Building a Distributed Task Queue in Go

Task queues are deceptively simple. You push a job, a worker picks it up, and it gets done. But once you need reliability, retries, and horizontal scaling, things get interesting fast.

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Why Build One?

Existing solutions like Celery and Sidekiq are great, but they come with opinions. I wanted something lightweight that spoke gRPC natively and could be embedded into existing Go services.

The Architecture

At its core, the queue is backed by Redis sorted sets. Each job gets a score based on its scheduled execution time, making delayed jobs trivial.

Workers poll the queue using BZPOPMIN, which blocks until a job is available. This keeps CPU usage near zero when idle.

Retry Strategy

Failed jobs get exponential backoff with jitter. After a configurable number of retries, they land in a dead-letter queue for manual inspection.

What I Learned

• Redis is not a database — but it's an excellent job broker if you accept its durability trade-offs.
• Idempotency matters — workers can crash mid-job, so every operation needs to be safe to retry.
• Observability first — I added Prometheus metrics from day one, and it paid off immediately during load testing.