concurrent.futures
— Launching parallel tasks¶
Added 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 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 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 aFuture
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 toExecutor.map()
. timeout can be an int or a float. If timeout is not specified orNone
, 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. WithThreadPoolExecutor
andInterpreterPoolExecutor
, chunksize has no effect.Changed in version 3.5: Added the chunksize argument.
- 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()
andExecutor.map()
made after shutdown will raiseRuntimeError
.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 isFalse
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 theExecutor
(waiting as ifExecutor.shutdown()
were called with wait set toTrue
):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)
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 usingatexit
. 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 thatThreadPoolExecutor
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 by5
, assuming thatThreadPoolExecutor
is often used to overlap I/O instead of CPU work and the number of workers should be higher than the number of workers forProcessPoolExecutor
.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¶
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 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 usingpickle
when sending them to the worker’s interpreter.Note
Functions defined in the
__main__
module cannot be pickled and thus cannot be used.Note
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
andbytes
, andmemoryview
. 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.
Note
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 isNone
or not given, it will default toos.process_cpu_count()
. If max_workers is less than or equal to0
, then aValueError
will be raised. On Windows, max_workers must be less than or equal to61
. If it is not thenValueError
will be raised. If max_workers isNone
, then the default chosen will be at most61
, even if more processors are available. mp_context can be amultiprocessing
context orNone
. It will be used to launch the workers. If mp_context isNone
or not given, the defaultmultiprocessing
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.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: Theos.fork()
function called internally to spawn workers may raise aDeprecationWarning
. Pass a mp_context configured to use a different start method. See theos.fork()
documentation for further explanation.Changed in version 3.13: max_workers uses
os.process_cpu_count()
by default, instead ofos.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 passmp_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 byExecutor.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 returnTrue
.
- 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 orNone
, 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 orNone
, 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 aBaseException
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 andExecutor
implementations.- set_running_or_notify_cancel()¶
This method should only be called by
Executor
implementations before executing the work associated with theFuture
and by unit tests.If the method returns
False
then theFuture
was cancelled, i.e.Future.cancel()
was called and returnedTrue
. Any threads waiting on theFuture
completing (i.e. throughas_completed()
orwait()
) will be woken up.If the method returns
True
then theFuture
was not cancelled and has been put in the running state, i.e. calls toFuture.running()
will returnTrue
.This method can only be called once and cannot be called after
Future.set_result()
orFuture.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 theFuture
is already done.
Module Functions¶
- concurrent.futures.wait(fs, timeout=None, return_when=ALL_COMPLETED)¶
Wait for the
Future
instances (possibly created by differentExecutor
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, nameddone
, contains the futures that completed (finished or cancelled futures) before the wait completed. The second set, namednot_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 differentExecutor
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 beforeas_completed()
is called will be yielded first. The returned iterator raises aTimeoutError
if__next__()
is called and the result isn’t available after timeout seconds from the original call toas_completed()
. timeout can be an int or float. If timeout is not specified orNone
, 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 aThreadPoolExecutor
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 aInterpreterPoolExecutor
has failed initializing.Added in version 3.14.
- exception concurrent.futures.interpreter.ExecutionFailed¶
Raised from
InterpreterPoolExecutor
when the given initializer fails or fromsubmit()
when there’s an uncaught exception from the submitted task.Added in version 3.14.
- exception concurrent.futures.process.BrokenProcessPool¶
Derived from
BrokenExecutor
(formerlyRuntimeError
), this exception class is raised when one of the workers of aProcessPoolExecutor
has terminated in a non-clean fashion (for example, if it was killed from the outside).Added in version 3.3.