data use case may be.

+

Given that it is going to save you and your team time and money, please help in considering financial support. This will +help the product grow into a sustainable and feature-full service.

+

Contact

+

Please contact Peter Flook +via Slack +or via email peter.flook@data.catering if you have any questions or queries.

+

Terms of service

+

Terms of service can be found here.

+

Privacy policy

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Privacy policy can be found here.

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circle{fill:var(--md-mermaid-label-bg-color)}.actor{fill:var(--md-mermaid-sequence-actor-bg-color);stroke:var(--md-mermaid-sequence-actor-border-color)}text.actor>tspan{fill:var(--md-mermaid-sequence-actor-fg-color);font-family:var(--md-mermaid-font-family)}line{stroke:var(--md-mermaid-sequence-actor-line-color)}.actor-man circle,.actor-man line{fill:var(--md-mermaid-sequence-actorman-bg-color);stroke:var(--md-mermaid-sequence-actorman-line-color)}.messageLine0,.messageLine1{stroke:var(--md-mermaid-sequence-message-line-color)}.note{fill:var(--md-mermaid-sequence-note-bg-color);stroke:var(--md-mermaid-sequence-note-border-color)}.loopText,.loopText>tspan,.messageText,.noteText>tspan{stroke:none;font-family:var(--md-mermaid-font-family)!important}.messageText{fill:var(--md-mermaid-sequence-message-fg-color)}.loopText,.loopText>tspan{fill:var(--md-mermaid-sequence-loop-fg-color)}.noteText>tspan{fill:var(--md-mermaid-sequence-note-fg-color)}#arrowhead 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t=R("input",e),r=O("meta",{name:"theme-color"});document.head.appendChild(r);let o=O("meta",{name:"color-scheme"});document.head.appendChild(o);let n=Rt("(prefers-color-scheme: light)");return $(()=>{let i=new w;return i.subscribe(a=>{if(document.body.setAttribute("data-md-color-switching",""),a.color.media==="(prefers-color-scheme)"){let s=matchMedia("(prefers-color-scheme: light)"),c=document.querySelector(s.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");a.color.scheme=c.getAttribute("data-md-color-scheme"),a.color.primary=c.getAttribute("data-md-color-primary"),a.color.accent=c.getAttribute("data-md-color-accent")}for(let[s,c]of Object.entries(a.color))document.body.setAttribute(`data-md-color-${s}`,c);for(let s=0;s{let a=ke("header"),s=window.getComputedStyle(a);return 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t=e.match(/^.+github\.com\/([^/]+)\/?([^/]+)?/i);if(t){let[,r,o]=t;return mi(r,o)}if(t=e.match(/^.+?([^/]*gitlab[^/]+)\/(.+?)\/?$/i),t){let[,r,o]=t;return fi(r,o)}return M}var Ja;function Xa(e){return Ja||(Ja=$(()=>{let t=__md_get("__source",sessionStorage);if(t)return W(t);if(pe("consent").length){let o=__md_get("__consent");if(!(o&&o.github))return M}return ui(e.href).pipe(T(o=>__md_set("__source",o,sessionStorage)))}).pipe(Me(()=>M),y(t=>Object.keys(t).length>0),m(t=>({facts:t})),ee(1)))}function di(e){let t=I(":scope > :last-child",e);return $(()=>{let r=new w;return r.subscribe(({facts:o})=>{t.appendChild(Sn(o)),t.classList.add("md-source__repository--active")}),Xa(e).pipe(T(o=>r.next(o)),A(()=>r.complete()),m(o=>j({ref:e},o)))})}function Za(e,{viewport$:t,header$:r}){return Ce(document.body).pipe(E(()=>gr(e,{header$:r,viewport$:t})),m(({offset:{y:o}})=>({hidden:o>=10})),ne("hidden"))}function hi(e,t){return $(()=>{let r=new w;return 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0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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r("em",-1,1),new r("en",-1,2),new r("ern",-1,1),new r("er",-1,1),new r("s",-1,3),new r("es",5,2)],f=[new r("en",-1,1),new r("er",-1,1),new r("st",-1,2),new r("est",2,1)],b=[new r("ig",-1,1),new r("lich",-1,1)],_=[new r("end",-1,1),new r("ig",-1,2),new r("ung",-1,1),new r("lich",-1,3),new r("isch",-1,2),new r("ik",-1,2),new r("heit",-1,3),new r("keit",-1,4)],p=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,32,8],g=[117,30,5],k=[117,30,4],v=new n;this.setCurrent=function(e){v.setCurrent(e)},this.getCurrent=function(){return v.getCurrent()},this.stem=function(){var e=v.cursor;return i(),v.cursor=e,t(),v.limit_backward=e,v.cursor=v.limit,a(),v.cursor=v.limit_backward,o(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.de.stemmer,"stemmer-de"),e.de.stopWordFilter=e.generateStopWordFilter("aber alle allem allen aller alles als also am an ander andere anderem anderen anderer anderes anderm andern anderr anders auch auf aus bei bin bis bist da damit dann das dasselbe dazu daß dein deine deinem deinen deiner deines dem demselben den denn denselben der derer derselbe derselben des desselben dessen dich die dies diese dieselbe dieselben diesem diesen dieser dieses dir doch dort du durch ein eine einem einen einer eines einig einige einigem einigen einiger einiges einmal er es etwas euch euer eure eurem euren eurer eures für gegen gewesen hab habe haben hat hatte hatten hier hin hinter ich ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres im in indem ins ist jede jedem jeden jeder jedes jene jenem jenen jener jenes jetzt kann kein keine keinem keinen keiner keines können könnte machen man manche manchem manchen mancher manches mein meine meinem meinen meiner meines mich mir mit muss musste nach nicht nichts noch nun nur ob oder ohne sehr sein seine seinem seinen seiner seines selbst sich sie sind so solche solchem solchen solcher solches soll sollte sondern sonst um und uns unse unsem unsen unser unses unter viel vom von vor war waren warst was weg weil weiter welche welchem welchen welcher welches wenn werde werden wie wieder will wir wird wirst wo wollen wollte während würde würden zu zum zur zwar zwischen über".split(" ")),e.Pipeline.registerFunction(e.de.stopWordFilter,"stopWordFilter-de")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.du.min.js b/assets/javascripts/lunr/min/lunr.du.min.js new file mode 100644 index 00000000..49a0f3f0 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.du.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Dutch` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");console.warn('[Lunr Languages] Please use the "nl" instead of the "du". The "nl" code is the standard code for Dutch language, and "du" will be removed in the next major versions.'),e.du=function(){this.pipeline.reset(),this.pipeline.add(e.du.trimmer,e.du.stopWordFilter,e.du.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.du.stemmer))},e.du.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.du.trimmer=e.trimmerSupport.generateTrimmer(e.du.wordCharacters),e.Pipeline.registerFunction(e.du.trimmer,"trimmer-du"),e.du.stemmer=function(){var r=e.stemmerSupport.Among,i=e.stemmerSupport.SnowballProgram,n=new function(){function e(){for(var e,r,i,o=C.cursor;;){if(C.bra=C.cursor,e=C.find_among(b,11))switch(C.ket=C.cursor,e){case 1:C.slice_from("a");continue;case 2:C.slice_from("e");continue;case 3:C.slice_from("i");continue;case 4:C.slice_from("o");continue;case 5:C.slice_from("u");continue;case 6:if(C.cursor>=C.limit)break;C.cursor++;continue}break}for(C.cursor=o,C.bra=o,C.eq_s(1,"y")?(C.ket=C.cursor,C.slice_from("Y")):C.cursor=o;;)if(r=C.cursor,C.in_grouping(q,97,232)){if(i=C.cursor,C.bra=i,C.eq_s(1,"i"))C.ket=C.cursor,C.in_grouping(q,97,232)&&(C.slice_from("I"),C.cursor=r);else if(C.cursor=i,C.eq_s(1,"y"))C.ket=C.cursor,C.slice_from("Y"),C.cursor=r;else if(n(r))break}else if(n(r))break}function n(e){return C.cursor=e,e>=C.limit||(C.cursor++,!1)}function o(){_=C.limit,f=_,t()||(_=C.cursor,_<3&&(_=3),t()||(f=C.cursor))}function t(){for(;!C.in_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}for(;!C.out_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}return!1}function s(){for(var e;;)if(C.bra=C.cursor,e=C.find_among(p,3))switch(C.ket=C.cursor,e){case 1:C.slice_from("y");break;case 2:C.slice_from("i");break;case 3:if(C.cursor>=C.limit)return;C.cursor++}}function u(){return _<=C.cursor}function c(){return f<=C.cursor}function a(){var e=C.limit-C.cursor;C.find_among_b(g,3)&&(C.cursor=C.limit-e,C.ket=C.cursor,C.cursor>C.limit_backward&&(C.cursor--,C.bra=C.cursor,C.slice_del()))}function l(){var e;w=!1,C.ket=C.cursor,C.eq_s_b(1,"e")&&(C.bra=C.cursor,u()&&(e=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-e,C.slice_del(),w=!0,a())))}function m(){var e;u()&&(e=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-e,C.eq_s_b(3,"gem")||(C.cursor=C.limit-e,C.slice_del(),a())))}function d(){var e,r,i,n,o,t,s=C.limit-C.cursor;if(C.ket=C.cursor,e=C.find_among_b(h,5))switch(C.bra=C.cursor,e){case 1:u()&&C.slice_from("heid");break;case 2:m();break;case 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r("",-1,6),new r("á",0,1),new r("ä",0,1),new r("é",0,2),new r("ë",0,2),new r("í",0,3),new r("ï",0,3),new r("ó",0,4),new r("ö",0,4),new r("ú",0,5),new r("ü",0,5)],p=[new r("",-1,3),new r("I",0,2),new r("Y",0,1)],g=[new r("dd",-1,-1),new r("kk",-1,-1),new r("tt",-1,-1)],h=[new r("ene",-1,2),new r("se",-1,3),new r("en",-1,2),new r("heden",2,1),new r("s",-1,3)],k=[new r("end",-1,1),new r("ig",-1,2),new r("ing",-1,1),new r("lijk",-1,3),new r("baar",-1,4),new r("bar",-1,5)],v=[new r("aa",-1,-1),new r("ee",-1,-1),new r("oo",-1,-1),new r("uu",-1,-1)],q=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],j=[1,0,0,17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],z=[17,67,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],C=new i;this.setCurrent=function(e){C.setCurrent(e)},this.getCurrent=function(){return C.getCurrent()},this.stem=function(){var r=C.cursor;return e(),C.cursor=r,o(),C.limit_backward=r,C.cursor=C.limit,d(),C.cursor=C.limit_backward,s(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return n.setCurrent(e),n.stem(),n.getCurrent()}):(n.setCurrent(e),n.stem(),n.getCurrent())}}(),e.Pipeline.registerFunction(e.du.stemmer,"stemmer-du"),e.du.stopWordFilter=e.generateStopWordFilter(" aan al alles als altijd andere ben bij daar dan dat de der deze die dit doch doen door dus een eens en er ge geen geweest haar had heb hebben heeft hem het hier hij hoe hun iemand iets ik in is ja je kan kon kunnen maar me meer men met mij mijn moet na naar niet niets nog nu of om omdat onder ons ook op over reeds te tegen toch toen tot u uit uw van veel voor want waren was wat werd wezen wie wil worden wordt zal ze zelf zich zij zijn zo zonder zou".split(" ")),e.Pipeline.registerFunction(e.du.stopWordFilter,"stopWordFilter-du")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.el.min.js b/assets/javascripts/lunr/min/lunr.el.min.js new file mode 100644 index 00000000..ace017bd --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.el.min.js @@ -0,0 +1 @@ +!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.el=function(){this.pipeline.reset(),void 0===this.searchPipeline&&this.pipeline.add(e.el.trimmer,e.el.normilizer),this.pipeline.add(e.el.stopWordFilter,e.el.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.el.stemmer))},e.el.wordCharacters="A-Za-zΑαΒβΓγΔδΕεΖζΗηΘθΙιΚκΛλΜμΝνΞξΟοΠπΡρΣσςΤτΥυΦφΧχΨψΩωΆάΈέΉήΊίΌόΎύΏώΪΐΫΰΐΰ",e.el.trimmer=e.trimmerSupport.generateTrimmer(e.el.wordCharacters),e.Pipeline.registerFunction(e.el.trimmer,"trimmer-el"),e.el.stemmer=function(){function e(e){return s.test(e)}function t(e){return/[ΑΕΗΙΟΥΩ]$/.test(e)}function r(e){return/[ΑΕΗΙΟΩ]$/.test(e)}function n(n){var s=n;if(n.length<3)return s;if(!e(n))return s;if(i.indexOf(n)>=0)return s;var u=new RegExp("(.*)("+Object.keys(l).join("|")+")$"),o=u.exec(s);return null!==o&&(s=o[1]+l[o[2]]),null!==(o=/^(.+?)(ΑΔΕΣ|ΑΔΩΝ)$/.exec(s))&&(s=o[1],/(ΟΚ|ΜΑΜ|ΜΑΝ|ΜΠΑΜΠ|ΠΑΤΕΡ|ΓΙΑΓΙ|ΝΤΑΝΤ|ΚΥΡ|ΘΕΙ|ΠΕΘΕΡ|ΜΟΥΣΑΜ|ΚΑΠΛΑΜ|ΠΑΡ|ΨΑΡ|ΤΖΟΥΡ|ΤΑΜΠΟΥΡ|ΓΑΛΑΤ|ΦΑΦΛΑΤ)$/.test(o[1])||(s+="ΑΔ")),null!==(o=/^(.+?)(ΕΔΕΣ|ΕΔΩΝ)$/.exec(s))&&(s=o[1],/(ΟΠ|ΙΠ|ΕΜΠ|ΥΠ|ΓΗΠ|ΔΑΠ|ΚΡΑΣΠ|ΜΙΛ)$/.test(o[1])&&(s+="ΕΔ")),null!==(o=/^(.+?)(ΟΥΔΕΣ|ΟΥΔΩΝ)$/.exec(s))&&(s=o[1],/(ΑΡΚ|ΚΑΛΙΑΚ|ΠΕΤΑΛ|ΛΙΧ|ΠΛΕΞ|ΣΚ|Σ|ΦΛ|ΦΡ|ΒΕΛ|ΛΟΥΛ|ΧΝ|ΣΠ|ΤΡΑΓ|ΦΕ)$/.test(o[1])&&(s+="ΟΥΔ")),null!==(o=/^(.+?)(ΕΩΣ|ΕΩΝ|ΕΑΣ|ΕΑ)$/.exec(s))&&(s=o[1],/^(Θ|Δ|ΕΛ|ΓΑΛ|Ν|Π|ΙΔ|ΠΑΡ|ΣΤΕΡ|ΟΡΦ|ΑΝΔΡ|ΑΝΤΡ)$/.test(o[1])&&(s+="Ε")),null!==(o=/^(.+?)(ΕΙΟ|ΕΙΟΣ|ΕΙΟΙ|ΕΙΑ|ΕΙΑΣ|ΕΙΕΣ|ΕΙΟΥ|ΕΙΟΥΣ|ΕΙΩΝ)$/.exec(s))&&o[1].length>4&&(s=o[1]),null!==(o=/^(.+?)(ΙΟΥΣ|ΙΑΣ|ΙΕΣ|ΙΟΣ|ΙΟΥ|ΙΟΙ|ΙΩΝ|ΙΟΝ|ΙΑ|ΙΟ)$/.exec(s))&&(s=o[1],(t(s)||s.length<2||/^(ΑΓ|ΑΓΓΕΛ|ΑΓΡ|ΑΕΡ|ΑΘΛ|ΑΚΟΥΣ|ΑΞ|ΑΣ|Β|ΒΙΒΛ|ΒΥΤ|Γ|ΓΙΑΓ|ΓΩΝ|Δ|ΔΑΝ|ΔΗΛ|ΔΗΜ|ΔΟΚΙΜ|ΕΛ|ΖΑΧΑΡ|ΗΛ|ΗΠ|ΙΔ|ΙΣΚ|ΙΣΤ|ΙΟΝ|ΙΩΝ|ΚΙΜΩΛ|ΚΟΛΟΝ|ΚΟΡ|ΚΤΗΡ|ΚΥΡ|ΛΑΓ|ΛΟΓ|ΜΑΓ|ΜΠΑΝ|ΜΠΡ|ΝΑΥΤ|ΝΟΤ|ΟΠΑΛ|ΟΞ|ΟΡ|ΟΣ|ΠΑΝΑΓ|ΠΑΤΡ|ΠΗΛ|ΠΗΝ|ΠΛΑΙΣ|ΠΟΝΤ|ΡΑΔ|ΡΟΔ|ΣΚ|ΣΚΟΡΠ|ΣΟΥΝ|ΣΠΑΝ|ΣΤΑΔ|ΣΥΡ|ΤΗΛ|ΤΙΜ|ΤΟΚ|ΤΟΠ|ΤΡΟΧ|ΦΙΛ|ΦΩΤ|Χ|ΧΙΛ|ΧΡΩΜ|ΧΩΡ)$/.test(o[1]))&&(s+="Ι"),/^(ΠΑΛ)$/.test(o[1])&&(s+="ΑΙ")),null!==(o=/^(.+?)(ΙΚΟΣ|ΙΚΟΝ|ΙΚΕΙΣ|ΙΚΟΙ|ΙΚΕΣ|ΙΚΟΥΣ|ΙΚΗ|ΙΚΗΣ|ΙΚΟ|ΙΚΑ|ΙΚΟΥ|ΙΚΩΝ|ΙΚΩΣ)$/.exec(s))&&(s=o[1],(t(s)||/^(ΑΔ|ΑΛ|ΑΜΑΝ|ΑΜΕΡ|ΑΜΜΟΧΑΛ|ΑΝΗΘ|ΑΝΤΙΔ|ΑΠΛ|ΑΤΤ|ΑΦΡ|ΒΑΣ|ΒΡΩΜ|ΓΕΝ|ΓΕΡ|Δ|ΔΙΚΑΝ|ΔΥΤ|ΕΙΔ|ΕΝΔ|ΕΞΩΔ|ΗΘ|ΘΕΤ|ΚΑΛΛΙΝ|ΚΑΛΠ|ΚΑΤΑΔ|ΚΟΥΖΙΝ|ΚΡ|ΚΩΔ|ΛΟΓ|Μ|ΜΕΡ|ΜΟΝΑΔ|ΜΟΥΛ|ΜΟΥΣ|ΜΠΑΓΙΑΤ|ΜΠΑΝ|ΜΠΟΛ|ΜΠΟΣ|ΜΥΣΤ|Ν|ΝΙΤ|ΞΙΚ|ΟΠΤ|ΠΑΝ|ΠΕΤΣ|ΠΙΚΑΝΤ|ΠΙΤΣ|ΠΛΑΣΤ|ΠΛΙΑΤΣ|ΠΟΝΤ|ΠΟΣΤΕΛΝ|ΠΡΩΤΟΔ|ΣΕΡΤ|ΣΗΜΑΝΤ|ΣΤΑΤ|ΣΥΝΑΔ|ΣΥΝΟΜΗΛ|ΤΕΛ|ΤΕΧΝ|ΤΡΟΠ|ΤΣΑΜ|ΥΠΟΔ|Φ|ΦΙΛΟΝ|ΦΥΛΟΔ|ΦΥΣ|ΧΑΣ)$/.test(o[1])||/(ΦΟΙΝ)$/.test(o[1]))&&(s+="ΙΚ")),"ΑΓΑΜΕ"===s&&(s="ΑΓΑΜ"),null!==(o=/^(.+?)(ΑΓΑΜΕ|ΗΣΑΜΕ|ΟΥΣΑΜΕ|ΗΚΑΜΕ|ΗΘΗΚΑΜΕ)$/.exec(s))&&(s=o[1]),null!==(o=/^(.+?)(ΑΜΕ)$/.exec(s))&&(s=o[1],/^(ΑΝΑΠ|ΑΠΟΘ|ΑΠΟΚ|ΑΠΟΣΤ|ΒΟΥΒ|ΞΕΘ|ΟΥΛ|ΠΕΘ|ΠΙΚΡ|ΠΟΤ|ΣΙΧ|Χ)$/.test(o[1])&&(s+="ΑΜ")),null!==(o=/^(.+?)(ΑΓΑΝΕ|ΗΣΑΝΕ|ΟΥΣΑΝΕ|ΙΟΝΤΑΝΕ|ΙΟΤΑΝΕ|ΙΟΥΝΤΑΝΕ|ΟΝΤΑΝΕ|ΟΤΑΝΕ|ΟΥΝΤΑΝΕ|ΗΚΑΝΕ|ΗΘΗΚΑΝΕ)$/.exec(s))&&(s=o[1],/^(ΤΡ|ΤΣ)$/.test(o[1])&&(s+="ΑΓΑΝ")),null!==(o=/^(.+?)(ΑΝΕ)$/.exec(s))&&(s=o[1],(r(s)||/^(ΒΕΤΕΡ|ΒΟΥΛΚ|ΒΡΑΧΜ|Γ|ΔΡΑΔΟΥΜ|Θ|ΚΑΛΠΟΥΖ|ΚΑΣΤΕΛ|ΚΟΡΜΟΡ|ΛΑΟΠΛ|ΜΩΑΜΕΘ|Μ|ΜΟΥΣΟΥΛΜΑΝ|ΟΥΛ|Π|ΠΕΛΕΚ|ΠΛ|ΠΟΛΙΣ|ΠΟΡΤΟΛ|ΣΑΡΑΚΑΤΣ|ΣΟΥΛΤ|ΤΣΑΡΛΑΤ|ΟΡΦ|ΤΣΙΓΓ|ΤΣΟΠ|ΦΩΤΟΣΤΕΦ|Χ|ΨΥΧΟΠΛ|ΑΓ|ΟΡΦ|ΓΑΛ|ΓΕΡ|ΔΕΚ|ΔΙΠΛ|ΑΜΕΡΙΚΑΝ|ΟΥΡ|ΠΙΘ|ΠΟΥΡΙΤ|Σ|ΖΩΝΤ|ΙΚ|ΚΑΣΤ|ΚΟΠ|ΛΙΧ|ΛΟΥΘΗΡ|ΜΑΙΝΤ|ΜΕΛ|ΣΙΓ|ΣΠ|ΣΤΕΓ|ΤΡΑΓ|ΤΣΑΓ|Φ|ΕΡ|ΑΔΑΠ|ΑΘΙΓΓ|ΑΜΗΧ|ΑΝΙΚ|ΑΝΟΡΓ|ΑΠΗΓ|ΑΠΙΘ|ΑΤΣΙΓΓ|ΒΑΣ|ΒΑΣΚ|ΒΑΘΥΓΑΛ|ΒΙΟΜΗΧ|ΒΡΑΧΥΚ|ΔΙΑΤ|ΔΙΑΦ|ΕΝΟΡΓ|ΘΥΣ|ΚΑΠΝΟΒΙΟΜΗΧ|ΚΑΤΑΓΑΛ|ΚΛΙΒ|ΚΟΙΛΑΡΦ|ΛΙΒ|ΜΕΓΛΟΒΙΟΜΗΧ|ΜΙΚΡΟΒΙΟΜΗΧ|ΝΤΑΒ|ΞΗΡΟΚΛΙΒ|ΟΛΙΓΟΔΑΜ|ΟΛΟΓΑΛ|ΠΕΝΤΑΡΦ|ΠΕΡΗΦ|ΠΕΡΙΤΡ|ΠΛΑΤ|ΠΟΛΥΔΑΠ|ΠΟΛΥΜΗΧ|ΣΤΕΦ|ΤΑΒ|ΤΕΤ|ΥΠΕΡΗΦ|ΥΠΟΚΟΠ|ΧΑΜΗΛΟΔΑΠ|ΨΗΛΟΤΑΒ)$/.test(o[1]))&&(s+="ΑΝ")),null!==(o=/^(.+?)(ΗΣΕΤΕ)$/.exec(s))&&(s=o[1]),null!==(o=/^(.+?)(ΕΤΕ)$/.exec(s))&&(s=o[1],(r(s)||/(ΟΔ|ΑΙΡ|ΦΟΡ|ΤΑΘ|ΔΙΑΘ|ΣΧ|ΕΝΔ|ΕΥΡ|ΤΙΘ|ΥΠΕΡΘ|ΡΑΘ|ΕΝΘ|ΡΟΘ|ΣΘ|ΠΥΡ|ΑΙΝ|ΣΥΝΔ|ΣΥΝ|ΣΥΝΘ|ΧΩΡ|ΠΟΝ|ΒΡ|ΚΑΘ|ΕΥΘ|ΕΚΘ|ΝΕΤ|ΡΟΝ|ΑΡΚ|ΒΑΡ|ΒΟΛ|ΩΦΕΛ)$/.test(o[1])||/^(ΑΒΑΡ|ΒΕΝ|ΕΝΑΡ|ΑΒΡ|ΑΔ|ΑΘ|ΑΝ|ΑΠΛ|ΒΑΡΟΝ|ΝΤΡ|ΣΚ|ΚΟΠ|ΜΠΟΡ|ΝΙΦ|ΠΑΓ|ΠΑΡΑΚΑΛ|ΣΕΡΠ|ΣΚΕΛ|ΣΥΡΦ|ΤΟΚ|Υ|Δ|ΕΜ|ΘΑΡΡ|Θ)$/.test(o[1]))&&(s+="ΕΤ")),null!==(o=/^(.+?)(ΟΝΤΑΣ|ΩΝΤΑΣ)$/.exec(s))&&(s=o[1],/^ΑΡΧ$/.test(o[1])&&(s+="ΟΝΤ"),/ΚΡΕ$/.test(o[1])&&(s+="ΩΝΤ")),null!==(o=/^(.+?)(ΟΜΑΣΤΕ|ΙΟΜΑΣΤΕ)$/.exec(s))&&(s=o[1],/^ΟΝ$/.test(o[1])&&(s+="ΟΜΑΣΤ")),null!==(o=/^(.+?)(ΙΕΣΤΕ)$/.exec(s))&&(s=o[1],/^(Π|ΑΠ|ΣΥΜΠ|ΑΣΥΜΠ|ΑΚΑΤΑΠ|ΑΜΕΤΑΜΦ)$/.test(o[1])&&(s+="ΙΕΣΤ")),null!==(o=/^(.+?)(ΕΣΤΕ)$/.exec(s))&&(s=o[1],/^(ΑΛ|ΑΡ|ΕΚΤΕΛ|Ζ|Μ|Ξ|ΠΑΡΑΚΑΛ|ΠΡΟ|ΝΙΣ)$/.test(o[1])&&(s+="ΕΣΤ")),null!==(o=/^(.+?)(ΗΘΗΚΑ|ΗΘΗΚΕΣ|ΗΘΗΚΕ)$/.exec(s))&&(s=o[1]),null!==(o=/^(.+?)(ΗΚΑ|ΗΚΕΣ|ΗΚΕ)$/.exec(s))&&(s=o[1],(/(ΣΚΩΛ|ΣΚΟΥΛ|ΝΑΡΘ|ΣΦ|ΟΘ|ΠΙΘ)$/.test(o[1])||/^(ΔΙΑΘ|Θ|ΠΑΡΑΚΑΤΑΘ|ΠΡΟΣΘ|ΣΥΝΘ)$/.test(o[1]))&&(s+="ΗΚ")),null!==(o=/^(.+?)(ΟΥΣΑ|ΟΥΣΕΣ|ΟΥΣΕ)$/.exec(s))&&(s=o[1],(t(s)||/^(ΦΑΡΜΑΚ|ΧΑΔ|ΑΓΚ|ΑΝΑΡΡ|ΒΡΟΜ|ΕΚΛΙΠ|ΛΑΜΠΙΔ|ΛΕΧ|Μ|ΠΑΤ|Ρ|Λ|ΜΕΔ|ΜΕΣΑΖ|ΥΠΟΤΕΙΝ|ΑΜ|ΑΙΘ|ΑΝΗΚ|ΔΕΣΠΟΖ|ΕΝΔΙΑΦΕΡ)$/.test(o[1])||/(ΠΟΔΑΡ|ΒΛΕΠ|ΠΑΝΤΑΧ|ΦΡΥΔ|ΜΑΝΤΙΛ|ΜΑΛΛ|ΚΥΜΑΤ|ΛΑΧ|ΛΗΓ|ΦΑΓ|ΟΜ|ΠΡΩΤ)$/.test(o[1]))&&(s+="ΟΥΣ")),null!==(o=/^(.+?)(ΑΓΑ|ΑΓΕΣ|ΑΓΕ)$/.exec(s))&&(s=o[1],(/^(ΑΒΑΣΤ|ΠΟΛΥΦ|ΑΔΗΦ|ΠΑΜΦ|Ρ|ΑΣΠ|ΑΦ|ΑΜΑΛ|ΑΜΑΛΛΙ|ΑΝΥΣΤ|ΑΠΕΡ|ΑΣΠΑΡ|ΑΧΑΡ|ΔΕΡΒΕΝ|ΔΡΟΣΟΠ|ΞΕΦ|ΝΕΟΠ|ΝΟΜΟΤ|ΟΛΟΠ|ΟΜΟΤ|ΠΡΟΣΤ|ΠΡΟΣΩΠΟΠ|ΣΥΜΠ|ΣΥΝΤ|Τ|ΥΠΟΤ|ΧΑΡ|ΑΕΙΠ|ΑΙΜΟΣΤ|ΑΝΥΠ|ΑΠΟΤ|ΑΡΤΙΠ|ΔΙΑΤ|ΕΝ|ΕΠΙΤ|ΚΡΟΚΑΛΟΠ|ΣΙΔΗΡΟΠ|Λ|ΝΑΥ|ΟΥΛΑΜ|ΟΥΡ|Π|ΤΡ|Μ)$/.test(o[1])||/(ΟΦ|ΠΕΛ|ΧΟΡΤ|ΛΛ|ΣΦ|ΡΠ|ΦΡ|ΠΡ|ΛΟΧ|ΣΜΗΝ)$/.test(o[1])&&!/^(ΨΟΦ|ΝΑΥΛΟΧ)$/.test(o[1])||/(ΚΟΛΛ)$/.test(o[1]))&&(s+="ΑΓ")),null!==(o=/^(.+?)(ΗΣΕ|ΗΣΟΥ|ΗΣΑ)$/.exec(s))&&(s=o[1],/^(Ν|ΧΕΡΣΟΝ|ΔΩΔΕΚΑΝ|ΕΡΗΜΟΝ|ΜΕΓΑΛΟΝ|ΕΠΤΑΝ|Ι)$/.test(o[1])&&(s+="ΗΣ")),null!==(o=/^(.+?)(ΗΣΤΕ)$/.exec(s))&&(s=o[1],/^(ΑΣΒ|ΣΒ|ΑΧΡ|ΧΡ|ΑΠΛ|ΑΕΙΜΝ|ΔΥΣΧΡ|ΕΥΧΡ|ΚΟΙΝΟΧΡ|ΠΑΛΙΜΨ)$/.test(o[1])&&(s+="ΗΣΤ")),null!==(o=/^(.+?)(ΟΥΝΕ|ΗΣΟΥΝΕ|ΗΘΟΥΝΕ)$/.exec(s))&&(s=o[1],/^(Ν|Ρ|ΣΠΙ|ΣΤΡΑΒΟΜΟΥΤΣ|ΚΑΚΟΜΟΥΤΣ|ΕΞΩΝ)$/.test(o[1])&&(s+="ΟΥΝ")),null!==(o=/^(.+?)(ΟΥΜΕ|ΗΣΟΥΜΕ|ΗΘΟΥΜΕ)$/.exec(s))&&(s=o[1],/^(ΠΑΡΑΣΟΥΣ|Φ|Χ|ΩΡΙΟΠΛ|ΑΖ|ΑΛΛΟΣΟΥΣ|ΑΣΟΥΣ)$/.test(o[1])&&(s+="ΟΥΜ")),null!=(o=/^(.+?)(ΜΑΤΟΙ|ΜΑΤΟΥΣ|ΜΑΤΟ|ΜΑΤΑ|ΜΑΤΩΣ|ΜΑΤΩΝ|ΜΑΤΟΣ|ΜΑΤΕΣ|ΜΑΤΗ|ΜΑΤΗΣ|ΜΑΤΟΥ)$/.exec(s))&&(s=o[1]+"Μ",/^(ΓΡΑΜ)$/.test(o[1])?s+="Α":/^(ΓΕ|ΣΤΑ)$/.test(o[1])&&(s+="ΑΤ")),null!==(o=/^(.+?)(ΟΥΑ)$/.exec(s))&&(s=o[1]+"ΟΥ"),n.length===s.length&&null!==(o=/^(.+?)(Α|ΑΓΑΤΕ|ΑΓΑΝ|ΑΕΙ|ΑΜΑΙ|ΑΝ|ΑΣ|ΑΣΑΙ|ΑΤΑΙ|ΑΩ|Ε|ΕΙ|ΕΙΣ|ΕΙΤΕ|ΕΣΑΙ|ΕΣ|ΕΤΑΙ|Ι|ΙΕΜΑΙ|ΙΕΜΑΣΤΕ|ΙΕΤΑΙ|ΙΕΣΑΙ|ΙΕΣΑΣΤΕ|ΙΟΜΑΣΤΑΝ|ΙΟΜΟΥΝ|ΙΟΜΟΥΝΑ|ΙΟΝΤΑΝ|ΙΟΝΤΟΥΣΑΝ|ΙΟΣΑΣΤΑΝ|ΙΟΣΑΣΤΕ|ΙΟΣΟΥΝ|ΙΟΣΟΥΝΑ|ΙΟΤΑΝ|ΙΟΥΜΑ|ΙΟΥΜΑΣΤΕ|ΙΟΥΝΤΑΙ|ΙΟΥΝΤΑΝ|Η|ΗΔΕΣ|ΗΔΩΝ|ΗΘΕΙ|ΗΘΕΙΣ|ΗΘΕΙΤΕ|ΗΘΗΚΑΤΕ|ΗΘΗΚΑΝ|ΗΘΟΥΝ|ΗΘΩ|ΗΚΑΤΕ|ΗΚΑΝ|ΗΣ|ΗΣΑΝ|ΗΣΑΤΕ|ΗΣΕΙ|ΗΣΕΣ|ΗΣΟΥΝ|ΗΣΩ|Ο|ΟΙ|ΟΜΑΙ|ΟΜΑΣΤΑΝ|ΟΜΟΥΝ|ΟΜΟΥΝΑ|ΟΝΤΑΙ|ΟΝΤΑΝ|ΟΝΤΟΥΣΑΝ|ΟΣ|ΟΣΑΣΤΑΝ|ΟΣΑΣΤΕ|ΟΣΟΥΝ|ΟΣΟΥΝΑ|ΟΤΑΝ|ΟΥ|ΟΥΜΑΙ|ΟΥΜΑΣΤΕ|ΟΥΝ|ΟΥΝΤΑΙ|ΟΥΝΤΑΝ|ΟΥΣ|ΟΥΣΑΝ|ΟΥΣΑΤΕ|Υ||ΥΑ|ΥΣ|Ω|ΩΝ|ΟΙΣ)$/.exec(s))&&(s=o[1]),null!=(o=/^(.+?)(ΕΣΤΕΡ|ΕΣΤΑΤ|ΟΤΕΡ|ΟΤΑΤ|ΥΤΕΡ|ΥΤΑΤ|ΩΤΕΡ|ΩΤΑΤ)$/.exec(s))&&(/^(ΕΞ|ΕΣ|ΑΝ|ΚΑΤ|Κ|ΠΡ)$/.test(o[1])||(s=o[1]),/^(ΚΑ|Μ|ΕΛΕ|ΛΕ|ΔΕ)$/.test(o[1])&&(s+="ΥΤ")),s}var 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étiez étions été étée étées étés êtes".split(" ")),e.Pipeline.registerFunction(e.fr.stopWordFilter,"stopWordFilter-fr")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.he.min.js b/assets/javascripts/lunr/min/lunr.he.min.js new file mode 100644 index 00000000..b863d3ea --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.he.min.js @@ -0,0 +1 @@ +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.he=function(){this.pipeline.reset(),this.pipeline.add(e.he.trimmer,e.he.stopWordFilter,e.he.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.he.stemmer))},e.he.wordCharacters="֑-״א-תa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.he.trimmer=e.trimmerSupport.generateTrimmer(e.he.wordCharacters),e.Pipeline.registerFunction(e.he.trimmer,"trimmer-he"),e.he.stemmer=function(){var e=this;return e.result=!1,e.preRemoved=!1,e.sufRemoved=!1,e.pre={pre1:"ה ו י ת",pre2:"ב כ ל מ ש כש",pre3:"הב הכ הל המ הש בש לכ",pre4:"וב וכ ול ומ וש",pre5:"מה שה כל",pre6:"מב מכ מל ממ מש",pre7:"בה בו בי בת כה כו כי כת לה לו לי לת",pre8:"ובה ובו ובי ובת וכה וכו וכי וכת ולה ולו ולי ולת"},e.suf={suf1:"ך כ ם ן נ",suf2:"ים ות וך וכ ום ון ונ הם הן יכ יך ינ ים",suf3:"תי תך תכ תם תן תנ",suf4:"ותי ותך ותכ ותם ותן ותנ",suf5:"נו כם כן הם הן",suf6:"ונו וכם וכן והם והן",suf7:"תכם תכן תנו תהם תהן",suf8:"הוא היא הם הן אני אתה את אנו אתם אתן",suf9:"ני נו כי כו כם כן תי תך תכ תם תן",suf10:"י ך כ ם ן נ ת"},e.patterns=JSON.parse('{"hebrewPatterns": [{"pt1": [{"c": "ה", "l": 0}]}, {"pt2": [{"c": "ו", "l": 0}]}, {"pt3": [{"c": "י", "l": 0}]}, {"pt4": [{"c": "ת", "l": 0}]}, {"pt5": [{"c": "מ", "l": 0}]}, {"pt6": [{"c": "ל", "l": 0}]}, {"pt7": [{"c": "ב", "l": 0}]}, {"pt8": [{"c": "כ", "l": 0}]}, {"pt9": [{"c": "ש", "l": 0}]}, {"pt10": [{"c": "כש", "l": 0}]}, {"pt11": [{"c": "בה", "l": 0}]}, {"pt12": [{"c": "וב", "l": 0}]}, {"pt13": [{"c": "וכ", "l": 0}]}, {"pt14": [{"c": "ול", "l": 0}]}, {"pt15": [{"c": "ומ", "l": 0}]}, {"pt16": [{"c": "וש", "l": 0}]}, {"pt17": [{"c": "הב", "l": 0}]}, {"pt18": [{"c": "הכ", "l": 0}]}, {"pt19": [{"c": "הל", "l": 0}]}, {"pt20": [{"c": "המ", "l": 0}]}, {"pt21": [{"c": "הש", "l": 0}]}, {"pt22": [{"c": "מה", "l": 0}]}, {"pt23": [{"c": "שה", "l": 0}]}, {"pt24": [{"c": "כל", "l": 0}]}]}'),e.execArray=["cleanWord","removeDiacritics","removeStopWords","normalizeHebrewCharacters"],e.stem=function(){var r=0;for(e.result=!1,e.preRemoved=!1,e.sufRemoved=!1;r=0)return!0},e.normalizeHebrewCharacters=function(){return e.word=e.word.replace("ך","כ"),e.word=e.word.replace("ם","מ"),e.word=e.word.replace("ן","נ"),e.word=e.word.replace("ף","פ"),e.word=e.word.replace("ץ","צ"),!1},function(r){return"function"==typeof r.update?r.update(function(r){return e.setCurrent(r),e.stem(),e.getCurrent()}):(e.setCurrent(r),e.stem(),e.getCurrent())}}(),e.Pipeline.registerFunction(e.he.stemmer,"stemmer-he"),e.he.stopWordFilter=e.generateStopWordFilter("אבל או אולי אותו אותי אותך אותם אותן אותנו אז אחר אחרות אחרי אחריכן אחרים אחרת אי איזה איך אין איפה אל אלה אלו אם אנחנו אני אף אפשר את אתה אתכם אתכן אתם אתן באיזה באיזו בגלל בין בלבד בעבור בעזרת בכל בכן בלי במידה במקום שבו ברוב בשביל בשעה ש בתוך גם דרך הוא היא היה היי היכן היתה היתי הם הן הנה הסיבה שבגללה הרי ואילו ואת זאת זה זות יהיה יוכל יוכלו יותר מדי יכול יכולה יכולות יכולים יכל יכלה יכלו יש כאן כאשר כולם כולן כזה כי כיצד כך כל כלל כמו כן כפי כש לא לאו לאיזותך לאן לבין לה להיות להם להן לו לזה לזות לי לך לכם לכן למה למעלה למעלה מ למטה למטה מ למעט למקום שבו למרות לנו לעבר לעיכן לפיכך לפני מאד מאחורי מאיזו סיבה מאין מאיפה מבלי מבעד מדוע מה מהיכן מול מחוץ מי מידע מכאן מכל מכן מלבד מן מנין מסוגל מעט מעטים מעל מצד מקום בו מתחת מתי נגד נגר נו עד עז על עלי עליו עליה עליהם עליך עלינו עם עצמה עצמהם עצמהן עצמו עצמי עצמם עצמן עצמנו פה רק שוב של שלה שלהם שלהן שלו שלי שלך שלכה שלכם שלכן שלנו שם תהיה תחת".split(" ")),e.Pipeline.registerFunction(e.he.stopWordFilter,"stopWordFilter-he")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.hi.min.js b/assets/javascripts/lunr/min/lunr.hi.min.js new file mode 100644 index 00000000..7dbc4140 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.hi.min.js @@ -0,0 +1 @@ +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hi=function(){this.pipeline.reset(),this.pipeline.add(e.hi.trimmer,e.hi.stopWordFilter,e.hi.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.hi.stemmer))},e.hi.wordCharacters="ऀ-ःऄ-एऐ-टठ-यर-िी-ॏॐ-य़ॠ-९॰-ॿa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.hi.trimmer=e.trimmerSupport.generateTrimmer(e.hi.wordCharacters),e.Pipeline.registerFunction(e.hi.trimmer,"trimmer-hi"),e.hi.stopWordFilter=e.generateStopWordFilter("अत अपना अपनी अपने अभी अंदर आदि आप इत्यादि इन इनका इन्हीं इन्हें इन्हों इस इसका इसकी इसके इसमें इसी इसे उन उनका उनकी उनके उनको उन्हीं उन्हें उन्हों उस उसके उसी उसे एक एवं एस ऐसे और कई कर करता करते करना करने करें कहते कहा का काफ़ी कि कितना किन्हें किन्हों किया किर किस किसी किसे की कुछ कुल के को कोई कौन कौनसा गया घर जब जहाँ जा जितना जिन जिन्हें जिन्हों जिस जिसे जीधर जैसा जैसे जो तक तब तरह तिन तिन्हें तिन्हों तिस तिसे तो था थी थे दबारा दिया दुसरा दूसरे दो द्वारा न नके नहीं ना निहायत नीचे ने पर पहले पूरा पे फिर बनी बही बहुत बाद बाला बिलकुल भी भीतर मगर मानो मे में यदि यह यहाँ यही या यिह ये रखें रहा रहे ऱ्वासा लिए लिये लेकिन व वग़ैरह वर्ग वह वहाँ वहीं वाले वुह वे वो सकता सकते सबसे सभी साथ साबुत साभ सारा से सो संग ही हुआ हुई हुए है हैं हो होता होती होते होना होने".split(" ")),e.hi.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.hi.tokenizer=function(i){if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var t=i.toString().toLowerCase().replace(/^\s+/,"");return r.cut(t).split("|")},e.Pipeline.registerFunction(e.hi.stemmer,"stemmer-hi"),e.Pipeline.registerFunction(e.hi.stopWordFilter,"stopWordFilter-hi")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.hu.min.js b/assets/javascripts/lunr/min/lunr.hu.min.js new file mode 100644 index 00000000..ed9d909f --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.hu.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Hungarian` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,n){"function"==typeof define&&define.amd?define(n):"object"==typeof exports?module.exports=n():n()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hu=function(){this.pipeline.reset(),this.pipeline.add(e.hu.trimmer,e.hu.stopWordFilter,e.hu.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.hu.stemmer))},e.hu.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.hu.trimmer=e.trimmerSupport.generateTrimmer(e.hu.wordCharacters),e.Pipeline.registerFunction(e.hu.trimmer,"trimmer-hu"),e.hu.stemmer=function(){var n=e.stemmerSupport.Among,r=e.stemmerSupport.SnowballProgram,i=new function(){function e(){var e,n=L.cursor;if(d=L.limit,L.in_grouping(W,97,252))for(;;){if(e=L.cursor,L.out_grouping(W,97,252))return L.cursor=e,L.find_among(g,8)||(L.cursor=e,e=L.limit)return void(d=e);L.cursor++}if(L.cursor=n,L.out_grouping(W,97,252)){for(;!L.in_grouping(W,97,252);){if(L.cursor>=L.limit)return;L.cursor++}d=L.cursor}}function i(){return d<=L.cursor}function a(){var 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e(),L.limit_backward=n,L.cursor=L.limit,c(),L.cursor=L.limit,o(),L.cursor=L.limit,w(),L.cursor=L.limit,l(),L.cursor=L.limit,u(),L.cursor=L.limit,k(),L.cursor=L.limit,f(),L.cursor=L.limit,b(),L.cursor=L.limit,m(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.hu.stemmer,"stemmer-hu"),e.hu.stopWordFilter=e.generateStopWordFilter("a abban ahhoz ahogy ahol aki akik akkor alatt amely amelyek amelyekben amelyeket amelyet amelynek ami amikor amit amolyan amíg annak arra arról az azok azon azonban azt aztán azután azzal azért be belül benne bár cikk cikkek cikkeket csak de e ebben eddig egy egyes egyetlen egyik egyre egyéb egész ehhez ekkor el ellen elsõ elég elõ elõször elõtt emilyen ennek erre ez ezek ezen ezt ezzel ezért fel felé hanem hiszen hogy hogyan igen ill ill. illetve ilyen ilyenkor ismét ison itt jobban jó jól kell kellett keressünk keresztül ki kívül között közül legalább legyen lehet lehetett lenne lenni lesz lett maga magát majd majd meg mellett mely melyek mert mi mikor milyen minden mindenki mindent mindig mint mintha mit mivel miért most már más másik még míg nagy nagyobb nagyon ne nekem neki nem nincs néha néhány nélkül olyan ott pedig persze rá s saját sem semmi sok sokat sokkal szemben szerint szinte számára talán tehát teljes tovább továbbá több ugyanis utolsó után utána vagy vagyis vagyok valaki valami valamint való van vannak vele vissza viszont volna volt voltak voltam voltunk által általában át én éppen és így õ õk õket össze úgy új újabb újra".split(" ")),e.Pipeline.registerFunction(e.hu.stopWordFilter,"stopWordFilter-hu")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.hy.min.js b/assets/javascripts/lunr/min/lunr.hy.min.js new file mode 100644 index 00000000..b37f7929 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.hy.min.js @@ -0,0 +1 @@ +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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avevo avrai avranno avrebbe avrebbero avrei avremmo avremo avreste avresti avrete avrà avrò avuta avute avuti avuto c che chi ci coi col come con contro cui da dagl dagli dai dal dall dalla dalle dallo degl degli dei del dell della delle dello di dov dove e ebbe ebbero ebbi ed era erano eravamo eravate eri ero essendo faccia facciamo facciano facciate faccio facemmo facendo facesse facessero facessi facessimo faceste facesti faceva facevamo facevano facevate facevi facevo fai fanno farai faranno farebbe farebbero farei faremmo faremo fareste faresti farete farà farò fece fecero feci fosse fossero fossi fossimo foste fosti fu fui fummo furono gli ha hai hanno ho i il in io l la le lei li lo loro lui ma mi mia mie miei mio ne negl negli nei nel nell nella nelle nello noi non nostra nostre nostri nostro o per perché più quale quanta quante quanti quanto quella quelle quelli quello questa queste questi questo sarai saranno sarebbe sarebbero sarei saremmo saremo sareste saresti sarete sarà sarò se sei si sia siamo siano siate siete sono sta stai stando stanno starai staranno starebbe starebbero starei staremmo staremo stareste staresti starete starà starò stava stavamo stavano stavate stavi stavo stemmo stesse stessero stessi stessimo steste stesti stette stettero stetti stia stiamo stiano stiate sto su sua sue sugl sugli sui sul sull sulla sulle sullo suo suoi ti tra tu tua tue tuo tuoi tutti tutto un una uno vi voi vostra vostre vostri vostro è".split(" ")),e.Pipeline.registerFunction(e.it.stopWordFilter,"stopWordFilter-it")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.ja.min.js b/assets/javascripts/lunr/min/lunr.ja.min.js new file mode 100644 index 00000000..5f254ebe --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.ja.min.js @@ -0,0 +1 @@ +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is 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Please include / require Lunr stemmer support before this script.");var r="2"==e.version[0];e.ja=function(){this.pipeline.reset(),this.pipeline.add(e.ja.trimmer,e.ja.stopWordFilter,e.ja.stemmer),r?this.tokenizer=e.ja.tokenizer:(e.tokenizer&&(e.tokenizer=e.ja.tokenizer),this.tokenizerFn&&(this.tokenizerFn=e.ja.tokenizer))};var t=new e.TinySegmenter;e.ja.tokenizer=function(i){var n,o,s,p,a,u,m,l,c,f;if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(t){return r?new 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")),e.Pipeline.registerFunction(e.ro.stopWordFilter,"stopWordFilter-ro")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.ru.min.js b/assets/javascripts/lunr/min/lunr.ru.min.js new file mode 100644 index 00000000..186cc485 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.ru.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Russian` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,n){"function"==typeof define&&define.amd?define(n):"object"==typeof exports?module.exports=n():n()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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Please include / require Lunr stemmer support before this script.");e.ta=function(){this.pipeline.reset(),this.pipeline.add(e.ta.trimmer,e.ta.stopWordFilter,e.ta.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.ta.stemmer))},e.ta.wordCharacters="஀-உஊ-ஏஐ-ஙச-ட஠-னப-யர-ஹ஺-ிீ-௉ொ-௏ௐ-௙௚-௟௠-௩௪-௯௰-௹௺-௿a-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.ta.trimmer=e.trimmerSupport.generateTrimmer(e.ta.wordCharacters),e.Pipeline.registerFunction(e.ta.trimmer,"trimmer-ta"),e.ta.stopWordFilter=e.generateStopWordFilter("அங்கு அங்கே அது அதை அந்த அவர் அவர்கள் அவள் அவன் அவை ஆக ஆகவே ஆகையால் ஆதலால் ஆதலினால் ஆனாலும் ஆனால் இங்கு இங்கே இது இதை இந்த இப்படி இவர் இவர்கள் இவள் இவன் இவை இவ்வளவு உனக்கு உனது உன் உன்னால் எங்கு எங்கே எது எதை எந்த எப்படி எவர் எவர்கள் எவள் எவன் எவை எவ்வளவு எனக்கு எனது எனவே என் என்ன என்னால் ஏது ஏன் தனது தன்னால் தானே தான் நாங்கள் நாம் நான் நீ நீங்கள்".split(" ")),e.ta.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var t=e.wordcut;t.init(),e.ta.tokenizer=function(r){if(!arguments.length||null==r||void 0==r)return[];if(Array.isArray(r))return r.map(function(t){return isLunr2?new e.Token(t.toLowerCase()):t.toLowerCase()});var i=r.toString().toLowerCase().replace(/^\s+/,"");return t.cut(i).split("|")},e.Pipeline.registerFunction(e.ta.stemmer,"stemmer-ta"),e.Pipeline.registerFunction(e.ta.stopWordFilter,"stopWordFilter-ta")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.te.min.js b/assets/javascripts/lunr/min/lunr.te.min.js new file mode 100644 index 00000000..9fa7a93b --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.te.min.js @@ -0,0 +1 @@ +!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.te=function(){this.pipeline.reset(),this.pipeline.add(e.te.trimmer,e.te.stopWordFilter,e.te.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.te.stemmer))},e.te.wordCharacters="ఀ-ఄఅ-ఔక-హా-ౌౕ-ౖౘ-ౚౠ-ౡౢ-ౣ౦-౯౸-౿఼ఽ్ౝ౷౤౥",e.te.trimmer=e.trimmerSupport.generateTrimmer(e.te.wordCharacters),e.Pipeline.registerFunction(e.te.trimmer,"trimmer-te"),e.te.stopWordFilter=e.generateStopWordFilter("అందరూ అందుబాటులో అడగండి అడగడం అడ్డంగా అనుగుణంగా అనుమతించు అనుమతిస్తుంది అయితే ఇప్పటికే ఉన్నారు ఎక్కడైనా ఎప్పుడు ఎవరైనా ఎవరో ఏ ఏదైనా ఏమైనప్పటికి ఒక ఒకరు కనిపిస్తాయి కాదు కూడా గా గురించి చుట్టూ చేయగలిగింది తగిన తర్వాత దాదాపు దూరంగా నిజంగా పై ప్రకారం ప్రక్కన మధ్య మరియు మరొక మళ్ళీ మాత్రమే మెచ్చుకో వద్ద వెంట వేరుగా వ్యతిరేకంగా సంబంధం".split(" ")),e.te.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var t=e.wordcut;t.init(),e.te.tokenizer=function(r){if(!arguments.length||null==r||void 0==r)return[];if(Array.isArray(r))return r.map(function(t){return isLunr2?new e.Token(t.toLowerCase()):t.toLowerCase()});var i=r.toString().toLowerCase().replace(/^\s+/,"");return t.cut(i).split("|")},e.Pipeline.registerFunction(e.te.stemmer,"stemmer-te"),e.Pipeline.registerFunction(e.te.stopWordFilter,"stopWordFilter-te")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.th.min.js b/assets/javascripts/lunr/min/lunr.th.min.js new file mode 100644 index 00000000..dee3aac6 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.th.min.js @@ -0,0 +1 @@ +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r="2"==e.version[0];e.th=function(){this.pipeline.reset(),this.pipeline.add(e.th.trimmer),r?this.tokenizer=e.th.tokenizer:(e.tokenizer&&(e.tokenizer=e.th.tokenizer),this.tokenizerFn&&(this.tokenizerFn=e.th.tokenizer))},e.th.wordCharacters="[฀-๿]",e.th.trimmer=e.trimmerSupport.generateTrimmer(e.th.wordCharacters),e.Pipeline.registerFunction(e.th.trimmer,"trimmer-th");var t=e.wordcut;t.init(),e.th.tokenizer=function(i){if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(t){return r?new e.Token(t):t});var n=i.toString().replace(/^\s+/,"");return t.cut(n).split("|")}}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.tr.min.js b/assets/javascripts/lunr/min/lunr.tr.min.js new file mode 100644 index 00000000..563f6ec1 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.tr.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Turkish` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(r,i){"function"==typeof define&&define.amd?define(i):"object"==typeof exports?module.exports=i():i()(r.lunr)}(this,function(){return function(r){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");r.tr=function(){this.pipeline.reset(),this.pipeline.add(r.tr.trimmer,r.tr.stopWordFilter,r.tr.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(r.tr.stemmer))},r.tr.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",r.tr.trimmer=r.trimmerSupport.generateTrimmer(r.tr.wordCharacters),r.Pipeline.registerFunction(r.tr.trimmer,"trimmer-tr"),r.tr.stemmer=function(){var i=r.stemmerSupport.Among,e=r.stemmerSupport.SnowballProgram,n=new function(){function r(r,i,e){for(;;){var n=Dr.limit-Dr.cursor;if(Dr.in_grouping_b(r,i,e)){Dr.cursor=Dr.limit-n;break}if(Dr.cursor=Dr.limit-n,Dr.cursor<=Dr.limit_backward)return!1;Dr.cursor--}return!0}function n(){var i,e;i=Dr.limit-Dr.cursor,r(Wr,97,305);for(var 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r=Dr.limit-Dr.cursor;return!(T()||(Dr.cursor=Dr.limit-r,E()||(Dr.cursor=Dr.limit-r,j()||(Dr.cursor=Dr.limit-r,Z()))))}function D(){if(A()){var r=Dr.limit-Dr.cursor;if(S()||(Dr.cursor=Dr.limit-r,W()||(Dr.cursor=Dr.limit-r,C()||(Dr.cursor=Dr.limit-r,P()||(Dr.cursor=Dr.limit-r,F()||(Dr.cursor=Dr.limit-r))))),T())return!1}return!0}function G(){if(W()){Dr.bra=Dr.cursor,Dr.slice_del();var r=Dr.limit-Dr.cursor;return Dr.ket=Dr.cursor,x()||(Dr.cursor=Dr.limit-r,E()||(Dr.cursor=Dr.limit-r,j()||(Dr.cursor=Dr.limit-r,T()||(Dr.cursor=Dr.limit-r)))),nr=!1,!1}return!0}function H(){if(!L())return!0;var r=Dr.limit-Dr.cursor;return!E()&&(Dr.cursor=Dr.limit-r,!j())}function I(){var r,i=Dr.limit-Dr.cursor;return!(S()||(Dr.cursor=Dr.limit-i,F()||(Dr.cursor=Dr.limit-i,P()||(Dr.cursor=Dr.limit-i,C()))))||(Dr.bra=Dr.cursor,Dr.slice_del(),r=Dr.limit-Dr.cursor,Dr.ket=Dr.cursor,T()||(Dr.cursor=Dr.limit-r),!1)}function J(){var r,i=Dr.limit-Dr.cursor;if(Dr.ket=Dr.cursor,nr=!0,B()&&(Dr.cursor=Dr.limit-i,D()&&(Dr.cursor=Dr.limit-i,G()&&(Dr.cursor=Dr.limit-i,H()&&(Dr.cursor=Dr.limit-i,I()))))){if(Dr.cursor=Dr.limit-i,!x())return;Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,r=Dr.limit-Dr.cursor,S()||(Dr.cursor=Dr.limit-r,W()||(Dr.cursor=Dr.limit-r,C()||(Dr.cursor=Dr.limit-r,P()||(Dr.cursor=Dr.limit-r,F()||(Dr.cursor=Dr.limit-r))))),T()||(Dr.cursor=Dr.limit-r)}Dr.bra=Dr.cursor,Dr.slice_del()}function K(){var r,i,e,n;if(Dr.ket=Dr.cursor,h()){if(r=Dr.limit-Dr.cursor,p())return 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r,i,e=Dr.limit-Dr.cursor;if(Dr.ket=Dr.cursor,!p()&&(Dr.cursor=Dr.limit-e,!f()&&(Dr.cursor=Dr.limit-e,!_())))return!1;if(Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,r=Dr.limit-Dr.cursor,a())Dr.bra=Dr.cursor,Dr.slice_del(),i=Dr.limit-Dr.cursor,Dr.ket=Dr.cursor,W()||(Dr.cursor=Dr.limit-i);else if(Dr.cursor=Dr.limit-r,!W())return!0;return Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,K(),!0}function R(){var r,i,e=Dr.limit-Dr.cursor;if(Dr.ket=Dr.cursor,W())return Dr.bra=Dr.cursor,Dr.slice_del(),void K();if(Dr.cursor=Dr.limit-e,Dr.ket=Dr.cursor,q())if(Dr.bra=Dr.cursor,Dr.slice_del(),r=Dr.limit-Dr.cursor,Dr.ket=Dr.cursor,d())Dr.bra=Dr.cursor,Dr.slice_del();else{if(Dr.cursor=Dr.limit-r,Dr.ket=Dr.cursor,!a()&&(Dr.cursor=Dr.limit-r,!m())){if(Dr.cursor=Dr.limit-r,Dr.ket=Dr.cursor,!W())return;if(Dr.bra=Dr.cursor,Dr.slice_del(),!K())return}Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,W()&&(Dr.bra=Dr.cursor,Dr.slice_del(),K())}else 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Register as an anonymous module. + define(factory) + } else if (typeof exports === 'object') { + /** + * Node. Does not work with strict CommonJS, but + * only CommonJS-like environments that support module.exports, + * like Node. + */ + module.exports = factory() + } else { + // Browser globals (root is window) + factory()(root.lunr); + } +}(this, function () { + /** + * Just return a value to define the module export. + * This example returns an object, but the module + * can return a function as the exported value. + */ + + return function(lunr) { + // TinySegmenter 0.1 -- Super compact Japanese tokenizer in Javascript + // (c) 2008 Taku Kudo + // TinySegmenter is freely distributable under the terms of a new BSD licence. + // For details, see http://chasen.org/~taku/software/TinySegmenter/LICENCE.txt + + function TinySegmenter() { + var patterns = { + "[一二三四五六七八九十百千万億兆]":"M", + "[一-龠々〆ヵヶ]":"H", + "[ぁ-ん]":"I", + "[ァ-ヴーｱ-ﾝﾞｰ]":"K", + "[a-zA-Zａ-ｚＡ-Ｚ]":"A", + "[0-9０-９]":"N" + } + this.chartype_ = []; + for (var i in patterns) { + var regexp = new RegExp(i); + this.chartype_.push([regexp, patterns[i]]); + } + + this.BIAS__ = -332 + this.BC1__ = {"HH":6,"II":2461,"KH":406,"OH":-1378}; + this.BC2__ = {"AA":-3267,"AI":2744,"AN":-878,"HH":-4070,"HM":-1711,"HN":4012,"HO":3761,"IA":1327,"IH":-1184,"II":-1332,"IK":1721,"IO":5492,"KI":3831,"KK":-8741,"MH":-3132,"MK":3334,"OO":-2920}; + this.BC3__ = {"HH":996,"HI":626,"HK":-721,"HN":-1307,"HO":-836,"IH":-301,"KK":2762,"MK":1079,"MM":4034,"OA":-1652,"OH":266}; + this.BP1__ = {"BB":295,"OB":304,"OO":-125,"UB":352}; + this.BP2__ = {"BO":60,"OO":-1762}; + this.BQ1__ = {"BHH":1150,"BHM":1521,"BII":-1158,"BIM":886,"BMH":1208,"BNH":449,"BOH":-91,"BOO":-2597,"OHI":451,"OIH":-296,"OKA":1851,"OKH":-1020,"OKK":904,"OOO":2965}; + this.BQ2__ = {"BHH":118,"BHI":-1159,"BHM":466,"BIH":-919,"BKK":-1720,"BKO":864,"OHH":-1139,"OHM":-181,"OIH":153,"UHI":-1146}; + this.BQ3__ = {"BHH":-792,"BHI":2664,"BII":-299,"BKI":419,"BMH":937,"BMM":8335,"BNN":998,"BOH":775,"OHH":2174,"OHM":439,"OII":280,"OKH":1798,"OKI":-793,"OKO":-2242,"OMH":-2402,"OOO":11699}; + this.BQ4__ = {"BHH":-3895,"BIH":3761,"BII":-4654,"BIK":1348,"BKK":-1806,"BMI":-3385,"BOO":-12396,"OAH":926,"OHH":266,"OHK":-2036,"ONN":-973}; 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+ score += this.ts_(this.UC6__[c6]); + score += this.ts_(this.BC1__[c2 + c3]); + score += this.ts_(this.BC2__[c3 + c4]); + score += this.ts_(this.BC3__[c4 + c5]); + score += this.ts_(this.TC1__[c1 + c2 + c3]); + score += this.ts_(this.TC2__[c2 + c3 + c4]); + score += this.ts_(this.TC3__[c3 + c4 + c5]); + score += this.ts_(this.TC4__[c4 + c5 + c6]); + // score += this.ts_(this.TC5__[c4 + c5 + c6]); + score += this.ts_(this.UQ1__[p1 + c1]); + score += this.ts_(this.UQ2__[p2 + c2]); + score += this.ts_(this.UQ3__[p3 + c3]); + score += this.ts_(this.BQ1__[p2 + c2 + c3]); + score += this.ts_(this.BQ2__[p2 + c3 + c4]); + score += this.ts_(this.BQ3__[p3 + c2 + c3]); + score += this.ts_(this.BQ4__[p3 + c3 + c4]); + score += this.ts_(this.TQ1__[p2 + c1 + c2 + c3]); + score += this.ts_(this.TQ2__[p2 + c2 + c3 + c4]); + score += this.ts_(this.TQ3__[p3 + c1 + c2 + c3]); + score += this.ts_(this.TQ4__[p3 + c2 + c3 + c4]); + var p = "O"; + if (score > 0) { + result.push(word); + word = ""; + p = "B"; + } + p1 = p2; + p2 = p3; + p3 = p; + word += seg[i]; + } + result.push(word); + + return result; + } + + lunr.TinySegmenter = TinySegmenter; + }; + +})); \ No newline at end of file diff --git a/assets/javascripts/lunr/wordcut.js b/assets/javascripts/lunr/wordcut.js new file mode 100644 index 00000000..0d898c9e --- /dev/null +++ b/assets/javascripts/lunr/wordcut.js @@ -0,0 +1,6708 @@ +(function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"){g=global}else if(typeof self!=="undefined"){g=self}else{g=this}(g.lunr || (g.lunr = {})).wordcut = f()}})(function(){var define,module,exports;return (function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o 1; + }) + this.addWords(words, false) + } + if(finalize){ + this.finalizeDict(); + } + }, + + dictSeek: function (l, r, ch, strOffset, pos) { + var ans = null; + while (l <= r) { + var m = Math.floor((l + r) / 2), + dict_item = this.dict[m], + len = dict_item.length; + if (len <= strOffset) { + l = m + 1; + } else { + var ch_ = dict_item[strOffset]; + if (ch_ < ch) { + l = m + 1; + } else if (ch_ > ch) { + r = m - 1; + } else { + ans = m; + if (pos == LEFT) { + r = m - 1; + } else { + l = m + 1; + } + } + } + } + return ans; + }, + + isFinal: function (acceptor) { + return this.dict[acceptor.l].length == acceptor.strOffset; + }, + + createAcceptor: function () { + return { + l: 0, + r: this.dict.length - 1, + strOffset: 0, + isFinal: false, + dict: this, + transit: function (ch) { + return this.dict.transit(this, ch); + }, + isError: false, + tag: "DICT", + w: 1, + type: "DICT" + }; + }, + + transit: function (acceptor, ch) { + var l = this.dictSeek(acceptor.l, + acceptor.r, + ch, + acceptor.strOffset, + LEFT); + if (l !== null) { + var r = this.dictSeek(l, + acceptor.r, + ch, + acceptor.strOffset, + RIGHT); + acceptor.l = l; + acceptor.r = r; + acceptor.strOffset++; + acceptor.isFinal = this.isFinal(acceptor); + } else { + acceptor.isError = true; + } + return acceptor; + }, + + sortuniq: function(a){ + return a.sort().filter(function(item, pos, arr){ + return !pos || item != arr[pos - 1]; + }) + }, + + flatten: function(a){ + //[[1,2],[3]] -> [1,2,3] + return [].concat.apply([], a); + } +}; +module.exports = WordcutDict; + +}).call(this,"/dist/tmp") +},{"glob":16,"path":22}],3:[function(require,module,exports){ +var WordRule = { + createAcceptor: function(tag) { + if (tag["WORD_RULE"]) + return null; + + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + var lch = ch.toLowerCase(); + if (lch >= "a" && lch <= "z") { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "WORD_RULE", + type: "WORD_RULE", + w: 1}; + } +}; + +var NumberRule = { + createAcceptor: function(tag) { + if (tag["NUMBER_RULE"]) + return null; + + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (ch >= "0" && ch <= "9") { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "NUMBER_RULE", + type: "NUMBER_RULE", + w: 1}; + } +}; + +var SpaceRule = { + tag: "SPACE_RULE", + createAcceptor: function(tag) { + + if (tag["SPACE_RULE"]) + return null; + + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (ch == " " || ch == "\t" || ch == "\r" || ch == "\n" || + ch == "\u00A0" || ch=="\u2003"//nbsp and emsp + ) { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: SpaceRule.tag, + w: 1, + type: "SPACE_RULE"}; + } +} + +var SingleSymbolRule = { + tag: "SINSYM", + createAcceptor: function(tag) { + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (this.strOffset == 0 && ch.match(/^[\@\(\)\/\,\-\."`]$/)) { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "SINSYM", + w: 1, + type: "SINSYM"}; + } +} + + +var LatinRules = [WordRule, SpaceRule, SingleSymbolRule, NumberRule]; + +module.exports = LatinRules; + +},{}],4:[function(require,module,exports){ +var _ = require("underscore") + , WordcutCore = require("./wordcut_core"); +var PathInfoBuilder = { + + /* + buildByPartAcceptors: function(path, acceptors, i) { + var + var genInfos = partAcceptors.reduce(function(genInfos, acceptor) { + + }, []); + + return genInfos; + } + */ + + buildByAcceptors: function(path, finalAcceptors, i) { + var self = this; + var infos = finalAcceptors.map(function(acceptor) { + var p = i - acceptor.strOffset + 1 + , _info = path[p]; + + var info = {p: p, + mw: _info.mw + (acceptor.mw === undefined ? 0 : acceptor.mw), + w: acceptor.w + _info.w, + unk: (acceptor.unk ? acceptor.unk : 0) + _info.unk, + type: acceptor.type}; + + if (acceptor.type == "PART") { + for(var j = p + 1; j <= i; j++) { + path[j].merge = p; + } + info.merge = p; + } + + return info; + }); + return infos.filter(function(info) { return info; }); + }, + + fallback: function(path, leftBoundary, text, i) { + var _info = path[leftBoundary]; + if (text[i].match(/[\u0E48-\u0E4E]/)) { + if (leftBoundary != 0) + leftBoundary = path[leftBoundary].p; + return {p: leftBoundary, + mw: 0, + w: 1 + _info.w, + unk: 1 + _info.unk, + type: "UNK"}; +/* } else if(leftBoundary > 0 && path[leftBoundary].type !== "UNK") { + leftBoundary = path[leftBoundary].p; + return {p: leftBoundary, + w: 1 + _info.w, + unk: 1 + _info.unk, + type: "UNK"}; */ + } else { + return {p: leftBoundary, + mw: _info.mw, + w: 1 + _info.w, + unk: 1 + _info.unk, + type: "UNK"}; + } + }, + + build: function(path, finalAcceptors, i, leftBoundary, text) { + var basicPathInfos = this.buildByAcceptors(path, finalAcceptors, i); + if (basicPathInfos.length > 0) { + return basicPathInfos; + } else { + return [this.fallback(path, leftBoundary, text, i)]; + } + } +}; + +module.exports = function() { + return _.clone(PathInfoBuilder); +} + +},{"./wordcut_core":8,"underscore":25}],5:[function(require,module,exports){ +var _ = require("underscore"); + + +var PathSelector = { + selectPath: function(paths) { + var path = paths.reduce(function(selectedPath, path) { + if (selectedPath == null) { + return path; + } else { + if (path.unk < selectedPath.unk) + return path; + if (path.unk == selectedPath.unk) { + if (path.mw < selectedPath.mw) + return path + if (path.mw == selectedPath.mw) { + if (path.w < selectedPath.w) + return path; + } + } + return selectedPath; + } + }, null); + return path; + }, + + createPath: function() { + return [{p:null, w:0, unk:0, type: "INIT", mw:0}]; + } +}; + +module.exports = function() { + return _.clone(PathSelector); +}; + +},{"underscore":25}],6:[function(require,module,exports){ +function isMatch(pat, offset, ch) { + if (pat.length <= offset) + return false; + var _ch = pat[offset]; + return _ch == ch || + (_ch.match(/[กข]/) && ch.match(/[ก-ฮ]/)) || + (_ch.match(/[มบ]/) && ch.match(/[ก-ฮ]/)) || + (_ch.match(/\u0E49/) && ch.match(/[\u0E48-\u0E4B]/)); +} + +var Rule0 = { + pat: "เหก็ม", + createAcceptor: function(tag) { + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (isMatch(Rule0.pat, this.strOffset,ch)) { + this.isFinal = (this.strOffset + 1 == Rule0.pat.length); + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "THAI_RULE", + type: "THAI_RULE", + w: 1}; + } +}; + +var PartRule = { + createAcceptor: function(tag) { + return {strOffset: 0, + patterns: [ + "แก", "เก", "ก้", "กก์", "กา", "กี", "กิ", "กืก" + ], + isFinal: false, + transit: function(ch) { + var offset = this.strOffset; + this.patterns = this.patterns.filter(function(pat) { + return isMatch(pat, offset, ch); + }); + + if (this.patterns.length > 0) { + var len = 1 + offset; + this.isFinal = this.patterns.some(function(pat) { + return pat.length == len; + }); + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "PART", + type: "PART", + unk: 1, + w: 1}; + } +}; + +var ThaiRules = [Rule0, PartRule]; + +module.exports = ThaiRules; + +},{}],7:[function(require,module,exports){ +var sys = require("sys") + , WordcutDict = require("./dict") + , WordcutCore = require("./wordcut_core") + , PathInfoBuilder = require("./path_info_builder") + , PathSelector = require("./path_selector") + , Acceptors = require("./acceptors") + , latinRules = require("./latin_rules") + , thaiRules = require("./thai_rules") + , _ = require("underscore"); + + +var Wordcut = Object.create(WordcutCore); +Wordcut.defaultPathInfoBuilder = PathInfoBuilder; +Wordcut.defaultPathSelector = PathSelector; +Wordcut.defaultAcceptors = Acceptors; +Wordcut.defaultLatinRules = latinRules; +Wordcut.defaultThaiRules = thaiRules; +Wordcut.defaultDict = WordcutDict; + + +Wordcut.initNoDict = function(dict_path) { + var self = this; + self.pathInfoBuilder = new self.defaultPathInfoBuilder; + self.pathSelector = new self.defaultPathSelector; + self.acceptors = new self.defaultAcceptors; + self.defaultLatinRules.forEach(function(rule) { + self.acceptors.creators.push(rule); + }); + self.defaultThaiRules.forEach(function(rule) { + self.acceptors.creators.push(rule); + }); +}; + +Wordcut.init = function(dict_path, withDefault, additionalWords) { + withDefault = withDefault || false; + this.initNoDict(); + var dict = _.clone(this.defaultDict); + dict.init(dict_path, withDefault, additionalWords); + this.acceptors.creators.push(dict); +}; + +module.exports = Wordcut; + +},{"./acceptors":1,"./dict":2,"./latin_rules":3,"./path_info_builder":4,"./path_selector":5,"./thai_rules":6,"./wordcut_core":8,"sys":28,"underscore":25}],8:[function(require,module,exports){ +var WordcutCore = { + + buildPath: function(text) { + var self = this + , path = self.pathSelector.createPath() + , leftBoundary = 0; + self.acceptors.reset(); + for (var i = 0; i < text.length; i++) { + var ch = text[i]; + self.acceptors.transit(ch); + + var possiblePathInfos = self + .pathInfoBuilder + .build(path, + self.acceptors.getFinalAcceptors(), + i, + leftBoundary, + text); + var selectedPath = self.pathSelector.selectPath(possiblePathInfos) + + path.push(selectedPath); + if (selectedPath.type !== "UNK") { + leftBoundary = i; + } + } + return path; + }, + + pathToRanges: function(path) { + var e = path.length - 1 + , ranges = []; + + while (e > 0) { + var info = path[e] + , s = info.p; + + if (info.merge !== undefined && ranges.length > 0) { + var r = ranges[ranges.length - 1]; + r.s = info.merge; + s = r.s; + } else { + ranges.push({s:s, e:e}); + } + e = s; + } + return ranges.reverse(); + }, + + rangesToText: function(text, ranges, delimiter) { + return ranges.map(function(r) { + return text.substring(r.s, r.e); + }).join(delimiter); + }, + + cut: function(text, delimiter) { + var path = this.buildPath(text) + , ranges = this.pathToRanges(path); + return this + .rangesToText(text, ranges, + (delimiter === undefined ? "|" : delimiter)); + }, + + cutIntoRanges: function(text, noText) { + var path = this.buildPath(text) + , ranges = this.pathToRanges(path); + + if (!noText) { + ranges.forEach(function(r) { + r.text = text.substring(r.s, r.e); + }); + } + return ranges; + }, + + cutIntoArray: function(text) { + var path = this.buildPath(text) + , ranges = this.pathToRanges(path); + + return ranges.map(function(r) { + return text.substring(r.s, r.e) + }); + } +}; + +module.exports = WordcutCore; + +},{}],9:[function(require,module,exports){ +// http://wiki.commonjs.org/wiki/Unit_Testing/1.0 +// +// THIS IS NOT TESTED NOR LIKELY TO WORK OUTSIDE V8! +// +// Originally from narwhal.js (http://narwhaljs.org) +// Copyright (c) 2009 Thomas Robinson <280north.com> +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files (the 'Software'), to +// deal in the Software without restriction, including without limitation the +// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or +// sell copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions: +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +// AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +// ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +// WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +// when used in node, this will actually load the util module we depend on +// versus loading the builtin util module as happens otherwise +// this is a bug in node module loading as far as I am concerned +var util = require('util/'); + +var pSlice = Array.prototype.slice; +var hasOwn = Object.prototype.hasOwnProperty; + +// 1. The assert module provides functions that throw +// AssertionError's when particular conditions are not met. The +// assert module must conform to the following interface. + +var assert = module.exports = ok; + +// 2. The AssertionError is defined in assert. +// new assert.AssertionError({ message: message, +// actual: actual, +// expected: expected }) + +assert.AssertionError = function AssertionError(options) { + this.name = 'AssertionError'; + this.actual = options.actual; + this.expected = options.expected; + this.operator = options.operator; + if (options.message) { + this.message = options.message; + this.generatedMessage = false; + } else { + this.message = getMessage(this); + this.generatedMessage = true; + } + var stackStartFunction = options.stackStartFunction || fail; + + if (Error.captureStackTrace) { + Error.captureStackTrace(this, stackStartFunction); + } + else { + // non v8 browsers so we can have a stacktrace + var err = new Error(); + if (err.stack) { + var out = err.stack; + + // try to strip useless frames + var fn_name = stackStartFunction.name; + var idx = out.indexOf('\n' + fn_name); + if (idx >= 0) { + // once we have located the function frame + // we need to strip out everything before it (and its line) + var next_line = out.indexOf('\n', idx + 1); + out = out.substring(next_line + 1); + } + + this.stack = out; + } + } +}; + +// assert.AssertionError instanceof Error +util.inherits(assert.AssertionError, Error); + +function replacer(key, value) { + if (util.isUndefined(value)) { + return '' + value; + } + if (util.isNumber(value) && !isFinite(value)) { + return value.toString(); + } + if (util.isFunction(value) || util.isRegExp(value)) { + return value.toString(); + } + return value; +} + +function truncate(s, n) { + if (util.isString(s)) { + return s.length < n ? s : s.slice(0, n); + } else { + return s; + } +} + +function getMessage(self) { + return truncate(JSON.stringify(self.actual, replacer), 128) + ' ' + + self.operator + ' ' + + truncate(JSON.stringify(self.expected, replacer), 128); +} + +// At present only the three keys mentioned above are used and +// understood by the spec. Implementations or sub modules can pass +// other keys to the AssertionError's constructor - they will be +// ignored. + +// 3. All of the following functions must throw an AssertionError +// when a corresponding condition is not met, with a message that +// may be undefined if not provided. All assertion methods provide +// both the actual and expected values to the assertion error for +// display purposes. + +function fail(actual, expected, message, operator, stackStartFunction) { + throw new assert.AssertionError({ + message: message, + actual: actual, + expected: expected, + operator: operator, + stackStartFunction: stackStartFunction + }); +} + +// EXTENSION! allows for well behaved errors defined elsewhere. +assert.fail = fail; + +// 4. Pure assertion tests whether a value is truthy, as determined +// by !!guard. +// assert.ok(guard, message_opt); +// This statement is equivalent to assert.equal(true, !!guard, +// message_opt);. To test strictly for the value true, use +// assert.strictEqual(true, guard, message_opt);. + +function ok(value, message) { + if (!value) fail(value, true, message, '==', assert.ok); +} +assert.ok = ok; + +// 5. The equality assertion tests shallow, coercive equality with +// ==. +// assert.equal(actual, expected, message_opt); + +assert.equal = function equal(actual, expected, message) { + if (actual != expected) fail(actual, expected, message, '==', assert.equal); +}; + +// 6. The non-equality assertion tests for whether two objects are not equal +// with != assert.notEqual(actual, expected, message_opt); + +assert.notEqual = function notEqual(actual, expected, message) { + if (actual == expected) { + fail(actual, expected, message, '!=', assert.notEqual); + } +}; + +// 7. The equivalence assertion tests a deep equality relation. +// assert.deepEqual(actual, expected, message_opt); + +assert.deepEqual = function deepEqual(actual, expected, message) { + if (!_deepEqual(actual, expected)) { + fail(actual, expected, message, 'deepEqual', assert.deepEqual); + } +}; + +function _deepEqual(actual, expected) { + // 7.1. All identical values are equivalent, as determined by ===. + if (actual === expected) { + return true; + + } else if (util.isBuffer(actual) && util.isBuffer(expected)) { + if (actual.length != expected.length) return false; + + for (var i = 0; i < actual.length; i++) { + if (actual[i] !== expected[i]) return false; + } + + return true; + + // 7.2. If the expected value is a Date object, the actual value is + // equivalent if it is also a Date object that refers to the same time. + } else if (util.isDate(actual) && util.isDate(expected)) { + return actual.getTime() === expected.getTime(); + + // 7.3 If the expected value is a RegExp object, the actual value is + // equivalent if it is also a RegExp object with the same source and + // properties (`global`, `multiline`, `lastIndex`, `ignoreCase`). + } else if (util.isRegExp(actual) && util.isRegExp(expected)) { + return actual.source === expected.source && + actual.global === expected.global && + actual.multiline === expected.multiline && + actual.lastIndex === expected.lastIndex && + actual.ignoreCase === expected.ignoreCase; + + // 7.4. Other pairs that do not both pass typeof value == 'object', + // equivalence is determined by ==. + } else if (!util.isObject(actual) && !util.isObject(expected)) { + return actual == expected; + + // 7.5 For all other Object pairs, including Array objects, equivalence is + // determined by having the same number of owned properties (as verified + // with Object.prototype.hasOwnProperty.call), the same set of keys + // (although not necessarily the same order), equivalent values for every + // corresponding key, and an identical 'prototype' property. Note: this + // accounts for both named and indexed properties on Arrays. + } else { + return objEquiv(actual, expected); + } +} + +function isArguments(object) { + return Object.prototype.toString.call(object) == '[object Arguments]'; +} + +function objEquiv(a, b) { + if (util.isNullOrUndefined(a) || util.isNullOrUndefined(b)) + return false; + // an identical 'prototype' property. + if (a.prototype !== b.prototype) return false; + // if one is a primitive, the other must be same + if (util.isPrimitive(a) || util.isPrimitive(b)) { + return a === b; + } + var aIsArgs = isArguments(a), + bIsArgs = isArguments(b); + if ((aIsArgs && !bIsArgs) || (!aIsArgs && bIsArgs)) + return false; + if (aIsArgs) { + a = pSlice.call(a); + b = pSlice.call(b); + return _deepEqual(a, b); + } + var ka = objectKeys(a), + kb = objectKeys(b), + key, i; + // having the same number of owned properties (keys incorporates + // hasOwnProperty) + if (ka.length != kb.length) + return false; + //the same set of keys (although not necessarily the same order), + ka.sort(); + kb.sort(); + //~~~cheap key test + for (i = ka.length - 1; i >= 0; i--) { + if (ka[i] != kb[i]) + return false; + } + //equivalent values for every corresponding key, and + //~~~possibly expensive deep test + for (i = ka.length - 1; i >= 0; i--) { + key = ka[i]; + if (!_deepEqual(a[key], b[key])) return false; + } + return true; +} + +// 8. The non-equivalence assertion tests for any deep inequality. +// assert.notDeepEqual(actual, expected, message_opt); + +assert.notDeepEqual = function notDeepEqual(actual, expected, message) { + if (_deepEqual(actual, expected)) { + fail(actual, expected, message, 'notDeepEqual', assert.notDeepEqual); + } +}; + +// 9. The strict equality assertion tests strict equality, as determined by ===. +// assert.strictEqual(actual, expected, message_opt); + +assert.strictEqual = function strictEqual(actual, expected, message) { + if (actual !== expected) { + fail(actual, expected, message, '===', assert.strictEqual); + } +}; + +// 10. The strict non-equality assertion tests for strict inequality, as +// determined by !==. assert.notStrictEqual(actual, expected, message_opt); + +assert.notStrictEqual = function notStrictEqual(actual, expected, message) { + if (actual === expected) { + fail(actual, expected, message, '!==', assert.notStrictEqual); + } +}; + +function expectedException(actual, expected) { + if (!actual || !expected) { + return false; + } + + if (Object.prototype.toString.call(expected) == '[object RegExp]') { + return expected.test(actual); + } else if (actual instanceof expected) { + return true; + } else if (expected.call({}, actual) === true) { + return true; + } + + return false; +} + +function _throws(shouldThrow, block, expected, message) { + var actual; + + if (util.isString(expected)) { + message = expected; + expected = null; + } + + try { + block(); + } catch (e) { + actual = e; + } + + message = (expected && expected.name ? ' (' + expected.name + ').' : '.') + + (message ? ' ' + message : '.'); + + if (shouldThrow && !actual) { + fail(actual, expected, 'Missing expected exception' + message); + } + + if (!shouldThrow && expectedException(actual, expected)) { + fail(actual, expected, 'Got unwanted exception' + message); + } + + if ((shouldThrow && actual && expected && + !expectedException(actual, expected)) || (!shouldThrow && actual)) { + throw actual; + } +} + +// 11. Expected to throw an error: +// assert.throws(block, Error_opt, message_opt); + +assert.throws = function(block, /*optional*/error, /*optional*/message) { + _throws.apply(this, [true].concat(pSlice.call(arguments))); +}; + +// EXTENSION! This is annoying to write outside this module. +assert.doesNotThrow = function(block, /*optional*/message) { + _throws.apply(this, [false].concat(pSlice.call(arguments))); +}; + +assert.ifError = function(err) { if (err) {throw err;}}; + +var objectKeys = Object.keys || function (obj) { + var keys = []; + for (var key in obj) { + if (hasOwn.call(obj, key)) keys.push(key); + } + return keys; +}; + +},{"util/":28}],10:[function(require,module,exports){ +'use strict'; +module.exports = balanced; +function balanced(a, b, str) { + if (a instanceof RegExp) a = maybeMatch(a, str); + if (b instanceof RegExp) b = maybeMatch(b, str); + + var r = range(a, b, str); + + return r && { + start: r[0], + end: r[1], + pre: str.slice(0, r[0]), + body: str.slice(r[0] + a.length, r[1]), + post: str.slice(r[1] + b.length) + }; +} + +function maybeMatch(reg, str) { + var m = str.match(reg); + return m ? m[0] : null; +} + +balanced.range = range; +function range(a, b, str) { + var begs, beg, left, right, result; + var ai = str.indexOf(a); + var bi = str.indexOf(b, ai + 1); + var i = ai; + + if (ai >= 0 && bi > 0) { + begs = []; + left = str.length; + + while (i >= 0 && !result) { + if (i == ai) { + begs.push(i); + ai = str.indexOf(a, i + 1); + } else if (begs.length == 1) { + result = [ begs.pop(), bi ]; + } else { + beg = begs.pop(); + if (beg < left) { + left = beg; + right = bi; + } + + bi = str.indexOf(b, i + 1); + } + + i = ai < bi && ai >= 0 ? ai : bi; + } + + if (begs.length) { + result = [ left, right ]; + } + } + + return result; +} + +},{}],11:[function(require,module,exports){ +var concatMap = require('concat-map'); +var balanced = require('balanced-match'); + +module.exports = expandTop; + +var escSlash = '\0SLASH'+Math.random()+'\0'; +var escOpen = '\0OPEN'+Math.random()+'\0'; +var escClose = '\0CLOSE'+Math.random()+'\0'; +var escComma = '\0COMMA'+Math.random()+'\0'; +var escPeriod = '\0PERIOD'+Math.random()+'\0'; + +function numeric(str) { + return parseInt(str, 10) == str + ? parseInt(str, 10) + : str.charCodeAt(0); +} + +function escapeBraces(str) { + return str.split('\\\\').join(escSlash) + .split('\\{').join(escOpen) + .split('\\}').join(escClose) + .split('\\,').join(escComma) + .split('\\.').join(escPeriod); +} + +function unescapeBraces(str) { + return str.split(escSlash).join('\\') + .split(escOpen).join('{') + .split(escClose).join('}') + .split(escComma).join(',') + .split(escPeriod).join('.'); +} + + +// Basically just str.split(","), but handling cases +// where we have nested braced sections, which should be +// treated as individual members, like {a,{b,c},d} +function parseCommaParts(str) { + if (!str) + return ['']; + + var parts = []; + var m = balanced('{', '}', str); + + if (!m) + return str.split(','); + + var pre = m.pre; + var body = m.body; + var post = m.post; + var p = pre.split(','); + + p[p.length-1] += '{' + body + '}'; + var postParts = parseCommaParts(post); + if (post.length) { + p[p.length-1] += postParts.shift(); + p.push.apply(p, postParts); + } + + parts.push.apply(parts, p); + + return parts; +} + +function expandTop(str) { + if (!str) + return []; + + // I don't know why Bash 4.3 does this, but it does. + // Anything starting with {} will have the first two bytes preserved + // but *only* at the top level, so {},a}b will not expand to anything, + // but a{},b}c will be expanded to [a}c,abc]. + // One could argue that this is a bug in Bash, but since the goal of + // this module is to match Bash's rules, we escape a leading {} + if (str.substr(0, 2) === '{}') { + str = '\\{\\}' + str.substr(2); + } + + return expand(escapeBraces(str), true).map(unescapeBraces); +} + +function identity(e) { + return e; +} + +function embrace(str) { + return '{' + str + '}'; +} +function isPadded(el) { + return /^-?0\d/.test(el); +} + +function lte(i, y) { + return i <= y; +} +function gte(i, y) { + return i >= y; +} + +function expand(str, isTop) { + var expansions = []; + + var m = balanced('{', '}', str); + if (!m || /\$$/.test(m.pre)) return [str]; + + var isNumericSequence = /^-?\d+\.\.-?\d+(?:\.\.-?\d+)?$/.test(m.body); + var isAlphaSequence = /^[a-zA-Z]\.\.[a-zA-Z](?:\.\.-?\d+)?$/.test(m.body); + var isSequence = isNumericSequence || isAlphaSequence; + var isOptions = m.body.indexOf(',') >= 0; + if (!isSequence && !isOptions) { + // {a},b} + if (m.post.match(/,.*\}/)) { + str = m.pre + '{' + m.body + escClose + m.post; + return expand(str); + } + return [str]; + } + + var n; + if (isSequence) { + n = m.body.split(/\.\./); + } else { + n = parseCommaParts(m.body); + if (n.length === 1) { + // x{{a,b}}y ==> x{a}y x{b}y + n = expand(n[0], false).map(embrace); + if (n.length === 1) { + var post = m.post.length + ? expand(m.post, false) + : ['']; + return post.map(function(p) { + return m.pre + n[0] + p; + }); + } + } + } + + // at this point, n is the parts, and we know it's not a comma set + // with a single entry. + + // no need to expand pre, since it is guaranteed to be free of brace-sets + var pre = m.pre; + var post = m.post.length + ? expand(m.post, false) + : ['']; + + var N; + + if (isSequence) { + var x = numeric(n[0]); + var y = numeric(n[1]); + var width = Math.max(n[0].length, n[1].length) + var incr = n.length == 3 + ? Math.abs(numeric(n[2])) + : 1; + var test = lte; + var reverse = y < x; + if (reverse) { + incr *= -1; + test = gte; + } + var pad = n.some(isPadded); + + N = []; + + for (var i = x; test(i, y); i += incr) { + var c; + if (isAlphaSequence) { + c = String.fromCharCode(i); + if (c === '\\') + c = ''; + } else { + c = String(i); + if (pad) { + var need = width - c.length; + if (need > 0) { + var z = new Array(need + 1).join('0'); + if (i < 0) + c = '-' + z + c.slice(1); + else + c = z + c; + } + } + } + N.push(c); + } + } else { + N = concatMap(n, function(el) { return expand(el, false) }); + } + + for (var j = 0; j < N.length; j++) { + for (var k = 0; k < post.length; k++) { + var expansion = pre + N[j] + post[k]; + if (!isTop || isSequence || expansion) + expansions.push(expansion); + } + } + + return expansions; +} + + +},{"balanced-match":10,"concat-map":13}],12:[function(require,module,exports){ + +},{}],13:[function(require,module,exports){ +module.exports = function (xs, fn) { + var res = []; + for (var i = 0; i < xs.length; i++) { + var x = fn(xs[i], i); + if (isArray(x)) res.push.apply(res, x); + else res.push(x); + } + return res; +}; + +var isArray = Array.isArray || function (xs) { + return Object.prototype.toString.call(xs) === '[object Array]'; +}; + +},{}],14:[function(require,module,exports){ +// Copyright Joyent, Inc. and other Node contributors. +// +// Permission is hereby granted, free of charge, to any person obtaining a +// copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to permit +// persons to whom the Software is furnished to do so, subject to the +// following conditions: +// +// The above copyright notice and this permission notice shall be included +// in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN +// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +// USE OR OTHER DEALINGS IN THE SOFTWARE. + +function EventEmitter() { + this._events = this._events || {}; + this._maxListeners = this._maxListeners || undefined; +} +module.exports = EventEmitter; + +// Backwards-compat with node 0.10.x +EventEmitter.EventEmitter = EventEmitter; + +EventEmitter.prototype._events = undefined; +EventEmitter.prototype._maxListeners = undefined; + +// By default EventEmitters will print a warning if more than 10 listeners are +// added to it. This is a useful default which helps finding memory leaks. +EventEmitter.defaultMaxListeners = 10; + +// Obviously not all Emitters should be limited to 10. This function allows +// that to be increased. Set to zero for unlimited. +EventEmitter.prototype.setMaxListeners = function(n) { + if (!isNumber(n) || n < 0 || isNaN(n)) + throw TypeError('n must be a positive number'); + this._maxListeners = n; + return this; +}; + +EventEmitter.prototype.emit = function(type) { + var er, handler, len, args, i, listeners; + + if (!this._events) + this._events = {}; + + // If there is no 'error' event listener then throw. + if (type === 'error') { + if (!this._events.error || + (isObject(this._events.error) && !this._events.error.length)) { + er = arguments[1]; + if (er instanceof Error) { + throw er; // Unhandled 'error' event + } + throw TypeError('Uncaught, unspecified "error" event.'); + } + } + + handler = this._events[type]; + + if (isUndefined(handler)) + return false; + + if (isFunction(handler)) { + switch (arguments.length) { + // fast cases + case 1: + handler.call(this); + break; + case 2: + handler.call(this, arguments[1]); + break; + case 3: + handler.call(this, arguments[1], arguments[2]); + break; + // slower + default: + len = arguments.length; + args = new Array(len - 1); + for (i = 1; i < len; i++) + args[i - 1] = arguments[i]; + handler.apply(this, args); + } + } else if (isObject(handler)) { + len = arguments.length; + args = new Array(len - 1); + for (i = 1; i < len; i++) + args[i - 1] = arguments[i]; + + listeners = handler.slice(); + len = listeners.length; + for (i = 0; i < len; i++) + listeners[i].apply(this, args); + } + + return true; +}; + +EventEmitter.prototype.addListener = function(type, listener) { + var m; + + if (!isFunction(listener)) + throw TypeError('listener must be a function'); + + if (!this._events) + this._events = {}; + + // To avoid recursion in the case that type === "newListener"! Before + // adding it to the listeners, first emit "newListener". + if (this._events.newListener) + this.emit('newListener', type, + isFunction(listener.listener) ? + listener.listener : listener); + + if (!this._events[type]) + // Optimize the case of one listener. Don't need the extra array object. + this._events[type] = listener; + else if (isObject(this._events[type])) + // If we've already got an array, just append. + this._events[type].push(listener); + else + // Adding the second element, need to change to array. + this._events[type] = [this._events[type], listener]; + + // Check for listener leak + if (isObject(this._events[type]) && !this._events[type].warned) { + var m; + if (!isUndefined(this._maxListeners)) { + m = this._maxListeners; + } else { + m = EventEmitter.defaultMaxListeners; + } + + if (m && m > 0 && this._events[type].length > m) { + this._events[type].warned = true; + console.error('(node) warning: possible EventEmitter memory ' + + 'leak detected. %d listeners added. ' + + 'Use emitter.setMaxListeners() to increase limit.', + this._events[type].length); + if (typeof console.trace === 'function') { + // not supported in IE 10 + console.trace(); + } + } + } + + return this; +}; + +EventEmitter.prototype.on = EventEmitter.prototype.addListener; + +EventEmitter.prototype.once = function(type, listener) { + if (!isFunction(listener)) + throw TypeError('listener must be a function'); + + var fired = false; + + function g() { + this.removeListener(type, g); + + if (!fired) { + fired = true; + listener.apply(this, arguments); + } + } + + g.listener = listener; + this.on(type, g); + + return this; +}; + +// emits a 'removeListener' event iff the listener was removed +EventEmitter.prototype.removeListener = function(type, listener) { + var list, position, length, i; + + if (!isFunction(listener)) + throw TypeError('listener must be a function'); + + if (!this._events || !this._events[type]) + return this; + + list = this._events[type]; + length = list.length; + position = -1; + + if (list === listener || + (isFunction(list.listener) && list.listener === listener)) { + delete this._events[type]; + if (this._events.removeListener) + this.emit('removeListener', type, listener); + + } else if (isObject(list)) { + for (i = length; i-- > 0;) { + if (list[i] === listener || + (list[i].listener && list[i].listener === listener)) { + position = i; + break; + } + } + + if (position < 0) + return this; + + if (list.length === 1) { + list.length = 0; + delete this._events[type]; + } else { + list.splice(position, 1); + } + + if (this._events.removeListener) + this.emit('removeListener', type, listener); + } + + return this; +}; + +EventEmitter.prototype.removeAllListeners = function(type) { + var key, listeners; + + if (!this._events) + return this; + + // not listening for removeListener, no need to emit + if (!this._events.removeListener) { + if (arguments.length === 0) + this._events = {}; + else if (this._events[type]) + delete this._events[type]; + return this; + } + + // emit removeListener for all listeners on all events + if (arguments.length === 0) { + for (key in this._events) { + if (key === 'removeListener') continue; + this.removeAllListeners(key); + } + this.removeAllListeners('removeListener'); + this._events = {}; + return this; + } + + listeners = this._events[type]; + + if (isFunction(listeners)) { + this.removeListener(type, listeners); + } else { + // LIFO order + while (listeners.length) + this.removeListener(type, listeners[listeners.length - 1]); + } + delete this._events[type]; + + return this; +}; + +EventEmitter.prototype.listeners = function(type) { + var ret; + if (!this._events || !this._events[type]) + ret = []; + else if (isFunction(this._events[type])) + ret = [this._events[type]]; + else + ret = this._events[type].slice(); + return ret; +}; + +EventEmitter.listenerCount = function(emitter, type) { + var ret; + if (!emitter._events || !emitter._events[type]) + ret = 0; + else if (isFunction(emitter._events[type])) + ret = 1; + else + ret = emitter._events[type].length; + return ret; +}; + +function isFunction(arg) { + return typeof arg === 'function'; +} + +function isNumber(arg) { + return typeof arg === 'number'; +} + +function isObject(arg) { + return typeof arg === 'object' && arg !== null; +} + +function isUndefined(arg) { + return arg === void 0; +} + +},{}],15:[function(require,module,exports){ +(function (process){ +exports.alphasort = alphasort +exports.alphasorti = alphasorti +exports.setopts = setopts +exports.ownProp = ownProp +exports.makeAbs = makeAbs +exports.finish = finish +exports.mark = mark +exports.isIgnored = isIgnored +exports.childrenIgnored = childrenIgnored + +function ownProp (obj, field) { + return Object.prototype.hasOwnProperty.call(obj, field) +} + +var path = require("path") +var minimatch = require("minimatch") +var isAbsolute = require("path-is-absolute") +var Minimatch = minimatch.Minimatch + +function alphasorti (a, b) { + return a.toLowerCase().localeCompare(b.toLowerCase()) +} + +function alphasort (a, b) { + return a.localeCompare(b) +} + +function setupIgnores (self, options) { + self.ignore = options.ignore || [] + + if (!Array.isArray(self.ignore)) + self.ignore = [self.ignore] + + if (self.ignore.length) { + self.ignore = self.ignore.map(ignoreMap) + } +} + +function ignoreMap (pattern) { + var gmatcher = null + if (pattern.slice(-3) === '/**') { + var gpattern = pattern.replace(/(\/\*\*)+$/, '') + gmatcher = new Minimatch(gpattern) + } + + return { + matcher: new Minimatch(pattern), + gmatcher: gmatcher + } +} + +function setopts (self, pattern, options) { + if (!options) + options = {} + + // base-matching: just use globstar for that. + if (options.matchBase && -1 === pattern.indexOf("/")) { + if (options.noglobstar) { + throw new Error("base matching requires globstar") + } + pattern = "**/" + pattern + } + + self.silent = !!options.silent + self.pattern = pattern + self.strict = options.strict !== false + self.realpath = !!options.realpath + self.realpathCache = options.realpathCache || Object.create(null) + self.follow = !!options.follow + self.dot = !!options.dot + self.mark = !!options.mark + self.nodir = !!options.nodir + if (self.nodir) + self.mark = true + self.sync = !!options.sync + self.nounique = !!options.nounique + self.nonull = !!options.nonull + self.nosort = !!options.nosort + self.nocase = !!options.nocase + self.stat = !!options.stat + self.noprocess = !!options.noprocess + + self.maxLength = options.maxLength || Infinity + self.cache = options.cache || Object.create(null) + self.statCache = options.statCache || Object.create(null) + self.symlinks = options.symlinks || Object.create(null) + + setupIgnores(self, options) + + self.changedCwd = false + var cwd = process.cwd() + if (!ownProp(options, "cwd")) + self.cwd = cwd + else { + self.cwd = options.cwd + self.changedCwd = path.resolve(options.cwd) !== cwd + } + + self.root = options.root || path.resolve(self.cwd, "/") + self.root = path.resolve(self.root) + if (process.platform === "win32") + self.root = self.root.replace(/\\/g, "/") + + self.nomount = !!options.nomount + + // disable comments and negation unless the user explicitly + // passes in false as the option. + options.nonegate = options.nonegate === false ? false : true + options.nocomment = options.nocomment === false ? false : true + deprecationWarning(options) + + self.minimatch = new Minimatch(pattern, options) + self.options = self.minimatch.options +} + +// TODO(isaacs): remove entirely in v6 +// exported to reset in tests +exports.deprecationWarned +function deprecationWarning(options) { + if (!options.nonegate || !options.nocomment) { + if (process.noDeprecation !== true && !exports.deprecationWarned) { + var msg = 'glob WARNING: comments and negation will be disabled in v6' + if (process.throwDeprecation) + throw new Error(msg) + else if (process.traceDeprecation) + console.trace(msg) + else + console.error(msg) + + exports.deprecationWarned = true + } + } +} + +function finish (self) { + var nou = self.nounique + var all = nou ? [] : Object.create(null) + + for (var i = 0, l = self.matches.length; i < l; i ++) { + var matches = self.matches[i] + if (!matches || Object.keys(matches).length === 0) { + if (self.nonull) { + // do like the shell, and spit out the literal glob + var literal = self.minimatch.globSet[i] + if (nou) + all.push(literal) + else + all[literal] = true + } + } else { + // had matches + var m = Object.keys(matches) + if (nou) + all.push.apply(all, m) + else + m.forEach(function (m) { + all[m] = true + }) + } + } + + if (!nou) + all = Object.keys(all) + + if (!self.nosort) + all = all.sort(self.nocase ? alphasorti : alphasort) + + // at *some* point we statted all of these + if (self.mark) { + for (var i = 0; i < all.length; i++) { + all[i] = self._mark(all[i]) + } + if (self.nodir) { + all = all.filter(function (e) { + return !(/\/$/.test(e)) + }) + } + } + + if (self.ignore.length) + all = all.filter(function(m) { + return !isIgnored(self, m) + }) + + self.found = all +} + +function mark (self, p) { + var abs = makeAbs(self, p) + var c = self.cache[abs] + var m = p + if (c) { + var isDir = c === 'DIR' || Array.isArray(c) + var slash = p.slice(-1) === '/' + + if (isDir && !slash) + m += '/' + else if (!isDir && slash) + m = m.slice(0, -1) + + if (m !== p) { + var mabs = makeAbs(self, m) + self.statCache[mabs] = self.statCache[abs] + self.cache[mabs] = self.cache[abs] + } + } + + return m +} + +// lotta situps... +function makeAbs (self, f) { + var abs = f + if (f.charAt(0) === '/') { + abs = path.join(self.root, f) + } else if (isAbsolute(f) || f === '') { + abs = f + } else if (self.changedCwd) { + abs = path.resolve(self.cwd, f) + } else { + abs = path.resolve(f) + } + return abs +} + + +// Return true, if pattern ends with globstar '**', for the accompanying parent directory. +// Ex:- If node_modules/** is the pattern, add 'node_modules' to ignore list along with it's contents +function isIgnored (self, path) { + if (!self.ignore.length) + return false + + return self.ignore.some(function(item) { + return item.matcher.match(path) || !!(item.gmatcher && item.gmatcher.match(path)) + }) +} + +function childrenIgnored (self, path) { + if (!self.ignore.length) + return false + + return self.ignore.some(function(item) { + return !!(item.gmatcher && item.gmatcher.match(path)) + }) +} + +}).call(this,require('_process')) +},{"_process":24,"minimatch":20,"path":22,"path-is-absolute":23}],16:[function(require,module,exports){ +(function (process){ +// Approach: +// +// 1. Get the minimatch set +// 2. For each pattern in the set, PROCESS(pattern, false) +// 3. Store matches per-set, then uniq them +// +// PROCESS(pattern, inGlobStar) +// Get the first [n] items from pattern that are all strings +// Join these together. This is PREFIX. +// If there is no more remaining, then stat(PREFIX) and +// add to matches if it succeeds. END. +// +// If inGlobStar and PREFIX is symlink and points to dir +// set ENTRIES = [] +// else readdir(PREFIX) as ENTRIES +// If fail, END +// +// with ENTRIES +// If pattern[n] is GLOBSTAR +// // handle the case where the globstar match is empty +// // by pruning it out, and testing the resulting pattern +// PROCESS(pattern[0..n] + pattern[n+1 .. $], false) +// // handle other cases. +// for ENTRY in ENTRIES (not dotfiles) +// // attach globstar + tail onto the entry +// // Mark that this entry is a globstar match +// PROCESS(pattern[0..n] + ENTRY + pattern[n .. $], true) +// +// else // not globstar +// for ENTRY in ENTRIES (not dotfiles, unless pattern[n] is dot) +// Test ENTRY against pattern[n] +// If fails, continue +// If passes, PROCESS(pattern[0..n] + item + pattern[n+1 .. $]) +// +// Caveat: +// Cache all stats and readdirs results to minimize syscall. Since all +// we ever care about is existence and directory-ness, we can just keep +// `true` for files, and [children,...] for directories, or `false` for +// things that don't exist. + +module.exports = glob + +var fs = require('fs') +var minimatch = require('minimatch') +var Minimatch = minimatch.Minimatch +var inherits = require('inherits') +var EE = require('events').EventEmitter +var path = require('path') +var assert = require('assert') +var isAbsolute = require('path-is-absolute') +var globSync = require('./sync.js') +var common = require('./common.js') +var alphasort = common.alphasort +var alphasorti = common.alphasorti +var setopts = common.setopts +var ownProp = common.ownProp +var inflight = require('inflight') +var util = require('util') +var childrenIgnored = common.childrenIgnored +var isIgnored = common.isIgnored + +var once = require('once') + +function glob (pattern, options, cb) { + if (typeof options === 'function') cb = options, options = {} + if (!options) options = {} + + if (options.sync) { + if (cb) + throw new TypeError('callback provided to sync glob') + return globSync(pattern, options) + } + + return new Glob(pattern, options, cb) +} + +glob.sync = globSync +var GlobSync = glob.GlobSync = globSync.GlobSync + +// old api surface +glob.glob = glob + +glob.hasMagic = function (pattern, options_) { + var options = util._extend({}, options_) + options.noprocess = true + + var g = new Glob(pattern, options) + var set = g.minimatch.set + if (set.length > 1) + return true + + for (var j = 0; j < set[0].length; j++) { + if (typeof set[0][j] !== 'string') + return true + } + + return false +} + +glob.Glob = Glob +inherits(Glob, EE) +function Glob (pattern, options, cb) { + if (typeof options === 'function') { + cb = options + options = null + } + + if (options && options.sync) { + if (cb) + throw new TypeError('callback provided to sync glob') + return new GlobSync(pattern, options) + } + + if (!(this instanceof Glob)) + return new Glob(pattern, options, cb) + + setopts(this, pattern, options) + this._didRealPath = false + + // process each pattern in the minimatch set + var n = this.minimatch.set.length + + // The matches are stored as {: true,...} so that + // duplicates are automagically pruned. + // Later, we do an Object.keys() on these. + // Keep them as a list so we can fill in when nonull is set. + this.matches = new Array(n) + + if (typeof cb === 'function') { + cb = once(cb) + this.on('error', cb) + this.on('end', function (matches) { + cb(null, matches) + }) + } + + var self = this + var n = this.minimatch.set.length + this._processing = 0 + this.matches = new Array(n) + + this._emitQueue = [] + this._processQueue = [] + this.paused = false + + if (this.noprocess) + return this + + if (n === 0) + return done() + + for (var i = 0; i < n; i ++) { + this._process(this.minimatch.set[i], i, false, done) + } + + function done () { + --self._processing + if (self._processing <= 0) + self._finish() + } +} + +Glob.prototype._finish = function () { + assert(this instanceof Glob) + if (this.aborted) + return + + if (this.realpath && !this._didRealpath) + return this._realpath() + + common.finish(this) + this.emit('end', this.found) +} + +Glob.prototype._realpath = function () { + if (this._didRealpath) + return + + this._didRealpath = true + + var n = this.matches.length + if (n === 0) + return this._finish() + + var self = this + for (var i = 0; i < this.matches.length; i++) + this._realpathSet(i, next) + + function next () { + if (--n === 0) + self._finish() + } +} + +Glob.prototype._realpathSet = function (index, cb) { + var matchset = this.matches[index] + if (!matchset) + return cb() + + var found = Object.keys(matchset) + var self = this + var n = found.length + + if (n === 0) + return cb() + + var set = this.matches[index] = Object.create(null) + found.forEach(function (p, i) { + // If there's a problem with the stat, then it means that + // one or more of the links in the realpath couldn't be + // resolved. just return the abs value in that case. + p = self._makeAbs(p) + fs.realpath(p, self.realpathCache, function (er, real) { + if (!er) + set[real] = true + else if (er.syscall === 'stat') + set[p] = true + else + self.emit('error', er) // srsly wtf right here + + if (--n === 0) { + self.matches[index] = set + cb() + } + }) + }) +} + +Glob.prototype._mark = function (p) { + return common.mark(this, p) +} + +Glob.prototype._makeAbs = function (f) { + return common.makeAbs(this, f) +} + +Glob.prototype.abort = function () { + this.aborted = true + this.emit('abort') +} + +Glob.prototype.pause = function () { + if (!this.paused) { + this.paused = true + this.emit('pause') + } +} + +Glob.prototype.resume = function () { + if (this.paused) { + this.emit('resume') + this.paused = false + if (this._emitQueue.length) { + var eq = this._emitQueue.slice(0) + this._emitQueue.length = 0 + for (var i = 0; i < eq.length; i ++) { + var e = eq[i] + this._emitMatch(e[0], e[1]) + } + } + if (this._processQueue.length) { + var pq = this._processQueue.slice(0) + this._processQueue.length = 0 + for (var i = 0; i < pq.length; i ++) { + var p = pq[i] + this._processing-- + this._process(p[0], p[1], p[2], p[3]) + } + } + } +} + +Glob.prototype._process = function (pattern, index, inGlobStar, cb) { + assert(this instanceof Glob) + assert(typeof cb === 'function') + + if (this.aborted) + return + + this._processing++ + if (this.paused) { + this._processQueue.push([pattern, index, inGlobStar, cb]) + return + } + + //console.error('PROCESS %d', this._processing, pattern) + + // Get the first [n] parts of pattern that are all strings. + var n = 0 + while (typeof pattern[n] === 'string') { + n ++ + } + // now n is the index of the first one that is *not* a string. + + // see if there's anything else + var prefix + switch (n) { + // if not, then this is rather simple + case pattern.length: + this._processSimple(pattern.join('/'), index, cb) + return + + case 0: + // pattern *starts* with some non-trivial item. + // going to readdir(cwd), but not include the prefix in matches. + prefix = null + break + + default: + // pattern has some string bits in the front. + // whatever it starts with, whether that's 'absolute' like /foo/bar, + // or 'relative' like '../baz' + prefix = pattern.slice(0, n).join('/') + break + } + + var remain = pattern.slice(n) + + // get the list of entries. + var read + if (prefix === null) + read = '.' + else if (isAbsolute(prefix) || isAbsolute(pattern.join('/'))) { + if (!prefix || !isAbsolute(prefix)) + prefix = '/' + prefix + read = prefix + } else + read = prefix + + var abs = this._makeAbs(read) + + //if ignored, skip _processing + if (childrenIgnored(this, read)) + return cb() + + var isGlobStar = remain[0] === minimatch.GLOBSTAR + if (isGlobStar) + this._processGlobStar(prefix, read, abs, remain, index, inGlobStar, cb) + else + this._processReaddir(prefix, read, abs, remain, index, inGlobStar, cb) +} + +Glob.prototype._processReaddir = function (prefix, read, abs, remain, index, inGlobStar, cb) { + var self = this + this._readdir(abs, inGlobStar, function (er, entries) { + return self._processReaddir2(prefix, read, abs, remain, index, inGlobStar, entries, cb) + }) +} + +Glob.prototype._processReaddir2 = function (prefix, read, abs, remain, index, inGlobStar, entries, cb) { + + // if the abs isn't a dir, then nothing can match! + if (!entries) + return cb() + + // It will only match dot entries if it starts with a dot, or if + // dot is set. Stuff like @(.foo|.bar) isn't allowed. + var pn = remain[0] + var negate = !!this.minimatch.negate + var rawGlob = pn._glob + var dotOk = this.dot || rawGlob.charAt(0) === '.' + + var matchedEntries = [] + for (var i = 0; i < entries.length; i++) { + var e = entries[i] + if (e.charAt(0) !== '.' || dotOk) { + var m + if (negate && !prefix) { + m = !e.match(pn) + } else { + m = e.match(pn) + } + if (m) + matchedEntries.push(e) + } + } + + //console.error('prd2', prefix, entries, remain[0]._glob, matchedEntries) + + var len = matchedEntries.length + // If there are no matched entries, then nothing matches. + if (len === 0) + return cb() + + // if this is the last remaining pattern bit, then no need for + // an additional stat *unless* the user has specified mark or + // stat explicitly. We know they exist, since readdir returned + // them. + + if (remain.length === 1 && !this.mark && !this.stat) { + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + if (prefix) { + if (prefix !== '/') + e = prefix + '/' + e + else + e = prefix + e + } + + if (e.charAt(0) === '/' && !this.nomount) { + e = path.join(this.root, e) + } + this._emitMatch(index, e) + } + // This was the last one, and no stats were needed + return cb() + } + + // now test all matched entries as stand-ins for that part + // of the pattern. + remain.shift() + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + var newPattern + if (prefix) { + if (prefix !== '/') + e = prefix + '/' + e + else + e = prefix + e + } + this._process([e].concat(remain), index, inGlobStar, cb) + } + cb() +} + +Glob.prototype._emitMatch = function (index, e) { + if (this.aborted) + return + + if (this.matches[index][e]) + return + + if (isIgnored(this, e)) + return + + if (this.paused) { + this._emitQueue.push([index, e]) + return + } + + var abs = this._makeAbs(e) + + if (this.nodir) { + var c = this.cache[abs] + if (c === 'DIR' || Array.isArray(c)) + return + } + + if (this.mark) + e = this._mark(e) + + this.matches[index][e] = true + + var st = this.statCache[abs] + if (st) + this.emit('stat', e, st) + + this.emit('match', e) +} + +Glob.prototype._readdirInGlobStar = function (abs, cb) { + if (this.aborted) + return + + // follow all symlinked directories forever + // just proceed as if this is a non-globstar situation + if (this.follow) + return this._readdir(abs, false, cb) + + var lstatkey = 'lstat\0' + abs + var self = this + var lstatcb = inflight(lstatkey, lstatcb_) + + if (lstatcb) + fs.lstat(abs, lstatcb) + + function lstatcb_ (er, lstat) { + if (er) + return cb() + + var isSym = lstat.isSymbolicLink() + self.symlinks[abs] = isSym + + // If it's not a symlink or a dir, then it's definitely a regular file. + // don't bother doing a readdir in that case. + if (!isSym && !lstat.isDirectory()) { + self.cache[abs] = 'FILE' + cb() + } else + self._readdir(abs, false, cb) + } +} + +Glob.prototype._readdir = function (abs, inGlobStar, cb) { + if (this.aborted) + return + + cb = inflight('readdir\0'+abs+'\0'+inGlobStar, cb) + if (!cb) + return + + //console.error('RD %j %j', +inGlobStar, abs) + if (inGlobStar && !ownProp(this.symlinks, abs)) + return this._readdirInGlobStar(abs, cb) + + if (ownProp(this.cache, abs)) { + var c = this.cache[abs] + if (!c || c === 'FILE') + return cb() + + if (Array.isArray(c)) + return cb(null, c) + } + + var self = this + fs.readdir(abs, readdirCb(this, abs, cb)) +} + +function readdirCb (self, abs, cb) { + return function (er, entries) { + if (er) + self._readdirError(abs, er, cb) + else + self._readdirEntries(abs, entries, cb) + } +} + +Glob.prototype._readdirEntries = function (abs, entries, cb) { + if (this.aborted) + return + + // if we haven't asked to stat everything, then just + // assume that everything in there exists, so we can avoid + // having to stat it a second time. + if (!this.mark && !this.stat) { + for (var i = 0; i < entries.length; i ++) { + var e = entries[i] + if (abs === '/') + e = abs + e + else + e = abs + '/' + e + this.cache[e] = true + } + } + + this.cache[abs] = entries + return cb(null, entries) +} + +Glob.prototype._readdirError = function (f, er, cb) { + if (this.aborted) + return + + // handle errors, and cache the information + switch (er.code) { + case 'ENOTSUP': // https://github.com/isaacs/node-glob/issues/205 + case 'ENOTDIR': // totally normal. means it *does* exist. + this.cache[this._makeAbs(f)] = 'FILE' + break + + case 'ENOENT': // not terribly unusual + case 'ELOOP': + case 'ENAMETOOLONG': + case 'UNKNOWN': + this.cache[this._makeAbs(f)] = false + break + + default: // some unusual error. Treat as failure. + this.cache[this._makeAbs(f)] = false + if (this.strict) { + this.emit('error', er) + // If the error is handled, then we abort + // if not, we threw out of here + this.abort() + } + if (!this.silent) + console.error('glob error', er) + break + } + + return cb() +} + +Glob.prototype._processGlobStar = function (prefix, read, abs, remain, index, inGlobStar, cb) { + var self = this + this._readdir(abs, inGlobStar, function (er, entries) { + self._processGlobStar2(prefix, read, abs, remain, index, inGlobStar, entries, cb) + }) +} + + +Glob.prototype._processGlobStar2 = function (prefix, read, abs, remain, index, inGlobStar, entries, cb) { + //console.error('pgs2', prefix, remain[0], entries) + + // no entries means not a dir, so it can never have matches + // foo.txt/** doesn't match foo.txt + if (!entries) + return cb() + + // test without the globstar, and with every child both below + // and replacing the globstar. + var remainWithoutGlobStar = remain.slice(1) + var gspref = prefix ? [ prefix ] : [] + var noGlobStar = gspref.concat(remainWithoutGlobStar) + + // the noGlobStar pattern exits the inGlobStar state + this._process(noGlobStar, index, false, cb) + + var isSym = this.symlinks[abs] + var len = entries.length + + // If it's a symlink, and we're in a globstar, then stop + if (isSym && inGlobStar) + return cb() + + for (var i = 0; i < len; i++) { + var e = entries[i] + if (e.charAt(0) === '.' && !this.dot) + continue + + // these two cases enter the inGlobStar state + var instead = gspref.concat(entries[i], remainWithoutGlobStar) + this._process(instead, index, true, cb) + + var below = gspref.concat(entries[i], remain) + this._process(below, index, true, cb) + } + + cb() +} + +Glob.prototype._processSimple = function (prefix, index, cb) { + // XXX review this. Shouldn't it be doing the mounting etc + // before doing stat? kinda weird? + var self = this + this._stat(prefix, function (er, exists) { + self._processSimple2(prefix, index, er, exists, cb) + }) +} +Glob.prototype._processSimple2 = function (prefix, index, er, exists, cb) { + + //console.error('ps2', prefix, exists) + + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + // If it doesn't exist, then just mark the lack of results + if (!exists) + return cb() + + if (prefix && isAbsolute(prefix) && !this.nomount) { + var trail = /[\/\\]$/.test(prefix) + if (prefix.charAt(0) === '/') { + prefix = path.join(this.root, prefix) + } else { + prefix = path.resolve(this.root, prefix) + if (trail) + prefix += '/' + } + } + + if (process.platform === 'win32') + prefix = prefix.replace(/\\/g, '/') + + // Mark this as a match + this._emitMatch(index, prefix) + cb() +} + +// Returns either 'DIR', 'FILE', or false +Glob.prototype._stat = function (f, cb) { + var abs = this._makeAbs(f) + var needDir = f.slice(-1) === '/' + + if (f.length > this.maxLength) + return cb() + + if (!this.stat && ownProp(this.cache, abs)) { + var c = this.cache[abs] + + if (Array.isArray(c)) + c = 'DIR' + + // It exists, but maybe not how we need it + if (!needDir || c === 'DIR') + return cb(null, c) + + if (needDir && c === 'FILE') + return cb() + + // otherwise we have to stat, because maybe c=true + // if we know it exists, but not what it is. + } + + var exists + var stat = this.statCache[abs] + if (stat !== undefined) { + if (stat === false) + return cb(null, stat) + else { + var type = stat.isDirectory() ? 'DIR' : 'FILE' + if (needDir && type === 'FILE') + return cb() + else + return cb(null, type, stat) + } + } + + var self = this + var statcb = inflight('stat\0' + abs, lstatcb_) + if (statcb) + fs.lstat(abs, statcb) + + function lstatcb_ (er, lstat) { + if (lstat && lstat.isSymbolicLink()) { + // If it's a symlink, then treat it as the target, unless + // the target does not exist, then treat it as a file. + return fs.stat(abs, function (er, stat) { + if (er) + self._stat2(f, abs, null, lstat, cb) + else + self._stat2(f, abs, er, stat, cb) + }) + } else { + self._stat2(f, abs, er, lstat, cb) + } + } +} + +Glob.prototype._stat2 = function (f, abs, er, stat, cb) { + if (er) { + this.statCache[abs] = false + return cb() + } + + var needDir = f.slice(-1) === '/' + this.statCache[abs] = stat + + if (abs.slice(-1) === '/' && !stat.isDirectory()) + return cb(null, false, stat) + + var c = stat.isDirectory() ? 'DIR' : 'FILE' + this.cache[abs] = this.cache[abs] || c + + if (needDir && c !== 'DIR') + return cb() + + return cb(null, c, stat) +} + +}).call(this,require('_process')) +},{"./common.js":15,"./sync.js":17,"_process":24,"assert":9,"events":14,"fs":12,"inflight":18,"inherits":19,"minimatch":20,"once":21,"path":22,"path-is-absolute":23,"util":28}],17:[function(require,module,exports){ +(function (process){ +module.exports = globSync +globSync.GlobSync = GlobSync + +var fs = require('fs') +var minimatch = require('minimatch') +var Minimatch = minimatch.Minimatch +var Glob = require('./glob.js').Glob +var util = require('util') +var path = require('path') +var assert = require('assert') +var isAbsolute = require('path-is-absolute') +var common = require('./common.js') +var alphasort = common.alphasort +var alphasorti = common.alphasorti +var setopts = common.setopts +var ownProp = common.ownProp +var childrenIgnored = common.childrenIgnored + +function globSync (pattern, options) { + if (typeof options === 'function' || arguments.length === 3) + throw new TypeError('callback provided to sync glob\n'+ + 'See: https://github.com/isaacs/node-glob/issues/167') + + return new GlobSync(pattern, options).found +} + +function GlobSync (pattern, options) { + if (!pattern) + throw new Error('must provide pattern') + + if (typeof options === 'function' || arguments.length === 3) + throw new TypeError('callback provided to sync glob\n'+ + 'See: https://github.com/isaacs/node-glob/issues/167') + + if (!(this instanceof GlobSync)) + return new GlobSync(pattern, options) + + setopts(this, pattern, options) + + if (this.noprocess) + return this + + var n = this.minimatch.set.length + this.matches = new Array(n) + for (var i = 0; i < n; i ++) { + this._process(this.minimatch.set[i], i, false) + } + this._finish() +} + +GlobSync.prototype._finish = function () { + assert(this instanceof GlobSync) + if (this.realpath) { + var self = this + this.matches.forEach(function (matchset, index) { + var set = self.matches[index] = Object.create(null) + for (var p in matchset) { + try { + p = self._makeAbs(p) + var real = fs.realpathSync(p, self.realpathCache) + set[real] = true + } catch (er) { + if (er.syscall === 'stat') + set[self._makeAbs(p)] = true + else + throw er + } + } + }) + } + common.finish(this) +} + + +GlobSync.prototype._process = function (pattern, index, inGlobStar) { + assert(this instanceof GlobSync) + + // Get the first [n] parts of pattern that are all strings. + var n = 0 + while (typeof pattern[n] === 'string') { + n ++ + } + // now n is the index of the first one that is *not* a string. + + // See if there's anything else + var prefix + switch (n) { + // if not, then this is rather simple + case pattern.length: + this._processSimple(pattern.join('/'), index) + return + + case 0: + // pattern *starts* with some non-trivial item. + // going to readdir(cwd), but not include the prefix in matches. + prefix = null + break + + default: + // pattern has some string bits in the front. + // whatever it starts with, whether that's 'absolute' like /foo/bar, + // or 'relative' like '../baz' + prefix = pattern.slice(0, n).join('/') + break + } + + var remain = pattern.slice(n) + + // get the list of entries. + var read + if (prefix === null) + read = '.' + else if (isAbsolute(prefix) || isAbsolute(pattern.join('/'))) { + if (!prefix || !isAbsolute(prefix)) + prefix = '/' + prefix + read = prefix + } else + read = prefix + + var abs = this._makeAbs(read) + + //if ignored, skip processing + if (childrenIgnored(this, read)) + return + + var isGlobStar = remain[0] === minimatch.GLOBSTAR + if (isGlobStar) + this._processGlobStar(prefix, read, abs, remain, index, inGlobStar) + else + this._processReaddir(prefix, read, abs, remain, index, inGlobStar) +} + + +GlobSync.prototype._processReaddir = function (prefix, read, abs, remain, index, inGlobStar) { + var entries = this._readdir(abs, inGlobStar) + + // if the abs isn't a dir, then nothing can match! + if (!entries) + return + + // It will only match dot entries if it starts with a dot, or if + // dot is set. Stuff like @(.foo|.bar) isn't allowed. + var pn = remain[0] + var negate = !!this.minimatch.negate + var rawGlob = pn._glob + var dotOk = this.dot || rawGlob.charAt(0) === '.' + + var matchedEntries = [] + for (var i = 0; i < entries.length; i++) { + var e = entries[i] + if (e.charAt(0) !== '.' || dotOk) { + var m + if (negate && !prefix) { + m = !e.match(pn) + } else { + m = e.match(pn) + } + if (m) + matchedEntries.push(e) + } + } + + var len = matchedEntries.length + // If there are no matched entries, then nothing matches. + if (len === 0) + return + + // if this is the last remaining pattern bit, then no need for + // an additional stat *unless* the user has specified mark or + // stat explicitly. We know they exist, since readdir returned + // them. + + if (remain.length === 1 && !this.mark && !this.stat) { + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + if (prefix) { + if (prefix.slice(-1) !== '/') + e = prefix + '/' + e + else + e = prefix + e + } + + if (e.charAt(0) === '/' && !this.nomount) { + e = path.join(this.root, e) + } + this.matches[index][e] = true + } + // This was the last one, and no stats were needed + return + } + + // now test all matched entries as stand-ins for that part + // of the pattern. + remain.shift() + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + var newPattern + if (prefix) + newPattern = [prefix, e] + else + newPattern = [e] + this._process(newPattern.concat(remain), index, inGlobStar) + } +} + + +GlobSync.prototype._emitMatch = function (index, e) { + var abs = this._makeAbs(e) + if (this.mark) + e = this._mark(e) + + if (this.matches[index][e]) + return + + if (this.nodir) { + var c = this.cache[this._makeAbs(e)] + if (c === 'DIR' || Array.isArray(c)) + return + } + + this.matches[index][e] = true + if (this.stat) + this._stat(e) +} + + +GlobSync.prototype._readdirInGlobStar = function (abs) { + // follow all symlinked directories forever + // just proceed as if this is a non-globstar situation + if (this.follow) + return this._readdir(abs, false) + + var entries + var lstat + var stat + try { + lstat = fs.lstatSync(abs) + } catch (er) { + // lstat failed, doesn't exist + return null + } + + var isSym = lstat.isSymbolicLink() + this.symlinks[abs] = isSym + + // If it's not a symlink or a dir, then it's definitely a regular file. + // don't bother doing a readdir in that case. + if (!isSym && !lstat.isDirectory()) + this.cache[abs] = 'FILE' + else + entries = this._readdir(abs, false) + + return entries +} + +GlobSync.prototype._readdir = function (abs, inGlobStar) { + var entries + + if (inGlobStar && !ownProp(this.symlinks, abs)) + return this._readdirInGlobStar(abs) + + if (ownProp(this.cache, abs)) { + var c = this.cache[abs] + if (!c || c === 'FILE') + return null + + if (Array.isArray(c)) + return c + } + + try { + return this._readdirEntries(abs, fs.readdirSync(abs)) + } catch (er) { + this._readdirError(abs, er) + return null + } +} + +GlobSync.prototype._readdirEntries = function (abs, entries) { + // if we haven't asked to stat everything, then just + // assume that everything in there exists, so we can avoid + // having to stat it a second time. + if (!this.mark && !this.stat) { + for (var i = 0; i < entries.length; i ++) { + var e = entries[i] + if (abs === '/') + e = abs + e + else + e = abs + '/' + e + this.cache[e] = true + } + } + + this.cache[abs] = entries + + // mark and cache dir-ness + return entries +} + +GlobSync.prototype._readdirError = function (f, er) { + // handle errors, and cache the information + switch (er.code) { + case 'ENOTSUP': // https://github.com/isaacs/node-glob/issues/205 + case 'ENOTDIR': // totally normal. means it *does* exist. + this.cache[this._makeAbs(f)] = 'FILE' + break + + case 'ENOENT': // not terribly unusual + case 'ELOOP': + case 'ENAMETOOLONG': + case 'UNKNOWN': + this.cache[this._makeAbs(f)] = false + break + + default: // some unusual error. Treat as failure. + this.cache[this._makeAbs(f)] = false + if (this.strict) + throw er + if (!this.silent) + console.error('glob error', er) + break + } +} + +GlobSync.prototype._processGlobStar = function (prefix, read, abs, remain, index, inGlobStar) { + + var entries = this._readdir(abs, inGlobStar) + + // no entries means not a dir, so it can never have matches + // foo.txt/** doesn't match foo.txt + if (!entries) + return + + // test without the globstar, and with every child both below + // and replacing the globstar. + var remainWithoutGlobStar = remain.slice(1) + var gspref = prefix ? [ prefix ] : [] + var noGlobStar = gspref.concat(remainWithoutGlobStar) + + // the noGlobStar pattern exits the inGlobStar state + this._process(noGlobStar, index, false) + + var len = entries.length + var isSym = this.symlinks[abs] + + // If it's a symlink, and we're in a globstar, then stop + if (isSym && inGlobStar) + return + + for (var i = 0; i < len; i++) { + var e = entries[i] + if (e.charAt(0) === '.' && !this.dot) + continue + + // these two cases enter the inGlobStar state + var instead = gspref.concat(entries[i], remainWithoutGlobStar) + this._process(instead, index, true) + + var below = gspref.concat(entries[i], remain) + this._process(below, index, true) + } +} + +GlobSync.prototype._processSimple = function (prefix, index) { + // XXX review this. Shouldn't it be doing the mounting etc + // before doing stat? kinda weird? + var exists = this._stat(prefix) + + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + // If it doesn't exist, then just mark the lack of results + if (!exists) + return + + if (prefix && isAbsolute(prefix) && !this.nomount) { + var trail = /[\/\\]$/.test(prefix) + if (prefix.charAt(0) === '/') { + prefix = path.join(this.root, prefix) + } else { + prefix = path.resolve(this.root, prefix) + if (trail) + prefix += '/' + } + } + + if (process.platform === 'win32') + prefix = prefix.replace(/\\/g, '/') + + // Mark this as a match + this.matches[index][prefix] = true +} + +// Returns either 'DIR', 'FILE', or false +GlobSync.prototype._stat = function (f) { + var abs = this._makeAbs(f) + var needDir = f.slice(-1) === '/' + + if (f.length > this.maxLength) + return false + + if (!this.stat && ownProp(this.cache, abs)) { + var c = this.cache[abs] + + if (Array.isArray(c)) + c = 'DIR' + + // It exists, but maybe not how we need it + if (!needDir || c === 'DIR') + return c + + if (needDir && c === 'FILE') + return false + + // otherwise we have to stat, because maybe c=true + // if we know it exists, but not what it is. + } + + var exists + var stat = this.statCache[abs] + if (!stat) { + var lstat + try { + lstat = fs.lstatSync(abs) + } catch (er) { + return false + } + + if (lstat.isSymbolicLink()) { + try { + stat = fs.statSync(abs) + } catch (er) { + stat = lstat + } + } else { + stat = lstat + } + } + + this.statCache[abs] = stat + + var c = stat.isDirectory() ? 