Question

我有一个脚本可以生成数百万个Celery任务，数据库中每行一个。有没有办法限制它，以便它不会完全淹没芹菜？

理想情况下，我想让Celery忙碌，但我不希望Celery队列的长度超过几十个任务，因为这只是浪费内存（特别是因为没有某种节流，脚本会增加数百万任务到队列几乎立即）。

Answer 1

过去几天我花了一些时间研究这个问题，然后想出了我称之为CeleryThrottle的对象。基本上，你告诉它你想要在队列中有多少项目，并且它尽力保持队列在那个大小和2倍大小之间。

所以这里是代码（假设Redis代理，但很容易改变）：

# coding=utf-8
from collections import deque

import time

import redis
from django.conf import settings
from django.utils.timezone import now


def get_queue_length(queue_name='celery'):
    """Get the number of tasks in a celery queue.

    :param queue_name: The name of the queue you want to inspect.
    :return: the number of items in the queue.
    """
    r = redis.StrictRedis(
        host=settings.REDIS_HOST,
        port=settings.REDIS_PORT,
        db=settings.REDIS_DATABASES['CELERY'],
    )
    return r.llen(queue_name)


class CeleryThrottle(object):
    """A class for throttling celery."""

    def __init__(self, min_items=100, queue_name='celery'):
        """Create a throttle to prevent celery run aways.

        :param min_items: The minimum number of items that should be enqueued. 
        A maximum of 2× this number may be created. This minimum value is not 
        guaranteed and so a number slightly higher than your max concurrency 
        should be used. Note that this number includes all tasks unless you use
        a specific queue for your processing.
        """
        self.min = min_items
        self.max = self.min * 2

        # Variables used to track the queue and wait-rate
        self.last_processed_count = 0
        self.count_to_do = self.max
        self.last_measurement = None
        self.first_run = True

        # Use a fixed-length queue to hold last N rates
        self.rates = deque(maxlen=15)
        self.avg_rate = self._calculate_avg()

        # For inspections
        self.queue_name = queue_name

    def _calculate_avg(self):
        return float(sum(self.rates)) / (len(self.rates) or 1)

    def _add_latest_rate(self):
        """Calculate the rate that the queue is processing items."""
        right_now = now()
        elapsed_seconds = (right_now - self.last_measurement).total_seconds()
        self.rates.append(self.last_processed_count / elapsed_seconds)
        self.last_measurement = right_now
        self.last_processed_count = 0
        self.avg_rate = self._calculate_avg()

    def maybe_wait(self):
        """Stall the calling function or let it proceed, depending on the queue.

        The idea here is to check the length of the queue as infrequently as 
        possible while keeping the number of items in the queue as closely 
        between self.min and self.max as possible.

        We do this by immediately enqueueing self.max items. After that, we 
        monitor the queue to determine how quickly it is processing items. Using 
        that rate we wait an appropriate amount of time or immediately press on.
        """
        self.last_processed_count += 1
        if self.count_to_do > 0:
            # Do not wait. Allow process to continue.
            if self.first_run:
                self.first_run = False
                self.last_measurement = now()
            self.count_to_do -= 1
            return

        self._add_latest_rate()
        task_count = get_queue_length(self.queue_name)
        if task_count > self.min:
            # Estimate how long the surplus will take to complete and wait that
            # long + 5% to ensure we're below self.min on next iteration.
            surplus_task_count = task_count - self.min
            wait_time = (surplus_task_count / self.avg_rate) * 1.05
            time.sleep(wait_time)

            # Assume we're below self.min due to waiting; max out the queue.
            if task_count < self.max:
                self.count_to_do = self.max - self.min
            return

        elif task_count <= self.min:
            # Add more items.
            self.count_to_do = self.max - task_count
            return

用法如下：

throttle = CeleryThrottle()
for item in really_big_list_of_items:
    throttle.maybe_wait()
    my_task.delay(item)

非常简单，希望非常灵活。有了这个，代码将监视您的队列，并在队列变得太长时添加等待循环。如果有更新，则在our github repo。

当它这样做时，它将跟踪任务的滚动平均速度，并且将尝试不比所需更频繁地检查队列长度。例如，如果任务每次运行需要两分钟，在将100个项目放入队列之后，它可能需要等待一段时间才能再次检查队列长度。这个脚本的一个更简单的版本可以在每次循环时检查队列长度，但这会增加不必要的延迟。这个版本试图以有时候错误为代价（在这种情况下队列低于min_items）。

如何限制创建芹菜任务的脚本比使用它们更快？

1 个答案: