2024Production

Distributed Task Processing

Scalable Celery-based task queue system handling 10k+ concurrent operations with Redis clustering and PostgreSQL optimization.

PythonCeleryRedisPostgreSQL

Distributed Task Processing

A highly scalable distributed task processing system built on Celery, capable of handling over 10,000 concurrent operations with robust fault tolerance and monitoring.

Overview

Designed and implemented a production-grade distributed task queue system to handle high-volume background processing for web applications at scale.

Key Features

  • 10k+ Concurrent Operations: Efficiently processes thousands of tasks simultaneously
  • Redis Clustering: High-availability message broker with failover support
  • PostgreSQL Optimization: Optimized database queries and connection pooling
  • Monitoring & Observability: Comprehensive metrics and alerting

Technical Architecture

The system leverages:

  • Celery for distributed task execution
  • Redis as the message broker and result backend
  • PostgreSQL for persistent storage with advanced optimization
  • Python for worker implementation

Performance

Achieved 99.9% uptime with average task processing time under 100ms and automatic retry mechanisms for failed tasks.