Two database replicas should have identical data. One has 50 million rows. Comparing row by row would take hours. Merkle trees find the differences by comparing a single hash.

Three replicas, one write. How many replicas need to acknowledge before the write is ‘done’? One? All three? The answer determines your consistency guarantees.

You added read replicas to scale reads. Now users update their profile and see the old version. Welcome to replica lag.

How do stale replicas catch up in distributed systems? Compare read repair and anti-entropy strategies with Merkle trees for healing data divergence.

Concurrent writes in distributed databases don’t merge automatically. Learn to detect conflicts with version vectors and resolve them without losing data.

Users see stale data after writes. It’s not a bug, it’s replication lag. Learn to handle read-after-write problems and causality violations in production.