* Cassandra backend: bumped driver to v3.24 to support Astra DB, adapted the backend code to that effect, introduced new setting `cassandra_secure_bundle_path` and updated the documentation to reflect this. * edits to docs - configuration for cassandra * Update requirements/extras/cassandra.txt Co-authored-by: Asif Saif Uddin <email@example.com> * Update README.rst Co-authored-by: Omer Katz <firstname.lastname@example.org> * Cassandra backend for Astra: more test coverage, more docs, driver version bumped Co-authored-by: Stefano Lottini <email@example.com> Co-authored-by: Asif Saif Uddin <firstname.lastname@example.org> Co-authored-by: Omer Katz <email@example.com>
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task, queue, job, async, rabbitmq, amqp, redis, python, distributed, actors
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What's a Task Queue?
Task queues are used as a mechanism to distribute work across threads or machines.
A task queue's input is a unit of work, called a task, dedicated worker processes then constantly monitor the queue for new work to perform.
Celery communicates via messages, usually using a broker to mediate between clients and workers. To initiate a task a client puts a message on the queue, the broker then delivers the message to a worker.
A Celery system can consist of multiple workers and brokers, giving way to high availability and horizontal scaling.
Language interoperability can also be achieved by using webhooks in such a way that the client enqueues an URL to be requested by a worker.
What do I need?
Celery version 5.2.0 runs on,
- Python (3.7, 3.8, 3.9, 3.10)
- PyPy3.7 (7.3.7+)
This is the version of celery which will support Python 3.7 or newer.
If you're running an older version of Python, you need to be running an older version of Celery:
- Python 2.6: Celery series 3.1 or earlier.
- Python 2.5: Celery series 3.0 or earlier.
- Python 2.4: Celery series 2.2 or earlier.
- Python 2.7: Celery 4.x series.
- Python 3.6: Celery 5.1 or earlier.
Celery is a project with minimal funding, so we don't support Microsoft Windows. Please don't open any issues related to that platform.
Celery is usually used with a message broker to send and receive messages. The RabbitMQ, Redis transports are feature complete, but there's also experimental support for a myriad of other solutions, including using SQLite for local development.
Celery can run on a single machine, on multiple machines, or even across datacenters.
If this is the first time you're trying to use Celery, or you're new to Celery v5.2.0 coming from previous versions then you should read our getting started tutorials:
Tutorial teaching you the bare minimum needed to get started with Celery.
A more complete overview, showing more features.
You can also get started with Celery by using a hosted broker transport CloudAMQP. The largest hosting provider of RabbitMQ is a proud sponsor of Celery.
Celery is easy to use and maintain, and does not need configuration files.
It has an active, friendly community you can talk to for support, like at our mailing-list, or the IRC channel.
Here's one of the simplest applications you can make:
from celery import Celery = Celery('hello', broker='amqp://guest@localhost//') app @app.task def hello(): return 'hello world'
Workers and clients will automatically retry in the event of connection loss or failure, and some brokers support HA in way of Primary/Primary or Primary/Replica replication.
A single Celery process can process millions of tasks a minute, with sub-millisecond round-trip latency (using RabbitMQ, py-librabbitmq, and optimized settings).
Almost every part of Celery can be extended or used on its own, Custom pool implementations, serializers, compression schemes, logging, schedulers, consumers, producers, broker transports, and much more.
- AMQP, Redis
- SQLAlchemy, Django ORM
- Apache Cassandra, IronCache, Elasticsearch
- pickle, json, yaml, msgpack.
- zlib, bzip2 compression.
- Cryptographic message signing.
Celery is easy to integrate with web frameworks, some of which even have integration packages:
Django not needed Pyramid pyramid_celery Pylons celery-pylons Flask not needed web2py web2py-celery Tornado tornado-celery
The integration packages aren't strictly necessary, but they can make development easier, and sometimes they add important hooks like closing database connections at
The latest documentation is hosted at Read The Docs, containing user guides, tutorials, and an API reference.
最新的中文文档托管在 https://www.celerycn.io/ 中，包含用户指南、教程、API接口等。
You can install Celery either via the Python Package Index (PyPI) or from source.
To install using
$ pip install -U Celery
Celery also defines a group of bundles that can be used to install Celery and the dependencies for a given feature.
You can specify these in your requirements or on the
pip command-line by using brackets. Multiple bundles can be specified by separating them by commas.
$ pip install "celery[amqp]" $ pip install "celery[amqp,redis,auth,msgpack]"
The following bundles are available:
for using the
for using the msgpack serializer.
for using the yaml serializer.
for using the
for using the
Transports and Backends
for using the RabbitMQ amqp python library.
for using Redis as a message transport or as a result backend.
for using Amazon SQS as a message transport.
for using the
for using Memcached as a result backend (using
for using Memcached as a result backend (pure-Python implementation).
for using Apache Cassandra/Astra DB as a result backend with the DataStax driver.
for using Azure Storage as a result backend (using
for using S3 Storage as a result backend.
for using Couchbase as a result backend.
for using ArangoDB as a result backend.
for using Elasticsearch as a result backend.
for using Riak as a result backend.
for using Azure Cosmos DB as a result backend (using
for using Zookeeper as a message transport.
for using SQLAlchemy as a result backend (supported).
for using the Pyro4 message transport (experimental).
for using the SoftLayer Message Queue transport (experimental).
for using the Consul.io Key/Value store as a message transport or result backend (experimental).
specifies the lowest version possible for Django support.
You should probably not use this in your requirements, it's here for informational purposes only.
Downloading and installing from source
Download the latest version of Celery from PyPI:
You can install it by doing the following,:
$ tar xvfz celery-0.0.0.tar.gz $ cd celery-0.0.0 $ python setup.py build # python setup.py install
The last command must be executed as a privileged user if you aren't currently using a virtualenv.
Using the development version
The Celery development version also requires the development versions of
You can install the latest snapshot of these using the following pip commands:
$ pip install https://github.com/celery/celery/zipball/master#egg=celery $ pip install https://github.com/celery/billiard/zipball/master#egg=billiard $ pip install https://github.com/celery/py-amqp/zipball/master#egg=amqp $ pip install https://github.com/celery/kombu/zipball/master#egg=kombu $ pip install https://github.com/celery/vine/zipball/master#egg=vine
Please see the Contributing section.
For discussions about the usage, development, and future of Celery, please join the celery-users mailing list.
Come chat with us on IRC. The #celery channel is located at the Libera Chat network.
If you have any suggestions, bug reports, or annoyances please report them to our issue tracker at https://github.com/celery/celery/issues/
This project exists thanks to all the people who contribute. Development of celery happens at GitHub: https://github.com/celery/celery
You're highly encouraged to participate in the development of celery. If you don't like GitHub (for some reason) you're welcome to send regular patches.
Be sure to also read the Contributing to Celery section in the documentation.
Thank you to all our backers! 🙏 [Become a backer]
Support this project by becoming a sponsor. Your logo will show up here with a link to your website. [Become a sponsor]
This software is licensed under the New BSD License. See the
LICENSE file in the top distribution directory for the full license text.