'DIR' : 'FILE' + this.cache[abs] = this.cache[abs] || c + + if (needDir && c !== 'DIR') + return false + + return c +} + +GlobSync.prototype._mark = function (p) { + return common.mark(this, p) +} + +GlobSync.prototype._makeAbs = function (f) { + return common.makeAbs(this, f) +} + +}).call(this,require('_process')) +},{"./common.js":15,"./glob.js":16,"_process":24,"assert":9,"fs":12,"minimatch":20,"path":22,"path-is-absolute":23,"util":28}],18:[function(require,module,exports){ +(function (process){ +var wrappy = require('wrappy') +var reqs = Object.create(null) +var once = require('once') + +module.exports = wrappy(inflight) + +function inflight (key, cb) { + if (reqs[key]) { + reqs[key].push(cb) + return null + } else { + reqs[key] = [cb] + return makeres(key) + } +} + +function makeres (key) { + return once(function RES () { + var cbs = reqs[key] + var len = cbs.length + var args = slice(arguments) + + // XXX It's somewhat ambiguous whether a new callback added in this + // pass should be queued for later execution if something in the + // list of callbacks throws, or if it should just be discarded. + // However, it's such an edge case that it hardly matters, and either + // choice is likely as surprising as the other. + // As it happens, we do go ahead and schedule it for later execution. + try { + for (var i = 0; i < len; i++) { + cbs[i].apply(null, args) + } + } finally { + if (cbs.length > len) { + // added more in the interim. + // de-zalgo, just in case, but don't call again. + cbs.splice(0, len) + process.nextTick(function () { + RES.apply(null, args) + }) + } else { + delete reqs[key] + } + } + }) +} + +function slice (args) { + var length = args.length + var array = [] + + for (var i = 0; i < length; i++) array[i] = args[i] + return array +} + +}).call(this,require('_process')) +},{"_process":24,"once":21,"wrappy":29}],19:[function(require,module,exports){ +if (typeof Object.create === 'function') { + // implementation from standard node.js 'util' module + module.exports = function inherits(ctor, superCtor) { + ctor.super_ = superCtor + ctor.prototype = Object.create(superCtor.prototype, { + constructor: { + value: ctor, + enumerable: false, + writable: true, + configurable: true + } + }); + }; +} else { + // old school shim for old browsers + module.exports = function inherits(ctor, superCtor) { + ctor.super_ = superCtor + var TempCtor = function () {} + TempCtor.prototype = superCtor.prototype + ctor.prototype = new TempCtor() + ctor.prototype.constructor = ctor + } +} + +},{}],20:[function(require,module,exports){ +module.exports = minimatch +minimatch.Minimatch = Minimatch + +var path = { sep: '/' } +try { + path = require('path') +} catch (er) {} + +var GLOBSTAR = minimatch.GLOBSTAR = Minimatch.GLOBSTAR = {} +var expand = require('brace-expansion') + +var plTypes = { + '!': { open: '(?:(?!(?:', close: '))[^/]*?)'}, + '?': { open: '(?:', close: ')?' }, + '+': { open: '(?:', close: ')+' }, + '*': { open: '(?:', close: ')*' }, + '@': { open: '(?:', close: ')' } +} + +// any single thing other than / +// don't need to escape / when using new RegExp() +var qmark = '[^/]' + +// * => any number of characters +var star = qmark + '*?' + +// ** when dots are allowed. Anything goes, except .. and . +// not (^ or / followed by one or two dots followed by $ or /), +// followed by anything, any number of times. +var twoStarDot = '(?:(?!(?:\\\/|^)(?:\\.{1,2})($|\\\/)).)*?' + +// not a ^ or / followed by a dot, +// followed by anything, any number of times. +var twoStarNoDot = '(?:(?!(?:\\\/|^)\\.).)*?' + +// characters that need to be escaped in RegExp. +var reSpecials = charSet('().*{}+?[]^$\\!') + +// "abc" -> { a:true, b:true, c:true } +function charSet (s) { + return s.split('').reduce(function (set, c) { + set[c] = true + return set + }, {}) +} + +// normalizes slashes. +var slashSplit = /\/+/ + +minimatch.filter = filter +function filter (pattern, options) { + options = options || {} + return function (p, i, list) { + return minimatch(p, pattern, options) + } +} + +function ext (a, b) { + a = a || {} + b = b || {} + var t = {} + Object.keys(b).forEach(function (k) { + t[k] = b[k] + }) + Object.keys(a).forEach(function (k) { + t[k] = a[k] + }) + return t +} + +minimatch.defaults = function (def) { + if (!def || !Object.keys(def).length) return minimatch + + var orig = minimatch + + var m = function minimatch (p, pattern, options) { + return orig.minimatch(p, pattern, ext(def, options)) + } + + m.Minimatch = function Minimatch (pattern, options) { + return new orig.Minimatch(pattern, ext(def, options)) + } + + return m +} + +Minimatch.defaults = function (def) { + if (!def || !Object.keys(def).length) return Minimatch + return minimatch.defaults(def).Minimatch +} + +function minimatch (p, pattern, options) { + if (typeof pattern !== 'string') { + throw new TypeError('glob pattern string required') + } + + if (!options) options = {} + + // shortcut: comments match nothing. + if (!options.nocomment && pattern.charAt(0) === '#') { + return false + } + + // "" only matches "" + if (pattern.trim() === '') return p === '' + + return new Minimatch(pattern, options).match(p) +} + +function Minimatch (pattern, options) { + if (!(this instanceof Minimatch)) { + return new Minimatch(pattern, options) + } + + if (typeof pattern !== 'string') { + throw new TypeError('glob pattern string required') + } + + if (!options) options = {} + pattern = pattern.trim() + + // windows support: need to use /, not \ + if (path.sep !== '/') { + pattern = pattern.split(path.sep).join('/') + } + + this.options = options + this.set = [] + this.pattern = pattern + this.regexp = null + this.negate = false + this.comment = false + this.empty = false + + // make the set of regexps etc. + this.make() +} + +Minimatch.prototype.debug = function () {} + +Minimatch.prototype.make = make +function make () { + // don't do it more than once. + if (this._made) return + + var pattern = this.pattern + var options = this.options + + // empty patterns and comments match nothing. + if (!options.nocomment && pattern.charAt(0) === '#') { + this.comment = true + return + } + if (!pattern) { + this.empty = true + return + } + + // step 1: figure out negation, etc. + this.parseNegate() + + // step 2: expand braces + var set = this.globSet = this.braceExpand() + + if (options.debug) this.debug = console.error + + this.debug(this.pattern, set) + + // step 3: now we have a set, so turn each one into a series of path-portion + // matching patterns. + // These will be regexps, except in the case of "**", which is + // set to the GLOBSTAR object for globstar behavior, + // and will not contain any / characters + set = this.globParts = set.map(function (s) { + return s.split(slashSplit) + }) + + this.debug(this.pattern, set) + + // glob --> regexps + set = set.map(function (s, si, set) { + return s.map(this.parse, this) + }, this) + + this.debug(this.pattern, set) + + // filter out everything that didn't compile properly. + set = set.filter(function (s) { + return s.indexOf(false) === -1 + }) + + this.debug(this.pattern, set) + + this.set = set +} + +Minimatch.prototype.parseNegate = parseNegate +function parseNegate () { + var pattern = this.pattern + var negate = false + var options = this.options + var negateOffset = 0 + + if (options.nonegate) return + + for (var i = 0, l = pattern.length + ; i < l && pattern.charAt(i) === '!' + ; i++) { + negate = !negate + negateOffset++ + } + + if (negateOffset) this.pattern = pattern.substr(negateOffset) + this.negate = negate +} + +// Brace expansion: +// a{b,c}d -> abd acd +// a{b,}c -> abc ac +// a{0..3}d -> a0d a1d a2d a3d +// a{b,c{d,e}f}g -> abg acdfg acefg +// a{b,c}d{e,f}g -> abdeg acdeg abdeg abdfg +// +// Invalid sets are not expanded. +// a{2..}b -> a{2..}b +// a{b}c -> a{b}c +minimatch.braceExpand = function (pattern, options) { + return braceExpand(pattern, options) +} + +Minimatch.prototype.braceExpand = braceExpand + +function braceExpand (pattern, options) { + if (!options) { + if (this instanceof Minimatch) { + options = this.options + } else { + options = {} + } + } + + pattern = typeof pattern === 'undefined' + ? this.pattern : pattern + + if (typeof pattern === 'undefined') { + throw new TypeError('undefined pattern') + } + + if (options.nobrace || + !pattern.match(/\{.*\}/)) { + // shortcut. no need to expand. + return [pattern] + } + + return expand(pattern) +} + +// parse a component of the expanded set. +// At this point, no pattern may contain "/" in it +// so we're going to return a 2d array, where each entry is the full +// pattern, split on '/', and then turned into a regular expression. +// A regexp is made at the end which joins each array with an +// escaped /, and another full one which joins each regexp with |. +// +// Following the lead of Bash 4.1, note that "**" only has special meaning +// when it is the *only* thing in a path portion. Otherwise, any series +// of * is equivalent to a single *. Globstar behavior is enabled by +// default, and can be disabled by setting options.noglobstar. +Minimatch.prototype.parse = parse +var SUBPARSE = {} +function parse (pattern, isSub) { + if (pattern.length > 1024 * 64) { + throw new TypeError('pattern is too long') + } + + var options = this.options + + // shortcuts + if (!options.noglobstar && pattern === '**') return GLOBSTAR + if (pattern === '') return '' + + var re = '' + var hasMagic = !!options.nocase + var escaping = false + // ? => one single character + var patternListStack = [] + var negativeLists = [] + var stateChar + var inClass = false + var reClassStart = -1 + var classStart = -1 + // . and .. never match anything that doesn't start with ., + // even when options.dot is set. + var patternStart = pattern.charAt(0) === '.' ? '' // anything + // not (start or / followed by . or .. followed by / or end) + : options.dot ? '(?!(?:^|\\\/)\\.{1,2}(?:$|\\\/))' + : '(?!\\.)' + var self = this + + function clearStateChar () { + if (stateChar) { + // we had some state-tracking character + // that wasn't consumed by this pass. + switch (stateChar) { + case '*': + re += star + hasMagic = true + break + case '?': + re += qmark + hasMagic = true + break + default: + re += '\\' + stateChar + break + } + self.debug('clearStateChar %j %j', stateChar, re) + stateChar = false + } + } + + for (var i = 0, len = pattern.length, c + ; (i < len) && (c = pattern.charAt(i)) + ; i++) { + this.debug('%s\t%s %s %j', pattern, i, re, c) + + // skip over any that are escaped. + if (escaping && reSpecials[c]) { + re += '\\' + c + escaping = false + continue + } + + switch (c) { + case '/': + // completely not allowed, even escaped. + // Should already be path-split by now. + return false + + case '\\': + clearStateChar() + escaping = true + continue + + // the various stateChar values + // for the "extglob" stuff. + case '?': + case '*': + case '+': + case '@': + case '!': + this.debug('%s\t%s %s %j <-- stateChar', pattern, i, re, c) + + // all of those are literals inside a class, except that + // the glob [!a] means [^a] in regexp + if (inClass) { + this.debug(' in class') + if (c === '!' && i === classStart + 1) c = '^' + re += c + continue + } + + // if we already have a stateChar, then it means + // that there was something like ** or +? in there. + // Handle the stateChar, then proceed with this one. + self.debug('call clearStateChar %j', stateChar) + clearStateChar() + stateChar = c + // if extglob is disabled, then +(asdf|foo) isn't a thing. + // just clear the statechar *now*, rather than even diving into + // the patternList stuff. + if (options.noext) clearStateChar() + continue + + case '(': + if (inClass) { + re += '(' + continue + } + + if (!stateChar) { + re += '\\(' + continue + } + + patternListStack.push({ + type: stateChar, + start: i - 1, + reStart: re.length, + open: plTypes[stateChar].open, + close: plTypes[stateChar].close + }) + // negation is (?:(?!js)[^/]*) + re += stateChar === '!' ? '(?:(?!(?:' : '(?:' + this.debug('plType %j %j', stateChar, re) + stateChar = false + continue + + case ')': + if (inClass || !patternListStack.length) { + re += '\\)' + continue + } + + clearStateChar() + hasMagic = true + var pl = patternListStack.pop() + // negation is (?:(?!js)[^/]*) + // The others are (?:) + re += pl.close + if (pl.type === '!') { + negativeLists.push(pl) + } + pl.reEnd = re.length + continue + + case '|': + if (inClass || !patternListStack.length || escaping) { + re += '\\|' + escaping = false + continue + } + + clearStateChar() + re += '|' + continue + + // these are mostly the same in regexp and glob + case '[': + // swallow any state-tracking char before the [ + clearStateChar() + + if (inClass) { + re += '\\' + c + continue + } + + inClass = true + classStart = i + reClassStart = re.length + re += c + continue + + case ']': + // a right bracket shall lose its special + // meaning and represent itself in + // a bracket expression if it occurs + // first in the list. -- POSIX.2 2.8.3.2 + if (i === classStart + 1 || !inClass) { + re += '\\' + c + escaping = false + continue + } + + // handle the case where we left a class open. + // "[z-a]" is valid, equivalent to "\[z-a\]" + if (inClass) { + // split where the last [ was, make sure we don't have + // an invalid re. if so, re-walk the contents of the + // would-be class to re-translate any characters that + // were passed through as-is + // TODO: It would probably be faster to determine this + // without a try/catch and a new RegExp, but it's tricky + // to do safely. For now, this is safe and works. + var cs = pattern.substring(classStart + 1, i) + try { + RegExp('[' + cs + ']') + } catch (er) { + // not a valid class! + var sp = this.parse(cs, SUBPARSE) + re = re.substr(0, reClassStart) + '\\[' + sp[0] + '\\]' + hasMagic = hasMagic || sp[1] + inClass = false + continue + } + } + + // finish up the class. + hasMagic = true + inClass = false + re += c + continue + + default: + // swallow any state char that wasn't consumed + clearStateChar() + + if (escaping) { + // no need + escaping = false + } else if (reSpecials[c] + && !(c === '^' && inClass)) { + re += '\\' + } + + re += c + + } // switch + } // for + + // handle the case where we left a class open. + // "[abc" is valid, equivalent to "\[abc" + if (inClass) { + // split where the last [ was, and escape it + // this is a huge pita. We now have to re-walk + // the contents of the would-be class to re-translate + // any characters that were passed through as-is + cs = pattern.substr(classStart + 1) + sp = this.parse(cs, SUBPARSE) + re = re.substr(0, reClassStart) + '\\[' + sp[0] + hasMagic = hasMagic || sp[1] + } + + // handle the case where we had a +( thing at the *end* + // of the pattern. + // each pattern list stack adds 3 chars, and we need to go through + // and escape any | chars that were passed through as-is for the regexp. + // Go through and escape them, taking care not to double-escape any + // | chars that were already escaped. + for (pl = patternListStack.pop(); pl; pl = patternListStack.pop()) { + var tail = re.slice(pl.reStart + pl.open.length) + this.debug('setting tail', re, pl) + // maybe some even number of \, then maybe 1 \, followed by a | + tail = tail.replace(/((?:\\{2}){0,64})(\\?)\|/g, function (_, $1, $2) { + if (!$2) { + // the | isn't already escaped, so escape it. + $2 = '\\' + } + + // need to escape all those slashes *again*, without escaping the + // one that we need for escaping the | character. As it works out, + // escaping an even number of slashes can be done by simply repeating + // it exactly after itself. That's why this trick works. + // + // I am sorry that you have to see this. + return $1 + $1 + $2 + '|' + }) + + this.debug('tail=%j\n %s', tail, tail, pl, re) + var t = pl.type === '*' ? star + : pl.type === '?' ? qmark + : '\\' + pl.type + + hasMagic = true + re = re.slice(0, pl.reStart) + t + '\\(' + tail + } + + // handle trailing things that only matter at the very end. + clearStateChar() + if (escaping) { + // trailing \\ + re += '\\\\' + } + + // only need to apply the nodot start if the re starts with + // something that could conceivably capture a dot + var addPatternStart = false + switch (re.charAt(0)) { + case '.': + case '[': + case '(': addPatternStart = true + } + + // Hack to work around lack of negative lookbehind in JS + // A pattern like: *.!(x).!(y|z) needs to ensure that a name + // like 'a.xyz.yz' doesn't match. So, the first negative + // lookahead, has to look ALL the way ahead, to the end of + // the pattern. + for (var n = negativeLists.length - 1; n > -1; n--) { + var nl = negativeLists[n] + + var nlBefore = re.slice(0, nl.reStart) + var nlFirst = re.slice(nl.reStart, nl.reEnd - 8) + var nlLast = re.slice(nl.reEnd - 8, nl.reEnd) + var nlAfter = re.slice(nl.reEnd) + + nlLast += nlAfter + + // Handle nested stuff like *(*.js|!(*.json)), where open parens + // mean that we should *not* include the ) in the bit that is considered + // "after" the negated section. + var openParensBefore = nlBefore.split('(').length - 1 + var cleanAfter = nlAfter + for (i = 0; i < openParensBefore; i++) { + cleanAfter = cleanAfter.replace(/\)[+*?]?/, '') + } + nlAfter = cleanAfter + + var dollar = '' + if (nlAfter === '' && isSub !== SUBPARSE) { + dollar = '$' + } + var newRe = nlBefore + nlFirst + nlAfter + dollar + nlLast + re = newRe + } + + // if the re is not "" at this point, then we need to make sure + // it doesn't match against an empty path part. + // Otherwise a/* will match a/, which it should not. + if (re !== '' && hasMagic) { + re = '(?=.)' + re + } + + if (addPatternStart) { + re = patternStart + re + } + + // parsing just a piece of a larger pattern. + if (isSub === SUBPARSE) { + return [re, hasMagic] + } + + // skip the regexp for non-magical patterns + // unescape anything in it, though, so that it'll be + // an exact match against a file etc. + if (!hasMagic) { + return globUnescape(pattern) + } + + var flags = options.nocase ? 'i' : '' + try { + var regExp = new RegExp('^' + re + '$', flags) + } catch (er) { + // If it was an invalid regular expression, then it can't match + // anything. This trick looks for a character after the end of + // the string, which is of course impossible, except in multi-line + // mode, but it's not a /m regex. + return new RegExp('$.') + } + + regExp._glob = pattern + regExp._src = re + + return regExp +} + +minimatch.makeRe = function (pattern, options) { + return new Minimatch(pattern, options || {}).makeRe() +} + +Minimatch.prototype.makeRe = makeRe +function makeRe () { + if (this.regexp || this.regexp === false) return this.regexp + + // at this point, this.set is a 2d array of partial + // pattern strings, or "**". + // + // It's better to use .match(). This function shouldn't + // be used, really, but it's pretty convenient sometimes, + // when you just want to work with a regex. + var set = this.set + + if (!set.length) { + this.regexp = false + return this.regexp + } + var options = this.options + + var twoStar = options.noglobstar ? star + : options.dot ? twoStarDot + : twoStarNoDot + var flags = options.nocase ? 'i' : '' + + var re = set.map(function (pattern) { + return pattern.map(function (p) { + return (p === GLOBSTAR) ? twoStar + : (typeof p === 'string') ? regExpEscape(p) + : p._src + }).join('\\\/') + }).join('|') + + // must match entire pattern + // ending in a * or ** will make it less strict. + re = '^(?:' + re + ')$' + + // can match anything, as long as it's not this. + if (this.negate) re = '^(?!' + re + ').*$' + + try { + this.regexp = new RegExp(re, flags) + } catch (ex) { + this.regexp = false + } + return this.regexp +} + +minimatch.match = function (list, pattern, options) { + options = options || {} + var mm = new Minimatch(pattern, options) + list = list.filter(function (f) { + return mm.match(f) + }) + if (mm.options.nonull && !list.length) { + list.push(pattern) + } + return list +} + +Minimatch.prototype.match = match +function match (f, partial) { + this.debug('match', f, this.pattern) + // short-circuit in the case of busted things. + // comments, etc. + if (this.comment) return false + if (this.empty) return f === '' + + if (f === '/' && partial) return true + + var options = this.options + + // windows: need to use /, not \ + if (path.sep !== '/') { + f = f.split(path.sep).join('/') + } + + // treat the test path as a set of pathparts. + f = f.split(slashSplit) + this.debug(this.pattern, 'split', f) + + // just ONE of the pattern sets in this.set needs to match + // in order for it to be valid. If negating, then just one + // match means that we have failed. + // Either way, return on the first hit. + + var set = this.set + this.debug(this.pattern, 'set', set) + + // Find the basename of the path by looking for the last non-empty segment + var filename + var i + for (i = f.length - 1; i >= 0; i--) { + filename = f[i] + if (filename) break + } + + for (i = 0; i < set.length; i++) { + var pattern = set[i] + var file = f + if (options.matchBase && pattern.length === 1) { + file = [filename] + } + var hit = this.matchOne(file, pattern, partial) + if (hit) { + if (options.flipNegate) return true + return !this.negate + } + } + + // didn't get any hits. this is success if it's a negative + // pattern, failure otherwise. + if (options.flipNegate) return false + return this.negate +} + +// set partial to true to test if, for example, +// "/a/b" matches the start of "/*/b/*/d" +// Partial means, if you run out of file before you run +// out of pattern, then that's fine, as long as all +// the parts match. +Minimatch.prototype.matchOne = function (file, pattern, partial) { + var options = this.options + + this.debug('matchOne', + { 'this': this, file: file, pattern: pattern }) + + this.debug('matchOne', file.length, pattern.length) + + for (var fi = 0, + pi = 0, + fl = file.length, + pl = pattern.length + ; (fi < fl) && (pi < pl) + ; fi++, pi++) { + this.debug('matchOne loop') + var p = pattern[pi] + var f = file[fi] + + this.debug(pattern, p, f) + + // should be impossible. + // some invalid regexp stuff in the set. + if (p === false) return false + + if (p === GLOBSTAR) { + this.debug('GLOBSTAR', [pattern, p, f]) + + // "**" + // a/**/b/**/c would match the following: + // a/b/x/y/z/c + // a/x/y/z/b/c + // a/b/x/b/x/c + // a/b/c + // To do this, take the rest of the pattern after + // the **, and see if it would match the file remainder. + // If so, return success. + // If not, the ** "swallows" a segment, and try again. + // This is recursively awful. + // + // a/**/b/**/c matching a/b/x/y/z/c + // - a matches a + // - doublestar + // - matchOne(b/x/y/z/c, b/**/c) + // - b matches b + // - doublestar + // - matchOne(x/y/z/c, c) -> no + // - matchOne(y/z/c, c) -> no + // - matchOne(z/c, c) -> no + // - matchOne(c, c) yes, hit + var fr = fi + var pr = pi + 1 + if (pr === pl) { + this.debug('** at the end') + // a ** at the end will just swallow the rest. + // We have found a match. + // however, it will not swallow /.x, unless + // options.dot is set. + // . and .. are *never* matched by **, for explosively + // exponential reasons. + for (; fi < fl; fi++) { + if (file[fi] === '.' || file[fi] === '..' || + (!options.dot && file[fi].charAt(0) === '.')) return false + } + return true + } + + // ok, let's see if we can swallow whatever we can. + while (fr < fl) { + var swallowee = file[fr] + + this.debug('\nglobstar while', file, fr, pattern, pr, swallowee) + + // XXX remove this slice. Just pass the start index. + if (this.matchOne(file.slice(fr), pattern.slice(pr), partial)) { + this.debug('globstar found match!', fr, fl, swallowee) + // found a match. + return true + } else { + // can't swallow "." or ".." ever. + // can only swallow ".foo" when explicitly asked. + if (swallowee === '.' || swallowee === '..' || + (!options.dot && swallowee.charAt(0) === '.')) { + this.debug('dot detected!', file, fr, pattern, pr) + break + } + + // ** swallows a segment, and continue. + this.debug('globstar swallow a segment, and continue') + fr++ + } + } + + // no match was found. + // However, in partial mode, we can't say this is necessarily over. + // If there's more *pattern* left, then + if (partial) { + // ran out of file + this.debug('\n>>> no match, partial?', file, fr, pattern, pr) + if (fr === fl) return true + } + return false + } + + // something other than ** + // non-magic patterns just have to match exactly + // patterns with magic have been turned into regexps. + var hit + if (typeof p === 'string') { + if (options.nocase) { + hit = f.toLowerCase() === p.toLowerCase() + } else { + hit = f === p + } + this.debug('string match', p, f, hit) + } else { + hit = f.match(p) + this.debug('pattern match', p, f, hit) + } + + if (!hit) return false + } + + // Note: ending in / means that we'll get a final "" + // at the end of the pattern. This can only match a + // corresponding "" at the end of the file. + // If the file ends in /, then it can only match a + // a pattern that ends in /, unless the pattern just + // doesn't have any more for it. But, a/b/ should *not* + // match "a/b/*", even though "" matches against the + // [^/]*? pattern, except in partial mode, where it might + // simply not be reached yet. + // However, a/b/ should still satisfy a/* + + // now either we fell off the end of the pattern, or we're done. + if (fi === fl && pi === pl) { + // ran out of pattern and filename at the same time. + // an exact hit! + return true + } else if (fi === fl) { + // ran out of file, but still had pattern left. + // this is ok if we're doing the match as part of + // a glob fs traversal. + return partial + } else if (pi === pl) { + // ran out of pattern, still have file left. + // this is only acceptable if we're on the very last + // empty segment of a file with a trailing slash. + // a/* should match a/b/ + var emptyFileEnd = (fi === fl - 1) && (file[fi] === '') + return emptyFileEnd + } + + // should be unreachable. + throw new Error('wtf?') +} + +// replace stuff like \* with * +function globUnescape (s) { + return s.replace(/\\(.)/g, '$1') +} + +function regExpEscape (s) { + return s.replace(/[-[\]{}()*+?.,\\^$|#\s]/g, '\\$&') +} + +},{"brace-expansion":11,"path":22}],21:[function(require,module,exports){ +var wrappy = require('wrappy') +module.exports = wrappy(once) +module.exports.strict = wrappy(onceStrict) + +once.proto = once(function () { + Object.defineProperty(Function.prototype, 'once', { + value: function () { + return once(this) + }, + configurable: true + }) + + Object.defineProperty(Function.prototype, 'onceStrict', { + value: function () { + return onceStrict(this) + }, + configurable: true + }) +}) + +function once (fn) { + var f = function () { + if (f.called) return f.value + f.called = true + return f.value = fn.apply(this, arguments) + } + f.called = false + return f +} + +function onceStrict (fn) { + var f = function () { + if (f.called) + throw new Error(f.onceError) + f.called = true + return f.value = fn.apply(this, arguments) + } + var name = fn.name || 'Function wrapped with `once`' + f.onceError = name + " shouldn't be called more than once" + f.called = false + return f +} + +},{"wrappy":29}],22:[function(require,module,exports){ +(function (process){ +// Copyright Joyent, Inc. and other Node contributors. +// +// Permission is hereby granted, free of charge, to any person obtaining a +// copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to permit +// persons to whom the Software is furnished to do so, subject to the +// following conditions: +// +// The above copyright notice and this permission notice shall be included +// in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN +// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +// USE OR OTHER DEALINGS IN THE SOFTWARE. + +// resolves . and .. elements in a path array with directory names there +// must be no slashes, empty elements, or device names (c:\) in the array +// (so also no leading and trailing slashes - it does not distinguish +// relative and absolute paths) +function normalizeArray(parts, allowAboveRoot) { + // if the path tries to go above the root, `up` ends up > 0 + var up = 0; + for (var i = parts.length - 1; i >= 0; i--) { + var last = parts[i]; + if (last === '.') { + parts.splice(i, 1); + } else if (last === '..') { + parts.splice(i, 1); + up++; + } else if (up) { + parts.splice(i, 1); + up--; + } + } + + // if the path is allowed to go above the root, restore leading ..s + if (allowAboveRoot) { + for (; up--; up) { + parts.unshift('..'); + } + } + + return parts; +} + +// Split a filename into [root, dir, basename, ext], unix version +// 'root' is just a slash, or nothing. +var splitPathRe = + /^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/; +var splitPath = function(filename) { + return splitPathRe.exec(filename).slice(1); +}; + +// path.resolve([from ...], to) +// posix version +exports.resolve = function() { + var resolvedPath = '', + resolvedAbsolute = false; + + for (var i = arguments.length - 1; i >= -1 && !resolvedAbsolute; i--) { + var path = (i >= 0) ? arguments[i] : process.cwd(); + + // Skip empty and invalid entries + if (typeof path !== 'string') { + throw new TypeError('Arguments to path.resolve must be strings'); + } else if (!path) { + continue; + } + + resolvedPath = path + '/' + resolvedPath; + resolvedAbsolute = path.charAt(0) === '/'; + } + + // At this point the path should be resolved to a full absolute path, but + // handle relative paths to be safe (might happen when process.cwd() fails) + + // Normalize the path + resolvedPath = normalizeArray(filter(resolvedPath.split('/'), function(p) { + return !!p; + }), !resolvedAbsolute).join('/'); + + return ((resolvedAbsolute ? '/' : '') + resolvedPath) || '.'; +}; + +// path.normalize(path) +// posix version +exports.normalize = function(path) { + var isAbsolute = exports.isAbsolute(path), + trailingSlash = substr(path, -1) === '/'; + + // Normalize the path + path = normalizeArray(filter(path.split('/'), function(p) { + return !!p; + }), !isAbsolute).join('/'); + + if (!path && !isAbsolute) { + path = '.'; + } + if (path && trailingSlash) { + path += '/'; + } + + return (isAbsolute ? '/' : '') + path; +}; + +// posix version +exports.isAbsolute = function(path) { + return path.charAt(0) === '/'; +}; + +// posix version +exports.join = function() { + var paths = Array.prototype.slice.call(arguments, 0); + return exports.normalize(filter(paths, function(p, index) { + if (typeof p !== 'string') { + throw new TypeError('Arguments to path.join must be strings'); + } + return p; + }).join('/')); +}; + + +// path.relative(from, to) +// posix version +exports.relative = function(from, to) { + from = exports.resolve(from).substr(1); + to = exports.resolve(to).substr(1); + + function trim(arr) { + var start = 0; + for (; start < arr.length; start++) { + if (arr[start] !== '') break; + } + + var end = arr.length - 1; + for (; end >= 0; end--) { + if (arr[end] !== '') break; + } + + if (start > end) return []; + return arr.slice(start, end - start + 1); + } + + var fromParts = trim(from.split('/')); + var toParts = trim(to.split('/')); + + var length = Math.min(fromParts.length, toParts.length); + var samePartsLength = length; + for (var i = 0; i < length; i++) { + if (fromParts[i] !== toParts[i]) { + samePartsLength = i; + break; + } + } + + var outputParts = []; + for (var i = samePartsLength; i < fromParts.length; i++) { + outputParts.push('..'); + } + + outputParts = outputParts.concat(toParts.slice(samePartsLength)); + + return outputParts.join('/'); +}; + +exports.sep = '/'; +exports.delimiter = ':'; + +exports.dirname = function(path) { + var result = splitPath(path), + root = result[0], + dir = result[1]; + + if (!root && !dir) { + // No dirname whatsoever + return '.'; + } + + if (dir) { + // It has a dirname, strip trailing slash + dir = dir.substr(0, dir.length - 1); + } + + return root + dir; +}; + + +exports.basename = function(path, ext) { + var f = splitPath(path)[2]; + // TODO: make this comparison case-insensitive on windows? + if (ext && f.substr(-1 * ext.length) === ext) { + f = f.substr(0, f.length - ext.length); + } + return f; +}; + + +exports.extname = function(path) { + return splitPath(path)[3]; +}; + +function filter (xs, f) { + if (xs.filter) return xs.filter(f); + var res = []; + for (var i = 0; i < xs.length; i++) { + if (f(xs[i], i, xs)) res.push(xs[i]); + } + return res; +} + +// String.prototype.substr - negative index don't work in IE8 +var substr = 'ab'.substr(-1) === 'b' + ? function (str, start, len) { return str.substr(start, len) } + : function (str, start, len) { + if (start < 0) start = str.length + start; + return str.substr(start, len); + } +; + +}).call(this,require('_process')) +},{"_process":24}],23:[function(require,module,exports){ +(function (process){ +'use strict'; + +function posix(path) { + return path.charAt(0) === '/'; +} + +function win32(path) { + // https://github.com/nodejs/node/blob/b3fcc245fb25539909ef1d5eaa01dbf92e168633/lib/path.js#L56 + var splitDeviceRe = /^([a-zA-Z]:|[\\\/]{2}[^\\\/]+[\\\/]+[^\\\/]+)?([\\\/])?([\s\S]*?)$/; + var result = splitDeviceRe.exec(path); + var device = result[1] || ''; + var isUnc = Boolean(device && device.charAt(1) !== ':'); + + // UNC paths are always absolute + return Boolean(result[2] || isUnc); +} + +module.exports = process.platform === 'win32' ? win32 : posix; +module.exports.posix = posix; +module.exports.win32 = win32; + +}).call(this,require('_process')) +},{"_process":24}],24:[function(require,module,exports){ +// shim for using process in browser +var process = module.exports = {}; + +// cached from whatever global is present so that test runners that stub it +// don't break things. But we need to wrap it in a try catch in case it is +// wrapped in strict mode code which doesn't define any globals. It's inside a +// function because try/catches deoptimize in certain engines. + +var cachedSetTimeout; +var cachedClearTimeout; + +function defaultSetTimout() { + throw new Error('setTimeout has not been defined'); +} +function defaultClearTimeout () { + throw new Error('clearTimeout has not been defined'); +} +(function () { + try { + if (typeof setTimeout === 'function') { + cachedSetTimeout = setTimeout; + } else { + cachedSetTimeout = defaultSetTimout; + } + } catch (e) { + cachedSetTimeout = defaultSetTimout; + } + try { + if (typeof clearTimeout === 'function') { + cachedClearTimeout = clearTimeout; + } else { + cachedClearTimeout = defaultClearTimeout; + } + } catch (e) { + cachedClearTimeout = defaultClearTimeout; + } +} ()) +function runTimeout(fun) { + if (cachedSetTimeout === setTimeout) { + //normal enviroments in sane situations + return setTimeout(fun, 0); + } + // if setTimeout wasn't available but was latter defined + if ((cachedSetTimeout === defaultSetTimout || !cachedSetTimeout) && setTimeout) { + cachedSetTimeout = setTimeout; + return setTimeout(fun, 0); + } + try { + // when when somebody has screwed with setTimeout but no I.E. maddness + return cachedSetTimeout(fun, 0); + } catch(e){ + try { + // When we are in I.E. but the script has been evaled so I.E. doesn't trust the global object when called normally + return cachedSetTimeout.call(null, fun, 0); + } catch(e){ + // same as above but when it's a version of I.E. that must have the global object for 'this', hopfully our context correct otherwise it will throw a global error + return cachedSetTimeout.call(this, fun, 0); + } + } + + +} +function runClearTimeout(marker) { + if (cachedClearTimeout === clearTimeout) { + //normal enviroments in sane situations + return clearTimeout(marker); + } + // if clearTimeout wasn't available but was latter defined + if ((cachedClearTimeout === defaultClearTimeout || !cachedClearTimeout) && clearTimeout) { + cachedClearTimeout = clearTimeout; + return clearTimeout(marker); + } + try { + // when when somebody has screwed with setTimeout but no I.E. maddness + return cachedClearTimeout(marker); + } catch (e){ + try { + // When we are in I.E. but the script has been evaled so I.E. doesn't trust the global object when called normally + return cachedClearTimeout.call(null, marker); + } catch (e){ + // same as above but when it's a version of I.E. that must have the global object for 'this', hopfully our context correct otherwise it will throw a global error. + // Some versions of I.E. have different rules for clearTimeout vs setTimeout + return cachedClearTimeout.call(this, marker); + } + } + + + +} +var queue = []; +var draining = false; +var currentQueue; +var queueIndex = -1; + +function cleanUpNextTick() { + if (!draining || !currentQueue) { + return; + } + draining = false; + if (currentQueue.length) { + queue = currentQueue.concat(queue); + } else { + queueIndex = -1; + } + if (queue.length) { + drainQueue(); + } +} + +function drainQueue() { + if (draining) { + return; + } + var timeout = runTimeout(cleanUpNextTick); + draining = true; + + var len = queue.length; + while(len) { + currentQueue = queue; + queue = []; + while (++queueIndex < len) { + if (currentQueue) { + currentQueue[queueIndex].run(); + } + } + queueIndex = -1; + len = queue.length; + } + currentQueue = null; + draining = false; + runClearTimeout(timeout); +} + +process.nextTick = function (fun) { + var args = new Array(arguments.length - 1); + if (arguments.length > 1) { + for (var i = 1; i < arguments.length; i++) { + args[i - 1] = arguments[i]; + } + } + queue.push(new Item(fun, args)); + if (queue.length === 1 && !draining) { + runTimeout(drainQueue); + } +}; + +// v8 likes predictible objects +function Item(fun, array) { + this.fun = fun; + this.array = array; +} +Item.prototype.run = function () { + this.fun.apply(null, this.array); +}; +process.title = 'browser'; +process.browser = true; +process.env = {}; +process.argv = []; +process.version = ''; // empty string to avoid regexp issues +process.versions = {}; + +function noop() {} + +process.on = noop; +process.addListener = noop; +process.once = noop; +process.off = noop; +process.removeListener = noop; +process.removeAllListeners = noop; +process.emit = noop; +process.prependListener = noop; +process.prependOnceListener = noop; + +process.listeners = function (name) { return [] } + +process.binding = function (name) { + throw new Error('process.binding is not supported'); +}; + +process.cwd = function () { return '/' }; +process.chdir = function (dir) { + throw new Error('process.chdir is not supported'); +}; +process.umask = function() { return 0; }; + +},{}],25:[function(require,module,exports){ +// Underscore.js 1.8.3 +// http://underscorejs.org +// (c) 2009-2015 Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors +// Underscore may be freely distributed under the MIT license. + +(function() { + + // Baseline setup + // -------------- + + // Establish the root object, `window` in the browser, or `exports` on the server. + var root = this; + + // Save the previous value of the `_` variable. + var previousUnderscore = root._; + + // Save bytes in the minified (but not gzipped) version: + var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype; + + // Create quick reference variables for speed access to core prototypes. + var + push = ArrayProto.push, + slice = ArrayProto.slice, + toString = ObjProto.toString, + hasOwnProperty = ObjProto.hasOwnProperty; + + // All **ECMAScript 5** native function implementations that we hope to use + // are declared here. + var + nativeIsArray = Array.isArray, + nativeKeys = Object.keys, + nativeBind = FuncProto.bind, + nativeCreate = Object.create; + + // Naked function reference for surrogate-prototype-swapping. + var Ctor = function(){}; + + // Create a safe reference to the Underscore object for use below. + var _ = function(obj) { + if (obj instanceof _) return obj; + if (!(this instanceof _)) return new _(obj); + this._wrapped = obj; + }; + + // Export the Underscore object for **Node.js**, with + // backwards-compatibility for the old `require()` API. If we're in + // the browser, add `_` as a global object. + if (typeof exports !== 'undefined') { + if (typeof module !== 'undefined' && module.exports) { + exports = module.exports = _; + } + exports._ = _; + } else { + root._ = _; + } + + // Current version. + _.VERSION = '1.8.3'; + + // Internal function that returns an efficient (for current engines) version + // of the passed-in callback, to be repeatedly applied in other Underscore + // functions. + var optimizeCb = function(func, context, argCount) { + if (context === void 0) return func; + switch (argCount == null ? 3 : argCount) { + case 1: return function(value) { + return func.call(context, value); + }; + case 2: return function(value, other) { + return func.call(context, value, other); + }; + case 3: return function(value, index, collection) { + return func.call(context, value, index, collection); + }; + case 4: return function(accumulator, value, index, collection) { + return func.call(context, accumulator, value, index, collection); + }; + } + return function() { + return func.apply(context, arguments); + }; + }; + + // A mostly-internal function to generate callbacks that can be applied + // to each element in a collection, returning the desired result — either + // identity, an arbitrary callback, a property matcher, or a property accessor. + var cb = function(value, context, argCount) { + if (value == null) return _.identity; + if (_.isFunction(value)) return optimizeCb(value, context, argCount); + if (_.isObject(value)) return _.matcher(value); + return _.property(value); + }; + _.iteratee = function(value, context) { + return cb(value, context, Infinity); + }; + + // An internal function for creating assigner functions. + var createAssigner = function(keysFunc, undefinedOnly) { + return function(obj) { + var length = arguments.length; + if (length < 2 || obj == null) return obj; + for (var index = 1; index < length; index++) { + var source = arguments[index], + keys = keysFunc(source), + l = keys.length; + for (var i = 0; i < l; i++) { + var key = keys[i]; + if (!undefinedOnly || obj[key] === void 0) obj[key] = source[key]; + } + } + return obj; + }; + }; + + // An internal function for creating a new object that inherits from another. + var baseCreate = function(prototype) { + if (!_.isObject(prototype)) return {}; + if (nativeCreate) return nativeCreate(prototype); + Ctor.prototype = prototype; + var result = new Ctor; + Ctor.prototype = null; + return result; + }; + + var property = function(key) { + return function(obj) { + return obj == null ? void 0 : obj[key]; + }; + }; + + // Helper for collection methods to determine whether a collection + // should be iterated as an array or as an object + // Related: http://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength + // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094 + var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1; + var getLength = property('length'); + var isArrayLike = function(collection) { + var length = getLength(collection); + return typeof length == 'number' && length >= 0 && length <= MAX_ARRAY_INDEX; + }; + + // Collection Functions + // -------------------- + + // The cornerstone, an `each` implementation, aka `forEach`. + // Handles raw objects in addition to array-likes. Treats all + // sparse array-likes as if they were dense. + _.each = _.forEach = function(obj, iteratee, context) { + iteratee = optimizeCb(iteratee, context); + var i, length; + if (isArrayLike(obj)) { + for (i = 0, length = obj.length; i < length; i++) { + iteratee(obj[i], i, obj); + } + } else { + var keys = _.keys(obj); + for (i = 0, length = keys.length; i < length; i++) { + iteratee(obj[keys[i]], keys[i], obj); + } + } + return obj; + }; + + // Return the results of applying the iteratee to each element. + _.map = _.collect = function(obj, iteratee, context) { + iteratee = cb(iteratee, context); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length, + results = Array(length); + for (var index = 0; index < length; index++) { + var currentKey = keys ? keys[index] : index; + results[index] = iteratee(obj[currentKey], currentKey, obj); + } + return results; + }; + + // Create a reducing function iterating left or right. + function createReduce(dir) { + // Optimized iterator function as using arguments.length + // in the main function will deoptimize the, see #1991. + function iterator(obj, iteratee, memo, keys, index, length) { + for (; index >= 0 && index < length; index += dir) { + var currentKey = keys ? keys[index] : index; + memo = iteratee(memo, obj[currentKey], currentKey, obj); + } + return memo; + } + + return function(obj, iteratee, memo, context) { + iteratee = optimizeCb(iteratee, context, 4); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length, + index = dir > 0 ? 0 : length - 1; + // Determine the initial value if none is provided. + if (arguments.length < 3) { + memo = obj[keys ? keys[index] : index]; + index += dir; + } + return iterator(obj, iteratee, memo, keys, index, length); + }; + } + + // **Reduce** builds up a single result from a list of values, aka `inject`, + // or `foldl`. + _.reduce = _.foldl = _.inject = createReduce(1); + + // The right-associative version of reduce, also known as `foldr`. + _.reduceRight = _.foldr = createReduce(-1); + + // Return the first value which passes a truth test. Aliased as `detect`. + _.find = _.detect = function(obj, predicate, context) { + var key; + if (isArrayLike(obj)) { + key = _.findIndex(obj, predicate, context); + } else { + key = _.findKey(obj, predicate, context); + } + if (key !== void 0 && key !== -1) return obj[key]; + }; + + // Return all the elements that pass a truth test. + // Aliased as `select`. + _.filter = _.select = function(obj, predicate, context) { + var results = []; + predicate = cb(predicate, context); + _.each(obj, function(value, index, list) { + if (predicate(value, index, list)) results.push(value); + }); + return results; + }; + + // Return all the elements for which a truth test fails. + _.reject = function(obj, predicate, context) { + return _.filter(obj, _.negate(cb(predicate)), context); + }; + + // Determine whether all of the elements match a truth test. + // Aliased as `all`. + _.every = _.all = function(obj, predicate, context) { + predicate = cb(predicate, context); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length; + for (var index = 0; index < length; index++) { + var currentKey = keys ? keys[index] : index; + if (!predicate(obj[currentKey], currentKey, obj)) return false; + } + return true; + }; + + // Determine if at least one element in the object matches a truth test. + // Aliased as `any`. + _.some = _.any = function(obj, predicate, context) { + predicate = cb(predicate, context); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length; + for (var index = 0; index < length; index++) { + var currentKey = keys ? keys[index] : index; + if (predicate(obj[currentKey], currentKey, obj)) return true; + } + return false; + }; + + // Determine if the array or object contains a given item (using `===`). + // Aliased as `includes` and `include`. + _.contains = _.includes = _.include = function(obj, item, fromIndex, guard) { + if (!isArrayLike(obj)) obj = _.values(obj); + if (typeof fromIndex != 'number' || guard) fromIndex = 0; + return _.indexOf(obj, item, fromIndex) >= 0; + }; + + // Invoke a method (with arguments) on every item in a collection. + _.invoke = function(obj, method) { + var args = slice.call(arguments, 2); + var isFunc = _.isFunction(method); + return _.map(obj, function(value) { + var func = isFunc ? method : value[method]; + return func == null ? func : func.apply(value, args); + }); + }; + + // Convenience version of a common use case of `map`: fetching a property. + _.pluck = function(obj, key) { + return _.map(obj, _.property(key)); + }; + + // Convenience version of a common use case of `filter`: selecting only objects + // containing specific `key:value` pairs. + _.where = function(obj, attrs) { + return _.filter(obj, _.matcher(attrs)); + }; + + // Convenience version of a common use case of `find`: getting the first object + // containing specific `key:value` pairs. + _.findWhere = function(obj, attrs) { + return _.find(obj, _.matcher(attrs)); + }; + + // Return the maximum element (or element-based computation). + _.max = function(obj, iteratee, context) { + var result = -Infinity, lastComputed = -Infinity, + value, computed; + if (iteratee == null && obj != null) { + obj = isArrayLike(obj) ? obj : _.values(obj); + for (var i = 0, length = obj.length; i < length; i++) { + value = obj[i]; + if (value > result) { + result = value; + } + } + } else { + iteratee = cb(iteratee, context); + _.each(obj, function(value, index, list) { + computed = iteratee(value, index, list); + if (computed > lastComputed || computed === -Infinity && result === -Infinity) { + result = value; + lastComputed = computed; + } + }); + } + return result; + }; + + // Return the minimum element (or element-based computation). + _.min = function(obj, iteratee, context) { + var result = Infinity, lastComputed = Infinity, + value, computed; + if (iteratee == null && obj != null) { + obj = isArrayLike(obj) ? obj : _.values(obj); + for (var i = 0, length = obj.length; i < length; i++) { + value = obj[i]; + if (value < result) { + result = value; + } + } + } else { + iteratee = cb(iteratee, context); + _.each(obj, function(value, index, list) { + computed = iteratee(value, index, list); + if (computed < lastComputed || computed === Infinity && result === Infinity) { + result = value; + lastComputed = computed; + } + }); + } + return result; + }; + + // Shuffle a collection, using the modern version of the + // [Fisher-Yates shuffle](http://en.wikipedia.org/wiki/Fisher–Yates_shuffle). + _.shuffle = function(obj) { + var set = isArrayLike(obj) ? obj : _.values(obj); + var length = set.length; + var shuffled = Array(length); + for (var index = 0, rand; index < length; index++) { + rand = _.random(0, index); + if (rand !== index) shuffled[index] = shuffled[rand]; + shuffled[rand] = set[index]; + } + return shuffled; + }; + + // Sample **n** random values from a collection. + // If **n** is not specified, returns a single random element. + // The internal `guard` argument allows it to work with `map`. + _.sample = function(obj, n, guard) { + if (n == null || guard) { + if (!isArrayLike(obj)) obj = _.values(obj); + return obj[_.random(obj.length - 1)]; + } + return _.shuffle(obj).slice(0, Math.max(0, n)); + }; + + // Sort the object's values by a criterion produced by an iteratee. + _.sortBy = function(obj, iteratee, context) { + iteratee = cb(iteratee, context); + return _.pluck(_.map(obj, function(value, index, list) { + return { + value: value, + index: index, + criteria: iteratee(value, index, list) + }; + }).sort(function(left, right) { + var a = left.criteria; + var b = right.criteria; + if (a !== b) { + if (a > b || a === void 0) return 1; + if (a < b || b === void 0) return -1; + } + return left.index - right.index; + }), 'value'); + }; + + // An internal function used for aggregate "group by" operations. + var group = function(behavior) { + return function(obj, iteratee, context) { + var result = {}; + iteratee = cb(iteratee, context); + _.each(obj, function(value, index) { + var key = iteratee(value, index, obj); + behavior(result, value, key); + }); + return result; + }; + }; + + // Groups the object's values by a criterion. Pass either a string attribute + // to group by, or a function that returns the criterion. + _.groupBy = group(function(result, value, key) { + if (_.has(result, key)) result[key].push(value); else result[key] = [value]; + }); + + // Indexes the object's values by a criterion, similar to `groupBy`, but for + // when you know that your index values will be unique. + _.indexBy = group(function(result, value, key) { + result[key] = value; + }); + + // Counts instances of an object that group by a certain criterion. Pass + // either a string attribute to count by, or a function that returns the + // criterion. + _.countBy = group(function(result, value, key) { + if (_.has(result, key)) result[key]++; else result[key] = 1; + }); + + // Safely create a real, live array from anything iterable. + _.toArray = function(obj) { + if (!obj) return []; + if (_.isArray(obj)) return slice.call(obj); + if (isArrayLike(obj)) return _.map(obj, _.identity); + return _.values(obj); + }; + + // Return the number of elements in an object. + _.size = function(obj) { + if (obj == null) return 0; + return isArrayLike(obj) ? obj.length : _.keys(obj).length; + }; + + // Split a collection into two arrays: one whose elements all satisfy the given + // predicate, and one whose elements all do not satisfy the predicate. + _.partition = function(obj, predicate, context) { + predicate = cb(predicate, context); + var pass = [], fail = []; + _.each(obj, function(value, key, obj) { + (predicate(value, key, obj) ? pass : fail).push(value); + }); + return [pass, fail]; + }; + + // Array Functions + // --------------- + + // Get the first element of an array. Passing **n** will return the first N + // values in the array. Aliased as `head` and `take`. The **guard** check + // allows it to work with `_.map`. + _.first = _.head = _.take = function(array, n, guard) { + if (array == null) return void 0; + if (n == null || guard) return array[0]; + return _.initial(array, array.length - n); + }; + + // Returns everything but the last entry of the array. Especially useful on + // the arguments object. Passing **n** will return all the values in + // the array, excluding the last N. + _.initial = function(array, n, guard) { + return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n))); + }; + + // Get the last element of an array. Passing **n** will return the last N + // values in the array. + _.last = function(array, n, guard) { + if (array == null) return void 0; + if (n == null || guard) return array[array.length - 1]; + return _.rest(array, Math.max(0, array.length - n)); + }; + + // Returns everything but the first entry of the array. Aliased as `tail` and `drop`. + // Especially useful on the arguments object. Passing an **n** will return + // the rest N values in the array. + _.rest = _.tail = _.drop = function(array, n, guard) { + return slice.call(array, n == null || guard ? 1 : n); + }; + + // Trim out all falsy values from an array. + _.compact = function(array) { + return _.filter(array, _.identity); + }; + + // Internal implementation of a recursive `flatten` function. + var flatten = function(input, shallow, strict, startIndex) { + var output = [], idx = 0; + for (var i = startIndex || 0, length = getLength(input); i < length; i++) { + var value = input[i]; + if (isArrayLike(value) && (_.isArray(value) || _.isArguments(value))) { + //flatten current level of array or arguments object + if (!shallow) value = flatten(value, shallow, strict); + var j = 0, len = value.length; + output.length += len; + while (j < len) { + output[idx++] = value[j++]; + } + } else if (!strict) { + output[idx++] = value; + } + } + return output; + }; + + // Flatten out an array, either recursively (by default), or just one level. + _.flatten = function(array, shallow) { + return flatten(array, shallow, false); + }; + + // Return a version of the array that does not contain the specified value(s). + _.without = function(array) { + return _.difference(array, slice.call(arguments, 1)); + }; + + // Produce a duplicate-free version of the array. If the array has already + // been sorted, you have the option of using a faster algorithm. + // Aliased as `unique`. + _.uniq = _.unique = function(array, isSorted, iteratee, context) { + if (!_.isBoolean(isSorted)) { + context = iteratee; + iteratee = isSorted; + isSorted = false; + } + if (iteratee != null) iteratee = cb(iteratee, context); + var result = []; + var seen = []; + for (var i = 0, length = getLength(array); i < length; i++) { + var value = array[i], + computed = iteratee ? iteratee(value, i, array) : value; + if (isSorted) { + if (!i || seen !== computed) result.push(value); + seen = computed; + } else if (iteratee) { + if (!_.contains(seen, computed)) { + seen.push(computed); + result.push(value); + } + } else if (!_.contains(result, value)) { + result.push(value); + } + } + return result; + }; + + // Produce an array that contains the union: each distinct element from all of + // the passed-in arrays. + _.union = function() { + return _.uniq(flatten(arguments, true, true)); + }; + + // Produce an array that contains every item shared between all the + // passed-in arrays. + _.intersection = function(array) { + var result = []; + var argsLength = arguments.length; + for (var i = 0, length = getLength(array); i < length; i++) { + var item = array[i]; + if (_.contains(result, item)) continue; + for (var j = 1; j < argsLength; j++) { + if (!_.contains(arguments[j], item)) break; + } + if (j === argsLength) result.push(item); + } + return result; + }; + + // Take the difference between one array and a number of other arrays. + // Only the elements present in just the first array will remain. + _.difference = function(array) { + var rest = flatten(arguments, true, true, 1); + return _.filter(array, function(value){ + return !_.contains(rest, value); + }); + }; + + // Zip together multiple lists into a single array -- elements that share + // an index go together. + _.zip = function() { + return _.unzip(arguments); + }; + + // Complement of _.zip. Unzip accepts an array of arrays and groups + // each array's elements on shared indices + _.unzip = function(array) { + var length = array && _.max(array, getLength).length || 0; + var result = Array(length); + + for (var index = 0; index < length; index++) { + result[index] = _.pluck(array, index); + } + return result; + }; + + // Converts lists into objects. Pass either a single array of `[key, value]` + // pairs, or two parallel arrays of the same length -- one of keys, and one of + // the corresponding values. + _.object = function(list, values) { + var result = {}; + for (var i = 0, length = getLength(list); i < length; i++) { + if (values) { + result[list[i]] = values[i]; + } else { + result[list[i][0]] = list[i][1]; + } + } + return result; + }; + + // Generator function to create the findIndex and findLastIndex functions + function createPredicateIndexFinder(dir) { + return function(array, predicate, context) { + predicate = cb(predicate, context); + var length = getLength(array); + var index = dir > 0 ? 0 : length - 1; + for (; index >= 0 && index < length; index += dir) { + if (predicate(array[index], index, array)) return index; + } + return -1; + }; + } + + // Returns the first index on an array-like that passes a predicate test + _.findIndex = createPredicateIndexFinder(1); + _.findLastIndex = createPredicateIndexFinder(-1); + + // Use a comparator function to figure out the smallest index at which + // an object should be inserted so as to maintain order. Uses binary search. + _.sortedIndex = function(array, obj, iteratee, context) { + iteratee = cb(iteratee, context, 1); + var value = iteratee(obj); + var low = 0, high = getLength(array); + while (low < high) { + var mid = Math.floor((low + high) / 2); + if (iteratee(array[mid]) < value) low = mid + 1; else high = mid; + } + return low; + }; + + // Generator function to create the indexOf and lastIndexOf functions + function createIndexFinder(dir, predicateFind, sortedIndex) { + return function(array, item, idx) { + var i = 0, length = getLength(array); + if (typeof idx == 'number') { + if (dir > 0) { + i = idx >= 0 ? idx : Math.max(idx + length, i); + } else { + length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1; + } + } else if (sortedIndex && idx && length) { + idx = sortedIndex(array, item); + return array[idx] === item ? idx : -1; + } + if (item !== item) { + idx = predicateFind(slice.call(array, i, length), _.isNaN); + return idx >= 0 ? idx + i : -1; + } + for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) { + if (array[idx] === item) return idx; + } + return -1; + }; + } + + // Return the position of the first occurrence of an item in an array, + // or -1 if the item is not included in the array. + // If the array is large and already in sort order, pass `true` + // for **isSorted** to use binary search. + _.indexOf = createIndexFinder(1, _.findIndex, _.sortedIndex); + _.lastIndexOf = createIndexFinder(-1, _.findLastIndex); + + // Generate an integer Array containing an arithmetic progression. A port of + // the native Python `range()` function. See + // [the Python documentation](http://docs.python.org/library/functions.html#range). + _.range = function(start, stop, step) { + if (stop == null) { + stop = start || 0; + start = 0; + } + step = step || 1; + + var length = Math.max(Math.ceil((stop - start) / step), 0); + var range = Array(length); + + for (var idx = 0; idx < length; idx++, start += step) { + range[idx] = start; + } + + return range; + }; + + // Function (ahem) Functions + // ------------------ + + // Determines whether to execute a function as a constructor + // or a normal function with the provided arguments + var executeBound = function(sourceFunc, boundFunc, context, callingContext, args) { + if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args); + var self = baseCreate(sourceFunc.prototype); + var result = sourceFunc.apply(self, args); + if (_.isObject(result)) return result; + return self; + }; + + // Create a function bound to a given object (assigning `this`, and arguments, + // optionally). Delegates to **ECMAScript 5**'s native `Function.bind` if + // available. + _.bind = function(func, context) { + if (nativeBind && func.bind === nativeBind) return nativeBind.apply(func, slice.call(arguments, 1)); + if (!_.isFunction(func)) throw new TypeError('Bind must be called on a function'); + var args = slice.call(arguments, 2); + var bound = function() { + return executeBound(func, bound, context, this, args.concat(slice.call(arguments))); + }; + return bound; + }; + + // Partially apply a function by creating a version that has had some of its + // arguments pre-filled, without changing its dynamic `this` context. _ acts + // as a placeholder, allowing any combination of arguments to be pre-filled. + _.partial = function(func) { + var boundArgs = slice.call(arguments, 1); + var bound = function() { + var position = 0, length = boundArgs.length; + var args = Array(length); + for (var i = 0; i < length; i++) { + args[i] = boundArgs[i] === _ ? arguments[position++] : boundArgs[i]; + } + while (position < arguments.length) args.push(arguments[position++]); + return executeBound(func, bound, this, this, args); + }; + return bound; + }; + + // Bind a number of an object's methods to that object. Remaining arguments + // are the method names to be bound. Useful for ensuring that all callbacks + // defined on an object belong to it. + _.bindAll = function(obj) { + var i, length = arguments.length, key; + if (length <= 1) throw new Error('bindAll must be passed function names'); + for (i = 1; i < length; i++) { + key = arguments[i]; + obj[key] = _.bind(obj[key], obj); + } + return obj; + }; + + // Memoize an expensive function by storing its results. + _.memoize = function(func, hasher) { + var memoize = function(key) { + var cache = memoize.cache; + var address = '' + (hasher ? hasher.apply(this, arguments) : key); + if (!_.has(cache, address)) cache[address] = func.apply(this, arguments); + return cache[address]; + }; + memoize.cache = {}; + return memoize; + }; + + // Delays a function for the given number of milliseconds, and then calls + // it with the arguments supplied. + _.delay = function(func, wait) { + var args = slice.call(arguments, 2); + return setTimeout(function(){ + return func.apply(null, args); + }, wait); + }; + + // Defers a function, scheduling it to run after the current call stack has + // cleared. + _.defer = _.partial(_.delay, _, 1); + + // Returns a function, that, when invoked, will only be triggered at most once + // during a given window of time. Normally, the throttled function will run + // as much as it can, without ever going more than once per `wait` duration; + // but if you'd like to disable the execution on the leading edge, pass + // `{leading: false}`. To disable execution on the trailing edge, ditto. + _.throttle = function(func, wait, options) { + var context, args, result; + var timeout = null; + var previous = 0; + if (!options) options = {}; + var later = function() { + previous = options.leading === false ? 0 : _.now(); + timeout = null; + result = func.apply(context, args); + if (!timeout) context = args = null; + }; + return function() { + var now = _.now(); + if (!previous && options.leading === false) previous = now; + var remaining = wait - (now - previous); + context = this; + args = arguments; + if (remaining <= 0 || remaining > wait) { + if (timeout) { + clearTimeout(timeout); + timeout = null; + } + previous = now; + result = func.apply(context, args); + if (!timeout) context = args = null; + } else if (!timeout && options.trailing !== false) { + timeout = setTimeout(later, remaining); + } + return result; + }; + }; + + // Returns a function, that, as long as it continues to be invoked, will not + // be triggered. The function will be called after it stops being called for + // N milliseconds. If `immediate` is passed, trigger the function on the + // leading edge, instead of the trailing. + _.debounce = function(func, wait, immediate) { + var timeout, args, context, timestamp, result; + + var later = function() { + var last = _.now() - timestamp; + + if (last < wait && last >= 0) { + timeout = setTimeout(later, wait - last); + } else { + timeout = null; + if (!immediate) { + result = func.apply(context, args); + if (!timeout) context = args = null; + } + } + }; + + return function() { + context = this; + args = arguments; + timestamp = _.now(); + var callNow = immediate && !timeout; + if (!timeout) timeout = setTimeout(later, wait); + if (callNow) { + result = func.apply(context, args); + context = args = null; + } + + return result; + }; + }; + + // Returns the first function passed as an argument to the second, + // allowing you to adjust arguments, run code before and after, and + // conditionally execute the original function. + _.wrap = function(func, wrapper) { + return _.partial(wrapper, func); + }; + + // Returns a negated version of the passed-in predicate. + _.negate = function(predicate) { + return function() { + return !predicate.apply(this, arguments); + }; + }; + + // Returns a function that is the composition of a list of functions, each + // consuming the return value of the function that follows. + _.compose = function() { + var args = arguments; + var start = args.length - 1; + return function() { + var i = start; + var result = args[start].apply(this, arguments); + while (i--) result = args[i].call(this, result); + return result; + }; + }; + + // Returns a function that will only be executed on and after the Nth call. + _.after = function(times, func) { + return function() { + if (--times < 1) { + return func.apply(this, arguments); + } + }; + }; + + // Returns a function that will only be executed up to (but not including) the Nth call. + _.before = function(times, func) { + var memo; + return function() { + if (--times > 0) { + memo = func.apply(this, arguments); + } + if (times <= 1) func = null; + return memo; + }; + }; + + // Returns a function that will be executed at most one time, no matter how + // often you call it. Useful for lazy initialization. + _.once = _.partial(_.before, 2); + + // Object Functions + // ---------------- + + // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed. + var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString'); + var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString', + 'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString']; + + function collectNonEnumProps(obj, keys) { + var nonEnumIdx = nonEnumerableProps.length; + var constructor = obj.constructor; + var proto = (_.isFunction(constructor) && constructor.prototype) || ObjProto; + + // Constructor is a special case. + var prop = 'constructor'; + if (_.has(obj, prop) && !_.contains(keys, prop)) keys.push(prop); + + while (nonEnumIdx--) { + prop = nonEnumerableProps[nonEnumIdx]; + if (prop in obj && obj[prop] !== proto[prop] && !_.contains(keys, prop)) { + keys.push(prop); + } + } + } + + // Retrieve the names of an object's own properties. + // Delegates to **ECMAScript 5**'s native `Object.keys` + _.keys = function(obj) { + if (!_.isObject(obj)) return []; + if (nativeKeys) return nativeKeys(obj); + var keys = []; + for (var key in obj) if (_.has(obj, key)) keys.push(key); + // Ahem, IE < 9. + if (hasEnumBug) collectNonEnumProps(obj, keys); + return keys; + }; + + // Retrieve all the property names of an object. + _.allKeys = function(obj) { + if (!_.isObject(obj)) return []; + var keys = []; + for (var key in obj) keys.push(key); + // Ahem, IE < 9. + if (hasEnumBug) collectNonEnumProps(obj, keys); + return keys; + }; + + // Retrieve the values of an object's properties. + _.values = function(obj) { + var keys = _.keys(obj); + var length = keys.length; + var values = Array(length); + for (var i = 0; i < length; i++) { + values[i] = obj[keys[i]]; + } + return values; + }; + + // Returns the results of applying the iteratee to each element of the object + // In contrast to _.map it returns an object + _.mapObject = function(obj, iteratee, context) { + iteratee = cb(iteratee, context); + var keys = _.keys(obj), + length = keys.length, + results = {}, + currentKey; + for (var index = 0; index < length; index++) { + currentKey = keys[index]; + results[currentKey] = iteratee(obj[currentKey], currentKey, obj); + } + return results; + }; + + // Convert an object into a list of `[key, value]` pairs. + _.pairs = function(obj) { + var keys = _.keys(obj); + var length = keys.length; + var pairs = Array(length); + for (var i = 0; i < length; i++) { + pairs[i] = [keys[i], obj[keys[i]]]; + } + return pairs; + }; + + // Invert the keys and values of an object. The values must be serializable. + _.invert = function(obj) { + var result = {}; + var keys = _.keys(obj); + for (var i = 0, length = keys.length; i < length; i++) { + result[obj[keys[i]]] = keys[i]; + } + return result; + }; + + // Return a sorted list of the function names available on the object. + // Aliased as `methods` + _.functions = _.methods = function(obj) { + var names = []; + for (var key in obj) { + if (_.isFunction(obj[key])) names.push(key); + } + return names.sort(); + }; + + // Extend a given object with all the properties in passed-in object(s). + _.extend = createAssigner(_.allKeys); + + // Assigns a given object with all the own properties in the passed-in object(s) + // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign) + _.extendOwn = _.assign = createAssigner(_.keys); + + // Returns the first key on an object that passes a predicate test + _.findKey = function(obj, predicate, context) { + predicate = cb(predicate, context); + var keys = _.keys(obj), key; + for (var i = 0, length = keys.length; i < length; i++) { + key = keys[i]; + if (predicate(obj[key], key, obj)) return key; + } + }; + + // Return a copy of the object only containing the whitelisted properties. + _.pick = function(object, oiteratee, context) { + var result = {}, obj = object, iteratee, keys; + if (obj == null) return result; + if (_.isFunction(oiteratee)) { + keys = _.allKeys(obj); + iteratee = optimizeCb(oiteratee, context); + } else { + keys = flatten(arguments, false, false, 1); + iteratee = function(value, key, obj) { return key in obj; }; + obj = Object(obj); + } + for (var i = 0, length = keys.length; i < length; i++) { + var key = keys[i]; + var value = obj[key]; + if (iteratee(value, key, obj)) result[key] = value; + } + return result; + }; + + // Return a copy of the object without the blacklisted properties. + _.omit = function(obj, iteratee, context) { + if (_.isFunction(iteratee)) { + iteratee = _.negate(iteratee); + } else { + var keys = _.map(flatten(arguments, false, false, 1), String); + iteratee = function(value, key) { + return !_.contains(keys, key); + }; + } + return _.pick(obj, iteratee, context); + }; + + // Fill in a given object with default properties. + _.defaults = createAssigner(_.allKeys, true); + + // Creates an object that inherits from the given prototype object. + // If additional properties are provided then they will be added to the + // created object. + _.create = function(prototype, props) { + var result = baseCreate(prototype); + if (props) _.extendOwn(result, props); + return result; + }; + + // Create a (shallow-cloned) duplicate of an object. + _.clone = function(obj) { + if (!_.isObject(obj)) return obj; + return _.isArray(obj) ? obj.slice() : _.extend({}, obj); + }; + + // Invokes interceptor with the obj, and then returns obj. + // The primary purpose of this method is to "tap into" a method chain, in + // order to perform operations on intermediate results within the chain. + _.tap = function(obj, interceptor) { + interceptor(obj); + return obj; + }; + + // Returns whether an object has a given set of `key:value` pairs. + _.isMatch = function(object, attrs) { + var keys = _.keys(attrs), length = keys.length; + if (object == null) return !length; + var obj = Object(object); + for (var i = 0; i < length; i++) { + var key = keys[i]; + if (attrs[key] !== obj[key] || !(key in obj)) return false; + } + return true; + }; + + + // Internal recursive comparison function for `isEqual`. + var eq = function(a, b, aStack, bStack) { + // Identical objects are equal. `0 === -0`, but they aren't identical. + // See the [Harmony `egal` proposal](http://wiki.ecmascript.org/doku.php?id=harmony:egal). + if (a === b) return a !== 0 || 1 / a === 1 / b; + // A strict comparison is necessary because `null == undefined`. + if (a == null || b == null) return a === b; + // Unwrap any wrapped objects. + if (a instanceof _) a = a._wrapped; + if (b instanceof _) b = b._wrapped; + // Compare `[[Class]]` names. + var className = toString.call(a); + if (className !== toString.call(b)) return false; + switch (className) { + // Strings, numbers, regular expressions, dates, and booleans are compared by value. + case '[object RegExp]': + // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i') + case '[object String]': + // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is + // equivalent to `new String("5")`. + return '' + a === '' + b; + case '[object Number]': + // `NaN`s are equivalent, but non-reflexive. + // Object(NaN) is equivalent to NaN + if (+a !== +a) return +b !== +b; + // An `egal` comparison is performed for other numeric values. + return +a === 0 ? 1 / +a === 1 / b : +a === +b; + case '[object Date]': + case '[object Boolean]': + // Coerce dates and booleans to numeric primitive values. Dates are compared by their + // millisecond representations. Note that invalid dates with millisecond representations + // of `NaN` are not equivalent. + return +a === +b; + } + + var areArrays = className === '[object Array]'; + if (!areArrays) { + if (typeof a != 'object' || typeof b != 'object') return false; + + // Objects with different constructors are not equivalent, but `Object`s or `Array`s + // from different frames are. + var aCtor = a.constructor, bCtor = b.constructor; + if (aCtor !== bCtor && !(_.isFunction(aCtor) && aCtor instanceof aCtor && + _.isFunction(bCtor) && bCtor instanceof bCtor) + && ('constructor' in a && 'constructor' in b)) { + return false; + } + } + // Assume equality for cyclic structures. The algorithm for detecting cyclic + // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`. + + // Initializing stack of traversed objects. + // It's done here since we only need them for objects and arrays comparison. + aStack = aStack || []; + bStack = bStack || []; + var length = aStack.length; + while (length--) { + // Linear search. Performance is inversely proportional to the number of + // unique nested structures. + if (aStack[length] === a) return bStack[length] === b; + } + + // Add the first object to the stack of traversed objects. + aStack.push(a); + bStack.push(b); + + // Recursively compare objects and arrays. + if (areArrays) { + // Compare array lengths to determine if a deep comparison is necessary. + length = a.length; + if (length !== b.length) return false; + // Deep compare the contents, ignoring non-numeric properties. + while (length--) { + if (!eq(a[length], b[length], aStack, bStack)) return false; + } + } else { + // Deep compare objects. + var keys = _.keys(a), key; + length = keys.length; + // Ensure that both objects contain the same number of properties before comparing deep equality. + if (_.keys(b).length !== length) return false; + while (length--) { + // Deep compare each member + key = keys[length]; + if (!(_.has(b, key) && eq(a[key], b[key], aStack, bStack))) return false; + } + } + // Remove the first object from the stack of traversed objects. + aStack.pop(); + bStack.pop(); + return true; + }; + + // Perform a deep comparison to check if two objects are equal. + _.isEqual = function(a, b) { + return eq(a, b); + }; + + // Is a given array, string, or object empty? + // An "empty" object has no enumerable own-properties. + _.isEmpty = function(obj) { + if (obj == null) return true; + if (isArrayLike(obj) && (_.isArray(obj) || _.isString(obj) || _.isArguments(obj))) return obj.length === 0; + return _.keys(obj).length === 0; + }; + + // Is a given value a DOM element? + _.isElement = function(obj) { + return !!(obj && obj.nodeType === 1); + }; + + // Is a given value an array? + // Delegates to ECMA5's native Array.isArray + _.isArray = nativeIsArray || function(obj) { + return toString.call(obj) === '[object Array]'; + }; + + // Is a given variable an object? + _.isObject = function(obj) { + var type = typeof obj; + return type === 'function' || type === 'object' && !!obj; + }; + + // Add some isType methods: isArguments, isFunction, isString, isNumber, isDate, isRegExp, isError. + _.each(['Arguments', 'Function', 'String', 'Number', 'Date', 'RegExp', 'Error'], function(name) { + _['is' + name] = function(obj) { + return toString.call(obj) === '[object ' + name + ']'; + }; + }); + + // Define a fallback version of the method in browsers (ahem, IE < 9), where + // there isn't any inspectable "Arguments" type. + if (!_.isArguments(arguments)) { + _.isArguments = function(obj) { + return _.has(obj, 'callee'); + }; + } + + // Optimize `isFunction` if appropriate. Work around some typeof bugs in old v8, + // IE 11 (#1621), and in Safari 8 (#1929). + if (typeof /./ != 'function' && typeof Int8Array != 'object') { + _.isFunction = function(obj) { + return typeof obj == 'function' || false; + }; + } + + // Is a given object a finite number? + _.isFinite = function(obj) { + return isFinite(obj) && !isNaN(parseFloat(obj)); + }; + + // Is the given value `NaN`? (NaN is the only number which does not equal itself). + _.isNaN = function(obj) { + return _.isNumber(obj) && obj !== +obj; + }; + + // Is a given value a boolean? + _.isBoolean = function(obj) { + return obj === true || obj === false || toString.call(obj) === '[object Boolean]'; + }; + + // Is a given value equal to null? + _.isNull = function(obj) { + return obj === null; + }; + + // Is a given variable undefined? + _.isUndefined = function(obj) { + return obj === void 0; + }; + + // Shortcut function for checking if an object has a given property directly + // on itself (in other words, not on a prototype). + _.has = function(obj, key) { + return obj != null && hasOwnProperty.call(obj, key); + }; + + // Utility Functions + // ----------------- + + // Run Underscore.js in *noConflict* mode, returning the `_` variable to its + // previous owner. Returns a reference to the Underscore object. + _.noConflict = function() { + root._ = previousUnderscore; + return this; + }; + + // Keep the identity function around for default iteratees. + _.identity = function(value) { + return value; + }; + + // Predicate-generating functions. Often useful outside of Underscore. + _.constant = function(value) { + return function() { + return value; + }; + }; + + _.noop = function(){}; + + _.property = property; + + // Generates a function for a given object that returns a given property. + _.propertyOf = function(obj) { + return obj == null ? function(){} : function(key) { + return obj[key]; + }; + }; + + // Returns a predicate for checking whether an object has a given set of + // `key:value` pairs. + _.matcher = _.matches = function(attrs) { + attrs = _.extendOwn({}, attrs); + return function(obj) { + return _.isMatch(obj, attrs); + }; + }; + + // Run a function **n** times. + _.times = function(n, iteratee, context) { + var accum = Array(Math.max(0, n)); + iteratee = optimizeCb(iteratee, context, 1); + for (var i = 0; i < n; i++) accum[i] = iteratee(i); + return accum; + }; + + // Return a random integer between min and max (inclusive). + _.random = function(min, max) { + if (max == null) { + max = min; + min = 0; + } + return min + Math.floor(Math.random() * (max - min + 1)); + }; + + // A (possibly faster) way to get the current timestamp as an integer. + _.now = Date.now || function() { + return new Date().getTime(); + }; + + // List of HTML entities for escaping. + var escapeMap = { + '&': '&', + '<': '<', + '>': '>', + '"': '"', + "'": ''', + '`': '`' + }; + var unescapeMap = _.invert(escapeMap); + + // Functions for escaping and unescaping strings to/from HTML interpolation. + var createEscaper = function(map) { + var escaper = function(match) { + return map[match]; + }; + // Regexes for identifying a key that needs to be escaped + var source = '(?:' + _.keys(map).join('|') + ')'; + var testRegexp = RegExp(source); + var replaceRegexp = RegExp(source, 'g'); + return function(string) { + string = string == null ? '' : '' + string; + return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string; + }; + }; + _.escape = createEscaper(escapeMap); + _.unescape = createEscaper(unescapeMap); + + // If the value of the named `property` is a function then invoke it with the + // `object` as context; otherwise, return it. + _.result = function(object, property, fallback) { + var value = object == null ? void 0 : object[property]; + if (value === void 0) { + value = fallback; + } + return _.isFunction(value) ? value.call(object) : value; + }; + + // Generate a unique integer id (unique within the entire client session). + // Useful for temporary DOM ids. + var idCounter = 0; + _.uniqueId = function(prefix) { + var id = ++idCounter + ''; + return prefix ? prefix + id : id; + }; + + // By default, Underscore uses ERB-style template delimiters, change the + // following template settings to use alternative delimiters. + _.templateSettings = { + evaluate : /<%([\s\S]+?)%>/g, + interpolate : /<%=([\s\S]+?)%>/g, + escape : /<%-([\s\S]+?)%>/g + }; + + // When customizing `templateSettings`, if you don't want to define an + // interpolation, evaluation or escaping regex, we need one that is + // guaranteed not to match. + var noMatch = /(.)^/; + + // Certain characters need to be escaped so that they can be put into a + // string literal. + var escapes = { + "'": "'", + '\\': '\\', + '\r': 'r', + '\n': 'n', + '\u2028': 'u2028', + '\u2029': 'u2029' + }; + + var escaper = /\\|'|\r|\n|\u2028|\u2029/g; + + var escapeChar = function(match) { + return '\\' + escapes[match]; + }; + + // JavaScript micro-templating, similar to John Resig's implementation. + // Underscore templating handles arbitrary delimiters, preserves whitespace, + // and correctly escapes quotes within interpolated code. + // NB: `oldSettings` only exists for backwards compatibility. + _.template = function(text, settings, oldSettings) { + if (!settings && oldSettings) settings = oldSettings; + settings = _.defaults({}, settings, _.templateSettings); + + // Combine delimiters into one regular expression via alternation. + var matcher = RegExp([ + (settings.escape || noMatch).source, + (settings.interpolate || noMatch).source, + (settings.evaluate || noMatch).source + ].join('|') + '|$', 'g'); + + // Compile the template source, escaping string literals appropriately. + var index = 0; + var source = "__p+='"; + text.replace(matcher, function(match, escape, interpolate, evaluate, offset) { + source += text.slice(index, offset).replace(escaper, escapeChar); + index = offset + match.length; + + if (escape) { + source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'"; + } else if (interpolate) { + source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'"; + } else if (evaluate) { + source += "';\n" + evaluate + "\n__p+='"; + } + + // Adobe VMs need the match returned to produce the correct offest. + return match; + }); + source += "';\n"; + + // If a variable is not specified, place data values in local scope. + if (!settings.variable) source = 'with(obj||{}){\n' + source + '}\n'; + + source = "var __t,__p='',__j=Array.prototype.join," + + "print=function(){__p+=__j.call(arguments,'');};\n" + + source + 'return __p;\n'; + + try { + var render = new Function(settings.variable || 'obj', '_', source); + } catch (e) { + e.source = source; + throw e; + } + + var template = function(data) { + return render.call(this, data, _); + }; + + // Provide the compiled source as a convenience for precompilation. + var argument = settings.variable || 'obj'; + template.source = 'function(' + argument + '){\n' + source + '}'; + + return template; + }; + + // Add a "chain" function. Start chaining a wrapped Underscore object. + _.chain = function(obj) { + var instance = _(obj); + instance._chain = true; + return instance; + }; + + // OOP + // --------------- + // If Underscore is called as a function, it returns a wrapped object that + // can be used OO-style. This wrapper holds altered versions of all the + // underscore functions. Wrapped objects may be chained. + + // Helper function to continue chaining intermediate results. + var result = function(instance, obj) { + return instance._chain ? _(obj).chain() : obj; + }; + + // Add your own custom functions to the Underscore object. + _.mixin = function(obj) { + _.each(_.functions(obj), function(name) { + var func = _[name] = obj[name]; + _.prototype[name] = function() { + var args = [this._wrapped]; + push.apply(args, arguments); + return result(this, func.apply(_, args)); + }; + }); + }; + + // Add all of the Underscore functions to the wrapper object. + _.mixin(_); + + // Add all mutator Array functions to the wrapper. + _.each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) { + var method = ArrayProto[name]; + _.prototype[name] = function() { + var obj = this._wrapped; + method.apply(obj, arguments); + if ((name === 'shift' || name === 'splice') && obj.length === 0) delete obj[0]; + return result(this, obj); + }; + }); + + // Add all accessor Array functions to the wrapper. + _.each(['concat', 'join', 'slice'], function(name) { + var method = ArrayProto[name]; + _.prototype[name] = function() { + return result(this, method.apply(this._wrapped, arguments)); + }; + }); + + // Extracts the result from a wrapped and chained object. + _.prototype.value = function() { + return this._wrapped; + }; + + // Provide unwrapping proxy for some methods used in engine operations + // such as arithmetic and JSON stringification. + _.prototype.valueOf = _.prototype.toJSON = _.prototype.value; + + _.prototype.toString = function() { + return '' + this._wrapped; + }; + + // AMD registration happens at the end for compatibility with AMD loaders + // that may not enforce next-turn semantics on modules. Even though general + // practice for AMD registration is to be anonymous, underscore registers + // as a named module because, like jQuery, it is a base library that is + // popular enough to be bundled in a third party lib, but not be part of + // an AMD load request. Those cases could generate an error when an + // anonymous define() is called outside of a loader request. + if (typeof define === 'function' && define.amd) { + define('underscore', [], function() { + return _; + }); + } +}.call(this)); + +},{}],26:[function(require,module,exports){ +arguments[4][19][0].apply(exports,arguments) +},{"dup":19}],27:[function(require,module,exports){ +module.exports = function isBuffer(arg) { + return arg && typeof arg === 'object' + && typeof arg.copy === 'function' + && typeof arg.fill === 'function' + && typeof arg.readUInt8 === 'function'; +} +},{}],28:[function(require,module,exports){ +(function (process,global){ +// Copyright Joyent, Inc. and other Node contributors. +// +// Permission is hereby granted, free of charge, to any person obtaining a +// copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to permit +// persons to whom the Software is furnished to do so, subject to the +// following conditions: +// +// The above copyright notice and this permission notice shall be included +// in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN +// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +// USE OR OTHER DEALINGS IN THE SOFTWARE. + +var formatRegExp = /%[sdj%]/g; +exports.format = function(f) { + if (!isString(f)) { + var objects = []; + for (var i = 0; i < arguments.length; i++) { + objects.push(inspect(arguments[i])); + } + return objects.join(' '); + } + + var i = 1; + var args = arguments; + var len = args.length; + var str = String(f).replace(formatRegExp, function(x) { + if (x === '%%') return '%'; + if (i >= len) return x; + switch (x) { + case '%s': return String(args[i++]); + case '%d': return Number(args[i++]); + case '%j': + try { + return JSON.stringify(args[i++]); + } catch (_) { + return '[Circular]'; + } + default: + return x; + } + }); + for (var x = args[i]; i < len; x = args[++i]) { + if (isNull(x) || !isObject(x)) { + str += ' ' + x; + } else { + str += ' ' + inspect(x); + } + } + return str; +}; + + +// Mark that a method should not be used. +// Returns a modified function which warns once by default. +// If --no-deprecation is set, then it is a no-op. +exports.deprecate = function(fn, msg) { + // Allow for deprecating things in the process of starting up. + if (isUndefined(global.process)) { + return function() { + return exports.deprecate(fn, msg).apply(this, arguments); + }; + } + + if (process.noDeprecation === true) { + return fn; + } + + var warned = false; + function deprecated() { + if (!warned) { + if (process.throwDeprecation) { + throw new Error(msg); + } else if (process.traceDeprecation) { + console.trace(msg); + } else { + console.error(msg); + } + warned = true; + } + return fn.apply(this, arguments); + } + + return deprecated; +}; + + +var debugs = {}; +var debugEnviron; +exports.debuglog = function(set) { + if (isUndefined(debugEnviron)) + debugEnviron = process.env.NODE_DEBUG || ''; + set = set.toUpperCase(); + if (!debugs[set]) { + if (new RegExp('\\b' + set + '\\b', 'i').test(debugEnviron)) { + var pid = process.pid; + debugs[set] = function() { + var msg = exports.format.apply(exports, arguments); + console.error('%s %d: %s', set, pid, msg); + }; + } else { + debugs[set] = function() {}; + } + } + return debugs[set]; +}; + + +/** + * Echos the value of a value. Trys to print the value out + * in the best way possible given the different types. + * + * @param {Object} obj The object to print out. + * @param {Object} opts Optional options object that alters the output. + */ +/* legacy: obj, showHidden, depth, colors*/ +function inspect(obj, opts) { + // default options + var ctx = { + seen: [], + stylize: stylizeNoColor + }; + // legacy... + if (arguments.length >= 3) ctx.depth = arguments[2]; + if (arguments.length >= 4) ctx.colors = arguments[3]; + if (isBoolean(opts)) { + // legacy... + ctx.showHidden = opts; + } else if (opts) { + // got an "options" object + exports._extend(ctx, opts); + } + // set default options + if (isUndefined(ctx.showHidden)) ctx.showHidden = false; + if (isUndefined(ctx.depth)) ctx.depth = 2; + if (isUndefined(ctx.colors)) ctx.colors = false; + if (isUndefined(ctx.customInspect)) ctx.customInspect = true; + if (ctx.colors) ctx.stylize = stylizeWithColor; + return formatValue(ctx, obj, ctx.depth); +} +exports.inspect = inspect; + + +// http://en.wikipedia.org/wiki/ANSI_escape_code#graphics +inspect.colors = { + 'bold' : [1, 22], + 'italic' : [3, 23], + 'underline' : [4, 24], + 'inverse' : [7, 27], + 'white' : [37, 39], + 'grey' : [90, 39], + 'black' : [30, 39], + 'blue' : [34, 39], + 'cyan' : [36, 39], + 'green' : [32, 39], + 'magenta' : [35, 39], + 'red' : [31, 39], + 'yellow' : [33, 39] +}; + +// Don't use 'blue' not visible on cmd.exe +inspect.styles = { + 'special': 'cyan', + 'number': 'yellow', + 'boolean': 'yellow', + 'undefined': 'grey', + 'null': 'bold', + 'string': 'green', + 'date': 'magenta', + // "name": intentionally not styling + 'regexp': 'red' +}; + + +function stylizeWithColor(str, styleType) { + var style = inspect.styles[styleType]; + + if (style) { + return '\u001b[' + inspect.colors[style][0] + 'm' + str + + '\u001b[' + inspect.colors[style][1] + 'm'; + } else { + return str; + } +} + + +function stylizeNoColor(str, styleType) { + return str; +} + + +function arrayToHash(array) { + var hash = {}; + + array.forEach(function(val, idx) { + hash[val] = true; + }); + + return hash; +} + + +function formatValue(ctx, value, recurseTimes) { + // Provide a hook for user-specified inspect functions. + // Check that value is an object with an inspect function on it + if (ctx.customInspect && + value && + isFunction(value.inspect) && + // Filter out the util module, it's inspect function is special + value.inspect !== exports.inspect && + // Also filter out any prototype objects using the circular check. + !(value.constructor && value.constructor.prototype === value)) { + var ret = value.inspect(recurseTimes, ctx); + if (!isString(ret)) { + ret = formatValue(ctx, ret, recurseTimes); + } + return ret; + } + + // Primitive types cannot have properties + var primitive = formatPrimitive(ctx, value); + if (primitive) { + return primitive; + } + + // Look up the keys of the object. + var keys = Object.keys(value); + var visibleKeys = arrayToHash(keys); + + if (ctx.showHidden) { + keys = Object.getOwnPropertyNames(value); + } + + // IE doesn't make error fields non-enumerable + // http://msdn.microsoft.com/en-us/library/ie/dww52sbt(v=vs.94).aspx + if (isError(value) + && (keys.indexOf('message') >= 0 || keys.indexOf('description') >= 0)) { + return formatError(value); + } + + // Some type of object without properties can be shortcutted. + if (keys.length === 0) { + if (isFunction(value)) { + var name = value.name ? ': ' + value.name : ''; + return ctx.stylize('[Function' + name + ']', 'special'); + } + if (isRegExp(value)) { + return ctx.stylize(RegExp.prototype.toString.call(value), 'regexp'); + } + if (isDate(value)) { + return ctx.stylize(Date.prototype.toString.call(value), 'date'); + } + if (isError(value)) { + return formatError(value); + } + } + + var base = '', array = false, braces = ['{', '}']; + + // Make Array say that they are Array + if (isArray(value)) { + array = true; + braces = ['[', ']']; + } + + // Make functions say that they are functions + if (isFunction(value)) { + var n = value.name ? ': ' + value.name : ''; + base = ' [Function' + n + ']'; + } + + // Make RegExps say that they are RegExps + if (isRegExp(value)) { + base = ' ' + RegExp.prototype.toString.call(value); + } + + // Make dates with properties first say the date + if (isDate(value)) { + base = ' ' + Date.prototype.toUTCString.call(value); + } + + // Make error with message first say the error + if (isError(value)) { + base = ' ' + formatError(value); + } + + if (keys.length === 0 && (!array || value.length == 0)) { + return braces[0] + base + braces[1]; + } + + if (recurseTimes < 0) { + if (isRegExp(value)) { + return ctx.stylize(RegExp.prototype.toString.call(value), 'regexp'); + } else { + return ctx.stylize('[Object]', 'special'); + } + } + + ctx.seen.push(value); + + var output; + if (array) { + output = formatArray(ctx, value, recurseTimes, visibleKeys, keys); + } else { + output = keys.map(function(key) { + return formatProperty(ctx, value, recurseTimes, visibleKeys, key, array); + }); + } + + ctx.seen.pop(); + + return reduceToSingleString(output, base, braces); +} + + +function formatPrimitive(ctx, value) { + if (isUndefined(value)) + return ctx.stylize('undefined', 'undefined'); + if (isString(value)) { + var simple = '\'' + JSON.stringify(value).replace(/^"|"$/g, '') + .replace(/'/g, "\\'") + .replace(/\\"/g, '"') + '\''; + return ctx.stylize(simple, 'string'); + } + if (isNumber(value)) + return ctx.stylize('' + value, 'number'); + if (isBoolean(value)) + return ctx.stylize('' + value, 'boolean'); + // For some reason typeof null is "object", so special case here. + if (isNull(value)) + return ctx.stylize('null', 'null'); +} + + +function formatError(value) { + return '[' + Error.prototype.toString.call(value) + ']'; +} + + +function formatArray(ctx, value, recurseTimes, visibleKeys, keys) { + var output = []; + for (var i = 0, l = value.length; i < l; ++i) { + if (hasOwnProperty(value, String(i))) { + output.push(formatProperty(ctx, value, recurseTimes, visibleKeys, + String(i), true)); + } else { + output.push(''); + } + } + keys.forEach(function(key) { + if (!key.match(/^\d+$/)) { + output.push(formatProperty(ctx, value, recurseTimes, visibleKeys, + key, true)); + } + }); + return output; +} + + +function formatProperty(ctx, value, recurseTimes, visibleKeys, key, array) { + var name, str, desc; + desc = Object.getOwnPropertyDescriptor(value, key) || { value: value[key] }; + if (desc.get) { + if (desc.set) { + str = ctx.stylize('[Getter/Setter]', 'special'); + } else { + str = ctx.stylize('[Getter]', 'special'); + } + } else { + if (desc.set) { + str = ctx.stylize('[Setter]', 'special'); + } + } + if (!hasOwnProperty(visibleKeys, key)) { + name = '[' + key + ']'; + } + if (!str) { + if (ctx.seen.indexOf(desc.value) < 0) { + if (isNull(recurseTimes)) { + str = formatValue(ctx, desc.value, null); + } else { + str = formatValue(ctx, desc.value, recurseTimes - 1); + } + if (str.indexOf('\n') > -1) { + if (array) { + str = str.split('\n').map(function(line) { + return ' ' + line; + }).join('\n').substr(2); + } else { + str = '\n' + str.split('\n').map(function(line) { + return ' ' + line; + }).join('\n'); + } + } + } else { + str = ctx.stylize('[Circular]', 'special'); + } + } + if (isUndefined(name)) { + if (array && key.match(/^\d+$/)) { + return str; + } + name = JSON.stringify('' + key); + if (name.match(/^"([a-zA-Z_][a-zA-Z_0-9]*)"$/)) { + name = name.substr(1, name.length - 2); + name = ctx.stylize(name, 'name'); + } else { + name = name.replace(/'/g, "\\'") + .replace(/\\"/g, '"') + .replace(/(^"|"$)/g, "'"); + name = ctx.stylize(name, 'string'); + } + } + + return name + ': ' + str; +} + + +function reduceToSingleString(output, base, braces) { + var numLinesEst = 0; + var length = output.reduce(function(prev, cur) { + numLinesEst++; + if (cur.indexOf('\n') >= 0) numLinesEst++; + return prev + cur.replace(/\u001b\[\d\d?m/g, '').length + 1; + }, 0); + + if (length > 60) { + return braces[0] + + (base === '' ? '' : base + '\n ') + + ' ' + + output.join(',\n ') + + ' ' + + braces[1]; + } + + return braces[0] + base + ' ' + output.join(', ') + ' ' + braces[1]; +} + + +// NOTE: These type checking functions intentionally don't use `instanceof` +// because it is fragile and can be easily faked with `Object.create()`. +function isArray(ar) { + return Array.isArray(ar); +} +exports.isArray = isArray; + +function isBoolean(arg) { + return typeof arg === 'boolean'; +} +exports.isBoolean = isBoolean; + +function isNull(arg) { + return arg === null; +} +exports.isNull = isNull; + +function isNullOrUndefined(arg) { + return arg == null; +} +exports.isNullOrUndefined = isNullOrUndefined; + +function isNumber(arg) { + return typeof arg === 'number'; +} +exports.isNumber = isNumber; + +function isString(arg) { + return typeof arg === 'string'; +} +exports.isString = isString; + +function isSymbol(arg) { + return typeof arg === 'symbol'; +} +exports.isSymbol = isSymbol; + +function isUndefined(arg) { + return arg === void 0; +} +exports.isUndefined = isUndefined; + +function isRegExp(re) { + return isObject(re) && objectToString(re) === '[object RegExp]'; +} +exports.isRegExp = isRegExp; + +function isObject(arg) { + return typeof arg === 'object' && arg !== null; +} +exports.isObject = isObject; + +function isDate(d) { + return isObject(d) && objectToString(d) === '[object Date]'; +} +exports.isDate = isDate; + +function isError(e) { + return isObject(e) && + (objectToString(e) === '[object Error]' || e instanceof Error); +} +exports.isError = isError; + +function isFunction(arg) { + return typeof arg === 'function'; +} +exports.isFunction = isFunction; + +function isPrimitive(arg) { + return arg === null || + typeof arg === 'boolean' || + typeof arg === 'number' || + typeof arg === 'string' || + typeof arg === 'symbol' || // ES6 symbol + typeof arg === 'undefined'; +} +exports.isPrimitive = isPrimitive; + +exports.isBuffer = require('./support/isBuffer'); + +function objectToString(o) { + return Object.prototype.toString.call(o); +} + + +function pad(n) { + return n < 10 ? '0' + n.toString(10) : n.toString(10); +} + + +var months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', + 'Oct', 'Nov', 'Dec']; + +// 26 Feb 16:19:34 +function timestamp() { + var d = new Date(); + var time = [pad(d.getHours()), + pad(d.getMinutes()), + pad(d.getSeconds())].join(':'); + return [d.getDate(), months[d.getMonth()], time].join(' '); +} + + +// log is just a thin wrapper to console.log that prepends a timestamp +exports.log = function() { + console.log('%s - %s', timestamp(), exports.format.apply(exports, arguments)); +}; + + +/** + * Inherit the prototype methods from one constructor into another. + * + * The Function.prototype.inherits from lang.js rewritten as a standalone + * function (not on Function.prototype). NOTE: If this file is to be loaded + * during bootstrapping this function needs to be rewritten using some native + * functions as prototype setup using normal JavaScript does not work as + * expected during bootstrapping (see mirror.js in r114903). + * + * @param {function} ctor Constructor function which needs to inherit the + * prototype. + * @param {function} superCtor Constructor function to inherit prototype from. + */ +exports.inherits = require('inherits'); + +exports._extend = function(origin, add) { + // Don't do anything if add isn't an object + if (!add || !isObject(add)) return origin; + + var keys = Object.keys(add); + var i = keys.length; + while (i--) { + origin[keys[i]] = add[keys[i]]; + } + return origin; +}; + +function hasOwnProperty(obj, prop) { + return Object.prototype.hasOwnProperty.call(obj, prop); +} + +}).call(this,require('_process'),typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {}) +},{"./support/isBuffer":27,"_process":24,"inherits":26}],29:[function(require,module,exports){ +// Returns a wrapper function that returns a wrapped callback +// The wrapper function should do some stuff, and return a +// presumably different callback function. +// This makes sure that own properties are retained, so that +// decorations and such are not lost along the way. +module.exports = wrappy +function wrappy (fn, cb) { + if (fn && cb) return wrappy(fn)(cb) + + if (typeof fn !== 'function') + throw new TypeError('need wrapper function') + + Object.keys(fn).forEach(function (k) { + wrapper[k] = fn[k] + }) + + return wrapper + + function wrapper() { + var args = new Array(arguments.length) + for (var i = 0; i < args.length; i++) { + args[i] = arguments[i] + } + var ret = fn.apply(this, args) + var cb = args[args.length-1] + if (typeof ret === 'function' && ret !== cb) { + Object.keys(cb).forEach(function (k) { + ret[k] = cb[k] + }) + } + return ret + } +} + +},{}]},{},[7])(7) +}); \ No newline at end of file diff --git a/assets/javascripts/workers/search.1e90e0fb.min.js b/assets/javascripts/workers/search.1e90e0fb.min.js new file mode 100644 index 00000000..ff43aedd --- /dev/null +++ b/assets/javascripts/workers/search.1e90e0fb.min.js @@ -0,0 +1,2 @@ +"use strict";(()=>{var xe=Object.create;var G=Object.defineProperty,ve=Object.defineProperties,Se=Object.getOwnPropertyDescriptor,Te=Object.getOwnPropertyDescriptors,Qe=Object.getOwnPropertyNames,Y=Object.getOwnPropertySymbols,Ee=Object.getPrototypeOf,X=Object.prototype.hasOwnProperty,be=Object.prototype.propertyIsEnumerable;var Z=Math.pow,J=(t,e,r)=>e in t?G(t,e,{enumerable:!0,configurable:!0,writable:!0,value:r}):t[e]=r,_=(t,e)=>{for(var r in e||(e={}))X.call(e,r)&&J(t,r,e[r]);if(Y)for(var r of Y(e))be.call(e,r)&&J(t,r,e[r]);return t},B=(t,e)=>ve(t,Te(e));var Le=(t,e)=>()=>(e||t((e={exports:{}}).exports,e),e.exports);var we=(t,e,r,n)=>{if(e&&typeof e=="object"||typeof e=="function")for(let i of Qe(e))!X.call(t,i)&&i!==r&&G(t,i,{get:()=>e[i],enumerable:!(n=Se(e,i))||n.enumerable});return t};var Pe=(t,e,r)=>(r=t!=null?xe(Ee(t)):{},we(e||!t||!t.__esModule?G(r,"default",{value:t,enumerable:!0}):r,t));var W=(t,e,r)=>new Promise((n,i)=>{var s=u=>{try{a(r.next(u))}catch(c){i(c)}},o=u=>{try{a(r.throw(u))}catch(c){i(c)}},a=u=>u.done?n(u.value):Promise.resolve(u.value).then(s,o);a((r=r.apply(t,e)).next())});var te=Le((K,ee)=>{(function(){var t=function(e){var r=new t.Builder;return r.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),r.searchPipeline.add(t.stemmer),e.call(r,r),r.build()};t.version="2.3.9";t.utils={},t.utils.warn=function(e){return function(r){e.console&&console.warn&&console.warn(r)}}(this),t.utils.asString=function(e){return e==null?"":e.toString()},t.utils.clone=function(e){if(e==null)return e;for(var r=Object.create(null),n=Object.keys(e),i=0;i0){var f=t.utils.clone(r)||{};f.position=[a,c],f.index=s.length,s.push(new t.Token(n.slice(a,o),f))}a=o+1}}return s},t.tokenizer.separator=/[\s\-]+/;t.Pipeline=function(){this._stack=[]},t.Pipeline.registeredFunctions=Object.create(null),t.Pipeline.registerFunction=function(e,r){r in this.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+r),e.label=r,t.Pipeline.registeredFunctions[e.label]=e},t.Pipeline.warnIfFunctionNotRegistered=function(e){var r=e.label&&e.label in this.registeredFunctions;r||t.utils.warn(`Function is not registered with pipeline. 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    Data Caterer
    Data Caterer
    Database
    Database
    Files
    Files
    Events
    Events
    API
    API
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    Files
    Event
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    Event...
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    Get Latest Schema
    Get Latest Schema
    Generate Data
    Generate Data
    Jobs
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    Services
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    Other Data Consumers
    Consume Data
    Consume Data
    Data Caterer
    Data Caterer
    Database
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    Files
    Files
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    Events
    API
    API
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    Validate/Clean Data
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    Services
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    Data Caterer
    Data Caterer
    Database
    Database
    Files
    Files
    Events
    Events
    API
    API
    Database
    Database
    Files
    Files
    Event
    Schema
    Event...
    API
    Definition
    API...
    Get Latest Schema
    Get Latest Schema
    Generate Data
    Generate Data
    Jobs
    Jobs
    Services
    Services
    Other Data Consumers
    Other Data Consumers
    Consume Data
    Consume Data
    Data Caterer
    Data Caterer
    Database
    Database
    Files
    Files
    Events
    Events
    API
    API
    Validate/Clean Data
    Validate/Clean Data
    Metadata
    Services
    Metadata...    Text is not SVG - cannot display     \ No newline at end of file diff --git a/get-started/docker/index.html b/get-started/docker/index.html new file mode 100644 index 00000000..0e421c95 --- /dev/null +++ b/get-started/docker/index.html @@ -0,0 +1,2364 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Get Started - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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    + + + + + + + +

