Service
Systems that survive worker crashes and traffic spikes
Background work, fan-out and shared state need explicit failure modes. We build queues, workers and event pipelines with recovery paths that operators can actually use.
Problems we solve
Jobs that disappear when workers die
List-popping alone is not crash-safe. Leases, reapers and dead-letter queues turn silent loss into recoverable work.
Rate limits that break across servers
In-process counters do not share state. Atomic Redis enforcement keeps limits correct under concurrency.
CPU-heavy work on the request path
Transcoding, document processing and fan-out belong in queues — so the API stays a control plane.
Capabilities
- Background job processing
- Queues and worker systems
- Event-driven architecture
- Distributed rate limiting
- Dead letter queues and retries
- Real-time fan-out patterns
Technologies
Tools used when they fit the problem — not the other way around.
RedisLuaBullMQRedis StreamsCeleryPM2WebSocketsSocket.IOk6Prometheus