You write a record, immediately read it back, and assert equality. The test fails. Not because of a bug, but because the read hit a replica that hasn’t caught up yet. Your test is correct. Your assertion timing isn’t.
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.
There are only two hard things in computer science: cache invalidation and naming things. Here’s why invalidation is so tricky, and what actually works.
How distributed systems track ‘what happened before what’ without trusting wall clocks. Lamport timestamps for ordering, vector clocks for detecting conflicts.
Read-your-writes and monotonic reads aren’t just buzzwords. They’re the difference between a database that feels broken and one that makes sense to users.
How do stale replicas catch up in distributed systems? Compare read repair and anti-entropy strategies with Merkle trees for healing data divergence.
Eventual consistency doesn’t mean milliseconds. Understand linearizability, causal consistency, and quorum reads to pick the right consistency model.