美文网首页python交流学习虫虫
Python并发编程之线程池/进程池

Python并发编程之线程池/进程池

作者: 78c40b03ee4e | 来源:发表于2019-03-04 15:45 被阅读32次

    原文来自开源中国

    前言

    python标准库提供线程和多处理模块来编写相应的多线程/多进程代码,但当项目达到一定规模时,频繁地创建/销毁进程或线程是非常消耗资源的,此时我们必须编写自己的线程池/进程池来交换时间空间。但是从Python3.2开始,标准库为我们提供了并发的。Futures模块,它提供两个类:ThreadPool Executor和ProcessPool Executor。它实现线程和多处理的进一步抽象,并为编写线程池/进程池提供直接支持。

    Executor和Future

    concurrent.futures模块的基础是Exectuor,Executor是一个抽象类,它不能被直接使用。但是它提供的两个子类ThreadPoolExecutor和ProcessPoolExecutor却是非常有用,顾名思义两者分别被用来创建线程池和进程池的代码。我们可以将相应的tasks直接放入线程池/进程池,不需要维护Queue来操心死锁的问题,线程池/进程池会自动帮我们调度。

    使用submit来操作线程池/进程池

    我们先通过下面这段代码来了解一下线程池的概念

    # example1.py
    from concurrent.futures import ThreadPoolExecutor
    import time
    def return_future_result(message):
        time.sleep(2)
        return message
    pool = ThreadPoolExecutor(max_workers=2)  # 创建一个最大可容纳2个task的线程池
    future1 = pool.submit(return_future_result, ("hello"))  # 往线程池里面加入一个task
    future2 = pool.submit(return_future_result, ("world"))  # 往线程池里面加入一个task
    print(future1.done())  # 判断task1是否结束
    time.sleep(3)
    print(future2.done())  # 判断task2是否结束
    print(future1.result())  # 查看task1返回的结果
    print(future2.result())  # 查看task2返回的结果
    

    让我们根据操作结果进行分析。我们使用submit方法将任务添加到线程池,submit返回一个将来的对象,这可以简单地理解为将来要完成的操作。在第一份印刷声明中,很明显我们的未来1由于时间的原因没有完成。睡眠(2),因为我们使用时间挂起了主线程。sleep(3),所以到第二个print语句时,线程池中的所有任务都已完成。

    ziwenxie :: ~ » python example1.py
    False
    True
    hello
    world
    # 在上述程序执行的过程中,通过ps命令我们可以看到三个线程同时在后台运行
    ziwenxie :: ~ » ps -eLf | grep python
    ziwenxie      8361  7557  8361  3    3 19:45 pts/0    00:00:00 python example1.py
    ziwenxie      8361  7557  8362  0    3 19:45 pts/0    00:00:00 python example1.py
    ziwenxie      8361  7557  8363  0    3 19:45 pts/0    00:00:00 python example1.py
    

    上面的代码我们也可以改写为进程池形式,api和线程池如出一辙,我就不罗嗦了。

    # example2.py
    from concurrent.futures import ProcessPoolExecutor
    import time
    def return_future_result(message):
        time.sleep(2)
        return message
    pool = ProcessPoolExecutor(max_workers=2)
    future1 = pool.submit(return_future_result, ("hello"))
    future2 = pool.submit(return_future_result, ("world"))
    print(future1.done())
    time.sleep(3)
    print(future2.done())
    print(future1.result())
    print(future2.result())
    

    下面是运行结果

    ziwenxie :: ~ » python example2.py
    False
    True
    hello
    world
    ziwenxie :: ~ » ps -eLf | grep python
    ziwenxie      8560  7557  8560  3    3 19:53 pts/0    00:00:00 python example2.py
    ziwenxie      8560  7557  8563  0    3 19:53 pts/0    00:00:00 python example2.py
    ziwenxie      8560  7557  8564  0    3 19:53 pts/0    00:00:00 python example2.py
    ziwenxie      8561  8560  8561  0    1 19:53 pts/0    00:00:00 python example2.py
    ziwenxie      8562  8560  8562  0    1 19:53 pts/0    00:00:00 python example2.py
    

