async-processing

Production-grade backend demonstrating asynchronous job processing with BullMQ, Redis, PostgreSQL, and Express. Features background workers, retry mechanisms, dead-letter queues, rate limiting, structured logging, and resilient REST APIs.

mern redis kubernetes docker argocd gitops ci-cd distributed-systems async-processing

Event-driven OpenMusic REST API built with Node.js, PostgreSQL, Redis, and RabbitMQ, featuring JWT authentication, server-side caching, async playlist export, and album cover upload.

Real-time distributed system template featuring Nginx TLS/rate-limiting, JWT auth, RabbitMQ worker queues and PostgreSQL audit tracing. Zero-config, single-command setup

A distributed asynchronous job processing system designed to handle concurrent workloads reliably using Redis queues, worker orchestration, retry mechanisms, dead-letter queues, and failure recovery workflows.

Async backend system for transforming compliance policies into structured checklists with AI, retry logic, and failure safe design

Demonstrates async processing patterns with Node.js. Calculate Fibonacci numbers without freezing - instant feedback, background processing, and smart caching. Applicable to image processing, reports, and any long-running tasks.

Async Document Processing System with real-time status tracking | Next.js | MongoDB | Tailwind

A beginner-friendly Node.js project demonstrating fast, slow, and asynchronous background job processing using Express, BullMQ, Redis, and Worker Threads. Handle CPU-intensive tasks efficiently ... ❤️

An observable, self-healing async job processing pipeline built with Fastify, MongoDB, BullMQ/Redis, and Kubernetes — designed to showcase DevOps practices (observability, autoscaling, CI/CD) on top of a MERN-style backend.

Privacy intelligence engine for analyzing web trackers, fingerprinting, cookies, and third-party data flows using scalable crawler pipelines.

Analytics Service is a high-performance, production-ready website analytics system built using Node.js, Express, Redis, and MongoDB. It is designed to handle high-volume event ingestion with sub-5ms response times, using Redis as an in-memory queue for fast, non-blocking data pipelines.