# `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.