What 3 Database Race Conditions Taught Me About Distributed Locks
When you start building backend systems with asynchronous workflows, everyone tells you about race conditions. You think, "Sure, I'll just throw a Redis lock on it and call it a day."
That's exactly what I thought, until production proved me wrong—three different times, in three different ways.
I was working on a system where synchronous APIs, async webhooks, and background workers were all trying to read and write the same MongoDB documents at the exact same time. The root problem was always the same: two writers, one document. But I quickly learned that there is no silver-bullet fix. The right way to handle a race condition depends entirely on what's actually happening inside that split-second execution window.
Here are the war stories, the trade-offs I had to make, and what this experience actually taught me about concurrency.