    Run Data Caterer

    +

    Quick start

    +

    Ensure you have docker installed and running.

    +
    git clone git@github.com:pflooky/data-caterer-example.git
+cd data-caterer-example && ./run.sh
+#check results under docker/sample/report/index.html folder
+
    +

    Report

    +

    Check the report generated under docker/data/custom/report/index.html.

    +

    Sample report can also be seen here

    + +

    30 day trial of the paid version can be accessed via these steps:

    +
      +
    1. Join the Slack Data Catering Slack group here
      2. +
    2. Get an API_KEY by using slash command /token in the Slack group (will only be visible to you)
      3. +
    3. +
      git clone git@github.com:pflooky/data-caterer-example.git
+cd data-caterer-example && export DATA_CATERING_API_KEY=<insert api key>
+./run.sh
+
      +
      4. +
    +

    If you want to check how long your trial has left, you can check back in the Slack group or type /token again.

    +

    Guided tour

    +

    Check out the starter guide here that will take your through +step by step. You can also check the other guides here to see the other possibilities of +what Data Caterer can achieve for you.

    + + + + + + + + + + + + + + + +
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    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 00000000..b6d0da0b --- /dev/null +++ b/index.html @@ -0,0 +1,2456 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Data Caterer is a metadata-driven data generation and +testing tool that aids in creating production-like data across both batch and event data systems. Run data validations +to ensure your systems have ingested it as expected, then clean up the data afterwards.

    + +

    Simplify your data testing

    + +

    Take away the pain and complexity of your data landscape and let Data Caterer handle it

    + +

    +Try now +

    +

    Data testing is difficult and fragmented

    + +
      +
    • Data being sent via messages, HTTP requests or files and getting stored in databases, file systems, etc.
      • +
    • Maintaining and updating tests with the latest schemas and business definitions
      • +
    • Different testing tools for services, jobs or data sources
      • +
    • Complex relationships between datasets and fields
      • +
    • Different scenarios, permutations, combinations and edge cases to cover
      • +
    +

    Current solutions only cover half the story

    + +
      +
    • Specific testing frameworks that support one or limited number of data sources or transport protocols
      • +
    • Under utilizing metadata from data catalogs or metadata discovery services
      • +
    • Testing teams having difficulties understanding when failures occur
      • +
    • Integration tests relying on external teams/services
      • +
    • Manually generating data, or worse, copying/masking production data into lower environments
      • +
    • Observability pushes towards being reactive rather than proactive
      • +
    +

    +Try now +

    +

    What you need is a reliable tool that can handle changes to your data landscape

    + +
    +

    High level overview of Data Caterer + High level overview of Data Caterer

    +
    +

    With Data Caterer, you get:

    +
      +
    • Ability to connect to any type of data source: files, SQL or no-SQL databases, messaging systems, HTTP
      • +
    • Discover metadata from your existing infrastructure and services
      • +
    • Gain confidence that bugs do not propagate to production
      • +
    • Be proactive in ensuring changes do not affect other data producers or consumers
      • +
    • Configurability to run the way you want
      • +
    +

    +Try now +

    +

    Tech Summary

    +

    Use the Java, Scala API, or YAML files to help with setup or customisation that are all run via a Docker image. Want to +get into details? Checkout the setup pages here to get code examples and guides that will take you +through scenarios and data sources.

    +

    Main features include:

    +
      +
    • Metadata discovery
      • +
    • Batch and event data generation
      • +
    • Maintain referential integrity across any dataset
      • +
    • Create custom data generation scenarios
      • +
    • Clean up generated data
      • +
    • Validate data
      • +
    • Suggest data validations
      • +
    +
    +

    Basic flow

    +
    +

    Check other run configurations here.

    +

    What is it

    +
    +
      +
    • +

      Data generation and testing tool

      +
      +

      Generate production like data to be consumed and validated.

      +
      • +
    • +

      Designed for any data source

      +
      +

      We aim to support pushing data to any data source, in any format.

      +
      • +
    • +

      Low/no code solution

      +
      +

      Can use the tool via either Scala, Java or YAML. Connect to data or metadata sources to generate data and validate.

      +
      • +
    • +

      Developer productivity tool

      +
      +

      If you are a new developer or seasoned veteran, cut down on your feedback loop when developing with data.

      +
      • +
    +
    +

    What it is not

    +
    +
      +
    • +

      Metadata storage/platform

      +
      +

      You could store and use metadata within the data generation/validation tasks but is not the recommended approach. +Rather, this metadata should be gathered from existing services who handle metadata on behalf of Data Caterer.

      +
      • +
    • +

      Data contract

      +
      +

      The focus of Data Caterer is on the data generation and testing, which can include details about how the data looks +like and how it behaves. But it does not encompass all the additional metadata that comes with a data contract such +as SLAs, security, etc.

      +
      • +
    • +

      Metrics from load testing

      +
      +

      Although millions of records can be generated, there are limited capabilities in terms of metric capturing.

      +
      • +
    +
    +

    +Try now +

    +

    Data Catering vs Other tools vs In-house

    + +

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Data CateringOther toolsIn-house
    Data flowBatch and events generation with validationBatch generation only or validation onlyDepends on architecture and design
    Time to results1 day1+ month to integrate, deploy and onboard1+ month to build and deploy
    SolutionConnect with your existing data ecosystem, automatic generation and validationManual UI data entry or via SDKDepends on engineer(s) building it
    +

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    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/js/open_in_new_tab.js b/js/open_in_new_tab.js new file mode 100644 index 00000000..964fb4c1 --- /dev/null +++ b/js/open_in_new_tab.js @@ -0,0 +1,45 @@ +// Description: Open external links in a new tab and PDF links in a new tab +// Source: https://jekyllcodex.org/without-plugin/new-window-fix/ + +//open external links in a new window +function external_new_window() { + for(let c = document.getElementsByTagName("a"), a = 0;a < c.length;a++) { + let b = c[a]; + if(b.getAttribute("href") && b.hostname !== location.hostname) { + b.target = "_blank"; + b.rel = "noopener"; + } + } +} +//open PDF links in a new window +function pdf_new_window () +{ + if (!document.getElementsByTagName) { + return false; + } + let links = document.getElementsByTagName("a"); + for (let eleLink=0; eleLink < links.length; eleLink ++) { + if ((links[eleLink].href.indexOf('.pdf') !== -1)||(links[eleLink].href.indexOf('.doc') !== -1)||(links[eleLink].href.indexOf('.docx') !== -1)) { + links[eleLink].onclick = + function() { + window.open(this.href); + return false; + } + } + } +} + +function apply_rules() { + external_new_window(); + pdf_new_window(); +} + +if (typeof document$ !== "undefined") { + // compatibility with mkdocs-material's instant loading feature + // based on code from https://github.com/timvink/mkdocs-charts-plugin + // Copyright (c) 2021 Tim Vink - MIT License + // fixes [Issue #2](https://github.com/JakubAndrysek/mkdocs-open-in-new-tab/issues/2) + document$.subscribe(function() { + apply_rules(); + }) +} \ No newline at end of file diff --git a/legal/privacy-policy/index.html b/legal/privacy-policy/index.html new file mode 100644 index 00000000..eefa3ac3 --- /dev/null +++ b/legal/privacy-policy/index.html @@ -0,0 +1,2429 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Privacy policy - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Privacy Policy

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    Last updated September 25, 2023

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    Data Caterer Policy on Privacy of Customer Personal Information

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    Peter John Flook is committed to protecting the privacy and security of your personal information obtained by reason of +your use +of Data Caterer. This policy explains the types of customer personal information we collect, how it is used, and the +steps +we take to ensure your personal information is handled appropriately.

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    Who is Peter John Flook?

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    For purposes of this Privacy Policy, “Peter John Flook” means Peter John Flook, the company developing and providing +Data Caterer and related websites and services.

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    What is personal information?

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    Personal information is information that refers to an individual specifically and is recorded in any form. Personal +information includes such things as age, income, date of birth, ethnic origin and credit records. Information about +individuals contained in the following documents is not considered personal information:

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    • public telephone directories, where the subscriber can refuse to be listed
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    • professional and business directories available to the public
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    • public registries and court records
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    • other publicly available printed and electronic publications
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    We are accountable to you

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    Peter John Flook is responsible for all personal information under its control. Our team is accountable for compliance +with these privacy and security principles.

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    Peter John Flook identifies the purpose for which your personal information is collected and will be used or disclosed. +If that purpose is not listed below we will do this before or at the time the information is actually being collected. +You will be deemed to consent to our use of your personal information for the purpose of:

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    • communicating with you generally
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    • processing your purchases
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    • processing and keeping track of transactions and reporting back to you
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    • protecting against fraud or error
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    • providing product and services requested by you
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    • recommending products and services that Peter John Flook believes will be of interest and provide value to you
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    • fulfilling any other purpose that would be reasonably apparent to the average person at the time we collect it from + you
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    Otherwise, Peter John Flook will obtain your express consent (by verbal, written or electronic agreement) to collect, +use or disclose your personal information. You can change your consent preferences at any time by contacting Peter John +Flook (please refer to the “How to contact us” section below).

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    We limit collection of your personal information

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    Peter John Flook collects only the information required to provide products and services to you. Peter John Flook will +collect personal information only by clear, fair and lawful means.

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    We receive and store any information you enter on our website or give us in any other way. You can choose not to provide +certain information, but then you might not be able to take advantage of many of our features.

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    Peter John Flook does not receive or store personal content saved to your local device while using Data Caterer.

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    We also receive and store certain types of information whenever you interact with us.

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    Information provided to Stripe

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    All purchases that are made through this site are processed securely and externally by Stripe. Unless you expressly +consent otherwise, we do not see or have access to any personal information that you may provide to Stripe, other than +information that is required in order to process your order and deliver your purchased items to you (eg, your name, +email address and billing/postal address).

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    We limit disclosure and retention of your personal information

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    Peter John Flook does not disclose personal information to any organization or person for any reason except the +following:

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    We employ other companies and individuals to perform functions on our behalf. Examples include fulfilling orders, +delivering packages, sending postal mail and e-mail, removing repetitive information from customer lists, analyzing +data, providing marketing assistance, processing credit card payments, and providing customer service. They have access +to personal information needed to perform their functions, but may not use it for other purposes. We may use service +providers located outside of Australia, and, if applicable, your personal information may be processed and stored in other +countries and therefore may be subject to disclosure under the laws of those countries. +As we continue to develop our business, we might sell or buy stores, subsidiaries, or business units. In such +transactions, customer information generally is one of the transferred business assets but remains subject to the +promises made in any pre-existing Privacy Notice (unless, of course, the customer consents otherwise). Also, in the +unlikely event that Peter John Flook or substantially all of its assets are acquired, customer information of course +will be one +of the transferred assets. +You are deemed to consent to disclosure of your personal information for those purposes. If your personal information is +shared with third parties, those third parties are bound by appropriate agreements with Peter John Flook to secure and +protect +the confidentiality of your personal information.

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    Peter John Flook retains your personal information only as long as it is required for our business relationship or as +required +by federal and provincial laws.

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    We keep your personal information up to date and accurate

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    Peter John Flook keeps your personal information up to date, accurate and relevant for its intended use.

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    You may request access to the personal information we have on record in order to review and amend the information, as +appropriate. In circumstances where your personal information has been provided by a third party, we will refer you to +that party (e.g. credit bureaus). To access your personal information, refer to the “How to contact us” section below.

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    The security of your personal information is a priority for Peter John Flook

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    We take steps to safeguard your personal information, regardless of the format in which it is held, including:

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    physical security measures such as restricted access facilities and locked filing cabinets +electronic security measures for computerized personal information such as password protection, database encryption and +personal identification numbers. We work to protect the security of your information during transmission by using +“Transport Layer Security” (TLS) protocol. +organizational processes such as limiting access to your personal information to a selected group of individuals +contractual obligations with third parties who need access to your personal information requiring them to protect and +secure your personal information +It’s important for you to protect against unauthorized access to your password and your computer. Be sure to sign off +when you’ve finished using any shared computer.

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    Our site may include third-party advertising and links to other websites. We do not provide any personally identifiable +customer information to these advertisers or third-party websites.

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    These third-party websites and advertisers, or Internet advertising companies working on their behalf, sometimes use +technology to send (or “serve”) the advertisements that appear on our website directly to your browser. They +automatically receive your IP address when this happens. They may also use cookies, JavaScript, web beacons (also known +as action tags or single-pixel gifs), and other technologies to measure the effectiveness of their ads and to +personalize advertising content. We do not have access to or control over cookies or other features that they may use, +and the information practices of these advertisers and third-party websites are not covered by this Privacy Notice. +Please contact them directly for more information about their privacy practices. In addition, the Network Advertising +Initiative offers useful information about Internet advertising companies (also called “ad networks” or “network +advertisers”), including information about how to opt-out of their information collection. You can access the Network +Advertising Initiative at http://www.networkadvertising.org.

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    Redirection to Stripe

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    In particular, when you submit an order to us, you may be automatically redirected to Stripe in order to complete the +required payment. The payment page that is provided by Stripe is not part of this site. As noted above, we are not privy +to any of the bank account, credit card or other personal information that you may provide to Stripe, other than +information that is required in order to process your order and deliver your purchased items to you (eg, your name, +email address and billing/postal address). We recommend that you refer to Stripe’s privacy statement if you would like +more information about how Stripe collects and handles your personal information.

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    We are open about our privacy and security policy

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    We are committed to providing you with understandable and easily available information about our policy and practices +related to management of your personal information. This policy and any related information is available at all times on +our website, https://data.catering/about/ under Privacy or on request. To contact us, refer to the “How +to contact us” section below.

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    We provide access to your personal information stored by Peter John Flook

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    You can request access to your personal information stored by Peter John Flook. To contact us, refer to the “How to +contact us” section below. Upon receiving such a request, Peter John Flook will:

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    inform you about what type of personal information we have on record or in our control, how it is used and to whom it +may have been disclosed +provide you with access to your information so you can review and verify the accuracy and completeness and request +changes to the information +make any necessary updates to your personal information +We respond to your questions, concerns and complaints about privacy +Peter John Flook responds in a timely manner to your questions, concerns and complaints about the privacy of your +personal +information and our privacy policies and procedures.

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    How to contact us

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    • by email at peter.flook@data.catering
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    Our business changes constantly, and this privacy notice will change also. We may e-mail periodic reminders of our +notices and conditions, unless you have instructed us not to, but you should check our website frequently to see +recent +changes. We are, however, committed to protecting your information and will never materially change our policies and +practices to make them less protective of customer information collected in the past without the consent of affected +customers.

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    Terms and Conditions

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    Last updated: September 25, 2023

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    Please read these terms and conditions carefully before using Our Service.

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    Interpretation and Definitions

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    Interpretation

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    The words of which the initial letter is capitalized have meanings defined under the following conditions. The following +definitions shall have the same meaning regardless of whether they appear in singular or in plural.

    +

    Definitions

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    For the purposes of these Terms and Conditions:

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    • Application means the software program provided by the Company downloaded by You on any electronic device, named + Data Caterer
      • +
    • Application Store means the digital distribution service operated and developed by Docker Inc. (“Docker”) in which + the Application has been downloaded.
      • +
    • Affiliate means an entity that controls, is controlled by or is under common control with a party, where "control" + means ownership of 50% or more of the shares, equity interest or other securities entitled to vote for election of + directors or other managing authority.
      • +
    • Country refers to: New South Wales, Australia
      • +
    • Company (referred to as either "the Company", "We", "Us" or "Our" in this Agreement) refers to Peter John Flook ( + ABN: 65153160916), 30 Anne William Drive, West Pennant Hills, 2125, NSW, Australia.
      • +
    • Device means any device that can access the Service such as a computer, a cellphone or a digital tablet.
      • +
    • Service refers to the Application.
      • +
    • Terms and Conditions (also referred as "Terms") mean these Terms and Conditions that form the entire agreement + between You and the Company regarding the use of the Service.
      • +
    • Third-party Social Media Service means any services or content (including data, information, products or services) + provided by a third party that may be displayed, included or made available by the Service.
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    • You means the individual accessing or using the Service, or the company, or other legal entity on behalf of which + such individual is accessing or using the Service, as applicable.
      • +
    +

    Acknowledgment

    +

    These are the Terms and Conditions governing the use of this Service and the agreement that operates between You and the +Company. These Terms and Conditions set out the rights and obligations of all users regarding the use of the Service.

    +

    Your access to and use of the Service is conditioned on Your acceptance of and compliance with these Terms and +Conditions. These Terms and Conditions apply to all visitors, users and others who access or use the Service.

    +

    By accessing or using the Service You agree to be bound by these Terms and Conditions. If You disagree with any part of +these Terms and Conditions then You may not access the Service.

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    You represent that you are over the age of 18. The Company does not permit those under 18 to use the Service.

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    Your access to and use of the Service is also conditioned on Your acceptance of and compliance with the Privacy Policy +of the Company. Our Privacy Policy describes Our policies and procedures on the collection, use and disclosure of Your +personal information when You use the Application or the Website and tells You about Your privacy rights and how the law +protects You. Please read Our Privacy Policy carefully before using Our Service.

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    Links to Other Websites

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    Our Service may contain links to third-party websites or services that are not owned or controlled by the Company.

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    The Company has no control over, and assumes no responsibility for, the content, privacy policies, or practices of any +third party websites or services. You further acknowledge and agree that the Company shall not be responsible or +liable, directly or indirectly, for any damage or loss caused or alleged to be caused by or in connection with the use +of or reliance on any such content, goods or services available on or through any such websites or services.

    +

    We strongly advise You to read the terms and conditions and privacy policies of any third-party websites or services +that You visit.

    +

    Termination

    +

    We may terminate or suspend Your access immediately, without prior notice or liability, for any reason whatsoever, +including without limitation if You breach these Terms and Conditions.

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    Upon termination, Your right to use the Service will cease immediately.

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    Limitation of Liability

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    Notwithstanding any damages that You might incur, the entire liability of the Company and any of its suppliers under any +provision of these Terms and Your exclusive remedy for all the foregoing shall be limited to the amount actually paid +by You through the Service or 100 USD if You haven't purchased anything through the Service.

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    To the maximum extent permitted by applicable law, in no event shall the Company or its suppliers be liable for any +special, incidental, indirect, or consequential damages whatsoever (including, but not limited to, damages for loss of +profits, loss of data or other information, for business interruption, for personal injury, loss of privacy arising out +of or in any way related to the use of or inability to use the Service, third-party software and/or third-party hardware +used with the Service, or otherwise in connection with any provision of these Terms), even if the Company or any +supplier has been advised of the possibility of such damages and even if the remedy fails of its essential purpose.

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    Some states do not allow the exclusion of implied warranties or limitation of liability for incidental or consequential +damages, which means that some of the above limitations may not apply. In these states, each party's liability will be +limited to the greatest extent permitted by law.

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    "AS IS" and "AS AVAILABLE" Disclaimer

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    The Service is provided to You "AS IS" and "AS AVAILABLE" and with all faults and defects without warranty of any kind. +To the maximum extent permitted under applicable law, the Company, on its own behalf and on behalf of its Affiliates and +its and their respective licensors and service providers, expressly disclaims all warranties, whether express, implied, +statutory or otherwise, with respect to the Service, including all implied warranties of merchantability, fitness for a +particular purpose, title and non-infringement, and warranties that may arise out of course of dealing, course of +performance, usage or trade practice. Without limitation to the foregoing, the Company provides no warranty or +undertaking, and makes no representation of any kind that the Service will meet Your requirements, achieve any intended +results, be compatible or work with any other software, applications, systems or services, operate without interruption, +meet any performance or reliability standards or be error free or that any errors or defects can or will be corrected.

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    Without limiting the foregoing, neither the Company nor any of the company's provider makes any representation or +warranty of any kind, express or implied: (i) as to the operation or availability of the Service, or the information, +content, and materials or products included thereon; (ii) that the Service will be uninterrupted or error-free; (iii) as +to the accuracy, reliability, or currency of any information or content provided through the Service; or (iv) that the +Service, its servers, the content, or e-mails sent from or on behalf of the Company are free of viruses, scripts, trojan +horses, worms, malware, time-bombs or other harmful components.

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    Some jurisdictions do not allow the exclusion of certain types of warranties or limitations on applicable statutory +rights of a consumer, so some or all of the above exclusions and limitations may not apply to You. But in such a case +the exclusions and limitations set forth in this section shall be applied to the greatest extent enforceable under +applicable law.

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    Governing Law

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    The laws of the Country, excluding its conflicts of law rules, shall govern this Terms and Your use of the Service. Your +use of the Application may also be subject to other local, state, national, or international laws.

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    Disputes Resolution

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    If You have any concern or dispute about the Service, You agree to first try to resolve the dispute informally by +contacting the Company.

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    For European Union (EU) Users

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    If You are a European Union consumer, you will benefit from any mandatory provisions of the law of the country in which +you are resident in.

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    United States Legal Compliance

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    You represent and warrant that (i) You are not located in a country that is subject to the United States government +embargo, or that has been designated by the United States government as a "terrorist supporting" country, and (ii) You +are not listed on any United States government list of prohibited or restricted parties.

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    Severability and Waiver

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    Severability

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    If any provision of these Terms is held to be unenforceable or invalid, such provision will be changed and interpreted +to accomplish the objectives of such provision to the greatest extent possible under applicable law and the remaining +provisions will continue in full force and effect.

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    Waiver

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    Except as provided herein, the failure to exercise a right or to require performance of an obligation under these Terms +shall not affect a party's ability to exercise such right or require such performance at any time thereafter nor shall +the waiver of a breach constitute a waiver of any subsequent breach.

    +

    Translation Interpretation

    +

    These Terms and Conditions may have been translated if We have made them available to You on our Service. +You agree that the original English text shall prevail in the case of a dispute.

    +

    Changes to These Terms and Conditions

    +

    We reserve the right, at Our sole discretion, to modify or replace these Terms at any time. If a revision is material We +will make reasonable efforts to provide at least 30 days' notice prior to any new terms taking effect. What constitutes +a material change will be determined at Our sole discretion.

    +

    By continuing to access or use Our Service after those revisions become effective, You agree to be bound by the revised +terms. If You do not agree to the new terms, in whole or in part, please stop using the website and the Service.

    +

    Contact Us

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    If you have any questions about these Terms and Conditions, You can contact us:

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    • By email: peter.flook@data.catering
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    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/sample/datafaker/expressions.txt b/sample/datafaker/expressions.txt new file mode 100644 index 00000000..55799fb8 --- /dev/null +++ b/sample/datafaker/expressions.txt @@ -0,0 +1,1024 @@ +Address.buildingNumber +Address.city +Address.cityName +Address.cityPrefix +Address.citySuffix +Address.country +Address.countryCode +Address.countyByZipCode +Address.fullAddress +Address.latLon +Address.latitude +Address.lonLat +Address.longitude +Address.mailBox +Address.postcode +Address.secondaryAddress +Address.state +Address.stateAbbr +Address.streetAddress +Address.streetAddressNumber +Address.streetName +Address.streetPrefix +Address.streetSuffix +Address.timeZone +Address.zipCode +Address.zipCodeByState +Address.zipCodePlus4 +Ancient.god +Ancient.hero +Ancient.primordial +Ancient.titan +Animal.genus +Animal.name +Animal.scientificName +Animal.species +App.author +App.name +App.version +Appliance.brand +Appliance.equipment +AquaTeenHungerForce.character +Artist.name +Australia.animals +Australia.locations +Australia.states +Avatar.image +Aviation.METAR +Aviation.aircraft +Aviation.airline +Aviation.airport +Aviation.flight +Aws.accountId +Aws.acmARN +Aws.albARN +Aws.albTargetGroupARN +Aws.region +Aws.route53ZoneId +Aws.securityGroupId +Aws.subnetId +Aws.vpcId +Azure.appServiceEnvironment +Azure.appServicePlan +Azure.applicationGateway +Azure.bastionHost +Azure.containerApps +Azure.containerAppsEnvironment +Azure.containerInstance +Azure.containerRegistry +Azure.cosmosDBDatabase +Azure.firewall +Azure.keyVault +Azure.loadBalancer +Azure.loadTesting +Azure.logAnalytics +Azure.managementGroup +Azure.mysqlDatabase +Azure.networkSecurityGroup +Azure.postgreSQLDatabase +Azure.region +Azure.resourceGroup +Azure.serviceBus +Azure.serviceBusQueue +Azure.serviceBusTopic +Azure.sqlDatabase +Azure.staticWebApp +Azure.storageAccount +Azure.subscriptionId +Azure.tenantId +Azure.virtualMachine +Azure.virtualNetwork +Azure.virtualWan +Babylon5.character +Babylon5.quote +BackToTheFuture.character +BackToTheFuture.date +BackToTheFuture.quote +Barcode.type +Baseball.coaches +Baseball.players +Baseball.positions +Baseball.teams +Basketball.coaches +Basketball.players +Basketball.positions +Basketball.teams +Battlefield1.classes +Battlefield1.faction +Battlefield1.map +Battlefield1.vehicle +Battlefield1.weapon +Beer.brand +Beer.hop +Beer.malt +Beer.name +Beer.style +Beer.yeast +BigBangTheory.character +BigBangTheory.quote +BloodType.aboTypes +BloodType.bloodGroup +BloodType.pTypes +BloodType.rhTypes +BojackHorseman.characters +BojackHorseman.quotes +BojackHorseman.tongueTwisters +Book.author +Book.genre +Book.publisher +Book.title +BossaNova.artist +BossaNova.song +Brand.car +Brand.sport +Brand.watch +BreakingBad.character +BreakingBad.episode +BrooklynNineNine.characters +BrooklynNineNine.quotes +Buffy.bigBads +Buffy.celebrities +Buffy.characters +Buffy.episodes +Buffy.quotes +Business.creditCardExpiry +Business.creditCardNumber +Business.creditCardType +Business.securityCode +CNPJ.invalid +CNPJ.valid +CPF.invalid +CPF.valid +Camera.brand +Camera.brandWithModel +Camera.model +Cannabis.brands +Cannabis.buzzwords +Cannabis.cannabinoidAbbreviations +Cannabis.cannabinoids +Cannabis.categories +Cannabis.healthBenefits +Cannabis.medicalUses +Cannabis.strains +Cannabis.terpenes +Cannabis.types +Cat.breed +Cat.name +Cat.registry +Chess.opening +Chess.player +Chess.title +Chess.tournament +Chiquito.expressions +Chiquito.jokes +Chiquito.sentences +Chiquito.terms +ChuckNorris.fact +ClashOfClans.defensiveBuilding +ClashOfClans.rank +ClashOfClans.troop +Code.asin +Code.ean13 +Code.ean8 +Code.gtin13 +Code.gtin8 +Code.imei +Code.isbn10 +Code.isbn13 +Code.isbnGroup +Code.isbnGs1 +Code.isbnRegistrant +Coffee.blendName +Coffee.body +Coffee.country +Coffee.descriptor +Coffee.intensifier +Coffee.name1 +Coffee.name2 +Coffee.notes +Coffee.region +Coffee.variety +Coin.flip +Color.hex +Color.name +Commerce.brand +Commerce.department +Commerce.material +Commerce.price +Commerce.productName +Commerce.promotionCode +Commerce.vendor +Community.character +Community.quote +Company.bs +Company.buzzword +Company.catchPhrase +Company.industry +Company.logo +Company.name +Company.profession +Company.suffix +Company.url +Compass.abbreviation +Compass.azimuth +Compass.word +Computer.brand +Computer.linux +Computer.macos +Computer.operatingSystem +Computer.platform +Computer.type +Computer.windows +Construction.heavyEquipment +Construction.materials +Construction.roles +Construction.standardCostCodes +Construction.subcontractCategories +Construction.trades +Control.alteredItem +Control.alteredWorldEvent +Control.character +Control.hiss +Control.location +Control.objectOfPower +Control.quote +Control.theBoard +Cosmere.allomancers +Cosmere.aons +Cosmere.feruchemists +Cosmere.heralds +Cosmere.knightsRadiant +Cosmere.metals +Cosmere.shardWorlds +Cosmere.shards +Cosmere.sprens +Cosmere.surges +Country.capital +Country.countryCode2 +Country.countryCode3 +Country.currency +Country.currencyCode +Country.flag +Country.name +CowboyBebop.character +CowboyBebop.episode +CowboyBebop.quote +CowboyBebop.song +Cricket.formats +Cricket.players +Cricket.teams +Cricket.tournaments +CryptoCoin.coin +CultureSeries.books +CultureSeries.civs +CultureSeries.cultureShipClassAbvs +CultureSeries.cultureShipClasses +CultureSeries.cultureShips +CultureSeries.planets +Currency.code +Currency.name +DarkSouls.classes +DarkSouls.covenants +DarkSouls.shield +DarkSouls.stats +DateAndTime.between +DateAndTime.birthday +DateAndTime.future +DateAndTime.past +DcComics.hero +DcComics.heroine +DcComics.name +DcComics.title +DcComics.villain +Demographic.demonym +Demographic.educationalAttainment +Demographic.maritalStatus +Demographic.race +Demographic.sex +Departed.actor +Departed.character +Departed.quote +Dessert.flavor +Dessert.topping +Dessert.variety 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00000000..698c7919 --- /dev/null +++ b/sample/report/html/data-sources.html @@ -0,0 +1,178 @@ + + + + Data Source Details - Data Caterer + + + +
    + + + Data Caterer + +

    Data Sources

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    Output Rows Per Second

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    +
    + + \ No newline at end of file diff --git a/sample/report/html/main.css b/sample/report/html/main.css new file mode 100644 index 00000000..e07128fc --- /dev/null +++ b/sample/report/html/main.css @@ -0,0 +1,173 @@ +.box-iframe { + float: left; + margin-right: 10px; +} + +body { + margin: 0; +} + +.top-banner { + height: fit-content; + background-color: #ff6e42; + padding: 0 .2rem; + display: flex; +} + +.top-banner span { + color: #f2f2f2; + font-size: 17px; + padding: 5px 6px; + display: flex; + align-items: center; +} + +.logo { + padding: 5px; + height: 45px; + width: auto; + display: flex; + align-items: center; + justify-content: center; +} + +.logo:hover { + background-color: #ff9100; + color: black; +} + +.top-banner img { + height: 35px; + width: auto; + display: flex; + justify-content: center; + vertical-align: middle; +} + +.topnav { + overflow: hidden; + background-color: #ff6e42; +} + +.topnav a { + float: left; + color: #f2f2f2; + text-align: center; + padding: 8px 10px; + text-decoration: none; + font-size: 17px; +} + +.topnav a:hover { + background-color: #ff9100; + color: black; +} + +.topnav a.active { + color: black; +} + +table { + overflow: hidden; + transition: max-height 0.2s ease-out; +} + +table.codegrid { + font-family: monospace; + font-size: 12px; + width: auto !important; +} + +table.statementlist { + width: auto !important; + font-size: 13px; +} + +table.codegrid td { + padding: 0 !important; + border: 0 !important +} + +table td.linenumber { + width: 40px !important; +} + +td { + white-space: normal +} + +.table thead th { + position: sticky; + top: 0; + z-index: 1; +} + +table, tr, td, th { + border-collapse: collapse; +} + +.table-collapsible { + max-height: 0; + overflow: hidden; + transition: max-height 0.2s ease-out; +} + +.collapsible { + background-color: lightgray; + color: black; + cursor: pointer; + width: 100%; + border: none; + text-align: left; + outline: none; +} + +.collapsible:after { + content: "\02795"; /* Unicode character for "plus" sign (+) */ + color: white; + float: right; +} + +.active:after { + content: "\2796"; /* Unicode character for "minus" sign (-) */ +} + +.outer-container { + display: flex; + flex-direction: column; + height: 100vh; +} + +.top-container { + height: 50%; + overflow: auto; + resize: vertical; +} + +.bottom-container { + flex: 1; + min-height: 0; + height: 50%; + overflow: auto; + resize: vertical; +} + +.slider { + text-align: center; + background-color: #dee2e6; + cursor: row-resize; + user-select: none; +} + +.selected-row { + background-color: #ff6e42 !important; +} + +.progress { + white-space: normal; + background-color: #d9534f; +} + +.progress-bar { + color: black; +} diff --git a/sample/report/html/steps.html b/sample/report/html/steps.html new file mode 100644 index 00000000..8bd4eb85 --- /dev/null +++ b/sample/report/html/steps.html @@ -0,0 +1,1233 @@ + + + + Step Details - Data Caterer + + + +
    + + + Data Caterer + +
    +
    +

    Steps

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    Field Details: + 89ba1628-281d-4503-bcc5-6f393db4b4cb +

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    + name + + string + + ✅ + + random + +
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    +
    +
    +
    +
    +
    +
    +
    ...
    +
    +
    +

    Field Details: + fec4ad7f-4f70-4ec0-971d-2a15b0e1619d +

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeNullableGenerator TypeGenerated Records Metadata Comparison
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    + Metadata Field + + Original Value + + Generated Value +
    + + count + + + + + 2000 +
    + + distinctCount + + + + + 950 +
    + + label + + + + + name +
    + + expression + + + #{Name.name} + + #{Name.name} +
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    + + avgLen + + + + + 15 +
    + + isPII + + + + + true +
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    + amount + + double + + ✅ + + random + +
    + + + + + + + + + + + + + + + + + + + + + + +
    + Metadata Field + + Original Value + + Generated Value +
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    +
    +
    +
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    +
    + + \ No newline at end of file diff --git a/sample/report/html/tasks.html b/sample/report/html/tasks.html new file mode 100644 index 00000000..f16c945c --- /dev/null +++ b/sample/report/html/tasks.html @@ -0,0 +1,105 @@ + + + + Task Details - Data Caterer + + + +
    + + + Data Caterer + +

    Tasks

    + + + + + + + + + + + + + + + + +
    NameSteps
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    + + \ No newline at end of file diff --git a/sample/report/html/validations.html b/sample/report/html/validations.html new file mode 100644 index 00000000..78b0ff3d --- /dev/null +++ b/sample/report/html/validations.html @@ -0,0 +1,410 @@ + + + + Validations - Data Caterer + + + +
    + + + Data Caterer + +

    Validations

    + + + + + + + + + + + + + + + + + + + + + +
    NameData SourcesDescriptionSuccess
    + + default_validation + + + + my_json + + + Validation of data sources after generating data + +
    +
    +
    + 1/2 (50.00%) +
    +
    + + default_validation + + + + my_csv + + + Validation of data sources after generating data + +
    +
    +
    + 2/2 (100.00%) +
    +

    Details

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    DescriptionData SourceOptionsSuccessWithin Error ThresholdValidationError Sample
    + Validate + + + my_json + + + + +
    +
    +
    + 2000/2000 (100.00%) +
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    + + expr + + + max(amount) < 100 +
    + + groupByColumns + + + account_id,name +
    + + aggregationColumn + + + amount +
    + + aggregationType + + + max +
    + + errorThreshold + + + 0.0 +
    +     		+ +
    + Validate + + + my_json + + + + +
    +
    +
    + 1000/2000 (50.00%) +
    +     		+ ❌ + + + + + + + + + + + + + + + +
    + + expr + + + count == 1 +
    + + groupByColumns + + + account_id,name +
    + + aggregationColumn + + + unique +
    + + aggregationType + + + count +
    + + errorThreshold + + + 0.0 +
    +     		+ + + + + + + + + + + + + + +
    + {"account_id":"ACC67262091","name":"Emiko Abernathy III","count":2} +
    + {"account_id":"ACC98264190","name":"Shirleen Weimann","count":2} +
    + {"account_id":"ACC70737722","name":"Shanelle Keebler","count":2} +
    + {"account_id":"ACC28941814","name":"Dr. Violette Green","count":2} +
    + {"account_id":"ACC42462487","name":"Marquetta Hayes","count":2} +
    +
    + account_number is a primary key + + + my_csv + + + + +
    +
    +
    + 1000/1000 (100.00%) +
    +     		+ ✅ + + + + + + + + + +
    + + expr + + + ISNOTNULL(account_number) +
    + + errorThreshold + + + 0.0 +
    +     		+ +
    + Some names follow a different pattern + + + my_csv + + + + +
    +
    +
    + 985/1000 (98.50%) +
    +     		+ ✅ + + + + + + + + + +
    + + expr + + + REGEXP(name, '[A-Z][a-z]+ [A-Z][a-z]+') +
    + + errorThreshold + + + 0.3 +
    +     		+ +
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    Data testing is difficult and fragmented
    • Data being sent via messages, HTTP requests or files and getting stored in databases, file systems, etc.
    • Maintaining and updating tests with the latest schemas and business definitions
    • Different testing tools for services, jobs or data sources
    • Complex relationships between datasets and fields
    • Different scenarios, permutations, combinations and edge cases to cover
    Current solutions only cover half the story
    • Specific testing frameworks that support one or limited number of data sources or transport protocols
    • Under utilizing metadata from data catalogs or metadata discovery services
    • Testing teams having difficulties understanding when failures occur
    • Integration tests relying on external teams/services
    • Manually generating data, or worse, copying/masking production data into lower environments
    • Observability pushes towards being reactive rather than proactive

    Try now

    What you need is a reliable tool that can handle changes to your data landscape

    With Data Caterer, you get:

    • Ability to connect to any type of data source: files, SQL or no-SQL databases, messaging systems, HTTP
    • Discover metadata from your existing infrastructure and services
    • Gain confidence that bugs do not propagate to production
    • Be proactive in ensuring changes do not affect other data producers or consumers
    • Configurability to run the way you want

    Try now

    "},{"location":"#tech-summary","title":"Tech Summary","text":"

    Use the Java, Scala API, or YAML files to help with setup or customisation that are all run via a Docker image. Want to get into details? Checkout the setup pages here to get code examples and guides that will take you through scenarios and data sources.

    Main features include:

    • Metadata discovery
    • Batch and event data generation
    • Maintain referential integrity across any dataset
    • Create custom data generation scenarios
    • Clean up generated data
    • Validate data
    • Suggest data validations

    Check other run configurations here.

    "},{"location":"#what-is-it","title":"What is it","text":"

    • Data generation and testing tool

      Generate production like data to be consumed and validated.

    • Designed for any data source

      We aim to support pushing data to any data source, in any format.

    • Low/no code solution

      Can use the tool via either Scala, Java or YAML. Connect to data or metadata sources to generate data and validate.

    • Developer productivity tool

      If you are a new developer or seasoned veteran, cut down on your feedback loop when developing with data.

    "},{"location":"#what-it-is-not","title":"What it is not","text":"

    • Metadata storage/platform

      You could store and use metadata within the data generation/validation tasks but is not the recommended approach. Rather, this metadata should be gathered from existing services who handle metadata on behalf of Data Caterer.

    • Data contract

      The focus of Data Caterer is on the data generation and testing, which can include details about how the data looks like and how it behaves. But it does not encompass all the additional metadata that comes with a data contract such as SLAs, security, etc.

    • Metrics from load testing

      Although millions of records can be generated, there are limited capabilities in terms of metric capturing.

    Try now

    Data Catering vs Other tools vs In-house

    Data Catering Other tools In-house Data flow Batch and events generation with validation Batch generation only or validation only Depends on architecture and design Time to results 1 day 1+ month to integrate, deploy and onboard 1+ month to build and deploy Solution Connect with your existing data ecosystem, automatic generation and validation Manual UI data entry or via SDK Depends on engineer(s) building it

    "},{"location":"about/","title":"About","text":"

    Hi, my name is Peter. I am a independent Software Developer, mainly focussing on data related services. My experience can be found on my LinkedIn.

    I have created Data Caterer to help serve individuals and companies with data generation and data testing. It is a complex area that has many edge cases or intricacies that are hard to summarise or turn into something actionable and repeatable. Through the use of metadata, Data Caterer can help simplify your data testing, simulating production environment data, aid in data debugging, or whatever your data use case may be.

    Given that it is going to save you and your team time and money, please help in considering financial support. This will help the product grow into a sustainable and feature-full service.

    "},{"location":"about/#contact","title":"Contact","text":"

    Please contact Peter Flook via Slack or via email peter.flook@data.catering if you have any questions or queries.

    "},{"location":"about/#terms-of-service","title":"Terms of service","text":"

    Terms of service can be found here.

    "},{"location":"about/#privacy-policy","title":"Privacy policy","text":"

    Privacy policy can be found here.

    "},{"location":"sponsor/","title":"Sponsor","text":"

    To have access to all the features of Data Caterer, you can subscribe according to your situation. You will not be charged by usage. As you continue to subscribe, you will have access to the latest version of Data Caterer as new bug fixes and features get published.

    This has been a passion project of mine where I have spent countless hours thinking of the idea, implementing, maintaining, documenting and updating it. I hope that it will help with developers and companies with their testing by saving time and effort, allowing you to focus on what is important. If you fall under this boat, please consider sponsorship to allow me to further maintain and upgrade the solution. Any contributions are much appreciated.

    Those who are wanting to use this project for open source applications, please contact me as I would be happy to contribute.

    This is inspired by the mkdocs-material project that follows the same model.

    "},{"location":"sponsor/#features","title":"Features","text":"
    • Metadata discovery
    • All data sources (see here for all data sources)
    • Batch and Event generation
    • Auto generation from data connections or metadata sources
    • Suggest data validations
    • Clean up generated data
    • Run as many times as you want, not charged by usage
    "},{"location":"sponsor/#tiers","title":"Tiers","text":""},{"location":"sponsor/#manage-subscription","title":"Manage Subscription","text":"

    Manage via this link

    "},{"location":"sponsor/#contact","title":"Contact","text":"

    Please contact Peter Flook via Slack or via email peter.flook@data.catering if you have any questions or queries.

    "},{"location":"use-case/","title":"Use cases","text":""},{"location":"use-case/#replicate-production-in-lower-environment","title":"Replicate production in lower environment","text":"

    Having a stable and reliable test environment is a challenge for a number of companies, especially where teams are asynchronously deploying and testing changes at faster rates. Data Caterer can help alleviate these issues by doing the following:

    1. Generates data with the latest schema changes and production like field values
    2. Run as a job on a daily/regular basis to replicate production traffic or data flows
    3. Validate data to ensure your system runs as expected
    4. Clean up data to avoid build up of generated data

    "},{"location":"use-case/#local-development","title":"Local development","text":"

    Similar to the above, being able to replicate production like data in your local environment can be key to developing more reliable code as you can test directly against data in your local computer. This has a number of benefits including:

    1. Fewer assumptions or ambiguities when the developer codes
    2. Direct feedback loop in local computer rather than waiting for test environment for more reliable test data
    3. No domain expertise required to understand the data
    4. Easy for new developers to be onboarded and developing/testing code for jobs/services
    "},{"location":"use-case/#systemintegration-testing","title":"System/integration testing","text":"

    When working with third-party, external or internal data providers, it can be difficult to have all setup ready to produce reliable data that abides by relationship contracts between each of the systems. You have to rely on these data providers in order for you to run your tests which may not align to their priorities. With Data Caterer, you can generate the same data that they would produce, along with maintaining referential integrity across the data providers, so that you can run your tests without relying on their systems being up and reliable in their corresponding lower environments.

    "},{"location":"use-case/#scenario-testing","title":"Scenario testing","text":"

    If you want to set up particular data scenarios, you can customise the generated data to fit your scenario. Once the data gets generated and is consumed, you can also run validations to ensure your system has consumed the data correctly. These scenarios can be put together from existing tasks or data sources can be enabled/disabled based on your requirement. Built into Data Caterer and controlled via feature flags, is the ability to test edge cases based on the data type of the fields used for data generation (enableEdgeCases flag within <field>.generator.options, see more here).

    "},{"location":"use-case/#data-debugging","title":"Data debugging","text":"

    When data related issues occur in production, it may be difficult to replicate in a lower or local environment. It could be related to specific fields not containing expected results, size of data is too large or missing corresponding referenced data. This becomes key to resolving the issue as you can directly code against the exact data scenario and have confidence that your code changes will fix the problem. Data Caterer can be used to generate the appropriate data in whichever environment you want to test your changes against.

    "},{"location":"use-case/#data-profiling","title":"Data profiling","text":"

    When using Data Caterer with the feature flag enableGeneratePlanAndTasks enabled (see here), metadata relating all the fields defined in the data sources you have configured will be generated via data profiling. You can run this as a standalone job (can disable enableGenerateData) so that you can focus on the profile of the data you are utilising. This can be run against your production data sources to ensure the metadata can be used to accurately generate data in other environments. This is a key feature of Data Caterer as no direct production connections need to be maintained to generate data in other environments (which can lead to serious concerns about data security as seen here).

    "},{"location":"use-case/#schema-gathering","title":"Schema gathering","text":"

    When using Data Caterer with the feature flag enableGeneratePlanAndTasks enabled (see here), all schemas of the data sources defined will be tracked in a common format (as tasks). This data, along with the data profiling metadata, could then feed back into your schema registries to help keep them up to date with your system.

    "},{"location":"get-started/docker/","title":"Run Data Caterer","text":""},{"location":"get-started/docker/#quick-start","title":"Quick start","text":"

    Ensure you have docker installed and running.

    git clone git@github.com:pflooky/data-caterer-example.git\ncd data-caterer-example && ./run.sh\n#check results under docker/sample/report/index.html folder\n
    "},{"location":"get-started/docker/#report","title":"Report","text":"

    Check the report generated under docker/data/custom/report/index.html.

    Sample report can also be seen here

    "},{"location":"get-started/docker/#paid-version-trial","title":"Paid Version Trial","text":"

    30 day trial of the paid version can be accessed via these steps:

    1. Join the Slack Data Catering Slack group here
    2. Get an API_KEY by using slash command /token in the Slack group (will only be visible to you)
    3. git clone git@github.com:pflooky/data-caterer-example.git\ncd data-caterer-example && export DATA_CATERING_API_KEY=<insert api key>\n./run.sh\n

    If you want to check how long your trial has left, you can check back in the Slack group or type /token again.

    "},{"location":"get-started/docker/#guided-tour","title":"Guided tour","text":"

    Check out the starter guide here that will take your through step by step. You can also check the other guides here to see the other possibilities of what Data Caterer can achieve for you.

    "},{"location":"legal/privacy-policy/","title":"Privacy Policy","text":"

    Last updated September 25, 2023

    "},{"location":"legal/privacy-policy/#data-caterer-policy-on-privacy-of-customer-personal-information","title":"Data Caterer Policy on Privacy of Customer Personal Information","text":"

    Peter John Flook is committed to protecting the privacy and security of your personal information obtained by reason of your use of Data Caterer. This policy explains the types of customer personal information we collect, how it is used, and the steps we take to ensure your personal information is handled appropriately.

    "},{"location":"legal/privacy-policy/#who-is-peter-john-flook","title":"Who is Peter John Flook?","text":"

    For purposes of this Privacy Policy, \u201cPeter John Flook\u201d means Peter John Flook, the company developing and providing Data Caterer and related websites and services.

    "},{"location":"legal/privacy-policy/#what-is-personal-information","title":"What is personal information?","text":"

    Personal information is information that refers to an individual specifically and is recorded in any form. Personal information includes such things as age, income, date of birth, ethnic origin and credit records. Information about individuals contained in the following documents is not considered personal information:

    • public telephone directories, where the subscriber can refuse to be listed
    • professional and business directories available to the public
    • public registries and court records
    • other publicly available printed and electronic publications
    "},{"location":"legal/privacy-policy/#we-are-accountable-to-you","title":"We are accountable to you","text":"

    Peter John Flook is responsible for all personal information under its control. Our team is accountable for compliance with these privacy and security principles.

    "},{"location":"legal/privacy-policy/#we-let-you-know-why-we-collect-and-use-your-personal-information-and-get-your-consent","title":"We let you know why we collect and use your personal information and get your consent","text":"

    Peter John Flook identifies the purpose for which your personal information is collected and will be used or disclosed. If that purpose is not listed below we will do this before or at the time the information is actually being collected. You will be deemed to consent to our use of your personal information for the purpose of:

    • communicating with you generally
    • processing your purchases
    • processing and keeping track of transactions and reporting back to you
    • protecting against fraud or error
    • providing product and services requested by you
    • recommending products and services that Peter John Flook believes will be of interest and provide value to you
    • fulfilling any other purpose that would be reasonably apparent to the average person at the time we collect it from you

    Otherwise, Peter John Flook will obtain your express consent (by verbal, written or electronic agreement) to collect, use or disclose your personal information. You can change your consent preferences at any time by contacting Peter John Flook (please refer to the \u201cHow to contact us\u201d section below).

    "},{"location":"legal/privacy-policy/#we-limit-collection-of-your-personal-information","title":"We limit collection of your personal information","text":"

    Peter John Flook collects only the information required to provide products and services to you. Peter John Flook will collect personal information only by clear, fair and lawful means.

    We receive and store any information you enter on our website or give us in any other way. You can choose not to provide certain information, but then you might not be able to take advantage of many of our features.

    Peter John Flook does not receive or store personal content saved to your local device while using Data Caterer.

    We also receive and store certain types of information whenever you interact with us.

    "},{"location":"legal/privacy-policy/#information-provided-to-stripe","title":"Information provided to Stripe","text":"

    All purchases that are made through this site are processed securely and externally by Stripe. Unless you expressly consent otherwise, we do not see or have access to any personal information that you may provide to Stripe, other than information that is required in order to process your order and deliver your purchased items to you (eg, your name, email address and billing/postal address).

    "},{"location":"legal/privacy-policy/#we-limit-disclosure-and-retention-of-your-personal-information","title":"We limit disclosure and retention of your personal information","text":"

    Peter John Flook does not disclose personal information to any organization or person for any reason except the following:

    We employ other companies and individuals to perform functions on our behalf. Examples include fulfilling orders, delivering packages, sending postal mail and e-mail, removing repetitive information from customer lists, analyzing data, providing marketing assistance, processing credit card payments, and providing customer service. They have access to personal information needed to perform their functions, but may not use it for other purposes. We may use service providers located outside of Australia, and, if applicable, your personal information may be processed and stored in other countries and therefore may be subject to disclosure under the laws of those countries. As we continue to develop our business, we might sell or buy stores, subsidiaries, or business units. In such transactions, customer information generally is one of the transferred business assets but remains subject to the promises made in any pre-existing Privacy Notice (unless, of course, the customer consents otherwise). Also, in the unlikely event that Peter John Flook or substantially all of its assets are acquired, customer information of course will be one of the transferred assets. You are deemed to consent to disclosure of your personal information for those purposes. If your personal information is shared with third parties, those third parties are bound by appropriate agreements with Peter John Flook to secure and protect the confidentiality of your personal information.

    Peter John Flook retains your personal information only as long as it is required for our business relationship or as required by federal and provincial laws.

    "},{"location":"legal/privacy-policy/#we-keep-your-personal-information-up-to-date-and-accurate","title":"We keep your personal information up to date and accurate","text":"

    Peter John Flook keeps your personal information up to date, accurate and relevant for its intended use.

    You may request access to the personal information we have on record in order to review and amend the information, as appropriate. In circumstances where your personal information has been provided by a third party, we will refer you to that party (e.g. credit bureaus). To access your personal information, refer to the \u201cHow to contact us\u201d section below.

    "},{"location":"legal/privacy-policy/#the-security-of-your-personal-information-is-a-priority-for-peter-john-flook","title":"The security of your personal information is a priority for Peter John Flook","text":"

    We take steps to safeguard your personal information, regardless of the format in which it is held, including:

    physical security measures such as restricted access facilities and locked filing cabinets electronic security measures for computerized personal information such as password protection, database encryption and personal identification numbers. We work to protect the security of your information during transmission by using \u201cTransport Layer Security\u201d (TLS) protocol. organizational processes such as limiting access to your personal information to a selected group of individuals contractual obligations with third parties who need access to your personal information requiring them to protect and secure your personal information It\u2019s important for you to protect against unauthorized access to your password and your computer. Be sure to sign off when you\u2019ve finished using any shared computer.

    "},{"location":"legal/privacy-policy/#what-about-third-party-advertisers-and-links-to-other-websites","title":"What About Third-Party Advertisers and Links to Other Websites?","text":"

    Our site may include third-party advertising and links to other websites. We do not provide any personally identifiable customer information to these advertisers or third-party websites.

    These third-party websites and advertisers, or Internet advertising companies working on their behalf, sometimes use technology to send (or \u201cserve\u201d) the advertisements that appear on our website directly to your browser. They automatically receive your IP address when this happens. They may also use cookies, JavaScript, web beacons (also known as action tags or single-pixel gifs), and other technologies to measure the effectiveness of their ads and to personalize advertising content. We do not have access to or control over cookies or other features that they may use, and the information practices of these advertisers and third-party websites are not covered by this Privacy Notice. Please contact them directly for more information about their privacy practices. In addition, the Network Advertising Initiative offers useful information about Internet advertising companies (also called \u201cad networks\u201d or \u201cnetwork advertisers\u201d), including information about how to opt-out of their information collection. You can access the Network Advertising Initiative at http://www.networkadvertising.org.

    "},{"location":"legal/privacy-policy/#redirection-to-stripe","title":"Redirection to Stripe","text":"

    In particular, when you submit an order to us, you may be automatically redirected to Stripe in order to complete the required payment. The payment page that is provided by Stripe is not part of this site. As noted above, we are not privy to any of the bank account, credit card or other personal information that you may provide to Stripe, other than information that is required in order to process your order and deliver your purchased items to you (eg, your name, email address and billing/postal address). We recommend that you refer to Stripe\u2019s privacy statement if you would like more information about how Stripe collects and handles your personal information.

    "},{"location":"legal/privacy-policy/#we-are-open-about-our-privacy-and-security-policy","title":"We are open about our privacy and security policy","text":"

    We are committed to providing you with understandable and easily available information about our policy and practices related to management of your personal information. This policy and any related information is available at all times on our website, https://data.catering/about/ under Privacy or on request. To contact us, refer to the \u201cHow to contact us\u201d section below.

    "},{"location":"legal/privacy-policy/#we-provide-access-to-your-personal-information-stored-by-peter-john-flook","title":"We provide access to your personal information stored by Peter John Flook","text":"

    You can request access to your personal information stored by Peter John Flook. To contact us, refer to the \u201cHow to contact us\u201d section below. Upon receiving such a request, Peter John Flook will:

    inform you about what type of personal information we have on record or in our control, how it is used and to whom it may have been disclosed provide you with access to your information so you can review and verify the accuracy and completeness and request changes to the information make any necessary updates to your personal information We respond to your questions, concerns and complaints about privacy Peter John Flook responds in a timely manner to your questions, concerns and complaints about the privacy of your personal information and our privacy policies and procedures.

    "},{"location":"legal/privacy-policy/#how-to-contact-us","title":"How to contact us","text":"
    • by email at peter.flook@data.catering

    Our business changes constantly, and this privacy notice will change also. We may e-mail periodic reminders of our notices and conditions, unless you have instructed us not to, but you should check our website frequently to see recent changes. We are, however, committed to protecting your information and will never materially change our policies and practices to make them less protective of customer information collected in the past without the consent of affected customers.

    "},{"location":"legal/terms-of-service/","title":"Terms and Conditions","text":"

    Last updated: September 25, 2023

    Please read these terms and conditions carefully before using Our Service.

    "},{"location":"legal/terms-of-service/#interpretation-and-definitions","title":"Interpretation and Definitions","text":""},{"location":"legal/terms-of-service/#interpretation","title":"Interpretation","text":"

    The words of which the initial letter is capitalized have meanings defined under the following conditions. The following definitions shall have the same meaning regardless of whether they appear in singular or in plural.

    "},{"location":"legal/terms-of-service/#definitions","title":"Definitions","text":"

    For the purposes of these Terms and Conditions:

    • Application means the software program provided by the Company downloaded by You on any electronic device, named Data Caterer
    • Application Store means the digital distribution service operated and developed by Docker Inc. (\u201cDocker\u201d) in which the Application has been downloaded.
    • Affiliate means an entity that controls, is controlled by or is under common control with a party, where \"control\" means ownership of 50% or more of the shares, equity interest or other securities entitled to vote for election of directors or other managing authority.
    • Country refers to: New South Wales, Australia
    • Company (referred to as either \"the Company\", \"We\", \"Us\" or \"Our\" in this Agreement) refers to Peter John Flook ( ABN: 65153160916), 30 Anne William Drive, West Pennant Hills, 2125, NSW, Australia.
    • Device means any device that can access the Service such as a computer, a cellphone or a digital tablet.
    • Service refers to the Application.
    • Terms and Conditions (also referred as \"Terms\") mean these Terms and Conditions that form the entire agreement between You and the Company regarding the use of the Service.
    • Third-party Social Media Service means any services or content (including data, information, products or services) provided by a third party that may be displayed, included or made available by the Service.
    • You means the individual accessing or using the Service, or the company, or other legal entity on behalf of which such individual is accessing or using the Service, as applicable.
    "},{"location":"legal/terms-of-service/#acknowledgment","title":"Acknowledgment","text":"

    These are the Terms and Conditions governing the use of this Service and the agreement that operates between You and the Company. These Terms and Conditions set out the rights and obligations of all users regarding the use of the Service.

    Your access to and use of the Service is conditioned on Your acceptance of and compliance with these Terms and Conditions. These Terms and Conditions apply to all visitors, users and others who access or use the Service.

    By accessing or using the Service You agree to be bound by these Terms and Conditions. If You disagree with any part of these Terms and Conditions then You may not access the Service.

    You represent that you are over the age of 18. The Company does not permit those under 18 to use the Service.

    Your access to and use of the Service is also conditioned on Your acceptance of and compliance with the Privacy Policy of the Company. Our Privacy Policy describes Our policies and procedures on the collection, use and disclosure of Your personal information when You use the Application or the Website and tells You about Your privacy rights and how the law protects You. Please read Our Privacy Policy carefully before using Our Service.

    "},{"location":"legal/terms-of-service/#links-to-other-websites","title":"Links to Other Websites","text":"

    Our Service may contain links to third-party websites or services that are not owned or controlled by the Company.

    The Company has no control over, and assumes no responsibility for, the content, privacy policies, or practices of any third party websites or services. You further acknowledge and agree that the Company shall not be responsible or liable, directly or indirectly, for any damage or loss caused or alleged to be caused by or in connection with the use of or reliance on any such content, goods or services available on or through any such websites or services.

    We strongly advise You to read the terms and conditions and privacy policies of any third-party websites or services that You visit.

    "},{"location":"legal/terms-of-service/#termination","title":"Termination","text":"

    We may terminate or suspend Your access immediately, without prior notice or liability, for any reason whatsoever, including without limitation if You breach these Terms and Conditions.

    Upon termination, Your right to use the Service will cease immediately.

    "},{"location":"legal/terms-of-service/#limitation-of-liability","title":"Limitation of Liability","text":"

    Notwithstanding any damages that You might incur, the entire liability of the Company and any of its suppliers under any provision of these Terms and Your exclusive remedy for all the foregoing shall be limited to the amount actually paid by You through the Service or 100 USD if You haven't purchased anything through the Service.

    To the maximum extent permitted by applicable law, in no event shall the Company or its suppliers be liable for any special, incidental, indirect, or consequential damages whatsoever (including, but not limited to, damages for loss of profits, loss of data or other information, for business interruption, for personal injury, loss of privacy arising out of or in any way related to the use of or inability to use the Service, third-party software and/or third-party hardware used with the Service, or otherwise in connection with any provision of these Terms), even if the Company or any supplier has been advised of the possibility of such damages and even if the remedy fails of its essential purpose.

