From 923debdbf9709117e2cead361cafb83e07913ddf Mon Sep 17 00:00:00 2001 From: Gabriel Pisa <61624649+folhesgabriel@users.noreply.github.com> Date: Tue, 9 Jul 2024 15:42:33 -0300 Subject: [PATCH] [documentacao] (#1720) * add new session in access_data_bq * fix: small fixes --------- Co-authored-by: Laura Amaral --- docs/docs/access_data_bq.md | 109 +++++++++++++++++- .../bq_metadada_table_partitions_clusters.gif | Bin 0 -> 1948534 bytes docs/docs/images/bq_query_estimated_costs.png | Bin 0 -> 27007 bytes .../bq_query_estimated_costs_row_security.png | Bin 0 -> 29619 bytes .../website_metadata_table_partitions.gif | Bin 0 -> 2213457 bytes 5 files changed, 107 insertions(+), 2 deletions(-) create mode 100644 docs/docs/images/bq_metadada_table_partitions_clusters.gif create mode 100644 docs/docs/images/bq_query_estimated_costs.png create mode 100644 docs/docs/images/bq_query_estimated_costs_row_security.png create mode 100644 docs/docs/images/website_metadata_table_partitions.gif diff --git a/docs/docs/access_data_bq.md b/docs/docs/access_data_bq.md index ff47457d7..7d951c0d6 100644 --- a/docs/docs/access_data_bq.md +++ b/docs/docs/access_data_bq.md @@ -14,6 +14,7 @@ Google. Você faz consultas ao banco em SQL direto do navegador com: Pronto(a) para começar? Nesta página você encontra: - [Primeiros passos](#primeiros-passos) +- [Entenda o uso gratuito do Big Query BQ](#entenda-o-uso-gratuito-do-big-query-bq) - [Tutoriais](#tutoriais) - [Manuais e Cursos de SQL](#manuais-e-cursos-de-sql) @@ -37,7 +38,7 @@ fazer queries em nosso *datalake* público. Pense no projeto como a "conta" na qual a Google vai contabilizar o quanto de processamento você já utilizou. **Não é necessário adicionar nenhum cartão ou forma de pagamento - O BigQuery inicia automaticamente no modo Sandbox, que permite você utilizar seus recursos sem adicionar um modo de pagamento. [Leia mais aqui](https://cloud.google.com/bigquery/docs/sandbox/?hl=pt).** - + ### Acessando o datalake da `basedosdados` O botão abaixo via te direcionar ao nosso projeto no Google BigQuery: @@ -64,7 +65,7 @@ Dentro do projeto existem dois níveis de organização dos dados, nível municipal) - **Cada tabela pertence a um único conjunto de dados** (ex: a tabela `municipio` em `br_ibge_populacao` é diferente de `municipio` em `br_bd_diretorios`) - + !!! Tip "[Veja aqui o guia do Google de como funciona a interface do BigQuery](https://cloud.google.com/bigquery/docs/bigquery-web-ui)." ![](images/bq_dataset_tables_structure.png){ width=100% } @@ -91,6 +92,108 @@ Só clicar em **Executar** e pronto! de consultas` - basta você completar com os campos e filtros que achar necessários. +## Entenda o uso gratuito do Big Query BQ + +Está seção é dedicada a apresentar dicas de como reduzir custos de processamento para aproveitar ao máximo os dados da BD! + +Para usuários que acessam os dados em projetos públicos como o da Base dos Dados o único tipo de custo associado se refere ao custo de **processamento das consultas**. A notícia boa, como mencionado acima, é que todo usuário possui *1 TB gratuito por mês para consultar livremente os dados do maior data lake público do Brasil*. Se você ainda não possui um projeto no BQ consulte [a sessão acima](https://basedosdados.github.io/mais/access_data_bq/#primeiros-passos) para criá-lo. + +- Conhecer o básico da interface do BQ é importante para o entendimento do artigo. Caso você não tenha familiariadade ou queria revisitar a interface, sugerimos 3 trilhas: +1. Nosso guia utilizando as [tabelas da RAIS - Relação Anual de Informações Sociais](https://dev.to/basedosdados/bigquery-101-45pk) +2. Nosso