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Correctness of dataflow systems

ACID transactions usually provide both timeliness (e.g., linearizability) and integrity (e.g., atomic commit) guarantees. Thus, if you approach application correctness from the point of view of ACID transactions, the distinction between timeliness and integrity is fairly inconsequential.

On the other hand, an interesting property of the event-based dataflow systems that we have discussed in this chapter is that they decouple timeliness and integrity. When processing event streams asynchronously, there is no guarantee of timeliness, unless you explicitly build consumers that wait for a message to arrive before returning. But integrity is in fact central to streaming systems.

Exactly-once or effectively-once semantics (see “Fault Tolerance” on page 476) is a mechanism for preserving integrity. If an event is lost, or if an event takes effect twice, the integrity of a data system could be violated. Thus, fault-tolerant message delivery and duplicate suppression (e.g., idempotent operations) are important for maintaining the integrity of a data system in the face of faults.

As we saw in the last section, reliable stream processing systems can preserve integrity without requiring distributed transactions and an atomic commit protocol, which means they can potentially achieve comparable correctness with much better performance and operational robustness. We achieved this integrity through a combination of mechanisms:

  • • Representing the content of the write operation as a single message, which can easily be written atomically—an approach that fits very well with event sourcing (see “Event Sourcing” on page 457)
  • • Deriving all other state updates from that single message using deterministic derivation functions, similarly to stored procedures (see “Actual Serial Execution” on page 252 and “Application code as a derivation function” on page 505)
  • • Passing a client-generated request ID through all these levels of processing, enabling end-to-end duplicate suppression and idempotence
  • • Making messages immutable and allowing derived data to be reprocessed from time to time, which makes it easier to recover from bugs (see “Advantages of immutable events” on page 460)

This combination of mechanisms seems to me a very promising direction for building fault-tolerant applications in the future.

 
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