This document describes the current stable version of Celery (5.3). For development docs, go here.
Concurrency with Eventlet¶
The Eventlet homepage describes it as a concurrent networking library for Python that allows you to change how you run your code, not how you write it.
Coroutines ensure that the developer uses a blocking style of programming that’s similar to threading, but provide the benefits of non-blocking I/O.
The event dispatch is implicit: meaning you can easily use Eventlet from the Python interpreter, or as a small part of a larger application.
Celery supports Eventlet as an alternative execution pool implementation and in some cases superior to prefork. However, you need to ensure one task doesn’t block the event loop too long. Generally, CPU-bound operations don’t go well with Eventlet. Also note that some libraries, usually with C extensions, cannot be monkeypatched and therefore cannot benefit from using Eventlet. Please refer to their documentation if you are not sure. For example, pylibmc does not allow cooperation with Eventlet but psycopg2 does when both of them are libraries with C extensions.
The prefork pool can take use of multiple processes, but how many is often limited to a few processes per CPU. With Eventlet you can efficiently spawn hundreds, or thousands of green threads. In an informal test with a feed hub system the Eventlet pool could fetch and process hundreds of feeds every second, while the prefork pool spent 14 seconds processing 100 feeds. Note that this is one of the applications async I/O is especially good at (asynchronous HTTP requests). You may want a mix of both Eventlet and prefork workers, and route tasks according to compatibility or what works best.
You can enable the Eventlet pool by using the
celery worker -P
$ celery -A proj worker -P eventlet -c 1000
See the Eventlet examples directory in the Celery distribution for some examples taking use of Eventlet support.