`concurrent.futures` --- 並列タスク実行¶

Added in version 3.2.

ソースコード: Lib/concurrent/futures/thread.py および Lib/concurrent/futures/process.py

concurrent.futures モジュールは、非同期に実行できる呼び出し可能オブジェクトの高水準のインターフェースを提供します。

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

Availability: not WASI.

このモジュールは WebAssembly では動作しないか、利用不可です。詳しくは、WebAssembly プラットフォームを見てください。

Executor オブジェクト¶

class concurrent.futures.Executor¶

非同期呼び出しを実行するためのメソッドを提供する抽象クラスです。このクラスを直接使ってはならず、具象サブクラスを介して使います。

submit(fn, /, *args, **kwargs)¶

呼び出し可能オブジェクト fn を、 fn(*args, **kwargs) として実行するようにスケジュールし、呼び出し可能オブジェクトの実行を表現する Future オブジェクトを返します。

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

map(fn, *iterables, timeout=None, chunksize=1)¶

Similar to map(fn, *iterables) except:

the iterables are collected immediately rather than lazily;
fn is executed asynchronously and several calls to fn may be made concurrently.

もし __next__() が呼ばれその結果が元々の Executor.map() の呼び出しから timeout 秒経った後も利用できない場合、返されるイテレータは TimeoutError を送出します。timeout は整数または浮動小数点数です。もし timeout が指定されないかの場合、待ち時間に制限はありません。

If a fn 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 and InterpreterPoolExecutor, chunksize has no effect.

バージョン 3.5 で変更: chunksize 引数が追加されました。

shutdown(wait=True, *, cancel_futures=False)¶

executor に対して、現在保留中のフューチャーが実行された後で、使用中のすべての資源を解放するように伝えます。シャットダウンにより後に Executor.submit() と Executor.map() を呼び出すと RuntimeError が送出されます。

wait が True の場合、すべての未完了のフューチャの実行が完了して Executor に関連付けられたリソースが解放されるまで、このメソッドは返りません。 wait が False の場合、このメソッドはすぐに返り、すべての未完了のフューチャの実行が完了したときに、 Executor に関連付けられたリソースが解放されます。 wait の値に関係なく、すべての未完了のフューチャの実行が完了するまで Python プログラム全体は終了しません。

If cancel_futures is True, this method will cancel all pending futures that the executor has not started running. Any futures that are completed or running won't be cancelled, regardless of the value of cancel_futures.

If both cancel_futures and wait are True, all futures that the executor has started running will be completed prior to this method returning. The remaining futures are cancelled.

with 文を使用することで、このメソッドを明示的に呼ばないようにできます。 with 文は Executor をシャットダウンします (wait を True にセットして Executor.shutdown() が呼ばれたかのように待ちます)。

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')

バージョン 3.9 で変更: cancel_futures が追加されました。

ThreadPoolExecutor¶

ThreadPoolExecutor はスレッドのプールを使用して非同期に呼び出しを行う、 Executor のサブクラスです。

Future に関連づけられた呼び出し可能オブジェクトが、別の Future の結果を待つ時にデッドロックすることがあります。例:

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)

以下でも同様です:

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=())¶

最大で max_workers 個のスレッドを非同期実行に使う Executor のサブクラスです。

All threads enqueued to ThreadPoolExecutor will be joined before the interpreter can exit. Note that the exit handler which does this is executed before any exit handlers added using atexit. This means exceptions in the main thread must be caught and handled in order to signal threads to exit gracefully. For this reason, it is recommended that ThreadPoolExecutor not be used for long-running tasks.

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

バージョン 3.5 で変更: max_workers が None か指定されない場合のデフォルト値はマシンのプロセッサの数に 5 を掛けたものになります。これは、 ThreadPoolExecutor は CPU の処理ではなく I/O をオーバーラップするのによく使用されるため、 ProcessPoolExecutor のワーカーの数よりもこのワーカーの数を増やすべきであるという想定に基づいています。

バージョン 3.6 で変更: Added the thread_name_prefix parameter to allow users to control the threading.Thread names for worker threads created by the pool for easier debugging.

バージョン 3.7 で変更: initializer と initargs 引数が追加されました。

バージョン 3.8 で変更: 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.

バージョン 3.13 で変更: Default value of max_workers is changed to min(32, (os.process_cpu_count() or 1) + 4).

