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
or
, or separate processes, using
. Each implements the same interface, which is defined by the abstract
class.
must not be confused with
, which is designed for use with
tasks and coroutines. See the
documentation for a detailed comparison of the two.
Executor Objects
classconcurrent.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
object representing the execution of the callable.
withThreadPoolExecutor(max_workers=1)asexecutor:future=executor.submit(pow,323,1235)print(future.result())map(fn, *iterables, timeout=None, chunksize=1, buffersize=None)
Similar to
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
if
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. It cancels all future calls of fn and closes the iterator. 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. It does not cancel future calls of fn.
The returned iterator has method close() which cancels all future calls of fn and discards the results of already finished calls if they are available.
When using
, 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
and
, chunksize has no effect.
Changed in version 3.5: Added the chunksize parameter.
Changed in version 3.14: Added the buffersize parameter.
Changed in version 3.16.0a0 (unreleased): The returned iterator is no longer automatically closed if a fn call raises an exception.
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
and
made after shutdown will raise
.
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
via the
statement, which will shutdown the Executor (waiting as if Executor.shutdown() were called with wait set to True):
importshutilwithThreadPoolExecutor(max_workers=4)ase: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
subclass that uses a pool of threads to execute calls asynchronously.
Deadlocks can occur when the callable associated with a
waits on the results of another Future. For example:
importtimedefwait_on_b():time.sleep(5)print(b.result())# b will never complete because it is waiting on a.return5defwait_on_a():time.sleep(5)print(a.result())# a will never complete because it is waiting on b.return6executor=ThreadPoolExecutor(max_workers=2)a=executor.submit(wait_on_b)b=executor.submit(wait_on_a)And:
defwait_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)future=executor.submit(wait_on_future)# Note: calling future.result() would also cause a deadlock because# the single worker thread is already waiting for wait_on_future().classconcurrent.futures.ThreadPoolExecutor(max_workers=None, thread_name_prefix='', initializer=None, initargs=())
An
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
, 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
.
Changed in version 3.6: Added the thread_name_prefix parameter to allow users to control the
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()or1)+4).
ThreadPoolExecutor Example
importconcurrent.futuresimporturllib.requestURLS=['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 contentsdefload_url(url,timeout):withurllib.request.urlopen(url,timeout=timeout)asconn:returnconn.read()# We can use a with statement to ensure threads are cleaned up promptlywithconcurrent.futures.ThreadPoolExecutor(max_workers=5)asexecutor:# Start the load operations and mark each future with its URLfuture_to_url={executor.submit(load_url,url,60):urlforurlinURLS}forfutureinconcurrent.futures.as_completed(future_to_url):url=future_to_url[future]try:data=future.result()exceptExceptionasexc:print('%r generated an exception: %s'%(url,exc))else:print('%r page is %d bytes'%(url,len(data)))InterpreterPoolExecutor
Added in version 3.14.
The
class uses a pool of interpreters to execute calls asynchronously. It is a
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
, 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
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
,
, 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
. One less efficient alternative is to serialize with
and then send the bytes over a shared
or
.
classconcurrent.futures.InterpreterPoolExecutor(max_workers=None, thread_name_prefix='', initializer=None, initargs=())
A
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
, 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
when sending them to the worker’s interpreter.
Note
The executor may replace uncaught exceptions from initializer with
.
Other caveats from parent
apply here.
and
work like normal, except the worker serializes the callable and arguments using
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
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
class is an
subclass that uses a pool of processes to execute calls asynchronously. ProcessPoolExecutor uses the
module, which allows it to side-step the
but also means that only picklable objects can be executed and returned.
The __main__ module must be importable by worker subprocesses. This means that
will not work in the interactive interpreter.
Calling
or
methods from a callable submitted to a
will result in deadlock.
Note that the restrictions on functions and arguments needing to picklable as per
apply when using
and
on a
. A function defined in a REPL or a lambda should not be expected to work.
classconcurrent.futures.ProcessPoolExecutor(max_workers=None, mp_context=None, initializer=None, initargs=(), max_tasks_per_child=None)
An
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
. If max_workers is less than or equal to 0, then a
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
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
.
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
, 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
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
context uses the "fork" start method: The
function called internally to spawn workers may raise a
. Pass a mp_context configured to use a different start method. See the os.fork() documentation for further explanation.
Changed in version 3.14: The default process start method (see
) changed away from fork. If you require the fork start method for ProcessPoolExecutor you must explicitly pass mp_context=multiprocessing.get_context("fork").
Changed in version 3.16.0a0 (unreleased): Fixed a deadlock (
) where the executor could hang after a worker process exited upon reaching its max_tasks_per_child limit while tasks remained queued.
terminate_workers()
Attempt to terminate all living worker processes immediately by calling
on each of them. Internally, it will also call
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.
kill_workers()
Attempt to kill all living worker processes immediately by calling
on each of them. Internally, it will also call
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.
ProcessPoolExecutor Example
importconcurrent.futuresimportmathPRIMES=[112272535095293,112582705942171,112272535095293,115280095190773,115797848077099,1099726899285419]defis_prime(n):ifn<2:returnFalseifn==2:returnTrueifn%2==0:returnFalsesqrt_n=int(math.floor(math.sqrt(n)))foriinrange(3,sqrt_n+1,2):ifn%i==0:returnFalsereturnTruedefmain():withconcurrent.futures.ProcessPoolExecutor()asexecutor:fornumber,primeinzip(PRIMES,executor.map(is_prime,PRIMES)):print('%d is prime: %s'%(number,prime))if__name__=='__main__':main()Future Objects
The
class encapsulates the asynchronous execution of a callable. Future instances are created by
.
classconcurrent.futures.Future
Encapsulates the asynchronous execution of a callable. Future instances are created by
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
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
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
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
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
subclass, it will be logged and ignored. If the callable raises a
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
implementations.
set_running_or_notify_cancel()
This method should only be called by
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.
was called and returned True. Any threads waiting on the Future completing (i.e. through
or
) 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
will return True.
This method can only be called once and cannot be called after
or
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
implementations and unit tests.
Changed in 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.
This method should only be used by
implementations and unit tests.
Changed in 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
instances (possibly created by different
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
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
.
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
instances (possibly created by different
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
if
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
– futures - execute computations asynchronouslyThe proposal which described this feature for inclusion in the Python standard library.
Exception classes
exceptionconcurrent.futures.CancelledError
Raised when a future is cancelled.
exceptionconcurrent.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.
exceptionconcurrent.futures.BrokenExecutor
Derived from
, 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.
exceptionconcurrent.futures.InvalidStateError
Raised when an operation is performed on a future that is not allowed in the current state.
Added in version 3.8.
exceptionconcurrent.futures.thread.BrokenThreadPool
Derived from
, this exception class is raised when one of the workers of a
has failed initializing.
Added in version 3.7.
exceptionconcurrent.futures.interpreter.BrokenInterpreterPool
Derived from
, this exception class is raised when one of the workers of a
has failed initializing.
Added in version 3.14.
exceptionconcurrent.futures.process.BrokenProcessPool
Derived from
(formerly
), this exception class is raised when one of the workers of a
has terminated in a non-clean fashion (for example, if it was killed from the outside).
Added in version 3.3.