This document describes the current stable version of Celery (5.2). For development docs, go here.

Contributors Guide to the Code

Philosophy

The API>RCP Precedence Rule

  • The API is more important than Readability

  • Readability is more important than Convention

  • Convention is more important than Performance

    • …unless the code is a proven hot-spot.

More important than anything else is the end-user API. Conventions must step aside, and any suffering is always alleviated if the end result is a better API.

Conventions and Idioms Used

Classes

Naming

  • Follows PEP 8.

  • Class names must be CamelCase.

  • but not if they’re verbs, verbs shall be lower_case:

    # - test case for a class
class TestMyClass(Case):                # BAD
    pass

class test_MyClass(Case):               # GOOD
    pass

# - test case for a function
class TestMyFunction(Case):             # BAD
    pass

class test_my_function(Case):           # GOOD
    pass

# - "action" class (verb)
class UpdateTwitterStatus:    # BAD
    pass

class update_twitter_status:    # GOOD
    pass

    Note

    Sometimes it makes sense to have a class mask as a function, and there’s precedence for this in the Python standard library (e.g., contextmanager). Celery examples include signature, chord, inspect, promise and more..

  • Factory functions and methods must be CamelCase (excluding verbs):

    class Celery:

    def consumer_factory(self):     # BAD
        ...

    def Consumer(self):             # GOOD
        ...

Default values

Class attributes serve as default values for the instance, as this means that they can be set by either instantiation or inheritance.

Example:

class Producer:
    active = True
    serializer = 'json'

    def __init__(self, serializer=None, active=None):
        self.serializer = serializer or self.serializer

        # must check for None when value can be false-y
        self.active = active if active is not None else self.active

A subclass can change the default value:

TaskProducer(Producer):
    serializer = 'pickle'

and the value can be set at instantiation:

>>> producer = TaskProducer(serializer='msgpack')

Exceptions

Custom exceptions raised by an objects methods and properties should be available as an attribute and documented in the method/property that throw.

This way a user doesn’t have to find out where to import the exception from, but rather use help(obj) and access the exception class from the instance directly.

Example:

class Empty(Exception):
    pass

class Queue:
    Empty = Empty

    def get(self):
        """Get the next item from the queue.

        :raises Queue.Empty: if there are no more items left.

        """
        try:
            return self.queue.popleft()
        except IndexError:
            raise self.Empty()

Composites

Similarly to exceptions, composite classes should be override-able by inheritance and/or instantiation. Common sense can be used when selecting what classes to include, but often it’s better to add one too many: predicting what users need to override is hard (this has saved us from many a monkey patch).

Example:

class Worker:
    Consumer = Consumer

    def __init__(self, connection, consumer_cls=None):
        self.Consumer = consumer_cls or self.Consumer

    def do_work(self):
        with self.Consumer(self.connection) as consumer:
            self.connection.drain_events()

Applications vs. “single mode”

In the beginning Celery was developed for Django, simply because this enabled us get the project started quickly, while also having a large potential user base.

In Django there’s a global settings object, so multiple Django projects can’t co-exist in the same process space, this later posed a problem for using Celery with frameworks that don’t have this limitation.

Therefore the app concept was introduced. When using apps you use ‘celery’ objects instead of importing things from Celery sub-modules, this (unfortunately) also means that Celery essentially has two API’s.

Here’s an example using Celery in single-mode:

from celery import task
from celery.task.control import inspect

from .models import CeleryStats

@task
def write_stats_to_db():
    stats = inspect().stats(timeout=1)
    for node_name, reply in stats:
        CeleryStats.objects.update_stat(node_name, stats)

and here’s the same using Celery app objects:

from .celery import celery
from .models import CeleryStats

@app.task
def write_stats_to_db():
    stats = celery.control.inspect().stats(timeout=1)
    for node_name, reply in stats:
        CeleryStats.objects.update_stat(node_name, stats)

In the example above the actual application instance is imported from a module in the project, this module could look something like this:

from celery import Celery

app = Celery(broker='amqp://')

Module Overview

  • celery.app

    This is the core of Celery: the entry-point for all functionality.

  • celery.loaders

    Every app must have a loader. The loader decides how configuration is read; what happens when the worker starts; when a task starts and ends; and so on.

    The loaders included are:

    • app

      Custom Celery app instances uses this loader by default.

    • default

      “single-mode” uses this loader by default.

    Extension loaders also exist, for example celery-pylons.

  • celery.worker

    This is the worker implementation.

  • celery.backends

    Task result backends live here.

  • celery.apps

    Major user applications: worker and beat. The command-line wrappers for these are in celery.bin (see below)

  • celery.bin

    Command-line applications. setup.py creates setuptools entry-points for these.

  • celery.concurrency

    Execution pool implementations (prefork, eventlet, gevent, solo, thread).

  • celery.db

    Database models for the SQLAlchemy database result backend. (should be moved into celery.backends.database)

  • celery.events

    Sending and consuming monitoring events, also includes curses monitor, event dumper and utilities to work with in-memory cluster state.

  • celery.execute.trace

    How tasks are executed and traced by the worker, and in eager mode.

  • celery.security

    Security related functionality, currently a serializer using cryptographic digests.

  • celery.task

    single-mode interface to creating tasks, and controlling workers.

  • t.unit (int distribution)

    The unit test suite.

  • celery.utils

    Utility functions used by the Celery code base. Much of it is there to be compatible across Python versions.

  • celery.contrib

    Additional public code that doesn’t fit into any other name-space.

Worker overview

  • celery.bin.worker:Worker

    This is the command-line interface to the worker.

    Responsibilities:

    • Daemonization when --detach set,

    • dropping privileges when using --uid/ --gid arguments

    • Installs “concurrency patches” (eventlet/gevent monkey patches).

    app.worker_main(argv) calls instantiate('celery.bin.worker:Worker')(app).execute_from_commandline(argv)

  • app.Worker -> celery.apps.worker:Worker

    Responsibilities: * sets up logging and redirects standard outs * installs signal handlers (TERM/HUP/STOP/USR1 (cry)/USR2 (rdb)) * prints banner and warnings (e.g., pickle warning) * handles the celery worker --purge argument

  • app.WorkController -> celery.worker.WorkController

    This is the real worker, built up around bootsteps.