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.