    使用map/wait来操作线程池/进程池

    除了submit,Exectuor还为我们提供了map方法,和内建的map用法类似,下面我们通过两个例子来比较一下两者的区别。

    使用submit操作回顾

    # example3.py
    import concurrent.futures
    import urllib.request
    URLS = ['http://httpbin.org', 'http://example.com/', 'https://api.github.com/']
    def load_url(url, timeout):
        with urllib.request.urlopen(url, timeout=timeout) as conn:
            return conn.read()
    # We can use a with statement to ensure threads are cleaned up promptly
    with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
        # Start the load operations and mark each future with its URL
        future_to_url = {executor.submit(load_url, url, 60): url for url in URLS}
        for future in concurrent.futures.as_completed(future_to_url):
            url = future_to_url[future]
            try:
                data = future.result()
            except Exception as exc:
                print('%r generated an exception: %s' % (url, exc))
            else:
                print('%r page is %d bytes' % (url, len(data)))
    

    从运行结果可以看出,as_completed不是按照URLS列表元素的顺序返回的。

    ziwenxie :: ~ » python example3.py
    'http://example.com/' page is 1270 byte
    'https://api.github.com/' page is 2039 bytes
    'http://httpbin.org' page is 12150 bytes
    

    使用map

    # example4.py
    import concurrent.futures
    import urllib.request
    URLS = ['http://httpbin.org', 'http://example.com/', 'https://api.github.com/']
    def load_url(url):
        with urllib.request.urlopen(url, timeout=60) as conn:
            return conn.read()
    # We can use a with statement to ensure threads are cleaned up promptly
    with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
        for url, data in zip(URLS, executor.map(load_url, URLS)):
            print('%r page is %d bytes' % (url, len(data)))
    

    从运行结果可以看出,map是按照URLS列表元素的顺序返回的,并且写出的代码更加简洁直观,我们可以根据具体的需求任选一种。

    ziwenxie :: ~ » python example4.py
    'http://httpbin.org' page is 12150 bytes
    'http://example.com/' page is 1270 bytes
    'https://api.github.com/' page is 2039 bytes
    

    第三种选择wait

    wait方法接会返回一个tuple(元组),tuple中包含两个set(集合),一个是completed(已完成的)另外一个是uncompleted(未完成的)。使用wait方法的一个优势就是获得更大的自由度,它接收三个参数FIRST_COMPLETED, FIRST_EXCEPTION 和ALL_COMPLETE,默认设置为ALL_COMPLETED。

    我们通过下面这个例子来看一下三个参数的区别

    from concurrent.futures import ThreadPoolExecutor, wait, as_completed
    from time import sleep
    from random import randint
    def return_after_random_secs(num):
        sleep(randint(1, 5))
        return "Return of {}".format(num)
    pool = ThreadPoolExecutor(5)
    futures = []
    for x in range(5):
        futures.append(pool.submit(return_after_random_secs, x))
    print(wait(futures))
    # print(wait(futures, timeout=None, return_when='FIRST_COMPLETED'))
    

    如果采用默认的ALL_COMPLETED,程序会阻塞直到线程池里面的所有任务都完成。

    ziwenxie :: ~ » python example5.py
    DoneAndNotDoneFutures(done={
    <Future at 0x7f0b06c9bc88 state=finished returned str>,
    <Future at 0x7f0b06cbaa90 state=finished returned str>,
    <Future at 0x7f0b06373898 state=finished returned str>,
    <Future at 0x7f0b06352ba8 state=finished returned str>,
    <Future at 0x7f0b06373b00 state=finished returned str>}, not_done=set())
    

    如果采用FIRST_COMPLETED参数,程序并不会等到线程池里面所有的任务都完成。

    ziwenxie :: ~ » python example5.py
    DoneAndNotDoneFutures(done={
    <Future at 0x7f84109edb00 state=finished returned str>,
    <Future at 0x7f840e2e9320 state=finished returned str>,
    <Future at 0x7f840f25ccc0 state=finished returned str>},
    not_done={<Future at 0x7f840e2e9ba8 state=running>,
    <Future at 0x7f840e2e9940 state=running>})
    

    小编推荐一个学python的学习qun 740,3222,34
    无论你是大牛还是小白,是想转行还是想入行都可以来了解一起进步一起学习!裙内有开发工具,很多干货和技术资料分享!

    相关文章

      网友评论

        本文标题:Python并发编程之线程池/进程池

        本文链接:https://www.haomeiwen.com/subject/wlsiuqtx.html