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Python 3.6.5 Documentation > "concurrent.futures" — Launching parallel tasks

"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)

Similar to "map(func, *iterables)" except:

* the *iterables* are collected immediately rather than
lazily;

* *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 *func* 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:

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.