ThreadPoolExecutor の例¶

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://nonexistent-subdomain.python.org/']

# 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)))

InterpreterPoolExecutor¶

The InterpreterPoolExecutor class uses a pool of interpreters to execute calls asynchronously. It is a ThreadPoolExecutor subclass, which means each worker is running in its own thread. The difference here is that each worker has its own interpreter, and runs each task using that interpreter.

The biggest benefit to using interpreters instead of only threads is true multi-core parallelism. Each interpreter has its own Global Interpreter Lock, so code running in one interpreter can run on one CPU core, while code in another interpreter runs unblocked on a different core.

The tradeoff is that writing concurrent code for use with multiple interpreters can take extra effort. However, this is because it forces you to be deliberate about how and when interpreters interact, and to be explicit about what data is shared between interpreters. This results in several benefits that help balance the extra effort, including true multi-core parallelism, For example, code written this way can make it easier to reason about concurrency. Another major benefit is that you don't have to deal with several of the big pain points of using threads, like race conditions.

Each worker's interpreter is isolated from all the other interpreters. "Isolated" means each interpreter has its own runtime state and operates completely independently. For example, if you redirect sys.stdout in one interpreter, it will not be automatically redirected any other interpreter. If you import a module in one interpreter, it is not automatically imported in any other. You would need to import the module separately in interpreter where you need it. In fact, each module imported in an interpreter is a completely separate object from the same module in a different interpreter, including sys, builtins, and even __main__.

Isolation means a mutable object, or other data, cannot be used by more than one interpreter at the same time. That effectively means interpreters cannot actually share such objects or data. Instead, each interpreter must have its own copy, and you will have to synchronize any changes between the copies manually. Immutable objects and data, like the builtin singletons, strings, and tuples of immutable objects, don't have these limitations.

Communicating and synchronizing between interpreters is most effectively done using dedicated tools, like those proposed in PEP 734. One less efficient alternative is to serialize with pickle and then send the bytes over a shared socket or pipe.

class concurrent.futures.InterpreterPoolExecutor(max_workers=None, thread_name_prefix='', initializer=None, initargs=(), shared=None)¶

A ThreadPoolExecutor subclass that executes calls asynchronously using a pool of at most max_workers threads. Each thread runs tasks in its own interpreter. The worker interpreters are isolated from each other, which means each has its own runtime state and that they can't share any mutable objects or other data. Each interpreter has its own Global Interpreter Lock, which means code run with this executor has true multi-core parallelism.

The optional initializer and initargs arguments have the same meaning as for ThreadPoolExecutor: the initializer is run when each worker is created, though in this case it is run.in the worker's interpreter. The executor serializes the initializer and initargs using pickle when sending them to the worker's interpreter.

注釈

Functions defined in the __main__ module cannot be pickled and thus cannot be used.

注釈

The executor may replace uncaught exceptions from initializer with ExecutionFailed.

The optional shared argument is a dict of objects that all interpreters in the pool share. The shared items are added to each interpreter's __main__ module. Not all objects are shareable. Shareable objects include the builtin singletons, str and bytes, and memoryview. See PEP 734 for more info.

Other caveats from parent ThreadPoolExecutor apply here.

submit() and map() work like normal, except the worker serializes the callable and arguments using pickle when sending them to its interpreter. The worker likewise serializes the return value when sending it back.

注釈

Functions defined in the __main__ module cannot be pickled and thus cannot be used.

When a worker's current task raises an uncaught exception, the worker always tries to preserve the exception as-is. If that is successful then it also sets the __cause__ to a corresponding ExecutionFailed instance, which contains a summary of the original exception. In the uncommon case that the worker is not able to preserve the original as-is then it directly preserves the corresponding ExecutionFailed instance instead.

ProcessPoolExecutor¶

ProcessPoolExecutor はプロセスプールを使って非同期呼び出しを実施する Executor のサブクラスです。ProcessPoolExecutor は multiprocessing モジュールを利用します。このため Global Interpreter Lock を回避することができますが、pickle 化できるオブジェクトしか実行したり返したりすることができません。

__main__ モジュールはワーカサブプロセスでインポート可能でなければなりません。すなわち、 ProcessPoolExecutor は対話的インタープリタでは動きません。

ProcessPoolExecutor に渡された呼び出し可能オブジェクトから Executor や Future メソッドを呼ぶとデッドロックに陥ります。

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

An Executor subclass that executes calls asynchronously using a pool of at most max_workers processes. If max_workers is None or not given, it will default to os.process_cpu_count(). If max_workers is less than or equal to 0, then a ValueError will be raised. On Windows, max_workers must be less than or equal to 61. If it is not then ValueError will be raised. If max_workers is None, then the default chosen will be at most 61, even if more processors are available. mp_context can be a multiprocessing context or None. It will be used to launch the workers. If mp_context is None or not given, the default multiprocessing context is used. See コンテキストと開始方式.

