Celery: Distributed Task Queue
Celery is an open source Python library for handling asynchronous tasks and job queues. It allows defining tasks that can be executed asynchronously, monitoring them, and getting notified when they are finished. Celery supports scheduling tasks and integrating with a variety of s
python
asynchronous
task-queue
job-queue
distributed
Celery: Distributed Task Queue for Python
Celery is an open source Python library for handling asynchronous tasks and job queues. It allows defining tasks that can be executed asynchronously, monitoring them, and getting notified when they are finished. Celery supports scheduling tasks and integrating with a variety of services.
What is Celery: Distributed Task Queue?
Celery is an asynchronous task queue/job queue based on distributed message passing. It is used for handling large volumes of tasks that need to be executed asynchronously, outside of the request-response cycle.
Some key features of Celery:
- Tasks can be assigned to queues and workers can subscribe to queues to receive and process tasks
- Supports scheduling tasks periodicially or at specific times
- Integrates seamlessly with popular frameworks like Django, Flask, etc
- Supports multiple brokers like Redis, RabbitMQ, SQLAlchemy, etc for message transport
- Can distribute tasks across machines and processes
- Has broad monitoring support including web dashboard, events, metrics, etc
Celery is a good choice for offloading intensive processing or IO bound jobs like sending emails, image processing, PDF generation, database updates, API calls, file translations, etc so they do not block the main application thread. It helps make applications highly scalable.
Celery: Distributed Task Queue Features
Features
- Distributed - Celery is designed to run on multiple nodes
- Async task queue - Allows defining, running and monitoring async tasks
- Scheduling - Supports scheduling tasks to run at specific times
- Integration - Integrates with many services like Redis, RabbitMQ, SQLAlchemy, Django, etc.
Pricing
- Open Source
Pros
Reliability - Tasks run distributed across nodes provides fault tolerance
Flexibility - Many configuration options to tune and optimize
Active community - Well maintained and good documentation
Cons
Complexity - Can have a steep learning curve
Overhead - Running a distributed system has overhead
Versioning - Upgrading Celery and dependencies can cause issues
Official Links
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