Distributed Task Queue
Build a distributed task queue with priority scheduling, retries with exponential backoff, dead letter queues, and a monitoring dashboard.
Overview
Create a Celery/Sidekiq-style task processing system. Producers enqueue tasks with priority and metadata. Workers consume tasks, process them, and report results. Handle failures with configurable retry policies, dead letter queues for poison messages, and a web dashboard for monitoring.
Distributed task queues are the backbone of every production backend system. They handle email sending, image processing, payment reconciliation, report generation, and any operation too slow or unreliable to run synchronously in an HTTP request handler. Building one from scratch (or deeply customizing BullMQ) teaches you the fundamental patterns that power systems like Celery, Sidekiq, RabbitMQ, and AWS SQS.
The engineering challenges are substantial: ensuring at-least-once delivery when workers crash, implementing priority scheduling that does not starve low-priority tasks, designing retry policies with exponential backoff and jitter to prevent thundering herd, routing failed tasks to dead letter queues for manual inspection, and building a real-time monitoring dashboard that shows queue health at a glance.
For interviews, task queue architecture comes up in nearly every system design discussion. Being able to reference a working implementation of reliable delivery, retry policies, idempotency patterns, and monitoring dashboards gives you concrete answers to questions that most candidates can only address theoretically.
Interview questions
- How do I implement reliable task delivery?
- How does exponential backoff work?
- What is a dead letter queue?
- How should the monitoring dashboard work?
- How do I implement task prioritization?
- How do I make task handlers idempotent?
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