# `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、Lib/concurrent/futures/process.py および Lib/concurrent/futures/interpreter.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 `InterpreterPoolExecutor`, or separate processes, using `ProcessPoolExecutor`. Each implements the same interface, which is defined by the abstract `Executor` class.

`concurrent.futures.Future` must not be confused with `asyncio.Future`, which is designed for use with `asyncio` tasks and coroutines. See the asyncio's Future documentation for a detailed comparison of the two.

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, buffersize=None)

    Similar to `map(fn, *iterables)` except:

    - The iterables are collected immediately rather than lazily, unless a buffersize is specified to limit the number of submitted tasks whose results have not yet been yielded. If the buffer is full, iteration over the iterables pauses until a result is yielded from the buffer.

    - 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。

    もし fn の呼び出しが例外を送出した場合、その例外はイテレータから値を受け取る時に送出されます。

    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。

    バージョン 3.14 で変更: buffersize 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.

    You can avoid having to call this method explicitly if you use the executor as a context manager via 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')

    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)
    future = executor.submit(wait_on_future)
    # Note: calling future.result() would also cause a deadlock because
    # the single worker thread is already waiting for 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: thread_name_prefix 引数が追加され、デバッグしやすくなるようにプールから作られたワーカスレッド `threading.Thread` の名前を管理できるようになりました。

  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

Added in version 3.14.

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 to 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=())

  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

  The executor may replace uncaught exceptions from initializer with `ExecutionFailed`.

  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.

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。

Note that the restrictions on functions and arguments needing to picklable as per `multiprocessing.Process` apply when using `submit()` and `map()` on a `ProcessPoolExecutor`. A function defined in a REPL or a lambda should not be expected to work.

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.

  注釈

  Bugs have been reported when using the max_tasks_per_child feature that can result in the `ProcessPoolExecutor` hanging in some circumstances. Follow its eventual resolution in gh-115634.

  バージョン 3.3 で変更: ワーカプロセスの1つが突然終了した場合、`BrokenProcessPool` エラーが送出されるようになりました。 以前は挙動は未定義でしたが、 executor や futures がフリーズしたりデッドロックを起こすことがしばしばでした。

  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")`.

  terminate_workers()

    Attempt to terminate all living worker processes immediately by calling `Process.terminate` on each of them. Internally, it will also call `Executor.shutdown()` to ensure that all other resources associated with the executor are freed.

    After calling this method the caller should no longer submit tasks to the executor.

    Added in version 3.14.

  kill_workers()

    Attempt to kill all living worker processes immediately by calling `Process.kill` on each of them. Internally, it will also call `Executor.shutdown()` to ensure that all other resources associated with the executor are freed.

    After calling this method the caller should no longer submit tasks to the executor.

    Added in version 3.14.

### 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.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.