Why Distributed Locks in Go Are Harder Than They Look

Distributed locking across multiple service instances appears straightforward — store a key in Redis and let only one worker proceed — but the real complexity lies in handling failure scenarios. Problems such as process crashes, network partitions, clock skew, and lock expiry during active processing can silently corrupt state or allow two workers to operate simultaneously. Unlike Go's sync.Mutex, which protects shared memory within a single process, a distributed lock relies on a remote system, introducing an entirely different class of failure modes. The recommended Redis pattern uses an atomic SET with NX and a TTL, combined with a unique random token so a worker can only release a lock it actually owns. Even this approach carries subtle bugs, such as a lock expiring before the protected operation completes, allowing a second worker to acquire it while the first continues executing.