Consistency and Completeness: Rethinking Distributed Stream Processing in Apache Kafka

The text delves into the evolution of stream processing, emphasizing the pivotal role of Apache Kafka and its component, Kafka Streams, in addressing challenges of consistency and completeness in data streaming. Initially perceived as a real-time, low-latency, yet potentially transient architecture, stream processing has advanced to offer robust correctness guarantees through mechanisms like exactly-once semantics and speculative processing. Kafka achieves this by integrating stream processing with persistent logging, utilizing idempotent and transactional writes for consistency, and revision-based approaches for completeness, even with out-of-order data. This approach allows Kafka to manage the trade-offs between latency, throughput, and correctness more flexibly compared to other frameworks that often incur higher latencies. The discussion also highlights a white paper, presented at the ACM SIGMOD International Conference, detailing these innovations and their applications in large-scale deployments by companies like Bloomberg and Expedia.

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