# `concurrent.futures` — Launching parallel tasks

Added in version 3.2.

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

This module does not work or is not available on WebAssembly. See WebAssembly platforms for more information.

## 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, 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.

    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.

    Changed in version 3.5: Added the chunksize parameter.

    Changed in version 3.14: Added the buffersize parameter.

  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 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')

    Changed in version 3.9: Added 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)
    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=())

  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.

  Changed in version 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`.

  Changed in version 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.

  Changed in version 3.7: Added the initializer and initargs arguments.

  Changed in version 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.

  Changed in version 3.13: Default value of max_workers is changed to `min(32, (os.process_cpu_count() or 1) + 4)`.

### 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://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.

  Note

  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

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.

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 Contexts and start methods.

  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.

  Note

  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.

  Changed in version 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.

  Changed in version 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.

  Changed in version 3.11: The max_tasks_per_child argument was added to allow users to control the lifetime of workers in the pool.

  Changed in version 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.

  Changed in version 3.13: max_workers uses `os.process_cpu_count()` by default, instead of `os.cpu_count()`.

  Changed in version 3.14: The default process start method (see Contexts and start methods) 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 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.

    Changed in version 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.

    Changed in version 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 indicates when this function should return. It must be one of the following constants:

  Constant | Description
  --- | ---
  concurrent.futures.FIRST_COMPLETED | The function will return when any future finishes or is cancelled.
  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 | The function will return when all futures finish or are cancelled.

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.

See also

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.

  Changed in version 3.11: This class was made an alias of `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.

  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.