Distributed Locking & High-Concurrency Caching Patterns with Redis and Lua Scripts

1. Distributed Mutex: Single-Instance vs. Redlock Protocol

In distributed microservice topologies where multiple application workers process shared resources (such as payment checkouts, link shortening hashes, or inventory allocation), concurrency control cannot rely on language-level mutexes. Redis provides high-performance distributed locking primitives using SET key value NX PX [ttl].

For mission-critical multi-master setups, the Redlock algorithm coordinates quorum across N independent Redis nodes, guaranteeing mutual exclusion even during single-node network partitions or sudden hardware failure.

2. Safe Lock Release via Atomic Lua Scripts

A critical failure mode in distributed locking occurs when a worker's lock lease expires while the task is still executing. If worker A simply calls DEL lock_key, it risks releasing a lock newly acquired by worker B. To ensure safety, lock release must be atomic and check token ownership:

-- Atomic Lua script for safe lock release
if redis.call("get", KEYS[1]) == ARGV[1] then
    return redis.call("del", KEYS[1])
else
    return 0
end

3. Cache Stampede (Thundering Herd) Prevention

When high-velocity cached keys expire, hundreds of concurrent incoming requests simultaneously detect a cache miss and overwhelm origin databases. YashLab implements two battle-tested strategies to eliminate stampedes:

• Probabilistic Early Expiration (XFetch): Workers probabilistically refresh the cache slightly before true TTL expiration based on compute delta.
• Distributed Mutex Lockout: Only one thread acquires the lock to compute origin data; remaining threads wait or return the stale cache entry gracefully.