13 Measuring data consistency and transactions

 

This chapter covers

  • Identifying and troubleshooting data inconsistencies across services
  • Tracking multi-step transactions using trace IDs and audit logs
  • Understanding why coordination breaks down in distributed workflows
  • Measuring consistency guarantees using sampling, invariants, and reconciliation

In a perfect system, data is always in sync. Every service sees the same state, updates happen atomically, and no user ever gets confused. In real life? Not so much.

In a distributed environment, consistency is a moving target. Services communicate over networks, store state independently, and occasionally forget to invite each other to the transaction. You’ll see orders that were paid but not shipped, emails confirming things that never got saved, or records that exist in one database but not another. The bugs are subtle, hard to reproduce, and often only show up at 2 a.m.

In this chapter, we’ll look at how to detect and diagnose these issues before your support team finds them first. We’ll start by identifying symptoms of inconsistency across services, then learn how to trace multi-step transactions that span service boundaries, and finally cover strategies for measuring and monitoring consistency guarantees in production systems, because “it worked in staging” is not a consistency model.

13.1 Troubleshooting inconsistencies across services

13.1.1 Inspecting time-based anomalies in event flows

13.1.2 Applying domain invariants to identify invalid states

13.2 Tracking and correlating multi-step transactions

13.2.1 Reviewing audit logs to reconstruct transaction steps

13.2.2 Replaying events or examining event logs for missing messages

13.3 Measuring and monitoring consistency guarantees

13.3.1 Verifying data integrity using checksums or hashes

13.3.2 Running reconciliation jobs to compare expected vs. actual state

13.4 Summary