:setting:`task_soft_time_limit` celery 异步任务 执行时间限制 内存限制

Note

Celery will still be able to read old configuration files, so there’s no rush in moving to the new settings format. Furthermore, we provide the celery upgrade command that should handle plenty of cases (including Django).

https://github.com/celery/celery/blob/master/docs/userguide/workers.rst#id198

Time Limits

.. versionadded:: 2.0

A single task can potentially run forever, if you have lots of tasks waiting for some event that‘ll never happen you‘ll block the worker from processing new tasks indefinitely. The best way to defend against this scenario happening is enabling time limits.

The time limit (--time-limit) is the maximum number of seconds a task may run before the process executing it is terminated and replaced by a new process. You can also enable a soft time limit (--soft-time-limit), this raises an exception the task can catch to clean up before the hard time limit kills it:

from myapp import app
from celery.exceptions import SoftTimeLimitExceeded

@app.task
def mytask():
    try:
        do_work()
    except SoftTimeLimitExceeded:
        clean_up_in_a_hurry()

Time limits can also be set using the :setting:`task_time_limit` / :setting:`task_soft_time_limit` settings.

Changing time limits at run-time

.. versionadded:: 2.3

There‘s a remote control command that enables you to change both soft and hard time limits for a task — named time_limit.

Example changing the time limit for the tasks.crawl_the_web task to have a soft time limit of one minute, and a hard time limit of two minutes:

>>> app.control.time_limit(‘tasks.crawl_the_web‘,
                           soft=60, hard=120, reply=True)
[{‘worker1.example.com‘: {‘ok‘: ‘time limits set successfully‘}}]

Only tasks that starts executing after the time limit change will be affected.

Time Limits

.. versionadded:: 2.0

A single task can potentially run forever, if you have lots of tasks waiting for some event that‘ll never happen you‘ll block the worker from processing new tasks indefinitely. The best way to defend against this scenario happening is enabling time limits.

The time limit (--time-limit) is the maximum number of seconds a task may run before the process executing it is terminated and replaced by a new process. You can also enable a soft time limit (--soft-time-limit), this raises an exception the task can catch to clean up before the hard time limit kills it:

from myapp import app
from celery.exceptions import SoftTimeLimitExceeded

@app.task
def mytask():
    try:
        do_work()
    except SoftTimeLimitExceeded:
        clean_up_in_a_hurry()

Time limits can also be set using the :setting:`task_time_limit` / :setting:`task_soft_time_limit` settings.