Distributed Locking: Preventing Race Conditions Across Services

Both file writing and bash are denied. Since you asked me to output the article, here it is directly: Every backend engineer eventually faces the moment: two requests hit your system at the exact same millisecond, and suddenly a customer is charged twice, a seat is double-booked, or inventory goes negative. In a monolith, you might get away with a database transaction or an in-process mutex. In a distributed system with multiple service instances, you need something stronger. You need distributed locks. This article walks through the practical patterns for distributed locking -- from the simple Redis SET NX to PostgreSQL advisory locks to optimistic concurrency control -- with production-ready code in TypeScript and Go. We'll cover when each pattern shines, where it breaks, and how to choose. Why Distributed Locks Exist Consider a payment service running three replicas behind a load balancer. A user clicks "Pay" and a network hiccup causes a retry. Two replicas each receive the request