    Some states do not allow the exclusion of implied warranties or limitation of liability for incidental or consequential damages, which means that some of the above limitations may not apply. In these states, each party's liability will be limited to the greatest extent permitted by law.

    "},{"location":"legal/terms-of-service/#as-is-and-as-available-disclaimer","title":"\"AS IS\" and \"AS AVAILABLE\" Disclaimer","text":"

    The Service is provided to You \"AS IS\" and \"AS AVAILABLE\" and with all faults and defects without warranty of any kind. To the maximum extent permitted under applicable law, the Company, on its own behalf and on behalf of its Affiliates and its and their respective licensors and service providers, expressly disclaims all warranties, whether express, implied, statutory or otherwise, with respect to the Service, including all implied warranties of merchantability, fitness for a particular purpose, title and non-infringement, and warranties that may arise out of course of dealing, course of performance, usage or trade practice. Without limitation to the foregoing, the Company provides no warranty or undertaking, and makes no representation of any kind that the Service will meet Your requirements, achieve any intended results, be compatible or work with any other software, applications, systems or services, operate without interruption, meet any performance or reliability standards or be error free or that any errors or defects can or will be corrected.

    Without limiting the foregoing, neither the Company nor any of the company's provider makes any representation or warranty of any kind, express or implied: (i) as to the operation or availability of the Service, or the information, content, and materials or products included thereon; (ii) that the Service will be uninterrupted or error-free; (iii) as to the accuracy, reliability, or currency of any information or content provided through the Service; or (iv) that the Service, its servers, the content, or e-mails sent from or on behalf of the Company are free of viruses, scripts, trojan horses, worms, malware, time-bombs or other harmful components.

    Some jurisdictions do not allow the exclusion of certain types of warranties or limitations on applicable statutory rights of a consumer, so some or all of the above exclusions and limitations may not apply to You. But in such a case the exclusions and limitations set forth in this section shall be applied to the greatest extent enforceable under applicable law.

    "},{"location":"legal/terms-of-service/#governing-law","title":"Governing Law","text":"

    The laws of the Country, excluding its conflicts of law rules, shall govern this Terms and Your use of the Service. Your use of the Application may also be subject to other local, state, national, or international laws.

    "},{"location":"legal/terms-of-service/#disputes-resolution","title":"Disputes Resolution","text":"

    If You have any concern or dispute about the Service, You agree to first try to resolve the dispute informally by contacting the Company.

    "},{"location":"legal/terms-of-service/#for-european-union-eu-users","title":"For European Union (EU) Users","text":"

    If You are a European Union consumer, you will benefit from any mandatory provisions of the law of the country in which you are resident in.

    "},{"location":"legal/terms-of-service/#united-states-legal-compliance","title":"United States Legal Compliance","text":"

    You represent and warrant that (i) You are not located in a country that is subject to the United States government embargo, or that has been designated by the United States government as a \"terrorist supporting\" country, and (ii) You are not listed on any United States government list of prohibited or restricted parties.

    "},{"location":"legal/terms-of-service/#severability-and-waiver","title":"Severability and Waiver","text":""},{"location":"legal/terms-of-service/#severability","title":"Severability","text":"

    If any provision of these Terms is held to be unenforceable or invalid, such provision will be changed and interpreted to accomplish the objectives of such provision to the greatest extent possible under applicable law and the remaining provisions will continue in full force and effect.

    "},{"location":"legal/terms-of-service/#waiver","title":"Waiver","text":"

    Except as provided herein, the failure to exercise a right or to require performance of an obligation under these Terms shall not affect a party's ability to exercise such right or require such performance at any time thereafter nor shall the waiver of a breach constitute a waiver of any subsequent breach.

    "},{"location":"legal/terms-of-service/#translation-interpretation","title":"Translation Interpretation","text":"

    These Terms and Conditions may have been translated if We have made them available to You on our Service. You agree that the original English text shall prevail in the case of a dispute.

    "},{"location":"legal/terms-of-service/#changes-to-these-terms-and-conditions","title":"Changes to These Terms and Conditions","text":"

    We reserve the right, at Our sole discretion, to modify or replace these Terms at any time. If a revision is material We will make reasonable efforts to provide at least 30 days' notice prior to any new terms taking effect. What constitutes a material change will be determined at Our sole discretion.

    By continuing to access or use Our Service after those revisions become effective, You agree to be bound by the revised terms. If You do not agree to the new terms, in whole or in part, please stop using the website and the Service.

    "},{"location":"legal/terms-of-service/#contact-us","title":"Contact Us","text":"

    If you have any questions about these Terms and Conditions, You can contact us:

    • By email: peter.flook@data.catering
    "},{"location":"setup/","title":"Setup","text":"

    All the configurations and customisation related to Data Caterer can be found under here.

    "},{"location":"setup/#guide","title":"Guide","text":"

    If you want a guided tour of using the Java or Scala API, you can follow one of the guides found here.

    "},{"location":"setup/#specific-configuration","title":"Specific Configuration","text":"
    • Configurations - Configurations relating to feature flags, folder pathways, metadata analysis
    • Connections - Explore the data source connections available
    • Generators - Choose and configure the type of generator you want used for fields
    • Validations - How to validate data to ensure your system is performing as expected
    • Foreign Keys - Define links between data elements across data sources
    • Deployment - Deploy Data Caterer as a job to your chosen environment
    • Advanced - Advanced usage of Data Caterer
    "},{"location":"setup/#high-level-run-configurations","title":"High Level Run Configurations","text":""},{"location":"setup/advanced/","title":"Advanced use cases","text":""},{"location":"setup/advanced/#special-data-formats","title":"Special data formats","text":"

    There are many options available for you to use when you have a scenario when data has to be a certain format.

    1. Create expression datafaker
      1. Can be used to create names, addresses, or anything that can be found under here
    2. Create regex
    "},{"location":"setup/advanced/#foreign-keys-across-data-sets","title":"Foreign keys across data sets","text":"

    Details for how you can configure foreign keys can be found here.

    "},{"location":"setup/advanced/#edge-cases","title":"Edge cases","text":"

    For each given data type, there are edge cases which can cause issues when your application processes the data. This can be controlled at a column level by including the following flag in the generator options:

    JavaScalaYAML 
    field()\n  .name(\"amount\")\n  .type(DoubleType.instance())\n  .enableEdgeCases(true)\n  .edgeCaseProbability(0.1)\n
    field\n  .name(\"amount\")\n  .`type`(DoubleType)\n  .enableEdgeCases(true)\n  .edgeCaseProbability(0.1)\n
    fields:\n  - name: \"amount\"\n    type: \"double\"\n    generator:\n      type: \"random\"\n      options:\n        enableEdgeCases: \"true\"\n        edgeCaseProb: 0.1\n

    If you want to know all the possible edge cases for each data type, can check the documentation here.

    "},{"location":"setup/advanced/#scenario-testing","title":"Scenario testing","text":"

    You can create specific scenarios by adjusting the metadata found in the plan and tasks to your liking. For example, if you had two data sources, a Postgres database and a parquet file, and you wanted to save account data into Postgres and transactions related to those accounts into a parquet file. You can alter the status column in the account data to only generate open accounts and define a foreign key between Postgres and parquet to ensure the same account_id is being used. Then in the parquet task, define 1 to 10 transactions per account_id to be generated.

    Postgres account generation example task Parquet transaction generation example task Plan

    "},{"location":"setup/advanced/#cloud-storage","title":"Cloud storage","text":""},{"location":"setup/advanced/#data-source","title":"Data source","text":"

    If you want to save the file types CSV, JSON, Parquet or ORC into cloud storage, you can do so via adding extra configurations. Below is an example for S3.

    JavaScalaYAML 
    var csvTask = csv(\"my_csv\", \"s3a://my-bucket/csv/accounts\")\n  .schema(\n    field().name(\"account_id\"),\n    ...\n  );\n\nvar s3Configuration = configuration()\n  .runtimeConfig(Map.of(\n    \"spark.hadoop.fs.s3a.directory.marker.retention\", \"keep\",\n    \"spark.hadoop.fs.s3a.bucket.all.committer.magic.enabled\", \"true\",\n    \"spark.hadoop.fs.defaultFS\", \"s3a://my-bucket\",\n    //can change to other credential providers as shown here\n    //https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers\n    \"spark.hadoop.fs.s3a.aws.credentials.provider\", \"org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider\",\n    \"spark.hadoop.fs.s3a.access.key\", \"access_key\",\n    \"spark.hadoop.fs.s3a.secret.key\", \"secret_key\"\n  ));\n\nexecute(s3Configuration, csvTask);\n
    val csvTask = csv(\"my_csv\", \"s3a://my-bucket/csv/accounts\")\n  .schema(\n    field.name(\"account_id\"),\n    ...\n  )\n\nval s3Configuration = configuration\n  .runtimeConfig(Map(\n    \"spark.hadoop.fs.s3a.directory.marker.retention\" -> \"keep\",\n    \"spark.hadoop.fs.s3a.bucket.all.committer.magic.enabled\" -> \"true\",\n    \"spark.hadoop.fs.defaultFS\" -> \"s3a://my-bucket\",\n    //can change to other credential providers as shown here\n    //https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers\n    \"spark.hadoop.fs.s3a.aws.credentials.provider\" -> \"org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider\",\n    \"spark.hadoop.fs.s3a.access.key\" -> \"access_key\",\n    \"spark.hadoop.fs.s3a.secret.key\" -> \"secret_key\"\n  ))\n\nexecute(s3Configuration, csvTask)\n
    folders {\n   generatedPlanAndTaskFolderPath = \"s3a://my-bucket/data-caterer/generated\"\n   planFilePath = \"s3a://my-bucket/data-caterer/generated/plan/customer-create-plan.yaml\"\n   taskFolderPath = \"s3a://my-bucket/data-caterer/generated/task\"\n}\n\nruntime {\n    config {\n        ...\n        #S3\n        \"spark.hadoop.fs.s3a.directory.marker.retention\" = \"keep\"\n        \"spark.hadoop.fs.s3a.bucket.all.committer.magic.enabled\" = \"true\"\n        \"spark.hadoop.fs.defaultFS\" = \"s3a://my-bucket\"\n        #can change to other credential providers as shown here\n        #https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers\n        \"spark.hadoop.fs.s3a.aws.credentials.provider\" = \"org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider\"\n        \"spark.hadoop.fs.s3a.access.key\" = \"access_key\"\n        \"spark.hadoop.fs.s3a.secret.key\" = \"secret_key\"\n   }\n}\n
    "},{"location":"setup/advanced/#storing-plantasks","title":"Storing plan/task(s)","text":"

    You can generate and store the plan/task files inside either AWS S3, Azure Blob Storage or Google GCS. This can be controlled via configuration set in the application.conf file where you can set something like the below:

    JavaScalaYAML 
    configuration()\n  .generatedReportsFolderPath(\"s3a://my-bucket/data-caterer/generated\")\n  .planFilePath(\"s3a://my-bucket/data-caterer/generated/plan/customer-create-plan.yaml\")\n  .taskFolderPath(\"s3a://my-bucket/data-caterer/generated/task\")\n  .runtimeConfig(Map.of(\n    \"spark.hadoop.fs.s3a.directory.marker.retention\", \"keep\",\n    \"spark.hadoop.fs.s3a.bucket.all.committer.magic.enabled\", \"true\",\n    \"spark.hadoop.fs.defaultFS\", \"s3a://my-bucket\",\n    //can change to other credential providers as shown here\n    //https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers\n    \"spark.hadoop.fs.s3a.aws.credentials.provider\", \"org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider\",\n    \"spark.hadoop.fs.s3a.access.key\", \"access_key\",\n    \"spark.hadoop.fs.s3a.secret.key\", \"secret_key\"\n  ));\n
    configuration\n  .generatedReportsFolderPath(\"s3a://my-bucket/data-caterer/generated\")\n  .planFilePath(\"s3a://my-bucket/data-caterer/generated/plan/customer-create-plan.yaml\")\n  .taskFolderPath(\"s3a://my-bucket/data-caterer/generated/task\")\n  .runtimeConfig(Map(\n    \"spark.hadoop.fs.s3a.directory.marker.retention\" -> \"keep\",\n    \"spark.hadoop.fs.s3a.bucket.all.committer.magic.enabled\" -> \"true\",\n    \"spark.hadoop.fs.defaultFS\" -> \"s3a://my-bucket\",\n    //can change to other credential providers as shown here\n    //https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers\n    \"spark.hadoop.fs.s3a.aws.credentials.provider\" -> \"org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider\",\n    \"spark.hadoop.fs.s3a.access.key\" -> \"access_key\",\n    \"spark.hadoop.fs.s3a.secret.key\" -> \"secret_key\"\n  ))\n
    folders {\n   generatedPlanAndTaskFolderPath = \"s3a://my-bucket/data-caterer/generated\"\n   planFilePath = \"s3a://my-bucket/data-caterer/generated/plan/customer-create-plan.yaml\"\n   taskFolderPath = \"s3a://my-bucket/data-caterer/generated/task\"\n}\n\nruntime {\n    config {\n        ...\n        #S3\n        \"spark.hadoop.fs.s3a.directory.marker.retention\" = \"keep\"\n        \"spark.hadoop.fs.s3a.bucket.all.committer.magic.enabled\" = \"true\"\n        \"spark.hadoop.fs.defaultFS\" = \"s3a://my-bucket\"\n        #can change to other credential providers as shown here\n        #https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers\n        \"spark.hadoop.fs.s3a.aws.credentials.provider\" = \"org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider\"\n        \"spark.hadoop.fs.s3a.access.key\" = \"access_key\"\n        \"spark.hadoop.fs.s3a.secret.key\" = \"secret_key\"\n   }\n}\n
    "},{"location":"setup/configuration/","title":"Configuration","text":"

    A number of configurations can be made and customised within Data Caterer to help control what gets run and/or where any metadata gets saved.

    These configurations are defined from within your Java or Scala class via configuration or for YAML file setup, application.conf file as seen here.

    "},{"location":"setup/configuration/#flags","title":"Flags","text":"

    Flags are used to control which processes are executed when you run Data Caterer.

    Config Default Paid Description enableGenerateData true N Enable/disable data generation enableCount true N Count the number of records generated. Can be disabled to improve performance enableFailOnError true N Whilst saving generated data, if there is an error, it will stop any further data from being generated enableSaveReports true N Enable/disable HTML reports summarising data generated, metadata of data generated (if enableSinkMetadata is enabled) and validation results (if enableValidation is enabled). Sample here enableSinkMetadata true N Run data profiling for the generated data. Shown in HTML reports if enableSaveSinkMetadata is enabled enableValidation false N Run validations as described in plan. Results can be viewed from logs or from HTML report if enableSaveSinkMetadata is enabled. Sample here enableGeneratePlanAndTasks false Y Enable/disable plan and task auto generation based off data source connections enableRecordTracking false Y Enable/disable which data records have been generated for any data source enableDeleteGeneratedRecords false Y Delete all generated records based off record tracking (if enableRecordTracking has been set to true) enableGenerateValidations false Y If enabled, it will generate validations based on the data sources defined. JavaScalaapplication.conf 
    configuration()\n  .enableGenerateData(true)\n  .enableCount(true)\n  .enableFailOnError(true)\n  .enableSaveReports(true)\n  .enableSinkMetadata(true)\n  .enableValidation(false)\n  .enableGeneratePlanAndTasks(false)\n  .enableRecordTracking(false)\n  .enableDeleteGeneratedRecords(false)\n  .enableGenerateValidations(false);\n
    configuration\n  .enableGenerateData(true)\n  .enableCount(true)\n  .enableFailOnError(true)\n  .enableSaveReports(true)\n  .enableSinkMetadata(true)\n  .enableValidation(false)\n  .enableGeneratePlanAndTasks(false)\n  .enableRecordTracking(false)\n  .enableDeleteGeneratedRecords(false)\n  .enableGenerateValidations(false)\n
    flags {\n  enableCount = false\n  enableCount = ${?ENABLE_COUNT}\n  enableGenerateData = true\n  enableGenerateData = ${?ENABLE_GENERATE_DATA}\n  enableFailOnError = true\n  enableFailOnError = ${?ENABLE_FAIL_ON_ERROR}\n  enableGeneratePlanAndTasks = false\n  enableGeneratePlanAndTasks = ${?ENABLE_GENERATE_PLAN_AND_TASKS}\n  enableRecordTracking = false\n  enableRecordTracking = ${?ENABLE_RECORD_TRACKING}\n  enableDeleteGeneratedRecords = false\n  enableDeleteGeneratedRecords = ${?ENABLE_DELETE_GENERATED_RECORDS}\n  enableGenerateValidations = false\n  enableGenerateValidations = ${?ENABLE_GENERATE_VALIDATIONS}\n}\n
    "},{"location":"setup/configuration/#folders","title":"Folders","text":"

    Depending on which flags are enabled, there are folders that get used to save metadata, store HTML reports or track the records generated.

    These folder pathways can be defined as a cloud storage pathway (i.e. s3a://my-bucket/task).

    Config Default Paid Description planFilePath /opt/app/plan/customer-create-plan.yaml N Plan file path to use when generating and/or validating data taskFolderPath /opt/app/task N Task folder path that contains all the task files (can have nested directories) validationFolderPath /opt/app/validation N Validation folder path that contains all the validation files (can have nested directories) generatedReportsFolderPath /opt/app/report N Where HTML reports get generated that contain information about data generated along with any validations performed generatedPlanAndTaskFolderPath /tmp Y Folder path where generated plan and task files will be saved recordTrackingFolderPath /opt/app/record-tracking Y Where record tracking parquet files get saved JavaScalaapplication.conf 
    configuration()\n  .planFilePath(\"/opt/app/custom/plan/postgres-plan.yaml\")\n  .taskFolderPath(\"/opt/app/custom/task\")\n  .validationFolderPath(\"/opt/app/custom/validation\")\n  .generatedReportsFolderPath(\"/opt/app/custom/report\")\n  .generatedPlanAndTaskFolderPath(\"/opt/app/custom/generated\")\n  .recordTrackingFolderPath(\"/opt/app/custom/record-tracking\");\n
    configuration\n  .planFilePath(\"/opt/app/custom/plan/postgres-plan.yaml\")\n  .taskFolderPath(\"/opt/app/custom/task\")\n  .validationFolderPath(\"/opt/app/custom/validation\")\n  .generatedReportsFolderPath(\"/opt/app/custom/report\")\n  .generatedPlanAndTaskFolderPath(\"/opt/app/custom/generated\")\n  .recordTrackingFolderPath(\"/opt/app/custom/record-tracking\")\n
    folders {\n  planFilePath = \"/opt/app/custom/plan/postgres-plan.yaml\"\n  planFilePath = ${?PLAN_FILE_PATH}\n  taskFolderPath = \"/opt/app/custom/task\"\n  taskFolderPath = ${?TASK_FOLDER_PATH}\n  validationFolderPath = \"/opt/app/custom/validation\"\n  validationFolderPath = ${?VALIDATION_FOLDER_PATH}\n  generatedReportsFolderPath = \"/opt/app/custom/report\"\n  generatedReportsFolderPath = ${?GENERATED_REPORTS_FOLDER_PATH}\n  generatedPlanAndTaskFolderPath = \"/opt/app/custom/generated\"\n  generatedPlanAndTaskFolderPath = ${?GENERATED_PLAN_AND_TASK_FOLDER_PATH}\n  recordTrackingFolderPath = \"/opt/app/custom/record-tracking\"\n  recordTrackingFolderPath = ${?RECORD_TRACKING_FOLDER_PATH}\n}\n
    "},{"location":"setup/configuration/#metadata","title":"Metadata","text":"

    When metadata gets generated, there are some configurations that can be altered to help with performance or accuracy related issues. Metadata gets generated from two processes: 1) if enableGeneratePlanAndTasks or 2) if enableSinkMetadata are enabled.

    During the generation of plan and tasks, data profiling is used to create the metadata for each of the fields defined in the data source. You may face issues if the number of records in the data source is large as data profiling is an expensive task. Similarly, it can be expensive when analysing the generated data if the number of records generated is large.

    Config Default Paid Description numRecordsFromDataSource 10000 Y Number of records read in from the data source that could be used for data profiling numRecordsForAnalysis 10000 Y Number of records used for data profiling from the records gathered in numRecordsFromDataSource oneOfMinCount 1000 Y Minimum number of records required before considering if a field can be of type oneOf oneOfDistinctCountVsCountThreshold 0.2 Y Threshold ratio to determine if a field is of type oneOf (i.e. a field called status that only contains open or closed. Distinct count = 2, total count = 10, ratio = 2 / 10 = 0.2 therefore marked as oneOf) numGeneratedSamples 10 N Number of sample records from generated data to take. Shown in HTML report JavaScalaapplication.conf 
    configuration()\n  .numRecordsFromDataSourceForDataProfiling(10000)\n  .numRecordsForAnalysisForDataProfiling(10000)\n  .oneOfMinCount(1000)\n  .oneOfDistinctCountVsCountThreshold(1000)\n  .numGeneratedSamples(10);\n
    configuration\n  .numRecordsFromDataSourceForDataProfiling(10000)\n  .numRecordsForAnalysisForDataProfiling(10000)\n  .oneOfMinCount(1000)\n  .oneOfDistinctCountVsCountThreshold(1000)\n  .numGeneratedSamples(10)\n
    metadata {\n  numRecordsFromDataSource = 10000\n  numRecordsForAnalysis = 10000\n  oneOfMinCount = 1000\n  oneOfDistinctCountVsCountThreshold = 0.2\n  numGeneratedSamples = 10\n}\n
    "},{"location":"setup/configuration/#generation","title":"Generation","text":"

    When generating data, you may have some limitations such as limited CPU or memory, large number of data sources, or data sources prone to failure under load. To help alleviate these issues or speed up performance, you can control the number of records that get generated in each batch.

    Config Default Paid Description numRecordsPerBatch 100000 N Number of records across all data sources to generate per batch numRecordsPerStep N Overrides the count defined in each step with this value if defined (i.e. if set to 1000, for each step, 1000 records will be generated) ScalaScalaapplication.conf 
    configuration()\n  .numRecordsPerBatch(100000)\n  .numRecordsPerStep(1000);\n
    configuration\n  .numRecordsPerBatch(100000)\n  .numRecordsPerStep(1000)\n
    generation {\n  numRecordsPerBatch = 100000\n  numRecordsPerStep = 1000\n}\n
    "},{"location":"setup/configuration/#runtime","title":"Runtime","text":"

    Given Data Caterer uses Spark as the base framework for data processing, you can configure the job as to your specifications via configuration as seen here.

    JavaScalaapplication.conf 
    configuration()\n  .master(\"local[*]\")\n  .runtimeConfig(Map.of(\"spark.driver.cores\", \"5\"))\n  .addRuntimeConfig(\"spark.driver.memory\", \"10g\");\n
    configuration\n  .master(\"local[*]\")\n  .runtimeConfig(Map(\"spark.driver.cores\" -> \"5\"))\n  .addRuntimeConfig(\"spark.driver.memory\" -> \"10g\")\n
    runtime {\n  master = \"local[*]\"\n  master = ${?DATA_CATERER_MASTER}\n  config {\n    \"spark.driver.cores\" = \"5\"\n    \"spark.driver.memory\" = \"10g\"\n  }\n}\n
    "},{"location":"setup/connection/","title":"Data Source Connections","text":"

    Details of all the connection configuration supported can be found in the below subsections for each type of connection.

    These configurations can be done via API or from configuration. Examples of both are shown for each data source below.

    "},{"location":"setup/connection/#supported-data-connections","title":"Supported Data Connections","text":"Data Source Type Data Source Sponsor Database Postgres, MySQL, Cassandra N File CSV, JSON, ORC, Parquet N Messaging Kafka, Solace Y HTTP REST API Y Metadata Marquez, OpenMetadata, OpenAPI/Swagger Y"},{"location":"setup/connection/#api","title":"API","text":"

    All connection details require a name. Depending on the data source, you can define additional options which may be used by the driver or connector for connecting to the data source.

    "},{"location":"setup/connection/#configuration-file","title":"Configuration file","text":"

    All connection details follow the same pattern.

    <connection format> {\n    <connection name> {\n        <key> = <value>\n    }\n}\n

    Overriding configuration

    When defining a configuration value that can be defined by a system property or environment variable at runtime, you can define that via the following:

    url = \"localhost\"\nurl = ${?POSTGRES_URL}\n

    The above defines that if there is a system property or environment variable named POSTGRES_URL, then that value will be used for the url, otherwise, it will default to localhost.

    "},{"location":"setup/connection/#data-sources","title":"Data sources","text":"

    To find examples of a task for each type of data source, please check out this page.

    "},{"location":"setup/connection/#file","title":"File","text":"

    Linked here is a list of generic options that can be included as part of your file data source configuration if required. Links to specific file type configurations can be found below.

    "},{"location":"setup/connection/#csv","title":"CSV","text":"JavaScalaapplication.conf 
    csv(\"customer_transactions\", \"/data/customer/transaction\")\n
    csv(\"customer_transactions\", \"/data/customer/transaction\")\n
    csv {\n  customer_transactions {\n    path = \"/data/customer/transaction\"\n    path = ${?CSV_PATH}\n  }\n}\n

    Other available configuration for CSV can be found here

    "},{"location":"setup/connection/#json","title":"JSON","text":"JavaScalaapplication.conf 
    json(\"customer_transactions\", \"/data/customer/transaction\")\n
    json(\"customer_transactions\", \"/data/customer/transaction\")\n
    json {\n  customer_transactions {\n    path = \"/data/customer/transaction\"\n    path = ${?JSON_PATH}\n  }\n}\n

    Other available configuration for JSON can be found here

    "},{"location":"setup/connection/#orc","title":"ORC","text":"JavaScalaapplication.conf 
    orc(\"customer_transactions\", \"/data/customer/transaction\")\n
    orc(\"customer_transactions\", \"/data/customer/transaction\")\n
    orc {\n  customer_transactions {\n    path = \"/data/customer/transaction\"\n    path = ${?ORC_PATH}\n  }\n}\n

    Other available configuration for ORC can be found here

    "},{"location":"setup/connection/#parquet","title":"Parquet","text":"JavaScalaapplication.conf 
    parquet(\"customer_transactions\", \"/data/customer/transaction\")\n
    parquet(\"customer_transactions\", \"/data/customer/transaction\")\n
    parquet {\n  customer_transactions {\n    path = \"/data/customer/transaction\"\n    path = ${?PARQUET_PATH}\n  }\n}\n

    Other available configuration for Parquet can be found here

    "},{"location":"setup/connection/#delta-not-supported-yet","title":"Delta (not supported yet)","text":"JavaScalaapplication.conf 
    delta(\"customer_transactions\", \"/data/customer/transaction\")\n
    delta(\"customer_transactions\", \"/data/customer/transaction\")\n
    delta {\n  customer_transactions {\n    path = \"/data/customer/transaction\"\n    path = ${?DELTA_PATH}\n  }\n}\n
    "},{"location":"setup/connection/#rmdbs","title":"RMDBS","text":"

    Follows the same configuration used by Spark as found here. Sample can be found below

    JavaScalaapplication.conf 
    postgres(\n    \"customer_postgres\",                            #name\n    \"jdbc:postgresql://localhost:5432/customer\",    #url\n    \"postgres\",                                     #username\n    \"postgres\"                                      #password\n)\n
    postgres(\n    \"customer_postgres\",                            #name\n    \"jdbc:postgresql://localhost:5432/customer\",    #url\n    \"postgres\",                                     #username\n    \"postgres\"                                      #password\n)\n
    jdbc {\n    customer_postgres {\n        url = \"jdbc:postgresql://localhost:5432/customer\"\n        url = ${?POSTGRES_URL}\n        user = \"postgres\"\n        user = ${?POSTGRES_USERNAME}\n        password = \"postgres\"\n        password = ${?POSTGRES_PASSWORD}\n        driver = \"org.postgresql.Driver\"\n    }\n}\n

    Ensure that the user has write permission, so it is able to save the table to the target tables.

    SQL Permission Statements 
    GRANT INSERT ON <schema>.<table> TO <user>;\n
    "},{"location":"setup/connection/#postgres","title":"Postgres","text":"

    Can see example API or Config definition for Postgres connection above.

    "},{"location":"setup/connection/#permissions","title":"Permissions","text":"

    Following permissions are required when generating plan and tasks:

    SQL Permission Statements 
    GRANT SELECT ON information_schema.tables TO < user >;\nGRANT SELECT ON information_schema.columns TO < user >;\nGRANT SELECT ON information_schema.key_column_usage TO < user >;\nGRANT SELECT ON information_schema.table_constraints TO < user >;\nGRANT SELECT ON information_schema.constraint_column_usage TO < user >;\n
    "},{"location":"setup/connection/#mysql","title":"MySQL","text":"JavaScalaapplication.conf 
    mysql(\n    \"customer_mysql\",                       #name\n    \"jdbc:mysql://localhost:3306/customer\", #url\n    \"root\",                                 #username\n    \"root\"                                  #password\n)\n
    mysql(\n    \"customer_mysql\",                       #name\n    \"jdbc:mysql://localhost:3306/customer\", #url\n    \"root\",                                 #username\n    \"root\"                                  #password\n)\n
    jdbc {\n    customer_mysql {\n        url = \"jdbc:mysql://localhost:3306/customer\"\n        user = \"root\"\n        password = \"root\"\n        driver = \"com.mysql.cj.jdbc.Driver\"\n    }\n}\n
    "},{"location":"setup/connection/#permissions_1","title":"Permissions","text":"

    Following permissions are required when generating plan and tasks:

    SQL Permission Statements 
    GRANT SELECT ON information_schema.columns TO < user >;\nGRANT SELECT ON information_schema.statistics TO < user >;\nGRANT SELECT ON information_schema.key_column_usage TO < user >;\n
    "},{"location":"setup/connection/#cassandra","title":"Cassandra","text":"

    Follows same configuration as defined by the Spark Cassandra Connector as found here

    JavaScalaapplication.conf 
    cassandra(\n    \"customer_cassandra\",   #name\n    \"localhost:9042\",       #url\n    \"cassandra\",            #username\n    \"cassandra\",            #password\n    Map.of()                #optional additional connection options\n)\n
    cassandra(\n    \"customer_cassandra\",   #name\n    \"localhost:9042\",       #url\n    \"cassandra\",            #username\n    \"cassandra\",            #password\n    Map()                #optional additional connection options\n)\n
    org.apache.spark.sql.cassandra {\n    customer_cassandra {\n        spark.cassandra.connection.host = \"localhost\"\n        spark.cassandra.connection.host = ${?CASSANDRA_HOST}\n        spark.cassandra.connection.port = \"9042\"\n        spark.cassandra.connection.port = ${?CASSANDRA_PORT}\n        spark.cassandra.auth.username = \"cassandra\"\n        spark.cassandra.auth.username = ${?CASSANDRA_USERNAME}\n        spark.cassandra.auth.password = \"cassandra\"\n        spark.cassandra.auth.password = ${?CASSANDRA_PASSWORD}\n    }\n}\n
    "},{"location":"setup/connection/#permissions_2","title":"Permissions","text":"

    Ensure that the user has write permission, so it is able to save the table to the target tables.

    CQL Permission Statements 
    GRANT INSERT ON <schema>.<table> TO <user>;\n

    Following permissions are required when enabling configuration.enableGeneratePlanAndTasks(true) as it will gather metadata information about tables and columns from the below tables.

    CQL Permission Statements 
    GRANT SELECT ON system_schema.tables TO <user>;\nGRANT SELECT ON system_schema.columns TO <user>;\n
    "},{"location":"setup/connection/#kafka","title":"Kafka","text":"

    Define your Kafka bootstrap server to connect and send generated data to corresponding topics. Topic gets set at a step level. Further details can be found here

    JavaScalaapplication.conf 
    kafka(\n    \"customer_kafka\",   #name\n    \"localhost:9092\"    #url\n)\n
    kafka(\n    \"customer_kafka\",   #name\n    \"localhost:9092\"    #url\n)\n
    kafka {\n    customer_kafka {\n        kafka.bootstrap.servers = \"localhost:9092\"\n        kafka.bootstrap.servers = ${?KAFKA_BOOTSTRAP_SERVERS}\n    }\n}\n

    When defining your schema for pushing data to Kafka, it follows a specific top level schema. An example can be found here . You can define the key, value, headers, partition or topic by following the linked schema.

    "},{"location":"setup/connection/#jms","title":"JMS","text":"

    Uses JNDI lookup to send messages to JMS queue. Ensure that the messaging system you are using has your queue/topic registered via JNDI otherwise a connection cannot be created.

    JavaScalaapplication.conf 
    solace(\n    \"customer_solace\",                                      #name\n    \"smf://localhost:55554\",                                #url\n    \"admin\",                                                #username\n    \"admin\",                                                #password\n    \"default\",                                              #vpn name\n    \"/jms/cf/default\",                                      #connection factory\n    \"com.solacesystems.jndi.SolJNDIInitialContextFactory\"   #initial context factory\n)\n
    solace(\n    \"customer_solace\",                                      #name\n    \"smf://localhost:55554\",                                #url\n    \"admin\",                                                #username\n    \"admin\",                                                #password\n    \"default\",                                              #vpn name\n    \"/jms/cf/default\",                                      #connection factory\n    \"com.solacesystems.jndi.SolJNDIInitialContextFactory\"   #initial context factory\n)\n
    jms {\n    customer_solace {\n        initialContextFactory = \"com.solacesystems.jndi.SolJNDIInitialContextFactory\"\n        connectionFactory = \"/jms/cf/default\"\n        url = \"smf://localhost:55555\"\n        url = ${?SOLACE_URL}\n        user = \"admin\"\n        user = ${?SOLACE_USER}\n        password = \"admin\"\n        password = ${?SOLACE_PASSWORD}\n        vpnName = \"default\"\n        vpnName = ${?SOLACE_VPN}\n    }\n}\n
    "},{"location":"setup/connection/#http","title":"HTTP","text":"

    Define any username and/or password needed for the HTTP requests. The url is defined in the tasks to allow for generated data to be populated in the url.

    JavaScalaapplication.conf 
    http(\n    \"customer_api\", #name\n    \"admin\",        #username\n    \"admin\"         #password\n)\n
    http(\n    \"customer_api\", #name\n    \"admin\",        #username\n    \"admin\"         #password\n)\n
    http {\n    customer_api {\n        user = \"admin\"\n        user = ${?HTTP_USER}\n        password = \"admin\"\n        password = ${?HTTP_PASSWORD}\n    }\n}\n
    "},{"location":"setup/deployment/","title":"Deployment","text":"

    Two main ways to deploy and run Data Caterer:

    • Docker
    • Helm
    "},{"location":"setup/deployment/#docker","title":"Docker","text":"

    To package up your class along with the Data Caterer base image, you can follow the Dockerfile that is created for you here.

    Then you can run the following:

    ./gradlew clean build\ndocker build -t <my_image_name>:<my_image_tag> .\n
    "},{"location":"setup/deployment/#helm","title":"Helm","text":"

    Link to sample helm on GitHub here

    Update the configuration to your own data connections and configuration or own image created from above.

    git clone git@github.com:pflooky/data-caterer-example.git\nhelm install data-caterer ./data-caterer-example/helm/data-caterer\n
    "},{"location":"setup/design/","title":"Design","text":"

    This document shows the thought process behind the design of Data Caterer to help give you insights as to how and why it was created to what it is today. Also, this serves as a reference for future design decisions which will get updated here and thus is a living document.

    "},{"location":"setup/design/#motivation","title":"Motivation","text":"

    The main difficulties that I faced as a developer and team lead relating to testing were:

    • Difficulty in testing with multiple data sources, both batch and real time
    • Reliance on other teams for stable environments or domain knowledge
    • Test environments with no reliable or consistent data flows
    • Complex data masking/anonymization solutions
    • Relying on production data (potential privacy and data breach issues)
    • Cost of data production issues can be very high
    • Unknown unknowns staying hidden until problems occur in production
    • Underutilised metadata
    "},{"location":"setup/design/#guiding-principles","title":"Guiding Principles","text":"

    These difficulties helped formed the basis of the principles for which Data Caterer should follow:

    • Data source agnostic: Connect to any batch or real time data sources for data generation or validation
    • Configurable: Run the application the way you want
    • Extensible: Allow for new innovations to seamlessly integrate with Data Caterer
    • Integrate with existing solutions: Utilise existing metadata to make it easy for users to use straight away
    • Secure: No production connections required, metadata based solution
    • Fast: Give developers fast feedback loops to encourage them to thoroughly test data flows
    "},{"location":"setup/design/#high-level-flow","title":"High level flow","text":"
    graph LR\n  subgraph userTasks [User Configuration]\n  dataGen[Data Generation]\n  dataValid[Data Validation]\n  runConf[Runtime Config]\n  end\n\n  subgraph dataProcessor [Processor]\n  dataCaterer[Data Caterer]\n  end\n\n  subgraph existingMetadata [Metadata]\n  metadataService[Metadata Services]\n  metadataDataSource[Data Sources]\n  end\n\n  subgraph output [Output]\n  outputDataSource[Data Sources]\n  report[Report]\n  end\n\n  dataGen --> dataCaterer\n  dataValid --> dataCaterer\n  runConf --> dataCaterer\n  direction TB\n  dataCaterer -.-> metadataService\n  dataCaterer -.-> metadataDataSource\n  direction LR\n  dataCaterer ---> outputDataSource\n  dataCaterer ---> report
    1. User Configuration
      1. Users define data generation, validation and runtime configuration
    2. Processor
      1. Engine will take user configuration to decide how to run
      2. User defined configuration merged with metadata from external sources
    3. Metadata
      1. Automatically retrieve schema, data profiling, relationship or validation rule metadata from data sources or metadata services
    4. Output
      1. Execute data generation and validation tasks on data sources
      2. Generate report summarising outcome
    "},{"location":"setup/foreign-key/","title":"Foreign Keys","text":"

    Foreign keys can be defined to represent the relationships between datasets where values are required to match for particular columns.

    "},{"location":"setup/foreign-key/#single-column","title":"Single column","text":"

    Define a column in one data source to match against another column. Below example shows a postgres data source with two tables, accounts and transactions that have a foreign key for account_id.

    JavaScalaYAML 
    var postgresAcc = postgres(\"my_postgres\", \"jdbc:...\")\n  .table(\"public.accounts\")\n  .schema(\n    field().name(\"account_id\"),\n    field().name(\"name\"),\n    ...\n  );\nvar postgresTxn = postgres(postgresAcc)\n  .table(\"public.transactions\")\n  .schema(\n    field().name(\"account_id\"),\n    field().name(\"full_name\"),\n    ...\n  );\n\nplan().addForeignKeyRelationship(\n  postgresAcc, \"account_id\",\n  List.of(Map.entry(postgresTxn, \"account_id\"))\n);\n
    val postgresAcc = postgres(\"my_postgres\", \"jdbc:...\")\n  .table(\"public.accounts\")\n  .schema(\n    field.name(\"account_id\"),\n    field.name(\"name\"),\n    ...\n  )\nval postgresTxn = postgres(postgresAcc)\n  .table(\"public.transactions\")\n  .schema(\n    field.name(\"account_id\"),\n    field.name(\"full_name\"),\n    ...\n  )\n\nplan.addForeignKeyRelationship(\n  postgresAcc, \"account_id\",\n  List(postgresTxn -> \"account_id\")\n)\n
    ---\nname: \"postgres_data\"\nsteps:\n  - name: \"accounts\"\n    type: \"postgres\"\n    options:\n      dbtable: \"account.accounts\"\n    schema:\n      fields:\n        - name: \"account_id\"\n        - name: \"name\"\n  - name: \"transactions\"\n    type: \"postgres\"\n    options:\n      dbtable: \"account.transactions\"\n    schema:\n      fields:\n        - name: \"account_id\"\n        - name: \"full_name\"\n---\nname: \"customer_create_plan\"\ndescription: \"Create customers in JDBC\"\ntasks:\n  - name: \"postgres_data\"\n    dataSourceName: \"my_postgres\"\n\nsinkOptions:\n  foreignKeys:\n    \"postgres.accounts.account_id\":\n      - \"postgres.transactions.account_id\"\n
    "},{"location":"setup/foreign-key/#multiple-columns","title":"Multiple columns","text":"

    You may have a scenario where multiple columns need to be aligned. From the same example, we want account_id and name from accounts to match with account_id and full_name to match in transactions respectively.

    JavaScalaYAML 
    var postgresAcc = postgres(\"my_postgres\", \"jdbc:...\")\n  .table(\"public.accounts\")\n  .schema(\n    field().name(\"account_id\"),\n    field().name(\"name\"),\n    ...\n  );\nvar postgresTxn = postgres(postgresAcc)\n  .table(\"public.transactions\")\n  .schema(\n    field().name(\"account_id\"),\n    field().name(\"full_name\"),\n    ...\n  );\n\nplan().addForeignKeyRelationship(\n  postgresAcc, List.of(\"account_id\", \"name\"),\n  List.of(Map.entry(postgresTxn, List.of(\"account_id\", \"full_name\")))\n);\n
    val postgresAcc = postgres(\"my_postgres\", \"jdbc:...\")\n  .table(\"public.accounts\")\n  .schema(\n    field.name(\"account_id\"),\n    field.name(\"name\"),\n    ...\n  )\nval postgresTxn = postgres(postgresAcc)\n  .table(\"public.transactions\")\n  .schema(\n    field.name(\"account_id\"),\n    field.name(\"full_name\"),\n    ...\n  )\n\nplan.addForeignKeyRelationship(\n  postgresAcc, List(\"account_id\", \"name\"),\n  List(postgresTxn -> List(\"account_id\", \"full_name\"))\n)\n
    ---\nname: \"postgres_data\"\nsteps:\n  - name: \"accounts\"\n    type: \"postgres\"\n    options:\n      dbtable: \"account.accounts\"\n    schema:\n      fields:\n        - name: \"account_id\"\n        - name: \"name\"\n  - name: \"transactions\"\n    type: \"postgres\"\n    options:\n      dbtable: \"account.transactions\"\n    schema:\n      fields:\n        - name: \"account_id\"\n        - name: \"full_name\"\n---\nname: \"customer_create_plan\"\ndescription: \"Create customers in JDBC\"\ntasks:\n  - name: \"postgres_data\"\n    dataSourceName: \"my_postgres\"\n\nsinkOptions:\n  foreignKeys:\n    \"my_postgres.accounts.account_id,name\":\n      - \"my_postgres.transactions.account_id,full_name\"\n
    "},{"location":"setup/foreign-key/#nested-column","title":"Nested column","text":"

    Your schema structure can have nested fields which can also be referenced as foreign keys. But to do so, you need to create a proxy field that gets omitted from the final saved data.

    In the example below, the nested customer_details.name field inside the json task needs to match with name from postgres. A new field in the json called _txn_name is used as a temporary column to facilitate the foreign key definition.

    JavaScalaYAML 
    var postgresAcc = postgres(\"my_postgres\", \"jdbc:...\")\n  .table(\"public.accounts\")\n  .schema(\n    field().name(\"account_id\"),\n    field().name(\"name\"),\n    ...\n  );\nvar jsonTask = json(\"my_json\", \"/tmp/json\")\n  .schema(\n    field().name(\"account_id\"),\n    field().name(\"customer_details\")\n      .schema(\n        field().name(\"name\").sql(\"_txn_name\"), #nested field will get value from '_txn_name'\n        ...\n      ),\n    field().name(\"_txn_name\").omit(true)       #value will not be included in output\n  );\n\nplan().addForeignKeyRelationship(\n  postgresAcc, List.of(\"account_id\", \"name\"),\n  List.of(Map.entry(jsonTask, List.of(\"account_id\", \"_txn_name\")))\n);\n
    val postgresAcc = postgres(\"my_postgres\", \"jdbc:...\")\n  .table(\"public.accounts\")\n  .schema(\n    field.name(\"account_id\"),\n    field.name(\"name\"),\n    ...\n  )\nvar jsonTask = json(\"my_json\", \"/tmp/json\")\n  .schema(\n    field.name(\"account_id\"),\n    field.name(\"customer_details\")\n      .schema(\n        field.name(\"name\").sql(\"_txn_name\"), #nested field will get value from '_txn_name'\n        ...\n      ), \n    field.name(\"_txn_name\").omit(true)       #value will not be included in output\n  )\n\nplan.addForeignKeyRelationship(\n  postgresAcc, List(\"account_id\", \"name\"),\n  List(jsonTask -> List(\"account_id\", \"_txn_name\"))\n)\n
    ---\n#postgres task yaml\nname: \"postgres_data\"\nsteps:\n  - name: \"accounts\"\n    type: \"postgres\"\n    options:\n      dbtable: \"account.accounts\"\n    schema:\n      fields:\n        - name: \"account_id\"\n        - name: \"name\"\n---\n#json task yaml\nname: \"json_data\"\nsteps:\n  - name: \"transactions\"\n    type: \"json\"\n    options:\n      dbtable: \"account.transactions\"\n    schema:\n      fields:\n        - name: \"account_id\"\n        - name: \"_txn_name\"\n          generator:\n            options:\n              omit: true\n        - name: \"cusotmer_details\"\n          schema:\n            fields:\n              name: \"name\"\n              generator:\n                type: \"sql\"\n                options:\n                  sql: \"_txn_name\"\n\n---\n#plan yaml\nname: \"customer_create_plan\"\ndescription: \"Create customers in JDBC\"\ntasks:\n  - name: \"postgres_data\"\n    dataSourceName: \"my_postgres\"\n  - name: \"json_data\"\n    dataSourceName: \"my_json\"\n\nsinkOptions:\n  foreignKeys:\n    \"my_postgres.accounts.account_id,name\":\n      - \"my_json.transactions.account_id,_txn_name\"\n
    "},{"location":"setup/validation/","title":"Validations","text":"

    Validations can be used to run data checks after you have run the data generator or even as a standalone task. A report summarising the success or failure of the validations is produced and can be examined for further investigation.

    • Basic - Basic column level validations
    • Group by/Aggregate - Run aggregates over grouped data, then validate
    • Upstream data source - Ensure record values exist in datasets based on other data sources or data generated
    • [Data Profile (Coming soon)] - Score how close the data profile of generated data is against the target data profile
    "},{"location":"setup/validation/#define-validations","title":"Define Validations","text":"

    Full example validation can be found below. For more details, check out each of the subsections defined further below.

    JavaScalaYAML 
    var csvTxns = csv(\"transactions\", \"/tmp/csv\", Map.of(\"header\", \"true\"))\n  .validations(\n    validation().col(\"amount\").lessThan(100),\n    validation().col(\"year\").isEqual(2021).errorThreshold(0.1),  //equivalent to if error percentage is > 10%, then fail\n    validation().col(\"name\").matches(\"Peter .*\").errorThreshold(200)  //equivalent to if number of errors is > 200, then fail\n  )\n  .validationWait(waitCondition().pause(1));\n\nvar conf = configuration().enableValidation(true);\n
    val csvTxns = csv(\"transactions\", \"/tmp/csv\", Map(\"header\" -> \"true\"))\n  .validations(\n    validation.col(\"amount\").lessThan(100),\n    validation.col(\"year\").isEqual(2021).errorThreshold(0.1),  //equivalent to if error percentage is > 10%, then fail\n    validation.col(\"name\").matches(\"Peter .*\").errorThreshold(200)  //equivalent to if number of errors is > 200, then fail\n  )  \n  .validationWait(waitCondition.pause(1))\n\nval conf = configuration.enableValidation(true)\n
    ---\nname: \"account_checks\"\ndataSources:\n  transactions:\n    options:\n      path: \"/tmp/csv\"\n    validations:\n      - expr: \"amount < 100\"\n      - expr: \"year == 2021\"\n        errorThreshold: 0.1   #equivalent to if error percentage is > 10%, then fail\n      - expr: \"REGEXP_LIKE(name, 'Peter .*')\"\n        errorThreshold: 200   #equivalent to if number of errors is > 200, then fail\n        description: \"Should be lots of Peters\"\n    waitCondition:\n      pauseInSeconds: 1\n
    "},{"location":"setup/validation/#wait-condition","title":"Wait Condition","text":"

    Once data has been generated, you may want to wait for a certain condition to be met before starting the data validations. This can be via:

    • Pause for seconds
    • When file is available
    • Data exists
    • Webhook
    "},{"location":"setup/validation/#pause","title":"Pause","text":"JavaScalaYAML 
    var csvTxns = csv(\"transactions\", \"/tmp/csv\", Map.of(\"header\", \"true\"))\n  .validationWait(waitCondition().pause(1));\n
    val csvTxns = csv(\"transactions\", \"/tmp/csv\", Map(\"header\" -> \"true\"))\n  .validationWait(waitCondition.pause(1))\n
    ---\nname: \"account_checks\"\ndataSources:\n  transactions:\n    options:\n      path: \"/tmp/csv\"\n    waitCondition:\n      pauseInSeconds: 1\n
    "},{"location":"setup/validation/#data-exists","title":"Data exists","text":"JavaScalaYAML 
    var csvTxns = csv(\"transactions\", \"/tmp/csv\", Map.of(\"header\", \"true\"))\n  .validationWaitDataExists(\"updated_date > DATE('2023-01-01')\");\n
    val csvTxns = csv(\"transactions\", \"/tmp/csv\", Map(\"header\" -> \"true\"))\n  .validationWaitDataExists(\"updated_date > DATE('2023-01-01')\")\n
    ---\nname: \"account_checks\"\ndataSources:\n  transactions:\n    options:\n      path: \"/tmp/csv\"\n    waitCondition:\n      dataSourceName: \"transactions\"\n      options:\n        path: \"/tmp/csv\"\n      expr: \"updated_date > DATE('2023-01-01')\"\n
    "},{"location":"setup/validation/#webhook","title":"Webhook","text":"JavaScalaYAML 
    var csvTxns = csv(\"transactions\", \"/tmp/csv\", Map.of(\"header\", \"true\"))\n  .validationWait(waitCondition().webhook(\"http://localhost:8080/finished\")); //by default, GET request successful when 200 status code\n\n//or\n\nvar csvTxnsWithStatusCodes = csv(\"transactions\", \"/tmp/csv\", Map.of(\"header\", \"true\"))\n  .validationWait(waitCondition().webhook(\"http://localhost:8080/finished\", \"GET\", 200, 202));  //successful if 200 or 202 status code\n\n//or\n\nvar csvTxnsWithExistingHttpConnection = csv(\"transactions\", \"/tmp/csv\", Map.of(\"header\", \"true\"))\n  .validationWait(waitCondition().webhook(\"my_http\", \"http://localhost:8080/finished\"));  //use connection configuration from existing 'my_http' connection definition\n
    val csvTxns = csv(\"transactions\", \"/tmp/csv\", Map(\"header\" -> \"true\"))\n  .validationWait(waitCondition.webhook(\"http://localhost:8080/finished\"))  //by default, GET request successful when 200 status code\n\n//or\n\nval csvTxnsWithStatusCodes = csv(\"transactions\", \"/tmp/csv\", Map(\"header\" -> \"true\"))\n  .validationWait(waitCondition.webhook(\"http://localhost:8080/finished\", \"GET\", 200, 202)) //successful if 200 or 202 status code\n\n//or\n\nval csvTxnsWithExistingHttpConnection = csv(\"transactions\", \"/tmp/csv\", Map(\"header\" -> \"true\"))\n  .validationWait(waitCondition.webhook(\"my_http\", \"http://localhost:8080/finished\")) //use connection configuration from existing 'my_http' connection definition\n
    ---\nname: \"account_checks\"\ndataSources:\n  transactions:\n    options:\n      path: \"/tmp/csv\"\n    waitCondition:\n      url: \"http://localhost:8080/finished\" #by default, GET request successful when 200 status code\n\n#or\n\n---\nname: \"account_checks\"\ndataSources:\n  transactions:\n    options:\n      path: \"/tmp/csv\"\n    waitCondition:\n      url: \"http://localhost:8080/finished\"\n      method: \"GET\"\n      statusCodes: [200, 202] #successful if 200 or 202 status code\n\n#or\n\n---\nname: \"account_checks\"\ndataSources:\n  transactions:\n    options:\n      path: \"/tmp/csv\"\n    waitCondition:\n      dataSourceName: \"my_http\" #use connection configuration from existing 'my_http' connection definition\n      url: \"http://localhost:8080/finished\"\n
    "},{"location":"setup/validation/#file-exists","title":"File exists","text":"JavaScalaYAML 
    var csvTxns = csv(\"transactions\", \"/tmp/csv\", Map.of(\"header\", \"true\"))\n  .validationWait(waitCondition().file(\"/tmp/json\"));\n
    val csvTxns = csv(\"transactions\", \"/tmp/csv\", Map.of(\"header\", \"true\"))\n  .validationWait(waitCondition.file(\"/tmp/json\"))\n
    ---\nname: \"account_checks\"\ndataSources:\n  transactions:\n    options:\n      path: \"/tmp/csv\"\n    waitCondition:\n      path: \"/tmp/json\"\n
    "},{"location":"setup/validation/#report","title":"Report","text":"

    Once run, it will produce a report like this.

    "},{"location":"setup/generator/count/","title":"Record Count","text":"

    There are options related to controlling the number of records generated that can help in generating the scenarios or data required.

    "},{"location":"setup/generator/count/#record-count_1","title":"Record Count","text":"

    Record count is the simplest as you define the total number of records you require for that particular step. For example, in the below step, it will generate 1000 records for the CSV file

    JavaScalaYAML 
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .count(1000);\n
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .count(1000)\n
    name: \"csv_file\"\nsteps:\n  - name: \"transactions\"\n    type: \"csv\"\n    options:\n      path: \"app/src/test/resources/sample/csv/transactions\"\n    count:\n      records: 1000\n
    "},{"location":"setup/generator/count/#generated-count","title":"Generated Count","text":"

    As like most things in Data Caterer, the count can be generated based on some metadata. For example, if I wanted to generate between 1000 and 2000 records, I could define that by the below configuration:

    JavaScalaYAML 
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .count(generator().min(1000).max(2000));\n
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .count(generator.min(1000).max(2000))\n
    name: \"csv_file\"\nsteps:\n  - name: \"transactions\"\n    type: \"csv\"\n    options:\n      path: \"app/src/test/resources/sample/csv/transactions\"\n    count:\n      generator:\n        type: \"random\"\n        options:\n          min: 1000\n          max: 2000\n
    "},{"location":"setup/generator/count/#per-column-count","title":"Per Column Count","text":"

    When defining a per column count, this allows you to generate records \"per set of columns\". This means that for a given set of columns, it will generate a particular amount of records per combination of values for those columns.

    One example of this would be when generating transactions relating to a customer, a customer may be defined by columns account_id, name. A number of transactions would be generated per account_id, name.

    You can also use a combination of the above two methods to generate the number of records per column.

    "},{"location":"setup/generator/count/#records","title":"Records","text":"

    When defining a base number of records within the perColumn configuration, it translates to creating (count.records * count.recordsPerColumn) records. This is a fixed number of records that will be generated each time, with no variation between runs.

    In the example below, we have count.records = 1000 and count.recordsPerColumn = 2. Which means that 1000 * 2 = 2000 records will be generated in total.

    JavaScalaYAML 
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .count(\n    count()\n      .records(1000)\n      .recordsPerColumn(2, \"account_id\", \"name\")\n  );\n
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .count(\n    count\n      .records(1000)\n      .recordsPerColumn(2, \"account_id\", \"name\")\n  )\n
    name: \"csv_file\"\nsteps:\n  - name: \"transactions\"\n    type: \"csv\"\n    options:\n      path: \"app/src/test/resources/sample/csv/transactions\"\n    count:\n      records: 1000\n      perColumn:\n        records: 2\n        columnNames:\n          - \"account_id\"\n          - \"name\"\n
    "},{"location":"setup/generator/count/#generated","title":"Generated","text":"

    You can also define a generator for the count per column. This can be used in scenarios where you want a variable number of records per set of columns.

    In the example below, it will generate between (count.records * count.perColumnGenerator.generator.min) = (1000 * 1) = 1000 and (count.records * count.perColumnGenerator.generator.max) = (1000 * 2) = 2000 records.

    JavaScalaYAML 
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .count(\n    count()\n      .records(1000)\n      .recordsPerColumnGenerator(generator().min(1).max(2), \"account_id\", \"name\")\n  );\n
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .count(\n    count\n      .records(1000)\n      .recordsPerColumnGenerator(generator.min(1).max(2), \"account_id\", \"name\")\n  )\n
    name: \"csv_file\"\nsteps:\n  - name: \"transactions\"\n    type: \"csv\"\n    options:\n      path: \"app/src/test/resources/sample/csv/transactions\"\n    count:\n      records: 1000\n      perColumn:\n        columnNames:\n          - \"account_id\"\n          - \"name\"\n        generator:\n          type: \"random\"\n          options:\n            min: 1\n            max: 2\n
    "},{"location":"setup/generator/data-generator/","title":"Data Generators","text":""},{"location":"setup/generator/data-generator/#data-types","title":"Data Types","text":"

    Below is a list of all supported data types for generating data:

    Data Type Spark Data Type Options Description string StringType minLen, maxLen, expression, enableNull integer IntegerType min, max, stddev, mean long LongType min, max, stddev, mean short ShortType min, max, stddev, mean decimal(precision, scale) DecimalType(precision, scale) min, max, stddev, mean double DoubleType min, max, stddev, mean float FloatType min, max, stddev, mean date DateType min, max, enableNull timestamp TimestampType min, max, enableNull boolean BooleanType binary BinaryType minLen, maxLen, enableNull byte ByteType array ArrayType arrayMinLen, arrayMaxLen, arrayType _ StructType Implicitly supported when a schema is defined for a field"},{"location":"setup/generator/data-generator/#options","title":"Options","text":""},{"location":"setup/generator/data-generator/#all-data-types","title":"All data types","text":"

    Some options are available to use for all types of data generators. Below is the list along with example and descriptions:

    Option Default Example Description enableEdgeCase false enableEdgeCase: \"true\" Enable/disable generated data to contain edge cases based on the data type. For example, integer data type has edge cases of (Int.MaxValue, Int.MinValue and 0) edgeCaseProbability 0.0 edgeCaseProb: \"0.1\" Probability of generating a random edge case value if enableEdgeCase is true isUnique false isUnique: \"true\" Enable/disable generated data to be unique for that column. Errors will be thrown when it is unable to generate unique data seed seed: \"1\" Defines the random seed for generating data for that particular column. It will override any seed defined at a global level sql sql: \"CASE WHEN amount < 10 THEN true ELSE false END\" Define any SQL statement for generating that columns value. Computation occurs after all non-SQL fields are generated. This means any columns used in the SQL cannot be based on other SQL generated columns. Data type of generated value from SQL needs to match data type defined for the field"},{"location":"setup/generator/data-generator/#string","title":"String","text":"Option Default Example Description minLen 1 minLen: \"2\" Ensures that all generated strings have at least length minLen maxLen 10 maxLen: \"15\" Ensures that all generated strings have at most length maxLen expression expression: \"#{Name.name}\"expression:\"#{Address.city}/#{Demographic.maritalStatus}\" Will generate a string based on the faker expression provided. All possible faker expressions can be found here Expression has to be in format #{<faker expression name>} enableNull false enableNull: \"true\" Enable/disable null values being generated nullProbability 0.0 nullProb: \"0.1\" Probability to generate null values if enableNull is true

    Edge cases: (\"\", \"\\n\", \"\\r\", \"\\t\", \" \", \"\\u0000\", \"\\ufff\", \"\u0130yi g\u00fcnler\", \"\u0421\u043f\u0430\u0441\u0438\u0431\u043e\", \"\u039a\u03b1\u03bb\u03b7\u03bc\u03ad\u03c1\u03b1\", \"\u0635\u0628\u0627\u062d \u0627\u0644\u062e\u064a\u0631\", \" F\u00f6rl\u00e5t\", \"\u4f60\u597d\u5417\", \"Nh\u00e0 v\u1ec7 sinh \u1edf \u0111\u00e2u\", \"\u3053\u3093\u306b\u3061\u306f\", \"\u0928\u092e\u0938\u094d\u0924\u0947\", \"\u0532\u0561\u0580\u0565\u0582\", \"\u0417\u0434\u0440\u0430\u0432\u0435\u0439\u0442\u0435\")

    "},{"location":"setup/generator/data-generator/#sample","title":"Sample","text":"JavaScalaYAML 
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .schema(\n    field()\n      .name(\"name\")\n      .type(StringType.instance())\n      .expression(\"#{Name.name}\")\n      .enableNull(true)\n      .nullProbability(0.1)\n      .minLength(4)\n      .maxLength(20)\n  );\n
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .schema(\n    field\n      .name(\"name\")\n      .`type`(StringType)\n      .expression(\"#{Name.name}\")\n      .enableNull(true)\n      .nullProbability(0.1)\n      .minLength(4)\n      .maxLength(20)\n  )\n
    name: \"csv_file\"\nsteps:\n  - name: \"transactions\"\n    type: \"csv\"\n    options:\n      path: \"app/src/test/resources/sample/csv/transactions\"\n    schema:\n      fields:\n        - name: \"name\"\n          type: \"string\"\n          generator:\n            options:\n              expression: \"#{Name.name}\"\n              enableNull: true\n              nullProb: 0.1\n              minLength: 4\n              maxLength: 20\n
    "},{"location":"setup/generator/data-generator/#numeric","title":"Numeric","text":"

    For all the numeric data types, there are 4 options to choose from: min, max and maxValue. Generally speaking, you only need to define one of min or minValue, similarly with max or maxValue. The reason why there are 2 options for each is because of when metadata is automatically gathered, we gather the statistics of the observed min and max values. Also, it will attempt to gather any restriction on the min or max value as defined by the data source (i.e. max value as per database type).

    "},{"location":"setup/generator/data-generator/#integerlongshort","title":"Integer/Long/Short","text":"Option Default Example Description min 0 min: \"2\" Ensures that all generated values are greater than or equal to min max 1000 max: \"25\" Ensures that all generated values are less than or equal to max stddev 1.0 stddev: \"2.0\" Standard deviation for normal distributed data mean max - min mean: \"5.0\" Mean for normal distributed data

    Edge cases Integer: (2147483647, -2147483648, 0) Edge cases Long: (9223372036854775807, -9223372036854775808, 0) Edge cases Short: (32767, -32768, 0)

    "},{"location":"setup/generator/data-generator/#sample_1","title":"Sample","text":"JavaScalaYAML 
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .schema(\n    field().name(\"year\").type(IntegerType.instance()).min(2020).max(2023),\n    field().name(\"customer_id\").type(LongType.instance()),\n    field().name(\"customer_group\").type(ShortType.instance())\n  );\n
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .schema(\n    field.name(\"year\").`type`(IntegerType).min(2020).max(2023),\n    field.name(\"customer_id\").`type`(LongType),\n    field.name(\"customer_group\").`type`(ShortType)\n  )\n
    name: \"csv_file\"\nsteps:\n  - name: \"transactions\"\n    ...\n    schema:\n      fields:\n        - name: \"year\"\n          type: \"integer\"\n          generator:\n            options:\n              min: 2020\n              max: 2023\n        - name: \"customer_id\"\n          type: \"long\"\n        - name: \"customer_group\"\n          type: \"short\"\n
    "},{"location":"setup/generator/data-generator/#decimal","title":"Decimal","text":"Option Default Example Description min 0 min: \"2\" Ensures that all generated values are greater than or equal to min max 1000 max: \"25\" Ensures that all generated values are less than or equal to max stddev 1.0 stddev: \"2.0\" Standard deviation for normal distributed data mean max - min mean: \"5.0\" Mean for normal distributed data numericPrecision 10 precision: \"25\" The maximum number of digits numericScale 0 scale: \"25\" The number of digits on the right side of the decimal point (has to be less than or equal to precision)

    Edge cases Decimal: (9223372036854775807, -9223372036854775808, 0)

    "},{"location":"setup/generator/data-generator/#sample_2","title":"Sample","text":"JavaScalaYAML 
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .schema(\n    field().name(\"balance\").type(DecimalType.instance()).numericPrecision(10).numericScale(5)\n  );\n
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .schema(\n    field.name(\"balance\").`type`(DecimalType).numericPrecision(10).numericScale(5)\n  )\n
    name: \"csv_file\"\nsteps:\n  - name: \"transactions\"\n    ...\n    schema:\n      fields:\n        - name: \"balance\"\n          type: \"decimal\"\n            generator:\n              options:\n                precision: 10\n                scale: 5\n
    "},{"location":"setup/generator/data-generator/#doublefloat","title":"Double/Float","text":"Option Default Example Description min 0.0 min: \"2.1\" Ensures that all generated values are greater than or equal to min max 1000.0 max: \"25.9\" Ensures that all generated values are less than or equal to max stddev 1.0 stddev: \"2.0\" Standard deviation for normal distributed data mean max - min mean: \"5.0\" Mean for normal distributed data

    Edge cases Double: (+infinity, 1.7976931348623157e+308, 4.9e-324, 0.0, -0.0, -1.7976931348623157e+308, -infinity, NaN) Edge cases Float: (+infinity, 3.4028235e+38, 1.4e-45, 0.0, -0.0, -3.4028235e+38, -infinity, NaN)

    "},{"location":"setup/generator/data-generator/#sample_3","title":"Sample","text":"JavaScalaYAML 
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .schema(\n    field().name(\"amount\").type(DoubleType.instance())\n  );\n
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .schema(\n    field.name(\"amount\").`type`(DoubleType)\n  )\n
    name: \"csv_file\"\nsteps:\n  - name: \"transactions\"\n    ...\n    schema:\n      fields:\n        - name: \"amount\"\n          type: \"double\"\n
    "},{"location":"setup/generator/data-generator/#date","title":"Date","text":"Option Default Example Description min now() - 365 days min: \"2023-01-31\" Ensures that all generated values are greater than or equal to min max now() max: \"2023-12-31\" Ensures that all generated values are less than or equal to max enableNull false enableNull: \"true\" Enable/disable null values being generated nullProbability 0.0 nullProb: \"0.1\" Probability to generate null values if enableNull is true

    Edge cases: (0001-01-01, 1582-10-15, 1970-01-01, 9999-12-31) (reference)

    "},{"location":"setup/generator/data-generator/#sample_4","title":"Sample","text":"JavaScalaYAML 
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .schema(\n    field().name(\"created_date\").type(DateType.instance()).min(java.sql.Date.valueOf(\"2020-01-01\"))\n  );\n
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .schema(\n    field.name(\"created_date\").`type`(DateType).min(java.sql.Date.valueOf(\"2020-01-01\"))\n  )\n
    name: \"csv_file\"\nsteps:\n  - name: \"transactions\"\n    ...\n    schema:\n      fields:\n        - name: \"created_date\"\n          type: \"date\"\n            generator:\n              options:\n                min: \"2020-01-01\"\n
    "},{"location":"setup/generator/data-generator/#timestamp","title":"Timestamp","text":"Option Default Example Description min now() - 365 days min: \"2023-01-31 23:10:10\" Ensures that all generated values are greater than or equal to min max now() max: \"2023-12-31 23:10:10\" Ensures that all generated values are less than or equal to max enableNull false enableNull: \"true\" Enable/disable null values being generated nullProbability 0.0 nullProb: \"0.1\" Probability to generate null values if enableNull is true

    Edge cases: (0001-01-01 00:00:00, 1582-10-15 23:59:59, 1970-01-01 00:00:00, 9999-12-31 23:59:59)

    "},{"location":"setup/generator/data-generator/#sample_5","title":"Sample","text":"JavaScalaYAML 
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .schema(\n    field().name(\"created_time\").type(TimestampType.instance()).min(java.sql.Timestamp.valueOf(\"2020-01-01 00:00:00\"))\n  );\n
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .schema(\n    field.name(\"created_time\").`type`(TimestampType).min(java.sql.Timestamp.valueOf(\"2020-01-01 00:00:00\"))\n  )\n
    name: \"csv_file\"\nsteps:\n  - name: \"transactions\"\n    ...\n    schema:\n      fields:\n        - name: \"created_time\"\n          type: \"timestamp\"\n            generator:\n              options:\n                min: \"2020-01-01 00:00:00\"\n
    "},{"location":"setup/generator/data-generator/#binary","title":"Binary","text":"Option Default Example Description minLen 1 minLen: \"2\" Ensures that all generated array of bytes have at least length minLen maxLen 20 maxLen: \"15\" Ensures that all generated array of bytes have at most length maxLen enableNull false enableNull: \"true\" Enable/disable null values being generated nullProbability 0.0 nullProb: \"0.1\" Probability to generate null values if enableNull is true

    Edge cases: (\"\", \"\\n\", \"\\r\", \"\\t\", \" \", \"\\u0000\", \"\\ufff\", -128, 127)

    "},{"location":"setup/generator/data-generator/#sample_6","title":"Sample","text":"JavaScalaYAML 
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .schema(\n    field().name(\"payload\").type(BinaryType.instance())\n  );\n
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .schema(\n    field.name(\"payload\").`type`(BinaryType)\n  )\n
    name: \"csv_file\"\nsteps:\n  - name: \"transactions\"\n    ...\n    schema:\n      fields:\n        - name: \"payload\"\n          type: \"binary\"\n
    "},{"location":"setup/generator/data-generator/#array","title":"Array","text":"Option Default Example Description arrayMinLen 0 arrayMinLen: \"2\" Ensures that all generated arrays have at least length arrayMinLen arrayMaxLen 5 arrayMaxLen: \"15\" Ensures that all generated arrays have at most length arrayMaxLen arrayType arrayType: \"double\" Inner data type of the array. Optional when using Java/Scala API. Allows for nested data types to be defined like struct enableNull false enableNull: \"true\" Enable/disable null values being generated nullProbability 0.0 nullProb: \"0.1\" Probability to generate null values if enableNull is true"},{"location":"setup/generator/data-generator/#sample_7","title":"Sample","text":"JavaScalaYAML 
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .schema(\n    field().name(\"last_5_amounts\").type(ArrayType.instance()).arrayType(\"double\")\n  );\n
    csv(\"transactions\", \"app/src/test/resources/sample/csv/transactions\")\n  .schema(\n    field.name(\"last_5_amounts\").`type`(ArrayType).arrayType(\"double\")\n  )\n
    name: \"csv_file\"\nsteps:\n  - name: \"transactions\"\n    ...\n    schema:\n      fields:\n        - name: \"last_5_amounts\"\n          type: \"array<double>\"\n
    "},{"location":"setup/generator/report/","title":"Report","text":"

    Data Caterer can be configured to produce a report of the data generated to help users understand what was run, how much data was generated, where it was generated, validation results and any associated metadata.

    "},{"location":"setup/generator/report/#sample","title":"Sample","text":"

    Once run, it will produce a report like this.

    "},{"location":"setup/guide/","title":"Guides","text":"

    Below are a list of guides you can follow to create your data generation for your use case.

    For any of the paid tier guides, you can use the trial version fo the app to try it out. Details on how to get the trial can be found here.

    "},{"location":"setup/guide/#scenarios","title":"Scenarios","text":"
    • First Data Generation - If you are new, this is the place to start
    • Multiple Records Per Column Value - How you can generate multiple records per set of columns
    • Foreign Keys Across Data Sources - Generate matching values across generated data sets
    • Data Validations - Run data validations after generating data
    • Auto Generate From Data Connection - Automatically generating data from just defining data sources
    • Delete Generated Data - Delete the generated data whilst leaving other data
    • Generate Batch and Event Data - Generate matching batch and event data
    "},{"location":"setup/guide/#data-sources","title":"Data Sources","text":"
    • Files (CSV, JSON, ORC, Parquet) - Generate data for popular file formats
    • Postgres - JDBC Postgres tables
    • Cassandra - Cassandra tables
    • Kafka - Kafka topics
    • Solace - Solace messages
    • Marquez - Generate data based on metadata in Marquez
    • OpenMetadata - Generate data based on metadata in OpenMetadata
    • HTTP - HTTP requests
    • Files (Fixed width) - (Soon to document) A variant of CSV but with no separator
    • MySql - (Soon to document) JDBC MySql tables
    "},{"location":"setup/guide/#yaml-files","title":"YAML Files","text":""},{"location":"setup/guide/#base-concept","title":"Base Concept","text":"

    The execution of the data generator is based on the concept of plans and tasks. A plan represent the set of tasks that need to be executed, along with other information that spans across tasks, such as foreign keys between data sources. A task represent the component(s) of a data source and its associated metadata so that it understands what the data should look like and how many steps (sub data sources) there are (i.e. tables in a database, topics in Kafka). Tasks can define one or more steps.

    "},{"location":"setup/guide/#plan","title":"Plan","text":""},{"location":"setup/guide/#foreign-keys","title":"Foreign Keys","text":"

    Define foreign keys across data sources in your plan to ensure generated data can match Link to associated task 1 Link to associated task 2

    "},{"location":"setup/guide/#task","title":"Task","text":"Data Source Type Data Source Sample Task Notes Database Postgres Sample Database MySQL Sample Database Cassandra Sample File CSV Sample File JSON Sample Contains nested schemas and use of SQL for generated values File Parquet Sample Partition by year column Kafka Kafka Sample Specific base schema to be used, define headers, key, value, etc. JMS Solace Sample JSON formatted message HTTP PUT Sample JSON formatted PUT body"},{"location":"setup/guide/#configuration","title":"Configuration","text":"

    Basic configuration

    "},{"location":"setup/guide/#docker-compose","title":"Docker-compose","text":"

    To see how it runs against different data sources, you can run using docker-compose and set DATA_SOURCE like below

    ./gradlew build\ncd docker\nDATA_SOURCE=postgres docker-compose up -d datacaterer\n

    Can set it to one of the following:

    • postgres
    • mysql
    • cassandra
    • solace
    • kafka
    • http
    "},{"location":"setup/guide/data-source/cassandra/","title":"Cassandra","text":"

    Info

    Writing data to Cassandra is a paid feature. Try the free trial here.

    Creating a data generator for Cassandra. You will build a Docker image that will be able to populate data in Cassandra for the tables you configure.

    "},{"location":"setup/guide/data-source/cassandra/#requirements","title":"Requirements","text":"
    • 20 minutes
    • Git
    • Gradle
    • Docker
    • Cassandra
    "},{"location":"setup/guide/data-source/cassandra/#get-started","title":"Get Started","text":"

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    git clone git@github.com:pflooky/data-caterer-example.git\n

    If you already have a Cassandra instance running, you can skip to this step.

    "},{"location":"setup/guide/data-source/cassandra/#cassandra-setup","title":"Cassandra Setup","text":"

    Next, let's make sure you have an instance of Cassandra up and running in your local environment. This will make it easy for us to iterate and check our changes.

    cd docker\ndocker-compose up -d cassandra\n
    "},{"location":"setup/guide/data-source/cassandra/#permissions","title":"Permissions","text":"

    Let's make a new user that has the required permissions needed to push data into the Cassandra tables we want.

    CQL Permission Statements 
    GRANT INSERT ON <schema>.<table> TO data_caterer_user;\n

    Following permissions are required when enabling configuration.enableGeneratePlanAndTasks(true) as it will gather metadata information about tables and columns from the below tables.

    CQL Permission Statements 
    GRANT SELECT ON system_schema.tables TO data_caterer_user;\nGRANT SELECT ON system_schema.columns TO data_caterer_user;\n
    "},{"location":"setup/guide/data-source/cassandra/#plan-setup","title":"Plan Setup","text":"

    Create a new Java or Scala class.

    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedCassandraJavaPlan.java
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedCassandraPlan.scala

    Make sure your class extends PlanRun.

    JavaScala 
    import com.github.pflooky.datacaterer.java.api.PlanRun;\n\npublic class MyAdvancedCassandraJavaPlan extends PlanRun {\n}\n
    import com.github.pflooky.datacaterer.api.PlanRun\n\nclass MyAdvancedCassandraPlan extends PlanRun {\n}\n

    This class defines where we need to define all of our configurations for generating data. There are helper variables and methods defined to make it simple and easy to use.

    "},{"location":"setup/guide/data-source/cassandra/#connection-configuration","title":"Connection Configuration","text":"

    Within our class, we can start by defining the connection properties to connect to Cassandra.

    JavaScala 
    var accountTask = cassandra(\n    \"customer_cassandra\",   //name\n    \"localhost:9042\",       //url\n    \"cassandra\",            //username\n    \"cassandra\",            //password\n    Map.of()                //optional additional connection options\n)\n

    Additional options such as SSL configuration, etc can be found here.

    val accountTask = cassandra(\n    \"customer_cassandra\",   //name\n    \"localhost:9042\",       //url\n    \"cassandra\",            //username\n    \"cassandra\",            //password\n    Map()                   //optional additional connection options\n)\n

    Additional options such as SSL configuration, etc can be found here.

    "},{"location":"setup/guide/data-source/cassandra/#schema","title":"Schema","text":"

    Let's create a task for inserting data into the account.accounts and account.account_status_history tables as defined underdocker/data/cql/customer.cql. This table should already be setup for you if you followed this step. We can check if the table is setup already via the following command:

    docker exec host.docker.internal cqlsh -e 'describe account.accounts; describe account.account_status_history;'\n

    Here we should see some output that looks like the below. This tells us what schema we need to follow when generating data. We need to define that alongside any metadata that is useful to add constraints on what are possible values the generated data should contain.

    CREATE TABLE account.accounts (\n    account_id text PRIMARY KEY,\n    amount double,\n    created_by text,\n    name text,\n    open_time timestamp,\n    status text\n)...\n\nCREATE TABLE account.account_status_history (\n    account_id text,\n    eod_date date,\n    status text,\n    updated_by text,\n    updated_time timestamp,\n    PRIMARY KEY (account_id, eod_date)\n)...\n

    Trimming the connection details to work with the docker-compose Cassandra, we have a base Cassandra connection to define the table and schema required. Let's define each field along with their corresponding data type. You will notice that the text fields do not have a data type defined. This is because the default data type is StringType which corresponds to text in Cassandra.

    JavaScala 
    {\n    var accountTask = cassandra(\"customer_cassandra\", \"host.docker.internal:9042\")\n            .table(\"account\", \"accounts\")\n            .schema(\n                    field().name(\"account_id\"),\n                    field().name(\"amount\").type(DoubleType.instance()),\n                    field().name(\"created_by\"),\n                    field().name(\"name\"),\n                    field().name(\"open_time\").type(TimestampType.instance()),\n                    field().name(\"status\")\n            );\n}\n
    val accountTask = cassandra(\"customer_cassandra\", \"host.docker.internal:9042\")\n  .table(\"account\", \"accounts\")\n  .schema(\n    field.name(\"account_id\"),\n    field.name(\"amount\").`type`(DoubleType),\n    field.name(\"created_by\"),\n    field.name(\"name\"),\n    field.name(\"open_time\").`type`(TimestampType),\n    field.name(\"status\")\n  )\n
    "},{"location":"setup/guide/data-source/cassandra/#field-metadata","title":"Field Metadata","text":"

    We could stop here and generate random data for the accounts table. But wouldn't it be more useful if we produced data that is closer to the structure of the data that would come in production? We can do this by defining various metadata that add guidelines that the data generator will understand when generating data.

    "},{"location":"setup/guide/data-source/cassandra/#account_id","title":"account_id","text":"

    account_id follows a particular pattern that where it starts with ACC and has 8 digits after it. This can be defined via a regex like below. Alongside, we also mention that it is the primary key to prompt ensure that unique values are generated.

    JavaScala 
    field().name(\"account_id\").regex(\"ACC[0-9]{8}\").primaryKey(true),\n
    field.name(\"account_id\").regex(\"ACC[0-9]{8}\").primaryKey(true),\n
    "},{"location":"setup/guide/data-source/cassandra/#amount","title":"amount","text":"

    amount the numbers shouldn't be too large, so we can define a min and max for the generated numbers to be between 1 and 1000.

    JavaScala 
    field().name(\"amount\").type(DoubleType.instance()).min(1).max(1000),\n
    field.name(\"amount\").`type`(DoubleType).min(1).max(1000),\n
    "},{"location":"setup/guide/data-source/cassandra/#name","title":"name","text":"

    name is a string that also follows a certain pattern, so we could also define a regex but here we will choose to leverage the DataFaker library and create an expression to generate real looking name. All possible faker expressions can be found here

    JavaScala 
    field().name(\"name\").expression(\"#{Name.name}\"),\n
    field.name(\"name\").expression(\"#{Name.name}\"),\n
    "},{"location":"setup/guide/data-source/cassandra/#open_time","title":"open_time","text":"

    open_time is a timestamp that we want to have a value greater than a specific date. We can define a min date by using java.sql.Date like below.

    JavaScala 
    field().name(\"open_time\").type(TimestampType.instance()).min(java.sql.Date.valueOf(\"2022-01-01\")),\n
    field.name(\"open_time\").`type`(TimestampType).min(java.sql.Date.valueOf(\"2022-01-01\")),\n
    "},{"location":"setup/guide/data-source/cassandra/#status","title":"status","text":"

    status is a field that can only obtain one of four values, open, closed, suspended or pending.

    JavaScala 
    field().name(\"status\").oneOf(\"open\", \"closed\", \"suspended\", \"pending\")\n
    field.name(\"status\").oneOf(\"open\", \"closed\", \"suspended\", \"pending\")\n
    "},{"location":"setup/guide/data-source/cassandra/#created_by","title":"created_by","text":"

    created_by is a field that is based on the status field where it follows the logic: if status is open or closed, then it is created_by eod else created_by event. This can be achieved by defining a SQL expression like below.

    JavaScala 
    field().name(\"created_by\").sql(\"CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END\"),\n
    field.name(\"created_by\").sql(\"CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END\"),\n

    Putting it all the fields together, our class should now look like this.

    JavaScala 
    var accountTask = cassandra(\"customer_cassandra\", \"host.docker.internal:9042\")\n        .table(\"account\", \"accounts\")\n        .schema(\n                field().name(\"account_id\").regex(\"ACC[0-9]{8}\").primaryKey(true),\n                field().name(\"amount\").type(DoubleType.instance()).min(1).max(1000),\n                field().name(\"created_by\").sql(\"CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END\"),\n                field().name(\"name\").expression(\"#{Name.name}\"),\n                field().name(\"open_time\").type(TimestampType.instance()).min(java.sql.Date.valueOf(\"2022-01-01\")),\n                field().name(\"status\").oneOf(\"open\", \"closed\", \"suspended\", \"pending\")\n        );\n
    val accountTask = cassandra(\"customer_cassandra\", \"host.docker.internal:9042\")\n  .table(\"account\", \"accounts\")\n  .schema(\n    field.name(\"account_id\").primaryKey(true),\n    field.name(\"amount\").`type`(DoubleType).min(1).max(1000),\n    field.name(\"created_by\").sql(\"CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END\"),\n    field.name(\"name\").expression(\"#{Name.name}\"),\n    field.name(\"open_time\").`type`(TimestampType).min(java.sql.Date.valueOf(\"2022-01-01\")),\n    field.name(\"status\").oneOf(\"open\", \"closed\", \"suspended\", \"pending\")\n  )\n
    "},{"location":"setup/guide/data-source/cassandra/#additional-configurations","title":"Additional Configurations","text":"

    At the end of data generation, a report gets generated that summarises the actions it performed. We can control the output folder of that report via configurations. We will also enable the unique check to ensure any unique fields will have unique values generated.

    JavaScala 
    var config = configuration()\n        .generatedReportsFolderPath(\"/opt/app/data/report\")\n        .enableUniqueCheck(true);\n
    val config = configuration\n  .generatedReportsFolderPath(\"/opt/app/data/report\")\n  .enableUniqueCheck(true)\n
    "},{"location":"setup/guide/data-source/cassandra/#execute","title":"Execute","text":"

    To tell Data Caterer that we want to run with the configurations along with the accountTask, we have to call execute . So our full plan run will look like this.

    JavaScala 
    public class MyAdvancedCassandraJavaPlan extends PlanRun {\n    {\n        var accountTask = cassandra(\"customer_cassandra\", \"host.docker.internal:9042\")\n                .table(\"account\", \"accounts\")\n                .schema(\n                        field().name(\"account_id\").regex(\"ACC[0-9]{8}\").primaryKey(true),\n                        field().name(\"amount\").type(DoubleType.instance()).min(1).max(1000),\n                        field().name(\"created_by\").sql(\"CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END\"),\n                        field().name(\"name\").expression(\"#{Name.name}\"),\n                        field().name(\"open_time\").type(TimestampType.instance()).min(java.sql.Date.valueOf(\"2022-01-01\")),\n                        field().name(\"status\").oneOf(\"open\", \"closed\", \"suspended\", \"pending\")\n                );\n\n        var config = configuration()\n                .generatedReportsFolderPath(\"/opt/app/data/report\")\n                .enableUniqueCheck(true);\n\n        execute(config, accountTask);\n    }\n}\n
    class MyAdvancedCassandraPlan extends PlanRun {\n  val accountTask = cassandra(\"customer_cassandra\", \"host.docker.internal:9042\")\n    .table(\"account\", \"accounts\")\n    .schema(\n      field.name(\"account_id\").primaryKey(true),\n      field.name(\"amount\").`type`(DoubleType).min(1).max(1000),\n      field.name(\"created_by\").sql(\"CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END\"),\n      field.name(\"name\").expression(\"#{Name.name}\"),\n      field.name(\"open_time\").`type`(TimestampType).min(java.sql.Date.valueOf(\"2022-01-01\")),\n      field.name(\"status\").oneOf(\"open\", \"closed\", \"suspended\", \"pending\")\n    )\n\n  val config = configuration\n    .generatedReportsFolderPath(\"/opt/app/data/report\")\n    .enableUniqueCheck(true)\n\n  execute(config, accountTask)\n}\n
    "},{"location":"setup/guide/data-source/cassandra/#run","title":"Run","text":"

    Now we can run via the script ./run.sh that is in the top level directory of the data-caterer-example to run the class we just created.

    ./run.sh\n#input class MyAdvancedCassandraJavaPlan or MyAdvancedCassandraPlan\n#after completing\ndocker exec docker-cassandraserver-1 cqlsh -e 'select count(1) from account.accounts;select * from account.accounts limit 10;'\n

    Your output should look like this.

     count\n-------\n  1000\n\n(1 rows)\n\nWarnings :\nAggregation query used without partition key\n\n\n account_id  | amount    | created_by         | name                   | open_time                       | status\n-------------+-----------+--------------------+------------------------+---------------------------------+-----------\n ACC13554145 | 917.00418 | zb CVvbBTTzitjo5fK |          Jan Sanford I | 2023-06-21 21:50:10.463000+0000 | suspended\n ACC19154140 |  46.99177 |             VH88H9 |       Clyde Bailey PhD | 2023-07-18 11:33:03.675000+0000 |      open\n ACC50587836 |  774.9872 |         GENANwPm t |           Sang Monahan | 2023-03-21 00:16:53.308000+0000 |    closed\n ACC67619387 | 452.86706 |       5msTpcBLStTH |         Jewell Gerlach | 2022-10-18 19:13:07.606000+0000 | suspended\n ACC69889784 |  14.69298 |           WDmOh7NT |          Dale Schulist | 2022-10-25 12:10:52.239000+0000 | suspended\n ACC41977254 |  51.26492 |          J8jAKzvj2 |           Norma Nienow | 2023-08-19 18:54:39.195000+0000 | suspended\n ACC40932912 | 349.68067 |   SLcJgKZdLp5ALMyg | Vincenzo Considine III | 2023-05-16 00:22:45.991000+0000 |    closed\n ACC20642011 | 658.40713 |          clyZRD4fI |  Lannie McLaughlin DDS | 2023-05-11 23:14:30.249000+0000 |      open\n ACC74962085 | 970.98218 |       ZLETTSnj4NpD |          Ima Jerde DVM | 2023-05-07 10:01:56.218000+0000 |   pending\n ACC72848439 | 481.64267 |                 cc |        Kyla Deckow DDS | 2023-08-16 13:28:23.362000+0000 | suspended\n\n(10 rows)\n

    Also check the HTML report, found at docker/sample/report/index.html, that gets generated to get an overview of what was executed.

    "},{"location":"setup/guide/data-source/http/","title":"HTTP Source","text":"

    Info

    Generating data based on OpenAPI/Swagger document and pushing to HTTP endpoint is a paid feature. Try the free trial here.

    Creating a data generator based on an OpenAPI/Swagger document.

    "},{"location":"setup/guide/data-source/http/#requirements","title":"Requirements","text":"
    • 10 minutes
    • Git
    • Gradle
    • Docker
    "},{"location":"setup/guide/data-source/http/#get-started","title":"Get Started","text":"

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    git clone git@github.com:pflooky/data-caterer-example.git\n
    "},{"location":"setup/guide/data-source/http/#http-setup","title":"HTTP Setup","text":"

    We will be using the http-bin docker image to help simulate a service with HTTP endpoints.

    Start it via:

    cd docker\ndocker-compose up -d http\ndocker ps\n
    "},{"location":"setup/guide/data-source/http/#plan-setup","title":"Plan Setup","text":"

    Create a new Java or Scala class.

    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedHttpJavaPlanRun.java
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedHttpPlanRun.scala

    Make sure your class extends PlanRun.

    JavaScala 
    import com.github.pflooky.datacaterer.java.api.PlanRun;\n...\n\npublic class MyAdvancedHttpJavaPlanRun extends PlanRun {\n    {\n        var conf = configuration().enableGeneratePlanAndTasks(true)\n            .generatedReportsFolderPath(\"/opt/app/data/report\");\n    }\n}\n
    import com.github.pflooky.datacaterer.api.PlanRun\n...\n\nclass MyAdvancedHttpPlanRun extends PlanRun {\n  val conf = configuration.enableGeneratePlanAndTasks(true)\n    .generatedReportsFolderPath(\"/opt/app/data/report\")\n}\n

    We will enable generate plan and tasks so that we can read from external sources for metadata and save the reports under a folder we can easily access.

    "},{"location":"setup/guide/data-source/http/#schema","title":"Schema","text":"

    We can point the schema of a data source to a OpenAPI/Swagger document or URL. For this example, we will use the OpenAPI document found under docker/mount/http/petstore.json in the data-caterer-example repo. This is a simplified version of the original OpenAPI spec that can be found here.

    We have kept the following endpoints to test out:

    • GET /pets - get all pets
    • POST /pets - create a new pet
    • GET /pets/{id} - get a pet by id
    • DELETE /pets/{id} - delete a pet by id
    JavaScala 
    var httpTask = http(\"my_http\")\n        .schema(metadataSource().openApi(\"/opt/app/mount/http/petstore.json\"))\n        .count(count().records(2));\n
    val httpTask = http(\"my_http\")\n  .schema(metadataSource.openApi(\"/opt/app/mount/http/petstore.json\"))\n  .count(count.records(2))\n

    The above defines that the schema will come from an OpenAPI document found on the pathway defined. It will then generate 2 requests per request method and endpoint combination.

    "},{"location":"setup/guide/data-source/http/#run","title":"Run","text":"

    Let's try run and see what happens.

    cd ..\n./run.sh\n#input class MyAdvancedHttpJavaPlanRun or MyAdvancedHttpPlanRun\n#after completing\ndocker logs -f docker-http-1\n

    It should look something like this.

    172.21.0.1 [06/Nov/2023:01:06:53 +0000] GET /anything/pets?tags%3DeXQxFUHVja+EYm%26limit%3D33895 HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:06:53 +0000] GET /anything/pets?tags%3DSXaFvAqwYGF%26tags%3DjdNRFONA%26limit%3D40975 HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:06:56 +0000] POST /anything/pets HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:06:56 +0000] POST /anything/pets HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:07:00 +0000] GET /anything/pets/kbH8D7rDuq HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:07:00 +0000] GET /anything/pets/REsa0tnu7dvekGDvxR HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:07:03 +0000] DELETE /anything/pets/EqrOr1dHFfKUjWb HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:07:03 +0000] DELETE /anything/pets/7WG7JHPaNxP HTTP/1.1 200 Host: host.docker.internal}\n

    Looks like we have some data now. But we can do better and add some enhancements to it.

    "},{"location":"setup/guide/data-source/http/#foreign-keys","title":"Foreign keys","text":"

    The four different requests that get sent could have the same id passed across to each of them if we define a foreign key relationship. This will make it more realistic to a real life scenario as pets get created and queried by a particular id value. We note that the id value is first used when a pet is created in the body of the POST request. Then it gets used as a path parameter in the DELETE and GET requests.

    To link them all together, we must follow a particular pattern when referring to request body, query parameter or path parameter columns.

    HTTP Type Column Prefix Example Request Body bodyContent bodyContent.id Path Parameter pathParam pathParamid Query Parameter queryParam queryParamid Header header headerContent_Type

    Also note, that when creating a foreign field definition for a HTTP data source, to refer to a specific endpoint and method, we have to follow the pattern of {http method}{http path}. For example, POST/pets. Let's apply this knowledge to link all the id values together.

    JavaScala 
    var myPlan = plan().addForeignKeyRelationship(\n        foreignField(\"my_http\", \"POST/pets\", \"bodyContent.id\"),     //source of foreign key value\n        foreignField(\"my_http\", \"DELETE/pets/{id}\", \"pathParamid\"),\n        foreignField(\"my_http\", \"GET/pets/{id}\", \"pathParamid\")\n);\n\nexecute(myPlan, conf, httpTask);\n
    val myPlan = plan.addForeignKeyRelationship(\n  foreignField(\"my_http\", \"POST/pets\", \"bodyContent.id\"),     //source of foreign key value\n  foreignField(\"my_http\", \"DELETE/pets/{id}\", \"pathParamid\"),\n  foreignField(\"my_http\", \"GET/pets/{id}\", \"pathParamid\")\n)\n\nexecute(myPlan, conf, httpTask)\n

    Let's test it out by running it again

    ./run.sh\n#input class MyAdvancedHttpJavaPlanRun or MyAdvancedHttpPlanRun\ndocker logs -f docker-http-1\n
    172.21.0.1 [06/Nov/2023:01:33:59 +0000] GET /anything/pets?limit%3D45971 HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:34:00 +0000] GET /anything/pets?limit%3D62015 HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:34:04 +0000] POST /anything/pets HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:34:05 +0000] POST /anything/pets HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:34:09 +0000] DELETE /anything/pets/5e HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:34:09 +0000] DELETE /anything/pets/IHPm2 HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:34:14 +0000] GET /anything/pets/IHPm2 HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:34:14 +0000] GET /anything/pets/5e HTTP/1.1 200 Host: host.docker.internal}\n

    Now we have the same id values being produced across the POST, DELETE and GET requests! What if we knew that the id values should follow a particular pattern?

    "},{"location":"setup/guide/data-source/http/#custom-metadata","title":"Custom metadata","text":"

    So given that we have defined a foreign key where the root of the foreign key values is from the POST request, we can update the metadata of the id column for the POST request and it will proliferate to the other endpoints as well. Given the id column is a nested column as noted in the foreign key, we can alter its metadata via the following:

    JavaScala 
    var httpTask = http(\"my_http\")\n        .schema(metadataSource().openApi(\"/opt/app/mount/http/petstore.json\"))\n        .schema(field().name(\"bodyContent\").schema(field().name(\"id\").regex(\"ID[0-9]{8}\")))\n        .count(count().records(2));\n
    val httpTask = http(\"my_http\")\n  .schema(metadataSource.openApi(\"/opt/app/mount/http/petstore.json\"))\n  .schema(field.name(\"bodyContent\").schema(field.name(\"id\").regex(\"ID[0-9]{8}\")))\n  .count(count.records(2))\n

    We first get the column bodyContent, then get the nested schema and get the column id and add metadata stating that id should follow the patter ID[0-9]{8}.

    Let's try run again, and hopefully we should see some proper ID values.

    ./run.sh\n#input class MyAdvancedHttpJavaPlanRun or MyAdvancedHttpPlanRun\ndocker logs -f docker-http-1\n
    172.21.0.1 [06/Nov/2023:01:45:45 +0000] GET /anything/pets?tags%3D10fWnNoDz%26limit%3D66804 HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:45:46 +0000] GET /anything/pets?tags%3DhyO6mI8LZUUpS HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:45:50 +0000] POST /anything/pets HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:45:51 +0000] POST /anything/pets HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:45:52 +0000] DELETE /anything/pets/ID55185420 HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:45:52 +0000] DELETE /anything/pets/ID20618951 HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:45:57 +0000] GET /anything/pets/ID55185420 HTTP/1.1 200 Host: host.docker.internal}\n172.21.0.1 [06/Nov/2023:01:45:57 +0000] GET /anything/pets/ID20618951 HTTP/1.1 200 Host: host.docker.internal}\n

    Great! Now we have replicated a production-like flow of HTTP requests.

    "},{"location":"setup/guide/data-source/http/#ordering","title":"Ordering","text":"

    If you wanted to change the ordering of the requests, you can alter the order from within the OpenAPI/Swagger document. This is particularly useful when you want to simulate the same flow that users would take when utilising your application (i.e. create account, query account, update account).

    "},{"location":"setup/guide/data-source/http/#rows-per-second","title":"Rows per second","text":"

    By default, Data Caterer will push requests per method and endpoint at a rate of around 5 requests per second. If you want to alter this value, you can do so via the below configuration. The lowest supported requests per second is 1.

    JavaScala 
    import com.github.pflooky.datacaterer.api.model.Constants;\n\n...\nvar httpTask = http(\"my_http\", Map.of(Constants.ROWS_PER_SECOND(), \"1\"))\n        ...\n
    import com.github.pflooky.datacaterer.api.model.Constants.ROWS_PER_SECOND\n\n...\nval httpTask = http(\"my_http\", options = Map(ROWS_PER_SECOND -> \"1\"))\n  ...\n

    Check out the full example under AdvancedHttpPlanRun in the example repo.

    "},{"location":"setup/guide/data-source/kafka/","title":"Kafka","text":"

    Info

    Writing data to Kafka is a paid feature. Try the free trial here.

    Creating a data generator for Kafka. You will build a Docker image that will be able to populate data in kafka for the topics you configure.

    "},{"location":"setup/guide/data-source/kafka/#requirements","title":"Requirements","text":"
    • 20 minutes
    • Git
    • Gradle
    • Docker
    • Kafka
    "},{"location":"setup/guide/data-source/kafka/#get-started","title":"Get Started","text":"

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    git clone git@github.com:pflooky/data-caterer-example.git\n

    If you already have a Kafka instance running, you can skip to this step.

    "},{"location":"setup/guide/data-source/kafka/#kafka-setup","title":"Kafka Setup","text":"

    Next, let's make sure you have an instance of Kafka up and running in your local environment. This will make it easy for us to iterate and check our changes.

    cd docker\ndocker-compose up -d kafka\n
    "},{"location":"setup/guide/data-source/kafka/#plan-setup","title":"Plan Setup","text":"

    Create a new Java or Scala class.

