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.
An abstract class that provides methods to execute calls asynchronously. It should not be used directly, but through its concrete subclasses.
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())
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.
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 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)
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).
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)))
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.
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.
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.
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").
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.
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.
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()
The Future class encapsulates the asynchronous execution of a callable.
Future instances are created by Executor.submit().
Encapsulates the asynchronous execution of a callable. Future
instances are created by Executor.submit() and should not be created
directly except for testing.
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.
Return True if the call was successfully cancelled.
Return True if the call is currently being executed and cannot be
cancelled.
Return True if the call was successfully cancelled or finished
running.
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.
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.
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.
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.
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.
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 |
|---|---|
|
The function will return when any future finishes or is cancelled. |
|
The function will return when any future finishes by raising an
exception. If no future raises an exception
then it is equivalent to |
|
The function will return when all futures finish or are cancelled. |
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
The proposal which described this feature for inclusion in the Python standard library.
Raised when a future is cancelled.
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.
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.
Raised when an operation is performed on a future that is not allowed in the current state.
Added in version 3.8.
Derived from BrokenExecutor, this exception
class is raised when one of the workers
of a ThreadPoolExecutor
has failed initializing.
Added in version 3.7.
Derived from BrokenThreadPool,
this exception class is raised when one of the workers
of a InterpreterPoolExecutor
has failed initializing.
Added in version 3.14.
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.