"concurrent.futures" --- Launching parallel tasks
*************************************************

Ajouté dans la 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.

Availability: not WASI.

This module does not work or is not available on WebAssembly. See
Plateformes WebAssembly 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.

      Modifié dans la version 3.5: Added the *chunksize* parameter.

      Modifié dans la 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')

      Modifié dans la 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)

Et :

   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.

   Modifié dans la 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".

   Modifié dans la 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.

   Modifié dans la version 3.7: Added the *initializer* and *initargs*
   arguments.

   Modifié dans la 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.

   Modifié dans la 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
=======================

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 Contextes et méthodes de
   démarrage.

   *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.

   Modifié dans la 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.

   Modifié dans la 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.

   Modifié dans la version 3.11: The *max_tasks_per_child* argument
   was added to allow users to control the lifetime of workers in the
   pool.

   Modifié dans la 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.

   Modifié dans la version 3.13: *max_workers* uses
   "os.process_cpu_count()" by default, instead of "os.cpu_count()".

   Modifié dans la version 3.14: The default process start method (see
   Contextes et méthodes de démarrage) 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.

      Ajouté dans la 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.

      Ajouté dans la 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.

      Modifié dans la 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.

      Modifié dans la 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* indique quand la fonction doit se terminer. Il peut
   prendre les valeurs suivantes :

   +----------------------------------------------------+----------------------------------------------------+
   | Constante                                          | Description                                        |
   |====================================================|====================================================|
   | concurrent.futures.FIRST_COMPLETED                 | La fonction se termine lorsque n'importe quel      |
   |                                                    | futur se termine ou est annulé.                    |
   +----------------------------------------------------+----------------------------------------------------+
   | 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                   | La fonction se termine lorsque les *futurs* sont   |
   |                                                    | tous finis ou annulés.                             |
   +----------------------------------------------------+----------------------------------------------------+

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.

Voir aussi:

  **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.

   Modifié dans la version 3.11: cette classe est devenue un alias 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.

   Ajouté dans la version 3.7.

exception concurrent.futures.InvalidStateError

   Raised when an operation is performed on a future that is not
   allowed in the current state.

   Ajouté dans la 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.

   Ajouté dans la 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.

   Ajouté dans la 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.

   Ajouté dans la 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).

   Ajouté dans la version 3.3.