acervo de [vídeos no youtube](https://www.youtube.com/@BasedosDados) +3. A introdução a interface [feita pelo Google](https://cloud.google.com/bigquery/docs/bigquery-web-ui?hl=pt-br#open-ui) + + +### Veja como usufruir ao máximo das consultas gratuitas + +Nesta seção, apresentamos algumas dicas simples para reduzir os custos das consultas no Big Query e aproveitar ao máximo os dados da BD! Antes de partir para os exemplos, apresentaremos o mecanismo básico de previsão de custos de processamento de consultas no Big Query (BQ). + +!!! Tip "Estimativas de custos" + No canto superior direito da interface do BQ é informado um aviso com estimativa do custo de processamento que será cobrado do seu projeto apos a execução da consulta. + + ![](images/bq_query_estimated_costs.png){ width=100% } + + +- Este é o mecanismo básico e prontamente acessível de previsibilidade dos custos de processamento. Infelizmente, não funciona para todas as tabelas. Por motivos de limitação interna do próprio Big Query, consultas à tabelas específicas não exibem estimativas de custos. É o caso das tabelas que possuem **Row Access Policy**. Isto é, tabelas onde o número de linhas acessíveis é limitada a depender do usuário. Este é o caso das tabelas que fazem parte do serviço [BDpro](https://info.basedosdados.org/bd-pro) + +- Exemplo da tabela `agencia` do conjunto `br_bcb_estban`. + + ![](images/bq_query_estimated_costs_row_security.png) { width=100% } + + +### DICA 1: **Selecione somente as colunas de interesse** + +- A arquitetura do Big Query utiliza o armazenamento orientado a colunas, isto é, cada coluna é armazenada separadamente. Esta característica tem uma implicação clara quanto aos custos de processamento: **quanto mais colunas forem selecionadas, maior será o custo.** + + +- **Evite**: Selecionar colunas em excesso + +```sql + SELECT * +``` + +- **Prática recomendada**: selecione somente as colunas de interesse para reduzir o custo final da consulta. + +```sql +SELECT coluna1, coluna2 +``` +- Veja este a diferença obtida com a tabela [`microdados`](https://basedosdados.org/dataset/5beeec93-cbf3-43f6-9eea-9bee6a0d1683?table=dea823a5-cad7-4014-b77c-4aa33b3b0541) do conjunto `br_ms_sim`. + + - **Sem seleção de colunas:** custo estimado 5.83 GB + - **Selecionando 3 colunas:** custo estimado 0.531 GB (531 MB) + +```sql +SELECT sequencial_obito, tipo_obito, data_obito FROM `basedosdados.br_ms_sim.microdados` +``` + + +- Para entender mais a fundo a arquitetura colunar, consulte a documentação oficial do [Big Query](https://cloud.google.com/bigquery/docs/storage_overview?hl=pt-br) + +### DICA 2: Utilize colunas particionadas e clusterizadas para filtrar os dados + +- As partições são divisões feitas em uma tabela para facilitar o gerenciamento e a consulta dos dados. No momento de execução da consulta, o Big Query ignora linhas que possuem um valor da partição diferente do utilizado no filtro. Isto normalmente reduz significativamente a quantidade de linhas lidas e, o que nos interessa, **reduz o custo de processamento**. + +- Clusters são agrupamentos organizados em uma tabela com base nos valores de uma ou mais colunas especificadas. Durante a execução de uma consulta, o BigQuery otimiza a leitura dos dados, acessando apenas os segmentos que contêm os valores relevantes das colunas de cluster. Isso significa que, ao invés de escanear toda a tabela, apenas as partes necessárias são lidas, o que geralmente reduz a quantidade de dados processados e, consequentemente, **reduz o custo de processamento.