How Redis Helps Maintain Consistency in Distributed Systems

A few months back, we were scaling out a background job system. The architecture looked solid: multiple worker nodes, Redis as a task queue, and a persistent database to store job results. But within days of production rollout, we started noticing symptoms of classic distributed inconsistency — duplicate job executions, race conditions in updates, and a few corrupted counters.

That’s when we decided to stop treating Redis as “just a cache” and started using it as a consistency enabler.

In this article, I’ll walk you through how we gradually layered Redis features to solve real problems in a distributed system — one step at a time.

It Started with Locks

Our first challenge was simple on the surface: ensure only one worker processes a job. Workers were polling a PostgreSQL table, and we had multiple instances racing to pick the same row.

Initially, we added a status column and used a SQL UPDATE ... WHERE status = 'queued' clause, but due to replication lag and connection pooling issues, duplicate pickups still happened.

So we introduced a Redis-based distributed lock.

Here’s what that looked like in Go:

func acquireLock(redisClient *redis.Client, key…