Distributed Locks in Go — Redlock Math, etcd & Split-Brain
Prerequisite: Part 6 of the System Design Masterclass. Read Part 5: Kafka & Event-Driven first. Distributed Locks in Go — Redlock Math, etcd & Split-Brain Executive Summary & Quick Answer: Distributed locks enforce mutual exclusion across independent microservice instances. Redis Redlock achieves high-performance locking across quorum master nodes with Lua-script atomicity, while etcd provides linearizable Raft-backed leases with fencing tokens to guarantee absolute safety under network partitions. Key Takeaways: Redlock Validity Formula: Lock validity equals $\text{TTL} - \text{elapsed_time} - \text{clock_drift}$; if validity $\le 0$, release immediately. Fencing Tokens: Monotonically increasing fencing tokens (e.g. etcd revision numbers) block delayed GC-paused lockholders at storage layer boundaries. Raft vs Redis Quorum: Use etcd for high-correctness financial transactions and Redis Redlock for high-throughput rate limiting or worker job distribution. What You’ll Learn That AI Won’t Tell You Redlock Clock Drift Math: Why unsynchronized system clocks (NTP drifts) allow two clients to acquire the same Redis lock, and how to verify with fencing tokens. Rsync Lock-Release Failures: The dangerous Lua script race condition when executing un-coordinated lock releases in Redis under network partitions. etcd Keep-Alive Overhead: How etcd’s HTTP/2 stream heartbeats impact cluster CPU utilization when holding thousands of concurrent locks. Why Do Race Conditions Occur in Distributed Systems? Key Concept: Race conditions occur across server processes when multiple servers independently read and then write shared state without coordination. A single-process mutex doesn’t help — you need a lock mechanism visible across all processes. ...