A distributed task queue for Python, enabling asynchronous execution of tasks. It’s focused on real-time processing, while also supporting task scheduling.
Celery
- Python
- Automation, Data Analytics, Process Control
- Cloud Computing, Software as a Service (SaaS)
Features:
- Asynchronous task execution, distributed task queues, scheduling (periodic tasks via Celery Beat), monitoring (e.g., Flower), multiple message broker support (RabbitMQ, Redis), result backends, workflow creation (canvas: chains, groups, chords), retries and error handling
Pricing:
- Free
- Robust and scalable for handling background tasks and long-running operations, flexible, widely used, good integration with web frameworks (Django, Flask), mature.
- Can be complex to set up and configure, especially for distributed environments, requires a message broker which adds another component to manage, debugging distributed tasks can be harder.
Best for:
- Developers needing to offload task execution from web request-response cycles, run background jobs, or build distributed, message-passing applications.