"concurrent.futures" --- Launching parallel tasks
*************************************************

3.2 版新加入.

**Source code:** Lib/concurrent/futures/thread.py and
Lib/concurrent/futures/process.py

======================================================================

The "concurrent.futures" module provides a high-level interface for
asynchronously executing callables.

The asynchronous execution can be performed with threads, using
"ThreadPoolExecutor", or separate processes, using
"ProcessPoolExecutor".  Both implement the same interface, which is
defined by the abstract "Executor" class.


Executor Objects
================

class concurrent.futures.Executor

   An abstract class that provides methods to execute calls
   asynchronously.  It should not be used directly, but through its
   concrete subclasses.

      submit(fn, *args, **kwargs)

         Schedules the callable, *fn*, to be executed as "fn(*args
         **kwargs)" and returns a "Future" object representing the
         execution of the callable.

            with ThreadPoolExecutor(max_workers=1) as executor:
                future = executor.submit(pow, 323, 1235)
                print(future.result())

      map(func, *iterables, timeout=None, chunksize=1)

         Similar to "map(func, *iterables)" except:

         * the *iterables* are collected immediately rather than
           lazily;

         * *func* is executed asynchronously and several calls to
           *func* may be made concurrently.

         The returned iterator raises a
         "concurrent.futures.TimeoutError" if "__next__()" is called
         and the result isn't available after *timeout* seconds from
         the original call to "Executor.map()". *timeout* can be an
         int or a float.  If *timeout* is not specified or "None",
         there is no limit to the wait time.

         If a *func* call raises an exception, then that exception
         will be raised when its value is retrieved from the iterator.

         When using "ProcessPoolExecutor", this method chops
         *iterables* into a number of chunks which it submits to the
         pool as separate tasks.  The (approximate) size of these
         chunks can be specified by setting *chunksize* to a positive
         integer.  For very long iterables, using a large value for
         *chunksize* can significantly improve performance compared to
         the default size of 1.  With "ThreadPoolExecutor",
         *chunksize* has no effect.

         3.5 版更變: Added the *chunksize* argument.

      shutdown(wait=True)

         Signal the executor that it should free any resources that it
         is using when the currently pending futures are done
         executing.  Calls to "Executor.submit()" and "Executor.map()"
         made after shutdown will raise "RuntimeError".

         If *wait* is "True" then this method will not return until
         all the pending futures are done executing and the resources
         associated with the executor have been freed.  If *wait* is
         "False" then this method will return immediately and the
         resources associated with the executor will be freed when all
         pending futures are done executing.  Regardless of the value
         of *wait*, the entire Python program will not exit until all
         pending futures are done executing.

         You can avoid having to call this method explicitly if you
         use the "with" statement, which will shutdown the "Executor"
         (waiting as if "Executor.shutdown()" were called with *wait*
         set to "True"):

            import shutil
            with ThreadPoolExecutor(max_workers=4) as e:
                e.submit(shutil.copy, 'src1.txt', 'dest1.txt')
                e.submit(shutil.copy, 'src2.txt', 'dest2.txt')
                e.submit(shutil.copy, 'src3.txt', 'dest3.txt')
                e.submit(shutil.copy, 'src4.txt', 'dest4.txt')


ThreadPoolExecutor
==================

"ThreadPoolExecutor" is an "Executor" subclass that uses a pool of
threads to execute calls asynchronously.

Deadlocks can occur when the callable associated with a "Future" waits
on the results of another "Future".  For example:

   import time
   def wait_on_b():
       time.sleep(5)
       print(b.result())  # b will never complete because it is waiting on a.
       return 5

   def wait_on_a():
       time.sleep(5)
       print(a.result())  # a will never complete because it is waiting on b.
       return 6


   executor = ThreadPoolExecutor(max_workers=2)
   a = executor.submit(wait_on_b)
   b = executor.submit(wait_on_a)

And:

   def wait_on_future():
       f = executor.submit(pow, 5, 2)
       # This will never complete because there is only one worker thread and
       # it is executing this function.
       print(f.result())

   executor = ThreadPoolExecutor(max_workers=1)
   executor.submit(wait_on_future)

class concurrent.futures.ThreadPoolExecutor(max_workers=None, thread_name_prefix='', initializer=None, initargs=())

   An "Executor" subclass that uses a pool of at most *max_workers*
   threads to execute calls asynchronously.