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 as any attempt to submit more jobs to the pool.

max_tasks_per_child is an optional argument that specifies the maximum number of tasks a single process can execute before it will exit and be replaced with a fresh worker process. By default max_tasks_per_child is None which means worker processes will live as long as the pool. When a max is specified, the "spawn" multiprocessing start method will be used by default in absence of a mp_context parameter. This feature is incompatible with the "fork" start method.

バージョン 3.3 で変更: 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.

バージョン 3.7 で変更: The mp_context argument was added to allow users to control the start_method for worker processes created by the pool.

initializer と initargs 引数が追加されました。

バージョン 3.11 で変更: The max_tasks_per_child argument was added to allow users to control the lifetime of workers in the pool.

バージョン 3.12 で変更: On POSIX systems, if your application has multiple threads and the multiprocessing context uses the "fork" start method: The os.fork() function called internally to spawn workers may raise a DeprecationWarning. Pass a mp_context configured to use a different start method. See the os.fork() documentation for further explanation.

バージョン 3.13 で変更: max_workers uses os.process_cpu_count() by default, instead of os.cpu_count().

バージョン 3.14 で変更: The default process start method (see コンテキストと開始方式) changed away from fork. If you require the fork start method for ProcessPoolExecutor you must explicitly pass mp_context=multiprocessing.get_context("fork").

ProcessPoolExecutor の例¶

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 オブジェクト¶

Future クラスは呼び出し可能オブジェクトの非同期実行をカプセル化します。 Future のインスタンスは Executor.submit() によって生成されます。

class concurrent.futures.Future¶

呼び出し可能オブジェクトの非同期実行をカプセル化します。 Future インスタンスは Executor.submit() で生成され、テストを除いて直接生成すべきではありません。

cancel()¶: Attempt to cancel the call. If the call is currently being executed or finished running and cannot be cancelled then the method will return False, otherwise the call will be cancelled and the method will return True.

cancelled()¶: 呼び出しが正常にキャンセルされた場合 True を返します。

running()¶: 現在呼び出しが実行中でキャンセルできない場合 True を返します。

done()¶: 呼び出しが正常にキャンセルされたか終了した場合 True を返します。

result(timeout=None)¶

呼び出しによって返された値を返します。もし呼び出しがまだ完了していなければ、このメソッドは timeout 秒の間、待機します。もし呼び出しが timeout 秒間の間に完了しなければ、 TimeoutError が送出されます。 timeout はintかfloatを指定できます。もし timeout が指定されていないか、 None であれば、待機時間に制限はありません。

future が完了する前にキャンセルされた場合 CancelledError が送出されます。

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

exception(timeout=None)¶

呼び出しによって送出された例外を返します。もし呼び出しがまだ完了されていなければ、このメソッドは timeout 秒だけ待機します。もし呼び出しが timeout 秒の間に完了しなければ、 TimeoutError が送出されます。 timeout にはintかfloatを指定できます。 timeout が指定されていないか、 None であれば、待機時間に制限はありません。

future が完了する前にキャンセルされた場合 CancelledError が送出されます。

呼び出しが例外を送出することなく完了した場合、None を返します。

add_done_callback(fn)¶

呼び出し可能な fn オブジェクトを future にアタッチします。futureがキャンセルされたか、実行を終了した際に、future をそのただ一つの引数として fn が呼び出されます。

追加された呼び出し可能オブジェクトは、追加された順番で呼びだされ、追加を行ったプロセスに属するスレッド中で呼び出されます。もし呼び出し可能オブジェクトが Exception のサブクラスを送出した場合、それはログに記録され無視されます。呼び出し可能オブジェクトが BaseException のサブクラスを送出した場合の動作は未定義です。

もしfutureがすでに完了しているか、キャンセル済みであれば、fn は即座に実行されます。

以下の Future メソッドは、ユニットテストでの使用と Executor を実装することを意図しています。

set_running_or_notify_cancel()¶

このメソッドは、Future に関連付けられたワークやユニットテストによるワークの実行前に、 Executor の実装によってのみ呼び出してください。

このメソッドが False を返す場合、 Future はキャンセルされています。つまり、 Future.cancel() が呼び出されて True が返っています。Future の完了を (as_completed() または wait() により) 待機するすべてのスレッドが起動します。

