python-2.7 – 每个任务名称的芹菜时间统计信息

2023年4月9日 228次阅读

我有一些相当繁忙的芹菜队列,但不确定哪些任务是有问题的.有没有办法汇总结果以确定哪些任务需要很长时间？我在2-4台服务器上有10-20名工人.

使用redis作为代理,也作为后端的结果.我注意到Flower上的忙碌队列,但无法弄清楚如何根据任务获得时间统计.

最佳答案 方法1：

如果在启动芹菜工作时启用了日志记录,则会记录每个任务的时间.

$celery worker -l info -A your_app --logfile celery.log

这将生成这样的日志

[2016-06-04 13:21:30,749: INFO/MainProcess] Task sig.add[a8b648eb-9674-44f0-90bd-71cfebe22f2f] succeeded in 0.00979363399983s: 3
[2016-06-04 13:21:30,973: INFO/MainProcess] Received task: sig.add[7fd422e6-8f48-4dd2-90de-e213afbedc38]
[2016-06-04 13:21:30,982: WARNING/Worker-2] called by small_task. LOL {'signal': <Signal: Signal>, 'result': 3, 'sender': <@task: sig.add of tasks:0x7fdf33146c50>}

您可以过滤成功的行.使用,[,：作为分隔符分割这些行,打印任务名称和每个行所用的时间,然后对所有行进行排序.

$grep ' succeeded in ' celery.log  | awk -F'[ :\[]' '{print $9, $13}' | sort 
awk: warning: escape sequence `\[' treated as plain `['
sig.add 0.00775764500031s
sig.add 0.00802627899975s
sig.foo 12.00813863099938s
sig.foo 15.00871706100043s
sig.foo 12.00979363399983s

如你所见,添加速度非常快foo很慢.

方法2：

Celery有task_prerun_handler,task_postrun_handler信号,它们在任务之前/之后运行.您可以连接跟踪时间的功能,然后记录某个时间.

from time import time
from celery.signals import task_prerun, task_postrun


tasks = {}
task_avg_time = {}
Average = namedtuple('Average', 'cum_avg count')


@task_prerun.connect
def task_prerun_handler(signal, sender, task_id, task, args, kwargs):
    tasks[task_id] = time()


@task_postrun.connect
def task_postrun_handler(signal, sender, task_id, task, args, kwargs, retval, state):
    try:
        cost = time() - tasks.pop(task_id)
    except KeyError:
        cost = None

    if not cost:
        return

    try:
        cum_avg, count = task_avg_time[task.name]
        new_count = count + 1
        new_avg = ((cum_avg * count) + cost) / new_count
        task_avg_time[task.name] = Average(new_avg, new_count)
    except KeyError:
        task_avg_time[task.name] = Average(cost, 1)

    # write to redis: task_avg_time

参考文献：https://stackoverflow.com/a/31731622/2698552

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