   *initializer* 是在每个工作者线程开始处调用的一个可选可调用对象。
   *initargs* 是传递给初始化器的元组参数。任何向池提交更多工作的尝试，
   *initializer* 都将引发一个异常，当前所有等待的工作都会引发一个
   "BrokenThreadPool"。

   3.5 版更變: If *max_workers* is "None" or not given, it will
   default to the number of processors on the machine, multiplied by
   "5", assuming that "ThreadPoolExecutor" is often used to overlap
   I/O instead of CPU work and the number of workers should be higher
   than the number of workers for "ProcessPoolExecutor".

   3.6 版新加入: 添加 *thread_name_prefix* 参数允许用户控制由线程池创
   建的 "threading.Thread" 工作线程名称以方便调试。

   3.7 版更變: Added the *initializer* and *initargs* arguments.

   3.8 版更變: Default value of *max_workers* is changed to "min(32,
   os.cpu_count() + 4)". This default value preserves at least 5
   workers for I/O bound tasks. It utilizes at most 32 CPU cores for
   CPU bound tasks which release the GIL. And it avoids using very
   large resources implicitly on many-core machines.ThreadPoolExecutor
   now reuses idle worker threads before starting *max_workers* worker
   threads too.


ThreadPoolExecutor Example
--------------------------

   import concurrent.futures
   import urllib.request

   URLS = ['http://www.foxnews.com/',
           'http://www.cnn.com/',
           'http://europe.wsj.com/',
           'http://www.bbc.co.uk/',
           'http://some-made-up-domain.com/']

   # Retrieve a single page and report the URL and contents
   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=5) 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)))


ProcessPoolExecutor
===================

The "ProcessPoolExecutor" class is an "Executor" subclass that uses a
pool of processes to execute calls asynchronously.
"ProcessPoolExecutor" uses the "multiprocessing" module, which allows
it to side-step the *Global Interpreter Lock* but also means that only
picklable objects can be executed and returned.

The "__main__" module must be importable by worker subprocesses. This
means that "ProcessPoolExecutor" will not work in the interactive
interpreter.

Calling "Executor" or "Future" methods from a callable submitted to a
"ProcessPoolExecutor" will result in deadlock.

class concurrent.futures.ProcessPoolExecutor(max_workers=None, mp_context=None, initializer=None, initargs=())

   异步执行调用的 "Executor" 子类使用一个最多有 *max_workers* 个进程的
   进程池。 如果 *max_workers* 为 "None" 或未给出，它将默认为机器的处
   理器个数。 如果 *max_workers* 小于等于 "0"，则将引发 "ValueError"。
   在 Windows 上，*max_workers* 必须小于等于 "61"，否则将引发
   "ValueError"。 如果 *max_workers* 为 "None"，则所选择的默认最多为
   "61"，即使存在更多处理器。 *mp_context* 可以是一个多进程上下文或是
   None。 它将被用来启动工作者。 如果 *mp_context* 为 "None" 或未给出
   ，将使用默认的多进程上下文。

   *initializer* is an optional callable that is called at the start
   of each worker process; *initargs* is a tuple of arguments passed
   to the initializer.  Should *initializer* raise an exception, all
   currently pending jobs will raise a "BrokenProcessPool", as well
   any attempt to submit more jobs to the pool.

   3.3 版更變: When one of the worker processes terminates abruptly, a
   "BrokenProcessPool" error is now raised.  Previously, behaviour was
   undefined but operations on the executor or its futures would often
   freeze or deadlock.