** + +- Como saber qual coluna foi utilizada para particionar e clusterizar uma tabela específica? + + 1. Pelos metadados na página de tabela no site da [BD](https://basedosdados.org/dataset/5beeec93-cbf3-43f6-9eea-9bee6a0d1683?table=dea823a5-cad7-4014-b77c-4aa33b3b0541) + + ![](images/website_metadata_table_partitions.gif) + + - Note que o campo **Partições no Big Query** elenca tanto as partições quanto os clusters. + + 2. Pelos metadados na página de 'Detalhes' no Big Query + + ![](images/bq_metadada_table_partitions_clusters.gif) + + - Note que são elencadas ambas informações: **partições** e **clusters**. Neste caso, a coluna **ano** foi definida como partição e a coluna **sigla_uf** como cluster. + +- **Prática recomendada**: sempre que possível, utilize colunas particionadas e clusterizadas para filtrar/agregar os dados. + +- **Exemplo** + - Consulta utilizado a coluna particionada como filtro: +```sql +SELECT sequencial_obito, tipo_obito, data_obito FROM `basedosdados.br_ms_sim.microdados` where ano = 2015 +``` + - **custo estimado**: 31.32 MB. A combinação de técnicas de seleção de colunas e filtro utilizando partição **reduziu o custo** estimado da consulta inicial de **5.83 GB** para somente **31.32 MB** + +### DICA 3: Muita atenção ao realizar joins entre tabelas + +- **Avalie a real necessidade do JOIN** + - Certifique-se de que o join é realmente necessário para a análise que você está realizando. Às vezes, operações alternativas como subconsultas ou agregações podem ser mais eficientes. + +- **Entenda a Lógica do JOIN** + - Diferentes tipos de joins (INNER, LEFT, RIGHT, FULL) têm diferentes implicações de desempenho e resultado. Gastar um tempinho entendo a melhor opção para seu objetivo de análise pode ajudar a ter um controle de custos mais eficiente. + - Um dos problemas mais comuns é a multiplicação de linhas indesejadas no resultado final. + - Para entender a fundo boas práticas e problemas recorrentes com joins sugerimos os guias [SQL Joins na prática](https://medium.com/@aneuk3/sql-joins-defcf817e8cf) e [Maximizando a Eficiência com JOIN em Consultas SQL para Combinar Tabelas](https://medium.com/comunidadeds/maximizando-a-eficiência-com-join-em-consultas-sql-para-combinar-tabelas-55bd3b62fa09) + +- **Utilize as dicas anteriores** + - Selecione somente colunas de interesse + - Faça uso das colunas particionadas para filtrar os dados + - Atente-se a estimativa de custos antes de executar a consulta + + ## Tutoriais ### Como navegar pelo BigQuery @@ -108,6 +211,8 @@ de *datasets* (conjuntos), *tables* (tabelas) ou *labels* (grupos). Construímos regras de nomeação simples e práticas para facilitar sua busca - [veja mais](../style_data). +### Entenda o uso gratuito do Big Query (BQ) + ### Conectando com o PowerBI O Power BI é uma das tecnologias mais populares para o desenvolvimento diff --git a/docs/docs/images/bq_metadada_table_partitions_clusters.gif b/docs/docs/images/bq_metadada_table_partitions_clusters.gif new file mode 100644 index 0000000000000000000000000000000000000000..a4c1c45471b5d152c9ed5321e3c12c811b5225c6 GIT binary patch literal 1948534 zcmW)nWmFW-*T+|-k?szcF6j^ulvwFnQlxu9Kw4TFfu*~9X;?a?yJJ~O2|-#~SlH+H z{NK%)Gc)&|GvE7SKKDDyACx2{Ey%GcU)(YY0ssIY00062AOHXq0DuDk2mk=}zgZv< z00IIaKmZg7fCB*tAOQ8hr9cn>1Oh-n04N9m2LTWu0P27Hfe-))0)RjOPzV4H0U#g% z)c+0zLIEHs00IR-p#V4(fPex}|BD2K13+*91P*}00dP0~0SBP|7aoWJfDix(0sut- z;0OQ$0YLpPCm;#{LIEHs02Bp)qW}mL0QLWgf`I@q2mpovz)%1f4geznU=#oh0D^%) zFbD{S0Krfo7!Cv@fM6653;=uBS2sj2n>LLfeFbEEYz`;;B7!C&`;9wLS3_yT^ z2rvi%h9JOD1Q?D0BM@K|0t`TbfhaHt1%{x&P!t%B0wYjh6bcLgz<>Z42mpfsU{C-I 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