When a Backend Bug Turned Into an AWS Infrastructure Problem
A real production incident from our app's backend: a one-line worker configuration issue caused unexpected Redis traffic, increased AWS usage, and stayed unnoticed for six days. Here’s how we traced it from an AWS cost anomaly back to the application code.
When a Backend Bug Turned Into an AWS Infrastructure Problem
Production bugs don't always look like application bugs. Sometimes, the first warning comes from your AWS bill.
While running our backend on AWS, a cost anomaly revealed unexpected network usage. Initially, we suspected EBS, but the real issue was inside our background worker.
What Happened?
Our worker was configured with:
- 2 concurrent jobs
- 1 job/second rate limit
The extra worker slot had no work but kept polling Redis, generating:
- 703 GB of data transfer
- 1.05 billion Redis round trips
- ~1,500 calls/second
Everything appeared healthy: no failed jobs, unhealthy queues, or obvious application errors.
Finding the Root Cause
We traced the AWS network usage back to Redis and discovered:
Worker concurrency > rate limiter capacity
The fix was a simple configuration change.
The Lesson
This incident showed us that AWS monitoring shouldn't stop at CPU and application health.
Application → Worker → Redis → Network → AWS Cost
A small backend configuration mistake can become a serious cloud infrastructure and cost problem.
The expensive part wasn't fixing the bug—it was letting it run for six days.
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