   3.7 版更變: The *mp_context* argument was added to allow users to
   control the start_method for worker processes created by the
   pool.Added the *initializer* and *initargs* arguments.


ProcessPoolExecutor Example
---------------------------

   import concurrent.futures
   import math

   PRIMES = [
       112272535095293,
       112582705942171,
       112272535095293,
       115280095190773,
       115797848077099,
       1099726899285419]

   def is_prime(n):
       if n < 2:
           return False
       if n == 2:
           return True
       if n % 2 == 0:
           return False

       sqrt_n = int(math.floor(math.sqrt(n)))
       for i in range(3, sqrt_n + 1, 2):
           if n % i == 0:
               return False
       return True

   def main():
       with concurrent.futures.ProcessPoolExecutor() as executor:
           for number, prime in zip(PRIMES, executor.map(is_prime, PRIMES)):
               print('%d is prime: %s' % (number, prime))

   if __name__ == '__main__':
       main()


Future Objects
==============

The "Future" class encapsulates the asynchronous execution of a
callable. "Future" instances are created by "Executor.submit()".

class concurrent.futures.Future

   Encapsulates the asynchronous execution of a callable.  "Future"
   instances are created by "Executor.submit()" and should not be
   created directly except for testing.

      cancel()

         尝试取消调用。 如果调用正在执行或已结束运行不能被取消则该方法
         将返回 "False"，否则调用会被取消并且该方法将返回 "True"。

      cancelled()

         Return "True" if the call was successfully cancelled.

      running()

         Return "True" if the call is currently being executed and
         cannot be cancelled.

      done()

         Return "True" if the call was successfully cancelled or
         finished running.

      result(timeout=None)

         Return the value returned by the call. If the call hasn't yet
         completed then this method will wait up to *timeout* seconds.
         If the call hasn't completed in *timeout* seconds, then a
         "concurrent.futures.TimeoutError" will be raised. *timeout*
         can be an int or float.  If *timeout* is not specified or
         "None", there is no limit to the wait time.

         If the future is cancelled before completing then
         "CancelledError" will be raised.

         If the call raised, this method will raise the same
         exception.

      exception(timeout=None)

         Return the exception raised by the call.  If the call hasn't
         yet completed then this method will wait up to *timeout*
         seconds.  If the call hasn't completed in *timeout* seconds,
         then a "concurrent.futures.TimeoutError" will be raised.
         *timeout* can be an int or float.  If *timeout* is not
         specified or "None", there is no limit to the wait time.

         If the future is cancelled before completing then
         "CancelledError" will be raised.

         If the call completed without raising, "None" is returned.

      add_done_callback(fn)

         Attaches the callable *fn* to the future.  *fn* will be
         called, with the future as its only argument, when the future
         is cancelled or finishes running.

         Added callables are called in the order that they were added
         and are always called in a thread belonging to the process
         that added them.  If the callable raises an "Exception"
         subclass, it will be logged and ignored.  If the callable
         raises a "BaseException" subclass, the behavior is undefined.

         If the future has already completed or been cancelled, *fn*
         will be called immediately.

   The following "Future" methods are meant for use in unit tests and
   "Executor" implementations.

      set_running_or_notify_cancel()

         This method should only be called by "Executor"
         implementations before executing the work associated with the
         "Future" and by unit tests.

         If the method returns "False" then the "Future" was
         cancelled, i.e. "Future.cancel()" was called and returned
         *True*.  Any threads waiting on the "Future" completing (i.e.
         through "as_completed()" or "wait()") will be woken up.

         If the method returns "True" then the "Future" was not
         cancelled and has been put in the running state, i.e. calls
         to "Future.running()" will return *True*.

         This method can only be called once and cannot be called
         after "Future.set_result()" or "Future.set_exception()" have
         been called.

      set_result(result)

         Sets the result of the work associated with the "Future" to
         *result*.