    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedKafkaJavaPlan.java
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedKafkaPlan.scala

    Make sure your class extends PlanRun.

    JavaScala 
    import com.github.pflooky.datacaterer.java.api.PlanRun;\n\npublic class MyAdvancedKafkaJavaPlan extends PlanRun {\n}\n
    import com.github.pflooky.datacaterer.api.PlanRun\n\nclass MyAdvancedKafkaPlan extends PlanRun {\n}\n

    This class defines where we need to define all of our configurations for generating data. There are helper variables and methods defined to make it simple and easy to use.

    "},{"location":"setup/guide/data-source/kafka/#connection-configuration","title":"Connection Configuration","text":"

    Within our class, we can start by defining the connection properties to connect to Kafka.

    JavaScala 
    var accountTask = kafka(\n    \"my_kafka\",       //name\n    \"localhost:9092\", //url\n    Map.of()          //optional additional connection options\n);\n

    Additional options can be found here.

    val accountTask = kafka(\n    \"my_kafka\",       //name\n    \"localhost:9092\", //url\n    Map()             //optional additional connection options\n)\n

    Additional options can be found here.

    "},{"location":"setup/guide/data-source/kafka/#schema","title":"Schema","text":"

    Let's create a task for inserting data into the account-topic that is already defined underdocker/data/kafka/setup_kafka.sh. This topic should already be setup for you if you followed this step. We can check if the topic is set up already via the following command:

    docker exec docker-kafkaserver-1 kafka-topics --bootstrap-server localhost:9092 --list\n

    Trimming the connection details to work with the docker-compose Kafka, we have a base Kafka connection to define the topic we will publish to. Let's define each field along with their corresponding data type. You will notice that the text fields do not have a data type defined. This is because the default data type is StringType.

    JavaScala 
    {\n    var kafkaTask = kafka(\"my_kafka\", \"kafkaserver:29092\")\n            .topic(\"account-topic\")\n            .schema(\n                    field().name(\"key\").sql(\"content.account_id\"),\n                    field().name(\"value\").sql(\"TO_JSON(content)\"),\n                    //field().name(\"partition\").type(IntegerType.instance()),  can define partition here\n                    field().name(\"headers\")\n                            .type(ArrayType.instance())\n                            .sql(\n                                    \"ARRAY(\" +\n                                            \"NAMED_STRUCT('key', 'account-id', 'value', TO_BINARY(content.account_id, 'utf-8')),\" +\n                                            \"NAMED_STRUCT('key', 'updated', 'value', TO_BINARY(content.details.updated_by.time, 'utf-8'))\" +\n                                            \")\"\n                            ),\n                    field().name(\"content\")\n                            .schema(\n                                    field().name(\"account_id\").regex(\"ACC[0-9]{8}\"),\n                                    field().name(\"year\").type(IntegerType.instance()),\n                                    field().name(\"amount\").type(DoubleType.instance()),\n                                    field().name(\"details\")\n                                            .schema(\n                                                    field().name(\"name\").expression(\"#{Name.name}\"),\n                                                    field().name(\"first_txn_date\").type(DateType.instance()).sql(\"ELEMENT_AT(SORT_ARRAY(content.transactions.txn_date), 1)\"),\n                                                    field().name(\"updated_by\")\n                                                            .schema(\n                                                                    field().name(\"user\"),\n                                                                    field().name(\"time\").type(TimestampType.instance())\n                                                            )\n                                            ),\n                                    field().name(\"transactions\").type(ArrayType.instance())\n                                            .schema(\n                                                    field().name(\"txn_date\").type(DateType.instance()).min(Date.valueOf(\"2021-01-01\")).max(\"2021-12-31\"),\n                                                    field().name(\"amount\").type(DoubleType.instance())\n                                            )\n                            ),\n                    field().name(\"tmp_year\").sql(\"content.year\").omit(true),\n                    field().name(\"tmp_name\").sql(\"content.details.name\").omit(true)\n            )\n}\n
    val kafkaTask = kafka(\"my_kafka\", \"kafkaserver:29092\")\n  .topic(\"account-topic\")\n  .schema(\n    field.name(\"key\").sql(\"content.account_id\"),\n    field.name(\"value\").sql(\"TO_JSON(content)\"),\n    //field.name(\"partition\").type(IntegerType),  can define partition here\n    field.name(\"headers\")\n      .`type`(ArrayType)\n      .sql(\n        \"\"\"ARRAY(\n          |  NAMED_STRUCT('key', 'account-id', 'value', TO_BINARY(content.account_id, 'utf-8')),\n          |  NAMED_STRUCT('key', 'updated', 'value', TO_BINARY(content.details.updated_by.time, 'utf-8'))\n          |)\"\"\".stripMargin\n      ),\n    field.name(\"content\")\n      .schema(\n        field.name(\"account_id\").regex(\"ACC[0-9]{8}\"),\n        field.name(\"year\").`type`(IntegerType).min(2021).max(2023),\n        field.name(\"amount\").`type`(DoubleType),\n        field.name(\"details\")\n          .schema(\n            field.name(\"name\").expression(\"#{Name.name}\"),\n            field.name(\"first_txn_date\").`type`(DateType).sql(\"ELEMENT_AT(SORT_ARRAY(content.transactions.txn_date), 1)\"),\n            field.name(\"updated_by\")\n              .schema(\n                field.name(\"user\"),\n                field.name(\"time\").`type`(TimestampType),\n              ),\n          ),\n        field.name(\"transactions\").`type`(ArrayType)\n          .schema(\n            field.name(\"txn_date\").`type`(DateType).min(Date.valueOf(\"2021-01-01\")).max(\"2021-12-31\"),\n            field.name(\"amount\").`type`(DoubleType),\n          )\n      ),\n    field.name(\"tmp_year\").sql(\"content.year\").omit(true),\n    field.name(\"tmp_name\").sql(\"content.details.name\").omit(true)\n  )\n
    "},{"location":"setup/guide/data-source/kafka/#fields","title":"Fields","text":"

    The schema defined for Kafka has a format that needs to be followed as noted above. Specifically, the required fields are: - value

    Whilst, the other fields are optional: - key - partition - headers

    "},{"location":"setup/guide/data-source/kafka/#headers","title":"headers","text":"

    headers follows a particular pattern that where it is of type array<struct<key: string,value: binary>>. To be able to generate data for this data type, we need to use an SQL expression like the one below. You will notice that in the value part, it refers to content.account_id where content is another field defined at the top level of the schema. This allows you to reference other values that have already been generated.

    JavaScala 
    field().name(\"headers\")\n        .type(ArrayType.instance())\n        .sql(\n                \"ARRAY(\" +\n                        \"NAMED_STRUCT('key', 'account-id', 'value', TO_BINARY(content.account_id, 'utf-8')),\" +\n                        \"NAMED_STRUCT('key', 'updated', 'value', TO_BINARY(content.details.updated_by.time, 'utf-8'))\" +\n                        \")\"\n        )\n
    field.name(\"headers\")\n  .`type`(ArrayType)\n  .sql(\n    \"\"\"ARRAY(\n      |  NAMED_STRUCT('key', 'account-id', 'value', TO_BINARY(content.account_id, 'utf-8')),\n      |  NAMED_STRUCT('key', 'updated', 'value', TO_BINARY(content.details.updated_by.time, 'utf-8'))\n      |)\"\"\".stripMargin\n  )\n
    "},{"location":"setup/guide/data-source/kafka/#transactions","title":"transactions","text":"

    transactions is an array that contains an inner structure of txn_date and amount. The size of the array generated can be controlled via arrayMinLength and arrayMaxLength.

    JavaScala 
    field().name(\"transactions\").type(ArrayType.instance())\n        .schema(\n                field().name(\"txn_date\").type(DateType.instance()).min(Date.valueOf(\"2021-01-01\")).max(\"2021-12-31\"),\n                field().name(\"amount\").type(DoubleType.instance())\n        )\n
    field.name(\"transactions\").`type`(ArrayType)\n  .schema(\n    field.name(\"txn_date\").`type`(DateType).min(Date.valueOf(\"2021-01-01\")).max(\"2021-12-31\"),\n    field.name(\"amount\").`type`(DoubleType),\n  )\n
    "},{"location":"setup/guide/data-source/kafka/#details","title":"details","text":"

    details is another example of a nested schema structure where it also has a nested structure itself in updated_by. One thing to note here is the first_txn_date field has a reference to the content.transactions array where it will sort the array by txn_date and get the first element.

    JavaScala 
    field().name(\"details\")\n        .schema(\n                field().name(\"name\").expression(\"#{Name.name}\"),\n                field().name(\"first_txn_date\").type(DateType.instance()).sql(\"ELEMENT_AT(SORT_ARRAY(content.transactions.txn_date), 1)\"),\n                field().name(\"updated_by\")\n                        .schema(\n                                field().name(\"user\"),\n                                field().name(\"time\").type(TimestampType.instance())\n                        )\n        )\n
    field.name(\"details\")\n  .schema(\n    field.name(\"name\").expression(\"#{Name.name}\"),\n    field.name(\"first_txn_date\").`type`(DateType).sql(\"ELEMENT_AT(SORT_ARRAY(content.transactions.txn_date), 1)\"),\n    field.name(\"updated_by\")\n      .schema(\n        field.name(\"user\"),\n        field.name(\"time\").`type`(TimestampType),\n      ),\n  )\n
    "},{"location":"setup/guide/data-source/kafka/#additional-configurations","title":"Additional Configurations","text":"

    At the end of data generation, a report gets generated that summarises the actions it performed. We can control the output folder of that report via configurations.

    JavaScala 
    var config = configuration()\n        .generatedReportsFolderPath(\"/opt/app/data/report\");\n
    val config = configuration\n  .generatedReportsFolderPath(\"/opt/app/data/report\")\n
    "},{"location":"setup/guide/data-source/kafka/#execute","title":"Execute","text":"

    To tell Data Caterer that we want to run with the configurations along with the kafkaTask, we have to call execute .

    "},{"location":"setup/guide/data-source/kafka/#run","title":"Run","text":"

    Now we can run via the script ./run.sh that is in the top level directory of the data-caterer-example to run the class we just created.

    ./run.sh\n#input class AdvancedKafkaJavaPlanRun or AdvancedKafkaPlanRun\n#after completing\ndocker exec docker-kafkaserver-1 kafka-console-consumer --bootstrap-server localhost:9092 --topic account-topic --from-beginning\n

    Your output should look like this.

    {\"account_id\":\"ACC56292178\",\"year\":2022,\"amount\":18338.627721151555,\"details\":{\"name\":\"Isaias Reilly\",\"first_txn_date\":\"2021-01-22\",\"updated_by\":{\"user\":\"FgYXbKDWdhHVc3\",\"time\":\"2022-12-30T13:49:07.309Z\"}},\"transactions\":[{\"txn_date\":\"2021-01-22\",\"amount\":30556.52125487579},{\"txn_date\":\"2021-10-29\",\"amount\":39372.302259554635},{\"txn_date\":\"2021-10-29\",\"amount\":61887.31389495968}]}\n{\"account_id\":\"ACC37729457\",\"year\":2022,\"amount\":96885.31758764731,\"details\":{\"name\":\"Randell Witting\",\"first_txn_date\":\"2021-06-30\",\"updated_by\":{\"user\":\"HCKYEBHN8AJ3TB\",\"time\":\"2022-12-02T02:05:01.144Z\"}},\"transactions\":[{\"txn_date\":\"2021-06-30\",\"amount\":98042.09647765031},{\"txn_date\":\"2021-10-06\",\"amount\":41191.43564742036},{\"txn_date\":\"2021-11-16\",\"amount\":78852.08184809204},{\"txn_date\":\"2021-10-09\",\"amount\":13747.157653571106}]}\n{\"account_id\":\"ACC23127317\",\"year\":2023,\"amount\":81164.49304198896,\"details\":{\"name\":\"Jed Wisozk\",\"updated_by\":{\"user\":\"9MBFZZ\",\"time\":\"2023-07-12T05:56:52.397Z\"}},\"transactions\":[]}\n

    Also check the HTML report, found at docker/sample/report/index.html, that gets generated to get an overview of what was executed.

    "},{"location":"setup/guide/data-source/marquez-metadata-source/","title":"Metadata Source","text":"

    Info

    Generating data based on an external metadata source is a paid feature. Try the free trial here.

    Creating a data generator for Postgres tables and CSV file based on metadata stored in Marquez ( follows OpenLineage API).

    "},{"location":"setup/guide/data-source/marquez-metadata-source/#requirements","title":"Requirements","text":"
    • 10 minutes
    • Git
    • Gradle
    • Docker
    "},{"location":"setup/guide/data-source/marquez-metadata-source/#get-started","title":"Get Started","text":"

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    git clone git@github.com:pflooky/data-caterer-example.git\n
    "},{"location":"setup/guide/data-source/marquez-metadata-source/#marquez-setup","title":"Marquez Setup","text":"

    You can follow the README found here to help with setting up Marquez in your local environment. This comes with an instance of Postgres which we will also be using as a data store for generated data.

    The command that was run for this example to help with setup of dummy data was ./docker/up.sh -a 5001 -m 5002 --seed.

    Check that the following url shows some data like below once you click on food_delivery from the ns drop down in the top right corner.

    "},{"location":"setup/guide/data-source/marquez-metadata-source/#postgres-setup","title":"Postgres Setup","text":"

    Since we will also be using the Marquez Postgres instance as a data source, we will set up a separate database to store the generated data in via:

    docker exec marquez-db psql -Upostgres -c 'CREATE DATABASE food_delivery'\n
    "},{"location":"setup/guide/data-source/marquez-metadata-source/#plan-setup","title":"Plan Setup","text":"

    Create a new Java or Scala class.

    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedMetadataSourceJavaPlanRun.java
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedMetadataSourcePlanRun.scala

    Make sure your class extends PlanRun.

    JavaScala 
    import com.github.pflooky.datacaterer.java.api.PlanRun;\n...\n\npublic class MyAdvancedMetadataSourceJavaPlanRun extends PlanRun {\n    {\n        var conf = configuration().enableGeneratePlanAndTasks(true)\n            .generatedReportsFolderPath(\"/opt/app/data/report\");\n    }\n}\n
    import com.github.pflooky.datacaterer.api.PlanRun\n...\n\nclass MyAdvancedMetadataSourcePlanRun extends PlanRun {\n  val conf = configuration.enableGeneratePlanAndTasks(true)\n    .generatedReportsFolderPath(\"/opt/app/data/report\")\n}\n

    We will enable generate plan and tasks so that we can read from external sources for metadata and save the reports under a folder we can easily access.

    "},{"location":"setup/guide/data-source/marquez-metadata-source/#schema","title":"Schema","text":"

    We can point the schema of a data source to our Marquez instance. For the Postgres data source, we will point to a namespace, which in Marquez or OpenLineage, represents a set of datasets. For the CSV data source, we will point to a specific namespace and dataset.

    "},{"location":"setup/guide/data-source/marquez-metadata-source/#single-schema","title":"Single Schema","text":"JavaScala 
    var csvTask = csv(\"my_csv\", \"/tmp/data/csv\", Map.of(\"saveMode\", \"overwrite\", \"header\", \"true\"))\n        .schema(metadataSource().marquez(\"http://localhost:5001\", \"food_delivery\", \"public.delivery_7_days\"))\n        .count(count().records(10));\n
    val csvTask = csv(\"my_csv\", \"/tmp/data/csv\", Map(\"saveMode\" -> \"overwrite\", \"header\" -> \"true\"))\n  .schema(metadataSource.marquez(\"http://localhost:5001\", \"food_delivery\", \"public.delivery_7_days\"))\n  .count(count.records(10))\n

    The above defines that the schema will come from Marquez, which is a type of metadata source that contains information about schemas. Specifically, it points to the food_delivery namespace and public.categories dataset to retrieve the schema information from.

    "},{"location":"setup/guide/data-source/marquez-metadata-source/#multiple-schemas","title":"Multiple Schemas","text":"JavaScala 
    var postgresTask = postgres(\"my_postgres\", \"jdbc:postgresql://host.docker.internal:5432/food_delivery\", \"postgres\", \"password\", Map.of())\n    .schema(metadataSource().marquez(\"http://host.docker.internal:5001\", \"food_delivery\"))\n    .count(count().records(10));\n
    val postgresTask = postgres(\"my_postgres\", \"jdbc:postgresql://host.docker.internal:5432/food_delivery\", \"postgres\", \"password\")\n  .schema(metadataSource.marquez(\"http://host.docker.internal:5001\", \"food_delivery\"))\n  .count(count.records(10))\n

    We now have pointed this Postgres instance to produce multiple schemas that are defined under the food_delivery namespace. Also note that we are using database food_delivery in Postgres to push our generated data to, and we have set the number of records per sub data source (in this case, per table) to be 10.

    "},{"location":"setup/guide/data-source/marquez-metadata-source/#run","title":"Run","text":"

    Let's try run and see what happens.

    cd ..\n./run.sh\n#input class MyAdvancedMetadataSourceJavaPlanRun or MyAdvancedMetadataSourcePlanRun\n#after completing\ndocker exec marquez-db psql -Upostgres -d food_delivery -c 'SELECT * FROM public.delivery_7_days'\n

    It should look something like this.

     order_id |     order_placed_on     |   order_dispatched_on   |   order_delivered_on    |         customer_email         |                     customer_address                     | menu_id | restaurant_id |                        restaurant_address\n   | menu_item_id | category_id | discount_id | city_id | driver_id\n----------+-------------------------+-------------------------+-------------------------+--------------------------------+----------------------------------------------------------+---------+---------------+---------------------------------------------------------------\n---+--------------+-------------+-------------+---------+-----------\n    38736 | 2023-02-05 06:05:23.755 | 2023-09-08 04:29:10.878 | 2023-09-03 23:58:34.285 | april.skiles@hotmail.com       | 5018 Lang Dam, Gaylordfurt, MO 35172                     |   59841 |         30971 | Suite 439 51366 Bartoletti Plains, West Lashawndamouth, CA 242\n42 |        55697 |       36370 |       21574 |   88022 |     16569\n     4376 | 2022-12-19 14:39:53.442 | 2023-08-30 07:40:06.948 | 2023-03-15 20:38:26.11  | adelina.balistreri@hotmail.com | Apt. 340 9146 Novella Motorway, East Troyhaven, UT 34773 |   66195 |         42765 | Suite 670 8956 Rob Fork, Rennershire, CA 04524\n   |        26516 |       81335 |       87615 |   27433 |     45649\n    11083 | 2022-10-30 12:46:38.692 | 2023-06-02 13:05:52.493 | 2022-11-27 18:38:07.873 | johnny.gleason@gmail.com       | Apt. 385 99701 Lemke Place, New Irvin, RI 73305          |   66427 |         44438 | 1309 Danny Cape, Weimanntown, AL 15865\n   |        41686 |       36508 |       34498 |   24191 |     92405\n    58759 | 2023-07-26 14:32:30.883 | 2022-12-25 11:04:08.561 | 2023-04-21 17:43:05.86  | isabelle.ohara@hotmail.com     | 2225 Evie Lane, South Ardella, SD 90805                  |   27106 |         25287 | Suite 678 3731 Dovie Park, Port Luigi, ID 08250\n   |        94205 |       66207 |       81051 |   52553 |     27483\n

    You can also try query some other tables. Let's also check what is in the CSV file.

    $ head docker/sample/csv/part-0000*\nmenu_item_id,category_id,discount_id,city_id,driver_id,order_id,order_placed_on,order_dispatched_on,order_delivered_on,customer_email,customer_address,menu_id,restaurant_id,restaurant_address\n72248,37098,80135,45888,5036,11090,2023-09-20T05:33:08.036+08:00,2023-05-16T23:10:57.119+08:00,2023-05-01T22:02:23.272+08:00,demetrice.rohan@hotmail.com,\"406 Harmony Rue, Wisozkburgh, MD 12282\",33762,9042,\"Apt. 751 0796 Ellan Flats, Lake Chetville, WI 81957\"\n41644,40029,48565,83373,89919,58359,2023-04-18T06:28:26.194+08:00,2022-10-15T18:17:48.998+08:00,2023-02-06T17:02:04.104+08:00,joannie.okuneva@yahoo.com,\"Suite 889 022 Susan Lane, Zemlakport, OR 56996\",27467,6216,\"Suite 016 286 Derick Grove, Dooleytown, NY 14664\"\n49299,53699,79675,40821,61764,72234,2023-07-16T21:33:48.739+08:00,2023-02-14T21:23:10.265+08:00,2023-09-18T02:08:51.433+08:00,ina.heller@yahoo.com,\"Suite 600 86844 Heller Island, New Celestinestad, DE 42622\",48002,12462,\"5418 Okuneva Mountain, East Blairchester, MN 04060\"\n83197,86141,11085,29944,81164,65382,2023-01-20T06:08:25.981+08:00,2023-01-11T13:24:32.968+08:00,2023-09-09T02:30:16.890+08:00,lakisha.bashirian@yahoo.com,\"Suite 938 534 Theodore Lock, Port Caitlynland, LA 67308\",69109,47727,\"4464 Stewart Tunnel, Marguritemouth, AR 56791\"\n

    Looks like we have some data now. But we can do better and add some enhancements to it.

    What if we wanted the same records in Postgres public.delivery_7_days to also show up in the CSV file? That's where we can use a foreign key definition.

    "},{"location":"setup/guide/data-source/marquez-metadata-source/#foreign-key","title":"Foreign Key","text":"

    We can take a look at the report (under docker/sample/report/index.html) to see what we need to do to create the foreign key. From the overview, you should see under Tasks there is a my_postgres task which has food_delivery_public.delivery_7_days as a step. Click on the link for food_delivery_public.delivery_7_days and it will take us to a page where we can find out about the columns used in this table. Click on the Fields button on the far right to see.

    We can copy all of a subset of fields that we want matched across the CSV file and Postgres. For this example, we will take all the fields.

    JavaScala 
    var myPlan = plan().addForeignKeyRelationship(\n        postgresTask, List.of(\"key\", \"tmp_year\", \"tmp_name\", \"value\"),\n        List.of(Map.entry(csvTask, List.of(\"account_number\", \"year\", \"name\", \"payload\")))\n);\n\nvar conf = ...\n\nexecute(myPlan, conf, postgresTask, csvTask);\n
    val foreignCols = List(\"order_id\", \"order_placed_on\", \"order_dispatched_on\", \"order_delivered_on\", \"customer_email\",\n  \"customer_address\", \"menu_id\", \"restaurant_id\", \"restaurant_address\", \"menu_item_id\", \"category_id\", \"discount_id\",\n  \"city_id\", \"driver_id\")\n\nval myPlan = plan.addForeignKeyRelationships(\n  csvTask, foreignCols,\n  List(foreignField(postgresTask, \"food_delivery_public.delivery_7_days\", foreignCols))\n)\n\nval conf = ...\n\nexecute(myPlan, conf, postgresTask, csvTask)\n

    Notice how we have defined the csvTask and foreignCols as the main foreign key but for postgresTask, we had to define it as a foreignField. This is because postgresTask has multiple tables within it, and we only want to define our foreign key with respect to the public.delivery_7_days table. We use the step name (can be seen from the report) to specify the table to target.

    To test this out, we will truncate the public.delivery_7_days table in Postgres first, and then try run again.

    docker exec marquez-db psql -Upostgres -d food_delivery -c 'TRUNCATE public.delivery_7_days'\n./run.sh\n#input class MyAdvancedMetadataSourceJavaPlanRun or MyAdvancedMetadataSourcePlanRun\ndocker exec marquez-db psql -Upostgres -d food_delivery -c 'SELECT * FROM public.delivery_7_days'\n
     order_id |     order_placed_on     |   order_dispatched_on   |   order_delivered_on    |        customer_email        |\n       customer_address                     | menu_id | restaurant_id |                   restaurant_address                   | menu\n_item_id | category_id | discount_id | city_id | driver_id\n----------+-------------------------+-------------------------+-------------------------+------------------------------+-------------\n--------------------------------------------+---------+---------------+--------------------------------------------------------+-----\n---------+-------------+-------------+---------+-----------\n    53333 | 2022-10-15 08:40:23.394 | 2023-01-23 09:42:48.397 | 2023-08-12 08:50:52.397 | normand.aufderhar@gmail.com  | Apt. 036 449\n27 Wilderman Forge, Marvinchester, CT 15952 |   40412 |         70130 | Suite 146 98176 Schaden Village, Grahammouth, SD 12354 |\n   90141 |       44210 |       83966 |   78614 |     77449\n

    Let's grab the first email from the Postgres table and check whether the same record exists in the CSV file.

    $ cat docker/sample/csv/part-0000* | grep normand.aufderhar\n90141,44210,83966,78614,77449,53333,2022-10-15T08:40:23.394+08:00,2023-01-23T09:42:48.397+08:00,2023-08-12T08:50:52.397+08:00,normand.aufderhar@gmail.com,\"Apt. 036 44927 Wilderman Forge, Marvinchester, CT 15952\",40412,70130,\"Suite 146 98176 Schaden Village, Grahammouth, SD 12354\"\n

    Great! Now we have the ability to get schema information from an external source, add our own foreign keys and generate data.

    Check out the full example under AdvancedMetadataSourcePlanRun in the example repo.

    "},{"location":"setup/guide/data-source/open-metadata-source/","title":"OpenMetadata Source","text":"

    Info

    Generating data based on an external metadata source is a paid feature. Try the free trial here.

    Creating a data generator for a JSON file based on metadata stored in OpenMetadata.

    "},{"location":"setup/guide/data-source/open-metadata-source/#requirements","title":"Requirements","text":"
    • 10 minutes
    • Git
    • Gradle
    • Docker
    "},{"location":"setup/guide/data-source/open-metadata-source/#get-started","title":"Get Started","text":"

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    git clone git@github.com:pflooky/data-caterer-example.git\n
    "},{"location":"setup/guide/data-source/open-metadata-source/#openmetadata-setup","title":"OpenMetadata Setup","text":"

    You can follow the local docker setup found here to help with setting up OpenMetadata in your local environment.

    If that page becomes outdated or the link doesn't work, below are the commands I used to run it:

    mkdir openmetadata-docker && cd openmetadata-docker\ncurl -sL https://github.com/open-metadata/OpenMetadata/releases/download/1.2.0-release/docker-compose.yml > docker-compose.yml\ndocker compose -f docker-compose.yml up --detach\n

    Check that the following url works and login with admin:admin. Then you should see some data like below:

    "},{"location":"setup/guide/data-source/open-metadata-source/#plan-setup","title":"Plan Setup","text":"

    Create a new Java or Scala class.

    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedOpenMetadataSourceJavaPlanRun.java
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedOpenMetadataSourcePlanRun.scala

    Make sure your class extends PlanRun.

    JavaScala 
    import com.github.pflooky.datacaterer.java.api.PlanRun;\n...\n\npublic class MyAdvancedOpenMetadataSourceJavaPlanRun extends PlanRun {\n    {\n        var conf = configuration().enableGeneratePlanAndTasks(true)\n            .generatedReportsFolderPath(\"/opt/app/data/report\");\n    }\n}\n
    import com.github.pflooky.datacaterer.api.PlanRun\n...\n\nclass MyAdvancedOpenMetadataSourcePlanRun extends PlanRun {\n  val conf = configuration.enableGeneratePlanAndTasks(true)\n    .generatedReportsFolderPath(\"/opt/app/data/report\")\n}\n

    We will enable generate plan and tasks so that we can read from external sources for metadata and save the reports under a folder we can easily access.

    "},{"location":"setup/guide/data-source/open-metadata-source/#schema","title":"Schema","text":"

    We can point the schema of a data source to our OpenMetadata instance. We will use a JSON data source so that we can show how nested data types are handled and how we could customise it.

    "},{"location":"setup/guide/data-source/open-metadata-source/#single-schema","title":"Single Schema","text":"JavaScala 
    import com.github.pflooky.datacaterer.api.model.Constants;\n...\n\nvar jsonTask = json(\"my_json\", \"/opt/app/data/json\", Map.of(\"saveMode\", \"overwrite\"))\n        .schema(metadataSource().openMetadataJava(\n            \"http://localhost:8585/api\",                                                              //url\n            Constants.OPEN_METADATA_AUTH_TYPE_OPEN_METADATA(),                                        //auth type\n            Map.of(                                                                                   //additional options (including auth options)\n                Constants.OPEN_METADATA_JWT_TOKEN(), \"abc123\",                                        //get from settings/bots/ingestion-bot\n                Constants.OPEN_METADATA_TABLE_FQN(), \"sample_data.ecommerce_db.shopify.raw_customer\"  //table fully qualified name\n            )\n        ))\n        .count(count().records(10));\n
    import com.github.pflooky.datacaterer.api.model.Constants.{OPEN_METADATA_AUTH_TYPE_OPEN_METADATA, OPEN_METADATA_JWT_TOKEN, OPEN_METADATA_TABLE_FQN, SAVE_MODE}\n...\n\nval jsonTask = json(\"my_json\", \"/opt/app/data/json\", Map(\"saveMode\" -> \"overwrite\"))\n  .schema(metadataSource.openMetadata(\n    \"http://localhost:8585/api\",                                                  //url\n    OPEN_METADATA_AUTH_TYPE_OPEN_METADATA,                                        //auth type\n    Map(                                                                          //additional options (including auth options)\n      OPEN_METADATA_JWT_TOKEN -> \"abc123\",                                        //get from settings/bots/ingestion-bot\n      OPEN_METADATA_TABLE_FQN -> \"sample_data.ecommerce_db.shopify.raw_customer\"  //table fully qualified name\n    )\n  ))\n  .count(count.records(10))\n

    The above defines that the schema will come from OpenMetadata, which is a type of metadata source that contains information about schemas. Specifically, it points to the sample_data.ecommerce_db.shopify.raw_customer table. You can check out the schema here to see what it looks like.

    "},{"location":"setup/guide/data-source/open-metadata-source/#run","title":"Run","text":"

    Let's try run and see what happens.

    cd ..\n./run.sh\n#input class MyAdvancedOpenMetadataSourceJavaPlanRun or MyAdvancedOpenMetadataSourcePlanRun\n#after completing\ncat docker/sample/json/part-00000-*\n

    It should look something like this.

    {\n  \"comments\": \"Mh6jqpD5e4M\",\n  \"creditcard\": \"6771839575926717\",\n  \"membership\": \"Za3wCQUl9E  EJj712\",\n  \"orders\": [\n    {\n      \"product_id\": \"Aa6NG0hxfHVq\",\n      \"price\": 16139,\n      \"onsale\": false,\n      \"tax\": 58134,\n      \"weight\": 40734,\n      \"others\": 45813,\n      \"vendor\": \"Kh\"\n    },\n    {\n      \"product_id\": \"zbHBY \",\n      \"price\": 17903,\n      \"onsale\": false,\n      \"tax\": 39526,\n      \"weight\": 9346,\n      \"others\": 52035,\n      \"vendor\": \"jbkbnXAa\"\n    },\n    {\n      \"product_id\": \"5qs3gakppd7Nw5\",\n      \"price\": 48731,\n      \"onsale\": true,\n      \"tax\": 81105,\n      \"weight\": 2004,\n      \"others\": 20465,\n      \"vendor\": \"nozCDMSXRPH Ev\"\n    },\n    {\n      \"product_id\": \"CA6h17ANRwvb\",\n      \"price\": 62102,\n      \"onsale\": true,\n      \"tax\": 96601,\n      \"weight\": 78849,\n      \"others\": 79453,\n      \"vendor\": \" ihVXEJz7E2EFS\"\n    }\n  ],\n  \"platform\": \"GLt9\",\n  \"preference\": {\n    \"key\": \"nmPmsPjg C\",\n    \"value\": true\n  },\n  \"shipping_address\": [\n    {\n      \"name\": \"Loren Bechtelar\",\n      \"street_address\": \"Suite 526 293 Rohan Road, Wunschshire, NE 25532\",\n      \"city\": \"South Norrisland\",\n      \"postcode\": \"56863\"\n    }\n  ],\n  \"shipping_date\": \"2022-11-03\",\n  \"transaction_date\": \"2023-02-01\",\n  \"customer\": {\n    \"username\": \"lance.murphy\",\n    \"name\": \"Zane Brakus DVM\",\n    \"sex\": \"7HcAaPiO\",\n    \"address\": \"594 Loida Haven, Gilland, MA 26071\",\n    \"mail\": \"Un3fhbvK2rEbenIYdnq\",\n    \"birthdate\": \"2023-01-31\"\n  }\n}\n

    Looks like we have some data now. But we can do better and add some enhancements to it.

    "},{"location":"setup/guide/data-source/open-metadata-source/#custom-metadata","title":"Custom metadata","text":"

    We can see from the data generated, that it isn't quite what we want. The metadata is not sufficient for us to produce production-like data yet. Let's try to add some enhancements to it.

    Let's make the platform field a choice field that can only be a set of certain values and the nested field customer.sex is also from a predefined set of values.

    JavaScala 
    var jsonTask = json(\"my_json\", \"/opt/app/data/json\", Map.of(\"saveMode\", \"overwrite\"))\n            .schema(\n                metadata...\n            ))\n            .schema(\n                field().name(\"platform\").oneOf(\"website\", \"mobile\"),\n                field().name(\"customer\").schema(field().name(\"sex\").oneOf(\"M\", \"F\", \"O\"))\n            )\n            .count(count().records(10));\n
    val jsonTask = json(\"my_json\", \"/opt/app/data/json\", Map(\"saveMode\" -> \"overwrite\"))\n  .schema(\n    metadata...\n  ))\n  .schema(\n    field.name(\"platform\").oneOf(\"website\", \"mobile\"),\n    field.name(\"customer\").schema(field.name(\"sex\").oneOf(\"M\", \"F\", \"O\"))\n  )\n  .count(count.records(10))\n

    Let's test it out by running it again

    ./run.sh\n#input class MyAdvancedMetadataSourceJavaPlanRun or MyAdvancedMetadataSourcePlanRun\ncat docker/sample/json/part-00000-*\n
    {\n  \"comments\": \"vqbPUm\",\n  \"creditcard\": \"6304867705548636\",\n  \"membership\": \"GZ1xOnpZSUOKN\",\n  \"orders\": [\n    {\n      \"product_id\": \"rgOokDAv\",\n      \"price\": 77367,\n      \"onsale\": false,\n      \"tax\": 61742,\n      \"weight\": 87855,\n      \"others\": 26857,\n      \"vendor\": \"04XHR64ImMr9T\"\n    }\n  ],\n  \"platform\": \"mobile\",\n  \"preference\": {\n    \"key\": \"IB5vNdWka\",\n    \"value\": true\n  },\n  \"shipping_address\": [\n    {\n      \"name\": \"Isiah Bins\",\n      \"street_address\": \"36512 Ross Spurs, Hillhaven, IA 18760\",\n      \"city\": \"Averymouth\",\n      \"postcode\": \"75818\"\n    },\n    {\n      \"name\": \"Scott Prohaska\",\n      \"street_address\": \"26573 Haley Ports, Dariusland, MS 90642\",\n      \"city\": \"Ashantimouth\",\n      \"postcode\": \"31792\"\n    },\n    {\n      \"name\": \"Rudolf Stamm\",\n      \"street_address\": \"Suite 878 0516 Danica Path, New Christiaport, ID 10525\",\n      \"city\": \"Doreathaport\",\n      \"postcode\": \"62497\"\n    }\n  ],\n  \"shipping_date\": \"2023-08-24\",\n  \"transaction_date\": \"2023-02-01\",\n  \"customer\": {\n    \"username\": \"jolie.cremin\",\n    \"name\": \"Fay Klein\",\n    \"sex\": \"O\",\n    \"address\": \"Apt. 174 5084 Volkman Creek, Hillborough, PA 61959\",\n    \"mail\": \"BiTmzb7\",\n    \"birthdate\": \"2023-04-07\"\n  }\n}\n

    Great! Now we have the ability to get schema information from an external source, add our own metadata and generate data.

    "},{"location":"setup/guide/data-source/open-metadata-source/#data-validation","title":"Data validation","text":"

    Another aspect of OpenMetadata that can be leveraged is the definition of data quality rules. These rules can be incorporated into your Data Caterer job as well by enabling data validations via enableGenerateValidations in configuration.

    JavaScala 
    var conf = configuration().enableGeneratePlanAndTasks(true)\n    .enableGenerateValidations(true)\n    .generatedReportsFolderPath(\"/opt/app/data/report\");\n\nexecute(conf, jsonTask);\n
    val conf = configuration.enableGeneratePlanAndTasks(true)\n  .enableGenerateValidations(true)\n  .generatedReportsFolderPath(\"/opt/app/data/report\")\n\nexecute(conf, jsonTask)\n

    Check out the full example under AdvancedOpenMetadataSourcePlanRun in the example repo.

    "},{"location":"setup/guide/data-source/solace/","title":"Solace","text":"

    Info

    Writing data to Solace is a paid feature. Try the free trial here.

    Creating a data generator for Solace. You will build a Docker image that will be able to populate data in Solace for the queues/topics you configure.

    "},{"location":"setup/guide/data-source/solace/#requirements","title":"Requirements","text":"
    • 20 minutes
    • Git
    • Gradle
    • Docker
    • Solace
    "},{"location":"setup/guide/data-source/solace/#get-started","title":"Get Started","text":"

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    git clone git@github.com:pflooky/data-caterer-example.git\n

    If you already have a Solace instance running, you can skip to this step.

    "},{"location":"setup/guide/data-source/solace/#solace-setup","title":"Solace Setup","text":"

    Next, let's make sure you have an instance of Solace up and running in your local environment. This will make it easy for us to iterate and check our changes.

    cd docker\ndocker-compose up -d solace\n

    Open up localhost:8080 and login with admin:admin and check there is the default VPN like below. Notice there is 2 queues/topics created. If you do not see 2 created, try to run the script found under docker/data/solace/setup_solace.sh and change the host to localhost.

    "},{"location":"setup/guide/data-source/solace/#plan-setup","title":"Plan Setup","text":"

    Create a new Java or Scala class.

    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedSolaceJavaPlan.java
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedSolacePlan.scala

    Make sure your class extends PlanRun.

    JavaScala 
    import com.github.pflooky.datacaterer.java.api.PlanRun;\n\npublic class MyAdvancedSolaceJavaPlan extends PlanRun {\n}\n
    import com.github.pflooky.datacaterer.api.PlanRun\n\nclass MyAdvancedSolacePlan extends PlanRun {\n}\n

    This class defines where we need to define all of our configurations for generating data. There are helper variables and methods defined to make it simple and easy to use.

    "},{"location":"setup/guide/data-source/solace/#connection-configuration","title":"Connection Configuration","text":"

    Within our class, we can start by defining the connection properties to connect to Solace.

    JavaScala 
    var accountTask = solace(\n    \"my_solace\",                        //name\n    \"smf://host.docker.internal:55554\", //url\n    Map.of()                            //optional additional connection options\n);\n

    Additional connection options can be found here.

    val accountTask = solace(\n    \"my_solace\",                        //name\n    \"smf://host.docker.internal:55554\", //url\n    Map()                               //optional additional connection options\n)\n

    Additional connection options can be found here.

    "},{"location":"setup/guide/data-source/solace/#schema","title":"Schema","text":"

    Let's create a task for inserting data into the rest_test_queue or rest_test_topic that is already created for us from this step.

    Trimming the connection details to work with the docker-compose Solace, we have a base Solace connection to define the JNDI destination we will publish to. Let's define each field along with their corresponding data type. You will notice that the text fields do not have a data type defined. This is because the default data type is StringType.

    JavaScala 
    {\n    var solaceTask = solace(\"my_solace\", \"smf://host.docker.internal:55554\")\n            .destination(\"/JNDI/Q/rest_test_queue\")\n            .schema(\n                    field().name(\"value\").sql(\"TO_JSON(content)\"),\n                    //field().name(\"partition\").type(IntegerType.instance()),   //can define message JMS priority here\n                    field().name(\"headers\")                                     //set message properties via headers field\n                            .type(HeaderType.getType())\n                            .sql(\n                                    \"ARRAY(\" +\n                                            \"NAMED_STRUCT('key', 'account-id', 'value', TO_BINARY(content.account_id, 'utf-8')),\" +\n                                            \"NAMED_STRUCT('key', 'updated', 'value', TO_BINARY(content.details.updated_by.time, 'utf-8'))\" +\n                                            \")\"\n                            ),\n                    field().name(\"content\")\n                            .schema(\n                                    field().name(\"account_id\").regex(\"ACC[0-9]{8}\"),\n                                    field().name(\"year\").type(IntegerType.instance()).min(2021).max(2023),\n                                    field().name(\"amount\").type(DoubleType.instance()),\n                                    field().name(\"details\")\n                                            .schema(\n                                                    field().name(\"name\").expression(\"#{Name.name}\"),\n                                                    field().name(\"first_txn_date\").type(DateType.instance()).sql(\"ELEMENT_AT(SORT_ARRAY(content.transactions.txn_date), 1)\"),\n                                                    field().name(\"updated_by\")\n                                                            .schema(\n                                                                    field().name(\"user\"),\n                                                                    field().name(\"time\").type(TimestampType.instance())\n                                                            )\n                                            ),\n                                    field().name(\"transactions\").type(ArrayType.instance())\n                                            .schema(\n                                                    field().name(\"txn_date\").type(DateType.instance()).min(Date.valueOf(\"2021-01-01\")).max(\"2021-12-31\"),\n                                                    field().name(\"amount\").type(DoubleType.instance())\n                                            )\n                            )\n            )\n            .count(count().records(10));\n}\n
    val solaceTask = solace(\"my_solace\", \"smf://host.docker.internal:55554\")\n  .destination(\"/JNDI/Q/rest_test_queue\")\n  .schema(\n    field.name(\"value\").sql(\"TO_JSON(content)\"),\n    //field.name(\"partition\").`type`(IntegerType),  //can define message JMS priority here\n    field.name(\"headers\")                           //set message properties via headers field\n      .`type`(HeaderType.getType)\n      .sql(\n        \"\"\"ARRAY(\n          |  NAMED_STRUCT('key', 'account-id', 'value', TO_BINARY(content.account_id, 'utf-8')),\n          |  NAMED_STRUCT('key', 'updated', 'value', TO_BINARY(content.details.updated_by.time, 'utf-8'))\n          |)\"\"\".stripMargin\n      ),\n    field.name(\"content\")\n      .schema(\n        field.name(\"account_id\").regex(\"ACC[0-9]{8}\"),\n        field.name(\"year\").`type`(IntegerType).min(2021).max(2023),\n        field.name(\"amount\").`type`(DoubleType),\n        field.name(\"details\")\n          .schema(\n            field.name(\"name\").expression(\"#{Name.name}\"),\n            field.name(\"first_txn_date\").`type`(DateType).sql(\"ELEMENT_AT(SORT_ARRAY(content.transactions.txn_date), 1)\"),\n            field.name(\"updated_by\")\n              .schema(\n                field.name(\"user\"),\n                field.name(\"time\").`type`(TimestampType),\n              ),\n          ),\n        field.name(\"transactions\").`type`(ArrayType)\n          .schema(\n            field.name(\"txn_date\").`type`(DateType).min(Date.valueOf(\"2021-01-01\")).max(\"2021-12-31\"),\n            field.name(\"amount\").`type`(DoubleType),\n          )\n      ),\n  ).count(count.records(10))\n
    "},{"location":"setup/guide/data-source/solace/#fields","title":"Fields","text":"

    The schema defined for Solace has a format that needs to be followed as noted above. Specifically, the required fields are:

    • value

    Whilst, the other fields are optional:

    • partition - refers to JMS priority of the message
    • headers - refers to JMS message properties
    "},{"location":"setup/guide/data-source/solace/#headers","title":"headers","text":"

    headers follows a particular pattern that where it is of type HeaderType.getType which behind the scenes, translates toarray<struct<key: string,value: binary>>. To be able to generate data for this data type, we need to use an SQL expression like the one below. You will notice that in thevalue part, it refers to content.account_id where content is another field defined at the top level of the schema. This allows you to reference other values that have already been generated.

    JavaScala 
    field().name(\"headers\")\n        .type(HeaderType.getType())\n        .sql(\n                \"ARRAY(\" +\n                        \"NAMED_STRUCT('key', 'account-id', 'value', TO_BINARY(content.account_id, 'utf-8')),\" +\n                        \"NAMED_STRUCT('key', 'updated', 'value', TO_BINARY(content.details.updated_by.time, 'utf-8'))\" +\n                        \")\"\n        )\n
    field.name(\"headers\")\n  .`type`(HeaderType.getType)\n  .sql(\n    \"\"\"ARRAY(\n      |  NAMED_STRUCT('key', 'account-id', 'value', TO_BINARY(content.account_id, 'utf-8')),\n      |  NAMED_STRUCT('key', 'updated', 'value', TO_BINARY(content.details.updated_by.time, 'utf-8'))\n      |)\"\"\".stripMargin\n  )\n
    "},{"location":"setup/guide/data-source/solace/#transactions","title":"transactions","text":"

    transactions is an array that contains an inner structure of txn_date and amount. The size of the array generated can be controlled via arrayMinLength and arrayMaxLength.

    JavaScala 
    field().name(\"transactions\").type(ArrayType.instance())\n        .schema(\n                field().name(\"txn_date\").type(DateType.instance()).min(Date.valueOf(\"2021-01-01\")).max(\"2021-12-31\"),\n                field().name(\"amount\").type(DoubleType.instance())\n        )\n
    field.name(\"transactions\").`type`(ArrayType)\n  .schema(\n    field.name(\"txn_date\").`type`(DateType).min(Date.valueOf(\"2021-01-01\")).max(\"2021-12-31\"),\n    field.name(\"amount\").`type`(DoubleType),\n  )\n
    "},{"location":"setup/guide/data-source/solace/#details","title":"details","text":"

    details is another example of a nested schema structure where it also has a nested structure itself in updated_by. One thing to note here is the first_txn_date field has a reference to the content.transactions array where it will sort the array by txn_date and get the first element.

    JavaScala 
    field().name(\"details\")\n        .schema(\n                field().name(\"name\").expression(\"#{Name.name}\"),\n                field().name(\"first_txn_date\").type(DateType.instance()).sql(\"ELEMENT_AT(SORT_ARRAY(content.transactions.txn_date), 1)\"),\n                field().name(\"updated_by\")\n                        .schema(\n                                field().name(\"user\"),\n                                field().name(\"time\").type(TimestampType.instance())\n                        )\n        )\n
    field.name(\"details\")\n  .schema(\n    field.name(\"name\").expression(\"#{Name.name}\"),\n    field.name(\"first_txn_date\").`type`(DateType).sql(\"ELEMENT_AT(SORT_ARRAY(content.transactions.txn_date), 1)\"),\n    field.name(\"updated_by\")\n      .schema(\n        field.name(\"user\"),\n        field.name(\"time\").`type`(TimestampType),\n      ),\n  )\n
    "},{"location":"setup/guide/data-source/solace/#additional-configurations","title":"Additional Configurations","text":"

    At the end of data generation, a report gets generated that summarises the actions it performed. We can control the output folder of that report via configurations.

    JavaScala 
    var config = configuration()\n        .generatedReportsFolderPath(\"/opt/app/data/report\");\n
    val config = configuration\n  .generatedReportsFolderPath(\"/opt/app/data/report\")\n
    "},{"location":"setup/guide/data-source/solace/#execute","title":"Execute","text":"

    To tell Data Caterer that we want to run with the configurations along with the kafkaTask, we have to call execute.

    "},{"location":"setup/guide/data-source/solace/#run","title":"Run","text":"

    Now we can run via the script ./run.sh that is in the top level directory of the data-caterer-example to run the class we just created.

    ./run.sh\n#input class AdvancedSolaceJavaPlanRun or AdvancedSolacePlanRun\n#after completing, check http://localhost:8080 from browser\n

    Your output should look like this.

    Unfortunately, there is no easy way to see the message content. You can check the message content from your application or service that consumes these messages.

    Also check the HTML report, found at docker/sample/report/index.html, that gets generated to get an overview of what was executed. Or view the sample report found here.

    "},{"location":"setup/guide/scenario/auto-generate-connection/","title":"Auto Generate From Data Connection","text":"

    Info

    Auto data generation from data connection is a paid feature. Try the free trial here.

    Creating a data generator based on only a data connection to Postgres.

    "},{"location":"setup/guide/scenario/auto-generate-connection/#requirements","title":"Requirements","text":"
    • 5 minutes
    • Git
    • Gradle
    • Docker
    "},{"location":"setup/guide/scenario/auto-generate-connection/#get-started","title":"Get Started","text":"

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    git clone git@github.com:pflooky/data-caterer-example.git\n
    "},{"location":"setup/guide/scenario/auto-generate-connection/#plan-setup","title":"Plan Setup","text":"

    Create a new Java or Scala class.

    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedAutomatedJavaPlanRun.java
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedAutomatedPlanRun.scala

    Make sure your class extends PlanRun.

    JavaScala 
    import com.github.pflooky.datacaterer.java.api.PlanRun;\n...\n\npublic class MyAdvancedAutomatedJavaPlanRun extends PlanRun {\n    {\n        var autoRun = configuration()\n                .postgres(\"my_postgres\", \"jdbc:postgresql://host.docker.internal:5432/customer\")  (1)\n                .enableGeneratePlanAndTasks(true)                                                 (2)\n                .generatedPlanAndTaskFolderPath(\"/opt/app/data/generated\")                        (3)\n                .enableUniqueCheck(true)                                                          (4)\n                .generatedReportsFolderPath(\"/opt/app/data/report\");\n\n        execute(autoRun);\n    }\n}\n
    import com.github.pflooky.datacaterer.api.PlanRun\n...\n\nclass MyAdvancedAutomatedPlanRun extends PlanRun {\n\n  val autoRun = configuration\n    .postgres(\"my_postgres\", \"jdbc:postgresql://host.docker.internal:5432/customer\")  (1)\n    .enableGeneratePlanAndTasks(true)                                                 (2)\n    .generatedPlanAndTaskFolderPath(\"/opt/app/data/generated\")                        (3)\n    .enableUniqueCheck(true)                                                          (4)\n    .generatedReportsFolderPath(\"/opt/app/data/report\")\n\n  execute(configuration = autoRun)\n}\n

    In the above code, we note the following:

    1. Data source configuration to a Postgres data source called my_postgres
    2. We have enabled the flag enableGeneratePlanAndTasks which tells Data Caterer to go to my_postgres and generate data for all the tables found under the database customer (which is defined in the connection string).
    3. The config generatedPlanAndTaskFolderPath defines where the metadata that is gathered from my_postgres should be saved at so that we could re-use it later.
    4. enableUniqueCheck is set to true to ensure that generated data is unique based on primary key or foreign key definitions.

    Note

    Unique check will only ensure generated data is unique. Any existing data in your data source is not taken into account, so generated data may fail to insert depending on the data source restrictions

    "},{"location":"setup/guide/scenario/auto-generate-connection/#postgres-setup","title":"Postgres Setup","text":"

    If you don't have your own Postgres up and running, you can set up and run an instance configured in the docker folder via.

    cd docker\ndocker-compose up -d postgres\ndocker exec docker-postgresserver-1 psql -Upostgres -d customer -c '\\dt+ account.*'\n

    This will create the tables found under docker/data/sql/postgres/customer.sql. You can change this file to contain your own tables. We can see there are 4 tables created for us, accounts, balances, transactions and mapping.

    "},{"location":"setup/guide/scenario/auto-generate-connection/#run","title":"Run","text":"

    Let's try run.

    cd ..\n./run.sh\n#input class MyAdvancedAutomatedJavaPlanRun or MyAdvancedAutomatedPlanRun\n#after completing\ndocker exec docker-postgresserver-1 psql -Upostgres -d customer -c 'select * from account.accounts limit 1;'\n

    It should look something like this.

       id   | account_number  | account_status | created_by | created_by_fixed_length | customer_id_int | customer_id_smallint | customer_id_bigint |   customer_id_decimal    | customer_id_real | customer_id_double | open_date  |     open_timestamp      | last_opened_time |                                                           payload_bytes\n--------+-----------------+----------------+------------+-------------------------+-----------------+----------------------+--------------------+--------------------------+------------------+--------------------+------------+-------------------------+------------------+------------------------------------------------------------------------------------------------------------------------------------\n 100414 | 5uROOVOUyQUbubN | h3H            | SfA0eZJcTm | CuRw                    |              13 |                   42 |               6041 | 76987.745612542900000000 |         91866.78 |  66400.37433202339 | 2023-03-05 | 2023-08-14 11:33:11.343 | 23:58:01.736     | \\x604d315d4547616e6a233050415373317274736f5e682d516132524f3d23233c37463463322f342d34376d597e665d6b3d395b4238284028622b7d6d2b4f5042\n(1 row)\n

    The data that gets inserted will follow the foreign keys that are defined within Postgres and also ensure the insertion order is correct.

    Also check the HTML report that gets generated under docker/sample/report/index.html. You can see a summary of what was generated along with other metadata.

    You can now look to play around with other tables or data sources and auto generate for them.

    "},{"location":"setup/guide/scenario/auto-generate-connection/#additional-topics","title":"Additional Topics","text":""},{"location":"setup/guide/scenario/auto-generate-connection/#learn-from-existing-data","title":"Learn From Existing Data","text":"

    If you have any existing data within your data source, Data Caterer will gather metadata about the existing data to help guide it when generating new data. There are configurations that can help tune the metadata analysis found here.

    "},{"location":"setup/guide/scenario/auto-generate-connection/#filter-out-schematables","title":"Filter Out Schema/Tables","text":"

    As part of your connection definition, you can define any schemas and/or tables your don't want to generate data for. In the example below, it will not generate any data for any tables under the history and audit schemas. Also, any table with the name balances or transactions in any schema will also not have data generated.

    JavaScala 
    var autoRun = configuration()\n        .postgres(\n              \"my_postgres\", \n              \"jdbc:postgresql://host.docker.internal:5432/customer\",\n              Map.of(\n                  \"filterOutSchema\", \"history, audit\",\n                  \"filterOutTable\", \"balances, transactions\")\n              )\n        )\n
    val autoRun = configuration\n  .postgres(\n    \"my_postgres\",\n    \"jdbc:postgresql://host.docker.internal:5432/customer\",\n    Map(\n      \"filterOutSchema\" -> \"history, audit\",\n      \"filterOutTable\" -> \"balances, transactions\")\n    )\n  )\n
    "},{"location":"setup/guide/scenario/auto-generate-connection/#define-record-count","title":"Define record count","text":"

    You can control the record count per sub data source via numRecordsPerStep.

    JavaScala 
    var autoRun = configuration()\n      ...\n      .numRecordsPerStep(100)\n\nexecute(autoRun)\n
    val autoRun = configuration\n  ...\n  .numRecordsPerStep(100)\n\nexecute(configuration = autoRun)\n
    "},{"location":"setup/guide/scenario/batch-and-event/","title":"Generate Batch and Event Data","text":"

    Info

    Generating event data is a paid feature. Try the free trial here.

    Creating a data generator for Kafka topic with matching records in a CSV file.

    "},{"location":"setup/guide/scenario/batch-and-event/#requirements","title":"Requirements","text":"
    • 5 minutes
    • Git
    • Gradle
    • Docker
    "},{"location":"setup/guide/scenario/batch-and-event/#get-started","title":"Get Started","text":"

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    git clone git@github.com:pflooky/data-caterer-example.git\n
    "},{"location":"setup/guide/scenario/batch-and-event/#kafka-setup","title":"Kafka Setup","text":"

    If you don't have your own Kafka up and running, you can set up and run an instance configured in the docker folder via.

    cd docker\ndocker-compose up -d kafka\ndocker exec docker-kafkaserver-1 kafka-topics --bootstrap-server localhost:9092 --list\n

    Let's create a task for inserting data into the account-topic that is already defined underdocker/data/kafka/setup_kafka.sh.

    "},{"location":"setup/guide/scenario/batch-and-event/#plan-setup","title":"Plan Setup","text":"

    Create a new Java or Scala class.

    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedBatchEventJavaPlanRun.java
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedBatchEventPlanRun.scala

    Make sure your class extends PlanRun.

    JavaScala 
    import com.github.pflooky.datacaterer.java.api.PlanRun;\n...\n\npublic class MyAdvancedBatchEventJavaPlanRun extends PlanRun {\n    {\n        var kafkaTask = new AdvancedKafkaJavaPlanRun().getKafkaTask();\n    }\n}\n
    import com.github.pflooky.datacaterer.api.PlanRun\n...\n\nclass MyAdvancedBatchEventPlanRun extends PlanRun {\n  val kafkaTask = new AdvancedKafkaPlanRun().kafkaTask\n}\n

    We will borrow the Kafka task that is already defined under the class AdvancedKafkaPlanRun or AdvancedKafkaJavaPlanRun. You can go through the Kafka guide here for more details.

    "},{"location":"setup/guide/scenario/batch-and-event/#schema","title":"Schema","text":"

    Let us set up the corresponding schema for the CSV file where we want to match the values that are generated for the Kafka messages.

    JavaScala 
    var kafkaTask = new AdvancedKafkaJavaPlanRun().getKafkaTask();\n\nvar csvTask = csv(\"my_csv\", \"/opt/app/data/csv/account\")\n        .schema(\n                field().name(\"account_number\"),\n                field().name(\"year\"),\n                field().name(\"name\"),\n                field().name(\"payload\")\n        );\n
    val kafkaTask = new AdvancedKafkaPlanRun().kafkaTask\n\nval csvTask = csv(\"my_csv\", \"/opt/app/data/csv/account\")\n  .schema(\n    field.name(\"account_number\"),\n    field.name(\"year\"),\n    field.name(\"name\"),\n    field.name(\"payload\")\n)\n

    This is a simple schema where we want to use the values and metadata that is already defined in the kafkaTask to determine what the data will look like for the CSV file. Even if we defined some metadata here, it would be overridden when we define our foreign key relationships.

    "},{"location":"setup/guide/scenario/batch-and-event/#foreign-keys","title":"Foreign Keys","text":"

    From the above CSV schema, we see note the following against the Kafka schema:

    • account_number in CSV needs to match with the account_id in Kafka
      • We see that account_id is referred to in the key column as field.name(\"key\").sql(\"content.account_id\")
    • year needs to match with content.year in Kafka, which is a nested field
      • We can only do foreign key relationships with top level fields, not nested fields. So we define a new column called tmp_year which will not appear in the final output for the Kafka messages but is used as an intermediate step field.name(\"tmp_year\").sql(\"content.year\").omit(true)
    • name needs to match with content.details.name in Kafka, also a nested field
      • Using the same logic as above, we define a temporary column called tmp_name which will take the value of the nested field but will be omitted field.name(\"tmp_name\").sql(\"content.details.name\").omit(true)
    • payload represents the whole JSON message sent to Kafka, which matches to value column

    Our foreign keys are therefore defined like below. Order is important when defining the list of columns. The index needs to match with the corresponding column in the other data source.

    JavaScala 
    var myPlan = plan().addForeignKeyRelationship(\n        kafkaTask, List.of(\"key\", \"tmp_year\", \"tmp_name\", \"value\"),\n        List.of(Map.entry(csvTask, List.of(\"account_number\", \"year\", \"name\", \"payload\")))\n);\n\nvar conf = configuration()\n      .generatedReportsFolderPath(\"/opt/app/data/report\");\n\nexecute(myPlan, conf, kafkaTask, csvTask);\n
    val myPlan = plan.addForeignKeyRelationship(\n    kafkaTask, List(\"key\", \"tmp_year\", \"tmp_name\", \"value\"),\n    List(csvTask -> List(\"account_number\", \"year\", \"name\", \"payload\"))\n)\n\nval conf = configuration.generatedReportsFolderPath(\"/opt/app/data/report\")\n\nexecute(myPlan, conf, kafkaTask, csvTask)\n
    "},{"location":"setup/guide/scenario/batch-and-event/#run","title":"Run","text":"

    Let's try run.

    cd ..\n./run.sh\n#input class MyAdvancedBatchEventJavaPlanRun or MyAdvancedBatchEventPlanRun\n#after completing\ndocker exec docker-kafkaserver-1 kafka-console-consumer --bootstrap-server localhost:9092 --topic account-topic --from-beginning\n

    It should look something like this.

    {\"account_id\":\"ACC03093143\",\"year\":2023,\"amount\":87990.37196728592,\"details\":{\"name\":\"Nadine Heidenreich Jr.\",\"first_txn_date\":\"2021-11-09\",\"updated_by\":{\"user\":\"YfEyJCe8ohrl0j IfyT\",\"time\":\"2022-09-26T20:47:53.404Z\"}},\"transactions\":[{\"txn_date\":\"2021-11-09\",\"amount\":97073.7914706189}]}\n{\"account_id\":\"ACC08764544\",\"year\":2021,\"amount\":28675.58758765888,\"details\":{\"name\":\"Delila Beer\",\"first_txn_date\":\"2021-05-19\",\"updated_by\":{\"user\":\"IzB5ksXu\",\"time\":\"2023-01-26T20:47:26.389Z\"}},\"transactions\":[{\"txn_date\":\"2021-10-01\",\"amount\":80995.23818711648},{\"txn_date\":\"2021-05-19\",\"amount\":92572.40049217848},{\"txn_date\":\"2021-12-11\",\"amount\":99398.79832225188}]}\n{\"account_id\":\"ACC62505420\",\"year\":2023,\"amount\":96125.3125884202,\"details\":{\"name\":\"Shawn Goodwin\",\"updated_by\":{\"user\":\"F3dqIvYp2pFtena4\",\"time\":\"2023-02-11T04:38:29.832Z\"}},\"transactions\":[]}\n

    Let's also check if there is a corresponding record in the CSV file.

    $ cat docker/sample/csv/account/part-0000* | grep ACC03093143\nACC03093143,2023,Nadine Heidenreich Jr.,\"{\\\"account_id\\\":\\\"ACC03093143\\\",\\\"year\\\":2023,\\\"amount\\\":87990.37196728592,\\\"details\\\":{\\\"name\\\":\\\"Nadine Heidenreich Jr.\\\",\\\"first_txn_date\\\":\\\"2021-11-09\\\",\\\"updated_by\\\":{\\\"user\\\":\\\"YfEyJCe8ohrl0j IfyT\\\",\\\"time\\\":\\\"2022-09-26T20:47:53.404Z\\\"}},\\\"transactions\\\":[{\\\"txn_date\\\":\\\"2021-11-09\\\",\\\"amount\\\":97073.7914706189}]}\"\n

    Great! The account, year, name and payload look to all match up.

    "},{"location":"setup/guide/scenario/batch-and-event/#additional-topics","title":"Additional Topics","text":""},{"location":"setup/guide/scenario/batch-and-event/#order-of-execution","title":"Order of execution","text":"

    You may notice that the events are generated first, then the CSV file. This is because as part of the execute function, we passed in the kafkaTask first, before the csvTask. You can change the order of execution by passing in csvTask before kafkaTask into the execute function.

    "},{"location":"setup/guide/scenario/data-validation/","title":"Data Validations","text":"

    Creating a data validator for a JSON file.

    "},{"location":"setup/guide/scenario/data-validation/#requirements","title":"Requirements","text":"
    • 5 minutes
    • Git
    • Gradle
    • Docker
    "},{"location":"setup/guide/scenario/data-validation/#get-started","title":"Get Started","text":"

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    git clone git@github.com:pflooky/data-caterer-example.git\n
    "},{"location":"setup/guide/scenario/data-validation/#data-setup","title":"Data Setup","text":"

    To aid in showing the functionality of data validations, we will first generate some data that our validations will run against. Run the below command and it will generate JSON files under docker/sample/json folder.

    ./run.sh JsonPlan\n
    "},{"location":"setup/guide/scenario/data-validation/#plan-setup","title":"Plan Setup","text":"

    Create a new Java or Scala class.

    • Java: src/main/java/com/github/pflooky/plan/MyValidationJavaPlan.java
    • Scala: src/main/scala/com/github/pflooky/plan/MyValidationPlan.scala

    Make sure your class extends PlanRun.

    JavaScala 
    import com.github.pflooky.datacaterer.java.api.PlanRun;\n...\n\npublic class MyValidationJavaPlan extends PlanRun {\n    {\n        var jsonTask = json(\"my_json\", \"/opt/app/data/json\");\n\n        var config = configuration()\n                .generatedReportsFolderPath(\"/opt/app/data/report\")\n                .enableValidation(true)\n                .enableGenerateData(false);\n\n        execute(config, jsonTask);\n    }\n}\n
    import com.github.pflooky.datacaterer.api.PlanRun\n...\n\nclass MyValidationPlan extends PlanRun {\n  val jsonTask = json(\"my_json\", \"/opt/app/data/json\")\n\n  val config = configuration\n    .generatedReportsFolderPath(\"/opt/app/data/report\")\n    .enableValidation(true)\n    .enableGenerateData(false)\n\n  execute(config, jsonTask)\n}\n

    As noted above, we create a JSON task that points to where the JSON data has been created at folder /opt/app/data/json . We also note that enableValidation is set to true and enableGenerateData to false to tell Data Catering, we only want to validate data.

    "},{"location":"setup/guide/scenario/data-validation/#validations","title":"Validations","text":"

    For reference, the schema in which we will be validating against looks like the below.

    .schema(\n  field.name(\"account_id\"),\n  field.name(\"year\").`type`(IntegerType),\n  field.name(\"balance\").`type`(DoubleType),\n  field.name(\"date\").`type`(DateType),\n  field.name(\"status\"),\n  field.name(\"update_history\").`type`(ArrayType)\n    .schema(\n      field.name(\"updated_time\").`type`(TimestampType),\n      field.name(\"status\").oneOf(\"open\", \"closed\", \"pending\", \"suspended\"),\n    ),\n  field.name(\"customer_details\")\n    .schema(\n      field.name(\"name\").expression(\"#{Name.name}\"),\n      field.name(\"age\").`type`(IntegerType),\n      field.name(\"city\").expression(\"#{Address.city}\")\n    )\n)\n
    "},{"location":"setup/guide/scenario/data-validation/#basic-validation","title":"Basic Validation","text":"

    Let's say our goal is to validate the customer_details.name field to ensure it conforms to the regex pattern [A-Z][a-z]+ [A-Z][a-z]+. Given the diversity in naming conventions across cultures and countries, variations such as middle names, suffixes, prefixes, or language-specific differences are tolerated to a certain extent. The validation considers an acceptable error threshold before marking it as failed.

    "},{"location":"setup/guide/scenario/data-validation/#validation-criteria","title":"Validation Criteria","text":"
    • Field to Validate: customer_details.name
    • Regex Pattern: [A-Z][a-z]+ [A-Z][a-z]+
    • Error Tolerance: If more than 10% do not match the regex, then fail.
    "},{"location":"setup/guide/scenario/data-validation/#considerations","title":"Considerations","text":"
    • Customisation
      • Adjust the regex pattern and error threshold based on your specific data schema and validation requirements.
      • For the full list of types of basic validations that can be used, check this page.
    • Understanding Tolerance
      • Be mindful of the error threshold, as it directly influences what percentage of deviations from the pattern is acceptable.
    JavaScala 
    validation().col(\"customer_details.name\")\n    .matches(\"[A-Z][a-z]+ [A-Z][a-z]+\")\n    .errorThreshold(0.1)                                      //<=10% failure rate is acceptable\n    .description(\"Names generally follow the same pattern\"),  //description to add context in report or other developers\n
    validation.col(\"customer_details.name\")\n  .matches(\"[A-Z][a-z]+ [A-Z][a-z]+\")\n  .errorThreshold(0.1)                                      //<=10% failure rate is acceptable\n  .description(\"Names generally follow the same pattern\"),  //description to add context in report or other developers\n
    "},{"location":"setup/guide/scenario/data-validation/#custom-validation","title":"Custom Validation","text":"

    There will be situation where you have a complex data setup and require you own custom logic to use for data validation. You can achieve this via setting your own SQL expression that returns a boolean value. An example is seen below where we want to check the array update_history, that each entry has updated_time greater than a certain timestamp.

    JavaScala 
    validation().expr(\"FORALL(update_history, x -> x.updated_time > TIMESTAMP('2022-01-01 00:00:00'))\"),\n
    validation.expr(\"FORALL(update_history, x -> x.updated_time > TIMESTAMP('2022-01-01 00:00:00'))\"),\n

    If you want to know what other SQL function are available for you to use, check this page.

    "},{"location":"setup/guide/scenario/data-validation/#group-by-validation","title":"Group By Validation","text":"

    There are scenarios where you want to validate against grouped values or the whole dataset via aggregations. An example would be validating that each customer's transactions sum is greater than 0.

    "},{"location":"setup/guide/scenario/data-validation/#validation-criteria_1","title":"Validation Criteria","text":"

    Line 1: validation.groupBy().count().isEqual(100)

    • Method Chaining
      • groupBy(): Group by whole dataset.
      • count(): Counts the number of dataset elements.
      • isEqual(100): Checks if the count is equal to 100.
    • Validation Rule
      • This line ensures that the count of the total dataset is exactly 100.

    Line 2: validation.groupBy(\"account_id\").max(\"balance\").lessThan(900)

    • Method Chaining
      • groupBy(\"account_id\"): Groups the data based on the account_id field.
      • max(\"balance\"): Calculates the maximum value of the balance field within each group.
      • lessThan(900): Checks if the maximum balance in each group is less than 900.
    • Validation Rule
      • This line ensures that, for each group identified by account_id the maximum balance is less than 900.
    "},{"location":"setup/guide/scenario/data-validation/#considerations_1","title":"Considerations","text":"
    • Adjust the errorThreshold or validation to your specification scenario. The full list of types of validations can be found here.
    • For the full list of types of group by validations that can be used, check this page.
    JavaScala 
    validation().groupBy().count().isEqual(100),\nvalidation().groupBy(\"account_id\").max(\"balance\").lessThan(900)\n
    validation.groupBy().count().isEqual(100),\nvalidation.groupBy(\"account_id\").max(\"balance\").lessThan(900)\n
    "},{"location":"setup/guide/scenario/data-validation/#sample-validation","title":"Sample Validation","text":"

    To try cover the majority of validation cases, the below has been created.

    JavaScala 
    var jsonTask = json(\"my_json\", \"/opt/app/data/json\")\n        .validations(\n                validation().col(\"customer_details.name\").matches(\"[A-Z][a-z]+ [A-Z][a-z]+\").errorThreshold(0.1).description(\"Names generally follow the same pattern\"),\n                validation().col(\"date\").isNotNull().errorThreshold(10),\n                validation().col(\"balance\").greaterThan(500),\n                validation().expr(\"YEAR(date) == year\"),\n                validation().col(\"status\").in(\"open\", \"closed\", \"pending\").errorThreshold(0.2).description(\"Could be new status introduced\"),\n                validation().col(\"customer_details.age\").greaterThan(18),\n                validation().expr(\"FORALL(update_history, x -> x.updated_time > TIMESTAMP('2022-01-01 00:00:00'))\"),\n                validation().col(\"update_history\").greaterThanSize(2),\n                validation().unique(\"account_id\"),\n                validation().groupBy().count().isEqual(1000),\n                validation().groupBy(\"account_id\").max(\"balance\").lessThan(900)\n        );\n\nvar config = configuration()\n        .generatedReportsFolderPath(\"/opt/app/data/report\")\n        .enableValidation(true)\n        .enableGenerateData(false);\n\nexecute(config, jsonTask);\n
    val jsonTask = json(\"my_json\", \"/opt/app/data/json\")\n  .validations(\n    validation.col(\"customer_details.name\").matches(\"[A-Z][a-z]+ [A-Z][a-z]+\").errorThreshold(0.1).description(\"Names generally follow the same pattern\"),\n    validation.col(\"date\").isNotNull.errorThreshold(10),\n    validation.col(\"balance\").greaterThan(500),\n    validation.expr(\"YEAR(date) == year\"),\n    validation.col(\"status\").in(\"open\", \"closed\", \"pending\").errorThreshold(0.2).description(\"Could be new status introduced\"),\n    validation.col(\"customer_details.age\").greaterThan(18),\n    validation.expr(\"FORALL(update_history, x -> x.updated_time > TIMESTAMP('2022-01-01 00:00:00'))\"),\n    validation.col(\"update_history\").greaterThanSize(2),\n    validation.unique(\"account_id\"),\n    validation.groupBy().count().isEqual(1000),\n    validation.groupBy(\"account_id\").max(\"balance\").lessThan(900)\n  )\n\nval config = configuration\n  .generatedReportsFolderPath(\"/opt/app/data/report\")\n  .enableValidation(true)\n  .enableGenerateData(false)\n\nexecute(config, jsonTask)\n
    "},{"location":"setup/guide/scenario/data-validation/#run","title":"Run","text":"

    Let's try run.

    ./run.sh\n#input class MyValidationJavaPlan or MyValidationPlan\n#after completing, check report at docker/sample/report/index.html\n

    It should look something like this.

    Check the full example at ValidationPlanRun inside the examples repo.

    "},{"location":"setup/guide/scenario/delete-generated-data/","title":"Delete Generated Data","text":"

    Info

    Delete generated data is a paid feature. Try the free trial here.

    Creating a data generator for Postgres and delete the generated data after using it.

    "},{"location":"setup/guide/scenario/delete-generated-data/#requirements","title":"Requirements","text":"
    • 5 minutes
    • Git
    • Gradle
    • Docker
    "},{"location":"setup/guide/scenario/delete-generated-data/#get-started","title":"Get Started","text":"

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    git clone git@github.com:pflooky/data-caterer-example.git\n
    "},{"location":"setup/guide/scenario/delete-generated-data/#plan-setup","title":"Plan Setup","text":"

    Create a new Java or Scala class.

    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedDeleteJavaPlanRun.java
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedDeletePlanRun.scala

    Make sure your class extends PlanRun.

    JavaScala 
    import com.github.pflooky.datacaterer.java.api.PlanRun;\n...\n\npublic class MyAdvancedDeleteJavaPlanRun extends PlanRun {\n    {\n        var autoRun = configuration()\n                .postgres(\"my_postgres\", \"jdbc:postgresql://host.docker.internal:5432/customer\")  (1)\n                .enableGeneratePlanAndTasks(true)                                                 (2)\n                .enableRecordTracking(true)                                                       (3)\n                .enableDeleteGeneratedRecords(false)                                              (4)\n                .enableUniqueCheck(true)\n                .generatedPlanAndTaskFolderPath(\"/opt/app/data/generated\")                        (5)\n                .recordTrackingFolderPath(\"/opt/app/data/recordTracking\")                         (6)\n                .generatedReportsFolderPath(\"/opt/app/data/report\");\n\n        execute(autoRun);\n   }\n}\n
    import com.github.pflooky.datacaterer.api.PlanRun\n...\n\nclass MyAdvancedDeletePlanRun extends PlanRun {\n\n  val autoRun = configuration\n    .postgres(\"my_postgres\", \"jdbc:postgresql://host.docker.internal:5432/customer\")  (1)\n    .enableGeneratePlanAndTasks(true)                                                 (2)\n    .enableRecordTracking(true)                                                       (3)\n    .enableDeleteGeneratedRecords(false)                                              (4)\n    .enableUniqueCheck(true)\n    .generatedPlanAndTaskFolderPath(\"/opt/app/data/generated\")                        (5)\n    .recordTrackingFolderPath(\"/opt/app/data/recordTracking\")                         (6)\n    .generatedReportsFolderPath(\"/opt/app/data/report\")\n\n  execute(configuration = autoRun)\n}\n

    In the above code we note the following:

    1. We have defined a Postgres connection called my_postgres
    2. enableGeneratePlanAndTasks is enabled to auto generate data for all tables under customer database
    3. enableRecordTracking is enabled to ensure that all generated records are tracked. This will get used when we want to delete data afterwards
    4. enableDeleteGeneratedRecords is disabled for now. We want to see the generated data first and delete sometime after
    5. generatedPlanAndTaskFolderPath is the folder path where we saved the metadata we have gathered from my_postgres
    6. recordTrackingFolderPath is the folder path where record tracking is maintained. We need to persist this data to ensure it is still available when we want to delete data
    "},{"location":"setup/guide/scenario/delete-generated-data/#postgres-setup","title":"Postgres Setup","text":"

    If you don't have your own Postgres up and running, you can set up and run an instance configured in the docker folder via.

    cd docker\ndocker-compose up -d postgres\ndocker exec docker-postgresserver-1 psql -Upostgres -d customer -c '\\dt+ account.*'\n

    This will create the tables found under docker/data/sql/postgres/customer.sql. You can change this file to contain your own tables. We can see there are 4 tables created for us, accounts, balances, transactions and mapping.

    "},{"location":"setup/guide/scenario/delete-generated-data/#run","title":"Run","text":"

    Let's try run.

    cd ..\n./run.sh\n#input class MyAdvancedDeleteJavaPlanRun or MyAdvancedDeletePlanRun\n#after completing\ndocker exec docker-postgresserver-1 psql -Upostgres -d customer -c 'select * from account.accounts limit 1'\n

    It should look something like this.

       id   | account_number  | account_status | created_by | created_by_fixed_length | customer_id_int | customer_id_smallint | customer_id_bigint |   customer_id_decimal    | customer_id_real | customer_id_double | open_date  |     open_timestamp      | last_opened_time |                                                           payload_bytes\n--------+-----------------+----------------+------------+-------------------------+-----------------+----------------------+--------------------+--------------------------+------------------+--------------------+------------+-------------------------+------------------+------------------------------------------------------------------------------------------------------------------------------------\n 100414 | 5uROOVOUyQUbubN | h3H            | SfA0eZJcTm | CuRw                    |              13 |                   42 |               6041 | 76987.745612542900000000 |         91866.78 |  66400.37433202339 | 2023-03-05 | 2023-08-14 11:33:11.343 | 23:58:01.736     | \\x604d315d4547616e6a233050415373317274736f5e682d516132524f3d23233c37463463322f342d34376d597e665d6b3d395b4238284028622b7d6d2b4f5042\n(1 row)\n

    The data that gets inserted will follow the foreign keys that are defined within Postgres and also ensure the insertion order is correct.

    Check the number of records via:

    docker exec docker-postgresserver-1 psql -Upostgres -d customer -c 'select count(1) from account.accounts'\n#open report under docker/sample/report/index.html\n
    "},{"location":"setup/guide/scenario/delete-generated-data/#delete","title":"Delete","text":"

    We are now at a stage where we want to delete the data that was generated. All we need to do is flip two flags.

    .enableDeleteGeneratedRecords(true)\n.enableGenerateData(false)  //we need to explicitly disable generating data\n

    Enable delete generated records and disable generating data.

    Before we run again, let us insert a record manually to see if that data will survive after running the job to delete the generated data.

    docker exec docker-postgresserver-1 psql -Upostgres -d customer -c \"insert into account.accounts (account_number) values ('my_account_number')\"\ndocker exec docker-postgresserver-1 psql -Upostgres -d customer -c \"select count(1) from account.accounts\"\n

    We now should have 1001 records in our account.accounts table. Let's delete the generated data now.

    ./run.sh\n#input class MyAdvancedDeleteJavaPlanRun or MyAdvancedDeletePlanRun\n#after completing\ndocker exec docker-postgresserver-1 psql -Upostgres -d customer -c 'select * from account.accounts limit 1'\ndocker exec docker-postgresserver-1 psql -Upostgres -d customer -c 'select count(1) from account.accounts'\n

    You should see that only 1 record is left, the one that we manually inserted. Great, now we can generate data reliably and also be able to clean it up.

    "},{"location":"setup/guide/scenario/delete-generated-data/#additional-topics","title":"Additional Topics","text":""},{"location":"setup/guide/scenario/delete-generated-data/#one-class-for-generating-another-for-deleting","title":"One class for generating, another for deleting?","text":"

    Yes, this is possible. There are two requirements: - the connection names used need to be the same across both classes - recordTrackingFolderPath needs to be set to the same value

    "},{"location":"setup/guide/scenario/delete-generated-data/#define-record-count","title":"Define record count","text":"

    You can control the record count per sub data source via numRecordsPerStep.

    JavaScala 
    var autoRun = configuration()\n      ...\n      .numRecordsPerStep(100)\n\nexecute(autoRun)\n
    val autoRun = configuration\n  ...\n  .numRecordsPerStep(100)\n\nexecute(configuration = autoRun)\n
    "},{"location":"setup/guide/scenario/first-data-generation/","title":"First Data Generation","text":"

    Creating a data generator for a CSV file.

    "},{"location":"setup/guide/scenario/first-data-generation/#requirements","title":"Requirements","text":"
    • 20 minutes
    • Git
    • Gradle
    • Docker
    "},{"location":"setup/guide/scenario/first-data-generation/#get-started","title":"Get Started","text":"

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    git clone git@github.com:pflooky/data-caterer-example.git\n
    "},{"location":"setup/guide/scenario/first-data-generation/#plan-setup","title":"Plan Setup","text":"

    Create a new Java or Scala class.

    • Java: src/main/java/com/github/pflooky/plan/MyCsvPlan.java
    • Scala: src/main/scala/com/github/pflooky/plan/MyCsvPlan.scala

    Make sure your class extends PlanRun.

    JavaScala 
    import com.github.pflooky.datacaterer.java.api.PlanRun;\n\npublic class MyCsvJavaPlan extends PlanRun {\n}\n
    import com.github.pflooky.datacaterer.api.PlanRun\n\nclass MyCsvPlan extends PlanRun {\n}\n

    This class defines where we need to define all of our configurations for generating data. There are helper variables and methods defined to make it simple and easy to use.

    "},{"location":"setup/guide/scenario/first-data-generation/#connection-configuration","title":"Connection Configuration","text":"

    When dealing with CSV files, we need to define a path for our generated CSV files to be saved at, along with any other high level configurations.

    JavaScala 
    csv(\n  \"customer_accounts\",              //name\n  \"/opt/app/data/customer/account\", //path\n  Map.of(\"header\", \"true\")          //optional additional options\n)\n

    Other additional options for CSV can be found here

    csv(\n  \"customer_accounts\",              //name\n  \"/opt/app/data/customer/account\", //path\n  Map(\"header\" -> \"true\")           //optional additional options\n)\n

    Other additional options for CSV can be found here

    "},{"location":"setup/guide/scenario/first-data-generation/#schema","title":"Schema","text":"

    Our CSV file that we generate should adhere to a defined schema where we can also define data types.

    Let's define each field along with their corresponding data type. You will notice that the string fields do not have a data type defined. This is because the default data type is StringType.

    JavaScala 
    var accountTask = csv(\"customer_accounts\", \"/opt/app/data/customer/account\", Map.of(\"header\", \"true\"))\n        .schema(\n                field().name(\"account_id\"),\n                field().name(\"balance\").type(DoubleType.instance()),\n                field().name(\"created_by\"),\n                field().name(\"name\"),\n                field().name(\"open_time\").type(TimestampType.instance()),\n                field().name(\"status\")\n        );\n
    val accountTask = csv(\"customer_accounts\", \"/opt/app/data/customer/account\", Map(\"header\" -> \"true\"))\n  .schema(\n    field.name(\"account_id\"),\n    field.name(\"balance\").`type`(DoubleType),\n    field.name(\"created_by\"),\n    field.name(\"name\"),\n    field.name(\"open_time\").`type`(TimestampType),\n    field.name(\"status\")\n  )\n
    "},{"location":"setup/guide/scenario/first-data-generation/#field-metadata","title":"Field Metadata","text":"

    We could stop here and generate random data for the accounts table. But wouldn't it be more useful if we produced data that is closer to the structure of the data that would come in production? We can do this by defining various metadata attributes that add guidelines that the data generator will understand when generating data.

    "},{"location":"setup/guide/scenario/first-data-generation/#account_id","title":"account_id","text":"
    • account_id follows a particular pattern that where it starts with ACC and has 8 digits after it. This can be defined via a regex like below. Alongside, we also mention that values are unique ensure that unique values are generated.
    JavaScala 
    field().name(\"account_id\").regex(\"ACC[0-9]{8}\").unique(true),\n
    field.name(\"account_id\").regex(\"ACC[0-9]{8}\").unique(true),\n
    "},{"location":"setup/guide/scenario/first-data-generation/#balance","title":"balance","text":"
    • balance let's make the numbers not too large, so we can define a min and max for the generated numbers to be between 1 and 1000.
    JavaScala 
    field().name(\"balance\").type(DoubleType.instance()).min(1).max(1000),\n
    field.name(\"balance\").`type`(DoubleType).min(1).max(1000),\n
    "},{"location":"setup/guide/scenario/first-data-generation/#name","title":"name","text":"
    • name is a string that also follows a certain pattern, so we could also define a regex but here we will choose to leverage the DataFaker library and create an expression to generate real looking name. All possible faker expressions can be found here
    JavaScala 
    field().name(\"name\").expression(\"#{Name.name}\"),\n
    field.name(\"name\").expression(\"#{Name.name}\"),\n
    "},{"location":"setup/guide/scenario/first-data-generation/#open_time","title":"open_time","text":"
    • open_time is a timestamp that we want to have a value greater than a specific date. We can define a min date by using java.sql.Date like below.
    JavaScala 
    field().name(\"open_time\").type(TimestampType.instance()).min(java.sql.Date.valueOf(\"2022-01-01\")),\n
    field.name(\"open_time\").`type`(TimestampType).min(java.sql.Date.valueOf(\"2022-01-01\")),\n
    "},{"location":"setup/guide/scenario/first-data-generation/#status","title":"status","text":"
    • status is a field that can only obtain one of four values, open, closed, suspended or pending.
    JavaScala 
    field().name(\"status\").oneOf(\"open\", \"closed\", \"suspended\", \"pending\")\n
    field.name(\"status\").oneOf(\"open\", \"closed\", \"suspended\", \"pending\")\n
    "},{"location":"setup/guide/scenario/first-data-generation/#created_by","title":"created_by","text":"
    • created_by is a field that is based on the status field where it follows the logic: if status is open or closed, then it is created_by eod else created_by event. This can be achieved by defining a SQL expression like below.
    JavaScala 
    field().name(\"created_by\").sql(\"CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END\"),\n
    field.name(\"created_by\").sql(\"CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END\"),\n

    Putting it all the fields together, our class should now look like this.

    JavaScala 
    var accountTask = csv(\"customer_accounts\", \"/opt/app/data/customer/account\", Map.of(\"header\", \"true\"))\n        .schema(\n                field().name(\"account_id\").regex(\"ACC[0-9]{8}\").unique(true),\n                field().name(\"balance\").type(DoubleType.instance()).min(1).max(1000),\n                field().name(\"created_by\").sql(\"CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END\"),\n                field().name(\"name\").expression(\"#{Name.name}\"),\n                field().name(\"open_time\").type(TimestampType.instance()).min(java.sql.Date.valueOf(\"2022-01-01\")),\n                field().name(\"status\").oneOf(\"open\", \"closed\", \"suspended\", \"pending\")\n        );\n
    val accountTask = csv(\"customer_accounts\", \"/opt/app/data/customer/account\", Map(\"header\" -> \"true\"))\n  .schema(\n    field.name(\"account_id\").regex(\"ACC[0-9]{8}\").unique(true),\n    field.name(\"balance\").`type`(DoubleType).min(1).max(1000),\n    field.name(\"created_by\").sql(\"CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END\"),\n    field.name(\"name\").expression(\"#{Name.name}\"),\n    field.name(\"open_time\").`type`(TimestampType).min(java.sql.Date.valueOf(\"2022-01-01\")),\n    field.name(\"status\").oneOf(\"open\", \"closed\", \"suspended\", \"pending\")\n  )\n
    "},{"location":"setup/guide/scenario/first-data-generation/#record-count","title":"Record Count","text":"

    We only want to generate 100 records, so that we can see what the output looks like. This is controlled at the accountTask level like below. If you want to generate more records, set it to the value you want.

    JavaScala 
    var accountTask = csv(\"customer_accounts\", \"/opt/app/data/customer/account\", Map.of(\"header\", \"true\"))\n        .schema(\n                ...\n        )\n        .count(count().records(100));\n
    val accountTask = csv(\"customer_accounts\", \"/opt/app/data/customer/account\", Map(\"header\" -> \"true\"))\n  .schema(\n    ...\n  )\n  .count(count.records(100))\n
    "},{"location":"setup/guide/scenario/first-data-generation/#additional-configurations","title":"Additional Configurations","text":"

    At the end of data generation, a report gets generated that summarises the actions it performed. We can control the output folder of that report via configurations. We will also enable the unique check to ensure any unique fields will have unique values generated.

    JavaScala 
    var config = configuration()\n        .generatedReportsFolderPath(\"/opt/app/data/report\")\n        .enableUniqueCheck(true);\n
    val config = configuration\n  .generatedReportsFolderPath(\"/opt/app/data/report\")\n  .enableUniqueCheck(true)\n
    "},{"location":"setup/guide/scenario/first-data-generation/#execute","title":"Execute","text":"

    To tell Data Caterer that we want to run with the configurations along with the accountTask, we have to call execute . So our full plan run will look like this.

    JavaScala 
    public class MyCsvJavaPlan extends PlanRun {\n    {\n        var accountTask = csv(\"customer_accounts\", \"/opt/app/data/customer/account\", Map.of(\"header\", \"true\"))\n                .schema(\n                        field().name(\"account_id\").regex(\"ACC[0-9]{8}\").unique(true),\n                        field().name(\"balance\").type(DoubleType.instance()).min(1).max(1000),\n                        field().name(\"created_by\").sql(\"CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END\"),\n                        field().name(\"name\").expression(\"#{Name.name}\"),\n                        field().name(\"open_time\").type(TimestampType.instance()).min(java.sql.Date.valueOf(\"2022-01-01\")),\n                        field().name(\"status\").oneOf(\"open\", \"closed\", \"suspended\", \"pending\")\n                );\n\n        var config = configuration()\n                .generatedReportsFolderPath(\"/opt/app/data/report\")\n                .enableUniqueCheck(true);\n\n        execute(config, accountTask);\n    }\n}\n
    class MyCsvPlan extends PlanRun {\n\n  val accountTask = csv(\"customer_accounts\", \"/opt/app/data/customer/account\", Map(\"header\" -> \"true\"))\n    .schema(\n      field.name(\"account_id\").regex(\"ACC[0-9]{8}\").unique(true),\n      field.name(\"balance\").`type`(DoubleType).min(1).max(1000),\n      field.name(\"created_by\").sql(\"CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END\"),\n      field.name(\"name\").expression(\"#{Name.name}\"),\n      field.name(\"open_time\").`type`(TimestampType).min(java.sql.Date.valueOf(\"2022-01-01\")),\n      field.name(\"status\").oneOf(\"open\", \"closed\", \"suspended\", \"pending\")\n    )\n    val config = configuration\n      .generatedReportsFolderPath(\"/opt/app/data/report\")\n      .enableUniqueCheck(true)\n\n    execute(config, accountTask)\n}\n
    "},{"location":"setup/guide/scenario/first-data-generation/#run","title":"Run","text":"

    Now we can run via the script ./run.sh that is in the top level directory of the data-caterer-example to run the class we just created.

    ./run.sh\n#input class MyCsvJavaPlan or MyCsvPlan\n#after completing\nhead docker/sample/customer/account/part-00000*\n

    Your output should look like this.

    account_id,balance,created_by,name,open_time,status\nACC06192462,853.9843359645766,eod,Hoyt Kertzmann MD,2023-07-22T11:17:01.713Z,closed\nACC15350419,632.5969895326234,eod,Dr. Claude White,2022-12-13T21:57:56.840Z,open\nACC25134369,592.0958847218986,eod,Fabian Rolfson,2023-04-26T04:54:41.068Z,open\nACC48021786,656.6413439322964,eod,Dewayne Stroman,2023-05-17T06:31:27.603Z,open\nACC26705211,447.2850352884595,event,Garrett Funk,2023-07-14T03:50:22.746Z,pending\nACC03150585,750.4568929015996,event,Natisha Reichel,2023-04-11T11:13:10.080Z,suspended\nACC29834210,686.4257811608622,event,Gisele Ondricka,2022-11-15T22:09:41.172Z,suspended\nACC39373863,583.5110618128994,event,Thaddeus Ortiz,2022-09-30T06:33:57.193Z,suspended\nACC39405798,989.2623959059525,eod,Shelby Reinger,2022-10-23T17:29:17.564Z,open\n

    Also check the HTML report, found at docker/sample/report/index.html, that gets generated to get an overview of what was executed.

    "},{"location":"setup/guide/scenario/first-data-generation/#join-with-another-csv","title":"Join With Another CSV","text":"

    Now that we have generated some accounts, let's also try to generate a set of transactions for those accounts in CSV format as well. The transactions could be in any other format, but to keep this simple, we will continue using CSV.

    We can define our schema the same way along with any additional metadata.

    JavaScala 
    var transactionTask = csv(\"customer_transactions\", \"/opt/app/data/customer/transaction\", Map.of(\"header\", \"true\"))\n        .schema(\n                field().name(\"account_id\"),\n                field().name(\"name\"),\n                field().name(\"amount\").type(DoubleType.instance()).min(1).max(100),\n                field().name(\"time\").type(TimestampType.instance()).min(java.sql.Date.valueOf(\"2022-01-01\")),\n                field().name(\"date\").type(DateType.instance()).sql(\"DATE(time)\")\n        );\n
    val transactionTask = csv(\"customer_transactions\", \"/opt/app/data/customer/transaction\", Map(\"header\" -> \"true\"))\n  .schema(\n    field.name(\"account_id\"),\n    field.name(\"full_name\"),\n    field.name(\"amount\").`type`(DoubleType).min(1).max(100),\n    field.name(\"time\").`type`(TimestampType).min(java.sql.Date.valueOf(\"2022-01-01\")),\n    field.name(\"date\").`type`(DateType).sql(\"DATE(time)\")\n  )\n
    "},{"location":"setup/guide/scenario/first-data-generation/#records-per-column","title":"Records Per Column","text":"

    Usually, for a given account_id, full_name, there should be multiple records for it as we want to simulate a customer having multiple transactions. We can achieve this through defining the number of records to generate in the count function.

    JavaScala 
    var transactionTask = csv(\"customer_transactions\", \"/opt/app/data/customer/transaction\", Map.of(\"header\", \"true\"))\n        .schema(\n                ...\n        )\n        .count(count().recordsPerColumn(5, \"account_id\", \"full_name\"));\n
    val transactionTask = csv(\"customer_transactions\", \"/opt/app/data/customer/transaction\", Map(\"header\" -> \"true\"))\n  .schema(\n    ...\n  )\n  .count(count.recordsPerColumn(5, \"account_id\", \"full_name\"))\n
    "},{"location":"setup/guide/scenario/first-data-generation/#random-records-per-column","title":"Random Records Per Column","text":"

    Above, you will notice that we are generating 5 records per account_id, full_name. This is okay but still not quite reflective of the real world. Sometimes, people have accounts with no transactions in them, or they could have many. We can accommodate for this via defining a random number of records per column.

    JavaScala 
    var transactionTask = csv(\"customer_transactions\", \"/opt/app/data/customer/transaction\", Map.of(\"header\", \"true\"))\n        .schema(\n                ...\n        )\n        .count(count().recordsPerColumnGenerator(generator().min(0).max(5), \"account_id\", \"full_name\"));\n
    val transactionTask = csv(\"customer_transactions\", \"/opt/app/data/customer/transaction\", Map(\"header\" -> \"true\"))\n  .schema(\n    ...\n  )\n  .count(count.recordsPerColumnGenerator(generator.min(0).max(5), \"account_id\", \"full_name\"))\n

    Here we set the minimum number of records per column to be 0 and the maximum to 5.

    "},{"location":"setup/guide/scenario/first-data-generation/#foreign-key","title":"Foreign Key","text":"

    In this scenario, we want to match the account_id in account to match the same column values in transaction. We also want to match name in account to full_name in transaction. This can be done via plan configuration like below.

    JavaScala 
    var myPlan = plan().addForeignKeyRelationship(\n        accountTask, List.of(\"account_id\", \"name\"), //the task and columns we want linked\n        List.of(Map.entry(transactionTask, List.of(\"account_id\", \"full_name\"))) //list of other tasks and their respective column names we want matched\n);\n
    val myPlan = plan.addForeignKeyRelationship(\n  accountTask, List(\"account_id\", \"name\"),  //the task and columns we want linked\n  List(transactionTask -> List(\"account_id\", \"full_name\"))  //list of other tasks and their respective column names we want matched\n)\n

    Now, stitching it all together for the execute function, our final plan should look like this.

    JavaScala 
    public class MyCsvJavaPlan extends PlanRun {\n    {\n        var accountTask = csv(\"customer_accounts\", \"/opt/app/data/customer/account\", Map.of(\"header\", \"true\"))\n                .schema(\n                        field().name(\"account_id\").regex(\"ACC[0-9]{8}\").unique(true),\n                        field().name(\"balance\").type(DoubleType.instance()).min(1).max(1000),\n                        field().name(\"created_by\").sql(\"CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END\"),\n                        field().name(\"name\").expression(\"#{Name.name}\"),\n                        field().name(\"open_time\").type(TimestampType.instance()).min(java.sql.Date.valueOf(\"2022-01-01\")),\n                        field().name(\"status\").oneOf(\"open\", \"closed\", \"suspended\", \"pending\")\n                )\n                .count(count().records(100));\n\n        var transactionTask = csv(\"customer_transactions\", \"/opt/app/data/customer/transaction\", Map.of(\"header\", \"true\"))\n                .schema(\n                        field().name(\"account_id\"),\n                        field().name(\"name\"),\n                        field().name(\"amount\").type(DoubleType.instance()).min(1).max(100),\n                        field().name(\"time\").type(TimestampType.instance()).min(java.sql.Date.valueOf(\"2022-01-01\")),\n                        field().name(\"date\").type(DateType.instance()).sql(\"DATE(time)\")\n                )\n                .count(count().recordsPerColumnGenerator(generator().min(0).max(5), \"account_id\", \"full_name\"));\n\n        var config = configuration()\n                .generatedReportsFolderPath(\"/opt/app/data/report\")\n                .enableUniqueCheck(true);\n\n        var myPlan = plan().addForeignKeyRelationship(\n                accountTask, List.of(\"account_id\", \"name\"),\n                List.of(Map.entry(transactionTask, List.of(\"account_id\", \"full_name\")))\n        );\n\n        execute(myPlan, config, accountTask, transactionTask);\n    }\n}\n
    class MyCsvPlan extends PlanRun {\n\n  val accountTask = csv(\"customer_accounts\", \"/opt/app/data/customer/account\", Map(\"header\" -> \"true\"))\n    .schema(\n      field.name(\"account_id\").regex(\"ACC[0-9]{8}\").unique(true),\n      field.name(\"balance\").`type`(DoubleType).min(1).max(1000),\n      field.name(\"created_by\").sql(\"CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END\"),\n      field.name(\"name\").expression(\"#{Name.name}\"),\n      field.name(\"open_time\").`type`(TimestampType).min(java.sql.Date.valueOf(\"2022-01-01\")),\n      field.name(\"status\").oneOf(\"open\", \"closed\", \"suspended\", \"pending\")\n    )\n    .count(count.records(100))\n\n  val transactionTask = csv(\"customer_transactions\", \"/opt/app/data/customer/transaction\", Map(\"header\" -> \"true\"))\n    .schema(\n      field.name(\"account_id\"),\n      field.name(\"name\"),\n      field.name(\"amount\").`type`(DoubleType).min(1).max(100),\n      field.name(\"time\").`type`(TimestampType).min(java.sql.Date.valueOf(\"2022-01-01\")),\n      field.name(\"date\").`type`(DateType).sql(\"DATE(time)\")\n    )\n    .count(count.recordsPerColumnGenerator(generator.min(0).max(5), \"account_id\", \"full_name\"))\n\n  val config = configuration\n    .generatedReportsFolderPath(\"/opt/app/data/report\")\n    .enableUniqueCheck(true)\n\n  val myPlan = plan.addForeignKeyRelationship(\n    accountTask, List(\"account_id\", \"name\"),\n    List(transactionTask -> List(\"account_id\", \"full_name\"))\n  )\n\n  execute(myPlan, config, accountTask, transactionTask)\n}\n

    Let's try run again.

