# Optimizing Distributed Locks in Go

##The Problem

Standard library mutexes work perfectly within the bounds of a single process. However, as we scaled our worker nodes to handle 50k events/sec, we started encountering significant data corruption in our user wallet service.

The root cause was a "thundering herd" scenario where multiple instances attempted to reconcile the same ledger entry simultaneously. Our initial Redis SETNX implementation failed to account for process crashes, leading to deadlocks.

##Solution Set

The atomic acquisition is handled via a Lua script to ensure that the existence check and the SET command happen in a single operation. This prevents the classic check-then-act race condition.

type DistributedLock struct {
    client *redis.Client
    key    string
    ttl    time.Duration
}
 
func (l *DistributedLock) TryLock(ctx context.Context) (bool, error) {
    const luaScript = `
        if redis.call("exists", KEYS[1]) == 0 then
            return redis.call("set", KEYS[1], ARGV[1], "PX", ARGV[2])
        end
        return nil
    `
    // Atomic acquire with backoff...
    ok, err := l.acquire(ctx, luaScript)
    return ok, err
}

##Benchmarks

> Environment: AWS c6g.2xlarge
> Commit: 8f3a2c9

While the in-memory mutex is orders of magnitude faster, it fundamentally cannot solve distributed coordination. The Lua script approach provided the best balance of safety and performance for our specific latency requirements.