README.md

#Insight Data Engineering project

FlockingComments.com

Motivation and description

Welcome to "FlockingComments", my Insight Data Engineering project. I'm a news addict, that's a fact, but the addiction goes deeper than this, I'm actually hooked on news comments as well...you know the comments section below every news article that makes you go "who the hell are these people commenting and how do they find the time?". Usually very poor in terms of content, often offensive, borederline useless and yet I spend hours reading them....

The project is not in anyway attempting to cure this disease, but to save time while I waste my time. Aggregating every comment from a lot of news sources seperated by subject, updated at all time and providing the live stream of tweets concerning all the news subject on a single page, now that's the dream.

Repository structure

The repository is structured in 3 independent branches corresponding to the 3 main parts of the application.

• First off, the master branch contains all the ingredient that make up the backend data handling pipline. Fully written in Scala, in hanles crawling, comments fetching, data ingestion (with Kafka), batch queries (with Spark), streaming analysis (with Storm), and of course data storage mainly in Hbase for a source of truth and sometimes mySQL when convinient. (more details below)

• The InsightJsonAPI branch contains a scala program based on Spray.io, which creates the views to the pipeline databases, web facing, the API serves JSON file to fetch comments, trends, or tweets from the pipeline. (more details in the brach's readme)

• The InsightClientApp branch contains an AngularJS client accessible on (http://flockingcomments.com that queries and serves the Json API on a client machine. As it is static js/HTML, it can be deployed retty much on any server. (more details in the brach's readme)

FlockingComments data pipline

I will describe here the contents of this master branch. It contains the main pieces of the data pipeline and most of the logic.

The classes are structured as follow

• src.main

  • InsightCommentsCrawler: Main class, separated in 3 sub programs that are ran by passing a different parameter: "RssCrawler", "InferTopics", "BatchLayer"

  • CommentsFetcher: Logic to fetch the comments from mulitple sources in a timely manner from the article links stored in Hbase, it utilizes heavily the classe in package externalAPIs

  • Utils: Object containing a few useful methods used throughout the program

  • rss

    • RSSReader: Logic to fetch and process the RSS feeds from different news sites, it also handles querying the Facebook API to get posts from journals official pages
    • models: Models (case classes) used in the RSS feed parsing to read/extract Json objects

  • externalAPIs: contains the logic to fetch extract and store the comments from Disqus/Facebook. It also contains the logic to fetch the Twitter Streaming API.

  • hbase: Logic to read/writes values from the Hbase Schema implemented (see below)

  • kafka: Connectors to read from and write to Kafka queues

  • spark: Classes utilizing Spark

    • BatchQueries: runs and stores distributed statistics about the comments, namely the most liked users across all site and the potential spam messages

    • TopicsFinder: uses Spark to compute the most recuring words in a set of titles either passe in memory or fetching a whole table in Hbase via an RDD

  • sql: Connector to mySQL used to store current values so they're easily queriable by the client API

  • storm: defines the topology created to filter and classify Tweets read from the Kafka queue, this class should be ran from the storm submitter.

  Pipeline overview

  

  

  For more detailed explanation of the data flow, check the blog post

  Hbase schema

  Hbase being the central datastore of this project, I'll give a few details concerning the schema used.

  article_links

  This table stores the article links as well as some meta information about each link (title, description)

  | | link1Hash | link2Hash | link3Hash | ... | |----------------------|-------------------------------------|-------------------------------------|-------------------------------------|-----| | Headline | Json object (url,title,description) | Json object (url,title,description) | Json object (url,title,description) | | | (future) sports | ... | ... | | | | (future) Politics... | | | | |

comments

the comments are denormalized twice because I want to be able to query the comments by topics and by user. This is why I store them twice

the first table comments contains the comments by topic, which make querying a topic memory efficient because rows are stored consistently within one region in Hbase.

Each Json object making up every cell is an Array structure as [{"comments":"the comment","from":"author","created_time":"comment_creation_time","likes_count":number_of_likes,"url":"url of origin"},{...},{...}]

row/column	URL1	URL2	URL3	URL...
Topic1	Json list containing all the comments for this URL			Json list containing all the comments for this URL
Topic2
Topic3		Json list Containing all the comments for this URL
Topic...			Json list Containing all the comments for this URL	Json list Containing all the comments for this URL

The table storing the comments per user is very similar except the rowkeys are the user names because again when querying for a particular user name, we only have to get one row, which is efficient.

row/column	URL1	URL2	URL3	URL...
User 1	Json list containing all the comments for this URL			Json list containing all the comments for this URL
User 2
User 3		Json list Containing all the comments for this URL
User...			Json list Containing all the comments for this URL	Json list Containing all the comments for this URL

The tweets are stored in a table similar to the comments table, the id of the tweets fetched from the Streaming API are used as column name.

The topics are stored in a table called topics, with 3 rows for the 3 different types of topics.

row/column	timestamp1	timestamp2	timestamp3	timestamp...
topics1h	Json list containing the top10 1h topics	Json list containing the top10 1h topics	Json list containing the top10 1h topics	Json list containing the top10 1h topics
topics12h	Json list containing the top10 12h topics	Json list containing the top10 12h topics	Json list containing the top10 12h topics	Json list containing the top10 12h topics
topicsalltime	Json list containing the top100 all time topics	Json list containing the top100 all time topics	Json list containing the top100 all time topics

The users statistics, like the total number of likes are also stored in a Hbase table in one row with the user in columns, which would allow to easily add later more aggregate statitics in different rows.

The Spam detection statistics are stored un a similar fashion.

How to install and run the pipeline.

First of all, this is a 4 weeks project, not everything is perfect ! But if you want to deploy the pipeline on your own system, here's how you should do it.

First install Zookeeper, Kafka, Storm, Spark, MySQL, Hbase and of course Scala (preferably with sbt). Create the Hbase schemas indicated above. Then you will need to create different supervisor config files to let your sytem run the different parts of the pipeline separately on your system.

Here's a sample from one of my EC2 node running the TopicsInfer part of the pipeline :

[program:TopicsInfer]
command=java -jar path/to/the/compiled.jar "InferTopics" "kafka-broker-host:9092"
user=nodeUser
autostart=true
autorestart=true
startsecs=30
startretries=1
log_stdout=true
log_stderr=true
logfile=/var/log/topicsInfer.log
logfile_maxbytes=20MB
logfile_backups=10

So you need to fill the subscription.xml file in the root folder with the RSS feeds you want to query and how they should be queried (Facebook, Disqus, or Internal Facebook), the tags to look for in the XML and for disqus comments, the forumId used by the website (put na for facebook)

Finally you need to start everything, launch the RSS crawler from the supervisor, then the TopicsInfer, then the BatchLayer. Once all is done, you can relax and start thinking about cloning the InsightJsonAPI located in the branch with the same name.