使用 asyncio.Queue 进行生产者-消费者流

问题描述

我对如何将 asyncio.Queue 用于特定的生产者-消费者模式感到困惑，在这种模式中，生产者和消费者同时独立运行.

I'm confused about how to use asyncio.Queue for a particular producer-consumer pattern in which both the producer and consumer operate concurrently and independently.

首先，考虑这个例子，它紧跟 docs forasyncio.Queue:

First, consider this example, which closely follows that from the docs for asyncio.Queue:

import asyncio
import random
import time

async def worker(name, queue):
    while True:
        sleep_for = await queue.get()
        await asyncio.sleep(sleep_for)
        queue.task_done()
        print(f'{name} has slept for {sleep_for:0.2f} seconds')

async def main(n):
    queue = asyncio.Queue()
    total_sleep_time = 0
    for _ in range(20):
        sleep_for = random.uniform(0.05, 1.0)
        total_sleep_time += sleep_for
        queue.put_nowait(sleep_for)
    tasks = []
    for i in range(n):
        task = asyncio.create_task(worker(f'worker-{i}', queue))
        tasks.append(task)
    started_at = time.monotonic()
    await queue.join()
    total_slept_for = time.monotonic() - started_at
    for task in tasks:
        task.cancel()
    # Wait until all worker tasks are cancelled.
    await asyncio.gather(*tasks, return_exceptions=True)
    print('====')
    print(f'3 workers slept in parallel for {total_slept_for:.2f} seconds')
    print(f'total expected sleep time: {total_sleep_time:.2f} seconds')

if __name__ == '__main__':
    import sys
    n = 3 if len(sys.argv) == 1 else sys.argv[1]
    asyncio.run(main())

关于这个脚本有一个更详细的细节:项目被同步放入队列，queue.put_nowait(sleep_for) 超过了传统的 for 循环.

There is one finer detail about this script: the items are put into the queue synchronously, with queue.put_nowait(sleep_for) over a conventional for-loop.

我的目标是创建一个使用 async def worker()(或 consumer())和 async def producer() 的脚本.两者都应安排为同时运行.没有任何消费者协程与生产者明确绑定或链接.

My goal is to create a script that uses async def worker() (or consumer()) and async def producer(). Both should be scheduled to run concurrently. No one consumer coroutine is explicitly tied to or chained from a producer.

如何修改上面的程序，使生产者成为自己的协程，可以与消费者/工人同时调度?

How can I modify the program above so that the producer(s) is its own coroutine that can be scheduled concurrently with the consumers/workers?

推荐答案

这个例子可以在不改变其基本逻辑的情况下进行推广:

The example can be generalized without changing its essential logic:

• 将插入循环移至单独的生产者协程.
• 在后台启动消费者，让他们在生产商品时对其进行处理.
• 在消费者运行的情况下，启动生产者并等待他们完成生产项目，例如 await producer() 或 await gather(*producers) 等.
• 所有生产者都完成后，等待消费者使用 await queue.join() 处理剩余的项目.
• 取消消费者，所有消费者现在都在无所事事地等待队列交付下一个项目，因为我们知道生产者已经完成，下一个项目永远不会到达.

• Move the insertion loop to a separate producer coroutine.
• Start the consumers in the background, letting them process the items as they are produced.
• With the consumers running, start the producers and wait for them to finish producing items, as with await producer() or await gather(*producers), etc.
• Once all producers are done, wait for consumers to process the remaining items with await queue.join().
• Cancel the consumers, all of which are now idly waiting for the queue to deliver the next item, which will never arrive as we know the producers are done.

以下是实现上述内容的示例:

Here is an example implementing the above:

import asyncio, random

async def rnd_sleep(t):
    # sleep for T seconds on average
    await asyncio.sleep(t * random.random() * 2)

async def producer(queue):
    while True:
        # produce a token and send it to a consumer
        token = random.random()
        print(f'produced {token}')
        if token < .05:
            break
        await queue.put(token)
        await rnd_sleep(.1)

async def consumer(queue):
    while True:
        token = await queue.get()
        # process the token received from a producer
        await rnd_sleep(.3)
        queue.task_done()
        print(f'consumed {token}')

async def main():
    queue = asyncio.Queue()

    # fire up the both producers and consumers
    producers = [asyncio.create_task(producer(queue))
                 for _ in range(3)]
    consumers = [asyncio.create_task(consumer(queue))
                 for _ in range(10)]

    # with both producers and consumers running, wait for
    # the producers to finish
    await asyncio.gather(*producers)
    print('---- done producing')

    # wait for the remaining tasks to be processed
    await queue.join()

    # cancel the consumers, which are now idle
    for c in consumers:
        c.cancel()

asyncio.run(main())

注意，在现实生活中的生产者和消费者中，尤其是那些涉及到网络访问的，你可能想捕捉处理过程中发生的与IO相关的异常.如果异常是可恢复的，就像大多数与网络相关的异常一样，您可以简单地捕获异常并记录错误.您仍应调用 task_done()，否则 queue.join() 将因未处理的项目而挂起.如果重新尝试处理该项目有意义，您可以在调用 task_done() 之前将其返回到队列中.例如:

Note that in real-life producers and consumers, especially those that involve network access, you probably want to catch IO-related exceptions that occur during processing. If the exception is recoverable, as most network-related exceptions are, you can simply catch the exception and log the error. You should still invoke task_done() because otherwise queue.join() will hang due to an unprocessed item. If it makes sense to re-try processing the item, you can return it into the queue prior to calling task_done(). For example:

# like the above, but handling exceptions during processing:
async def consumer(queue):
    while True:
        token = await queue.get()
        try:
            # this uses aiohttp or whatever
            await process(token)
        except aiohttp.ClientError as e:
            print(f"Error processing token {token}: {e}")
            # If it makes sense, return the token to the queue to be
            # processed again. (You can use a counter to avoid
            # processing a faulty token infinitely.)
            #await queue.put(token)
        queue.task_done()
        print(f'consumed {token}')

这篇关于使用 asyncio.Queue 进行生产者-消费者流的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！