queue-controller

Context

The client ran multiple concurrent job queues across their operations infrastructure. When something went wrong — a job stalled, a queue backed up, a worker crashed — there was no centralized way to see it. Investigation was manual, restarts were manual, and the operations team had no metrics to plan capacity against.

Problem

Static scheduling couldn’t adapt to varying load patterns. Job failures required someone to SSH in, figure out what happened, and restart things by hand. The operations team was effectively flying blind — no aggregate view of queue health, no historical data for trend analysis, and no way to automatically react when a queue started falling behind.

What I built

queue-controller — an end-to-end queue management platform.

Real-time monitoring

A live dashboard showing queue depth, processing rates, and error counts, backed by WebSocket updates for instant state reflection. Historical metrics gave the operations team the data they needed for capacity planning.

Adaptive scheduling

Dynamic worker allocation based on current queue depth and processing velocity. Priority-based routing ensured critical jobs were processed first. Automatic retry with exponential backoff handled transient failures without human intervention.

Infrastructure

Redis-backed queue storage for high-throughput operations, Docker-containerized services deployed on AWS, and a React frontend for the monitoring dashboard.

Note

This project was developed under NDA. Technical details are limited to publicly shareable information.