このメソッドが True を返す場合、 Future はキャンセルされて、実行状態に移行されています。つまり、 Future.running() を呼び出すと True が返ります。

このメソッドは、一度だけ呼び出すことができ、Future.set_result() または Future.set_exception() がキャンセルされた後には呼び出すことができません。

set_result(result)¶

Future に関連付けられたワークの結果を result に設定します。

このメソッドは、 Executor の実装またはユニットテストによってのみ使用してください。

バージョン 3.8 で変更: This method raises concurrent.futures.InvalidStateError if the Future is already done.

set_exception(exception)¶

Future に関連付けられたワークの結果を Exception exception に設定します。

このメソッドは、 Executor の実装またはユニットテストによってのみ使用してください。

バージョン 3.8 で変更: This method raises concurrent.futures.InvalidStateError if the Future is already done.

モジュール関数¶

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

Wait for the Future instances (possibly created by different Executor instances) given by fs to complete. Duplicate futures given to fs are removed and will be returned only once. Returns a named 2-tuple of sets. The first set, named done, contains the futures that completed (finished or cancelled futures) before the wait completed. The second set, named not_done, contains the futures that did not complete (pending or running futures).

timeout で結果を返すまで待機する最大秒数を指定できます。timeout は整数か浮動小数点数をとります。timeout が指定されないか None の場合、無期限に待機します。

return_when でこの関数がいつ結果を返すか指定します。指定できる値は以下の定数のどれか一つです:

定数	説明
concurrent.futures.FIRST_COMPLETED¶	いずれかのフューチャが終了したかキャンセルされたときに返します。
concurrent.futures.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`.
concurrent.futures.ALL_COMPLETED¶	すべてのフューチャが終了したかキャンセルされたときに返します。

concurrent.futures.as_completed(fs, timeout=None)¶: Returns an iterator over the Future instances (possibly created by different Executor instances) given by fs that yields futures as they complete (finished or cancelled futures). Any futures given by fs that are duplicated will be returned once. Any futures that completed before as_completed() is called will be yielded first. The returned iterator raises a TimeoutError if __next__() is called and the result isn't available after timeout seconds from the original call to as_completed(). timeout can be an int or float. If timeout is not specified or None, there is no limit to the wait time.

参考

PEP 3148 -- futures - execute computations asynchronously: この機能を Python 標準ライブラリに含めることを述べた提案です。

例外クラス¶

exception concurrent.futures.CancelledError¶: future がキャンセルされたときに送出されます。

exception concurrent.futures.TimeoutError¶: A deprecated alias of TimeoutError, raised when a future operation exceeds the given timeout.

バージョン 3.11 で変更: このクラスは TimeoutError のエイリアスになりました。

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.

Added in version 3.7.

exception concurrent.futures.InvalidStateError¶: Raised when an operation is performed on a future that is not allowed in the current state.

Added in version 3.8.

exception concurrent.futures.thread.BrokenThreadPool¶: Derived from BrokenExecutor, this exception class is raised when one of the workers of a ThreadPoolExecutor has failed initializing.

Added in version 3.7.

exception concurrent.futures.interpreter.BrokenInterpreterPool¶: Derived from BrokenThreadPool, this exception class is raised when one of the workers of a InterpreterPoolExecutor has failed initializing.

Added in version 3.14.

exception concurrent.futures.interpreter.ExecutionFailed¶: Raised from InterpreterPoolExecutor when the given initializer fails or from submit() when there's an uncaught exception from the submitted task.

Added in version 3.14.

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).

Added in version 3.3.

`concurrent.futures` --- 並列タスク実行¶

Executor オブジェクト¶

ThreadPoolExecutor¶

ThreadPoolExecutor の例¶

InterpreterPoolExecutor¶

ProcessPoolExecutor¶

ProcessPoolExecutor の例¶

Future オブジェクト¶

モジュール関数¶

例外クラス¶

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concurrent.futures --- 並列タスク実行¶

Executor オブジェクト¶

ThreadPoolExecutor¶

ThreadPoolExecutor の例¶

InterpreterPoolExecutor¶

ProcessPoolExecutor¶

ProcessPoolExecutor の例¶

Future オブジェクト¶

モジュール関数¶

例外クラス¶

`concurrent.futures` --- 並列タスク実行¶