         This method should only be used by "Executor" implementations
         and unit tests.

         3.8 版更變: This method raises
         "concurrent.futures.InvalidStateError" if the "Future" is
         already done.

      set_exception(exception)

         Sets the result of the work associated with the "Future" to
         the "Exception" *exception*.

         This method should only be used by "Executor" implementations
         and unit tests.

         3.8 版更變: This method raises
         "concurrent.futures.InvalidStateError" if the "Future" is
         already done.


Module Functions
================

concurrent.futures.wait(fs, timeout=None, return_when=ALL_COMPLETED)

   等待 *fs* 指定的 "Future" 实例（可能由不同的 "Executor" 实例创建）
   完成。 返回一个由集合构成的具名  2 元组。 第一个集合名称为 "done"，
   包含在等待完成之前已完成的期程（包括正常结束或被取消的期程）。 第二
   个集合名称为 "not_done"，包含未完成的期程（包括挂起的或正在运行的期
   程）。

   *timeout* can be used to control the maximum number of seconds to
   wait before returning.  *timeout* can be an int or float.  If
   *timeout* is not specified or "None", there is no limit to the wait
   time.

   *return_when* indicates when this function should return.  It must
   be one of the following constants:

   +-------------------------------+------------------------------------------+
   | Constant                      | 描述                                     |
   |===============================|==========================================|
   | "FIRST_COMPLETED"             | The function will return when any future |
   |                               | finishes or is cancelled.                |
   +-------------------------------+------------------------------------------+
   | "FIRST_EXCEPTION"             | The function will return when any future |
   |                               | finishes by raising an exception.  If no |
   |                               | future raises an exception then it is    |
   |                               | equivalent to "ALL_COMPLETED".           |
   +-------------------------------+------------------------------------------+
   | "ALL_COMPLETED"               | The function will return when all        |
   |                               | futures finish or are cancelled.         |
   +-------------------------------+------------------------------------------+

concurrent.futures.as_completed(fs, timeout=None)

   返回一个包含 *fs* 所指定的 "Future" 实例（可能由不同的 "Executor"
   实例创建）的迭代器，这些实例会在完成时生成期程（包括正常结束或被取
   消的期程）。 任何由 *fs* 所指定的重复期程将只被返回一次。 任何在
   "as_completed()" 被调用之前完成的期程将优先被生成。 如果
   "__next__()" 被调用并且在对 "as_completed()" 的原始调用 *timeout*
   秒之后结果仍不可用，则返回的迭代器将引发
   "concurrent.futures.TimeoutError"。 *timeout* 可以为整数或浮点数。
   如果 *timeout* 未指定或为 "None"，则不限制等待时间。

也參考:

  **PEP 3148** -- futures - execute computations asynchronously
     The proposal which described this feature for inclusion in the
     Python standard library.


Exception classes
=================

exception concurrent.futures.CancelledError

   Raised when a future is cancelled.

exception concurrent.futures.TimeoutError

   Raised when a future operation exceeds the given timeout.

exception concurrent.futures.BrokenExecutor

   Derived from "RuntimeError", this exception class is raised when an
   executor is broken for some reason, and cannot be used to submit or
   execute new tasks.

   3.7 版新加入.

exception concurrent.futures.InvalidStateError

   Raised when an operation is performed on a future that is not
   allowed in the current state.

   3.8 版新加入.

exception concurrent.futures.thread.BrokenThreadPool

   Derived from "BrokenExecutor", this exception class is raised when
   one of the workers of a "ThreadPoolExecutor" has failed
   initializing.

   3.7 版新加入.

exception concurrent.futures.process.BrokenProcessPool

   Derived from "BrokenExecutor" (formerly "RuntimeError"), this
   exception class is raised when one of the workers of a
   "ProcessPoolExecutor" has terminated in a non-clean fashion (for
   example, if it was killed from the outside).

   3.3 版新加入.
