Distributed Systems Mastery
A complete roadmap to understanding core concepts: CAP theorem, consistent hashing, consensus, idempotency, and resilience patterns.
Articles in this series
Consistent Hashing: The Algorithm Behind Every Scalable Distributed System
Adding one cache server shouldn't invalidate every key. Consistent hashing with virtual nodes and bounded loads — full Go and Java implementations.
Understanding Raft Consensus: The Algorithm That Keeps Your Database Honest
How Raft works in production: leader election, log replication, safety guarantees, and failure modes in etcd and CockroachDB.
Idempotency Patterns: Building Retry-Safe Distributed Systems
Why exactly-once is a myth, and how idempotency keys, database constraints, and the outbox pattern make retries safe in Go and Java.
Building Resilient Distributed Systems with Go
Circuit breakers, bulkhead isolation, timeout propagation, and idempotent retries in Go — the patterns for surviving component failures.
Caching Strategies at Scale
Four caching patterns with Go: cache-aside, write-through, write-behind, stampede prevention, and event-based invalidation.
Scaling Redis for High-Throughput Systems
Scale Redis: cluster sharding, hot key replication, connection pooling, pipelining, and eviction tuning for 1M+ ops/sec.
