What are the challenges and solutions for ensuring data integrity in distributed databases?

Official answer

Start with a concrete invariant, such as “an order ID is unique” or “a transfer cannot create money.” Distribution introduces concurrent updates, delayed replicas, duplicate messages and partial failures that can violate an application-level rule if coordination is insufficient.

Choose transaction and consistency semantics that cover the invariant's scope. MVCC provides versioned access and can support different isolation levels; it is not a universal conflict-resolution policy. Last-write-wins can discard a valid concurrent update, especially when timestamps or clocks are problematic.

Cross-partition atomic commit and replicated-log consensus solve different tasks. Two-phase commit coordinates an all-or-nothing decision and can block under failures. Replicating participants or the coordinator with consensus can improve fault tolerance, but simply naming Raft does not remove the need for a complete commit protocol.

During a partition, the CAP tradeoff concerns linearizable consistency and availability under the theorem's definitions. It does not say every system must discard one property at all times, nor does it describe every business integrity rule.

Use stable operation IDs, bounded retries, explicit conflict policies and reconciliation. Test ambiguous outcomes, concurrent writers, stale reads and recovery. Select guarantees from the actual database and configuration, then verify that the application uses them correctly.