使用 Django+Celery 进行开发而不运行 `celeryd`？
在开发中，运行 celeryd 以及 Django 开发服务器有点麻烦。例如，是否可以要求 celery 在开发过程中同步运行任务？或者类似的东西？
In development, it's a bit of a hassle to run the celeryd as well as the Django development server. Is it possible to, for example, ask celery to run tasks synchronously during development? Or something similar?
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是的，您可以通过在设置中设置
CELERY_TASK_ALWAYS_EAGER = True来实现此目的。（常量以前称为
CELERY_ALWAYS_EAGER）http://docs.celeryproject.org/en/最新/userguide/configuration.html#task-execution-settings
Yes you can do this by setting
CELERY_TASK_ALWAYS_EAGER = Truein your settings.(Constant was previously called
CELERY_ALWAYS_EAGER)http://docs.celeryproject.org/en/latest/userguide/configuration.html#task-execution-settings
django-celery 中还有一个自定义 Django 测试运行器，可以帮助处理 CELERY_ALWAYS_EAGER。
阅读有关在 Django 中使用 Celery 的更多信息 文档。
There's also a custom Django test runner in django-celery that helps with CELERY_ALWAYS_EAGER.
Read more about using Celery with Django on the docs.
在 Celery 的
4.0版本中，CELERY_ALWAYS_EAGER设置已替换为 Djangosettings.py或task_always_eager< 中的CELERY_TASK_ALWAYS_EAGER/code> 原生位于celeryconf中。由于 celery 配置从 3.x 版本到 4.x 版本的大量更改分布在许多行中，因此我建议使用内置的设置迁移工具。
celery升级设置--django来源：
http:// docs.celeryproject.org/en/latest/whatsnew-4.0.html#step-4-upgrade-to-celery-4-0
In version
4.0of CeleryCELERY_ALWAYS_EAGERsetting was replaced byCELERY_TASK_ALWAYS_EAGERin Djangosettings.pyortask_always_eagernatively incelery conf.Since the numerous changes in the celery configuration from version 3.x to 4.x are spread over many lines, I suggest to use the built-in settings migration tool.
celery upgrade settings --djangosource:
http://docs.celeryproject.org/en/latest/whatsnew-4.0.html#step-4-upgrade-to-celery-4-0
经过 5 年多的编写 Celery 任务后，我注意到我开发的一种模式可以帮助测试和简化开发 - 我意识到，如果我的 Celery 任务是我通常放入 < 中的常规 Python 函数的薄包装器，那就更好了代码>myproject.impl包。 Celery 任务可能包含一些严格与 Celery 相关的逻辑，例如使用分布式锁定、显式重试逻辑等。
After 5+ years of writing Celery tasks I have noticed a pattern I have developed that can help with testing and ease of development - I realised it is much better if my Celery tasks are thin wrappers around the regular Python functions that I typically put in
myproject.implpackage. Celery tasks may contain some strictly Celery related logic, like using distributed locking for an example, explicit retry logic, etc.