March 2025
Beginner to intermediate
260 pages
7h 25m
Portuguese (Portugal, Brazil)
Content preview from Bancos de dados de streaming
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Capítulo 6. Consistência
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Se estás familiarizado com bases de dados, a consistência é um dado adquirido. Sabes que os resultados das tuas consultas vão ser consistentes com os dados de entrada. Agora imagina que te atreves a atravessar a ponte da base de dados para o mundo do streaming. Podes contar com garantias de consistência semelhantes, mesmo com a complexidade adicional dos dados que chegam tarde e fora de ordem, bem como com a ênfase na baixa latência e no elevado rendimento?
Para os processadores de fluxo clássicos, a resposta é não. Eles garantem uma forma mais fraca de consistência chamada consistência eventual. Para casos de utilização de processamento de fluxos clássicos, que envolvem frequentemente agregações de dados em janelas, a consistência eventual é perfeita e também permite pipelines de dados com latência ultra-baixa, com um rendimento muito elevado e em escala extremamente grande. O problema é que, se vieres do mundo das bases de dados, a consistência eventual pode tornar-se uma experiência confusa e contra-intuitiva - especialmente em combinação com dados não janelados.
Neste capítulo, vamos usar um exemplo do domínio bancário para demonstrar o que pode correr mal em processadores de stream eventualmente consistentes como o Flink, o ksqlDB e o Proton se seguires apenas as tuas intuições do mundo das bases de dados.
Curiosamente, alguns sistemas ...
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