Python并发编程之线程池/进程池
exoj9984
8年前
<h2>引言</h2> <p>Python标准库为我们提供了threading和multiprocessing模块编写相应的多线程/多进程代码，但是当项目达到一定的规模，频繁创建/销毁进程或者线程是非常消耗资源的，这个时候我们就要编写自己的线程池/进程池，以空间换时间。但从Python3.2开始，标准库为我们提供了 <strong>concurrent.futures</strong> 模块，它提供了ThreadPool Executor 和ProcessPoolExecutor两个类，实现了对threading和multiprocessing的进一步抽象，对编写线程池/进程池提供了直接的支持。</p> <h2>Executor和Future</h2> <p>concurrent.futures模块的基础是 <strong>Exectuor</strong> ，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。</p> <p>Future这个概念相信有java和nodejs下编程经验的朋友肯定不陌生了， <strong>你可以把它理解为一个在未来完成的操作</strong> ，这是异步编程的基础，传统编程模式下比如我们操作queue.get的时候，在等待返回结果之前会产生阻塞，cpu不能让出来做其他事情，而Future的引入帮助我们在等待的这段时间可以完成其他的操作。关于在Python中进行异步IO可以阅读完本文之后参考我的 <a href="/misc/goto?guid=4959734145735582111" rel="nofollow,noindex">Python并发编程之协程/异步IO</a> 。</p> <p>p.s: 如果你依然在坚守Python2.x，请先安装futures模块。</p> <pre> <code class="language-python">pip install futures</code></pre> <h2>使用submit来操作线程池/进程池</h2> <p>我们先通过下面这段代码来了解一下线程池的概念</p> <pre> <code class="language-python"># 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</code></pre> <p>我们根据运行结果来分析一下。我们使用 <em>submit</em> 方法来往线程池中加入一个task，submit返回一个 <em>Future对象</em> ，对于Future对象可以简单地理解为一个在未来完成的操作。在第一个print语句中很明显因为time.sleep(2)的原因我们的future1没有完成，因为我们使用time.sleep(3)暂停了主线程，所以到第二个print语句的时候我们线程池里的任务都已经全部结束。</p> <pre> <code class="language-python">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</code></pre> <p>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。</p> <pre> <code class="language-python"># 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())</code></pre> <p>下面是运行结果</p> <pre> <code class="language-python">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</code></pre> <h2>使用map/wait来操作线程池/进程池</h2> <p>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。</p> <h3>使用submit操作回顾</h3> <pre> <code class="language-python"># 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)))</code></pre> <p>从运行结果可以看出， <strong>as_completed不是按照URLS列表元素的顺序返回的</strong> 。</p> <pre> <code class="language-python">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</code></pre> <h3>使用map</h3> <pre> <code class="language-python"># 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)))</code></pre> <p>从运行结果可以看出， <strong>map是按照URLS列表元素的顺序返回的</strong> ，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。</p> <pre> <code class="language-python">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</code></pre> <h3>第三种选择wait</h3> <p>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。</p> <p>我们通过下面这个例子来看一下三个参数的区别</p> <pre> <code class="language-python">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'))</code></pre> <p>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。</p> <pre> <code class="language-python">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())</code></pre> <p>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。</p> <pre> <code class="language-python">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>})</code></pre> <h2>思考题</h2> <p>写一个小程序对比multiprocessing.pool(ThreadPool)和ProcessPollExecutor(ThreadPoolExecutor)在执行效率上的差距，结合上面提到的Future思考为什么会造成这样的结果。</p> <p> </p> <p>来自：http://developer.51cto.com/art/201701/527525.htm</p> <p> </p>