Insights, patterns, and hard-won lessons on distributed data systems — storage, streaming, consistency, and scale.
Every distributed data system is an answer to the same uncomfortable question: what do you do when part of your system fails but the rest keeps running? On a single machine, a crash takes everything down together — …
Many distributed databases — Dynamo-style stores like Cassandra and Riak — don’t make you choose consistency once and for all. They let you tune it per request using quorums. The mechanism is simple arithmetic with …
Underneath nearly every database is one of two storage engines: a B-tree or an LSM-tree. The choice shapes write throughput, read latency, space usage, and how the system behaves under pressure. It’s worth …