    #clean up old data\nrm -rf docker/sample/customer/account\n./run.sh\n#input class MyCsvJavaPlan or MyCsvPlan\n#after completing, let's pick an account and check the transactions for that account\naccount=$(tail -1 docker/sample/customer/account/part-00000* | awk -F \",\" '{print $1 \",\" $4}')\necho $account\ncat docker/sample/customer/transaction/part-00000* | grep $account\n

    It should look something like this.

    ACC29117767,Willodean Sauer\nACC29117767,Willodean Sauer,84.99145871948083,2023-05-14T09:55:51.439Z,2023-05-14\nACC29117767,Willodean Sauer,58.89345733567232,2022-11-22T07:38:20.143Z,2022-11-22\n

    Congratulations! You have now made a data generator that has simulated a real world data scenario. You can check the DocumentationJavaPlanRun.java or DocumentationPlanRun.scala files as well to check that your plan is the same.

    We can now look to consume this CSV data from a job or service. Usually, once we have consumed the data, we would also want to check and validate that our consumer has correctly ingested the data.

    "},{"location":"setup/guide/scenario/first-data-generation/#validate","title":"Validate","text":"

    In this scenario, our consumer will read in the CSV file, do some transformations, and then save the data to Postgres. Let's try to configure data validations for the data that gets pushed into Postgres.

    "},{"location":"setup/guide/scenario/first-data-generation/#postgres-setup","title":"Postgres setup","text":"

    First, we define our connection properties for Postgres. You can check out the full options available here.

    JavaScala 
    var postgresValidateTask = postgres(\n    \"my_postgres\",                                          //connection name\n    \"jdbc:postgresql://host.docker.internal:5432/customer\", //url\n    \"postgres\",                                             //username\n    \"password\"                                              //password\n).table(\"account\", \"transactions\");\n
    val postgresValidateTask = postgres(\n  \"my_postgres\",                                          //connection name\n  \"jdbc:postgresql://host.docker.internal:5432/customer\", //url\n  \"postgres\",                                             //username\n  \"password\"                                              //password\n).table(\"account\", \"transactions\")\n

    We can connect and access the data inside the table account.transactions. Now to define our data validations.

    "},{"location":"setup/guide/scenario/first-data-generation/#validations","title":"Validations","text":"

    For full information about validation options and configurations, check here. Below, we have an example that should give you a good understanding of what validations are possible.

    JavaScala 
    var postgresValidateTask = postgres(...)\n        .table(\"account\", \"transactions\")\n        .validations(\n                validation().col(\"account_id\").isNotNull(),\n                validation().col(\"name\").matches(\"[A-Z][a-z]+ [A-Z][a-z]+\").errorThreshold(0.2).description(\"Some names have different formats\"),\n                validation().col(\"balance\").greaterThanOrEqual(0).errorThreshold(10).description(\"Account can have negative balance if overdraft\"),\n                validation().expr(\"CASE WHEN status == 'closed' THEN isNotNull(close_date) ELSE isNull(close_date) END\"),\n                validation().unique(\"account_id\", \"name\"),\n                validation().groupBy(\"account_id\", \"name\").max(\"login_retry\").lessThan(10)\n        );\n
    val postgresValidateTask = postgres(...)\n  .table(\"account\", \"transactions\")\n  .validations(\n    validation.col(\"account_id\").isNotNull,\n    validation.col(\"name\").matches(\"[A-Z][a-z]+ [A-Z][a-z]+\").errorThreshold(0.2).description(\"Some names have different formats\"),\n    validation.col(\"balance\").greaterThanOrEqual(0).errorThreshold(10).description(\"Account can have negative balance if overdraft\"),\n    validation.expr(\"CASE WHEN status == 'closed' THEN isNotNull(close_date) ELSE isNull(close_date) END\"),\n    validation.unique(\"account_id\", \"name\"),\n    validation.groupBy(\"account_id\", \"name\").max(\"login_retry\").lessThan(10)\n  )\n
    "},{"location":"setup/guide/scenario/first-data-generation/#name_1","title":"name","text":"

    For all values in the name column, we check if they match the regex [A-Z][a-z]+ [A-Z][a-z]+. As we know in the real world, names do not always follow the same pattern, so we allow for an errorThreshold before marking the validation as failed. Here, we define the errorThreshold to be 0.2, which means, if the error percentage is greater than 20%, then fail the validation. We also append on a helpful description so other developers/users can understand the context of the validation.

    "},{"location":"setup/guide/scenario/first-data-generation/#balance_1","title":"balance","text":"

    We check that all balance values are greater than or equal to 0. This time, we have a slightly different errorThreshold as it is set to 10, which means, if the number of errors is greater than 10, then fail the validation.

    "},{"location":"setup/guide/scenario/first-data-generation/#expr","title":"expr","text":"

    Sometimes, we may need to include the values of multiple columns to validate a certain condition. This is where we can use expr to define a SQL expression that returns a boolean. In this scenario, we are checking if the status column has value closed, then the close_date should be not null, otherwise, close_date is null.

    "},{"location":"setup/guide/scenario/first-data-generation/#unique","title":"unique","text":"

    We check whether the combination of account_id and name are unique within the dataset. You can define one or more columns for unique validations.

    "},{"location":"setup/guide/scenario/first-data-generation/#groupby","title":"groupBy","text":"

    There may be some business rule that states the number of login_retry should be less than 10 for each account. We can check this via a group by validation where we group by the account_id, name, take the maximum value for login_retry per account_id,name combination, then check if it is less than 10.

    You can now look to play around with other configurations or data sources to meet your needs. Also, make sure to explore the docs further as it can guide you on what can be configured.

    "},{"location":"setup/guide/scenario/records-per-column/","title":"Multiple Records Per Column","text":"

    Creating a data generator for a CSV file where there are multiple records per column values.

    "},{"location":"setup/guide/scenario/records-per-column/#requirements","title":"Requirements","text":"
    • 5 minutes
    • Git
    • Gradle
    • Docker
    "},{"location":"setup/guide/scenario/records-per-column/#get-started","title":"Get Started","text":"

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    git clone git@github.com:pflooky/data-caterer-example.git\n
    "},{"location":"setup/guide/scenario/records-per-column/#plan-setup","title":"Plan Setup","text":"

    Create a new Java or Scala class.

    • Java: src/main/java/com/github/pflooky/plan/MyMultipleRecordsPerColJavaPlan.java
    • Scala: src/main/scala/com/github/pflooky/plan/MyMultipleRecordsPerColPlan.scala

    Make sure your class extends PlanRun.

    JavaScala 
    import com.github.pflooky.datacaterer.java.api.PlanRun;\n...\n\npublic class MyMultipleRecordsPerColJavaPlan extends PlanRun {\n    {\n        var transactionTask = csv(\"customer_transactions\", \"/opt/app/data/customer/transaction\", Map.of(\"header\", \"true\"))\n                .schema(\n                        field().name(\"account_id\"),\n                        field().name(\"full_name\"),\n                        field().name(\"amount\").type(DoubleType.instance()).min(1).max(100),\n                        field().name(\"time\").type(TimestampType.instance()).min(java.sql.Date.valueOf(\"2022-01-01\")),\n                        field().name(\"date\").type(DateType.instance()).sql(\"DATE(time)\")\n                );\n\n        var config = configuration()\n                .generatedReportsFolderPath(\"/opt/app/data/report\")\n                .enableUniqueCheck(true);\n\n        execute(config, transactionTask);\n    }\n}\n
    import com.github.pflooky.datacaterer.api.PlanRun\n...\n\nclass MyMultipleRecordsPerColPlan extends PlanRun {\n\n  val transactionTask = csv(\"customer_transactions\", \"/opt/app/data/customer/transaction\", Map(\"header\" -> \"true\"))\n    .schema(\n      field.name(\"account_id\").regex(\"ACC[0-9]{8}\"), \n      field.name(\"full_name\").expression(\"#{Name.name}\"), \n      field.name(\"amount\").`type`(DoubleType.instance).min(1).max(100),\n      field.name(\"time\").`type`(TimestampType.instance).min(java.sql.Date.valueOf(\"2022-01-01\")), \n      field.name(\"date\").`type`(DateType.instance).sql(\"DATE(time)\")\n    )\n\n  val config = configuration\n    .generatedReportsFolderPath(\"/opt/app/data/report\")\n\n  execute(config, transactionTask)\n}\n
    "},{"location":"setup/guide/scenario/records-per-column/#record-count","title":"Record Count","text":"

    By default, tasks will generate 1000 records. You can alter this value via the count configuration which can be applied to individual tasks. For example, in Scala, csv(...).count(count.records(100)) to generate only 100 records.

    "},{"location":"setup/guide/scenario/records-per-column/#records-per-column","title":"Records Per Column","text":"

    In this scenario, for a given account_id, full_name, there should be multiple records for it as we want to simulate a customer having multiple transactions. We can achieve this through defining the number of records to generate in the count function.

    JavaScala 
    var transactionTask = csv(\"customer_transactions\", \"/opt/app/data/customer/transaction\", Map.of(\"header\", \"true\"))\n        .schema(\n                ...\n        )\n        .count(count().recordsPerColumn(5, \"account_id\", \"full_name\"));\n
    val transactionTask = csv(\"customer_transactions\", \"/opt/app/data/customer/transaction\", Map(\"header\" -> \"true\"))\n  .schema(\n    ...\n  )\n  .count(count.recordsPerColumn(5, \"account_id\", \"full_name\"))\n

    This will generate 1000 * 5 = 5000 records as the default number of records is set (1000) and per account_id, full_name from the initial 1000 records, 5 records will be generated.

    "},{"location":"setup/guide/scenario/records-per-column/#random-records-per-column","title":"Random Records Per Column","text":"

    Generating 5 records per column is okay but still not quite reflective of the real world. Sometimes, people have accounts with no transactions in them, or they could have many. We can accommodate for this via defining a random number of records per column.

    JavaScala 
    var transactionTask = csv(\"customer_transactions\", \"/opt/app/data/customer/transaction\", Map.of(\"header\", \"true\"))\n        .schema(\n                ...\n        )\n        .count(count().recordsPerColumnGenerator(generator().min(0).max(5), \"account_id\", \"full_name\"));\n
    val transactionTask = csv(\"customer_transactions\", \"/opt/app/data/customer/transaction\", Map(\"header\" -> \"true\"))\n  .schema(\n    ...\n  )\n  .count(count.recordsPerColumnGenerator(generator.min(0).max(5), \"account_id\", \"full_name\"))\n

    Here we set the minimum number of records per column to be 0 and the maximum to 5. This will follow a uniform distribution so the average number of records per account is 2.5. We could also define other metadata, just like we did with fields, when defining the generator. For example, we could set standardDeviation and mean for the number of records generated per column to follow a normal distribution.

    "},{"location":"setup/guide/scenario/records-per-column/#run","title":"Run","text":"

    Let's try run.

    #clean up old data\nrm -rf docker/sample/customer/account\n./run.sh\n#input class MyMultipleRecordsPerColJavaPlan or MyMultipleRecordsPerColPlan\n#after completing\nhead docker/sample/customer/transaction/part-00000*\n

    It should look something like this.

    ACC29117767,Willodean Sauer\nACC29117767,Willodean Sauer,84.99145871948083,2023-05-14T09:55:51.439Z,2023-05-14\nACC29117767,Willodean Sauer,58.89345733567232,2022-11-22T07:38:20.143Z,2022-11-22\n

    You can now look to play around with other count configurations found here.

    "},{"location":"setup/validation/basic-validation/","title":"Basic Validations","text":"

    Run validations on a column to ensure the values adhere to your requirement. Can be set to complex validation logic via SQL expression as well if needed (see here).

    "},{"location":"setup/validation/basic-validation/#equal","title":"Equal","text":"

    Ensure all data in column is equal to certain value. Value can be of any data type. Can use isEqualCol to define SQL expression that can reference other columns.

    JavaScalaYAML 
    validation().col(\"year\").isEqual(2021),\nvalidation().col(\"year\").isEqualCol(\"YEAR(date)\"),\n
    validation.col(\"year\").isEqual(2021),\nvalidation.col(\"year\").isEqualCol(\"YEAR(date)\"),\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"year == 2021\"\n
    "},{"location":"setup/validation/basic-validation/#not-equal","title":"Not Equal","text":"

    Ensure all data in column is not equal to certain value. Value can be of any data type. Can use isNotEqualCol to define SQL expression that can reference other columns.

    JavaScalaYAML 
    validation().col(\"year\").isNotEqual(2021),\nvalidation().col(\"year\").isNotEqualCol(\"YEAR(date)\"),\n
    validation.col(\"year\").isNotEqual(2021)\nvalidation.col(\"year\").isEqualCol(\"YEAR(date)\"),\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"year != 2021\"\n
    "},{"location":"setup/validation/basic-validation/#null","title":"Null","text":"

    Ensure all data in column is null.

    JavaScalaYAML 
    validation().col(\"year\").isNull()\n
    validation.col(\"year\").isNull\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"ISNULL(year)\"\n
    "},{"location":"setup/validation/basic-validation/#not-null","title":"Not Null","text":"

    Ensure all data in column is not null.

    JavaScalaYAML 
    validation().col(\"year\").isNotNull()\n
    validation.col(\"year\").isNotNull\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"ISNOTNULL(year)\"\n
    "},{"location":"setup/validation/basic-validation/#contains","title":"Contains","text":"

    Ensure all data in column is contains certain string. Column has to have type string.

    JavaScalaYAML 
    validation().col(\"name\").contains(\"peter\")\n
    validation.col(\"name\").contains(\"peter\")\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"CONTAINS(name, 'peter')\"\n
    "},{"location":"setup/validation/basic-validation/#not-contains","title":"Not Contains","text":"

    Ensure all data in column does not contain certain string. Column has to have type string.

    JavaScalaYAML 
    validation().col(\"name\").notContains(\"peter\")\n
    validation.col(\"name\").notContains(\"peter\")\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"!CONTAINS(name, 'peter')\"\n
    "},{"location":"setup/validation/basic-validation/#unique","title":"Unique","text":"

    Ensure all data in column is unique.

    JavaScalaYAML 
    validation().unique(\"account_id\", \"name\")\n
    validation.unique(\"account_id\", \"name\")\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - unique: [\"account_id\", \"name\"]\n
    "},{"location":"setup/validation/basic-validation/#less-than","title":"Less Than","text":"

    Ensure all data in column is less than certain value. Can use lessThanCol to define SQL expression that can reference other columns.

    JavaScalaYAML 
    validation().col(\"amount\").lessThan(100),\nvalidation().col(\"amount\").lessThanCol(\"balance + 1\"),\n
    validation.col(\"amount\").lessThan(100),\nvalidation.col(\"amount\").lessThanCol(\"balance + 1\"),\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"amount < 100\"\n      - expr: \"amount < balance + 1\"\n
    "},{"location":"setup/validation/basic-validation/#less-than-or-equal","title":"Less Than Or Equal","text":"

    Ensure all data in column is less than or equal to certain value. Can use lessThanOrEqualCol to define SQL expression that can reference other columns.

    JavaScalaYAML 
    validation().col(\"amount\").lessThanOrEqual(100),\nvalidation().col(\"amount\").lessThanOrEqualCol(\"balance + 1\"),\n
    validation.col(\"amount\").lessThanOrEqual(100),\nvalidation.col(\"amount\").lessThanCol(\"balance + 1\"),\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"amount <= 100\"\n      - expr: \"amount <= balance + 1\"\n
    "},{"location":"setup/validation/basic-validation/#greater-than","title":"Greater Than","text":"

    Ensure all data in column is greater than certain value. Can use greaterThanCol to define SQL expression that can reference other columns.

    JavaScalaYAML 
    validation().col(\"amount\").greaterThan(100),\nvalidation().col(\"amount\").greaterThanCol(\"balance\"),\n
    validation.col(\"amount\").greaterThan(100),\nvalidation.col(\"amount\").greaterThanCol(\"balance\"),\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"amount > 100\"\n      - expr: \"amount > balance\"\n
    "},{"location":"setup/validation/basic-validation/#greater-than-or-equal","title":"Greater Than Or Equal","text":"

    Ensure all data in column is greater than or equal to certain value. Can use greaterThanOrEqualCol to define SQL expression that can reference other columns.

    JavaScalaYAML 
    validation().col(\"amount\").greaterThanOrEqual(100),\nvalidation().col(\"amount\").greaterThanOrEqualCol(\"balance\"),\n
    validation.col(\"amount\").greaterThanOrEqual(100),\nvalidation.col(\"amount\").greaterThanOrEqualCol(\"balance\"),\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"amount >= 100\"\n      - expr: \"amount >= balance\"\n
    "},{"location":"setup/validation/basic-validation/#between","title":"Between","text":"

    Ensure all data in column is between two values. Can use betweenCol to define SQL expression that references other columns.

    JavaScalaYAML 
    validation().col(\"amount\").between(100, 200),\nvalidation().col(\"amount\").betweenCol(\"balance * 0.9\", \"balance * 1.1\"),\n
    validation.col(\"amount\").between(100, 200),\nvalidation.col(\"amount\").betweenCol(\"balance * 0.9\", \"balance * 1.1\"),\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"amount BETWEEN 100 AND 200\"\n      - expr: \"amount BETWEEN balance * 0.9 AND balance * 1.1\"\n
    "},{"location":"setup/validation/basic-validation/#not-between","title":"Not Between","text":"

    Ensure all data in column is not between two values. Can use notBetweenCol to define SQL expression that references other columns.

    JavaScalaYAML 
    validation().col(\"amount\").notBetween(100, 200),\nvalidation().col(\"amount\").notBetweenCol(\"balance * 0.9\", \"balance * 1.1\"),\n
    validation.col(\"amount\").notBetween(100, 200)\nvalidation.col(\"amount\").notBetweenCol(\"balance * 0.9\", \"balance * 1.1\"),\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"amount NOT BETWEEN 100 AND 200\"\n      - expr: \"amount NOT BETWEEN balance * 0.9 AND balance * 1.1\"\n
    "},{"location":"setup/validation/basic-validation/#in","title":"In","text":"

    Ensure all data in column is in set of defined values.

    JavaScalaYAML 
    validation().col(\"status\").in(\"open\", \"closed\")\n
    validation.col(\"status\").in(\"open\", \"closed\")\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"status IN ('open', 'closed')\"\n
    "},{"location":"setup/validation/basic-validation/#matches","title":"Matches","text":"

    Ensure all data in column matches certain regex expression.

    JavaScalaYAML 
    validation().col(\"account_id\").matches(\"ACC[0-9]{8}\")\n
    validation.col(\"account_id\").matches(\"ACC[0-9]{8}\")\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"REGEXP(account_id, ACC[0-9]{8})\"\n
    "},{"location":"setup/validation/basic-validation/#not-matches","title":"Not Matches","text":"

    Ensure all data in column does not match certain regex expression.

    JavaScalaYAML 
    validation().col(\"account_id\").notMatches(\"^acc.*\")\n
    validation.col(\"account_id\").notMatches(\"^acc.*\")\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"!REGEXP(account_id, '^acc.*')\"\n
    "},{"location":"setup/validation/basic-validation/#starts-with","title":"Starts With","text":"

    Ensure all data in column starts with certain string. Column has to have type string.

    JavaScalaYAML 
    validation().col(\"account_id\").startsWith(\"ACC\")\n
    validation.col(\"account_id\").startsWith(\"ACC\")\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"STARTSWITH(account_id, 'ACC')\"\n
    "},{"location":"setup/validation/basic-validation/#not-starts-with","title":"Not Starts With","text":"

    Ensure all data in column does not start with certain string. Column has to have type string.

    JavaScalaYAML 
    validation().col(\"account_id\").notStartsWith(\"ACC\")\n
    validation.col(\"account_id\").notStartsWith(\"ACC\")\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"!STARTSWITH(account_id, 'ACC')\"\n
    "},{"location":"setup/validation/basic-validation/#ends-with","title":"Ends With","text":"

    Ensure all data in column ends with certain string. Column has to have type string.

    JavaScalaYAML 
    validation().col(\"account_id\").endsWith(\"ACC\")\n
    validation.col(\"account_id\").endsWith(\"ACC\")\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"ENDWITH(account_id, 'ACC')\"\n
    "},{"location":"setup/validation/basic-validation/#not-ends-with","title":"Not Ends With","text":"

    Ensure all data in column does not end with certain string. Column has to have type string.

    JavaScalaYAML 
    validation().col(\"account_id\").notEndsWith(\"ACC\")\n
    validation.col(\"account_id\").notEndsWith(\"ACC\")\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"!ENDWITH(account_id, 'ACC')\"\n
    "},{"location":"setup/validation/basic-validation/#size","title":"Size","text":"

    Ensure all data in column has certain size. Column has to have type array or map.

    JavaScalaYAML 
    validation().col(\"transactions\").size(5)\n
    validation.col(\"transactions\").size(5)\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"SIZE(transactions, 5)\"\n
    "},{"location":"setup/validation/basic-validation/#not-size","title":"Not Size","text":"

    Ensure all data in column does not have certain size. Column has to have type array or map.

    JavaScalaYAML 
    validation().col(\"transactions\").notSize(5)\n
    validation.col(\"transactions\").notSize(5)\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"SIZE(transactions) != 5\"\n
    "},{"location":"setup/validation/basic-validation/#less-than-size","title":"Less Than Size","text":"

    Ensure all data in column has size less than certain value. Column has to have type array or map.

    JavaScalaYAML 
    validation().col(\"transactions\").lessThanSize(5)\n
    validation.col(\"transactions\").lessThanSize(5)\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"SIZE(transactions) < 5\"\n
    "},{"location":"setup/validation/basic-validation/#less-than-or-equal-size","title":"Less Than Or Equal Size","text":"

    Ensure all data in column has size less than or equal to certain value. Column has to have type array or map.

    JavaScalaYAML 
    validation().col(\"transactions\").lessThanOrEqualSize(5)\n
    validation.col(\"transactions\").lessThanOrEqualSize(5)\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"SIZE(transactions) <= 5\"\n
    "},{"location":"setup/validation/basic-validation/#greater-than-size","title":"Greater Than Size","text":"

    Ensure all data in column has size greater than certain value. Column has to have type array or map.

    JavaScalaYAML 
    validation().col(\"transactions\").greaterThanSize(5)\n
    validation.col(\"transactions\").greaterThanSize(5)\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"SIZE(transactions) > 5\"\n
    "},{"location":"setup/validation/basic-validation/#greater-than-or-equal-size","title":"Greater Than Or Equal Size","text":"

    Ensure all data in column has size greater than or equal to certain value. Column has to have type array or map.

    JavaScalaYAML 
    validation().col(\"transactions\").greaterThanOrEqualSize(5)\n
    validation.col(\"transactions\").greaterThanOrEqualSize(5)\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"SIZE(transactions) >= 5\"\n
    "},{"location":"setup/validation/basic-validation/#luhn-check","title":"Luhn Check","text":"

    Ensure all data in column passes luhn check. Luhn check is used to validate credit card numbers and certain identification numbers (see here for more details).

    JavaScalaYAML 
    validation().col(\"credit_card\").luhnCheck()\n
    validation.col(\"credit_card\").luhnCheck\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"LUHN_CHECK(credit_card)\"\n
    "},{"location":"setup/validation/basic-validation/#has-type","title":"Has Type","text":"

    Ensure all data in column has certain data type.

    JavaScalaYAML 
    validation().col(\"id\").hasType(\"string\")\n
    validation.col(\"id\").hasType(\"string\")\n
    ---\nname: \"account_checks\"\ndataSources:\n  ...\n    validations:\n      - expr: \"TYPEOF(id) == 'string'\"\n
    "},{"location":"setup/validation/basic-validation/#expression","title":"Expression","text":"

    Ensure all data in column adheres to SQL expression defined that returns back a boolean. You can define complex logic in here that could combine multiple columns.

    For example, CASE WHEN status == 'open' THEN balance > 0 ELSE balance == 0 END would check all rows with status open to have balance greater than 0, otherwise, check the balance is 0.

    JavaScalaYAML 
    var csvTxns = csv(\"transactions\", \"/tmp/csv\", Map.of(\"header\", \"true\"))\n  .validations(\n    validation().expr(\"amount < 100\"),\n    validation().expr(\"year == 2021\").errorThreshold(0.1),  //equivalent to if error percentage is > 10%, then fail\n    validation().expr(\"REGEXP_LIKE(name, 'Peter .*')\").errorThreshold(200)  //equivalent to if number of errors is > 200, then fail\n  );\n\nvar conf = configuration().enableValidation(true);\n
    val csvTxns = csv(\"transactions\", \"/tmp/csv\", Map(\"header\" -> \"true\"))\n  .validations(\n    validation.expr(\"amount < 100\"),\n    validation.expr(\"year == 2021\").errorThreshold(0.1),  //equivalent to if error percentage is > 10%, then fail\n    validation.expr(\"REGEXP_LIKE(name, 'Peter .*')\").errorThreshold(200)  //equivalent to if number of errors is > 200, then fail\n  )\n\nval conf = configuration.enableValidation(true)\n
    ---\nname: \"account_checks\"\ndataSources:\n  transactions:\n    options:\n      path: \"/tmp/csv\"\n    validations:\n      - expr: \"amount < 100\"\n      - expr: \"year == 2021\"\n        errorThreshold: 0.1   #equivalent to if error percentage is > 10%, then fail\n      - expr: \"REGEXP_LIKE(name, 'Peter .*')\"\n        errorThreshold: 200   #equivalent to if number of errors is > 200, then fail\n        description: \"Should be lots of Peters\"\n\n#enableValidation inside application.conf\n
    "},{"location":"setup/validation/group-by-validation/","title":"Group By Validation","text":"

    If you want to run aggregations based on a particular set of columns or just the whole dataset, you can do so via group by validations. An example would be checking that the sum of amount is less than 1000 per account_id, year. The validations applied can be one of the validations from the basic validation set found here.

    "},{"location":"setup/validation/group-by-validation/#record-count","title":"Record count","text":"

    Check the number of records across the whole dataset.

    JavaScala 
    validation().groupBy().count().lessThan(1000)\n
    validation.groupBy().count().lessThan(1000)\n
    "},{"location":"setup/validation/group-by-validation/#record-count-per-group","title":"Record count per group","text":"

    Check the number of records for each group.

    JavaScala 
    validation().groupBy(\"account_id\", \"year\").count().lessThan(10)\n
    validation.groupBy(\"account_id\", \"year\").count().lessThan(10)\n
    "},{"location":"setup/validation/group-by-validation/#sum","title":"Sum","text":"

    Check the sum of a columns values for each group adheres to validation.

    JavaScala 
    validation().groupBy(\"account_id\", \"year\").sum(\"amount\").lessThan(1000)\n
    validation.groupBy(\"account_id\", \"year\").sum(\"amount\").lessThan(1000)\n
    "},{"location":"setup/validation/group-by-validation/#count","title":"Count","text":"

    Check the count for each group adheres to validation.

    JavaScala 
    validation().groupBy(\"account_id\", \"year\").count(\"amount\").lessThan(10)\n
    validation.groupBy(\"account_id\", \"year\").count(\"amount\").lessThan(10)\n
    "},{"location":"setup/validation/group-by-validation/#min","title":"Min","text":"

    Check the min for each group adheres to validation.

    JavaScala 
    validation().groupBy(\"account_id\", \"year\").min(\"amount\").greaterThan(0)\n
    validation.groupBy(\"account_id\", \"year\").min(\"amount\").greaterThan(0)\n
    "},{"location":"setup/validation/group-by-validation/#max","title":"Max","text":"

    Check the max for each group adheres to validation.

    JavaScala 
    validation().groupBy(\"account_id\", \"year\").max(\"amount\").lessThanOrEqual(100)\n
    validation.groupBy(\"account_id\", \"year\").max(\"amount\").lessThanOrEqual(100)\n
    "},{"location":"setup/validation/group-by-validation/#average","title":"Average","text":"

    Check the average for each group adheres to validation.

    JavaScala 
    validation().groupBy(\"account_id\", \"year\").avg(\"amount\").between(40, 60)\n
    validation.groupBy(\"account_id\", \"year\").avg(\"amount\").between(40, 60)\n
    "},{"location":"setup/validation/group-by-validation/#standard-deviation","title":"Standard deviation","text":"

    Check the standard deviation for each group adheres to validation.

    JavaScala 
    validation().groupBy(\"account_id\", \"year\").stddev(\"amount\").between(0.5, 0.6)\n
    validation.groupBy(\"account_id\", \"year\").stddev(\"amount\").between(0.5, 0.6)\n
    "},{"location":"setup/validation/upstream-data-source-validation/","title":"Upstream Data Source Validation","text":"

    If you want to run data validations based on data generated or data from another data source, you can use the upstream data source validations. An example would be generating a Parquet file that gets ingested by a job and inserted into Postgres. The validations can then check for each account_id generated in the Parquet, it exists in account_number column in Postgres. The validations can be chained with basic and group by validations or even other upstream data sources, to cover any complex validations.

    "},{"location":"setup/validation/upstream-data-source-validation/#basic-join","title":"Basic join","text":"

    Join across datasets by particular columns. Then run validations on the joined dataset. You will notice that the data source name is appended onto the column names when joined (i.e. my_first_json_customer_details), to ensure column names do not clash and make it obvious which columns are being validated.

    In the below example, we check that the for the same account_id, then customer_details.name in the my_first_json dataset should equal to the name column in the my_second_json.

    JavaScala 
    var firstJsonTask = json(\"my_first_json\", \"/tmp/data/first_json\")\n  .schema(\n    field().name(\"account_id\").regex(\"ACC[0-9]{8}\"),\n    field().name(\"customer_details\")\n      .schema(\n        field().name(\"name\").expression(\"#{Name.name}\")\n      )\n  );\n\nvar secondJsonTask = json(\"my_second_json\", \"/tmp/data/second_json\")\n  .validations(\n    validation().upstreamData(firstJsonTask)\n      .joinColumns(\"account_id\")\n      .withValidation(\n        validation().col(\"my_first_json_customer_details.name\")\n          .isEqualCol(\"name\")\n      )\n  );\n
    val firstJsonTask = json(\"my_first_json\", \"/tmp/data/first_json\")\n  .schema(\n    field.name(\"account_id\").regex(\"ACC[0-9]{8}\"),\n    field.name(\"customer_details\")\n      .schema(\n        field.name(\"name\").expression(\"#{Name.name}\")\n      )\n  )\n\nval secondJsonTask = json(\"my_second_json\", \"/tmp/data/second_json\")\n  .validations(\n    validation.upstreamData(firstJsonTask)\n      .joinColumns(\"account_id\")\n      .withValidation(\n        validation.col(\"my_first_json_customer_details.name\")\n          .isEqualCol(\"name\")\n      )\n  )\n
    "},{"location":"setup/validation/upstream-data-source-validation/#join-expression","title":"Join expression","text":"

    Define join expression to link two datasets together. This can be any SQL expression that returns a boolean value. Useful in situations where join is based on transformations or complex logic.

    In the below example, we have to use CONCAT SQL function to combine 'ACC' and account_number to join with account_id column in my_first_json dataset.

    JavaScala 
    var firstJsonTask = json(\"my_first_json\", \"/tmp/data/first_json\")\n  .schema(\n    field().name(\"account_id\").regex(\"ACC[0-9]{8}\"),\n    field().name(\"customer_details\")\n      .schema(\n        field().name(\"name\").expression(\"#{Name.name}\")\n      )\n  );\n\nvar secondJsonTask = json(\"my_second_json\", \"/tmp/data/second_json\")\n  .validations(\n    validation().upstreamData(firstJsonTask)\n      .joinExpr(\"my_first_json_account_id == CONCAT('ACC', account_number)\")\n      .withValidation(\n        validation().col(\"my_first_json_customer_details.name\")\n          .isEqualCol(\"name\")\n      )\n  );\n
    val firstJsonTask = json(\"my_first_json\", \"/tmp/data/first_json\")\n  .schema(\n    field.name(\"account_id\").regex(\"ACC[0-9]{8}\"),\n    field.name(\"customer_details\")\n      .schema(\n        field.name(\"name\").expression(\"#{Name.name}\")\n      )\n  )\n\nval secondJsonTask = json(\"my_second_json\", \"/tmp/data/second_json\")\n  .validations(\n    validation.upstreamData(firstJsonTask)\n      .joinExpr(\"my_first_json_account_id == CONCAT('ACC', account_number)\")\n      .withValidation(\n        validation.col(\"my_first_json_customer_details.name\")\n          .isEqualCol(\"name\")\n      )\n  )\n
    "},{"location":"setup/validation/upstream-data-source-validation/#different-join-type","title":"Different join type","text":"

    By default, an outer join is used to gather columns from both datasets together for validation. But there may be scenarios where you want to control the join type.

    Possible join types include: - inner - outer, full, fullouter, full_outer - leftouter, left, left_outer - rightouter, right, right_outer - leftsemi, left_semi, semi - leftanti, left_anti, anti - cross

    In the example below, we do an anti join by column account_id and check if there are no records. This essentially checks that all account_id's from my_second_json exist in my_first_json. The second validation also does something similar but does an outer join (by default) and checks that the joined dataset has 30 records.

    JavaScala 
    var firstJsonTask = json(\"my_first_json\", \"/tmp/data/first_json\")\n  .schema(\n    field().name(\"account_id\").regex(\"ACC[0-9]{8}\"),\n    field().name(\"customer_details\")\n      .schema(\n        field().name(\"name\").expression(\"#{Name.name}\")\n      )\n  );\n\nvar secondJsonTask = json(\"my_second_json\", \"/tmp/data/second_json\")\n  .validations(\n    validation().upstreamData(firstJsonTask)\n      .joinColumns(\"account_id\")\n      .joinType(\"anti\")\n      .withValidation(validation().count().isEqual(0)),\n    validation().upstreamData(firstJsonTask)\n      .joinColumns(\"account_id\")\n      .withValidation(validation().count().isEqual(30))\n  );\n
    val firstJsonTask = json(\"my_first_json\", \"/tmp/data/first_json\")\n  .schema(\n    field.name(\"account_id\").regex(\"ACC[0-9]{8}\"),\n    field.name(\"customer_details\")\n      .schema(\n        field.name(\"name\").expression(\"#{Name.name}\")\n      )\n  )\n\nval secondJsonTask = json(\"my_second_json\", \"/tmp/data/second_json\")\n  .validations(\n    validation.upstreamData(firstJsonTask)\n      .joinColumns(\"account_id\")\n      .joinType(\"anti\")\n      .withValidation(validation.count().isEqual(0)),\n    validation.upstreamData(firstJsonTask)\n      .joinColumns(\"account_id\")\n      .withValidation(validation.count().isEqual(30))\n  )\n
    "},{"location":"setup/validation/upstream-data-source-validation/#join-then-group-by-validation","title":"Join then group by validation","text":"

    We can apply aggregate or group by validations to the resulting joined dataset as the withValidation method accepts any type of validation.

    Here we group by account_id, my_first_json_balance to check that when the amount field is summed up per group, it is between 0.8 and 1.2 times the balance.

    JavaScala 
    var firstJsonTask = json(\"my_first_json\", \"/tmp/data/first_json\")\n  .schema(\n    field().name(\"account_id\").regex(\"ACC[0-9]{8}\"),\n    field().name(\"balance\").type(DoubleType.instance()).min(10).max(1000),\n    field().name(\"customer_details\")\n      .schema(\n        field().name(\"name\").expression(\"#{Name.name}\")\n      )\n  );\n\nvar secondJsonTask = json(\"my_second_json\", \"/tmp/data/second_json\")\n  .validations(\n    validation().upstreamData(firstJsonTask).joinColumns(\"account_id\")\n      .withValidation(\n        validation().groupBy(\"account_id\", \"my_first_json_balance\")\n          .sum(\"amount\")\n          .betweenCol(\"my_first_json_balance * 0.8\", \"my_first_json_balance * 1.2\")\n      )\n  );\n
    val firstJsonTask = json(\"my_first_json\", \"/tmp/data/first_json\")\n  .schema(\n    field.name(\"account_id\").regex(\"ACC[0-9]{8}\"),\n    field.name(\"balance\").`type`(DoubleType).min(10).max(1000),\n    field.name(\"customer_details\")\n      .schema(\n        field.name(\"name\").expression(\"#{Name.name}\")\n      )\n  )\n\nval secondJsonTask = json(\"my_second_json\", \"/tmp/data/second_json\")\n  .validations(\n    validation.upstreamData(firstJsonTask).joinColumns(\"account_id\")\n      .withValidation(\n        validation.groupBy(\"account_id\", \"my_first_json_balance\")\n          .sum(\"amount\")\n          .betweenCol(\"my_first_json_balance * 0.8\", \"my_first_json_balance * 1.2\")\n      )\n  )\n
    "},{"location":"setup/validation/upstream-data-source-validation/#chained-validations","title":"Chained validations","text":"

    Given that the withValidation method accepts any other type of validation, you can chain other upstream data sources with it. Here we will show a third upstream data source being checked to ensure 30 records exists after joining them together by account_id.

    JavaScala 
    var firstJsonTask = json(\"my_first_json\", \"/tmp/data/first_json\")\n  .schema(\n    field().name(\"account_id\").regex(\"ACC[0-9]{8}\"),\n    field().name(\"balance\").type(DoubleType.instance()).min(10).max(1000),\n    field().name(\"customer_details\")\n      .schema(\n        field().name(\"name\").expression(\"#{Name.name}\")\n      )\n  )\n  .count(count().records(10));\n\nvar thirdJsonTask = json(\"my_third_json\", \"/tmp/data/third_json\")\n  .schema(\n    field().name(\"account_id\"),\n    field().name(\"amount\").type(IntegerType.instance()).min(1).max(100),\n    field().name(\"name\").expression(\"#{Name.name}\")\n  )\n  .count(count().records(10));\n\nvar secondJsonTask = json(\"my_second_json\", \"/tmp/data/second_json\")\n  .validations(\n    validation().upstreamData(firstJsonTask)\n      .joinColumns(\"account_id\")\n      .withValidation(\n        validation().upstreamData(thirdJsonTask)\n          .joinColumns(\"account_id\")\n          .withValidation(validation().count().isEqual(30))\n      )\n  );\n
    val firstJsonTask = json(\"my_first_json\", \"/tmp/data/first_json\")\n  .schema(\n    field.name(\"account_id\").regex(\"ACC[0-9]{8}\"),\n    field.name(\"balance\").`type`(DoubleType).min(10).max(1000),\n    field.name(\"customer_details\")\n      .schema(\n        field.name(\"name\").expression(\"#{Name.name}\")\n      )\n  )\n  .count(count.records(10))\n\nval thirdJsonTask = json(\"my_third_json\", \"/tmp/data/third_json\")\n  .schema(\n    field.name(\"account_id\"),\n    field.name(\"amount\").`type`(IntegerType).min(1).max(100),\n    field.name(\"name\").expression(\"#{Name.name}\"),\n  )\n  .count(count.records(10))\n\nval secondJsonTask = json(\"my_second_json\", \"/tmp/data/second_json\")\n  .validations(\n    validation.upstreamData(firstJsonTask).joinColumns(\"account_id\")\n      .withValidation(\n        validation.groupBy(\"account_id\", \"my_first_json_balance\")\n          .sum(\"amount\")\n          .betweenCol(\"my_first_json_balance * 0.8\", \"my_first_json_balance * 1.2\")\n      ),\n  )\n
    "},{"location":"use-case/business-value/","title":"Business Value","text":"

    Below is a list of the business related benefits from using Data Caterer which may be applicable for your use case.

    Problem Data Caterer Solution Resources Effects Reliable test data creation - Profile existing data- Create scenarios- Generate data Software Engineers, QA, Testers Cost reduction in labor, more time spent on development, more bugs caught before production Faster development cycles - Generate data in local, test, UAT, pre-prod- Run different scenarios Software Engineers, QA, Testers More defects caught in lower environments, features pushed to production faster, common framework used across all environments Data compliance - Profiling existing data- Generate based on metadata- No complex masking- No production data used in lower environments Audit and compliance No chance for production data breaches Storage costs - Delete generated data- Test specific scenarios Infrastructure Lower data storage costs, less time spent on data management and clean up Schema evolution - Create metadata from data sources- Generate data based off fresh metadata Software Engineers, QA, Testers Less time spent altering tests due to schema changes, ease of use between environments and application versions"},{"location":"use-case/comparison/","title":"Comparison to similar tools","text":"

    I have tried to include all the companies found in the list here from Mostly AI blog post and used information that is publicly available.

    The companies/products not shown below either have:

    • a website with insufficient information about the technology side of data generation/validation
    • no/little documentation
    • don't have a free, no sign-up version of their app to use
    "},{"location":"use-case/comparison/#data-generation","title":"Data Generation","text":"Tool Description Cost Pros Cons Clearbox AI Python based data generation tool via ML Unclear Python SDK UI interface Detect private data Report generation Batch data only No data clean up Limited/no documentation Curiosity Software Platform solution for test data management Unclear Extensive documentation Generate data based off test cases UI interface Web/API/UI/mobile testing No quick start No SDK Many components that may not be required No event generation support DataCebo Synthetic Data Vault Python based data generation tool via ML Unclear Python SDK Report generation Data quality checks Business logic constraints No data connection support No data clean up No foreign key support Datafaker Realistic data generation library Free SDK for many languages Simple, easy to use Extensible Open source Generate realistic values No data connection support No data clean up No validation No foreign key support DBLDatagen Python based data generation tool Free Python SDK Open source Good documentation Customisable scenarios Customisable column generation Generate from existing data/schemas Plugin third-party libraries Limited support if issues Code required No data clean up No data validation Gatling HTTP API load testing tool Free (Open Source)Gatling Enterprise, usage based, starts from \u20ac89 per month, 1 user, 6.25 hours of testing Kotlin, Java & Scala SDK Widely used Open source Clear documentation Extensive testing/validation support Customisable scenarios Report generation Only supports HTTP, JMS and JDBC No data clean up Data feeders not based off metadata Gretel Python based data generation tool via ML Usage based, starts from $295 per month, $2.20 per credit, assumed USD CLI & Python SDK UI interface Training and re-use of models Detect private data Customisable scenarios Batch data only No relationships between data sources Only simple foreign key relations defined No data clean up Charge by usage Howso Python based data generation tool via ML Unclear Python SDK Playground to try Open source library Customisable scenarios No support for data sources No data validation No data clean up Mostly AI Python based data generation tool via ML Usage based, Enterprise 1 user, 100 columns, 100K rows $3,100 per month, assumed USD Report generation Non-technical users can use UI Customisable scenarios Charge by usage Batch data only No data clean up Confusing use of 'smart select' for multiple foreign keys Limited custom column generation logic Multiple deployment components No SDK Octopize Python based data generation tool via ML Unclear Python & R SDK Report generation API for metadata Customisable scenarios Input data source is only CSV Multiple manual steps before starting Quickstart is not a quickstart Documentation lacks code examples Synthesized Python based data generation tool via ML Unclear CLI & Python SDK API for metadata IDE setup Data quality checks Not sure what is SDK & TDK Charge by usage No report of what was generated No relationships between data sources Tonic Platform solution for generating data Unclear UI interface Good documentation Detect private data Support for encrypted columns Report generation Alerting Batch data only Multiple deployment components No relationships between data sources No data validation No data clean up No SDK (only API) Difficult to embed complex business logic YData Python based data generation tool via ML. Platform solution as well Unclear Python SDK Open source Detect private data Compare datasets Report generation No data connection support Batch data only No data clean up Separate data generation and data validation No foreign key support"},{"location":"use-case/comparison/#use-of-ml-models","title":"Use of ML models","text":"

    You may notice that the majority of data generators use machine learning (ML) models to learn from your existing datasets to generate new data. Below are some pros and cons to the approach.

    Pros

    • Simple setup
    • Ability to reproduce complex logic
    • Flexible to accept all types of data

    Cons

    • Long time for model learning
    • Black box of logic
    • Maintain, store and update of ML models
    • Restriction on input data lengths
    • May not maintain referential integrity
    • Require deeper understanding of ML models for fine-tuning
    • Accuracy may be worse than non-ML models
    "},{"location":"use-case/roadmap/","title":"Roadmap","text":"

    Items below summarise the roadmap of Data Caterer. As each task gets completed, it will be documented and linked.

    Feature Description Sub Tasks Data source support Batch or real time data sources that can be added to Data Caterer. Support data sources that users want - AWS, GCP and Azure related data services ( cloud storage)- Deltalake- RabbitMQ- ActiveMQ- MongoDB- Elasticsearch- Snowflake- Databricks- Pulsar Metadata discovery Allow for schema and data profiling from external metadata sources - HTTP (OpenAPI spec)- JMS- Read from samples- OpenLineage metadata (Marquez)- OpenMetadata- ODCS (Open Data Contract Standard)- Amundsen- Datahub- Solace Event Portal- Airflow- DBT Developer API Scala/Java interface for developers/testers to create data generation and validation tasks - Scala- Java Report generation Generate a report that summarises the data generation or validation results - Report for data generated and validation rules UI portal Allow users to access a UI to input data generation or validation tasks. Also be able to view report results - Metadata stored in database- Store data generation/validation run information in file/database Integration with data validation tools Derive data validation rules from existing data validation tools - Great Expectation- DBT constraints- SodaCL- MonteCarlo Data validation rule suggestions Based on metadata, generate data validation rules appropriate for the dataset - Suggest basic data validations (yet to document) Wait conditions before data validation Define certain conditions to be met before starting data validations - Webhook- File exists- Data exists via SQL expression- Pause Validation types Ability to define simple/complex data validations - Basic validations- Aggregates (sum of amount per account is > 500)- Ordering (transactions are ordered by date)- Relationship (at least one account entry in history table per account in accounts table)- Data profile (how close the generated data profile is compared to the expected data profile)- Column name (check column count, column names, ordering) Data generation record count Generate scenarios where there are one to many, many to many situations relating to record count. Also ability to cover all edge cases or scenarios - Cover all possible cases (i.e. record for each combination of oneOf values, positive/negative values etc.)- Ability to override edge cases Alerting When tasks have completed, ability to define alerts based on certain conditions - Slack- Email Metadata enhancements Based on data profiling or inference, can add to existing metadata - PII detection (can integrate with Presidio)- Relationship detection across data sources- SQL generation- Ordering information Data cleanup Ability to clean up generated data - Clean up generated data- Clean up data in consumer data sinks- Clean up data from real time sources (i.e. DELETE HTTP endpoint, delete events in JMS) Trial version Trial version of the full app for users to test out all the features - Trial app to try out all features Code generation Based on metadata or existing classes, code for data generation and validation could be generated - Code generation- Schema generation from Scala/Java class Real time response data validations Ability to define data validations based on the response from real time data sources (e.g. HTTP response) - HTTP response data validation"},{"location":"use-case/blog/shift-left-data-quality/","title":"Shifting Data Quality Left with Data Catering","text":""},{"location":"use-case/blog/shift-left-data-quality/#empowering-proactive-data-management","title":"Empowering Proactive Data Management","text":"

    In the ever-evolving landscape of data-driven decision-making, ensuring data quality is non-negotiable. Traditionally, data quality has been a concern addressed late in the development lifecycle, often leading to reactive measures and increased costs. However, a paradigm shift is underway with the adoption of a \"shift left\" approach, placing data quality at the forefront of the development process.

    "},{"location":"use-case/blog/shift-left-data-quality/#today","title":"Today","text":"
    graph LR\n  subgraph badQualityData[<b>Manually generated data, limited data scenarios</b>]\n  local[<b>Local</b>\\nManual test, unit test]\n  dev[<b>Dev</b>\\nManual test, integration test]\n  stg[<b>Staging</b>\\nSanity checks]\n  end\n\n  subgraph qualityData[<b>Reliable data, the true test</b>]\n  prod[<b>Production</b>\\nData quality checks, monitoring, observaibility]\n  end\n\n  style badQualityData fill:#d9534f,fill-opacity:0.7\n  style qualityData fill:#5cb85c,fill-opacity:0.7\n\n  local --> dev\n  dev --> stg\n  stg --> prod
    "},{"location":"use-case/blog/shift-left-data-quality/#with-data-caterer","title":"With Data Caterer","text":"
    graph LR\n  subgraph qualityData[<b>Reliable data for testing anywhere<br>Common testing tool</b>]\n  direction LR\n  local[<b>Local</b>\\nManual test, unit test]\n  dev[<b>Dev</b>\\nManual test, integration test]\n  stg[<b>Staging</b>\\nSanity checks]\n  prod[<b>Production</b>\\nData quality checks, monitoring, observaibility]\n  end\n\n  style qualityData fill:#5cb85c,fill-opacity:0.7\n\n  local --> dev\n  dev --> stg\n  stg --> prod
    "},{"location":"use-case/blog/shift-left-data-quality/#understanding-the-shift-left-approach","title":"Understanding the Shift Left Approach","text":"

    \"Shift left\" is a philosophy that advocates for addressing tasks and concerns earlier in the development lifecycle. Applied to data quality, it means tackling data issues as early as possible, ideally during the development and testing phases. This approach aims to catch data anomalies, inaccuracies, or inconsistencies before they propagate through the system, reducing the likelihood of downstream errors.

    "},{"location":"use-case/blog/shift-left-data-quality/#data-caterer-the-catalyst-for-shifting-left","title":"Data Caterer: The Catalyst for Shifting Left","text":"

    Enter Data Caterer, a metadata-driven data generation and validation tool designed to empower organizations in shifting data quality left. By incorporating Data Caterer into the early stages of development, teams can proactively test complex data flows, validate data sources, and ensure data quality before it reaches downstream processes.

    "},{"location":"use-case/blog/shift-left-data-quality/#key-advantages-of-shifting-data-quality-left-with-data-caterer","title":"Key Advantages of Shifting Data Quality Left with Data Caterer","text":"
    1. Early Issue Detection:
      • Identify data quality issues early in the development process, reducing the risk of errors downstream.
    2. Proactive Validation:
      • Validate data sources and complex data flows in a simplified manner, promoting a proactive approach to data quality.
    3. Efficient Testing Across Sources:
      • Seamlessly test data across various sources, including databases, file formats, HTTP, and messaging, all within your local laptop or development environment.
      • Fast feedback loop to motivate developers to ensure thorough testing of data scenarios.
    4. Integration with Development Pipelines:
      • Easily integrate Data Caterer as a task in your development pipelines, ensuring that data quality is a continuous consideration rather than an isolated event.
    5. Integration with Existing Metadata:
      • By harnessing the power of existing metadata from data catalogs, schema registries, or other data validation tools, Data Caterer streamlines the process, automating the generation and validation of your data effortlessly.
    6. Improved Collaboration:
      • Facilitate collaboration between developers, testers, and data professionals by providing a common platform for early data validation.
    "},{"location":"use-case/blog/shift-left-data-quality/#realizing-the-vision-of-proactive-data-quality","title":"Realizing the Vision of Proactive Data Quality","text":"

    As organizations strive for excellence in their data-driven endeavors, the shift left approach with Data Caterer becomes a strategic imperative. By instilling a proactive data quality culture, teams can minimize the risk of costly errors, enhance the reliability of their data, and streamline the entire development lifecycle.

    In conclusion, the marriage of the shift left philosophy and Data Caterer brings forth a new era of data management, where data quality is not just a final checkpoint but an integral part of every development milestone. Embrace the shift left approach with Data Caterer and empower your teams to build robust, high-quality data solutions from the very beginning.

    Shift Left, Validate Early, and Accelerate with Data Caterer.

    "}]} \ No newline at end of file diff --git a/setup/advanced/index.html b/setup/advanced/index.html new file mode 100644 index 00000000..a4cfddb9 --- /dev/null +++ b/setup/advanced/index.html @@ -0,0 +1,2609 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Advanced - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Advanced use cases

    +

    Special data formats

    +

    There are many options available for you to use when you have a scenario when data has to be a certain format.

    +
      +
    1. Create expression datafaker
        +
      1. Can be used to create names, addresses, or anything that can be found + under here
        2. +
      +
      2. +
    2. Create regex
      3. +
    +

    Foreign keys across data sets

    +

    Multiple data source foreign key example

    +

    Details for how you can configure foreign keys can be found here.

    +

    Edge cases

    +

    For each given data type, there are edge cases which can cause issues when your application processes the data. +This can be controlled at a column level by including the following flag in the generator options:

    +
    +
    +
    +
    field()
+  .name("amount")
+  .type(DoubleType.instance())
+  .enableEdgeCases(true)
+  .edgeCaseProbability(0.1)
+
    +
    +
    +
    field
+  .name("amount")
+  .`type`(DoubleType)
+  .enableEdgeCases(true)
+  .edgeCaseProbability(0.1)
+
    +
    +
    +
    fields:
+  - name: "amount"
+    type: "double"
+    generator:
+      type: "random"
+      options:
+        enableEdgeCases: "true"
+        edgeCaseProb: 0.1
+
    +
    +
    +
    +

    If you want to know all the possible edge cases for each data +type, can check the documentation here.

    +

    Scenario testing

    +

    You can create specific scenarios by adjusting the metadata found in the plan and tasks to your liking.
    +For example, if you had two data sources, a Postgres database and a parquet file, and you wanted to save account data +into Postgres and transactions related to those accounts into a parquet file. +You can alter the status column in the account data to only generate open accounts +and define a foreign key between Postgres and parquet to ensure the same account_id is being used.
    +Then in the parquet task, define 1 to 10 transactions per account_id to be generated.

    +

    Postgres account generation example task
    +Parquet transaction generation example task
    +Plan

    +

    Cloud storage

    +

    Data source

    +

    If you want to save the file types CSV, JSON, Parquet or ORC into cloud storage, you can do so via adding extra +configurations. Below is an example for S3.

    +
    +
    +
    +
    var csvTask = csv("my_csv", "s3a://my-bucket/csv/accounts")
+  .schema(
+    field().name("account_id"),
+    ...
+  );
+
+var s3Configuration = configuration()
+  .runtimeConfig(Map.of(
+    "spark.hadoop.fs.s3a.directory.marker.retention", "keep",
+    "spark.hadoop.fs.s3a.bucket.all.committer.magic.enabled", "true",
+    "spark.hadoop.fs.defaultFS", "s3a://my-bucket",
+    //can change to other credential providers as shown here
+    //https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers
+    "spark.hadoop.fs.s3a.aws.credentials.provider", "org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider",
+    "spark.hadoop.fs.s3a.access.key", "access_key",
+    "spark.hadoop.fs.s3a.secret.key", "secret_key"
+  ));
+
+execute(s3Configuration, csvTask);
+
    +
    +
    +
    val csvTask = csv("my_csv", "s3a://my-bucket/csv/accounts")
+  .schema(
+    field.name("account_id"),
+    ...
+  )
+
+val s3Configuration = configuration
+  .runtimeConfig(Map(
+    "spark.hadoop.fs.s3a.directory.marker.retention" -> "keep",
+    "spark.hadoop.fs.s3a.bucket.all.committer.magic.enabled" -> "true",
+    "spark.hadoop.fs.defaultFS" -> "s3a://my-bucket",
+    //can change to other credential providers as shown here
+    //https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers
+    "spark.hadoop.fs.s3a.aws.credentials.provider" -> "org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider",
+    "spark.hadoop.fs.s3a.access.key" -> "access_key",
+    "spark.hadoop.fs.s3a.secret.key" -> "secret_key"
+  ))
+
+execute(s3Configuration, csvTask)
+
    +
    +
    +
    folders {
+   generatedPlanAndTaskFolderPath = "s3a://my-bucket/data-caterer/generated"
+   planFilePath = "s3a://my-bucket/data-caterer/generated/plan/customer-create-plan.yaml"
+   taskFolderPath = "s3a://my-bucket/data-caterer/generated/task"
+}
+
+runtime {
+    config {
+        ...
+        #S3
+        "spark.hadoop.fs.s3a.directory.marker.retention" = "keep"
+        "spark.hadoop.fs.s3a.bucket.all.committer.magic.enabled" = "true"
+        "spark.hadoop.fs.defaultFS" = "s3a://my-bucket"
+        #can change to other credential providers as shown here
+        #https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers
+        "spark.hadoop.fs.s3a.aws.credentials.provider" = "org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider"
+        "spark.hadoop.fs.s3a.access.key" = "access_key"
+        "spark.hadoop.fs.s3a.secret.key" = "secret_key"
+   }
+}
+
    +
    +
    +
    +

    Storing plan/task(s)

    +

    You can generate and store the plan/task files inside either AWS S3, Azure Blob Storage or Google GCS. +This can be controlled via configuration set in the application.conf file where you can set something like the below:

    +
    +
    +
    +
    configuration()
+  .generatedReportsFolderPath("s3a://my-bucket/data-caterer/generated")
+  .planFilePath("s3a://my-bucket/data-caterer/generated/plan/customer-create-plan.yaml")
+  .taskFolderPath("s3a://my-bucket/data-caterer/generated/task")
+  .runtimeConfig(Map.of(
+    "spark.hadoop.fs.s3a.directory.marker.retention", "keep",
+    "spark.hadoop.fs.s3a.bucket.all.committer.magic.enabled", "true",
+    "spark.hadoop.fs.defaultFS", "s3a://my-bucket",
+    //can change to other credential providers as shown here
+    //https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers
+    "spark.hadoop.fs.s3a.aws.credentials.provider", "org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider",
+    "spark.hadoop.fs.s3a.access.key", "access_key",
+    "spark.hadoop.fs.s3a.secret.key", "secret_key"
+  ));
+
    +
    +
    +
    configuration
+  .generatedReportsFolderPath("s3a://my-bucket/data-caterer/generated")
+  .planFilePath("s3a://my-bucket/data-caterer/generated/plan/customer-create-plan.yaml")
+  .taskFolderPath("s3a://my-bucket/data-caterer/generated/task")
+  .runtimeConfig(Map(
+    "spark.hadoop.fs.s3a.directory.marker.retention" -> "keep",
+    "spark.hadoop.fs.s3a.bucket.all.committer.magic.enabled" -> "true",
+    "spark.hadoop.fs.defaultFS" -> "s3a://my-bucket",
+    //can change to other credential providers as shown here
+    //https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers
+    "spark.hadoop.fs.s3a.aws.credentials.provider" -> "org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider",
+    "spark.hadoop.fs.s3a.access.key" -> "access_key",
+    "spark.hadoop.fs.s3a.secret.key" -> "secret_key"
+  ))
+
    +
    +
    +
    folders {
+   generatedPlanAndTaskFolderPath = "s3a://my-bucket/data-caterer/generated"
+   planFilePath = "s3a://my-bucket/data-caterer/generated/plan/customer-create-plan.yaml"
+   taskFolderPath = "s3a://my-bucket/data-caterer/generated/task"
+}
+
+runtime {
+    config {
+        ...
+        #S3
+        "spark.hadoop.fs.s3a.directory.marker.retention" = "keep"
+        "spark.hadoop.fs.s3a.bucket.all.committer.magic.enabled" = "true"
+        "spark.hadoop.fs.defaultFS" = "s3a://my-bucket"
+        #can change to other credential providers as shown here
+        #https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers
+        "spark.hadoop.fs.s3a.aws.credentials.provider" = "org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider"
+        "spark.hadoop.fs.s3a.access.key" = "access_key"
+        "spark.hadoop.fs.s3a.secret.key" = "secret_key"
+   }
+}
+
    +
    +
    +
    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/configuration/index.html b/setup/configuration/index.html new file mode 100644 index 00000000..feebeab1 --- /dev/null +++ b/setup/configuration/index.html @@ -0,0 +1,2774 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Configuration - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Configuration

    +

    A number of configurations can be made and customised within Data Caterer to help control what gets run and/or where any +metadata gets saved.

    +

    These configurations are defined from within your Java or Scala class via configuration or for YAML file setup, +application.conf file as seen +here.

    +

    Flags

    +

    Flags are used to control which processes are executed when you run Data Caterer.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ConfigDefaultPaidDescription
    enableGenerateDatatrueNEnable/disable data generation
    enableCounttrueNCount the number of records generated. Can be disabled to improve performance
    enableFailOnErrortrueNWhilst saving generated data, if there is an error, it will stop any further data from being generated
    enableSaveReportstrueNEnable/disable HTML reports summarising data generated, metadata of data generated (if enableSinkMetadata is enabled) and validation results (if enableValidation is enabled). Sample here
    enableSinkMetadatatrueNRun data profiling for the generated data. Shown in HTML reports if enableSaveSinkMetadata is enabled
    enableValidationfalseNRun validations as described in plan. Results can be viewed from logs or from HTML report if enableSaveSinkMetadata is enabled. Sample here
    enableGeneratePlanAndTasksfalseYEnable/disable plan and task auto generation based off data source connections
    enableRecordTrackingfalseYEnable/disable which data records have been generated for any data source
    enableDeleteGeneratedRecordsfalseYDelete all generated records based off record tracking (if enableRecordTracking has been set to true)
    enableGenerateValidationsfalseYIf enabled, it will generate validations based on the data sources defined.
    +
    +
    +
    +
    configuration()
+  .enableGenerateData(true)
+  .enableCount(true)
+  .enableFailOnError(true)
+  .enableSaveReports(true)
+  .enableSinkMetadata(true)
+  .enableValidation(false)
+  .enableGeneratePlanAndTasks(false)
+  .enableRecordTracking(false)
+  .enableDeleteGeneratedRecords(false)
+  .enableGenerateValidations(false);
+
    +
    +
    +
    configuration
+  .enableGenerateData(true)
+  .enableCount(true)
+  .enableFailOnError(true)
+  .enableSaveReports(true)
+  .enableSinkMetadata(true)
+  .enableValidation(false)
+  .enableGeneratePlanAndTasks(false)
+  .enableRecordTracking(false)
+  .enableDeleteGeneratedRecords(false)
+  .enableGenerateValidations(false)
+
    +
    +
    +
    flags {
+  enableCount = false
+  enableCount = ${?ENABLE_COUNT}
+  enableGenerateData = true
+  enableGenerateData = ${?ENABLE_GENERATE_DATA}
+  enableFailOnError = true
+  enableFailOnError = ${?ENABLE_FAIL_ON_ERROR}
+  enableGeneratePlanAndTasks = false
+  enableGeneratePlanAndTasks = ${?ENABLE_GENERATE_PLAN_AND_TASKS}
+  enableRecordTracking = false
+  enableRecordTracking = ${?ENABLE_RECORD_TRACKING}
+  enableDeleteGeneratedRecords = false
+  enableDeleteGeneratedRecords = ${?ENABLE_DELETE_GENERATED_RECORDS}
+  enableGenerateValidations = false
+  enableGenerateValidations = ${?ENABLE_GENERATE_VALIDATIONS}
+}
+
    +
    +
    +
    +

    Folders

    +

    Depending on which flags are enabled, there are folders that get used to save metadata, store HTML reports or track the +records generated.

    +

    These folder pathways can be defined as a cloud storage pathway (i.e. s3a://my-bucket/task).

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ConfigDefaultPaidDescription
    planFilePath/opt/app/plan/customer-create-plan.yamlNPlan file path to use when generating and/or validating data
    taskFolderPath/opt/app/taskNTask folder path that contains all the task files (can have nested directories)
    validationFolderPath/opt/app/validationNValidation folder path that contains all the validation files (can have nested directories)
    generatedReportsFolderPath/opt/app/reportNWhere HTML reports get generated that contain information about data generated along with any validations performed
    generatedPlanAndTaskFolderPath/tmpYFolder path where generated plan and task files will be saved
    recordTrackingFolderPath/opt/app/record-trackingYWhere record tracking parquet files get saved
    +
    +
    +
    +
    configuration()
+  .planFilePath("/opt/app/custom/plan/postgres-plan.yaml")
+  .taskFolderPath("/opt/app/custom/task")
+  .validationFolderPath("/opt/app/custom/validation")
+  .generatedReportsFolderPath("/opt/app/custom/report")
+  .generatedPlanAndTaskFolderPath("/opt/app/custom/generated")
+  .recordTrackingFolderPath("/opt/app/custom/record-tracking");
+
    +
    +
    +
    configuration
+  .planFilePath("/opt/app/custom/plan/postgres-plan.yaml")
+  .taskFolderPath("/opt/app/custom/task")
+  .validationFolderPath("/opt/app/custom/validation")
+  .generatedReportsFolderPath("/opt/app/custom/report")
+  .generatedPlanAndTaskFolderPath("/opt/app/custom/generated")
+  .recordTrackingFolderPath("/opt/app/custom/record-tracking")
+
    +
    +
    +
    folders {
+  planFilePath = "/opt/app/custom/plan/postgres-plan.yaml"
+  planFilePath = ${?PLAN_FILE_PATH}
+  taskFolderPath = "/opt/app/custom/task"
+  taskFolderPath = ${?TASK_FOLDER_PATH}
+  validationFolderPath = "/opt/app/custom/validation"
+  validationFolderPath = ${?VALIDATION_FOLDER_PATH}
+  generatedReportsFolderPath = "/opt/app/custom/report"
+  generatedReportsFolderPath = ${?GENERATED_REPORTS_FOLDER_PATH}
+  generatedPlanAndTaskFolderPath = "/opt/app/custom/generated"
+  generatedPlanAndTaskFolderPath = ${?GENERATED_PLAN_AND_TASK_FOLDER_PATH}
+  recordTrackingFolderPath = "/opt/app/custom/record-tracking"
+  recordTrackingFolderPath = ${?RECORD_TRACKING_FOLDER_PATH}
+}
+
    +
    +
    +
    +

    Metadata

    +

    When metadata gets generated, there are some configurations that can be altered to help with performance or accuracy +related issues. +Metadata gets generated from two processes: 1) if enableGeneratePlanAndTasks or 2) if enableSinkMetadata are +enabled.

    +

    During the generation of plan and tasks, data profiling is used to create the metadata for each of the fields defined in +the data source. +You may face issues if the number of records in the data source is large as data profiling is an expensive task. +Similarly, it can be expensive +when analysing the generated data if the number of records generated is large.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ConfigDefaultPaidDescription
    numRecordsFromDataSource10000YNumber of records read in from the data source that could be used for data profiling
    numRecordsForAnalysis10000YNumber of records used for data profiling from the records gathered in numRecordsFromDataSource
    oneOfMinCount1000YMinimum number of records required before considering if a field can be of type oneOf
    oneOfDistinctCountVsCountThreshold0.2YThreshold ratio to determine if a field is of type oneOf (i.e. a field called status that only contains open or closed. Distinct count = 2, total count = 10, ratio = 2 / 10 = 0.2 therefore marked as oneOf)
    numGeneratedSamples10NNumber of sample records from generated data to take. Shown in HTML report
    +
    +
    +
    +
    configuration()
+  .numRecordsFromDataSourceForDataProfiling(10000)
+  .numRecordsForAnalysisForDataProfiling(10000)
+  .oneOfMinCount(1000)
+  .oneOfDistinctCountVsCountThreshold(1000)
+  .numGeneratedSamples(10);
+
    +
    +
    +
    configuration
+  .numRecordsFromDataSourceForDataProfiling(10000)
+  .numRecordsForAnalysisForDataProfiling(10000)
+  .oneOfMinCount(1000)
+  .oneOfDistinctCountVsCountThreshold(1000)
+  .numGeneratedSamples(10)
+
    +
    +
    +
    metadata {
+  numRecordsFromDataSource = 10000
+  numRecordsForAnalysis = 10000
+  oneOfMinCount = 1000
+  oneOfDistinctCountVsCountThreshold = 0.2
+  numGeneratedSamples = 10
+}
+
    +
    +
    +
    +

    Generation

    +

    When generating data, you may have some limitations such as limited CPU or memory, large number of data sources, or data +sources prone to failure under load. +To help alleviate these issues or speed up performance, you can control the number of records that get generated in each +batch.

    + + + + + + + + + + + + + + + + + + + + + + + +
    ConfigDefaultPaidDescription
    numRecordsPerBatch100000NNumber of records across all data sources to generate per batch
    numRecordsPerStepNOverrides the count defined in each step with this value if defined (i.e. if set to 1000, for each step, 1000 records will be generated)
    +
    +
    +
    +
    configuration()
+  .numRecordsPerBatch(100000)
+  .numRecordsPerStep(1000);
+
    +
    +
    +
    configuration
+  .numRecordsPerBatch(100000)
+  .numRecordsPerStep(1000)
+
    +
    +
    +
    generation {
+  numRecordsPerBatch = 100000
+  numRecordsPerStep = 1000
+}
+
    +
    +
    +
    +

    Runtime

    +

    Given Data Caterer uses Spark as the base framework for data processing, you can configure the job as to your +specifications via configuration as seen here.

    +
    +
    +
    +
    configuration()
+  .master("local[*]")
+  .runtimeConfig(Map.of("spark.driver.cores", "5"))
+  .addRuntimeConfig("spark.driver.memory", "10g");
+
    +
    +
    +
    configuration
+  .master("local[*]")
+  .runtimeConfig(Map("spark.driver.cores" -> "5"))
+  .addRuntimeConfig("spark.driver.memory" -> "10g")
+
    +
    +
    +
    runtime {
+  master = "local[*]"
+  master = ${?DATA_CATERER_MASTER}
+  config {
+    "spark.driver.cores" = "5"
+    "spark.driver.memory" = "10g"
+  }
+}
+
    +
    +
    +
    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/connection/index.html b/setup/connection/index.html new file mode 100644 index 00000000..32afac7f --- /dev/null +++ b/setup/connection/index.html @@ -0,0 +1,3048 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Connection - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Data Source Connections

    +

    Details of all the connection configuration supported can be found in the below subsections for each type of connection.

    +

    These configurations can be done via API or from configuration. Examples of both are shown for each data source below.

    +

    Supported Data Connections

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Data Source TypeData SourceSponsor
    DatabasePostgres, MySQL, CassandraN
    FileCSV, JSON, ORC, ParquetN
    MessagingKafka, SolaceY
    HTTPREST APIY
    MetadataMarquez, OpenMetadata, OpenAPI/SwaggerY
    +

    API

    +

    All connection details require a name. Depending on the data source, you can define additional options which may be used +by the driver or connector for connecting to the data source.

    +

    Configuration file

    +

    All connection details follow the same pattern.

    +
    <connection format> {
+    <connection name> {
+        <key> = <value>
+    }
+}
+
    +
    +

    Overriding configuration

    +

    When defining a configuration value that can be defined by a system property or environment variable at runtime, you can +define that via the following:

    +
    url = "localhost"
+url = ${?POSTGRES_URL}
+
    +

    The above defines that if there is a system property or environment variable named POSTGRES_URL, then that value will +be used for the url, otherwise, it will default to localhost.

    +
    +

    Data sources

    +

    To find examples of a task for each type of data source, please check out this page.

    +

    File

    +

    Linked here is a list of generic options +that can be included as part of your file data source configuration if required. Links to specific file type +configurations can be found below.

    +

    CSV

    +
    +
    +
    +
    csv("customer_transactions", "/data/customer/transaction")
+
    +
    +
    +
    csv("customer_transactions", "/data/customer/transaction")
+
    +
    +
    +
    csv {
+  customer_transactions {
+    path = "/data/customer/transaction"
+    path = ${?CSV_PATH}
+  }
+}
+
    +
    +
    +
    +

    Other available configuration for CSV can be found here

    +

    JSON

    +
    +
    +
    +
    json("customer_transactions", "/data/customer/transaction")
+
    +
    +
    +
    json("customer_transactions", "/data/customer/transaction")
+
    +
    +
    +
    json {
+  customer_transactions {
+    path = "/data/customer/transaction"
+    path = ${?JSON_PATH}
+  }
+}
+
    +
    +
    +
    +

    Other available configuration for JSON can be found here

    +

    ORC

    +
    +
    +
    +
    orc("customer_transactions", "/data/customer/transaction")
+
    +
    +
    +
    orc("customer_transactions", "/data/customer/transaction")
+
    +
    +
    +
    orc {
+  customer_transactions {
+    path = "/data/customer/transaction"
+    path = ${?ORC_PATH}
+  }
+}
+
    +
    +
    +
    +

    Other available configuration for ORC can be found here

    +

    Parquet

    +
    +
    +
    +
    parquet("customer_transactions", "/data/customer/transaction")
+
    +
    +
    +
    parquet("customer_transactions", "/data/customer/transaction")
+
    +
    +
    +
    parquet {
+  customer_transactions {
+    path = "/data/customer/transaction"
+    path = ${?PARQUET_PATH}
+  }
+}
+
    +
    +
    +
    +

    Other available configuration for Parquet can be found here

    +

    Delta (not supported yet)

    +
    +
    +
    +
    delta("customer_transactions", "/data/customer/transaction")
+
    +
    +
    +
    delta("customer_transactions", "/data/customer/transaction")
+
    +
    +
    +
    delta {
+  customer_transactions {
+    path = "/data/customer/transaction"
+    path = ${?DELTA_PATH}
+  }
+}
+
    +
    +
    +
    +

    RMDBS

    +

    Follows the same configuration used by Spark as +found here.
    +Sample can be found below

    +
    +
    +
    +
    postgres(
+    "customer_postgres",                            #name
+    "jdbc:postgresql://localhost:5432/customer",    #url
+    "postgres",                                     #username
+    "postgres"                                      #password
+)
+
    +
    +
    +
    postgres(
+    "customer_postgres",                            #name
+    "jdbc:postgresql://localhost:5432/customer",    #url
+    "postgres",                                     #username
+    "postgres"                                      #password
+)
+
    +
    +
    +
    jdbc {
+    customer_postgres {
+        url = "jdbc:postgresql://localhost:5432/customer"
+        url = ${?POSTGRES_URL}
+        user = "postgres"
+        user = ${?POSTGRES_USERNAME}
+        password = "postgres"
+        password = ${?POSTGRES_PASSWORD}
+        driver = "org.postgresql.Driver"
+    }
+}
+
    +
    +
    +
    +

    Ensure that the user has write permission, so it is able to save the table to the target tables.

    +
    +SQL Permission Statements +
    GRANT INSERT ON <schema>.<table> TO <user>;
+
    +
    +

    Postgres

    +

    Can see example API or Config definition for Postgres connection above.

    +
    Permissions
    +

    Following permissions are required when generating plan and tasks:

    +
    +SQL Permission Statements +
    GRANT SELECT ON information_schema.tables TO < user >;
+GRANT SELECT ON information_schema.columns TO < user >;
+GRANT SELECT ON information_schema.key_column_usage TO < user >;
+GRANT SELECT ON information_schema.table_constraints TO < user >;
+GRANT SELECT ON information_schema.constraint_column_usage TO < user >;
+
    +
    +

    MySQL

    +
    +
    +
    +
    mysql(
+    "customer_mysql",                       #name
+    "jdbc:mysql://localhost:3306/customer", #url
+    "root",                                 #username
+    "root"                                  #password
+)
+
    +
    +
    +
    mysql(
+    "customer_mysql",                       #name
+    "jdbc:mysql://localhost:3306/customer", #url
+    "root",                                 #username
+    "root"                                  #password
+)
+
    +
    +
    +
    jdbc {
+    customer_mysql {
+        url = "jdbc:mysql://localhost:3306/customer"
+        user = "root"
+        password = "root"
+        driver = "com.mysql.cj.jdbc.Driver"
+    }
+}
+
    +
    +
    +
    +
    Permissions
    +

    Following permissions are required when generating plan and tasks:

    +
    +SQL Permission Statements +
    GRANT SELECT ON information_schema.columns TO < user >;
+GRANT SELECT ON information_schema.statistics TO < user >;
+GRANT SELECT ON information_schema.key_column_usage TO < user >;
+
    +
    +

    Cassandra

    +

    Follows same configuration as defined by the Spark Cassandra Connector as +found here

    +
    +
    +
    +
    cassandra(
+    "customer_cassandra",   #name
+    "localhost:9042",       #url
+    "cassandra",            #username
+    "cassandra",            #password
+    Map.of()                #optional additional connection options
+)
+
    +
    +
    +
    cassandra(
+    "customer_cassandra",   #name
+    "localhost:9042",       #url
+    "cassandra",            #username
+    "cassandra",            #password
+    Map()                #optional additional connection options
+)
+
    +
    +
    +
    org.apache.spark.sql.cassandra {
+    customer_cassandra {
+        spark.cassandra.connection.host = "localhost"
+        spark.cassandra.connection.host = ${?CASSANDRA_HOST}
+        spark.cassandra.connection.port = "9042"
+        spark.cassandra.connection.port = ${?CASSANDRA_PORT}
+        spark.cassandra.auth.username = "cassandra"
+        spark.cassandra.auth.username = ${?CASSANDRA_USERNAME}
+        spark.cassandra.auth.password = "cassandra"
+        spark.cassandra.auth.password = ${?CASSANDRA_PASSWORD}
+    }
+}
+
    +
    +
    +
    +
    Permissions
    +

    Ensure that the user has write permission, so it is able to save the table to the target tables.

    +
    +CQL Permission Statements +
    GRANT INSERT ON <schema>.<table> TO <user>;
+
    +
    +

    Following permissions are required when enabling configuration.enableGeneratePlanAndTasks(true) as it will gather +metadata information about tables and columns from the below tables.

    +
    +CQL Permission Statements +
    GRANT SELECT ON system_schema.tables TO <user>;
+GRANT SELECT ON system_schema.columns TO <user>;
+
    +
    +

    Kafka

    +

    Define your Kafka bootstrap server to connect and send generated data to corresponding topics. Topic gets set at a step +level.
    +Further details can be +found here

    +
    +
    +
    +
    kafka(
+    "customer_kafka",   #name
+    "localhost:9092"    #url
+)
+
    +
    +
    +
    kafka(
+    "customer_kafka",   #name
+    "localhost:9092"    #url
+)
+
    +
    +
    +
    kafka {
+    customer_kafka {
+        kafka.bootstrap.servers = "localhost:9092"
+        kafka.bootstrap.servers = ${?KAFKA_BOOTSTRAP_SERVERS}
+    }
+}
+
    +
    +
    +
    +

    When defining your schema for pushing data to Kafka, it follows a specific top level schema.
    +An example can be +found here +. You can define the key, value, headers, partition or topic by following the linked schema.

    +

    JMS

    +

    Uses JNDI lookup to send messages to JMS queue. Ensure that the messaging system you are using has your queue/topic +registered +via JNDI otherwise a connection cannot be created.

    +
    +
    +
    +
    solace(
+    "customer_solace",                                      #name
+    "smf://localhost:55554",                                #url
+    "admin",                                                #username
+    "admin",                                                #password
+    "default",                                              #vpn name
+    "/jms/cf/default",                                      #connection factory
+    "com.solacesystems.jndi.SolJNDIInitialContextFactory"   #initial context factory
+)
+
    +
    +
    +
    solace(
+    "customer_solace",                                      #name
+    "smf://localhost:55554",                                #url
+    "admin",                                                #username
+    "admin",                                                #password
+    "default",                                              #vpn name
+    "/jms/cf/default",                                      #connection factory
+    "com.solacesystems.jndi.SolJNDIInitialContextFactory"   #initial context factory
+)
+
    +
    +
    +
    jms {
+    customer_solace {
+        initialContextFactory = "com.solacesystems.jndi.SolJNDIInitialContextFactory"
+        connectionFactory = "/jms/cf/default"
+        url = "smf://localhost:55555"
+        url = ${?SOLACE_URL}
+        user = "admin"
+        user = ${?SOLACE_USER}
+        password = "admin"
+        password = ${?SOLACE_PASSWORD}
+        vpnName = "default"
+        vpnName = ${?SOLACE_VPN}
+    }
+}
+
    +
    +
    +
    +

    HTTP

    +

    Define any username and/or password needed for the HTTP requests.
    +The url is defined in the tasks to allow for generated data to be populated in the url.

    +
    +
    +
    +
    http(
+    "customer_api", #name
+    "admin",        #username
+    "admin"         #password
+)
+
    +
    +
    +
    http(
+    "customer_api", #name
+    "admin",        #username
+    "admin"         #password
+)
+
    +
    +
    +
    http {
+    customer_api {
+        user = "admin"
+        user = ${?HTTP_USER}
+        password = "admin"
+        password = ${?HTTP_PASSWORD}
+    }
+}
+
    +
    +
    +
    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/deployment/index.html b/setup/deployment/index.html new file mode 100644 index 00000000..0648a69c --- /dev/null +++ b/setup/deployment/index.html @@ -0,0 +1,2370 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Deployment - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
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    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Deployment

    +

    Two main ways to deploy and run Data Caterer:

    +
      +
    • Docker
      • +
    • Helm
      • +
    +

    Docker

    +

    To package up your class along with the Data Caterer base image, you can follow +the Dockerfile that is created for you here.

    +

    Then you can run the following:

    +
    ./gradlew clean build
+docker build -t <my_image_name>:<my_image_tag> .
+
    +

    Helm

    +

    Link to sample helm on GitHub here

    +

    Update +the configuration +to your own data connections and configuration or own image created from above.

    +
    git clone git@github.com:pflooky/data-caterer-example.git
+helm install data-caterer ./data-caterer-example/helm/data-caterer
+
    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/design/index.html b/setup/design/index.html new file mode 100644 index 00000000..49f119fc --- /dev/null +++ b/setup/design/index.html @@ -0,0 +1,2437 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Design - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Design

    +

    This document shows the thought process behind the design of Data Caterer to help give you insights as to how and why +it was created to what it is today. Also, this serves as a reference for future design decisions which will get updated +here and thus is a living document.

    +

    Motivation

    +

    The main difficulties that I faced as a developer and team lead relating to testing were:

    +
      +
    • Difficulty in testing with multiple data sources, both batch and real time
      • +
    • Reliance on other teams for stable environments or domain knowledge
      • +
    • Test environments with no reliable or consistent data flows
      • +
    • Complex data masking/anonymization solutions
      • +
    • Relying on production data (potential privacy and data breach issues)
      • +
    • Cost of data production issues can be very high
      • +
    • Unknown unknowns staying hidden until problems occur in production
      • +
    • Underutilised metadata
      • +
    +

    Guiding Principles

    +

    These difficulties helped formed the basis of the principles for which Data Caterer should follow:

    +
      +
    • Data source agnostic: Connect to any batch or real time data sources for data generation or validation
      • +
    • Configurable: Run the application the way you want
      • +
    • Extensible: Allow for new innovations to seamlessly integrate with Data Caterer
      • +
    • Integrate with existing solutions: Utilise existing metadata to make it easy for users to use straight away
      • +
    • Secure: No production connections required, metadata based solution
      • +
    • Fast: Give developers fast feedback loops to encourage them to thoroughly test data flows
      • +
    +

    High level flow

    +
    graph LR
+  subgraph userTasks [User Configuration]
+  dataGen[Data Generation]
+  dataValid[Data Validation]
+  runConf[Runtime Config]
+  end
+
+  subgraph dataProcessor [Processor]
+  dataCaterer[Data Caterer]
+  end
+
+  subgraph existingMetadata [Metadata]
+  metadataService[Metadata Services]
+  metadataDataSource[Data Sources]
+  end
+
+  subgraph output [Output]
+  outputDataSource[Data Sources]
+  report[Report]
+  end
+
+  dataGen --> dataCaterer
+  dataValid --> dataCaterer
+  runConf --> dataCaterer
+  direction TB
+  dataCaterer -.-> metadataService
+  dataCaterer -.-> metadataDataSource
+  direction LR
+  dataCaterer ---> outputDataSource
+  dataCaterer ---> report
    +
      +
    1. User Configuration
        +
      1. Users define data generation, validation and runtime configuration
        2. +
      +
      2. +
    2. Processor
        +
      1. Engine will take user configuration to decide how to run
        2. +
      2. User defined configuration merged with metadata from external sources
        3. +
      +
      3. +
    3. Metadata
        +
      1. Automatically retrieve schema, data profiling, relationship or validation rule metadata from data sources or metadata services
        2. +
      +
      4. +
    4. Output
        +
      1. Execute data generation and validation tasks on data sources
        2. +
      2. Generate report summarising outcome
        3. +
      +
      5. +
    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/foreign-key/index.html b/setup/foreign-key/index.html new file mode 100644 index 00000000..abf99f5e --- /dev/null +++ b/setup/foreign-key/index.html @@ -0,0 +1,2646 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Foreign Keys - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    + + + + + + + + + +
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    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Foreign Keys

    +

    Multiple data source foreign key example

    +

    Foreign keys can be defined to represent the relationships between datasets where values are required to match for +particular columns.

    +

    Single column

    +

    Define a column in one data source to match against another column.
    +Below example shows a postgres data source with two tables, accounts and transactions that have a foreign key +for account_id.

    +
    +
    +
    +
    var postgresAcc = postgres("my_postgres", "jdbc:...")
+  .table("public.accounts")
+  .schema(
+    field().name("account_id"),
+    field().name("name"),
+    ...
+  );
+var postgresTxn = postgres(postgresAcc)
+  .table("public.transactions")
+  .schema(
+    field().name("account_id"),
+    field().name("full_name"),
+    ...
+  );
+
+plan().addForeignKeyRelationship(
+  postgresAcc, "account_id",
+  List.of(Map.entry(postgresTxn, "account_id"))
+);
+
    +
    +
    +
    val postgresAcc = postgres("my_postgres", "jdbc:...")
+  .table("public.accounts")
+  .schema(
+    field.name("account_id"),
+    field.name("name"),
+    ...
+  )
+val postgresTxn = postgres(postgresAcc)
+  .table("public.transactions")
+  .schema(
+    field.name("account_id"),
+    field.name("full_name"),
+    ...
+  )
+
+plan.addForeignKeyRelationship(
+  postgresAcc, "account_id",
+  List(postgresTxn -> "account_id")
+)
+
    +
    +
    +
    ---
+name: "postgres_data"
+steps:
+  - name: "accounts"
+    type: "postgres"
+    options:
+      dbtable: "account.accounts"
+    schema:
+      fields:
+        - name: "account_id"
+        - name: "name"
+  - name: "transactions"
+    type: "postgres"
+    options:
+      dbtable: "account.transactions"
+    schema:
+      fields:
+        - name: "account_id"
+        - name: "full_name"
+---
+name: "customer_create_plan"
+description: "Create customers in JDBC"
+tasks:
+  - name: "postgres_data"
+    dataSourceName: "my_postgres"
+
+sinkOptions:
+  foreignKeys:
+    "postgres.accounts.account_id":
+      - "postgres.transactions.account_id"
+
    +
    +
    +
    +

    Multiple columns

    +

    You may have a scenario where multiple columns need to be aligned. From the same example, we want account_id +and name from accounts to match with account_id and full_name to match in transactions respectively.

    +
    +
    +
    +
    var postgresAcc = postgres("my_postgres", "jdbc:...")
+  .table("public.accounts")
+  .schema(
+    field().name("account_id"),
+    field().name("name"),
+    ...
+  );
+var postgresTxn = postgres(postgresAcc)
+  .table("public.transactions")
+  .schema(
+    field().name("account_id"),
+    field().name("full_name"),
+    ...
+  );
+
+plan().addForeignKeyRelationship(
+  postgresAcc, List.of("account_id", "name"),
+  List.of(Map.entry(postgresTxn, List.of("account_id", "full_name")))
+);
+
    +
    +
    +
    val postgresAcc = postgres("my_postgres", "jdbc:...")
+  .table("public.accounts")
+  .schema(
+    field.name("account_id"),
+    field.name("name"),
+    ...
+  )
+val postgresTxn = postgres(postgresAcc)
+  .table("public.transactions")
+  .schema(
+    field.name("account_id"),
+    field.name("full_name"),
+    ...
+  )
+
+plan.addForeignKeyRelationship(
+  postgresAcc, List("account_id", "name"),
+  List(postgresTxn -> List("account_id", "full_name"))
+)
+
    +
    +
    +
    ---
+name: "postgres_data"
+steps:
+  - name: "accounts"
+    type: "postgres"
+    options:
+      dbtable: "account.accounts"
+    schema:
+      fields:
+        - name: "account_id"
+        - name: "name"
+  - name: "transactions"
+    type: "postgres"
+    options:
+      dbtable: "account.transactions"
+    schema:
+      fields:
+        - name: "account_id"
+        - name: "full_name"
+---
+name: "customer_create_plan"
+description: "Create customers in JDBC"
+tasks:
+  - name: "postgres_data"
+    dataSourceName: "my_postgres"
+
+sinkOptions:
+  foreignKeys:
+    "my_postgres.accounts.account_id,name":
+      - "my_postgres.transactions.account_id,full_name"
+
    +
    +
    +
    +

    Nested column

    +

    Your schema structure can have nested fields which can also be referenced as foreign keys. But to do so, you need to +create a proxy field that gets omitted from the final saved data.

    +

    In the example below, the nested customer_details.name field inside the json task needs to match with name +from postgres. A new field in the json called _txn_name is used as a temporary column to facilitate the foreign +key definition.

    +
    +
    +
    +
    var postgresAcc = postgres("my_postgres", "jdbc:...")
+  .table("public.accounts")
+  .schema(
+    field().name("account_id"),
+    field().name("name"),
+    ...
+  );
+var jsonTask = json("my_json", "/tmp/json")
+  .schema(
+    field().name("account_id"),
+    field().name("customer_details")
+      .schema(
+        field().name("name").sql("_txn_name"), #nested field will get value from '_txn_name'
+        ...
+      ),
+    field().name("_txn_name").omit(true)       #value will not be included in output
+  );
+
+plan().addForeignKeyRelationship(
+  postgresAcc, List.of("account_id", "name"),
+  List.of(Map.entry(jsonTask, List.of("account_id", "_txn_name")))
+);
+
    +
    +
    +
    val postgresAcc = postgres("my_postgres", "jdbc:...")
+  .table("public.accounts")
+  .schema(
+    field.name("account_id"),
+    field.name("name"),
+    ...
+  )
+var jsonTask = json("my_json", "/tmp/json")
+  .schema(
+    field.name("account_id"),
+    field.name("customer_details")
+      .schema(
+        field.name("name").sql("_txn_name"), #nested field will get value from '_txn_name'
+        ...
+      ), 
+    field.name("_txn_name").omit(true)       #value will not be included in output
+  )
+
+plan.addForeignKeyRelationship(
+  postgresAcc, List("account_id", "name"),
+  List(jsonTask -> List("account_id", "_txn_name"))
+)
+
    +
    +
    +
    ---
+#postgres task yaml
+name: "postgres_data"
+steps:
+  - name: "accounts"
+    type: "postgres"
+    options:
+      dbtable: "account.accounts"
+    schema:
+      fields:
+        - name: "account_id"
+        - name: "name"
+---
+#json task yaml
+name: "json_data"
+steps:
+  - name: "transactions"
+    type: "json"
+    options:
+      dbtable: "account.transactions"
+    schema:
+      fields:
+        - name: "account_id"
+        - name: "_txn_name"
+          generator:
+            options:
+              omit: true
+        - name: "cusotmer_details"
+          schema:
+            fields:
+              name: "name"
+              generator:
+                type: "sql"
+                options:
+                  sql: "_txn_name"
+
+---
+#plan yaml
+name: "customer_create_plan"
+description: "Create customers in JDBC"
+tasks:
+  - name: "postgres_data"
+    dataSourceName: "my_postgres"
+  - name: "json_data"
+    dataSourceName: "my_json"
+
+sinkOptions:
+  foreignKeys:
+    "my_postgres.accounts.account_id,name":
+      - "my_json.transactions.account_id,_txn_name"
+
    +
    +
    +
    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/generator/count/index.html b/setup/generator/count/index.html new file mode 100644 index 00000000..1ca3eed7 --- /dev/null +++ b/setup/generator/count/index.html @@ -0,0 +1,2565 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Record Count - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Record Count

    +

    There are options related to controlling the number of records generated that can help in generating the scenarios or data required.

    +

    Record Count

    +

    Record count is the simplest as you define the total number of records you require for that particular step. +For example, in the below step, it will generate 1000 records for the CSV file

    +
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .count(1000);
+
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .count(1000)
+
    +
    +
    +
    name: "csv_file"
+steps:
+  - name: "transactions"
+    type: "csv"
+    options:
+      path: "app/src/test/resources/sample/csv/transactions"
+    count:
+      records: 1000
+
    +
    +
    +
    +

    Generated Count

    +

    As like most things in Data Caterer, the count can be generated based on some metadata. +For example, if I wanted to generate between 1000 and 2000 records, I could define that by the below configuration:

    +
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .count(generator().min(1000).max(2000));
+
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .count(generator.min(1000).max(2000))
+
    +
    +
    +
    name: "csv_file"
+steps:
+  - name: "transactions"
+    type: "csv"
+    options:
+      path: "app/src/test/resources/sample/csv/transactions"
+    count:
+      generator:
+        type: "random"
+        options:
+          min: 1000
+          max: 2000
+
    +
    +
    +
    +

    Per Column Count

    +

    When defining a per column count, this allows you to generate records "per set of columns". +This means that for a given set of columns, it will generate a particular amount of records per combination of values for those columns.

    +

    One example of this would be when generating transactions relating to a customer, a customer may be defined by columns account_id, name. +A number of transactions would be generated per account_id, name.

    +

    You can also use a combination of the above two methods to generate the number of records per column.

    +

    Records

    +

    When defining a base number of records within the perColumn configuration, it translates to creating (count.records * count.recordsPerColumn) records.
    +This is a fixed number of records that will be generated each time, with no variation between runs.

    +

    In the example below, we have count.records = 1000 and count.recordsPerColumn = 2. Which means that 1000 * 2 = 2000 records will be generated +in total.

    +
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .count(
+    count()
+      .records(1000)
+      .recordsPerColumn(2, "account_id", "name")
+  );
+
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .count(
+    count
+      .records(1000)
+      .recordsPerColumn(2, "account_id", "name")
+  )
+
    +
    +
    +
    name: "csv_file"
+steps:
+  - name: "transactions"
+    type: "csv"
+    options:
+      path: "app/src/test/resources/sample/csv/transactions"
+    count:
+      records: 1000
+      perColumn:
+        records: 2
+        columnNames:
+          - "account_id"
+          - "name"
+
    +
    +
    +
    +

    Generated

    +

    You can also define a generator for the count per column. This can be used in scenarios where you want a variable number of records +per set of columns.

    +

    In the example below, it will generate between (count.records * count.perColumnGenerator.generator.min) = (1000 * 1) = 1000 and +(count.records * count.perColumnGenerator.generator.max) = (1000 * 2) = 2000 records.

    +
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .count(
+    count()
+      .records(1000)
+      .recordsPerColumnGenerator(generator().min(1).max(2), "account_id", "name")
+  );
+
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .count(
+    count
+      .records(1000)
+      .recordsPerColumnGenerator(generator.min(1).max(2), "account_id", "name")
+  )
+
    +
    +
    +
    name: "csv_file"
+steps:
+  - name: "transactions"
+    type: "csv"
+    options:
+      path: "app/src/test/resources/sample/csv/transactions"
+    count:
+      records: 1000
+      perColumn:
+        columnNames:
+          - "account_id"
+          - "name"
+        generator:
+          type: "random"
+          options:
+            min: 1
+            max: 2
+
    +
    +
    +
    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/generator/data-generator/index.html b/setup/generator/data-generator/index.html new file mode 100644 index 00000000..4e974714 --- /dev/null +++ b/setup/generator/data-generator/index.html @@ -0,0 +1,3395 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Data Generator - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Data Generators

    +

    Data Types

    +

    Below is a list of all supported data types for generating data:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Data TypeSpark Data TypeOptionsDescription
    stringStringTypeminLen, maxLen, expression, enableNull
    integerIntegerTypemin, max, stddev, mean
    longLongTypemin, max, stddev, mean
    shortShortTypemin, max, stddev, mean
    decimal(precision, scale)DecimalType(precision, scale)min, max, stddev, mean
    doubleDoubleTypemin, max, stddev, mean
    floatFloatTypemin, max, stddev, mean
    dateDateTypemin, max, enableNull
    timestampTimestampTypemin, max, enableNull
    booleanBooleanType
    binaryBinaryTypeminLen, maxLen, enableNull
    byteByteType
    arrayArrayTypearrayMinLen, arrayMaxLen, arrayType
    _StructTypeImplicitly supported when a schema is defined for a field
    +

    Options

    +

    All data types

    +

    Some options are available to use for all types of data generators. Below is the list along with example and +descriptions:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    OptionDefaultExampleDescription
    enableEdgeCasefalseenableEdgeCase: "true"Enable/disable generated data to contain edge cases based on the data type. For example, integer data type has edge cases of (Int.MaxValue, Int.MinValue and 0)
    edgeCaseProbability0.0edgeCaseProb: "0.1"Probability of generating a random edge case value if enableEdgeCase is true
    isUniquefalseisUnique: "true"Enable/disable generated data to be unique for that column. Errors will be thrown when it is unable to generate unique data
    seedseed: "1"Defines the random seed for generating data for that particular column. It will override any seed defined at a global level
    sqlsql: "CASE WHEN amount < 10 THEN true ELSE false END"Define any SQL statement for generating that columns value. Computation occurs after all non-SQL fields are generated. This means any columns used in the SQL cannot be based on other SQL generated columns. Data type of generated value from SQL needs to match data type defined for the field
    +

    String

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    OptionDefaultExampleDescription
    minLen1minLen: "2"Ensures that all generated strings have at least length minLen
    maxLen10maxLen: "15"Ensures that all generated strings have at most length maxLen
    expressionexpression: "#{Name.name}"
    expression:"#{Address.city}/#{Demographic.maritalStatus}"    		Will generate a string based on the faker expression provided. All possible faker expressions can be found here
    Expression has to be in format #{<faker expression name>}
    enableNullfalseenableNull: "true"Enable/disable null values being generated
    nullProbability0.0nullProb: "0.1"Probability to generate null values if enableNull is true
    +

    Edge cases: ("", "\n", "\r", "\t", " ", "\u0000", "\ufff", "İyi günler", "Спасибо", "Καλημέρα", "صباح الخير", " +Förlåt", "你好吗", "Nhà vệ sinh ở đâu", "こんにちは", "नमस्ते", "Բարեւ", "Здравейте")

    +

    Sample

    +
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .schema(
+    field()
+      .name("name")
+      .type(StringType.instance())
+      .expression("#{Name.name}")
+      .enableNull(true)
+      .nullProbability(0.1)
+      .minLength(4)
+      .maxLength(20)
+  );
+
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .schema(
+    field
+      .name("name")
+      .`type`(StringType)
+      .expression("#{Name.name}")
+      .enableNull(true)
+      .nullProbability(0.1)
+      .minLength(4)
+      .maxLength(20)
+  )
+
    +
    +
    +
    name: "csv_file"
+steps:
+  - name: "transactions"
+    type: "csv"
+    options:
+      path: "app/src/test/resources/sample/csv/transactions"
+    schema:
+      fields:
+        - name: "name"
+          type: "string"
+          generator:
+            options:
+              expression: "#{Name.name}"
+              enableNull: true
+              nullProb: 0.1
+              minLength: 4
+              maxLength: 20
+
    +
    +
    +
    +

    Numeric

    +

    For all the numeric data types, there are 4 options to choose from: min, max and maxValue. +Generally speaking, you only need to define one of min or minValue, similarly with max or maxValue.
    +The reason why there are 2 options for each is because of when metadata is automatically gathered, we gather the +statistics of the observed min and max values. Also, it will attempt to gather any restriction on the min or max value +as defined by the data source (i.e. max value as per database type).

