`concurrent.futures` — Launching parallel tasks¶

Adicionado na versão 3.2.

Código-fonte: Lib/concurrent/futures/thread.py e 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 InterpreterPoolExecutor, or separate processes, using ProcessPoolExecutor. Each implements the same interface, which is defined by the abstract Executor class.

Disponibilidade

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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(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.

The returned iterator raises a 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 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.

Alterado na versão 3.5: Adicionado o argumento chunksize.

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

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.

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 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')

Alterado na versão 3.9: Adicionado cancel_futures.

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.

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.

Alterado na versão 3.5: 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.

Alterado na versão 3.6: 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.

Alterado na versão 3.7: Added the initializer and initargs arguments.

Alterado na versão 3.8: 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.

Alterado na versão 3.13: Default value of max_workers is changed to min(32, (os.process_cpu_count() or 1) + 4).

Exemplo de ThreadPoolExecutor¶

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 nrace 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.

Nota

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

Nota

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.

Nota

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`¶

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=(), 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 Contextos e métodos de inicialização.

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.

Alterado na versão 3.3: 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.

Alterado na versão 3.7: 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.

Alterado na versão 3.11: The max_tasks_per_child argument was added to allow users to control the lifetime of workers in the pool.

Alterado na versão 3.12: 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.

Alterado na versão 3.13: max_workers uses os.process_cpu_count() by default, instead of os.cpu_count().

Alterado na versão 3.14: The default process start method (see Contextos e métodos de inicialização) changed away from fork. If you require the fork start method for ProcessPoolExecutor you must explicitly pass mp_context=multiprocessing.get_context("fork").

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()¶: 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()¶: 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 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 an exception, 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 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.

Alterado na versão 3.8: 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.

Alterado na versão 3.8: This method raises concurrent.futures.InvalidStateError if the Future is already done.

Module Functions¶

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 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 indica quando esta função deve retornar. Ele deve ser uma das seguintes constantes:

Constante	Descrição
concurrent.futures.FIRST_COMPLETED¶	A função irá retornar quando qualquer futuro terminar ou for cancelado.
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¶	A função irá retornar quando todos os futuros encerrarem ou forem cancelados.

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.

Ver também

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

Alterado na versão 3.11: Esta classe foi feita como um apelido de TimeoutError.

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.

Adicionado na versão 3.7.

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

Adicionado na versão 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.

Adicionado na versão 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.

Adicionado na versão 3.14.0a1 (unreleased).

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.

Adicionado na versão 3.14.0a1 (unreleased).

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).

Adicionado na versão 3.3.

`concurrent.futures` — Launching parallel tasks¶

Executor Objects¶

ThreadPoolExecutor¶

Exemplo de ThreadPoolExecutor¶

InterpreterPoolExecutor¶

`ProcessPoolExecutor`¶

ProcessPoolExecutor Example¶

Future Objects¶

Module Functions¶

Exception classes¶

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concurrent.futures — Launching parallel tasks¶

Executor Objects¶

ThreadPoolExecutor¶

Exemplo de ThreadPoolExecutor¶

InterpreterPoolExecutor¶

`ProcessPoolExecutor`¶

ProcessPoolExecutor Example¶

Future Objects¶

Module Functions¶

Exception classes¶

`concurrent.futures` — Launching parallel tasks¶