Python并发编程之线程池/进程池

   <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 concurrent.futures 模块，它提供了ThreadPoolExecutor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 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。</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， 你可以把它理解为一个在未来完成的操作 ，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。</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">pipinstallfutures  </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.futuresimport 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 :: ~ » pythonexample1.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 | greppython  ziwenxie      8361  7557  8361  3    3 19:45 pts/0    00:00:00 pythonexample1.py  ziwenxie      8361  7557  8362  0    3 19:45 pts/0    00:00:00 pythonexample1.py  ziwenxie      8361  7557  8363  0    3 19:45 pts/0    00:00:00 pythonexample1.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.futuresimport 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 :: ~ » pythonexample2.py  False  True  hello  world  ziwenxie :: ~ » ps -eLf | greppython  ziwenxie      8560  7557  8560  3    3 19:53 pts/0    00:00:00 pythonexample2.py  ziwenxie      8560  7557  8563  0    3 19:53 pts/0    00:00:00 pythonexample2.py  ziwenxie      8560  7557  8564  0    3 19:53 pts/0    00:00:00 pythonexample2.py  ziwenxie      8561  8560  8561  0    1 19:53 pts/0    00:00:00 pythonexample2.py  ziwenxie      8562  8560  8562  0    1 19:53 pts/0    00:00:00 pythonexample2.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): urlfor urlin URLS}      for futurein 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 :: ~ » pythonexample3.py  'http://example.com/' pageis 1270 byte  'https://api.github.com/' pageis 2039 bytes  'http://httpbin.org' pageis 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, datain 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 :: ~ » pythonexample4.py  'http://httpbin.org' pageis 12150 bytes  'http://example.com/' pageis 1270 bytes  'https://api.github.com/' pageis 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.futuresimport 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 :: ~ » pythonexample5.py  DoneAndNotDoneFutures(done={  <Futureat 0x7f0b06c9bc88 state=finishedreturnedstr>,  <Futureat 0x7f0b06cbaa90 state=finishedreturnedstr>,  <Futureat 0x7f0b06373898 state=finishedreturnedstr>,  <Futureat 0x7f0b06352ba8 state=finishedreturnedstr>,  <Futureat 0x7f0b06373b00 state=finishedreturnedstr>}, 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 :: ~ » pythonexample5.py  DoneAndNotDoneFutures(done={  <Futureat 0x7f84109edb00 state=finishedreturnedstr>,  <Futureat 0x7f840e2e9320 state=finishedreturnedstr>,  <Futureat 0x7f840f25ccc0 state=finishedreturnedstr>},  not_done={<Futureat 0x7f840e2e9ba8 state=running>,  <Futureat 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://python.jobbole.com/87272/</p>    <p> </p>