    +

    Integer/Long/Short

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    OptionDefaultExampleDescription
    min0min: "2"Ensures that all generated values are greater than or equal to min
    max1000max: "25"Ensures that all generated values are less than or equal to max
    stddev1.0stddev: "2.0"Standard deviation for normal distributed data
    meanmax - minmean: "5.0"Mean for normal distributed data
    +

    Edge cases Integer: (2147483647, -2147483648, 0)
    +Edge cases Long: (9223372036854775807, -9223372036854775808, 0)
    +Edge cases Short: (32767, -32768, 0)

    +
    Sample
    +
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .schema(
+    field().name("year").type(IntegerType.instance()).min(2020).max(2023),
+    field().name("customer_id").type(LongType.instance()),
+    field().name("customer_group").type(ShortType.instance())
+  );
+
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .schema(
+    field.name("year").`type`(IntegerType).min(2020).max(2023),
+    field.name("customer_id").`type`(LongType),
+    field.name("customer_group").`type`(ShortType)
+  )
+
    +
    +
    +
    name: "csv_file"
+steps:
+  - name: "transactions"
+    ...
+    schema:
+      fields:
+        - name: "year"
+          type: "integer"
+          generator:
+            options:
+              min: 2020
+              max: 2023
+        - name: "customer_id"
+          type: "long"
+        - name: "customer_group"
+          type: "short"
+
    +
    +
    +
    +

    Decimal

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    OptionDefaultExampleDescription
    min0min: "2"Ensures that all generated values are greater than or equal to min
    max1000max: "25"Ensures that all generated values are less than or equal to max
    stddev1.0stddev: "2.0"Standard deviation for normal distributed data
    meanmax - minmean: "5.0"Mean for normal distributed data
    numericPrecision10precision: "25"The maximum number of digits
    numericScale0scale: "25"The number of digits on the right side of the decimal point (has to be less than or equal to precision)
    +

    Edge cases Decimal: (9223372036854775807, -9223372036854775808, 0)

    +
    Sample
    +
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .schema(
+    field().name("balance").type(DecimalType.instance()).numericPrecision(10).numericScale(5)
+  );
+
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .schema(
+    field.name("balance").`type`(DecimalType).numericPrecision(10).numericScale(5)
+  )
+
    +
    +
    +
    name: "csv_file"
+steps:
+  - name: "transactions"
+    ...
+    schema:
+      fields:
+        - name: "balance"
+          type: "decimal"
+            generator:
+              options:
+                precision: 10
+                scale: 5
+
    +
    +
    +
    +

    Double/Float

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    OptionDefaultExampleDescription
    min0.0min: "2.1"Ensures that all generated values are greater than or equal to min
    max1000.0max: "25.9"Ensures that all generated values are less than or equal to max
    stddev1.0stddev: "2.0"Standard deviation for normal distributed data
    meanmax - minmean: "5.0"Mean for normal distributed data
    +

    Edge cases Double: (+infinity, 1.7976931348623157e+308, 4.9e-324, 0.0, -0.0, -1.7976931348623157e+308, -infinity, +NaN)
    +Edge cases Float: (+infinity, 3.4028235e+38, 1.4e-45, 0.0, -0.0, -3.4028235e+38, -infinity, NaN)

    +
    Sample
    +
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .schema(
+    field().name("amount").type(DoubleType.instance())
+  );
+
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .schema(
+    field.name("amount").`type`(DoubleType)
+  )
+
    +
    +
    +
    name: "csv_file"
+steps:
+  - name: "transactions"
+    ...
+    schema:
+      fields:
+        - name: "amount"
+          type: "double"
+
    +
    +
    +
    +

    Date

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    OptionDefaultExampleDescription
    minnow() - 365 daysmin: "2023-01-31"Ensures that all generated values are greater than or equal to min
    maxnow()max: "2023-12-31"Ensures that all generated values are less than or equal to max
    enableNullfalseenableNull: "true"Enable/disable null values being generated
    nullProbability0.0nullProb: "0.1"Probability to generate null values if enableNull is true
    +

    Edge cases: (0001-01-01, 1582-10-15, 1970-01-01, 9999-12-31) +(reference)

    +

    Sample

    +
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .schema(
+    field().name("created_date").type(DateType.instance()).min(java.sql.Date.valueOf("2020-01-01"))
+  );
+
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .schema(
+    field.name("created_date").`type`(DateType).min(java.sql.Date.valueOf("2020-01-01"))
+  )
+
    +
    +
    +
    name: "csv_file"
+steps:
+  - name: "transactions"
+    ...
+    schema:
+      fields:
+        - name: "created_date"
+          type: "date"
+            generator:
+              options:
+                min: "2020-01-01"
+
    +
    +
    +
    +

    Timestamp

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    OptionDefaultExampleDescription
    minnow() - 365 daysmin: "2023-01-31 23:10:10"Ensures that all generated values are greater than or equal to min
    maxnow()max: "2023-12-31 23:10:10"Ensures that all generated values are less than or equal to max
    enableNullfalseenableNull: "true"Enable/disable null values being generated
    nullProbability0.0nullProb: "0.1"Probability to generate null values if enableNull is true
    +

    Edge cases: (0001-01-01 00:00:00, 1582-10-15 23:59:59, 1970-01-01 00:00:00, 9999-12-31 23:59:59)

    +

    Sample

    +
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .schema(
+    field().name("created_time").type(TimestampType.instance()).min(java.sql.Timestamp.valueOf("2020-01-01 00:00:00"))
+  );
+
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .schema(
+    field.name("created_time").`type`(TimestampType).min(java.sql.Timestamp.valueOf("2020-01-01 00:00:00"))
+  )
+
    +
    +
    +
    name: "csv_file"
+steps:
+  - name: "transactions"
+    ...
+    schema:
+      fields:
+        - name: "created_time"
+          type: "timestamp"
+            generator:
+              options:
+                min: "2020-01-01 00:00:00"
+
    +
    +
    +
    +

    Binary

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    OptionDefaultExampleDescription
    minLen1minLen: "2"Ensures that all generated array of bytes have at least length minLen
    maxLen20maxLen: "15"Ensures that all generated array of bytes have at most length maxLen
    enableNullfalseenableNull: "true"Enable/disable null values being generated
    nullProbability0.0nullProb: "0.1"Probability to generate null values if enableNull is true
    +

    Edge cases: ("", "\n", "\r", "\t", " ", "\u0000", "\ufff", -128, 127)

    +

    Sample

    +
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .schema(
+    field().name("payload").type(BinaryType.instance())
+  );
+
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .schema(
+    field.name("payload").`type`(BinaryType)
+  )
+
    +
    +
    +
    name: "csv_file"
+steps:
+  - name: "transactions"
+    ...
+    schema:
+      fields:
+        - name: "payload"
+          type: "binary"
+
    +
    +
    +
    +

    Array

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    OptionDefaultExampleDescription
    arrayMinLen0arrayMinLen: "2"Ensures that all generated arrays have at least length arrayMinLen
    arrayMaxLen5arrayMaxLen: "15"Ensures that all generated arrays have at most length arrayMaxLen
    arrayTypearrayType: "double"Inner data type of the array. Optional when using Java/Scala API. Allows for nested data types to be defined like struct
    enableNullfalseenableNull: "true"Enable/disable null values being generated
    nullProbability0.0nullProb: "0.1"Probability to generate null values if enableNull is true
    +

    Sample

    +
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .schema(
+    field().name("last_5_amounts").type(ArrayType.instance()).arrayType("double")
+  );
+
    +
    +
    +
    csv("transactions", "app/src/test/resources/sample/csv/transactions")
+  .schema(
+    field.name("last_5_amounts").`type`(ArrayType).arrayType("double")
+  )
+
    +
    +
    +
    name: "csv_file"
+steps:
+  - name: "transactions"
+    ...
+    schema:
+      fields:
+        - name: "last_5_amounts"
+          type: "array<double>"
+
    +
    +
    +
    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/generator/report/index.html b/setup/generator/report/index.html new file mode 100644 index 00000000..838ad026 --- /dev/null +++ b/setup/generator/report/index.html @@ -0,0 +1,2362 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Report - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Report

    +

    Data Caterer can be configured to produce a report of the data generated to help users understand what was run, how much +data was generated, where it was generated, validation results and any associated metadata.

    +

    Sample

    +

    Once run, it will produce a report like this.

    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/guide/data-source/cassandra/index.html b/setup/guide/data-source/cassandra/index.html new file mode 100644 index 00000000..f6e45d83 --- /dev/null +++ b/setup/guide/data-source/cassandra/index.html @@ -0,0 +1,2969 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Cassandra - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Cassandra

    +
    +

    Info

    +

    Writing data to Cassandra is a paid feature. Try the free trial here.

    +
    +

    Creating a data generator for Cassandra. You will build a Docker image that will be able to populate data in Cassandra +for the tables you configure.

    +

    Requirements

    +
      +
    • 20 minutes
      • +
    • Git
      • +
    • Gradle
      • +
    • Docker
      • +
    • Cassandra
      • +
    +

    Get Started

    +

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    +
    git clone git@github.com:pflooky/data-caterer-example.git
+
    +

    If you already have a Cassandra instance running, you can skip to this step.

    +

    Cassandra Setup

    +

    Next, let's make sure you have an instance of Cassandra up and running in your local environment. This will make it +easy for us to iterate and check our changes.

    +
    cd docker
+docker-compose up -d cassandra
+
    +

    Permissions

    +

    Let's make a new user that has the required permissions needed to push data into the Cassandra tables we want.

    +
    +CQL Permission Statements +
    GRANT INSERT ON <schema>.<table> TO data_caterer_user;
+
    +
    +

    Following permissions are required when enabling configuration.enableGeneratePlanAndTasks(true) as it will gather +metadata information about tables and columns from the below tables.

    +
    +CQL Permission Statements +
    GRANT SELECT ON system_schema.tables TO data_caterer_user;
+GRANT SELECT ON system_schema.columns TO data_caterer_user;
+
    +
    +

    Plan Setup

    +

    Create a new Java or Scala class.

    +
      +
    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedCassandraJavaPlan.java
      • +
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedCassandraPlan.scala
      • +
    +

    Make sure your class extends PlanRun.

    +
    +
    +
    +
    import com.github.pflooky.datacaterer.java.api.PlanRun;
+
+public class MyAdvancedCassandraJavaPlan extends PlanRun {
+}
+
    +
    +
    +
    import com.github.pflooky.datacaterer.api.PlanRun
+
+class MyAdvancedCassandraPlan extends PlanRun {
+}
+
    +
    +
    +
    +

    This class defines where we need to define all of our configurations for generating data. There are helper variables and +methods defined to make it simple and easy to use.

    +

    Connection Configuration

    +

    Within our class, we can start by defining the connection properties to connect to Cassandra.

    +
    +
    +
    +
    var accountTask = cassandra(
+    "customer_cassandra",   //name
+    "localhost:9042",       //url
+    "cassandra",            //username
+    "cassandra",            //password
+    Map.of()                //optional additional connection options
+)
+
    +

    Additional options such as SSL configuration, etc can be found here.

    +
    +
    +
    val accountTask = cassandra(
+    "customer_cassandra",   //name
+    "localhost:9042",       //url
+    "cassandra",            //username
+    "cassandra",            //password
+    Map()                   //optional additional connection options
+)
+
    +

    Additional options such as SSL configuration, etc can be found here.

    +
    +
    +
    +

    Schema

    +

    Let's create a task for inserting data into the account.accounts and account.account_status_history tables as +defined underdocker/data/cql/customer.cql. This table should already be setup for you if you followed this +step. We can check if the table is setup already via the following command:

    +
    docker exec host.docker.internal cqlsh -e 'describe account.accounts; describe account.account_status_history;'
+
    +

    Here we should see some output that looks like the below. This tells us what schema we need to follow when generating +data. We need to define that alongside any metadata that is useful to add constraints on what are possible values the +generated data should contain.

    +
    CREATE TABLE account.accounts (
+    account_id text PRIMARY KEY,
+    amount double,
+    created_by text,
+    name text,
+    open_time timestamp,
+    status text
+)...
+
+CREATE TABLE account.account_status_history (
+    account_id text,
+    eod_date date,
+    status text,
+    updated_by text,
+    updated_time timestamp,
+    PRIMARY KEY (account_id, eod_date)
+)...
+
    +

    Trimming the connection details to work with the docker-compose Cassandra, we have a base Cassandra connection to define +the table and schema required. Let's define each field along with their corresponding data type. You will notice that +the text fields do not have a data type defined. This is because the default data type is StringType which +corresponds to text in Cassandra.

    +
    +
    +
    +
    {
+    var accountTask = cassandra("customer_cassandra", "host.docker.internal:9042")
+            .table("account", "accounts")
+            .schema(
+                    field().name("account_id"),
+                    field().name("amount").type(DoubleType.instance()),
+                    field().name("created_by"),
+                    field().name("name"),
+                    field().name("open_time").type(TimestampType.instance()),
+                    field().name("status")
+            );
+}
+
    +
    +
    +
    val accountTask = cassandra("customer_cassandra", "host.docker.internal:9042")
+  .table("account", "accounts")
+  .schema(
+    field.name("account_id"),
+    field.name("amount").`type`(DoubleType),
+    field.name("created_by"),
+    field.name("name"),
+    field.name("open_time").`type`(TimestampType),
+    field.name("status")
+  )
+
    +
    +
    +
    +

    Field Metadata

    +

    We could stop here and generate random data for the accounts table. But wouldn't it be more useful if we produced data +that is closer to the structure of the data that would come in production? We can do this by defining various metadata +that add guidelines that the data generator will understand when generating data.

    +
    account_id
    +

    account_id follows a particular pattern that where it starts with ACC and has 8 digits after it. +This can be defined via a regex like below. Alongside, we also mention that it is the primary key to prompt ensure that +unique values are generated.

    +
    +
    +
    +
    field().name("account_id").regex("ACC[0-9]{8}").primaryKey(true),
+
    +
    +
    +
    field.name("account_id").regex("ACC[0-9]{8}").primaryKey(true),
+
    +
    +
    +
    +
    amount
    +

    amount the numbers shouldn't be too large, so we can define a min and max for the generated numbers to be between +1 and 1000.

    +
    +
    +
    +
    field().name("amount").type(DoubleType.instance()).min(1).max(1000),
+
    +
    +
    +
    field.name("amount").`type`(DoubleType).min(1).max(1000),
+
    +
    +
    +
    +
    name
    +

    name is a string that also follows a certain pattern, so we could also define a regex but here we will choose to +leverage the DataFaker library and create an expression to generate real looking name. All possible faker expressions +can be found here

    +
    +
    +
    +
    field().name("name").expression("#{Name.name}"),
+
    +
    +
    +
    field.name("name").expression("#{Name.name}"),
+
    +
    +
    +
    +
    open_time
    +

    open_time is a timestamp that we want to have a value greater than a specific date. We can define a min date by using +java.sql.Date like below.

    +
    +
    +
    +
    field().name("open_time").type(TimestampType.instance()).min(java.sql.Date.valueOf("2022-01-01")),
+
    +
    +
    +
    field.name("open_time").`type`(TimestampType).min(java.sql.Date.valueOf("2022-01-01")),
+
    +
    +
    +
    +
    status
    +

    status is a field that can only obtain one of four values, open, closed, suspended or pending.

    +
    +
    +
    +
    field().name("status").oneOf("open", "closed", "suspended", "pending")
+
    +
    +
    +
    field.name("status").oneOf("open", "closed", "suspended", "pending")
+
    +
    +
    +
    +
    created_by
    +

    created_by is a field that is based on the status field where it follows the logic: if status is open or closed, then +it is created_by eod else created_by event. This can be achieved by defining a SQL expression like below.

    +
    +
    +
    +
    field().name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
+
    +
    +
    +
    field.name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
+
    +
    +
    +
    +

    Putting it all the fields together, our class should now look like this.

    +
    +
    +
    +
    var accountTask = cassandra("customer_cassandra", "host.docker.internal:9042")
+        .table("account", "accounts")
+        .schema(
+                field().name("account_id").regex("ACC[0-9]{8}").primaryKey(true),
+                field().name("amount").type(DoubleType.instance()).min(1).max(1000),
+                field().name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
+                field().name("name").expression("#{Name.name}"),
+                field().name("open_time").type(TimestampType.instance()).min(java.sql.Date.valueOf("2022-01-01")),
+                field().name("status").oneOf("open", "closed", "suspended", "pending")
+        );
+
    +
    +
    +
    val accountTask = cassandra("customer_cassandra", "host.docker.internal:9042")
+  .table("account", "accounts")
+  .schema(
+    field.name("account_id").primaryKey(true),
+    field.name("amount").`type`(DoubleType).min(1).max(1000),
+    field.name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
+    field.name("name").expression("#{Name.name}"),
+    field.name("open_time").`type`(TimestampType).min(java.sql.Date.valueOf("2022-01-01")),
+    field.name("status").oneOf("open", "closed", "suspended", "pending")
+  )
+
    +
    +
    +
    +

    Additional Configurations

    +

    At the end of data generation, a report gets generated that summarises the actions it performed. We can control the +output folder of that report via configurations. We will also enable the unique check to ensure any unique fields will +have unique values generated.

    +
    +
    +
    +
    var config = configuration()
+        .generatedReportsFolderPath("/opt/app/data/report")
+        .enableUniqueCheck(true);
+
    +
    +
    +
    val config = configuration
+  .generatedReportsFolderPath("/opt/app/data/report")
+  .enableUniqueCheck(true)
+
    +
    +
    +
    +

    Execute

    +

    To tell Data Caterer that we want to run with the configurations along with the accountTask, we have to call execute +. So our full plan run will look like this.

    +
    +
    +
    +
    public class MyAdvancedCassandraJavaPlan extends PlanRun {
+    {
+        var accountTask = cassandra("customer_cassandra", "host.docker.internal:9042")
+                .table("account", "accounts")
+                .schema(
+                        field().name("account_id").regex("ACC[0-9]{8}").primaryKey(true),
+                        field().name("amount").type(DoubleType.instance()).min(1).max(1000),
+                        field().name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
+                        field().name("name").expression("#{Name.name}"),
+                        field().name("open_time").type(TimestampType.instance()).min(java.sql.Date.valueOf("2022-01-01")),
+                        field().name("status").oneOf("open", "closed", "suspended", "pending")
+                );
+
+        var config = configuration()
+                .generatedReportsFolderPath("/opt/app/data/report")
+                .enableUniqueCheck(true);
+
+        execute(config, accountTask);
+    }
+}
+
    +
    +
    +
    class MyAdvancedCassandraPlan extends PlanRun {
+  val accountTask = cassandra("customer_cassandra", "host.docker.internal:9042")
+    .table("account", "accounts")
+    .schema(
+      field.name("account_id").primaryKey(true),
+      field.name("amount").`type`(DoubleType).min(1).max(1000),
+      field.name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
+      field.name("name").expression("#{Name.name}"),
+      field.name("open_time").`type`(TimestampType).min(java.sql.Date.valueOf("2022-01-01")),
+      field.name("status").oneOf("open", "closed", "suspended", "pending")
+    )
+
+  val config = configuration
+    .generatedReportsFolderPath("/opt/app/data/report")
+    .enableUniqueCheck(true)
+
+  execute(config, accountTask)
+}
+
    +
    +
    +
    +

    Run

    +

    Now we can run via the script ./run.sh that is in the top level directory of the data-caterer-example to run the class we just +created.

    +
    ./run.sh
+#input class MyAdvancedCassandraJavaPlan or MyAdvancedCassandraPlan
+#after completing
+docker exec docker-cassandraserver-1 cqlsh -e 'select count(1) from account.accounts;select * from account.accounts limit 10;'
+
    +

    Your output should look like this.

    +
     count
+-------
+  1000
+
+(1 rows)
+
+Warnings :
+Aggregation query used without partition key
+
+
+ account_id  | amount    | created_by         | name                   | open_time                       | status
+-------------+-----------+--------------------+------------------------+---------------------------------+-----------
+ ACC13554145 | 917.00418 | zb CVvbBTTzitjo5fK |          Jan Sanford I | 2023-06-21 21:50:10.463000+0000 | suspended
+ ACC19154140 |  46.99177 |             VH88H9 |       Clyde Bailey PhD | 2023-07-18 11:33:03.675000+0000 |      open
+ ACC50587836 |  774.9872 |         GENANwPm t |           Sang Monahan | 2023-03-21 00:16:53.308000+0000 |    closed
+ ACC67619387 | 452.86706 |       5msTpcBLStTH |         Jewell Gerlach | 2022-10-18 19:13:07.606000+0000 | suspended
+ ACC69889784 |  14.69298 |           WDmOh7NT |          Dale Schulist | 2022-10-25 12:10:52.239000+0000 | suspended
+ ACC41977254 |  51.26492 |          J8jAKzvj2 |           Norma Nienow | 2023-08-19 18:54:39.195000+0000 | suspended
+ ACC40932912 | 349.68067 |   SLcJgKZdLp5ALMyg | Vincenzo Considine III | 2023-05-16 00:22:45.991000+0000 |    closed
+ ACC20642011 | 658.40713 |          clyZRD4fI |  Lannie McLaughlin DDS | 2023-05-11 23:14:30.249000+0000 |      open
+ ACC74962085 | 970.98218 |       ZLETTSnj4NpD |          Ima Jerde DVM | 2023-05-07 10:01:56.218000+0000 |   pending
+ ACC72848439 | 481.64267 |                 cc |        Kyla Deckow DDS | 2023-08-16 13:28:23.362000+0000 | suspended
+
+(10 rows)
+
    +

    Also check the HTML report, found at docker/sample/report/index.html, that gets generated to get an overview of what +was executed.

    +

    Sample report

    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/guide/data-source/http/index.html b/setup/guide/data-source/http/index.html new file mode 100644 index 00000000..aa2a2cc6 --- /dev/null +++ b/setup/guide/data-source/http/index.html @@ -0,0 +1,2748 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + HTTP - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    HTTP Source

    +
    +

    Info

    +

    Generating data based on OpenAPI/Swagger document and pushing to HTTP endpoint is a paid feature. Try the free trial here.

    +
    +

    Creating a data generator based on an OpenAPI/Swagger document.

    +

    Generate HTTP requests

    +

    Requirements

    +
      +
    • 10 minutes
      • +
    • Git
      • +
    • Gradle
      • +
    • Docker
      • +
    +

    Get Started

    +

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    +
    git clone git@github.com:pflooky/data-caterer-example.git
+
    +

    HTTP Setup

    +

    We will be using the http-bin docker image to help simulate a service with HTTP endpoints.

    +

    Start it via:

    +
    cd docker
+docker-compose up -d http
+docker ps
+
    +

    Plan Setup

    +

    Create a new Java or Scala class.

    +
      +
    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedHttpJavaPlanRun.java
      • +
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedHttpPlanRun.scala
      • +
    +

    Make sure your class extends PlanRun.

    +
    +
    +
    +
    import com.github.pflooky.datacaterer.java.api.PlanRun;
+...
+
+public class MyAdvancedHttpJavaPlanRun extends PlanRun {
+    {
+        var conf = configuration().enableGeneratePlanAndTasks(true)
+            .generatedReportsFolderPath("/opt/app/data/report");
+    }
+}
+
    +
    +
    +
    import com.github.pflooky.datacaterer.api.PlanRun
+...
+
+class MyAdvancedHttpPlanRun extends PlanRun {
+  val conf = configuration.enableGeneratePlanAndTasks(true)
+    .generatedReportsFolderPath("/opt/app/data/report")
+}
+
    +
    +
    +
    +

    We will enable generate plan and tasks so that we can read from external sources for metadata and save the reports +under a folder we can easily access.

    +

    Schema

    +

    We can point the schema of a data source to a OpenAPI/Swagger document or URL. For this example, we will use the OpenAPI +document found under docker/mount/http/petstore.json in the data-caterer-example repo. This is a simplified version of +the original OpenAPI spec that can be found here.

    +

    We have kept the following endpoints to test out:

    +
      +
    • GET /pets - get all pets
      • +
    • POST /pets - create a new pet
      • +
    • GET /pets/{id} - get a pet by id
      • +
    • DELETE /pets/{id} - delete a pet by id
      • +
    +
    +
    +
    +
    var httpTask = http("my_http")
+        .schema(metadataSource().openApi("/opt/app/mount/http/petstore.json"))
+        .count(count().records(2));
+
    +
    +
    +
    val httpTask = http("my_http")
+  .schema(metadataSource.openApi("/opt/app/mount/http/petstore.json"))
+  .count(count.records(2))
+
    +
    +
    +
    +

    The above defines that the schema will come from an OpenAPI document found on the pathway defined. It will then generate +2 requests per request method and endpoint combination.

    +

    Run

    +

    Let's try run and see what happens.

    +
    cd ..
+./run.sh
+#input class MyAdvancedHttpJavaPlanRun or MyAdvancedHttpPlanRun
+#after completing
+docker logs -f docker-http-1
+
    +

    It should look something like this.

    +
    172.21.0.1 [06/Nov/2023:01:06:53 +0000] GET /anything/pets?tags%3DeXQxFUHVja+EYm%26limit%3D33895 HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:06:53 +0000] GET /anything/pets?tags%3DSXaFvAqwYGF%26tags%3DjdNRFONA%26limit%3D40975 HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:06:56 +0000] POST /anything/pets HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:06:56 +0000] POST /anything/pets HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:07:00 +0000] GET /anything/pets/kbH8D7rDuq HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:07:00 +0000] GET /anything/pets/REsa0tnu7dvekGDvxR HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:07:03 +0000] DELETE /anything/pets/EqrOr1dHFfKUjWb HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:07:03 +0000] DELETE /anything/pets/7WG7JHPaNxP HTTP/1.1 200 Host: host.docker.internal}
+
    +

    Looks like we have some data now. But we can do better and add some enhancements to it.

    +

    Foreign keys

    +

    The four different requests that get sent could have the same id passed across to each of them if we define a foreign +key relationship. This will make it more realistic to a real life scenario as pets get created and queried by a +particular id value. We note that the id value is first used when a pet is created in the body of the POST request. +Then it gets used as a path parameter in the DELETE and GET requests.

    +

    To link them all together, we must follow a particular pattern when referring to request body, query parameter or path +parameter columns.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    HTTP TypeColumn PrefixExample
    Request BodybodyContentbodyContent.id
    Path ParameterpathParampathParamid
    Query ParameterqueryParamqueryParamid
    HeaderheaderheaderContent_Type
    +

    Also note, that when creating a foreign field definition for a HTTP data source, to refer to a specific endpoint and +method, we have to follow the pattern of {http method}{http path}. For example, POST/pets. Let's apply this +knowledge to link all the id values together.

    +
    +
    +
    +
    var myPlan = plan().addForeignKeyRelationship(
+        foreignField("my_http", "POST/pets", "bodyContent.id"),     //source of foreign key value
+        foreignField("my_http", "DELETE/pets/{id}", "pathParamid"),
+        foreignField("my_http", "GET/pets/{id}", "pathParamid")
+);
+
+execute(myPlan, conf, httpTask);
+
    +
    +
    +
    val myPlan = plan.addForeignKeyRelationship(
+  foreignField("my_http", "POST/pets", "bodyContent.id"),     //source of foreign key value
+  foreignField("my_http", "DELETE/pets/{id}", "pathParamid"),
+  foreignField("my_http", "GET/pets/{id}", "pathParamid")
+)
+
+execute(myPlan, conf, httpTask)
+
    +
    +
    +
    +

    Let's test it out by running it again

    +
    ./run.sh
+#input class MyAdvancedHttpJavaPlanRun or MyAdvancedHttpPlanRun
+docker logs -f docker-http-1
+
    +
    172.21.0.1 [06/Nov/2023:01:33:59 +0000] GET /anything/pets?limit%3D45971 HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:34:00 +0000] GET /anything/pets?limit%3D62015 HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:34:04 +0000] POST /anything/pets HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:34:05 +0000] POST /anything/pets HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:34:09 +0000] DELETE /anything/pets/5e HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:34:09 +0000] DELETE /anything/pets/IHPm2 HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:34:14 +0000] GET /anything/pets/IHPm2 HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:34:14 +0000] GET /anything/pets/5e HTTP/1.1 200 Host: host.docker.internal}
+
    +

    Now we have the same id values being produced across the POST, DELETE and GET requests! What if we knew that the id +values should follow a particular pattern?

    +

    Custom metadata

    +

    So given that we have defined a foreign key where the root of the foreign key values is from the POST request, we can +update the metadata of the id column for the POST request and it will proliferate to the other endpoints as well. +Given the id column is a nested column as noted in the foreign key, we can alter its metadata via the following:

    +
    +
    +
    +
    var httpTask = http("my_http")
+        .schema(metadataSource().openApi("/opt/app/mount/http/petstore.json"))
+        .schema(field().name("bodyContent").schema(field().name("id").regex("ID[0-9]{8}")))
+        .count(count().records(2));
+
    +
    +
    +
    val httpTask = http("my_http")
+  .schema(metadataSource.openApi("/opt/app/mount/http/petstore.json"))
+  .schema(field.name("bodyContent").schema(field.name("id").regex("ID[0-9]{8}")))
+  .count(count.records(2))
+
    +
    +
    +
    +

    We first get the column bodyContent, then get the nested schema and get the column id and add metadata stating that +id should follow the patter ID[0-9]{8}.

    +

    Let's try run again, and hopefully we should see some proper ID values.

    +
    ./run.sh
+#input class MyAdvancedHttpJavaPlanRun or MyAdvancedHttpPlanRun
+docker logs -f docker-http-1
+
    +
    172.21.0.1 [06/Nov/2023:01:45:45 +0000] GET /anything/pets?tags%3D10fWnNoDz%26limit%3D66804 HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:45:46 +0000] GET /anything/pets?tags%3DhyO6mI8LZUUpS HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:45:50 +0000] POST /anything/pets HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:45:51 +0000] POST /anything/pets HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:45:52 +0000] DELETE /anything/pets/ID55185420 HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:45:52 +0000] DELETE /anything/pets/ID20618951 HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:45:57 +0000] GET /anything/pets/ID55185420 HTTP/1.1 200 Host: host.docker.internal}
+172.21.0.1 [06/Nov/2023:01:45:57 +0000] GET /anything/pets/ID20618951 HTTP/1.1 200 Host: host.docker.internal}
+
    +

    Great! Now we have replicated a production-like flow of HTTP requests.

    +

    Ordering

    +

    If you wanted to change the ordering of the requests, you can alter the order from within the OpenAPI/Swagger document. +This is particularly useful when you want to simulate the same flow that users would take when utilising your +application (i.e. create account, query account, update account).

    +

    Rows per second

    +

    By default, Data Caterer will push requests per method and endpoint at a rate of around 5 requests per second. If you +want to alter this value, you can do so via the below configuration. The lowest supported requests per second is 1.

    +
    +
    +
    +
    import com.github.pflooky.datacaterer.api.model.Constants;
+
+...
+var httpTask = http("my_http", Map.of(Constants.ROWS_PER_SECOND(), "1"))
+        ...
+
    +
    +
    +
    import com.github.pflooky.datacaterer.api.model.Constants.ROWS_PER_SECOND
+
+...
+val httpTask = http("my_http", options = Map(ROWS_PER_SECOND -> "1"))
+  ...
+
    +
    +
    +
    +

    Check out the full example under AdvancedHttpPlanRun in the example repo.

    + + + + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/guide/data-source/kafka/index.html b/setup/guide/data-source/kafka/index.html new file mode 100644 index 00000000..31fd5077 --- /dev/null +++ b/setup/guide/data-source/kafka/index.html @@ -0,0 +1,2831 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Kafka - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Kafka

    +
    +

    Info

    +

    Writing data to Kafka is a paid feature. Try the free trial here.

    +
    +

    Creating a data generator for Kafka. You will build a Docker image that will be able to populate data in kafka +for the topics you configure.

    +

    Requirements

    +
      +
    • 20 minutes
      • +
    • Git
      • +
    • Gradle
      • +
    • Docker
      • +
    • Kafka
      • +
    +

    Get Started

    +

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    +
    git clone git@github.com:pflooky/data-caterer-example.git
+
    +

    If you already have a Kafka instance running, you can skip to this step.

    +

    Kafka Setup

    +

    Next, let's make sure you have an instance of Kafka up and running in your local environment. This will make it +easy for us to iterate and check our changes.

    +
    cd docker
+docker-compose up -d kafka
+
    +

    Plan Setup

    +

    Create a new Java or Scala class.

    +
      +
    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedKafkaJavaPlan.java
      • +
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedKafkaPlan.scala
      • +
    +

    Make sure your class extends PlanRun.

    +
    +
    +
    +
    import com.github.pflooky.datacaterer.java.api.PlanRun;
+
+public class MyAdvancedKafkaJavaPlan extends PlanRun {
+}
+
    +
    +
    +
    import com.github.pflooky.datacaterer.api.PlanRun
+
+class MyAdvancedKafkaPlan extends PlanRun {
+}
+
    +
    +
    +
    +

    This class defines where we need to define all of our configurations for generating data. There are helper variables and +methods defined to make it simple and easy to use.

    +

    Connection Configuration

    +

    Within our class, we can start by defining the connection properties to connect to Kafka.

    +
    +
    +
    +
    var accountTask = kafka(
+    "my_kafka",       //name
+    "localhost:9092", //url
+    Map.of()          //optional additional connection options
+);
+
    +

    Additional options can be found here.

    +
    +
    +
    val accountTask = kafka(
+    "my_kafka",       //name
+    "localhost:9092", //url
+    Map()             //optional additional connection options
+)
+
    +

    Additional options can be found here.

    +
    +
    +
    +

    Schema

    +

    Let's create a task for inserting data into the account-topic that is already +defined underdocker/data/kafka/setup_kafka.sh. This topic should already be setup for you if you followed this +step. We can check if the topic is set up already via the following command:

    +
    docker exec docker-kafkaserver-1 kafka-topics --bootstrap-server localhost:9092 --list
+
    +

    Trimming the connection details to work with the docker-compose Kafka, we have a base Kafka connection to define +the topic we will publish to. Let's define each field along with their corresponding data type. You will notice that +the text fields do not have a data type defined. This is because the default data type is StringType.

    +
    +
    +
    +
    {
+    var kafkaTask = kafka("my_kafka", "kafkaserver:29092")
+            .topic("account-topic")
+            .schema(
+                    field().name("key").sql("content.account_id"),
+                    field().name("value").sql("TO_JSON(content)"),
+                    //field().name("partition").type(IntegerType.instance()),  can define partition here
+                    field().name("headers")
+                            .type(ArrayType.instance())
+                            .sql(
+                                    "ARRAY(" +
+                                            "NAMED_STRUCT('key', 'account-id', 'value', TO_BINARY(content.account_id, 'utf-8'))," +
+                                            "NAMED_STRUCT('key', 'updated', 'value', TO_BINARY(content.details.updated_by.time, 'utf-8'))" +
+                                            ")"
+                            ),
+                    field().name("content")
+                            .schema(
+                                    field().name("account_id").regex("ACC[0-9]{8}"),
+                                    field().name("year").type(IntegerType.instance()),
+                                    field().name("amount").type(DoubleType.instance()),
+                                    field().name("details")
+                                            .schema(
+                                                    field().name("name").expression("#{Name.name}"),
+                                                    field().name("first_txn_date").type(DateType.instance()).sql("ELEMENT_AT(SORT_ARRAY(content.transactions.txn_date), 1)"),
+                                                    field().name("updated_by")
+                                                            .schema(
+                                                                    field().name("user"),
+                                                                    field().name("time").type(TimestampType.instance())
+                                                            )
+                                            ),
+                                    field().name("transactions").type(ArrayType.instance())
+                                            .schema(
+                                                    field().name("txn_date").type(DateType.instance()).min(Date.valueOf("2021-01-01")).max("2021-12-31"),
+                                                    field().name("amount").type(DoubleType.instance())
+                                            )
+                            ),
+                    field().name("tmp_year").sql("content.year").omit(true),
+                    field().name("tmp_name").sql("content.details.name").omit(true)
+            )
+}
+
    +
    +
    +
    val kafkaTask = kafka("my_kafka", "kafkaserver:29092")
+  .topic("account-topic")
+  .schema(
+    field.name("key").sql("content.account_id"),
+    field.name("value").sql("TO_JSON(content)"),
+    //field.name("partition").type(IntegerType),  can define partition here
+    field.name("headers")
+      .`type`(ArrayType)
+      .sql(
+        """ARRAY(
+          |  NAMED_STRUCT('key', 'account-id', 'value', TO_BINARY(content.account_id, 'utf-8')),
+          |  NAMED_STRUCT('key', 'updated', 'value', TO_BINARY(content.details.updated_by.time, 'utf-8'))
+          |)""".stripMargin
+      ),
+    field.name("content")
+      .schema(
+        field.name("account_id").regex("ACC[0-9]{8}"),
+        field.name("year").`type`(IntegerType).min(2021).max(2023),
+        field.name("amount").`type`(DoubleType),
+        field.name("details")
+          .schema(
+            field.name("name").expression("#{Name.name}"),
+            field.name("first_txn_date").`type`(DateType).sql("ELEMENT_AT(SORT_ARRAY(content.transactions.txn_date), 1)"),
+            field.name("updated_by")
+              .schema(
+                field.name("user"),
+                field.name("time").`type`(TimestampType),
+              ),
+          ),
+        field.name("transactions").`type`(ArrayType)
+          .schema(
+            field.name("txn_date").`type`(DateType).min(Date.valueOf("2021-01-01")).max("2021-12-31"),
+            field.name("amount").`type`(DoubleType),
+          )
+      ),
+    field.name("tmp_year").sql("content.year").omit(true),
+    field.name("tmp_name").sql("content.details.name").omit(true)
+  )
+
    +
    +
    +
    +

    Fields

    +

    The schema defined for Kafka has a format that needs to be followed as noted above. Specifically, the required fields are: +- value

    +

    Whilst, the other fields are optional: +- key +- partition +- headers

    +
    headers
    +

    headers follows a particular pattern that where it is of type array<struct<key: string,value: binary>>. To be able +to generate data for this data type, we need to use an SQL expression like the one below. You will notice that in the +value part, it refers to content.account_id where content is another field defined at the top level of the schema. +This allows you to reference other values that have already been generated.

    +
    +
    +
    +
    field().name("headers")
+        .type(ArrayType.instance())
+        .sql(
+                "ARRAY(" +
+                        "NAMED_STRUCT('key', 'account-id', 'value', TO_BINARY(content.account_id, 'utf-8'))," +
+                        "NAMED_STRUCT('key', 'updated', 'value', TO_BINARY(content.details.updated_by.time, 'utf-8'))" +
+                        ")"
+        )
+
    +
    +
    +
    field.name("headers")
+  .`type`(ArrayType)
+  .sql(
+    """ARRAY(
+      |  NAMED_STRUCT('key', 'account-id', 'value', TO_BINARY(content.account_id, 'utf-8')),
+      |  NAMED_STRUCT('key', 'updated', 'value', TO_BINARY(content.details.updated_by.time, 'utf-8'))
+      |)""".stripMargin
+  )
+
    +
    +
    +
    +
    transactions
    +

    transactions is an array that contains an inner structure of txn_date and amount. The size of the array generated +can be controlled via arrayMinLength and arrayMaxLength.

    +
    +
    +
    +
    field().name("transactions").type(ArrayType.instance())
+        .schema(
+                field().name("txn_date").type(DateType.instance()).min(Date.valueOf("2021-01-01")).max("2021-12-31"),
+                field().name("amount").type(DoubleType.instance())
+        )
+
    +
    +
    +
    field.name("transactions").`type`(ArrayType)
+  .schema(
+    field.name("txn_date").`type`(DateType).min(Date.valueOf("2021-01-01")).max("2021-12-31"),
+    field.name("amount").`type`(DoubleType),
+  )
+
    +
    +
    +
    +
    details
    +

    details is another example of a nested schema structure where it also has a nested structure itself in updated_by. +One thing to note here is the first_txn_date field has a reference to the content.transactions array where it will +sort the array by txn_date and get the first element.

    +
    +
    +
    +
    field().name("details")
+        .schema(
+                field().name("name").expression("#{Name.name}"),
+                field().name("first_txn_date").type(DateType.instance()).sql("ELEMENT_AT(SORT_ARRAY(content.transactions.txn_date), 1)"),
+                field().name("updated_by")
+                        .schema(
+                                field().name("user"),
+                                field().name("time").type(TimestampType.instance())
+                        )
+        )
+
    +
    +
    +
    field.name("details")
+  .schema(
+    field.name("name").expression("#{Name.name}"),
+    field.name("first_txn_date").`type`(DateType).sql("ELEMENT_AT(SORT_ARRAY(content.transactions.txn_date), 1)"),
+    field.name("updated_by")
+      .schema(
+        field.name("user"),
+        field.name("time").`type`(TimestampType),
+      ),
+  )
+
    +
    +
    +
    +

    Additional Configurations

    +

    At the end of data generation, a report gets generated that summarises the actions it performed. We can control the +output folder of that report via configurations.

    +
    +
    +
    +
    var config = configuration()
+        .generatedReportsFolderPath("/opt/app/data/report");
+
    +
    +
    +
    val config = configuration
+  .generatedReportsFolderPath("/opt/app/data/report")
+
    +
    +
    +
    +

    Execute

    +

    To tell Data Caterer that we want to run with the configurations along with the kafkaTask, we have to call execute +.

    +

    Run

    +

    Now we can run via the script ./run.sh that is in the top level directory of the data-caterer-example to run the class we just +created.

    +
    ./run.sh
+#input class AdvancedKafkaJavaPlanRun or AdvancedKafkaPlanRun
+#after completing
+docker exec docker-kafkaserver-1 kafka-console-consumer --bootstrap-server localhost:9092 --topic account-topic --from-beginning
+
    +

    Your output should look like this.

    +
    {"account_id":"ACC56292178","year":2022,"amount":18338.627721151555,"details":{"name":"Isaias Reilly","first_txn_date":"2021-01-22","updated_by":{"user":"FgYXbKDWdhHVc3","time":"2022-12-30T13:49:07.309Z"}},"transactions":[{"txn_date":"2021-01-22","amount":30556.52125487579},{"txn_date":"2021-10-29","amount":39372.302259554635},{"txn_date":"2021-10-29","amount":61887.31389495968}]}
+{"account_id":"ACC37729457","year":2022,"amount":96885.31758764731,"details":{"name":"Randell Witting","first_txn_date":"2021-06-30","updated_by":{"user":"HCKYEBHN8AJ3TB","time":"2022-12-02T02:05:01.144Z"}},"transactions":[{"txn_date":"2021-06-30","amount":98042.09647765031},{"txn_date":"2021-10-06","amount":41191.43564742036},{"txn_date":"2021-11-16","amount":78852.08184809204},{"txn_date":"2021-10-09","amount":13747.157653571106}]}
+{"account_id":"ACC23127317","year":2023,"amount":81164.49304198896,"details":{"name":"Jed Wisozk","updated_by":{"user":"9MBFZZ","time":"2023-07-12T05:56:52.397Z"}},"transactions":[]}
+
    +

    Also check the HTML report, found at docker/sample/report/index.html, that gets generated to get an overview of what +was executed.

    +

    Sample report

    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/guide/data-source/marquez-metadata-source/index.html b/setup/guide/data-source/marquez-metadata-source/index.html new file mode 100644 index 00000000..07ccf5ba --- /dev/null +++ b/setup/guide/data-source/marquez-metadata-source/index.html @@ -0,0 +1,2707 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Marquez Metadata Source - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Metadata Source

    +
    +

    Info

    +

    Generating data based on an external metadata source is a paid feature. Try the free trial here.

    +
    +

    Creating a data generator for Postgres tables and CSV file based on metadata stored in Marquez ( +follows OpenLineage API).

    +

    Requirements

    +
      +
    • 10 minutes
      • +
    • Git
      • +
    • Gradle
      • +
    • Docker
      • +
    +

    Get Started

    +

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    +
    git clone git@github.com:pflooky/data-caterer-example.git
+
    +

    Marquez Setup

    +

    You can follow the README found here to help with setting up Marquez in +your local environment. This comes with an instance of Postgres which we will also be using as a data store for +generated data.

    +

    The command that was run for this example to help with setup of dummy data was ./docker/up.sh -a 5001 -m 5002 --seed.

    +

    Check that the following url shows some data like below once you click on food_delivery +from the ns drop down in the top right corner.

    +

    Marquez dashboard

    +

    Postgres Setup

    +

    Since we will also be using the Marquez Postgres instance as a data source, we will set up a separate database to store +the generated data in via:

    +
    docker exec marquez-db psql -Upostgres -c 'CREATE DATABASE food_delivery'
+
    +

    Plan Setup

    +

    Create a new Java or Scala class.

    +
      +
    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedMetadataSourceJavaPlanRun.java
      • +
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedMetadataSourcePlanRun.scala
      • +
    +

    Make sure your class extends PlanRun.

    +
    +
    +
    +
    import com.github.pflooky.datacaterer.java.api.PlanRun;
+...
+
+public class MyAdvancedMetadataSourceJavaPlanRun extends PlanRun {
+    {
+        var conf = configuration().enableGeneratePlanAndTasks(true)
+            .generatedReportsFolderPath("/opt/app/data/report");
+    }
+}
+
    +
    +
    +
    import com.github.pflooky.datacaterer.api.PlanRun
+...
+
+class MyAdvancedMetadataSourcePlanRun extends PlanRun {
+  val conf = configuration.enableGeneratePlanAndTasks(true)
+    .generatedReportsFolderPath("/opt/app/data/report")
+}
+
    +
    +
    +
    +

    We will enable generate plan and tasks so that we can read from external sources for metadata and save the reports +under a folder we can easily access.

    +

    Schema

    +

    We can point the schema of a data source to our Marquez instance. For the Postgres data source, we will point to a +namespace, which in Marquez or OpenLineage, represents a set of datasets. For the CSV data source, we will point to +a specific namespace and dataset.

    +
    Single Schema
    +
    +
    +
    +
    var csvTask = csv("my_csv", "/tmp/data/csv", Map.of("saveMode", "overwrite", "header", "true"))
+        .schema(metadataSource().marquez("http://localhost:5001", "food_delivery", "public.delivery_7_days"))
+        .count(count().records(10));
+
    +
    +
    +
    val csvTask = csv("my_csv", "/tmp/data/csv", Map("saveMode" -> "overwrite", "header" -> "true"))
+  .schema(metadataSource.marquez("http://localhost:5001", "food_delivery", "public.delivery_7_days"))
+  .count(count.records(10))
+
    +
    +
    +
    +

    The above defines that the schema will come from Marquez, which is a type of metadata source that contains information +about schemas. Specifically, it points to the food_delivery namespace and public.categories dataset to retrieve the +schema information from.

    +
    Multiple Schemas
    +
    +
    +
    +
    var postgresTask = postgres("my_postgres", "jdbc:postgresql://host.docker.internal:5432/food_delivery", "postgres", "password", Map.of())
+    .schema(metadataSource().marquez("http://host.docker.internal:5001", "food_delivery"))
+    .count(count().records(10));
+
    +
    +
    +
    val postgresTask = postgres("my_postgres", "jdbc:postgresql://host.docker.internal:5432/food_delivery", "postgres", "password")
+  .schema(metadataSource.marquez("http://host.docker.internal:5001", "food_delivery"))
+  .count(count.records(10))
+
    +
    +
    +
    +

    We now have pointed this Postgres instance to produce multiple schemas that are defined under the food_delivery +namespace. Also note that we are using database food_delivery in Postgres to push our generated data to, and we have +set the number of records per sub data source (in this case, per table) to be 10.

    +

    Run

    +

    Let's try run and see what happens.

    +
    cd ..
+./run.sh
+#input class MyAdvancedMetadataSourceJavaPlanRun or MyAdvancedMetadataSourcePlanRun
+#after completing
+docker exec marquez-db psql -Upostgres -d food_delivery -c 'SELECT * FROM public.delivery_7_days'
+
    +

    It should look something like this.

    +
     order_id |     order_placed_on     |   order_dispatched_on   |   order_delivered_on    |         customer_email         |                     customer_address                     | menu_id | restaurant_id |                        restaurant_address
+   | menu_item_id | category_id | discount_id | city_id | driver_id
+----------+-------------------------+-------------------------+-------------------------+--------------------------------+----------------------------------------------------------+---------+---------------+---------------------------------------------------------------
+---+--------------+-------------+-------------+---------+-----------
+    38736 | 2023-02-05 06:05:23.755 | 2023-09-08 04:29:10.878 | 2023-09-03 23:58:34.285 | april.skiles@hotmail.com       | 5018 Lang Dam, Gaylordfurt, MO 35172                     |   59841 |         30971 | Suite 439 51366 Bartoletti Plains, West Lashawndamouth, CA 242
+42 |        55697 |       36370 |       21574 |   88022 |     16569
+     4376 | 2022-12-19 14:39:53.442 | 2023-08-30 07:40:06.948 | 2023-03-15 20:38:26.11  | adelina.balistreri@hotmail.com | Apt. 340 9146 Novella Motorway, East Troyhaven, UT 34773 |   66195 |         42765 | Suite 670 8956 Rob Fork, Rennershire, CA 04524
+   |        26516 |       81335 |       87615 |   27433 |     45649
+    11083 | 2022-10-30 12:46:38.692 | 2023-06-02 13:05:52.493 | 2022-11-27 18:38:07.873 | johnny.gleason@gmail.com       | Apt. 385 99701 Lemke Place, New Irvin, RI 73305          |   66427 |         44438 | 1309 Danny Cape, Weimanntown, AL 15865
+   |        41686 |       36508 |       34498 |   24191 |     92405
+    58759 | 2023-07-26 14:32:30.883 | 2022-12-25 11:04:08.561 | 2023-04-21 17:43:05.86  | isabelle.ohara@hotmail.com     | 2225 Evie Lane, South Ardella, SD 90805                  |   27106 |         25287 | Suite 678 3731 Dovie Park, Port Luigi, ID 08250
+   |        94205 |       66207 |       81051 |   52553 |     27483
+
    +

    You can also try query some other tables. Let's also check what is in the CSV file.

    +
    $ head docker/sample/csv/part-0000*
+menu_item_id,category_id,discount_id,city_id,driver_id,order_id,order_placed_on,order_dispatched_on,order_delivered_on,customer_email,customer_address,menu_id,restaurant_id,restaurant_address
+72248,37098,80135,45888,5036,11090,2023-09-20T05:33:08.036+08:00,2023-05-16T23:10:57.119+08:00,2023-05-01T22:02:23.272+08:00,demetrice.rohan@hotmail.com,"406 Harmony Rue, Wisozkburgh, MD 12282",33762,9042,"Apt. 751 0796 Ellan Flats, Lake Chetville, WI 81957"
+41644,40029,48565,83373,89919,58359,2023-04-18T06:28:26.194+08:00,2022-10-15T18:17:48.998+08:00,2023-02-06T17:02:04.104+08:00,joannie.okuneva@yahoo.com,"Suite 889 022 Susan Lane, Zemlakport, OR 56996",27467,6216,"Suite 016 286 Derick Grove, Dooleytown, NY 14664"
+49299,53699,79675,40821,61764,72234,2023-07-16T21:33:48.739+08:00,2023-02-14T21:23:10.265+08:00,2023-09-18T02:08:51.433+08:00,ina.heller@yahoo.com,"Suite 600 86844 Heller Island, New Celestinestad, DE 42622",48002,12462,"5418 Okuneva Mountain, East Blairchester, MN 04060"
+83197,86141,11085,29944,81164,65382,2023-01-20T06:08:25.981+08:00,2023-01-11T13:24:32.968+08:00,2023-09-09T02:30:16.890+08:00,lakisha.bashirian@yahoo.com,"Suite 938 534 Theodore Lock, Port Caitlynland, LA 67308",69109,47727,"4464 Stewart Tunnel, Marguritemouth, AR 56791"
+
    +

    Looks like we have some data now. But we can do better and add some enhancements to it.

    +

    What if we wanted the same records in Postgres public.delivery_7_days to also show up in the CSV file? That's where we +can use a foreign key definition.

    +

    Foreign Key

    +

    We can take a look at the report (under docker/sample/report/index.html) to see what we need to do to create the +foreign key. From the overview, you should see under Tasks there is a my_postgres task which has +food_delivery_public.delivery_7_days as a step. Click on the link for food_delivery_public.delivery_7_days and it +will take us to a page where we can find out about the columns used in this table. Click on the Fields button on the +far right to see.

    +

    We can copy all of a subset of fields that we want matched across the CSV file and Postgres. For this example, we will +take all the fields.

    +
    +
    +
    +
    var myPlan = plan().addForeignKeyRelationship(
+        postgresTask, List.of("key", "tmp_year", "tmp_name", "value"),
+        List.of(Map.entry(csvTask, List.of("account_number", "year", "name", "payload")))
+);
+
+var conf = ...
+
+execute(myPlan, conf, postgresTask, csvTask);
+
    +
    +
    +
    val foreignCols = List("order_id", "order_placed_on", "order_dispatched_on", "order_delivered_on", "customer_email",
+  "customer_address", "menu_id", "restaurant_id", "restaurant_address", "menu_item_id", "category_id", "discount_id",
+  "city_id", "driver_id")
+
+val myPlan = plan.addForeignKeyRelationships(
+  csvTask, foreignCols,
+  List(foreignField(postgresTask, "food_delivery_public.delivery_7_days", foreignCols))
+)
+
+val conf = ...
+
+execute(myPlan, conf, postgresTask, csvTask)
+
    +
    +
    +
    +

    Notice how we have defined the csvTask and foreignCols as the main foreign key but for postgresTask, we had to +define it as a foreignField. This is because postgresTask has multiple tables within it, and we only want to define +our foreign key with respect to the public.delivery_7_days table. We use the step name (can be seen from the report) +to specify the table to target.

    +

    To test this out, we will truncate the public.delivery_7_days table in Postgres first, and then try run again.

    +
    docker exec marquez-db psql -Upostgres -d food_delivery -c 'TRUNCATE public.delivery_7_days'
+./run.sh
+#input class MyAdvancedMetadataSourceJavaPlanRun or MyAdvancedMetadataSourcePlanRun
+docker exec marquez-db psql -Upostgres -d food_delivery -c 'SELECT * FROM public.delivery_7_days'
+
    +
     order_id |     order_placed_on     |   order_dispatched_on   |   order_delivered_on    |        customer_email        |
+       customer_address                     | menu_id | restaurant_id |                   restaurant_address                   | menu
+_item_id | category_id | discount_id | city_id | driver_id
+----------+-------------------------+-------------------------+-------------------------+------------------------------+-------------
+--------------------------------------------+---------+---------------+--------------------------------------------------------+-----
+---------+-------------+-------------+---------+-----------
+    53333 | 2022-10-15 08:40:23.394 | 2023-01-23 09:42:48.397 | 2023-08-12 08:50:52.397 | normand.aufderhar@gmail.com  | Apt. 036 449
+27 Wilderman Forge, Marvinchester, CT 15952 |   40412 |         70130 | Suite 146 98176 Schaden Village, Grahammouth, SD 12354 |
+   90141 |       44210 |       83966 |   78614 |     77449
+
    +

    Let's grab the first email from the Postgres table and check whether the same record exists in the CSV file.

    +
    $ cat docker/sample/csv/part-0000* | grep normand.aufderhar
+90141,44210,83966,78614,77449,53333,2022-10-15T08:40:23.394+08:00,2023-01-23T09:42:48.397+08:00,2023-08-12T08:50:52.397+08:00,normand.aufderhar@gmail.com,"Apt. 036 44927 Wilderman Forge, Marvinchester, CT 15952",40412,70130,"Suite 146 98176 Schaden Village, Grahammouth, SD 12354"
+
    +

    Great! Now we have the ability to get schema information from an external source, add our own foreign keys and generate +data.

    +

    Check out the full example under AdvancedMetadataSourcePlanRun in the example repo.

    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/guide/data-source/open-metadata-source/index.html b/setup/guide/data-source/open-metadata-source/index.html new file mode 100644 index 00000000..f30f645b --- /dev/null +++ b/setup/guide/data-source/open-metadata-source/index.html @@ -0,0 +1,2785 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + OpenMetadata Source - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    OpenMetadata Source

    +
    +

    Info

    +

    Generating data based on an external metadata source is a paid feature. Try the free trial here.

    +
    +

    Creating a data generator for a JSON file based on metadata stored +in OpenMetadata.

    +

    Requirements

    +
      +
    • 10 minutes
      • +
    • Git
      • +
    • Gradle
      • +
    • Docker
      • +
    +

    Get Started

    +

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    +
    git clone git@github.com:pflooky/data-caterer-example.git
+
    +

    OpenMetadata Setup

    +

    You can follow the local docker setup found +here to help with setting up +OpenMetadata in your local environment.

    +

    If that page becomes outdated or the link doesn't work, below are the commands I used to run it:

    +
    mkdir openmetadata-docker && cd openmetadata-docker
+curl -sL https://github.com/open-metadata/OpenMetadata/releases/download/1.2.0-release/docker-compose.yml > docker-compose.yml
+docker compose -f docker-compose.yml up --detach
+
    +

    Check that the following url works and login with admin:admin. Then you should see some data +like below:

    +

    OpenMetadata dashboard

    +

    Plan Setup

    +

    Create a new Java or Scala class.

    +
      +
    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedOpenMetadataSourceJavaPlanRun.java
      • +
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedOpenMetadataSourcePlanRun.scala
      • +
    +

    Make sure your class extends PlanRun.

    +
    +
    +
    +
    import com.github.pflooky.datacaterer.java.api.PlanRun;
+...
+
+public class MyAdvancedOpenMetadataSourceJavaPlanRun extends PlanRun {
+    {
+        var conf = configuration().enableGeneratePlanAndTasks(true)
+            .generatedReportsFolderPath("/opt/app/data/report");
+    }
+}
+
    +
    +
    +
    import com.github.pflooky.datacaterer.api.PlanRun
+...
+
+class MyAdvancedOpenMetadataSourcePlanRun extends PlanRun {
+  val conf = configuration.enableGeneratePlanAndTasks(true)
+    .generatedReportsFolderPath("/opt/app/data/report")
+}
+
    +
    +
    +
    +

    We will enable generate plan and tasks so that we can read from external sources for metadata and save the reports +under a folder we can easily access.

    +

    Schema

    +

    We can point the schema of a data source to our OpenMetadata instance. We will use a JSON data source so that we can +show how nested data types are handled and how we could customise it.

    +
    Single Schema
    +
    +
    +
    +
    import com.github.pflooky.datacaterer.api.model.Constants;
+...
+
+var jsonTask = json("my_json", "/opt/app/data/json", Map.of("saveMode", "overwrite"))
+        .schema(metadataSource().openMetadataJava(
+            "http://localhost:8585/api",                                                              //url
+            Constants.OPEN_METADATA_AUTH_TYPE_OPEN_METADATA(),                                        //auth type
+            Map.of(                                                                                   //additional options (including auth options)
+                Constants.OPEN_METADATA_JWT_TOKEN(), "abc123",                                        //get from settings/bots/ingestion-bot
+                Constants.OPEN_METADATA_TABLE_FQN(), "sample_data.ecommerce_db.shopify.raw_customer"  //table fully qualified name
+            )
+        ))
+        .count(count().records(10));
+
    +
    +
    +
    import com.github.pflooky.datacaterer.api.model.Constants.{OPEN_METADATA_AUTH_TYPE_OPEN_METADATA, OPEN_METADATA_JWT_TOKEN, OPEN_METADATA_TABLE_FQN, SAVE_MODE}
+...
+
+val jsonTask = json("my_json", "/opt/app/data/json", Map("saveMode" -> "overwrite"))
+  .schema(metadataSource.openMetadata(
+    "http://localhost:8585/api",                                                  //url
+    OPEN_METADATA_AUTH_TYPE_OPEN_METADATA,                                        //auth type
+    Map(                                                                          //additional options (including auth options)
+      OPEN_METADATA_JWT_TOKEN -> "abc123",                                        //get from settings/bots/ingestion-bot
+      OPEN_METADATA_TABLE_FQN -> "sample_data.ecommerce_db.shopify.raw_customer"  //table fully qualified name
+    )
+  ))
+  .count(count.records(10))
+
    +
    +
    +
    +

    The above defines that the schema will come from OpenMetadata, which is a type of metadata source that contains +information about schemas. Specifically, it points to the sample_data.ecommerce_db.shopify.raw_customer table. You +can check out the schema here to +see what it looks like.

    +

    Run

    +

    Let's try run and see what happens.

    +
    cd ..
+./run.sh
+#input class MyAdvancedOpenMetadataSourceJavaPlanRun or MyAdvancedOpenMetadataSourcePlanRun
+#after completing
+cat docker/sample/json/part-00000-*
+
    +

    It should look something like this.

    +
    {
+  "comments": "Mh6jqpD5e4M",
+  "creditcard": "6771839575926717",
+  "membership": "Za3wCQUl9E  EJj712",
+  "orders": [
+    {
+      "product_id": "Aa6NG0hxfHVq",
+      "price": 16139,
+      "onsale": false,
+      "tax": 58134,
+      "weight": 40734,
+      "others": 45813,
+      "vendor": "Kh"
+    },
+    {
+      "product_id": "zbHBY ",
+      "price": 17903,
+      "onsale": false,
+      "tax": 39526,
+      "weight": 9346,
+      "others": 52035,
+      "vendor": "jbkbnXAa"
+    },
+    {
+      "product_id": "5qs3gakppd7Nw5",
+      "price": 48731,
+      "onsale": true,
+      "tax": 81105,
+      "weight": 2004,
+      "others": 20465,
+      "vendor": "nozCDMSXRPH Ev"
+    },
+    {
+      "product_id": "CA6h17ANRwvb",
+      "price": 62102,
+      "onsale": true,
+      "tax": 96601,
+      "weight": 78849,
+      "others": 79453,
+      "vendor": " ihVXEJz7E2EFS"
+    }
+  ],
+  "platform": "GLt9",
+  "preference": {
+    "key": "nmPmsPjg C",
+    "value": true
+  },
+  "shipping_address": [
+    {
+      "name": "Loren Bechtelar",
+      "street_address": "Suite 526 293 Rohan Road, Wunschshire, NE 25532",
+      "city": "South Norrisland",
+      "postcode": "56863"
+    }
+  ],
+  "shipping_date": "2022-11-03",
+  "transaction_date": "2023-02-01",
+  "customer": {
+    "username": "lance.murphy",
+    "name": "Zane Brakus DVM",
+    "sex": "7HcAaPiO",
+    "address": "594 Loida Haven, Gilland, MA 26071",
+    "mail": "Un3fhbvK2rEbenIYdnq",
+    "birthdate": "2023-01-31"
+  }
+}
+
    +

    Looks like we have some data now. But we can do better and add some enhancements to it.

    +

    Custom metadata

    +

    We can see from the data generated, that it isn't quite what we want. The metadata is not sufficient for us to produce +production-like data yet. Let's try to add some enhancements to it.

    +

    Let's make the platform field a choice field that can only be a set of certain values and the nested +field customer.sex is also from a predefined set of values.

    +
    +
    +
    +
    var jsonTask = json("my_json", "/opt/app/data/json", Map.of("saveMode", "overwrite"))
+            .schema(
+                metadata...
+            ))
+            .schema(
+                field().name("platform").oneOf("website", "mobile"),
+                field().name("customer").schema(field().name("sex").oneOf("M", "F", "O"))
+            )
+            .count(count().records(10));
+
    +
    +
    +
    val jsonTask = json("my_json", "/opt/app/data/json", Map("saveMode" -> "overwrite"))
+  .schema(
+    metadata...
+  ))
+  .schema(
+    field.name("platform").oneOf("website", "mobile"),
+    field.name("customer").schema(field.name("sex").oneOf("M", "F", "O"))
+  )
+  .count(count.records(10))
+
    +
    +
    +
    +

    Let's test it out by running it again

    +
    ./run.sh
+#input class MyAdvancedMetadataSourceJavaPlanRun or MyAdvancedMetadataSourcePlanRun
+cat docker/sample/json/part-00000-*
+
    +
    {
+  "comments": "vqbPUm",
+  "creditcard": "6304867705548636",
+  "membership": "GZ1xOnpZSUOKN",
+  "orders": [
+    {
+      "product_id": "rgOokDAv",
+      "price": 77367,
+      "onsale": false,
+      "tax": 61742,
+      "weight": 87855,
+      "others": 26857,
+      "vendor": "04XHR64ImMr9T"
+    }
+  ],
+  "platform": "mobile",
+  "preference": {
+    "key": "IB5vNdWka",
+    "value": true
+  },
+  "shipping_address": [
+    {
+      "name": "Isiah Bins",
+      "street_address": "36512 Ross Spurs, Hillhaven, IA 18760",
+      "city": "Averymouth",
+      "postcode": "75818"
+    },
+    {
+      "name": "Scott Prohaska",
+      "street_address": "26573 Haley Ports, Dariusland, MS 90642",
+      "city": "Ashantimouth",
+      "postcode": "31792"
+    },
+    {
+      "name": "Rudolf Stamm",
+      "street_address": "Suite 878 0516 Danica Path, New Christiaport, ID 10525",
+      "city": "Doreathaport",
+      "postcode": "62497"
+    }
+  ],
+  "shipping_date": "2023-08-24",
+  "transaction_date": "2023-02-01",
+  "customer": {
+    "username": "jolie.cremin",
+    "name": "Fay Klein",
+    "sex": "O",
+    "address": "Apt. 174 5084 Volkman Creek, Hillborough, PA 61959",
+    "mail": "BiTmzb7",
+    "birthdate": "2023-04-07"
+  }
+}
+
    +

    Great! Now we have the ability to get schema information from an external source, add our own metadata and generate +data.

    +

    Data validation

    +

    Another aspect of OpenMetadata that can be leveraged is the definition of data quality rules. These rules can be +incorporated into your Data Caterer job as well by enabling data validations via enableGenerateValidations in +configuration.

    +
    +
    +
    +
    var conf = configuration().enableGeneratePlanAndTasks(true)
+    .enableGenerateValidations(true)
+    .generatedReportsFolderPath("/opt/app/data/report");
+
+execute(conf, jsonTask);
+
    +
    +
    +
    val conf = configuration.enableGeneratePlanAndTasks(true)
+  .enableGenerateValidations(true)
+  .generatedReportsFolderPath("/opt/app/data/report")
+
+execute(conf, jsonTask)
+
    +
    +
    +
    +

    Check out the full example under AdvancedOpenMetadataSourcePlanRun in the example repo.

    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/guide/data-source/solace/index.html b/setup/guide/data-source/solace/index.html new file mode 100644 index 00000000..5f6c1102 --- /dev/null +++ b/setup/guide/data-source/solace/index.html @@ -0,0 +1,2835 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Solace - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Solace

    +
    +

    Info

    +

    Writing data to Solace is a paid feature. Try the free trial here.

    +
    +

    Creating a data generator for Solace. You will build a Docker image that will be able to populate data in Solace +for the queues/topics you configure.

    +

    Generate Solace messages

    +

    Requirements

    +
      +
    • 20 minutes
      • +
    • Git
      • +
    • Gradle
      • +
    • Docker
      • +
    • Solace
      • +
    +

    Get Started

    +

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    +
    git clone git@github.com:pflooky/data-caterer-example.git
+
    +

    If you already have a Solace instance running, you can skip to this step.

    +

    Solace Setup

    +

    Next, let's make sure you have an instance of Solace up and running in your local environment. This will make it +easy for us to iterate and check our changes.

    +
    cd docker
+docker-compose up -d solace
+
    +

    Open up localhost:8080 and login with admin:admin and check there is the default VPN like +below. Notice there is 2 queues/topics created. If you do not see 2 created, try to run the script found under +docker/data/solace/setup_solace.sh and change the host to localhost.

    +

    Solace dashboard

    +

    Plan Setup

    +

    Create a new Java or Scala class.

    +
      +
    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedSolaceJavaPlan.java
      • +
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedSolacePlan.scala
      • +
    +

    Make sure your class extends PlanRun.

    +
    +
    +
    +
    import com.github.pflooky.datacaterer.java.api.PlanRun;
+
+public class MyAdvancedSolaceJavaPlan extends PlanRun {
+}
+
    +
    +
    +
    import com.github.pflooky.datacaterer.api.PlanRun
+
+class MyAdvancedSolacePlan extends PlanRun {
+}
+
    +
    +
    +
    +

    This class defines where we need to define all of our configurations for generating data. There are helper variables and +methods defined to make it simple and easy to use.

    +

    Connection Configuration

    +

    Within our class, we can start by defining the connection properties to connect to Solace.

    +
    +
    +
    +
    var accountTask = solace(
+    "my_solace",                        //name
+    "smf://host.docker.internal:55554", //url
+    Map.of()                            //optional additional connection options
+);
+
    +

    Additional connection options can be found here.

    +
    +
    +
    val accountTask = solace(
+    "my_solace",                        //name
+    "smf://host.docker.internal:55554", //url
+    Map()                               //optional additional connection options
+)
+
    +

    Additional connection options can be found here.

    +
    +
    +
    +

    Schema

    +

    Let's create a task for inserting data into the rest_test_queue or rest_test_topic that is already created for us +from this step.

    +

    Trimming the connection details to work with the docker-compose Solace, we have a base Solace connection to define +the JNDI destination we will publish to. Let's define each field along with their corresponding data type. You will +notice +that the text fields do not have a data type defined. This is because the default data type is StringType.

    +
    +
    +
    +
    {
+    var solaceTask = solace("my_solace", "smf://host.docker.internal:55554")
+            .destination("/JNDI/Q/rest_test_queue")
+            .schema(
+                    field().name("value").sql("TO_JSON(content)"),
+                    //field().name("partition").type(IntegerType.instance()),   //can define message JMS priority here
+                    field().name("headers")                                     //set message properties via headers field
+                            .type(HeaderType.getType())
+                            .sql(
+                                    "ARRAY(" +
+                                            "NAMED_STRUCT('key', 'account-id', 'value', TO_BINARY(content.account_id, 'utf-8'))," +
+                                            "NAMED_STRUCT('key', 'updated', 'value', TO_BINARY(content.details.updated_by.time, 'utf-8'))" +
+                                            ")"
+                            ),
+                    field().name("content")
+                            .schema(
+                                    field().name("account_id").regex("ACC[0-9]{8}"),
+                                    field().name("year").type(IntegerType.instance()).min(2021).max(2023),
+                                    field().name("amount").type(DoubleType.instance()),
+                                    field().name("details")
+                                            .schema(
+                                                    field().name("name").expression("#{Name.name}"),
+                                                    field().name("first_txn_date").type(DateType.instance()).sql("ELEMENT_AT(SORT_ARRAY(content.transactions.txn_date), 1)"),
+                                                    field().name("updated_by")
+                                                            .schema(
+                                                                    field().name("user"),
+                                                                    field().name("time").type(TimestampType.instance())
+                                                            )
+                                            ),
+                                    field().name("transactions").type(ArrayType.instance())
+                                            .schema(
+                                                    field().name("txn_date").type(DateType.instance()).min(Date.valueOf("2021-01-01")).max("2021-12-31"),
+                                                    field().name("amount").type(DoubleType.instance())
+                                            )
+                            )
+            )
+            .count(count().records(10));
+}
+
    +
    +
    +
    val solaceTask = solace("my_solace", "smf://host.docker.internal:55554")
+  .destination("/JNDI/Q/rest_test_queue")
+  .schema(
+    field.name("value").sql("TO_JSON(content)"),
+    //field.name("partition").`type`(IntegerType),  //can define message JMS priority here
+    field.name("headers")                           //set message properties via headers field
+      .`type`(HeaderType.getType)
+      .sql(
+        """ARRAY(
+          |  NAMED_STRUCT('key', 'account-id', 'value', TO_BINARY(content.account_id, 'utf-8')),
+          |  NAMED_STRUCT('key', 'updated', 'value', TO_BINARY(content.details.updated_by.time, 'utf-8'))
+          |)""".stripMargin
+      ),
+    field.name("content")
+      .schema(
+        field.name("account_id").regex("ACC[0-9]{8}"),
+        field.name("year").`type`(IntegerType).min(2021).max(2023),
+        field.name("amount").`type`(DoubleType),
+        field.name("details")
+          .schema(
+            field.name("name").expression("#{Name.name}"),
+            field.name("first_txn_date").`type`(DateType).sql("ELEMENT_AT(SORT_ARRAY(content.transactions.txn_date), 1)"),
+            field.name("updated_by")
+              .schema(
+                field.name("user"),
+                field.name("time").`type`(TimestampType),
+              ),
+          ),
+        field.name("transactions").`type`(ArrayType)
+          .schema(
+            field.name("txn_date").`type`(DateType).min(Date.valueOf("2021-01-01")).max("2021-12-31"),
+            field.name("amount").`type`(DoubleType),
+          )
+      ),
+  ).count(count.records(10))
+
    +
    +
    +
    +

    Fields

    +

    The schema defined for Solace has a format that needs to be followed as noted above. Specifically, the required fields +are:

    +
      +
    • value
      • +
    +

    Whilst, the other fields are optional:

    +
      +
    • partition - refers to JMS priority of the message
      • +
    • headers - refers to JMS message properties
      • +
    +
    headers
    +

    headers follows a particular pattern that where it is of type HeaderType.getType which behind the scenes, translates +toarray<struct<key: string,value: binary>>. To be able to generate data for this data type, we need to use an SQL +expression like the one below. You will notice that in thevalue part, it refers to content.account_id where +content is another field defined at the top level of the schema. This allows you to reference other values that have +already been generated.

    +
    +
    +
    +
    field().name("headers")
+        .type(HeaderType.getType())
+        .sql(
+                "ARRAY(" +
+                        "NAMED_STRUCT('key', 'account-id', 'value', TO_BINARY(content.account_id, 'utf-8'))," +
+                        "NAMED_STRUCT('key', 'updated', 'value', TO_BINARY(content.details.updated_by.time, 'utf-8'))" +
+                        ")"
+        )
+
    +
    +
    +
    field.name("headers")
+  .`type`(HeaderType.getType)
+  .sql(
+    """ARRAY(
+      |  NAMED_STRUCT('key', 'account-id', 'value', TO_BINARY(content.account_id, 'utf-8')),
+      |  NAMED_STRUCT('key', 'updated', 'value', TO_BINARY(content.details.updated_by.time, 'utf-8'))
+      |)""".stripMargin
+  )
+
    +
    +
    +
    +
    transactions
    +

    transactions is an array that contains an inner structure of txn_date and amount. The size of the array generated +can be controlled via arrayMinLength and arrayMaxLength.

    +
    +
    +
    +
    field().name("transactions").type(ArrayType.instance())
+        .schema(
+                field().name("txn_date").type(DateType.instance()).min(Date.valueOf("2021-01-01")).max("2021-12-31"),
+                field().name("amount").type(DoubleType.instance())
+        )
+
    +
    +
    +
    field.name("transactions").`type`(ArrayType)
+  .schema(
+    field.name("txn_date").`type`(DateType).min(Date.valueOf("2021-01-01")).max("2021-12-31"),
+    field.name("amount").`type`(DoubleType),
+  )
+
    +
    +
    +
    +
    details
    +

    details is another example of a nested schema structure where it also has a nested structure itself in updated_by. +One thing to note here is the first_txn_date field has a reference to the content.transactions array where it will +sort the array by txn_date and get the first element.

    +
    +
    +
    +
    field().name("details")
+        .schema(
+                field().name("name").expression("#{Name.name}"),
+                field().name("first_txn_date").type(DateType.instance()).sql("ELEMENT_AT(SORT_ARRAY(content.transactions.txn_date), 1)"),
+                field().name("updated_by")
+                        .schema(
+                                field().name("user"),
+                                field().name("time").type(TimestampType.instance())
+                        )
+        )
+
    +
    +
    +
    field.name("details")
+  .schema(
+    field.name("name").expression("#{Name.name}"),
+    field.name("first_txn_date").`type`(DateType).sql("ELEMENT_AT(SORT_ARRAY(content.transactions.txn_date), 1)"),
+    field.name("updated_by")
+      .schema(
+        field.name("user"),
+        field.name("time").`type`(TimestampType),
+      ),
+  )
+
    +
    +
    +
    +

    Additional Configurations

    +

    At the end of data generation, a report gets generated that summarises the actions it performed. We can control the +output folder of that report via configurations.

    +
    +
    +
    +
    var config = configuration()
+        .generatedReportsFolderPath("/opt/app/data/report");
+
    +
    +
    +
    val config = configuration
+  .generatedReportsFolderPath("/opt/app/data/report")
+
    +
    +
    +
    +

    Execute

    +

    To tell Data Caterer that we want to run with the configurations along with the kafkaTask, we have to call execute.

    +

    Run

    +

    Now we can run via the script ./run.sh that is in the top level directory of the data-caterer-example to run the +class we just created.

    +
    ./run.sh
+#input class AdvancedSolaceJavaPlanRun or AdvancedSolacePlanRun
+#after completing, check http://localhost:8080 from browser
+
    +

    Your output should look like this.

    +

    Solace messages queued

    +

    Unfortunately, there is no easy way to see the message content. You can check the message content from your application +or service that consumes these messages.

    +

    Also check the HTML report, found at docker/sample/report/index.html, that gets generated to get an overview of what +was executed. Or view the sample report found here.

    + + + + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/guide/index.html b/setup/guide/index.html new file mode 100644 index 00000000..852235b7 --- /dev/null +++ b/setup/guide/index.html @@ -0,0 +1,2591 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Guides - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Guides

    +

    Below are a list of guides you can follow to create your data generation for your use case.

    +

    For any of the paid tier guides, you can use the trial version fo the app to try it out. Details on how to get +the trial can be found here.

    +

    Scenarios

    +
    + +
    +

    Data Sources

    +
    + +
    +

    YAML Files

    +

    Base Concept

    +

    The execution of the data generator is based on the concept of plans and tasks. A plan represent the set of tasks that +need to be executed, +along with other information that spans across tasks, such as foreign keys between data sources.
    +A task represent the component(s) of a data source and its associated metadata so that it understands what the data +should look like +and how many steps (sub data sources) there are (i.e. tables in a database, topics in Kafka). Tasks can define one or +more steps.

    +

    Plan

    +

    Foreign Keys

    +

    Define foreign keys across data sources in your plan to ensure generated data can match
    +Link to associated task 1
    +Link to associated task 2

    +

    Task

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Data Source TypeData SourceSample TaskNotes
    DatabasePostgresSample
    DatabaseMySQLSample
    DatabaseCassandraSample
    FileCSVSample
    FileJSONSampleContains nested schemas and use of SQL for generated values
    FileParquetSamplePartition by year column
    KafkaKafkaSampleSpecific base schema to be used, define headers, key, value, etc.
    JMSSolaceSampleJSON formatted message
    HTTPPUTSampleJSON formatted PUT body
    +

    Configuration

    +

    Basic configuration

    +

    Docker-compose

    +

    To see how it runs against different data sources, you can run using docker-compose and set DATA_SOURCE like below

    +
    ./gradlew build
+cd docker
+DATA_SOURCE=postgres docker-compose up -d datacaterer
+
    +

    Can set it to one of the following:

    +
      +
    • postgres
      • +
    • mysql
      • +
    • cassandra
      • +
    • solace
      • +
    • kafka
      • +
    • http
      • +
    + + + + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/guide/scenario/auto-generate-connection/index.html b/setup/guide/scenario/auto-generate-connection/index.html new file mode 100644 index 00000000..7a2940f8 --- /dev/null +++ b/setup/guide/scenario/auto-generate-connection/index.html @@ -0,0 +1,2641 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Auto Generate From Data Connection - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Auto Generate From Data Connection

    +
    +

    Info

    +

    Auto data generation from data connection is a paid feature. Try the free trial here.

    +
    +

    Creating a data generator based on only a data connection to Postgres.

    +

    Requirements

    +
      +
    • 5 minutes
      • +
    • Git
      • +
    • Gradle
      • +
    • Docker
      • +
    +

    Get Started

    +

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    +
    git clone git@github.com:pflooky/data-caterer-example.git
+
    +

    Plan Setup

    +

    Create a new Java or Scala class.

    +
      +
    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedAutomatedJavaPlanRun.java
      • +
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedAutomatedPlanRun.scala
      • +
    +

    Make sure your class extends PlanRun.

    +
    +
    +
    +
    import com.github.pflooky.datacaterer.java.api.PlanRun;
+...
+
+public class MyAdvancedAutomatedJavaPlanRun extends PlanRun {
+    {
+        var autoRun = configuration()
+                .postgres("my_postgres", "jdbc:postgresql://host.docker.internal:5432/customer")  (1)
+                .enableGeneratePlanAndTasks(true)                                                 (2)
+                .generatedPlanAndTaskFolderPath("/opt/app/data/generated")                        (3)
+                .enableUniqueCheck(true)                                                          (4)
+                .generatedReportsFolderPath("/opt/app/data/report");
+
+        execute(autoRun);
+    }
+}
+
    +
    +
    +
    import com.github.pflooky.datacaterer.api.PlanRun
+...
+
+class MyAdvancedAutomatedPlanRun extends PlanRun {
+
+  val autoRun = configuration
+    .postgres("my_postgres", "jdbc:postgresql://host.docker.internal:5432/customer")  (1)
+    .enableGeneratePlanAndTasks(true)                                                 (2)
+    .generatedPlanAndTaskFolderPath("/opt/app/data/generated")                        (3)
+    .enableUniqueCheck(true)                                                          (4)
+    .generatedReportsFolderPath("/opt/app/data/report")
+
+  execute(configuration = autoRun)
+}
+
    +
    +
    +
    +

    In the above code, we note the following:

    +
      +
    1. Data source configuration to a Postgres data source called my_postgres
      2. +
    2. We have enabled the flag enableGeneratePlanAndTasks which tells Data Caterer to go to my_postgres and generate + data for all the tables found under the database customer (which is defined in the connection string).
      3. +
    3. The config generatedPlanAndTaskFolderPath defines where the metadata that is gathered from my_postgres should be + saved at so that we could re-use it later.
      4. +
    4. enableUniqueCheck is set to true to ensure that generated data is unique based on primary key or foreign key + definitions.
      5. +
    +
    +

    Note

    +

    Unique check will only ensure generated data is unique. Any existing data in your data source is not taken into +account, so generated data may fail to insert depending on the data source restrictions

    +
    +

    Postgres Setup

    +

    If you don't have your own Postgres up and running, you can set up and run an instance configured in the docker +folder via.

    +
    cd docker
+docker-compose up -d postgres
+docker exec docker-postgresserver-1 psql -Upostgres -d customer -c '\dt+ account.*'
+
    +

    This will create the tables found under docker/data/sql/postgres/customer.sql. You can change this file to contain +your own tables. We can see there are 4 tables created for us, accounts, balances, transactions and mapping.

    +

    Run

    +

    Let's try run.

    +
    cd ..
+./run.sh
+#input class MyAdvancedAutomatedJavaPlanRun or MyAdvancedAutomatedPlanRun
+#after completing
+docker exec docker-postgresserver-1 psql -Upostgres -d customer -c 'select * from account.accounts limit 1;'
+
    +

    It should look something like this.

    +
       id   | account_number  | account_status | created_by | created_by_fixed_length | customer_id_int | customer_id_smallint | customer_id_bigint |   customer_id_decimal    | customer_id_real | customer_id_double | open_date  |     open_timestamp      | last_opened_time |                                                           payload_bytes
+--------+-----------------+----------------+------------+-------------------------+-----------------+----------------------+--------------------+--------------------------+------------------+--------------------+------------+-------------------------+------------------+------------------------------------------------------------------------------------------------------------------------------------
+ 100414 | 5uROOVOUyQUbubN | h3H            | SfA0eZJcTm | CuRw                    |              13 |                   42 |               6041 | 76987.745612542900000000 |         91866.78 |  66400.37433202339 | 2023-03-05 | 2023-08-14 11:33:11.343 | 23:58:01.736     | \x604d315d4547616e6a233050415373317274736f5e682d516132524f3d23233c37463463322f342d34376d597e665d6b3d395b4238284028622b7d6d2b4f5042
+(1 row)
+
    +

    The data that gets inserted will follow the foreign keys that are defined within Postgres and also ensure the insertion +order is correct.

    +

    Also check the HTML report that gets generated under docker/sample/report/index.html. You can see a summary of what +was generated along with other metadata.

    +

    You can now look to play around with other tables or data sources and auto generate for them.

    +

    Additional Topics

    +

    Learn From Existing Data

    +

    If you have any existing data within your data source, Data Caterer will gather metadata about the existing data to +help guide it when generating new data. There are configurations that can help tune the metadata analysis found +here.

    +

    Filter Out Schema/Tables

    +

    As part of your connection definition, you can define any schemas and/or tables your don't want to generate data for. In +the example below, it will not generate any data for any tables under the history and audit schemas. Also, any +table with the name balances or transactions in any schema will also not have data generated.

    +
    +
    +
    +
    var autoRun = configuration()
+        .postgres(
+              "my_postgres", 
+              "jdbc:postgresql://host.docker.internal:5432/customer",
+              Map.of(
+                  "filterOutSchema", "history, audit",
+                  "filterOutTable", "balances, transactions")
+              )
+        )
+
    +
    +
    +
    val autoRun = configuration
+  .postgres(
+    "my_postgres",
+    "jdbc:postgresql://host.docker.internal:5432/customer",
+    Map(
+      "filterOutSchema" -> "history, audit",
+      "filterOutTable" -> "balances, transactions")
+    )
+  )
+
    +
    +
    +
    +

    Define record count

    +

    You can control the record count per sub data source via numRecordsPerStep.

    +
    +
    +
    +
    var autoRun = configuration()
+      ...
+      .numRecordsPerStep(100)
+
+execute(autoRun)
+
    +
    +
    +
    val autoRun = configuration
+  ...
+  .numRecordsPerStep(100)
+
+execute(configuration = autoRun)
+
    +
    +
    +
    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/guide/scenario/batch-and-event/index.html b/setup/guide/scenario/batch-and-event/index.html new file mode 100644 index 00000000..5d54e74d --- /dev/null +++ b/setup/guide/scenario/batch-and-event/index.html @@ -0,0 +1,2647 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Generate Batch and Event Data - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Generate Batch and Event Data

    +
    +

    Info

    +

    Generating event data is a paid feature. Try the free trial here.

    +
    +

    Creating a data generator for Kafka topic with matching records in a CSV file.

    +

    Requirements

    +
      +
    • 5 minutes
      • +
    • Git
      • +
    • Gradle
      • +
    • Docker
      • +
    +

    Get Started

    +

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    +
    git clone git@github.com:pflooky/data-caterer-example.git
+
    +

    Kafka Setup

    +

    If you don't have your own Kafka up and running, you can set up and run an instance configured in the docker +folder via.

    +
    cd docker
+docker-compose up -d kafka
+docker exec docker-kafkaserver-1 kafka-topics --bootstrap-server localhost:9092 --list
+
    +

    Let's create a task for inserting data into the account-topic that is already defined +underdocker/data/kafka/setup_kafka.sh.

    +

    Plan Setup

    +

    Create a new Java or Scala class.

    +
      +
    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedBatchEventJavaPlanRun.java
      • +
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedBatchEventPlanRun.scala
      • +
    +

    Make sure your class extends PlanRun.

    +
    +
    +
    +
    import com.github.pflooky.datacaterer.java.api.PlanRun;
+...
+
+public class MyAdvancedBatchEventJavaPlanRun extends PlanRun {
+    {
+        var kafkaTask = new AdvancedKafkaJavaPlanRun().getKafkaTask();
+    }
+}
+
    +
    +
    +
    import com.github.pflooky.datacaterer.api.PlanRun
+...
+
+class MyAdvancedBatchEventPlanRun extends PlanRun {
+  val kafkaTask = new AdvancedKafkaPlanRun().kafkaTask
+}
+
    +
    +
    +
    +

    We will borrow the Kafka task that is already defined under the class AdvancedKafkaPlanRun +or AdvancedKafkaJavaPlanRun. You can go through the Kafka guide here for more details.

    +

    Schema

    +

    Let us set up the corresponding schema for the CSV file where we want to match the values that are generated for the +Kafka messages.

    +
    +
    +
    +
    var kafkaTask = new AdvancedKafkaJavaPlanRun().getKafkaTask();
+
+var csvTask = csv("my_csv", "/opt/app/data/csv/account")
+        .schema(
+                field().name("account_number"),
+                field().name("year"),
+                field().name("name"),
+                field().name("payload")
+        );
+
    +
    +
    +
    val kafkaTask = new AdvancedKafkaPlanRun().kafkaTask
+
+val csvTask = csv("my_csv", "/opt/app/data/csv/account")
+  .schema(
+    field.name("account_number"),
+    field.name("year"),
+    field.name("name"),
+    field.name("payload")
+)
+
    +
    +
    +
    +

    This is a simple schema where we want to use the values and metadata that is already defined in the kafkaTask to +determine what the data will look like for the CSV file. Even if we defined some metadata here, it would be overridden +when we define our foreign key relationships.

    +

    Foreign Keys

    +

    From the above CSV schema, we see note the following against the Kafka schema:

    +
      +
    • account_number in CSV needs to match with the account_id in Kafka
        +
      • We see that account_id is referred to in the key column as field.name("key").sql("content.account_id")
        • +
      +
      • +
    • year needs to match with content.year in Kafka, which is a nested field
        +
      • We can only do foreign key relationships with top level fields, not nested fields. So we define a new column + called tmp_year which will not appear in the final output for the Kafka messages but is used as an intermediate + step field.name("tmp_year").sql("content.year").omit(true)
        • +
      +
      • +
    • name needs to match with content.details.name in Kafka, also a nested field
        +
      • Using the same logic as above, we define a temporary column called tmp_name which will take the value of the + nested field but will be omitted field.name("tmp_name").sql("content.details.name").omit(true)
        • +
      +
      • +
    • payload represents the whole JSON message sent to Kafka, which matches to value column
      • +
    +

    Our foreign keys are therefore defined like below. Order is important when defining the list of columns. The index needs +to match with the corresponding column in the other data source.

    +
    +
    +
    +
    var myPlan = plan().addForeignKeyRelationship(
+        kafkaTask, List.of("key", "tmp_year", "tmp_name", "value"),
+        List.of(Map.entry(csvTask, List.of("account_number", "year", "name", "payload")))
+);
+
+var conf = configuration()
+      .generatedReportsFolderPath("/opt/app/data/report");
+
+execute(myPlan, conf, kafkaTask, csvTask);
+
    +
    +
    +
    val myPlan = plan.addForeignKeyRelationship(
+    kafkaTask, List("key", "tmp_year", "tmp_name", "value"),
+    List(csvTask -> List("account_number", "year", "name", "payload"))
+)
+
+val conf = configuration.generatedReportsFolderPath("/opt/app/data/report")
+
+execute(myPlan, conf, kafkaTask, csvTask)
+
    +
    +
    +
    +

    Run

    +

    Let's try run.

    +
    cd ..
+./run.sh
+#input class MyAdvancedBatchEventJavaPlanRun or MyAdvancedBatchEventPlanRun
+#after completing
+docker exec docker-kafkaserver-1 kafka-console-consumer --bootstrap-server localhost:9092 --topic account-topic --from-beginning
+
    +

    It should look something like this.

    +
    {"account_id":"ACC03093143","year":2023,"amount":87990.37196728592,"details":{"name":"Nadine Heidenreich Jr.","first_txn_date":"2021-11-09","updated_by":{"user":"YfEyJCe8ohrl0j IfyT","time":"2022-09-26T20:47:53.404Z"}},"transactions":[{"txn_date":"2021-11-09","amount":97073.7914706189}]}
+{"account_id":"ACC08764544","year":2021,"amount":28675.58758765888,"details":{"name":"Delila Beer","first_txn_date":"2021-05-19","updated_by":{"user":"IzB5ksXu","time":"2023-01-26T20:47:26.389Z"}},"transactions":[{"txn_date":"2021-10-01","amount":80995.23818711648},{"txn_date":"2021-05-19","amount":92572.40049217848},{"txn_date":"2021-12-11","amount":99398.79832225188}]}
+{"account_id":"ACC62505420","year":2023,"amount":96125.3125884202,"details":{"name":"Shawn Goodwin","updated_by":{"user":"F3dqIvYp2pFtena4","time":"2023-02-11T04:38:29.832Z"}},"transactions":[]}
+
    +

    Let's also check if there is a corresponding record in the CSV file.

    +
    $ cat docker/sample/csv/account/part-0000* | grep ACC03093143
+ACC03093143,2023,Nadine Heidenreich Jr.,"{\"account_id\":\"ACC03093143\",\"year\":2023,\"amount\":87990.37196728592,\"details\":{\"name\":\"Nadine Heidenreich Jr.\",\"first_txn_date\":\"2021-11-09\",\"updated_by\":{\"user\":\"YfEyJCe8ohrl0j IfyT\",\"time\":\"2022-09-26T20:47:53.404Z\"}},\"transactions\":[{\"txn_date\":\"2021-11-09\",\"amount\":97073.7914706189}]}"
+
    +

    Great! The account, year, name and payload look to all match up.

    +

    Additional Topics

    +

    Order of execution

    +

    You may notice that the events are generated first, then the CSV file. This is because as part of the execute +function, we passed in the kafkaTask first, before the csvTask. You can change the order of execution by +passing in csvTask before kafkaTask into the execute function.

    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/guide/scenario/data-validation/index.html b/setup/guide/scenario/data-validation/index.html new file mode 100644 index 00000000..abba7d41 --- /dev/null +++ b/setup/guide/scenario/data-validation/index.html @@ -0,0 +1,2812 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Data Validations - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Data Validations

    +

    Creating a data validator for a JSON file.

    +

    Example data validation report

    +

    Requirements

    +
      +
    • 5 minutes
      • +
    • Git
      • +
    • Gradle
      • +
    • Docker
      • +
    +

    Get Started

    +

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    +
    git clone git@github.com:pflooky/data-caterer-example.git
+
    +

    Data Setup

    +

    To aid in showing the functionality of data validations, we will first generate some data that our validations will run +against. Run the below command and it will generate JSON files under docker/sample/json folder.

    +
    ./run.sh JsonPlan
+
    +

    Plan Setup

    +

    Create a new Java or Scala class.

    +
      +
    • Java: src/main/java/com/github/pflooky/plan/MyValidationJavaPlan.java
      • +
    • Scala: src/main/scala/com/github/pflooky/plan/MyValidationPlan.scala
      • +
    +

    Make sure your class extends PlanRun.

    +
    +
    +
    +
    import com.github.pflooky.datacaterer.java.api.PlanRun;
+...
+
+public class MyValidationJavaPlan extends PlanRun {
+    {
+        var jsonTask = json("my_json", "/opt/app/data/json");
+
+        var config = configuration()
+                .generatedReportsFolderPath("/opt/app/data/report")
+                .enableValidation(true)
+                .enableGenerateData(false);
+
+        execute(config, jsonTask);
+    }
+}
+
    +
    +
    +
    import com.github.pflooky.datacaterer.api.PlanRun
+...
+
+class MyValidationPlan extends PlanRun {
+  val jsonTask = json("my_json", "/opt/app/data/json")
+
+  val config = configuration
+    .generatedReportsFolderPath("/opt/app/data/report")
+    .enableValidation(true)
+    .enableGenerateData(false)
+
+  execute(config, jsonTask)
+}
+
    +
    +
    +
    +

    As noted above, we create a JSON task that points to where the JSON data has been created at folder /opt/app/data/json +. We also note that enableValidation is set to true and enableGenerateData to false to tell Data Catering, we +only want to validate data.

    +

    Validations

    +

    For reference, the schema in which we will be validating against looks like the below.

    +
    .schema(
+  field.name("account_id"),
+  field.name("year").`type`(IntegerType),
+  field.name("balance").`type`(DoubleType),
+  field.name("date").`type`(DateType),
+  field.name("status"),
+  field.name("update_history").`type`(ArrayType)
+    .schema(
+      field.name("updated_time").`type`(TimestampType),
+      field.name("status").oneOf("open", "closed", "pending", "suspended"),
+    ),
+  field.name("customer_details")
+    .schema(
+      field.name("name").expression("#{Name.name}"),
+      field.name("age").`type`(IntegerType),
+      field.name("city").expression("#{Address.city}")
+    )
+)
+
    +

    Basic Validation

    +

    Let's say our goal is to validate the customer_details.name field to ensure it conforms to the regex +pattern [A-Z][a-z]+ [A-Z][a-z]+. Given the diversity in naming conventions across cultures and countries, variations +such as middle names, suffixes, prefixes, or language-specific differences are tolerated to a certain extent. The +validation considers an acceptable error threshold before marking it as failed.

