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

New in version 3.2.

**Source code:** Lib/concurrent/futures/thread.py and
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 separate processes, using
"ProcessPoolExecutor".  Both implement the same interface, which is
defined by the abstract "Executor" class.


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(func, *iterables, timeout=None, chunksize=1)

         Equivalent to "map(func, *iterables)" except *func* is
         executed asynchronously and several calls to *func* may be
         made concurrently.  The returned iterator raises a
         "concurrent.futures.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 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", *chunksize* has no
         effect.

         Changed in version 3.5: Added the *chunksize* argument.

      shutdown(wait=True)

         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.

         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')


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='')

   An "Executor" subclass that uses a pool of at most *max_workers*
   threads to execute calls asynchronously.

   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".

   New in version 3.6: The *thread_name_prefix* argument was added to
   allow users to control the threading.Thread names for worker
   threads created by the pool for easier debugging.


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://some-made-up-domain.com/']

   # 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)))


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)

   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 the number of processors on
   the machine. If *max_workers* is lower or equal to "0", then a
   "ValueError" will be raised.

   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.


ProcessPoolExecutor Example
---------------------------

   import concurrent.futures
   import math

   PRIMES = [
       112272535095293,
       112582705942171,
       112272535095293,
       115280095190773,
       115797848077099,
       1099726899285419]

   def is_prime(n):
       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 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
         "concurrent.futures.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, 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 "concurrent.futures.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.

      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.


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.  Returns a named
   2-tuple of sets.  The first set, named "done", contains the futures
   that completed (finished or were cancelled) before the wait
   completed.  The second set, named "not_done", contains uncompleted
   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                              |
   +===============================+==========================================+
   | "FIRST_COMPLETED"             | The function will return when any future |
   |                               | finishes or is cancelled.                |
   +-------------------------------+------------------------------------------+
   | "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".           |
   +-------------------------------+------------------------------------------+
   | "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 were cancelled). 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
   "concurrent.futures.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

   Raised when a future operation exceeds the given timeout.

exception concurrent.futures.process.BrokenProcessPool

   Derived from "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).

   New in version 3.3.