    +
    Validation Criteria
    +
      +
    • Field to Validate: customer_details.name
      • +
    • Regex Pattern: [A-Z][a-z]+ [A-Z][a-z]+
      • +
    • Error Tolerance: If more than 10% do not match the regex, then fail.
      • +
    +
    Considerations
    +
      +
    • Customisation
        +
      • Adjust the regex pattern and error threshold based on your specific data schema and validation requirements.
        • +
      • For the full list of types of basic validations that can be + used, check this page.
        • +
      +
      • +
    • Understanding Tolerance
        +
      • Be mindful of the error threshold, as it directly influences what percentage of deviations from the pattern is + acceptable.
        • +
      +
      • +
    +
    +
    +
    +
    validation().col("customer_details.name")
+    .matches("[A-Z][a-z]+ [A-Z][a-z]+")
+    .errorThreshold(0.1)                                      //<=10% failure rate is acceptable
+    .description("Names generally follow the same pattern"),  //description to add context in report or other developers
+
    +
    +
    +
    validation.col("customer_details.name")
+  .matches("[A-Z][a-z]+ [A-Z][a-z]+")
+  .errorThreshold(0.1)                                      //<=10% failure rate is acceptable
+  .description("Names generally follow the same pattern"),  //description to add context in report or other developers
+
    +
    +
    +
    +
    Custom Validation
    +

    There will be situation where you have a complex data setup and require you own custom logic to use for data validation. +You can achieve this via setting your own SQL expression that returns a boolean value. An example is seen below where +we want to check the array update_history, that each entry has updated_time greater than a certain timestamp.

    +
    +
    +
    +
    validation().expr("FORALL(update_history, x -> x.updated_time > TIMESTAMP('2022-01-01 00:00:00'))"),
+
    +
    +
    +
    validation.expr("FORALL(update_history, x -> x.updated_time > TIMESTAMP('2022-01-01 00:00:00'))"),
+
    +
    +
    +
    +

    If you want to know what other SQL function are available for you to +use, check this page.

    +

    Group By Validation

    +

    There are scenarios where you want to validate against grouped values or the whole dataset via aggregations. An example +would be validating that each customer's transactions sum is greater than 0.

    +
    Validation Criteria
    +

    Line 1: validation.groupBy().count().isEqual(100)

    +
      +
    • Method Chaining
        +
      • groupBy(): Group by whole dataset.
        • +
      • count(): Counts the number of dataset elements.
        • +
      • isEqual(100): Checks if the count is equal to 100.
        • +
      +
      • +
    • Validation Rule
        +
      • This line ensures that the count of the total dataset is exactly 100.
        • +
      +
      • +
    +

    Line 2: validation.groupBy("account_id").max("balance").lessThan(900)

    +
      +
    • Method Chaining
        +
      • groupBy("account_id"): Groups the data based on the account_id field.
        • +
      • max("balance"): Calculates the maximum value of the balance field within each group.
        • +
      • lessThan(900): Checks if the maximum balance in each group is less than 900.
        • +
      +
      • +
    • Validation Rule
        +
      • This line ensures that, for each group identified by account_id the maximum balance is less than 900.
        • +
      +
      • +
    +
    Considerations
    + +
    +
    +
    +
    validation().groupBy().count().isEqual(100),
+validation().groupBy("account_id").max("balance").lessThan(900)
+
    +
    +
    +
    validation.groupBy().count().isEqual(100),
+validation.groupBy("account_id").max("balance").lessThan(900)
+
    +
    +
    +
    +

    Sample Validation

    +

    To try cover the majority of validation cases, the below has been created.

    +
    +
    +
    +
    var jsonTask = json("my_json", "/opt/app/data/json")
+        .validations(
+                validation().col("customer_details.name").matches("[A-Z][a-z]+ [A-Z][a-z]+").errorThreshold(0.1).description("Names generally follow the same pattern"),
+                validation().col("date").isNotNull().errorThreshold(10),
+                validation().col("balance").greaterThan(500),
+                validation().expr("YEAR(date) == year"),
+                validation().col("status").in("open", "closed", "pending").errorThreshold(0.2).description("Could be new status introduced"),
+                validation().col("customer_details.age").greaterThan(18),
+                validation().expr("FORALL(update_history, x -> x.updated_time > TIMESTAMP('2022-01-01 00:00:00'))"),
+                validation().col("update_history").greaterThanSize(2),
+                validation().unique("account_id"),
+                validation().groupBy().count().isEqual(1000),
+                validation().groupBy("account_id").max("balance").lessThan(900)
+        );
+
+var config = configuration()
+        .generatedReportsFolderPath("/opt/app/data/report")
+        .enableValidation(true)
+        .enableGenerateData(false);
+
+execute(config, jsonTask);
+
    +
    +
    +
    val jsonTask = json("my_json", "/opt/app/data/json")
+  .validations(
+    validation.col("customer_details.name").matches("[A-Z][a-z]+ [A-Z][a-z]+").errorThreshold(0.1).description("Names generally follow the same pattern"),
+    validation.col("date").isNotNull.errorThreshold(10),
+    validation.col("balance").greaterThan(500),
+    validation.expr("YEAR(date) == year"),
+    validation.col("status").in("open", "closed", "pending").errorThreshold(0.2).description("Could be new status introduced"),
+    validation.col("customer_details.age").greaterThan(18),
+    validation.expr("FORALL(update_history, x -> x.updated_time > TIMESTAMP('2022-01-01 00:00:00'))"),
+    validation.col("update_history").greaterThanSize(2),
+    validation.unique("account_id"),
+    validation.groupBy().count().isEqual(1000),
+    validation.groupBy("account_id").max("balance").lessThan(900)
+  )
+
+val config = configuration
+  .generatedReportsFolderPath("/opt/app/data/report")
+  .enableValidation(true)
+  .enableGenerateData(false)
+
+execute(config, jsonTask)
+
    +
    +
    +
    +

    Run

    +

    Let's try run.

    +
    ./run.sh
+#input class MyValidationJavaPlan or MyValidationPlan
+#after completing, check report at docker/sample/report/index.html
+
    +

    It should look something like this.

    + + +

    Check the full example at ValidationPlanRun inside the examples repo.

    + + + + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/guide/scenario/delete-generated-data/index.html b/setup/guide/scenario/delete-generated-data/index.html new file mode 100644 index 00000000..3eeeaa48 --- /dev/null +++ b/setup/guide/scenario/delete-generated-data/index.html @@ -0,0 +1,2632 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Delete Generated Data - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Delete Generated Data

    +
    +

    Info

    +

    Delete generated data is a paid feature. Try the free trial here.

    +
    +

    Creating a data generator for Postgres and delete the generated data after using it.

    +

    Requirements

    +
      +
    • 5 minutes
      • +
    • Git
      • +
    • Gradle
      • +
    • Docker
      • +
    +

    Get Started

    +

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    +
    git clone git@github.com:pflooky/data-caterer-example.git
+
    +

    Plan Setup

    +

    Create a new Java or Scala class.

    +
      +
    • Java: src/main/java/com/github/pflooky/plan/MyAdvancedDeleteJavaPlanRun.java
      • +
    • Scala: src/main/scala/com/github/pflooky/plan/MyAdvancedDeletePlanRun.scala
      • +
    +

    Make sure your class extends PlanRun.

    +
    +
    +
    +
    import com.github.pflooky.datacaterer.java.api.PlanRun;
+...
+
+public class MyAdvancedDeleteJavaPlanRun extends PlanRun {
+    {
+        var autoRun = configuration()
+                .postgres("my_postgres", "jdbc:postgresql://host.docker.internal:5432/customer")  (1)
+                .enableGeneratePlanAndTasks(true)                                                 (2)
+                .enableRecordTracking(true)                                                       (3)
+                .enableDeleteGeneratedRecords(false)                                              (4)
+                .enableUniqueCheck(true)
+                .generatedPlanAndTaskFolderPath("/opt/app/data/generated")                        (5)
+                .recordTrackingFolderPath("/opt/app/data/recordTracking")                         (6)
+                .generatedReportsFolderPath("/opt/app/data/report");
+
+        execute(autoRun);
+   }
+}
+
    +
    +
    +
    import com.github.pflooky.datacaterer.api.PlanRun
+...
+
+class MyAdvancedDeletePlanRun extends PlanRun {
+
+  val autoRun = configuration
+    .postgres("my_postgres", "jdbc:postgresql://host.docker.internal:5432/customer")  (1)
+    .enableGeneratePlanAndTasks(true)                                                 (2)
+    .enableRecordTracking(true)                                                       (3)
+    .enableDeleteGeneratedRecords(false)                                              (4)
+    .enableUniqueCheck(true)
+    .generatedPlanAndTaskFolderPath("/opt/app/data/generated")                        (5)
+    .recordTrackingFolderPath("/opt/app/data/recordTracking")                         (6)
+    .generatedReportsFolderPath("/opt/app/data/report")
+
+  execute(configuration = autoRun)
+}
+
    +
    +
    +
    +

    In the above code we note the following:

    +
      +
    1. We have defined a Postgres connection called my_postgres
      2. +
    2. enableGeneratePlanAndTasks is enabled to auto generate data for all tables under customer database
      3. +
    3. enableRecordTracking is enabled to ensure that all generated records are tracked. This will get used when we want + to delete data afterwards
      4. +
    4. enableDeleteGeneratedRecords is disabled for now. We want to see the generated data first and delete sometime after
      5. +
    5. generatedPlanAndTaskFolderPath is the folder path where we saved the metadata we have gathered from my_postgres
      6. +
    6. recordTrackingFolderPath is the folder path where record tracking is maintained. We need to persist this data to + ensure it is still available when we want to delete data
      7. +
    +

    Postgres Setup

    +

    If you don't have your own Postgres up and running, you can set up and run an instance configured in the docker +folder via.

    +
    cd docker
+docker-compose up -d postgres
+docker exec docker-postgresserver-1 psql -Upostgres -d customer -c '\dt+ account.*'
+
    +

    This will create the tables found under docker/data/sql/postgres/customer.sql. You can change this file to contain +your own tables. We can see there are 4 tables created for us, accounts, balances, transactions and mapping.

    +

    Run

    +

    Let's try run.

    +
    cd ..
+./run.sh
+#input class MyAdvancedDeleteJavaPlanRun or MyAdvancedDeletePlanRun
+#after completing
+docker exec docker-postgresserver-1 psql -Upostgres -d customer -c 'select * from account.accounts limit 1'
+
    +

    It should look something like this.

    +
       id   | account_number  | account_status | created_by | created_by_fixed_length | customer_id_int | customer_id_smallint | customer_id_bigint |   customer_id_decimal    | customer_id_real | customer_id_double | open_date  |     open_timestamp      | last_opened_time |                                                           payload_bytes
+--------+-----------------+----------------+------------+-------------------------+-----------------+----------------------+--------------------+--------------------------+------------------+--------------------+------------+-------------------------+------------------+------------------------------------------------------------------------------------------------------------------------------------
+ 100414 | 5uROOVOUyQUbubN | h3H            | SfA0eZJcTm | CuRw                    |              13 |                   42 |               6041 | 76987.745612542900000000 |         91866.78 |  66400.37433202339 | 2023-03-05 | 2023-08-14 11:33:11.343 | 23:58:01.736     | \x604d315d4547616e6a233050415373317274736f5e682d516132524f3d23233c37463463322f342d34376d597e665d6b3d395b4238284028622b7d6d2b4f5042
+(1 row)
+
    +

    The data that gets inserted will follow the foreign keys that are defined within Postgres and also ensure the insertion +order is correct.

    +

    Check the number of records via:

    +
    docker exec docker-postgresserver-1 psql -Upostgres -d customer -c 'select count(1) from account.accounts'
+#open report under docker/sample/report/index.html
+
    +

    Delete

    +

    We are now at a stage where we want to delete the data that was generated. All we need to do is flip two flags.

    +
    .enableDeleteGeneratedRecords(true)
+.enableGenerateData(false)  //we need to explicitly disable generating data
+
    +

    Enable delete generated records and disable generating data.

    +

    Before we run again, let us insert a record manually to see if that data will survive after running the job to delete +the generated data.

    +
    docker exec docker-postgresserver-1 psql -Upostgres -d customer -c "insert into account.accounts (account_number) values ('my_account_number')"
+docker exec docker-postgresserver-1 psql -Upostgres -d customer -c "select count(1) from account.accounts"
+
    +

    We now should have 1001 records in our account.accounts table. Let's delete the generated data now.

    +
    ./run.sh
+#input class MyAdvancedDeleteJavaPlanRun or MyAdvancedDeletePlanRun
+#after completing
+docker exec docker-postgresserver-1 psql -Upostgres -d customer -c 'select * from account.accounts limit 1'
+docker exec docker-postgresserver-1 psql -Upostgres -d customer -c 'select count(1) from account.accounts'
+
    +

    You should see that only 1 record is left, the one that we manually inserted. Great, now we can generate data reliably +and also be able to clean it up.

    +

    Additional Topics

    +

    One class for generating, another for deleting?

    +

    Yes, this is possible. There are two requirements: +- the connection names used need to be the same across both classes +- recordTrackingFolderPath needs to be set to the same value

    +

    Define record count

    +

    You can control the record count per sub data source via numRecordsPerStep.

    +
    +
    +
    +
    var autoRun = configuration()
+      ...
+      .numRecordsPerStep(100)
+
+execute(autoRun)
+
    +
    +
    +
    val autoRun = configuration
+  ...
+  .numRecordsPerStep(100)
+
+execute(configuration = autoRun)
+
    +
    +
    +
    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/guide/scenario/first-data-generation/index.html b/setup/guide/scenario/first-data-generation/index.html new file mode 100644 index 00000000..5497f58b --- /dev/null +++ b/setup/guide/scenario/first-data-generation/index.html @@ -0,0 +1,3358 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Files (CSV, JSON, ORC, Parquet) - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    First Data Generation

    +

    Creating a data generator for a CSV file.

    +

    Generate CSV files

    +

    Requirements

    +
      +
    • 20 minutes
      • +
    • Git
      • +
    • Gradle
      • +
    • Docker
      • +
    +

    Get Started

    +

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    +
    git clone git@github.com:pflooky/data-caterer-example.git
+
    +

    Plan Setup

    +

    Create a new Java or Scala class.

    +
      +
    • Java: src/main/java/com/github/pflooky/plan/MyCsvPlan.java
      • +
    • Scala: src/main/scala/com/github/pflooky/plan/MyCsvPlan.scala
      • +
    +

    Make sure your class extends PlanRun.

    +
    +
    +
    +
    import com.github.pflooky.datacaterer.java.api.PlanRun;
+
+public class MyCsvJavaPlan extends PlanRun {
+}
+
    +
    +
    +
    import com.github.pflooky.datacaterer.api.PlanRun
+
+class MyCsvPlan extends PlanRun {
+}
+
    +
    +
    +
    +

    This class defines where we need to define all of our configurations for generating data. There are helper variables and +methods defined to make it simple and easy to use.

    +

    Connection Configuration

    +

    When dealing with CSV files, we need to define a path for our generated CSV files to be saved at, along with any other +high level configurations.

    +
    +
    +
    +
    csv(
+  "customer_accounts",              //name
+  "/opt/app/data/customer/account", //path
+  Map.of("header", "true")          //optional additional options
+)
+
    +

    Other additional options for CSV can be found here

    +
    +
    +
    csv(
+  "customer_accounts",              //name
+  "/opt/app/data/customer/account", //path
+  Map("header" -> "true")           //optional additional options
+)
+
    +

    Other additional options for CSV can be found here

    +
    +
    +
    +

    Schema

    +

    Our CSV file that we generate should adhere to a defined schema where we can also define data types.

    +

    Let's define each field along with their corresponding data type. You will notice that the string fields do not have a +data type defined. This is because the default data type is StringType.

    +
    +
    +
    +
    var accountTask = csv("customer_accounts", "/opt/app/data/customer/account", Map.of("header", "true"))
+        .schema(
+                field().name("account_id"),
+                field().name("balance").type(DoubleType.instance()),
+                field().name("created_by"),
+                field().name("name"),
+                field().name("open_time").type(TimestampType.instance()),
+                field().name("status")
+        );
+
    +
    +
    +
    val accountTask = csv("customer_accounts", "/opt/app/data/customer/account", Map("header" -> "true"))
+  .schema(
+    field.name("account_id"),
+    field.name("balance").`type`(DoubleType),
+    field.name("created_by"),
+    field.name("name"),
+    field.name("open_time").`type`(TimestampType),
+    field.name("status")
+  )
+
    +
    +
    +
    +

    Field Metadata

    +

    We could stop here and generate random data for the accounts table. But wouldn't it be more useful if we produced data +that is closer to the structure of the data that would come in production? We can do this by defining various metadata +attributes that add guidelines that the data generator will understand when generating data.

    +
    account_id
    +
      +
    • account_id follows a particular pattern that where it starts with ACC and has 8 digits after it. + This can be defined via a regex like below. Alongside, we also mention that values are unique ensure that + unique values are generated.
      • +
    +
    +
    +
    +
    field().name("account_id").regex("ACC[0-9]{8}").unique(true),
+
    +
    +
    +
    field.name("account_id").regex("ACC[0-9]{8}").unique(true),
+
    +
    +
    +
    +
    balance
    +
      +
    • balance let's make the numbers not too large, so we can define a min and max for the generated numbers to be between + 1 and 1000.
      • +
    +
    +
    +
    +
    field().name("balance").type(DoubleType.instance()).min(1).max(1000),
+
    +
    +
    +
    field.name("balance").`type`(DoubleType).min(1).max(1000),
+
    +
    +
    +
    +
    name
    +
      +
    • name is a string that also follows a certain pattern, so we could also define a regex but here we will choose to + leverage the DataFaker library and create an expression to generate real looking name. All possible faker + expressions + can be found here
      • +
    +
    +
    +
    +
    field().name("name").expression("#{Name.name}"),
+
    +
    +
    +
    field.name("name").expression("#{Name.name}"),
+
    +
    +
    +
    +
    open_time
    +
      +
    • open_time is a timestamp that we want to have a value greater than a specific date. We can define a min date by + using + java.sql.Date like below.
      • +
    +
    +
    +
    +
    field().name("open_time").type(TimestampType.instance()).min(java.sql.Date.valueOf("2022-01-01")),
+
    +
    +
    +
    field.name("open_time").`type`(TimestampType).min(java.sql.Date.valueOf("2022-01-01")),
+
    +
    +
    +
    +
    status
    +
      +
    • status is a field that can only obtain one of four values, open, closed, suspended or pending.
      • +
    +
    +
    +
    +
    field().name("status").oneOf("open", "closed", "suspended", "pending")
+
    +
    +
    +
    field.name("status").oneOf("open", "closed", "suspended", "pending")
+
    +
    +
    +
    +
    created_by
    +
      +
    • created_by is a field that is based on the status field where it follows the + logic: if status is open or closed, then + it is created_by eod else created_by event. This can be achieved by defining a SQL expression like below.
      • +
    +
    +
    +
    +
    field().name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
+
    +
    +
    +
    field.name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
+
    +
    +
    +
    +

    Putting it all the fields together, our class should now look like this.

    +
    +
    +
    +
    var accountTask = csv("customer_accounts", "/opt/app/data/customer/account", Map.of("header", "true"))
+        .schema(
+                field().name("account_id").regex("ACC[0-9]{8}").unique(true),
+                field().name("balance").type(DoubleType.instance()).min(1).max(1000),
+                field().name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
+                field().name("name").expression("#{Name.name}"),
+                field().name("open_time").type(TimestampType.instance()).min(java.sql.Date.valueOf("2022-01-01")),
+                field().name("status").oneOf("open", "closed", "suspended", "pending")
+        );
+
    +
    +
    +
    val accountTask = csv("customer_accounts", "/opt/app/data/customer/account", Map("header" -> "true"))
+  .schema(
+    field.name("account_id").regex("ACC[0-9]{8}").unique(true),
+    field.name("balance").`type`(DoubleType).min(1).max(1000),
+    field.name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
+    field.name("name").expression("#{Name.name}"),
+    field.name("open_time").`type`(TimestampType).min(java.sql.Date.valueOf("2022-01-01")),
+    field.name("status").oneOf("open", "closed", "suspended", "pending")
+  )
+
    +
    +
    +
    +

    Record Count

    +

    We only want to generate 100 records, so that we can see what the output looks like. This is controlled at the +accountTask level like below. If you want to generate more records, set it to the value you want.

    +
    +
    +
    +
    var accountTask = csv("customer_accounts", "/opt/app/data/customer/account", Map.of("header", "true"))
+        .schema(
+                ...
+        )
+        .count(count().records(100));
+
    +
    +
    +
    val accountTask = csv("customer_accounts", "/opt/app/data/customer/account", Map("header" -> "true"))
+  .schema(
+    ...
+  )
+  .count(count.records(100))
+
    +
    +
    +
    +

    Additional Configurations

    +

    At the end of data generation, a report gets generated that summarises the actions it performed. We can control the +output folder of that report via configurations. We will also enable the unique check to ensure any unique fields will +have unique values generated.

    +
    +
    +
    +
    var config = configuration()
+        .generatedReportsFolderPath("/opt/app/data/report")
+        .enableUniqueCheck(true);
+
    +
    +
    +
    val config = configuration
+  .generatedReportsFolderPath("/opt/app/data/report")
+  .enableUniqueCheck(true)
+
    +
    +
    +
    +

    Execute

    +

    To tell Data Caterer that we want to run with the configurations along with the accountTask, we have to call execute +. So our full plan run will look like this.

    +
    +
    +
    +
    public class MyCsvJavaPlan extends PlanRun {
+    {
+        var accountTask = csv("customer_accounts", "/opt/app/data/customer/account", Map.of("header", "true"))
+                .schema(
+                        field().name("account_id").regex("ACC[0-9]{8}").unique(true),
+                        field().name("balance").type(DoubleType.instance()).min(1).max(1000),
+                        field().name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
+                        field().name("name").expression("#{Name.name}"),
+                        field().name("open_time").type(TimestampType.instance()).min(java.sql.Date.valueOf("2022-01-01")),
+                        field().name("status").oneOf("open", "closed", "suspended", "pending")
+                );
+
+        var config = configuration()
+                .generatedReportsFolderPath("/opt/app/data/report")
+                .enableUniqueCheck(true);
+
+        execute(config, accountTask);
+    }
+}
+
    +
    +
    +
    class MyCsvPlan extends PlanRun {
+
+  val accountTask = csv("customer_accounts", "/opt/app/data/customer/account", Map("header" -> "true"))
+    .schema(
+      field.name("account_id").regex("ACC[0-9]{8}").unique(true),
+      field.name("balance").`type`(DoubleType).min(1).max(1000),
+      field.name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
+      field.name("name").expression("#{Name.name}"),
+      field.name("open_time").`type`(TimestampType).min(java.sql.Date.valueOf("2022-01-01")),
+      field.name("status").oneOf("open", "closed", "suspended", "pending")
+    )
+    val config = configuration
+      .generatedReportsFolderPath("/opt/app/data/report")
+      .enableUniqueCheck(true)
+
+    execute(config, accountTask)
+}
+
    +
    +
    +
    +

    Run

    +

    Now we can run via the script ./run.sh that is in the top level directory of the data-caterer-example to run the +class we just +created.

    +
    ./run.sh
+#input class MyCsvJavaPlan or MyCsvPlan
+#after completing
+head docker/sample/customer/account/part-00000*
+
    +

    Your output should look like this.

    +
    account_id,balance,created_by,name,open_time,status
+ACC06192462,853.9843359645766,eod,Hoyt Kertzmann MD,2023-07-22T11:17:01.713Z,closed
+ACC15350419,632.5969895326234,eod,Dr. Claude White,2022-12-13T21:57:56.840Z,open
+ACC25134369,592.0958847218986,eod,Fabian Rolfson,2023-04-26T04:54:41.068Z,open
+ACC48021786,656.6413439322964,eod,Dewayne Stroman,2023-05-17T06:31:27.603Z,open
+ACC26705211,447.2850352884595,event,Garrett Funk,2023-07-14T03:50:22.746Z,pending
+ACC03150585,750.4568929015996,event,Natisha Reichel,2023-04-11T11:13:10.080Z,suspended
+ACC29834210,686.4257811608622,event,Gisele Ondricka,2022-11-15T22:09:41.172Z,suspended
+ACC39373863,583.5110618128994,event,Thaddeus Ortiz,2022-09-30T06:33:57.193Z,suspended
+ACC39405798,989.2623959059525,eod,Shelby Reinger,2022-10-23T17:29:17.564Z,open
+
    +

    Also check the HTML report, found at docker/sample/report/index.html, that gets generated to get an overview of what +was executed.

    +

    Sample report

    +

    Join With Another CSV

    +

    Now that we have generated some accounts, let's also try to generate a set of transactions for those accounts in CSV +format as well. The transactions could be in any other format, but to keep this simple, we will continue using CSV.

    +

    We can define our schema the same way along with any additional metadata.

    +
    +
    +
    +
    var transactionTask = csv("customer_transactions", "/opt/app/data/customer/transaction", Map.of("header", "true"))
+        .schema(
+                field().name("account_id"),
+                field().name("name"),
+                field().name("amount").type(DoubleType.instance()).min(1).max(100),
+                field().name("time").type(TimestampType.instance()).min(java.sql.Date.valueOf("2022-01-01")),
+                field().name("date").type(DateType.instance()).sql("DATE(time)")
+        );
+
    +
    +
    +
    val transactionTask = csv("customer_transactions", "/opt/app/data/customer/transaction", Map("header" -> "true"))
+  .schema(
+    field.name("account_id"),
+    field.name("full_name"),
+    field.name("amount").`type`(DoubleType).min(1).max(100),
+    field.name("time").`type`(TimestampType).min(java.sql.Date.valueOf("2022-01-01")),
+    field.name("date").`type`(DateType).sql("DATE(time)")
+  )
+
    +
    +
    +
    +

    Records Per Column

    +

    Usually, for a given account_id, full_name, there should be multiple records for it as we want to simulate a customer +having multiple transactions. We can achieve this through defining the number of records to generate in the count +function.

    +
    +
    +
    +
    var transactionTask = csv("customer_transactions", "/opt/app/data/customer/transaction", Map.of("header", "true"))
+        .schema(
+                ...
+        )
+        .count(count().recordsPerColumn(5, "account_id", "full_name"));
+
    +
    +
    +
    val transactionTask = csv("customer_transactions", "/opt/app/data/customer/transaction", Map("header" -> "true"))
+  .schema(
+    ...
+  )
+  .count(count.recordsPerColumn(5, "account_id", "full_name"))
+
    +
    +
    +
    +
    Random Records Per Column
    +

    Above, you will notice that we are generating 5 records per account_id, full_name. This is okay but still not quite +reflective of the real world. Sometimes, people have accounts with no transactions in them, or they could have many. We +can accommodate for this via defining a random number of records per column.

    +
    +
    +
    +
    var transactionTask = csv("customer_transactions", "/opt/app/data/customer/transaction", Map.of("header", "true"))
+        .schema(
+                ...
+        )
+        .count(count().recordsPerColumnGenerator(generator().min(0).max(5), "account_id", "full_name"));
+
    +
    +
    +
    val transactionTask = csv("customer_transactions", "/opt/app/data/customer/transaction", Map("header" -> "true"))
+  .schema(
+    ...
+  )
+  .count(count.recordsPerColumnGenerator(generator.min(0).max(5), "account_id", "full_name"))
+
    +
    +
    +
    +

    Here we set the minimum number of records per column to be 0 and the maximum to 5.

    +

    Foreign Key

    +

    In this scenario, we want to match the account_id in account to match the same column values in transaction. We +also want to match name in account to full_name in transaction. This can be done via plan configuration like +below.

    +
    +
    +
    +
    var myPlan = plan().addForeignKeyRelationship(
+        accountTask, List.of("account_id", "name"), //the task and columns we want linked
+        List.of(Map.entry(transactionTask, List.of("account_id", "full_name"))) //list of other tasks and their respective column names we want matched
+);
+
    +
    +
    +
    val myPlan = plan.addForeignKeyRelationship(
+  accountTask, List("account_id", "name"),  //the task and columns we want linked
+  List(transactionTask -> List("account_id", "full_name"))  //list of other tasks and their respective column names we want matched
+)
+
    +
    +
    +
    +

    Now, stitching it all together for the execute function, our final plan should look like this.

    +
    +
    +
    +
    public class MyCsvJavaPlan extends PlanRun {
+    {
+        var accountTask = csv("customer_accounts", "/opt/app/data/customer/account", Map.of("header", "true"))
+                .schema(
+                        field().name("account_id").regex("ACC[0-9]{8}").unique(true),
+                        field().name("balance").type(DoubleType.instance()).min(1).max(1000),
+                        field().name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
+                        field().name("name").expression("#{Name.name}"),
+                        field().name("open_time").type(TimestampType.instance()).min(java.sql.Date.valueOf("2022-01-01")),
+                        field().name("status").oneOf("open", "closed", "suspended", "pending")
+                )
+                .count(count().records(100));
+
+        var transactionTask = csv("customer_transactions", "/opt/app/data/customer/transaction", Map.of("header", "true"))
+                .schema(
+                        field().name("account_id"),
+                        field().name("name"),
+                        field().name("amount").type(DoubleType.instance()).min(1).max(100),
+                        field().name("time").type(TimestampType.instance()).min(java.sql.Date.valueOf("2022-01-01")),
+                        field().name("date").type(DateType.instance()).sql("DATE(time)")
+                )
+                .count(count().recordsPerColumnGenerator(generator().min(0).max(5), "account_id", "full_name"));
+
+        var config = configuration()
+                .generatedReportsFolderPath("/opt/app/data/report")
+                .enableUniqueCheck(true);
+
+        var myPlan = plan().addForeignKeyRelationship(
+                accountTask, List.of("account_id", "name"),
+                List.of(Map.entry(transactionTask, List.of("account_id", "full_name")))
+        );
+
+        execute(myPlan, config, accountTask, transactionTask);
+    }
+}
+
    +
    +
    +
    class MyCsvPlan extends PlanRun {
+
+  val accountTask = csv("customer_accounts", "/opt/app/data/customer/account", Map("header" -> "true"))
+    .schema(
+      field.name("account_id").regex("ACC[0-9]{8}").unique(true),
+      field.name("balance").`type`(DoubleType).min(1).max(1000),
+      field.name("created_by").sql("CASE WHEN status IN ('open', 'closed') THEN 'eod' ELSE 'event' END"),
+      field.name("name").expression("#{Name.name}"),
+      field.name("open_time").`type`(TimestampType).min(java.sql.Date.valueOf("2022-01-01")),
+      field.name("status").oneOf("open", "closed", "suspended", "pending")
+    )
+    .count(count.records(100))
+
+  val transactionTask = csv("customer_transactions", "/opt/app/data/customer/transaction", Map("header" -> "true"))
+    .schema(
+      field.name("account_id"),
+      field.name("name"),
+      field.name("amount").`type`(DoubleType).min(1).max(100),
+      field.name("time").`type`(TimestampType).min(java.sql.Date.valueOf("2022-01-01")),
+      field.name("date").`type`(DateType).sql("DATE(time)")
+    )
+    .count(count.recordsPerColumnGenerator(generator.min(0).max(5), "account_id", "full_name"))
+
+  val config = configuration
+    .generatedReportsFolderPath("/opt/app/data/report")
+    .enableUniqueCheck(true)
+
+  val myPlan = plan.addForeignKeyRelationship(
+    accountTask, List("account_id", "name"),
+    List(transactionTask -> List("account_id", "full_name"))
+  )
+
+  execute(myPlan, config, accountTask, transactionTask)
+}
+
    +
    +
    +
    +

    Let's try run again.

    +
    #clean up old data
+rm -rf docker/sample/customer/account
+./run.sh
+#input class MyCsvJavaPlan or MyCsvPlan
+#after completing, let's pick an account and check the transactions for that account
+account=$(tail -1 docker/sample/customer/account/part-00000* | awk -F "," '{print $1 "," $4}')
+echo $account
+cat docker/sample/customer/transaction/part-00000* | grep $account
+
    +

    It should look something like this.

    +
    ACC29117767,Willodean Sauer
+ACC29117767,Willodean Sauer,84.99145871948083,2023-05-14T09:55:51.439Z,2023-05-14
+ACC29117767,Willodean Sauer,58.89345733567232,2022-11-22T07:38:20.143Z,2022-11-22
+
    +

    Congratulations! You have now made a data generator that has simulated a real world data scenario. You can check the +DocumentationJavaPlanRun.java or DocumentationPlanRun.scala files as well to check that your plan is the same.

    +

    We can now look to consume this CSV data from a job or service. Usually, once we have consumed the data, we would also +want to check and validate that our consumer has correctly ingested the data.

    +

    Validate

    +

    In this scenario, our consumer will read in the CSV file, do some transformations, and then save the data to Postgres. +Let's try to configure data validations for the data that gets pushed into Postgres.

    +

    Postgres setup

    +

    First, we define our connection properties for Postgres. You can check out the full options available +here.

    +
    +
    +
    +
    var postgresValidateTask = postgres(
+    "my_postgres",                                          //connection name
+    "jdbc:postgresql://host.docker.internal:5432/customer", //url
+    "postgres",                                             //username
+    "password"                                              //password
+).table("account", "transactions");
+
    +
    +
    +
    val postgresValidateTask = postgres(
+  "my_postgres",                                          //connection name
+  "jdbc:postgresql://host.docker.internal:5432/customer", //url
+  "postgres",                                             //username
+  "password"                                              //password
+).table("account", "transactions")
+
    +
    +
    +
    +

    We can connect and access the data inside the table account.transactions. Now to define our data validations.

    +

    Validations

    +

    For full information about validation options and configurations, check here. +Below, we have an example that should give you a good understanding of what validations are possible.

    +
    +
    +
    +
    var postgresValidateTask = postgres(...)
+        .table("account", "transactions")
+        .validations(
+                validation().col("account_id").isNotNull(),
+                validation().col("name").matches("[A-Z][a-z]+ [A-Z][a-z]+").errorThreshold(0.2).description("Some names have different formats"),
+                validation().col("balance").greaterThanOrEqual(0).errorThreshold(10).description("Account can have negative balance if overdraft"),
+                validation().expr("CASE WHEN status == 'closed' THEN isNotNull(close_date) ELSE isNull(close_date) END"),
+                validation().unique("account_id", "name"),
+                validation().groupBy("account_id", "name").max("login_retry").lessThan(10)
+        );
+
    +
    +
    +
    val postgresValidateTask = postgres(...)
+  .table("account", "transactions")
+  .validations(
+    validation.col("account_id").isNotNull,
+    validation.col("name").matches("[A-Z][a-z]+ [A-Z][a-z]+").errorThreshold(0.2).description("Some names have different formats"),
+    validation.col("balance").greaterThanOrEqual(0).errorThreshold(10).description("Account can have negative balance if overdraft"),
+    validation.expr("CASE WHEN status == 'closed' THEN isNotNull(close_date) ELSE isNull(close_date) END"),
+    validation.unique("account_id", "name"),
+    validation.groupBy("account_id", "name").max("login_retry").lessThan(10)
+  )
+
    +
    +
    +
    +
    name
    +

    For all values in the name column, we check if they match the regex [A-Z][a-z]+ [A-Z][a-z]+. As we know in the real +world, names do not always follow the same pattern, so we allow for an errorThreshold before marking the validation +as failed. Here, we define the errorThreshold to be 0.2, which means, if the error percentage is greater than 20%, +then fail the validation. We also append on a helpful description so other developers/users can understand the context +of the validation.

    +
    balance
    +

    We check that all balance values are greater than or equal to 0. This time, we have a slightly different +errorThreshold as it is set to 10, which means, if the number of errors is greater than 10, then fail the +validation.

    +
    expr
    +

    Sometimes, we may need to include the values of multiple columns to validate a certain condition. This is where we can +use expr to define a SQL expression that returns a boolean. In this scenario, we are checking if the status column +has value closed, then the close_date should be not null, otherwise, close_date is null.

    +
    unique
    +

    We check whether the combination of account_id and name are unique within the dataset. You can define one or more +columns for unique validations.

    +
    groupBy
    +

    There may be some business rule that states the number of login_retry should be less than 10 for each account. We can +check this via a group by validation where we group by the account_id, name, take the maximum value +for login_retry per account_id,name combination, then check if it is less than 10.

    +

    You can now look to play around with other configurations or data sources to meet your needs. Also, make sure to explore +the docs further as it can guide you on what can be configured.

    + + + + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/guide/scenario/records-per-column/index.html b/setup/guide/scenario/records-per-column/index.html new file mode 100644 index 00000000..68e5a4cd --- /dev/null +++ b/setup/guide/scenario/records-per-column/index.html @@ -0,0 +1,2596 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Multiple Records Per Column Value - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Multiple Records Per Column

    +

    Creating a data generator for a CSV file where there are multiple records per column values.

    +

    Requirements

    +
      +
    • 5 minutes
      • +
    • Git
      • +
    • Gradle
      • +
    • Docker
      • +
    +

    Get Started

    +

    First, we will clone the data-caterer-example repo which will already have the base project setup required.

    +
    git clone git@github.com:pflooky/data-caterer-example.git
+
    +

    Plan Setup

    +

    Create a new Java or Scala class.

    +
      +
    • Java: src/main/java/com/github/pflooky/plan/MyMultipleRecordsPerColJavaPlan.java
      • +
    • Scala: src/main/scala/com/github/pflooky/plan/MyMultipleRecordsPerColPlan.scala
      • +
    +

    Make sure your class extends PlanRun.

    +
    +
    +
    +
    import com.github.pflooky.datacaterer.java.api.PlanRun;
+...
+
+public class MyMultipleRecordsPerColJavaPlan extends PlanRun {
+    {
+        var transactionTask = csv("customer_transactions", "/opt/app/data/customer/transaction", Map.of("header", "true"))
+                .schema(
+                        field().name("account_id"),
+                        field().name("full_name"),
+                        field().name("amount").type(DoubleType.instance()).min(1).max(100),
+                        field().name("time").type(TimestampType.instance()).min(java.sql.Date.valueOf("2022-01-01")),
+                        field().name("date").type(DateType.instance()).sql("DATE(time)")
+                );
+
+        var config = configuration()
+                .generatedReportsFolderPath("/opt/app/data/report")
+                .enableUniqueCheck(true);
+
+        execute(config, transactionTask);
+    }
+}
+
    +
    +
    +
    import com.github.pflooky.datacaterer.api.PlanRun
+...
+
+class MyMultipleRecordsPerColPlan extends PlanRun {
+
+  val transactionTask = csv("customer_transactions", "/opt/app/data/customer/transaction", Map("header" -> "true"))
+    .schema(
+      field.name("account_id").regex("ACC[0-9]{8}"), 
+      field.name("full_name").expression("#{Name.name}"), 
+      field.name("amount").`type`(DoubleType.instance).min(1).max(100),
+      field.name("time").`type`(TimestampType.instance).min(java.sql.Date.valueOf("2022-01-01")), 
+      field.name("date").`type`(DateType.instance).sql("DATE(time)")
+    )
+
+  val config = configuration
+    .generatedReportsFolderPath("/opt/app/data/report")
+
+  execute(config, transactionTask)
+}
+
    +
    +
    +
    +

    Record Count

    +

    By default, tasks will generate 1000 records. You can alter this value via the count configuration which can be +applied to individual tasks. For example, in Scala, csv(...).count(count.records(100)) to generate only 100 records.

    +

    Records Per Column

    +

    In this scenario, for a given account_id, full_name, there should be multiple records for it as we want to simulate a +customer having multiple transactions. We can achieve this through defining the number of records to generate in +the count function.

    +
    +
    +
    +
    var transactionTask = csv("customer_transactions", "/opt/app/data/customer/transaction", Map.of("header", "true"))
+        .schema(
+                ...
+        )
+        .count(count().recordsPerColumn(5, "account_id", "full_name"));
+
    +
    +
    +
    val transactionTask = csv("customer_transactions", "/opt/app/data/customer/transaction", Map("header" -> "true"))
+  .schema(
+    ...
+  )
+  .count(count.recordsPerColumn(5, "account_id", "full_name"))
+
    +
    +
    +
    +

    This will generate 1000 * 5 = 5000 records as the default number of records is set (1000) and +per account_id, full_name from the initial 1000 records, 5 records will be generated.

    +

    Random Records Per Column

    +

    Generating 5 records per column is okay but still not quite reflective of the real world. Sometimes, people have +accounts with no transactions in them, or they could have many. We can accommodate for this via defining a random number +of records per column.

    +
    +
    +
    +
    var transactionTask = csv("customer_transactions", "/opt/app/data/customer/transaction", Map.of("header", "true"))
+        .schema(
+                ...
+        )
+        .count(count().recordsPerColumnGenerator(generator().min(0).max(5), "account_id", "full_name"));
+
    +
    +
    +
    val transactionTask = csv("customer_transactions", "/opt/app/data/customer/transaction", Map("header" -> "true"))
+  .schema(
+    ...
+  )
+  .count(count.recordsPerColumnGenerator(generator.min(0).max(5), "account_id", "full_name"))
+
    +
    +
    +
    +

    Here we set the minimum number of records per column to be 0 and the maximum to 5. This will follow a uniform +distribution so the average number of records per account is 2.5. We could also define other metadata, +just like we did with fields, when defining the generator. For example, we could set standardDeviation and mean for +the number of records generated per column to follow a normal distribution.

    +

    Run

    +

    Let's try run.

    +
    #clean up old data
+rm -rf docker/sample/customer/account
+./run.sh
+#input class MyMultipleRecordsPerColJavaPlan or MyMultipleRecordsPerColPlan
+#after completing
+head docker/sample/customer/transaction/part-00000*
+
    +

    It should look something like this.

    +
    ACC29117767,Willodean Sauer
+ACC29117767,Willodean Sauer,84.99145871948083,2023-05-14T09:55:51.439Z,2023-05-14
+ACC29117767,Willodean Sauer,58.89345733567232,2022-11-22T07:38:20.143Z,2022-11-22
+
    +

    You can now look to play around with other count configurations found here.

    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/index.html b/setup/index.html new file mode 100644 index 00000000..3aada0a3 --- /dev/null +++ b/setup/index.html @@ -0,0 +1,2380 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Setup - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Setup

    +

    All the configurations and customisation related to Data Caterer can be found under here.

    +

    Guide

    +

    If you want a guided tour of using the Java or Scala API, you can follow one of the guides found here.

    +

    Specific Configuration

    +
    +
      +
    • Configurations - Configurations relating to feature flags, folder pathways, metadata + analysis
      • +
    • Connections - Explore the data source connections available
      • +
    • Generators - Choose and configure the type of generator you want used for + fields
      • +
    • Validations - How to validate data to ensure your system is performing as expected
      • +
    • Foreign Keys - Define links between data elements across data sources
      • +
    • Deployment - Deploy Data Caterer as a job to your chosen environment
      • +
    • Advanced - Advanced usage of Data Caterer
      • +
    +
    +

    High Level Run Configurations

    +

    High level run configurations

    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/validation/basic-validation/index.html b/setup/validation/basic-validation/index.html new file mode 100644 index 00000000..1a444378 --- /dev/null +++ b/setup/validation/basic-validation/index.html @@ -0,0 +1,3392 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Basic - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Basic Validations

    +

    Run validations on a column to ensure the values adhere to your requirement. Can be set to complex validation logic +via SQL expression as well if needed (see here).

    +

    Equal

    +

    Ensure all data in column is equal to certain value. Value can be of any data type. Can use isEqualCol to define SQL +expression that can reference other columns.

    +
    +
    +
    +
    validation().col("year").isEqual(2021),
+validation().col("year").isEqualCol("YEAR(date)"),
+
    +
    +
    +
    validation.col("year").isEqual(2021),
+validation.col("year").isEqualCol("YEAR(date)"),
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "year == 2021"
+
    +
    +
    +
    +

    Not Equal

    +

    Ensure all data in column is not equal to certain value. Value can be of any data type. Can use isNotEqualCol to +define SQL expression that can reference other columns.

    +
    +
    +
    +
    validation().col("year").isNotEqual(2021),
+validation().col("year").isNotEqualCol("YEAR(date)"),
+
    +
    +
    +
    validation.col("year").isNotEqual(2021)
+validation.col("year").isEqualCol("YEAR(date)"),
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "year != 2021"
+
    +
    +
    +
    +

    Null

    +

    Ensure all data in column is null.

    +
    +
    +
    +
    validation().col("year").isNull()
+
    +
    +
    +
    validation.col("year").isNull
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "ISNULL(year)"
+
    +
    +
    +
    +

    Not Null

    +

    Ensure all data in column is not null.

    +
    +
    +
    +
    validation().col("year").isNotNull()
+
    +
    +
    +
    validation.col("year").isNotNull
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "ISNOTNULL(year)"
+
    +
    +
    +
    +

    Contains

    +

    Ensure all data in column is contains certain string. Column has to have type string.

    +
    +
    +
    +
    validation().col("name").contains("peter")
+
    +
    +
    +
    validation.col("name").contains("peter")
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "CONTAINS(name, 'peter')"
+
    +
    +
    +
    +

    Not Contains

    +

    Ensure all data in column does not contain certain string. Column has to have type string.

    +
    +
    +
    +
    validation().col("name").notContains("peter")
+
    +
    +
    +
    validation.col("name").notContains("peter")
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "!CONTAINS(name, 'peter')"
+
    +
    +
    +
    +

    Unique

    +

    Ensure all data in column is unique.

    +
    +
    +
    +
    validation().unique("account_id", "name")
+
    +
    +
    +
    validation.unique("account_id", "name")
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - unique: ["account_id", "name"]
+
    +
    +
    +
    +

    Less Than

    +

    Ensure all data in column is less than certain value. Can use lessThanCol to define SQL expression that can reference +other columns.

    +
    +
    +
    +
    validation().col("amount").lessThan(100),
+validation().col("amount").lessThanCol("balance + 1"),
+
    +
    +
    +
    validation.col("amount").lessThan(100),
+validation.col("amount").lessThanCol("balance + 1"),
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "amount < 100"
+      - expr: "amount < balance + 1"
+
    +
    +
    +
    +

    Less Than Or Equal

    +

    Ensure all data in column is less than or equal to certain value. Can use lessThanOrEqualCol to define SQL expression +that can reference other columns.

    +
    +
    +
    +
    validation().col("amount").lessThanOrEqual(100),
+validation().col("amount").lessThanOrEqualCol("balance + 1"),
+
    +
    +
    +
    validation.col("amount").lessThanOrEqual(100),
+validation.col("amount").lessThanCol("balance + 1"),
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "amount <= 100"
+      - expr: "amount <= balance + 1"
+
    +
    +
    +
    +

    Greater Than

    +

    Ensure all data in column is greater than certain value. Can use greaterThanCol to define SQL expression +that can reference other columns.

    +
    +
    +
    +
    validation().col("amount").greaterThan(100),
+validation().col("amount").greaterThanCol("balance"),
+
    +
    +
    +
    validation.col("amount").greaterThan(100),
+validation.col("amount").greaterThanCol("balance"),
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "amount > 100"
+      - expr: "amount > balance"
+
    +
    +
    +
    +

    Greater Than Or Equal

    +

    Ensure all data in column is greater than or equal to certain value. Can use greaterThanOrEqualCol to define SQL +expression that can reference other columns.

    +
    +
    +
    +
    validation().col("amount").greaterThanOrEqual(100),
+validation().col("amount").greaterThanOrEqualCol("balance"),
+
    +
    +
    +
    validation.col("amount").greaterThanOrEqual(100),
+validation.col("amount").greaterThanOrEqualCol("balance"),
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "amount >= 100"
+      - expr: "amount >= balance"
+
    +
    +
    +
    +

    Between

    +

    Ensure all data in column is between two values. Can use betweenCol to define SQL expression that references other +columns.

    +
    +
    +
    +
    validation().col("amount").between(100, 200),
+validation().col("amount").betweenCol("balance * 0.9", "balance * 1.1"),
+
    +
    +
    +
    validation.col("amount").between(100, 200),
+validation.col("amount").betweenCol("balance * 0.9", "balance * 1.1"),
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "amount BETWEEN 100 AND 200"
+      - expr: "amount BETWEEN balance * 0.9 AND balance * 1.1"
+
    +
    +
    +
    +

    Not Between

    +

    Ensure all data in column is not between two values. Can use notBetweenCol to define SQL expression that references +other columns.

    +
    +
    +
    +
    validation().col("amount").notBetween(100, 200),
+validation().col("amount").notBetweenCol("balance * 0.9", "balance * 1.1"),
+
    +
    +
    +
    validation.col("amount").notBetween(100, 200)
+validation.col("amount").notBetweenCol("balance * 0.9", "balance * 1.1"),
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "amount NOT BETWEEN 100 AND 200"
+      - expr: "amount NOT BETWEEN balance * 0.9 AND balance * 1.1"
+
    +
    +
    +
    +

    In

    +

    Ensure all data in column is in set of defined values.

    +
    +
    +
    +
    validation().col("status").in("open", "closed")
+
    +
    +
    +
    validation.col("status").in("open", "closed")
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "status IN ('open', 'closed')"
+
    +
    +
    +
    +

    Matches

    +

    Ensure all data in column matches certain regex expression.

    +
    +
    +
    +
    validation().col("account_id").matches("ACC[0-9]{8}")
+
    +
    +
    +
    validation.col("account_id").matches("ACC[0-9]{8}")
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "REGEXP(account_id, ACC[0-9]{8})"
+
    +
    +
    +
    +

    Not Matches

    +

    Ensure all data in column does not match certain regex expression.

    +
    +
    +
    +
    validation().col("account_id").notMatches("^acc.*")
+
    +
    +
    +
    validation.col("account_id").notMatches("^acc.*")
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "!REGEXP(account_id, '^acc.*')"
+
    +
    +
    +
    +

    Starts With

    +

    Ensure all data in column starts with certain string. Column has to have type string.

    +
    +
    +
    +
    validation().col("account_id").startsWith("ACC")
+
    +
    +
    +
    validation.col("account_id").startsWith("ACC")
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "STARTSWITH(account_id, 'ACC')"
+
    +
    +
    +
    +

    Not Starts With

    +

    Ensure all data in column does not start with certain string. Column has to have type string.

    +
    +
    +
    +
    validation().col("account_id").notStartsWith("ACC")
+
    +
    +
    +
    validation.col("account_id").notStartsWith("ACC")
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "!STARTSWITH(account_id, 'ACC')"
+
    +
    +
    +
    +

    Ends With

    +

    Ensure all data in column ends with certain string. Column has to have type string.

    +
    +
    +
    +
    validation().col("account_id").endsWith("ACC")
+
    +
    +
    +
    validation.col("account_id").endsWith("ACC")
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "ENDWITH(account_id, 'ACC')"
+
    +
    +
    +
    +

    Not Ends With

    +

    Ensure all data in column does not end with certain string. Column has to have type string.

    +
    +
    +
    +
    validation().col("account_id").notEndsWith("ACC")
+
    +
    +
    +
    validation.col("account_id").notEndsWith("ACC")
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "!ENDWITH(account_id, 'ACC')"
+
    +
    +
    +
    +

    Size

    +

    Ensure all data in column has certain size. Column has to have type array or map.

    +
    +
    +
    +
    validation().col("transactions").size(5)
+
    +
    +
    +
    validation.col("transactions").size(5)
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "SIZE(transactions, 5)"
+
    +
    +
    +
    +

    Not Size

    +

    Ensure all data in column does not have certain size. Column has to have type array or map.

    +
    +
    +
    +
    validation().col("transactions").notSize(5)
+
    +
    +
    +
    validation.col("transactions").notSize(5)
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "SIZE(transactions) != 5"
+
    +
    +
    +
    +

    Less Than Size

    +

    Ensure all data in column has size less than certain value. Column has to have type array or map.

    +
    +
    +
    +
    validation().col("transactions").lessThanSize(5)
+
    +
    +
    +
    validation.col("transactions").lessThanSize(5)
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "SIZE(transactions) < 5"
+
    +
    +
    +
    +

    Less Than Or Equal Size

    +

    Ensure all data in column has size less than or equal to certain value. Column has to have type array or map.

    +
    +
    +
    +
    validation().col("transactions").lessThanOrEqualSize(5)
+
    +
    +
    +
    validation.col("transactions").lessThanOrEqualSize(5)
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "SIZE(transactions) <= 5"
+
    +
    +
    +
    +

    Greater Than Size

    +

    Ensure all data in column has size greater than certain value. Column has to have type array or map.

    +
    +
    +
    +
    validation().col("transactions").greaterThanSize(5)
+
    +
    +
    +
    validation.col("transactions").greaterThanSize(5)
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "SIZE(transactions) > 5"
+
    +
    +
    +
    +

    Greater Than Or Equal Size

    +

    Ensure all data in column has size greater than or equal to certain value. Column has to have type array or map.

    +
    +
    +
    +
    validation().col("transactions").greaterThanOrEqualSize(5)
+
    +
    +
    +
    validation.col("transactions").greaterThanOrEqualSize(5)
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "SIZE(transactions) >= 5"
+
    +
    +
    +
    +

    Luhn Check

    +

    Ensure all data in column passes luhn check. Luhn check is used to validate credit card numbers and certain +identification numbers (see here for more details).

    +
    +
    +
    +
    validation().col("credit_card").luhnCheck()
+
    +
    +
    +
    validation.col("credit_card").luhnCheck
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "LUHN_CHECK(credit_card)"
+
    +
    +
    +
    +

    Has Type

    +

    Ensure all data in column has certain data type.

    +
    +
    +
    +
    validation().col("id").hasType("string")
+
    +
    +
    +
    validation.col("id").hasType("string")
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  ...
+    validations:
+      - expr: "TYPEOF(id) == 'string'"
+
    +
    +
    +
    +

    Expression

    +

    Ensure all data in column adheres to SQL expression defined that returns back a boolean. You can define complex logic in +here that could combine multiple columns.

    +

    For example, CASE WHEN status == 'open' THEN balance > 0 ELSE balance == 0 END would check all rows with status +open to have balance greater than 0, otherwise, check the balance is 0.

    +
    +
    +
    +
    var csvTxns = csv("transactions", "/tmp/csv", Map.of("header", "true"))
+  .validations(
+    validation().expr("amount < 100"),
+    validation().expr("year == 2021").errorThreshold(0.1),  //equivalent to if error percentage is > 10%, then fail
+    validation().expr("REGEXP_LIKE(name, 'Peter .*')").errorThreshold(200)  //equivalent to if number of errors is > 200, then fail
+  );
+
+var conf = configuration().enableValidation(true);
+
    +
    +
    +
    val csvTxns = csv("transactions", "/tmp/csv", Map("header" -> "true"))
+  .validations(
+    validation.expr("amount < 100"),
+    validation.expr("year == 2021").errorThreshold(0.1),  //equivalent to if error percentage is > 10%, then fail
+    validation.expr("REGEXP_LIKE(name, 'Peter .*')").errorThreshold(200)  //equivalent to if number of errors is > 200, then fail
+  )
+
+val conf = configuration.enableValidation(true)
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  transactions:
+    options:
+      path: "/tmp/csv"
+    validations:
+      - expr: "amount < 100"
+      - expr: "year == 2021"
+        errorThreshold: 0.1   #equivalent to if error percentage is > 10%, then fail
+      - expr: "REGEXP_LIKE(name, 'Peter .*')"
+        errorThreshold: 200   #equivalent to if number of errors is > 200, then fail
+        description: "Should be lots of Peters"
+
+#enableValidation inside application.conf
+
    +
    +
    +
    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/validation/group-by-validation/index.html b/setup/validation/group-by-validation/index.html new file mode 100644 index 00000000..3c9f7f6a --- /dev/null +++ b/setup/validation/group-by-validation/index.html @@ -0,0 +1,2550 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Group by/Aggregate - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Group By Validation

    +

    If you want to run aggregations based on a particular set of columns or just the whole dataset, you can do so via group +by validations. An example would be checking that the sum of amount is less than 1000 per account_id, year. The +validations applied can be one of the validations from the basic validation set found here.

    +

    Record count

    +

    Check the number of records across the whole dataset.

    +
    +
    +
    +
    validation().groupBy().count().lessThan(1000)
+
    +
    +
    +
    validation.groupBy().count().lessThan(1000)
+
    +
    +
    +
    +

    Record count per group

    +

    Check the number of records for each group.

    +
    +
    +
    +
    validation().groupBy("account_id", "year").count().lessThan(10)
+
    +
    +
    +
    validation.groupBy("account_id", "year").count().lessThan(10)
+
    +
    +
    +
    +

    Sum

    +

    Check the sum of a columns values for each group adheres to validation.

    +
    +
    +
    +
    validation().groupBy("account_id", "year").sum("amount").lessThan(1000)
+
    +
    +
    +
    validation.groupBy("account_id", "year").sum("amount").lessThan(1000)
+
    +
    +
    +
    +

    Count

    +

    Check the count for each group adheres to validation.

    +
    +
    +
    +
    validation().groupBy("account_id", "year").count("amount").lessThan(10)
+
    +
    +
    +
    validation.groupBy("account_id", "year").count("amount").lessThan(10)
+
    +
    +
    +
    +

    Min

    +

    Check the min for each group adheres to validation.

    +
    +
    +
    +
    validation().groupBy("account_id", "year").min("amount").greaterThan(0)
+
    +
    +
    +
    validation.groupBy("account_id", "year").min("amount").greaterThan(0)
+
    +
    +
    +
    +

    Max

    +

    Check the max for each group adheres to validation.

    +
    +
    +
    +
    validation().groupBy("account_id", "year").max("amount").lessThanOrEqual(100)
+
    +
    +
    +
    validation.groupBy("account_id", "year").max("amount").lessThanOrEqual(100)
+
    +
    +
    +
    +

    Average

    +

    Check the average for each group adheres to validation.

    +
    +
    +
    +
    validation().groupBy("account_id", "year").avg("amount").between(40, 60)
+
    +
    +
    +
    validation.groupBy("account_id", "year").avg("amount").between(40, 60)
+
    +
    +
    +
    +

    Standard deviation

    +

    Check the standard deviation for each group adheres to validation.

    +
    +
    +
    +
    validation().groupBy("account_id", "year").stddev("amount").between(0.5, 0.6)
+
    +
    +
    +
    validation.groupBy("account_id", "year").stddev("amount").between(0.5, 0.6)
+
    +
    +
    +
    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/validation/index.html b/setup/validation/index.html new file mode 100644 index 00000000..d94bcdea --- /dev/null +++ b/setup/validation/index.html @@ -0,0 +1,2651 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Validations - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Validations

    +

    Validations can be used to run data checks after you have run the data generator or even as a standalone task. A report +summarising the success or failure of the validations is produced and can be examined for further investigation.

    +
    +
      +
    • Basic - Basic column level validations
      • +
    • Group by/Aggregate - Run aggregates over grouped data, then validate
      • +
    • Upstream data source - Ensure record values exist in datasets based on other data sources or data generated
      • +
    • [Data Profile (Coming soon)] - Score how close the data profile of generated data is against the target data profile
      • +
    +
    +

    Define Validations

    +

    Full example validation can be found below. For more details, check out each of the subsections defined further below.

    +
    +
    +
    +
    var csvTxns = csv("transactions", "/tmp/csv", Map.of("header", "true"))
+  .validations(
+    validation().col("amount").lessThan(100),
+    validation().col("year").isEqual(2021).errorThreshold(0.1),  //equivalent to if error percentage is > 10%, then fail
+    validation().col("name").matches("Peter .*").errorThreshold(200)  //equivalent to if number of errors is > 200, then fail
+  )
+  .validationWait(waitCondition().pause(1));
+
+var conf = configuration().enableValidation(true);
+
    +
    +
    +
    val csvTxns = csv("transactions", "/tmp/csv", Map("header" -> "true"))
+  .validations(
+    validation.col("amount").lessThan(100),
+    validation.col("year").isEqual(2021).errorThreshold(0.1),  //equivalent to if error percentage is > 10%, then fail
+    validation.col("name").matches("Peter .*").errorThreshold(200)  //equivalent to if number of errors is > 200, then fail
+  )  
+  .validationWait(waitCondition.pause(1))
+
+val conf = configuration.enableValidation(true)
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  transactions:
+    options:
+      path: "/tmp/csv"
+    validations:
+      - expr: "amount < 100"
+      - expr: "year == 2021"
+        errorThreshold: 0.1   #equivalent to if error percentage is > 10%, then fail
+      - expr: "REGEXP_LIKE(name, 'Peter .*')"
+        errorThreshold: 200   #equivalent to if number of errors is > 200, then fail
+        description: "Should be lots of Peters"
+    waitCondition:
+      pauseInSeconds: 1
+
    +
    +
    +
    +

    Wait Condition

    +

    Once data has been generated, you may want to wait for a certain condition to be met before starting the data +validations. This can be via:

    +
      +
    • Pause for seconds
      • +
    • When file is available
      • +
    • Data exists
      • +
    • Webhook
      • +
    +

    Pause

    +
    +
    +
    +
    var csvTxns = csv("transactions", "/tmp/csv", Map.of("header", "true"))
+  .validationWait(waitCondition().pause(1));
+
    +
    +
    +
    val csvTxns = csv("transactions", "/tmp/csv", Map("header" -> "true"))
+  .validationWait(waitCondition.pause(1))
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  transactions:
+    options:
+      path: "/tmp/csv"
+    waitCondition:
+      pauseInSeconds: 1
+
    +
    +
    +
    +

    Data exists

    +
    +
    +
    +
    var csvTxns = csv("transactions", "/tmp/csv", Map.of("header", "true"))
+  .validationWaitDataExists("updated_date > DATE('2023-01-01')");
+
    +
    +
    +
    val csvTxns = csv("transactions", "/tmp/csv", Map("header" -> "true"))
+  .validationWaitDataExists("updated_date > DATE('2023-01-01')")
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  transactions:
+    options:
+      path: "/tmp/csv"
+    waitCondition:
+      dataSourceName: "transactions"
+      options:
+        path: "/tmp/csv"
+      expr: "updated_date > DATE('2023-01-01')"
+
    +
    +
    +
    +

    Webhook

    +
    +
    +
    +
    var csvTxns = csv("transactions", "/tmp/csv", Map.of("header", "true"))
+  .validationWait(waitCondition().webhook("http://localhost:8080/finished")); //by default, GET request successful when 200 status code
+
+//or
+
+var csvTxnsWithStatusCodes = csv("transactions", "/tmp/csv", Map.of("header", "true"))
+  .validationWait(waitCondition().webhook("http://localhost:8080/finished", "GET", 200, 202));  //successful if 200 or 202 status code
+
+//or
+
+var csvTxnsWithExistingHttpConnection = csv("transactions", "/tmp/csv", Map.of("header", "true"))
+  .validationWait(waitCondition().webhook("my_http", "http://localhost:8080/finished"));  //use connection configuration from existing 'my_http' connection definition
+
    +
    +
    +
    val csvTxns = csv("transactions", "/tmp/csv", Map("header" -> "true"))
+  .validationWait(waitCondition.webhook("http://localhost:8080/finished"))  //by default, GET request successful when 200 status code
+
+//or
+
+val csvTxnsWithStatusCodes = csv("transactions", "/tmp/csv", Map("header" -> "true"))
+  .validationWait(waitCondition.webhook("http://localhost:8080/finished", "GET", 200, 202)) //successful if 200 or 202 status code
+
+//or
+
+val csvTxnsWithExistingHttpConnection = csv("transactions", "/tmp/csv", Map("header" -> "true"))
+  .validationWait(waitCondition.webhook("my_http", "http://localhost:8080/finished")) //use connection configuration from existing 'my_http' connection definition
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  transactions:
+    options:
+      path: "/tmp/csv"
+    waitCondition:
+      url: "http://localhost:8080/finished" #by default, GET request successful when 200 status code
+
+#or
+
+---
+name: "account_checks"
+dataSources:
+  transactions:
+    options:
+      path: "/tmp/csv"
+    waitCondition:
+      url: "http://localhost:8080/finished"
+      method: "GET"
+      statusCodes: [200, 202] #successful if 200 or 202 status code
+
+#or
+
+---
+name: "account_checks"
+dataSources:
+  transactions:
+    options:
+      path: "/tmp/csv"
+    waitCondition:
+      dataSourceName: "my_http" #use connection configuration from existing 'my_http' connection definition
+      url: "http://localhost:8080/finished"
+
    +
    +
    +
    +

    File exists

    +
    +
    +
    +
    var csvTxns = csv("transactions", "/tmp/csv", Map.of("header", "true"))
+  .validationWait(waitCondition().file("/tmp/json"));
+
    +
    +
    +
    val csvTxns = csv("transactions", "/tmp/csv", Map.of("header", "true"))
+  .validationWait(waitCondition.file("/tmp/json"))
+
    +
    +
    +
    ---
+name: "account_checks"
+dataSources:
+  transactions:
+    options:
+      path: "/tmp/csv"
+    waitCondition:
+      path: "/tmp/json"
+
    +
    +
    +
    +

    Report

    +

    Once run, it will produce a report like this.

    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/validation/upstream-data-source-validation/index.html b/setup/validation/upstream-data-source-validation/index.html new file mode 100644 index 00000000..00e2558e --- /dev/null +++ b/setup/validation/upstream-data-source-validation/index.html @@ -0,0 +1,2696 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Upstream - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + + + + +
    +
    +
    + + + + +
    + + + + + + + + + + + +
    + + + + + + + +

    Upstream Data Source Validation

    +

    If you want to run data validations based on data generated or data from another data source, you can use the upstream +data source validations. An example would be generating a Parquet file that gets ingested by a job and inserted into +Postgres. The validations can then check for each account_id generated in the Parquet, it exists in account_number +column in Postgres. The validations can be chained with basic and group by validations or even other upstream data +sources, to cover any complex validations.

    +

    Basic join

    +

    Join across datasets by particular columns. Then run validations on the joined dataset. You will notice that the data +source name is appended onto the column names when joined (i.e. my_first_json_customer_details), to ensure column +names do not clash and make it obvious which columns are being validated.

    +

    In the below example, we check that the for the same account_id, then customer_details.name in the my_first_json +dataset should equal to the name column in the my_second_json.

    +
    +
    +
    +
    var firstJsonTask = json("my_first_json", "/tmp/data/first_json")
+  .schema(
+    field().name("account_id").regex("ACC[0-9]{8}"),
+    field().name("customer_details")
+      .schema(
+        field().name("name").expression("#{Name.name}")
+      )
+  );
+
+var secondJsonTask = json("my_second_json", "/tmp/data/second_json")
+  .validations(
+    validation().upstreamData(firstJsonTask)
+      .joinColumns("account_id")
+      .withValidation(
+        validation().col("my_first_json_customer_details.name")
+          .isEqualCol("name")
+      )
+  );
+
    +
    +
    +
    val firstJsonTask = json("my_first_json", "/tmp/data/first_json")
+  .schema(
+    field.name("account_id").regex("ACC[0-9]{8}"),
+    field.name("customer_details")
+      .schema(
+        field.name("name").expression("#{Name.name}")
+      )
+  )
+
+val secondJsonTask = json("my_second_json", "/tmp/data/second_json")
+  .validations(
+    validation.upstreamData(firstJsonTask)
+      .joinColumns("account_id")
+      .withValidation(
+        validation.col("my_first_json_customer_details.name")
+          .isEqualCol("name")
+      )
+  )
+
    +
    +
    +
    +

    Join expression

    +

    Define join expression to link two datasets together. This can be any SQL expression that returns a boolean value. +Useful in situations where join is based on transformations or complex logic.

    +

    In the below example, we have to use CONCAT SQL function to combine 'ACC' and account_number to join with +account_id column in my_first_json dataset.

    +
    +
    +
    +
    var firstJsonTask = json("my_first_json", "/tmp/data/first_json")
+  .schema(
+    field().name("account_id").regex("ACC[0-9]{8}"),
+    field().name("customer_details")
+      .schema(
+        field().name("name").expression("#{Name.name}")
+      )
+  );
+
+var secondJsonTask = json("my_second_json", "/tmp/data/second_json")
+  .validations(
+    validation().upstreamData(firstJsonTask)
+      .joinExpr("my_first_json_account_id == CONCAT('ACC', account_number)")
+      .withValidation(
+        validation().col("my_first_json_customer_details.name")
+          .isEqualCol("name")
+      )
+  );
+
    +
    +
    +
    val firstJsonTask = json("my_first_json", "/tmp/data/first_json")
+  .schema(
+    field.name("account_id").regex("ACC[0-9]{8}"),
+    field.name("customer_details")
+      .schema(
+        field.name("name").expression("#{Name.name}")
+      )
+  )
+
+val secondJsonTask = json("my_second_json", "/tmp/data/second_json")
+  .validations(
+    validation.upstreamData(firstJsonTask)
+      .joinExpr("my_first_json_account_id == CONCAT('ACC', account_number)")
+      .withValidation(
+        validation.col("my_first_json_customer_details.name")
+          .isEqualCol("name")
+      )
+  )
+
    +
    +
    +
    +

    Different join type

    +

    By default, an outer join is used to gather columns from both datasets together for validation. But there may be +scenarios where you want to control the join type.

    +

    Possible join types include: +- inner +- outer, full, fullouter, full_outer +- leftouter, left, left_outer +- rightouter, right, right_outer +- leftsemi, left_semi, semi +- leftanti, left_anti, anti +- cross

    +

    In the example below, we do an anti join by column account_id and check if there are no records. This essentially +checks that all account_id's from my_second_json exist in my_first_json. The second validation also does something +similar but does an outer join (by default) and checks that the joined dataset has 30 records.

    +
    +
    +
    +
    var firstJsonTask = json("my_first_json", "/tmp/data/first_json")
+  .schema(
+    field().name("account_id").regex("ACC[0-9]{8}"),
+    field().name("customer_details")
+      .schema(
+        field().name("name").expression("#{Name.name}")
+      )
+  );
+
+var secondJsonTask = json("my_second_json", "/tmp/data/second_json")
+  .validations(
+    validation().upstreamData(firstJsonTask)
+      .joinColumns("account_id")
+      .joinType("anti")
+      .withValidation(validation().count().isEqual(0)),
+    validation().upstreamData(firstJsonTask)
+      .joinColumns("account_id")
+      .withValidation(validation().count().isEqual(30))
+  );
+
    +
    +
    +
    val firstJsonTask = json("my_first_json", "/tmp/data/first_json")
+  .schema(
+    field.name("account_id").regex("ACC[0-9]{8}"),
+    field.name("customer_details")
+      .schema(
+        field.name("name").expression("#{Name.name}")
+      )
+  )
+
+val secondJsonTask = json("my_second_json", "/tmp/data/second_json")
+  .validations(
+    validation.upstreamData(firstJsonTask)
+      .joinColumns("account_id")
+      .joinType("anti")
+      .withValidation(validation.count().isEqual(0)),
+    validation.upstreamData(firstJsonTask)
+      .joinColumns("account_id")
+      .withValidation(validation.count().isEqual(30))
+  )
+
    +
    +
    +
    +

    Join then group by validation

    +

    We can apply aggregate or group by validations to the resulting joined dataset as the withValidation method accepts +any type of validation.

    +

    Here we group by account_id, my_first_json_balance to check that when the amount field is summed up per group, it is +between 0.8 and 1.2 times the balance.

    +
    +
    +
    +
    var firstJsonTask = json("my_first_json", "/tmp/data/first_json")
+  .schema(
+    field().name("account_id").regex("ACC[0-9]{8}"),
+    field().name("balance").type(DoubleType.instance()).min(10).max(1000),
+    field().name("customer_details")
+      .schema(
+        field().name("name").expression("#{Name.name}")
+      )
+  );
+
+var secondJsonTask = json("my_second_json", "/tmp/data/second_json")
+  .validations(
+    validation().upstreamData(firstJsonTask).joinColumns("account_id")
+      .withValidation(
+        validation().groupBy("account_id", "my_first_json_balance")
+          .sum("amount")
+          .betweenCol("my_first_json_balance * 0.8", "my_first_json_balance * 1.2")
+      )
+  );
+
    +
    +
    +
    val firstJsonTask = json("my_first_json", "/tmp/data/first_json")
+  .schema(
+    field.name("account_id").regex("ACC[0-9]{8}"),
+    field.name("balance").`type`(DoubleType).min(10).max(1000),
+    field.name("customer_details")
+      .schema(
+        field.name("name").expression("#{Name.name}")
+      )
+  )
+
+val secondJsonTask = json("my_second_json", "/tmp/data/second_json")
+  .validations(
+    validation.upstreamData(firstJsonTask).joinColumns("account_id")
+      .withValidation(
+        validation.groupBy("account_id", "my_first_json_balance")
+          .sum("amount")
+          .betweenCol("my_first_json_balance * 0.8", "my_first_json_balance * 1.2")
+      )
+  )
+
    +
    +
    +
    +

    Chained validations

    +

    Given that the withValidation method accepts any other type of validation, you can chain other upstream data sources +with it. Here we will show a third upstream data source being checked to ensure 30 records exists after joining them +together by account_id.

    +
    +
    +
    +
    var firstJsonTask = json("my_first_json", "/tmp/data/first_json")
+  .schema(
+    field().name("account_id").regex("ACC[0-9]{8}"),
+    field().name("balance").type(DoubleType.instance()).min(10).max(1000),
+    field().name("customer_details")
+      .schema(
+        field().name("name").expression("#{Name.name}")
+      )
+  )
+  .count(count().records(10));
+
+var thirdJsonTask = json("my_third_json", "/tmp/data/third_json")
+  .schema(
+    field().name("account_id"),
+    field().name("amount").type(IntegerType.instance()).min(1).max(100),
+    field().name("name").expression("#{Name.name}")
+  )
+  .count(count().records(10));
+
+var secondJsonTask = json("my_second_json", "/tmp/data/second_json")
+  .validations(
+    validation().upstreamData(firstJsonTask)
+      .joinColumns("account_id")
+      .withValidation(
+        validation().upstreamData(thirdJsonTask)
+          .joinColumns("account_id")
+          .withValidation(validation().count().isEqual(30))
+      )
+  );
+
    +
    +
    +
    val firstJsonTask = json("my_first_json", "/tmp/data/first_json")
+  .schema(
+    field.name("account_id").regex("ACC[0-9]{8}"),
+    field.name("balance").`type`(DoubleType).min(10).max(1000),
+    field.name("customer_details")
+      .schema(
+        field.name("name").expression("#{Name.name}")
+      )
+  )
+  .count(count.records(10))
+
+val thirdJsonTask = json("my_third_json", "/tmp/data/third_json")
+  .schema(
+    field.name("account_id"),
+    field.name("amount").`type`(IntegerType).min(1).max(100),
+    field.name("name").expression("#{Name.name}"),
+  )
+  .count(count.records(10))
+
+val secondJsonTask = json("my_second_json", "/tmp/data/second_json")
+  .validations(
+    validation.upstreamData(firstJsonTask).joinColumns("account_id")
+      .withValidation(
+        validation.groupBy("account_id", "my_first_json_balance")
+          .sum("amount")
+          .betweenCol("my_first_json_balance * 0.8", "my_first_json_balance * 1.2")
+      ),
+  )
+
    +
    +
    +
    + + + + + + + + + + + + + + + +
    +
    + + + + + +
    + + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 00000000..23afcc7c --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,193 @@ + + + + https://data.catering/ + 2023-11-30 + daily + + + https://data.catering/about/ + 2023-11-30 + daily + + + https://data.catering/sponsor/ + 2023-11-30 + daily + + + https://data.catering/use-case/ + 2023-11-30 + daily + + + https://data.catering/get-started/docker/ + 2023-11-30 + daily + + + https://data.catering/legal/privacy-policy/ + 2023-11-30 + daily + + + https://data.catering/legal/terms-of-service/ + 2023-11-30 + daily + + + https://data.catering/setup/ + 2023-11-30 + daily + + + https://data.catering/setup/advanced/ + 2023-11-30 + daily + + + https://data.catering/setup/configuration/ + 2023-11-30 + daily + + + https://data.catering/setup/connection/ + 2023-11-30 + daily + + + https://data.catering/setup/deployment/ + 2023-11-30 + daily + + + https://data.catering/setup/design/ + 2023-11-30 + daily + + + https://data.catering/setup/foreign-key/ + 2023-11-30 + daily + + + https://data.catering/setup/validation/ + 2023-11-30 + daily + + + https://data.catering/setup/generator/count/ + 2023-11-30 + daily + + + https://data.catering/setup/generator/data-generator/ + 2023-11-30 + daily + + + https://data.catering/setup/generator/report/ + 2023-11-30 + daily + + + https://data.catering/setup/guide/ + 2023-11-30 + daily + + + https://data.catering/setup/guide/data-source/cassandra/ + 2023-11-30 + daily + + + https://data.catering/setup/guide/data-source/http/ + 2023-11-30 + daily + + + https://data.catering/setup/guide/data-source/kafka/ + 2023-11-30 + daily + + + https://data.catering/setup/guide/data-source/marquez-metadata-source/ + 2023-11-30 + daily + + + https://data.catering/setup/guide/data-source/open-metadata-source/ + 2023-11-30 + daily + + + https://data.catering/setup/guide/data-source/solace/ + 2023-11-30 + daily + + + https://data.catering/setup/guide/scenario/auto-generate-connection/ + 2023-11-30 + daily + + + https://data.catering/setup/guide/scenario/batch-and-event/ + 2023-11-30 + daily + + + https://data.catering/setup/guide/scenario/data-validation/ + 2023-11-30 + daily + + + https://data.catering/setup/guide/scenario/delete-generated-data/ + 2023-11-30 + daily + + + https://data.catering/setup/guide/scenario/first-data-generation/ + 2023-11-30 + daily + + + https://data.catering/setup/guide/scenario/records-per-column/ + 2023-11-30 + daily + + + https://data.catering/setup/validation/basic-validation/ + 2023-11-30 + daily + + + https://data.catering/setup/validation/group-by-validation/ + 2023-11-30 + daily + + + https://data.catering/setup/validation/upstream-data-source-validation/ + 2023-11-30 + daily + + + https://data.catering/use-case/business-value/ + 2023-11-30 + daily + + + https://data.catering/use-case/comparison/ + 2023-11-30 + daily + + + https://data.catering/use-case/roadmap/ + 2023-11-30 + daily + + + https://data.catering/use-case/blog/shift-left-data-quality/ + 2023-11-30 + daily + + \ No newline at end of file diff --git a/sitemap.xml.gz b/sitemap.xml.gz new file mode 100644 index 0000000000000000000000000000000000000000..00aa9d7309d585a075e324baa2798a17fa6e9c10 GIT binary patch literal 559 zcmV+~0?_>*iwFpvTWDnh|8r?{Wo=<_E_iKh0M(emlG`8*hVT0n8Q%%X?xpP{-rGJw z`v3~a4ik(B0;lQI7iY7(o}7B>$iCPJ8Gj>wfz0mp_uy9}CUz9|&33(MRu~Lf7y5nk z>*t&L*1Wb)yOKO(URCG7`{tN){Gs*ba#^#>I6&kzrCzf+)&}dlQE%4YoAzl{uW5ky zr`xsjWZG#IuESW!ur`n})sO0(p8Dplz|nl>tU+Ts>yR8B#{8EtwOFi=4+EypG9@V6G~X zcjhXSu{H)))G5X^IwNkb>nc^EOenA;1XDFAlFm@*TwfC8x)X>+3~1!?S&W{p1BNV> zk6kY`b{!?GaO#9tnSdjBX9Xumj{$4))~dizk*dHi$f;L-HHFhAjFhCtv^a~B(QZ@C zR8p-LYI(>IY=Wf#Pu@#mJwbO`Okf8{=YoIKfEniYh2T@fuvB&?53*_5V1VREL&@~9 zP1K)D;RWCW`~k8#s=C}M9K{xe56m~6h_7`Xn9*ZZ$A?U;n%-FidFK*mHLr!167z(H zl4TV!)wJ|w2;$531F$pVwLRnO6P2hA*CjER$T=YlYPR7B=KnmhJQQ{TnJ3nQVlfR3 xvF0BOj$|UR)8W6^nf6q5dvsk^9=rSMdoER?`9B;z+-@~%+P{XJ*@Jo+007jj7l;4= literal 0 HcmV?d00001 diff --git a/sponsor/index.html b/sponsor/index.html new file mode 100644 index 00000000..1de60ce3 --- /dev/null +++ b/sponsor/index.html @@ -0,0 +1,2346 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + Sponsor - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + + +
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    + + + + +
    + + + + + + + + + +
    + + + + + + + +

    Sponsor

    +

    To have access to all the features of Data Caterer, you can subscribe according to your situation. You will not be +charged by usage. As you continue to subscribe, you will have access to the latest version of Data Caterer as new +bug fixes and features get published.

    +

    This has been a passion project of mine where I have spent countless hours thinking of the idea, implementing, +maintaining, documenting and updating it. I hope that it will help with developers and companies with their testing +by saving time and effort, allowing you to focus on what is important. If you fall under this boat, please consider +sponsorship to allow me to further maintain and upgrade the solution. Any contributions are much appreciated.

    +

    Those who are wanting to use this project for open source applications, please contact me as I would be +happy to contribute.

    +

    This is inspired by the mkdocs-material project that +follows the same model.

    +

    Features

    +
      +
    • Metadata discovery
      • +
    • All data sources (see here for all data sources)
      • +
    • Batch and Event generation
      • +
    • Auto generation from data connections or metadata sources
      • +
    • Suggest data validations
      • +
    • Clean up generated data
      • +
    • Run as many times as you want, not charged by usage
      • +
    +

    Tiers

    + +

    +

    +

    Manage Subscription

    +

    Manage via this link

    +

    Contact

    +

    Please contact Peter Flook +via Slack +or via email peter.flook@data.catering if you have any questions or queries.

    + + + + + + + + + + + + + + + +
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    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/stylesheets/extra.css b/stylesheets/extra.css new file mode 100644 index 00000000..f8a1f93a --- /dev/null +++ b/stylesheets/extra.css @@ -0,0 +1,23 @@ +/* +#fbcccc -> hsl(0, 85%, 89%) +#36699F +#2d659e +#7786a7 +*/ +.center-content { + justify-content: center; + align-items: center; + display: flex; +} + +.content-spaced { + margin: 48px !important; +} + +.button-spaced { + margin: 32px; +} + +.red-cross { + color: #d9534f; +} diff --git a/use-case/blog/shift-left-data-quality/index.html b/use-case/blog/shift-left-data-quality/index.html new file mode 100644 index 00000000..f74b7b1a --- /dev/null +++ b/use-case/blog/shift-left-data-quality/index.html @@ -0,0 +1,2518 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + Shift Left Data Quality - Data Catering + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + + + + +
    + + + + + + + +

    Shifting Data Quality Left with Data Catering

    +

    Empowering Proactive Data Management

    +

    In the ever-evolving landscape of data-driven decision-making, ensuring data quality is non-negotiable. Traditionally, +data quality has been a concern addressed late in the development lifecycle, often leading to reactive measures and +increased costs. However, a paradigm shift is underway with the adoption of a "shift left" approach, placing data +quality at the forefront of the development process.

    +

    Today

    +
    graph LR
+  subgraph badQualityData[<b>Manually generated data, limited data scenarios</b>]
+  local[<b>Local</b>\nManual test, unit test]
+  dev[<b>Dev</b>\nManual test, integration test]
+  stg[<b>Staging</b>\nSanity checks]
+  end
+
+  subgraph qualityData[<b>Reliable data, the true test</b>]
+  prod[<b>Production</b>\nData quality checks, monitoring, observaibility]
+  end
+
+  style badQualityData fill:#d9534f,fill-opacity:0.7
+  style qualityData fill:#5cb85c,fill-opacity:0.7
+
+  local --> dev
+  dev --> stg
+  stg --> prod
    +

    With Data Caterer

    +
    graph LR
+  subgraph qualityData[<b>Reliable data for testing anywhere<br>Common testing tool</b>]
+  direction LR
+  local[<b>Local</b>\nManual test, unit test]
+  dev[<b>Dev</b>\nManual test, integration test]
+  stg[<b>Staging</b>\nSanity checks]
+  prod[<b>Production</b>\nData quality checks, monitoring, observaibility]
+  end
+
+  style qualityData fill:#5cb85c,fill-opacity:0.7
+
+  local --> dev
+  dev --> stg
+  stg --> prod
    +

    Understanding the Shift Left Approach

    +

    "Shift left" is a philosophy that advocates for addressing tasks and concerns earlier in the development lifecycle. +Applied to data quality, it means tackling data issues as early as possible, ideally during the development and testing +phases. This approach aims to catch data anomalies, inaccuracies, or inconsistencies before they propagate through the +system, reducing the likelihood of downstream errors.

    +

    Data Caterer: The Catalyst for Shifting Left

    +

    Enter Data Caterer, a metadata-driven data generation and validation tool designed to empower organizations in shifting +data quality left. By incorporating Data Caterer into the early stages of development, teams can proactively test +complex data flows, validate data sources, and ensure data quality before it reaches downstream processes.

    +

    Key Advantages of Shifting Data Quality Left with Data Caterer

    +
      +
    1. Early Issue Detection:
        +
      • Identify data quality issues early in the development process, reducing the risk of errors downstream.
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      2. +
    2. Proactive Validation:
        +
      • Validate data sources and complex data flows in a simplified manner, promoting a proactive approach to data quality.
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      3. +
    3. Efficient Testing Across Sources:
        +
      • Seamlessly test data across various sources, including databases, file formats, HTTP, and messaging, all within + your local laptop or development environment.
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      • Fast feedback loop to motivate developers to ensure thorough testing of data scenarios.
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      4. +
    4. Integration with Development Pipelines:
        +
      • Easily integrate Data Caterer as a task in your development pipelines, ensuring that data quality is a continuous + consideration rather than an isolated event.
        • +
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      5. +
    5. Integration with Existing Metadata:
        +
      • By harnessing the power of existing metadata from data catalogs, schema registries, or other data validation tools, + Data Caterer streamlines the process, automating the generation and validation of your data effortlessly.
        • +
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      6. +
    6. Improved Collaboration:
        +
      • Facilitate collaboration between developers, testers, and data professionals by providing a common platform for + early data validation.
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      7. +
    +

    Realizing the Vision of Proactive Data Quality

    +

    As organizations strive for excellence in their data-driven endeavors, the shift left approach with Data Caterer +becomes a strategic imperative. By instilling a proactive data quality culture, teams can minimize the risk of costly +errors, enhance the reliability of their data, and streamline the entire development lifecycle.

    +

    In conclusion, the marriage of the shift left philosophy and Data Caterer brings forth a new era of data management, +where data quality is not just a final checkpoint but an integral part of every development milestone. Embrace the shift +left approach with Data Caterer and empower your teams to build robust, high-quality data solutions from the very +beginning.

    +

    Shift Left, Validate Early, and Accelerate with Data Caterer.

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    Business Value

    +

    Below is a list of the business related benefits from using Data Caterer which may be applicable for your use case.

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    ProblemData Caterer SolutionResourcesEffects
    Reliable test data creation- Profile existing data
    - Create scenarios
    - Generate data    		Software Engineers, QA, TestersCost reduction in labor, more time spent on development, more bugs caught before production
    Faster development cycles- Generate data in local, test, UAT, pre-prod
    - Run different scenarios    		Software Engineers, QA, TestersMore defects caught in lower environments, features pushed to production faster, common framework used across all environments
    Data compliance- Profiling existing data
    - Generate based on metadata
    - No complex masking
    - No production data used in lower environments    		Audit and complianceNo chance for production data breaches
    Storage costs- Delete generated data
    - Test specific scenarios    		InfrastructureLower data storage costs, less time spent on data management and clean up
    Schema evolution- Create metadata from data sources
    - Generate data based off fresh metadata    		Software Engineers, QA, TestersLess time spent altering tests due to schema changes, ease of use between environments and application versions
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    Comparison to similar tools

    +

    I have tried to include all the companies found in the list +here from Mostly AI blog post and used information that is publicly +available.

    +

    The companies/products not shown below either have:

    +
      +
    • a website with insufficient information about the technology side of data generation/validation
      • +
    • no/little documentation
      • +
    • don't have a free, no sign-up version of their app to use
      • +
    +

    Data Generation

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ToolDescriptionCostProsCons
    Clearbox AIPython based data generation tool via MLUnclear✅ Python SDK
    ✅ UI interface
    ✅ Detect private data
    ✅ Report generation    		 Batch data only
    No data clean up
    Limited/no documentation
    Curiosity SoftwarePlatform solution for test data managementUnclear✅ Extensive documentation
    ✅ Generate data based off test cases
    ✅ UI interface
    ✅ Web/API/UI/mobile testing    		 No quick start
    No SDK
    Many components that may not be required
    No event generation support
    DataCebo Synthetic Data VaultPython based data generation tool via MLUnclear✅ Python SDK
    ✅ Report generation
    ✅ Data quality checks
    ✅ Business logic constraints    		 No data connection support
    No data clean up
    No foreign key support
    DatafakerRealistic data generation libraryFree✅ SDK for many languages
    ✅ Simple, easy to use
    ✅ Extensible
    ✅ Open source
    ✅ Generate realistic values    		 No data connection support
    No data clean up
    No validation
    No foreign key support
    DBLDatagenPython based data generation toolFree✅ Python SDK
    ✅ Open source
    ✅ Good documentation
    ✅ Customisable scenarios
    ✅ Customisable column generation
    ✅ Generate from existing data/schemas
    ✅ Plugin third-party libraries    		 Limited support if issues
    Code required
    No data clean up
    No data validation
    GatlingHTTP API load testing toolFree (Open Source)
    Gatling Enterprise, usage based, starts from €89 per month, 1 user, 6.25 hours of testing    		✅ Kotlin, Java & Scala SDK
    ✅ Widely used
    ✅ Open source
    ✅ Clear documentation
    ✅ Extensive testing/validation support
    ✅ Customisable scenarios
    ✅ Report generation    		 Only supports HTTP, JMS and JDBC
    No data clean up
    Data feeders not based off metadata
    GretelPython based data generation tool via MLUsage based, starts from $295 per month, $2.20 per credit, assumed USD✅ CLI & Python SDK
    ✅ UI interface
    ✅ Training and re-use of models
    ✅ Detect private data
    ✅ Customisable scenarios    		 Batch data only
    No relationships between data sources
    Only simple foreign key relations defined
    No data clean up
    Charge by usage
    HowsoPython based data generation tool via MLUnclear✅ Python SDK
    ✅ Playground to try
    ✅ Open source library
    ✅ Customisable scenarios    		 No support for data sources
    No data validation
    No data clean up
    Mostly AIPython based data generation tool via MLUsage based, Enterprise 1 user, 100 columns, 100K rows $3,100 per month, assumed USD✅ Report generation
    ✅ Non-technical users can use UI
    ✅ Customisable scenarios    		 Charge by usage
    Batch data only
    No data clean up
    Confusing use of 'smart select' for multiple foreign keys
    Limited custom column generation logic
    Multiple deployment components
    No SDK
    OctopizePython based data generation tool via MLUnclear✅ Python & R SDK
    ✅ Report generation
    ✅ API for metadata
    ✅ Customisable scenarios    		 Input data source is only CSV
    Multiple manual steps before starting
    Quickstart is not a quickstart
    Documentation lacks code examples
    SynthesizedPython based data generation tool via MLUnclear✅ CLI & Python SDK
    ✅ API for metadata
    ✅ IDE setup
    ✅ Data quality checks    		 Not sure what is SDK & TDK
    Charge by usage
    No report of what was generated
    No relationships between data sources
    TonicPlatform solution for generating dataUnclear✅ UI interface
    ✅ Good documentation
    ✅ Detect private data
    ✅ Support for encrypted columns
    ✅ Report generation
    ✅ Alerting    		 Batch data only
    Multiple deployment components
    No relationships between data sources
    No data validation
    No data clean up
    No SDK (only API)
    Difficult to embed complex business logic
    YDataPython based data generation tool via ML. Platform solution as wellUnclear✅ Python SDK
    ✅ Open source
    ✅ Detect private data
    ✅ Compare datasets
    ✅ Report generation    		 No data connection support
    Batch data only
    No data clean up
    Separate data generation and data validation
    No foreign key support
    +

    Use of ML models

    +

    You may notice that the majority of data generators use machine learning (ML) models to learn from your existing +datasets to generate new data. Below are some pros and cons to the approach.

    +

    Pros

    +
      +
    • Simple setup
      • +
    • Ability to reproduce complex logic
      • +
    • Flexible to accept all types of data
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    Cons

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    • Long time for model learning
      • +
    • Black box of logic
      • +
    • Maintain, store and update of ML models
      • +
    • Restriction on input data lengths
      • +
    • May not maintain referential integrity
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    • Require deeper understanding of ML models for fine-tuning
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    • Accuracy may be worse than non-ML models
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    Use cases

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    Replicate production in lower environment

    +

    Having a stable and reliable test environment is a challenge for a number of companies, especially where teams are +asynchronously deploying and testing changes at faster rates. Data Caterer can help alleviate these issues by doing +the following:

    +
      +
    1. Generates data with the latest schema changes and production like field values
      2. +
    2. Run as a job on a daily/regular basis to replicate production traffic or data flows
      3. +
    3. Validate data to ensure your system runs as expected
      4. +
    4. Clean up data to avoid build up of generated data
      5. +
    +

    Replicate production flow

    +

    Local development

    +

    Similar to the above, being able to replicate production like data in your local environment can be key to developing +more reliable code as you can test directly against data in your local computer. This has a number of benefits +including:

    +
      +
    1. Fewer assumptions or ambiguities when the developer codes
      2. +
    2. Direct feedback loop in local computer rather than waiting for test environment for more reliable test data
      3. +
    3. No domain expertise required to understand the data
      4. +
    4. Easy for new developers to be onboarded and developing/testing code for jobs/services
      5. +
    +

    System/integration testing

    +

    When working with third-party, external or internal data providers, it can be difficult to have all setup ready to +produce reliable data that abides by relationship contracts between each of the systems. You have to rely on these data +providers in order for you to run your tests which may not align to their priorities. With Data Caterer, you can +generate the same data that they would produce, along with maintaining referential integrity across the data providers, +so that you can run your tests without relying on their systems being up and reliable in their corresponding +lower environments.

    +

    Scenario testing

    +

    If you want to set up particular data scenarios, you can customise the generated data to fit your scenario. Once the +data gets generated and is consumed, you can also run validations to ensure your system has consumed the data correctly. +These scenarios can be put together from existing tasks or data sources can be enabled/disabled based on your +requirement. Built into Data Caterer and controlled via feature flags, is the ability to test edge cases based on the +data type of the fields used for data generation (enableEdgeCases flag within <field>.generator.options, see more +here).

    +

    Data debugging

    +

    When data related issues occur in production, it may be difficult to replicate in a lower or local environment. It could +be related to specific fields not containing expected results, size of data is too large or missing corresponding +referenced data. This becomes key to resolving the issue as you can directly code against the exact data scenario and +have confidence that your code changes will fix the problem. Data Caterer can be used to generate the appropriate data +in whichever environment you want to test your changes against.

    +

    Data profiling

    +

    When using Data Caterer with the feature flag enableGeneratePlanAndTasks enabled +(see here), metadata relating all the fields defined in the data sources you have +configured will be generated via data profiling. You can run this as a standalone job (can disable enableGenerateData) +so that you can focus on the profile of the data you are utilising. This can be run against your production data sources +to ensure the metadata can be used to accurately generate data in other environments. This is a key feature of Data +Caterer as no direct production connections need to be maintained to generate data in other environments (which can +lead to serious concerns about data security as seen here).

    +

    Schema gathering

    +

    When using Data Caterer with the feature flag enableGeneratePlanAndTasks enabled +(see here), all schemas of the data sources defined will be tracked in a common format (as +tasks). This data, along with the data profiling metadata, could then feed back into your schema registries to help keep +them up to date with your system.

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    Roadmap

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    Items below summarise the roadmap of Data Caterer. As each task gets completed, it will be documented and linked.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    FeatureDescriptionSub Tasks
    Data source supportBatch or real time data sources that can be added to Data Caterer. Support data sources that users want- AWS, GCP and Azure related data services (✅ cloud storage)
    - Deltalake
    - RabbitMQ
    - ActiveMQ
    - MongoDB
    - Elasticsearch
    - Snowflake
    - Databricks
    - Pulsar
    Metadata discoveryAllow for schema and data profiling from external metadata sources- ✅ HTTP (OpenAPI spec)
    - JMS
    - Read from samples
    - ✅ OpenLineage metadata (Marquez)
    - ✅ OpenMetadata
    - ODCS (Open Data Contract Standard)
    - Amundsen
    - Datahub
    - Solace Event Portal
    - Airflow
    - DBT
    Developer APIScala/Java interface for developers/testers to create data generation and validation tasks- ✅ Scala
    - ✅ Java
    Report generationGenerate a report that summarises the data generation or validation results- ✅ Report for data generated and validation rules
    UI portalAllow users to access a UI to input data generation or validation tasks. Also be able to view report results- Metadata stored in database
    - Store data generation/validation run information in file/database
    Integration with data validation toolsDerive data validation rules from existing data validation tools- Great Expectation
    - DBT constraints
    - SodaCL
    - MonteCarlo
    Data validation rule suggestionsBased on metadata, generate data validation rules appropriate for the dataset- ✅ Suggest basic data validations (yet to document)
    Wait conditions before data validationDefine certain conditions to be met before starting data validations- ✅ Webhook
    - ✅ File exists
    - ✅ Data exists via SQL expression
    - ✅ Pause
    Validation typesAbility to define simple/complex data validations- ✅ Basic validations
    - ✅ Aggregates (sum of amount per account is > 500)
    - Ordering (transactions are ordered by date)
    - ✅ Relationship (at least one account entry in history table per account in accounts table)
    - Data profile (how close the generated data profile is compared to the expected data profile)
    - Column name (check column count, column names, ordering)
    Data generation record countGenerate scenarios where there are one to many, many to many situations relating to record count. Also ability to cover all edge cases or scenarios- Cover all possible cases (i.e. record for each combination of oneOf values, positive/negative values etc.)
    - Ability to override edge cases
    AlertingWhen tasks have completed, ability to define alerts based on certain conditions- Slack
    - Email
    Metadata enhancementsBased on data profiling or inference, can add to existing metadata- PII detection (can integrate with Presidio)
    - Relationship detection across data sources
    - SQL generation
    - Ordering information
    Data cleanupAbility to clean up generated data- ✅ Clean up generated data
    - Clean up data in consumer data sinks
    - Clean up data from real time sources (i.e. DELETE HTTP endpoint, delete events in JMS)
    Trial versionTrial version of the full app for users to test out all the features- ✅ Trial app to try out all features
    Code generationBased on metadata or existing classes, code for data generation and validation could be generated- Code generation
    - Schema generation from Scala/Java class
    Real time response data validationsAbility to define data validations based on the response from real time data sources (e.g. HTTP response)- HTTP response data validation
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