python-peps/pep-3148.txt

PEP:               3148
Title:             futures - execute computations asynchronously
Version:           $Revision$
Last-Modified:     $Date$
Author:            Brian Quinlan <brian@sweetapp.com>
Status:            Draft
Type:              Standards Track
Content-Type:      text/x-rst
Created:           16-Oct-2009
Python-Version:    3.2
Post-History:

========
Abstract
========

This PEP proposes a design for a package that facilitates the evaluation of
callables using threads and processes.

==========
Motivation
==========

Python currently has powerful primitives to construct multi-threaded and
multi-process applications but parallelizing simple operations requires a lot of
work i.e. explicitly launching processes/threads, constructing a work/results
queue, and waiting for completion or some other termination condition (e.g.
failure, timeout). It is also difficult to design an application with a global
process/thread limit when each component invents its own parallel execution
strategy.

=============
Specification
=============

Check Prime Example
-------------------

::

    import 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

    with futures.ProcessPoolExecutor() as executor:
        for number, is_prime in zip(PRIMES, executor.map(is_prime, PRIMES)):
            print('%d is prime: %s' % (number, is_prime))

Web Crawl Example
-----------------

::

    import 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/']

    def load_url(url, timeout):
        return urllib.request.urlopen(url, timeout=timeout).read()

    with futures.ThreadPoolExecutor(max_workers=5) as executor:
        future_to_url = dict((executor.submit(load_url, url, 60), url)
                             for url in URLS)

    for future in futures.as_completed(future_to_url):
        url = future_to_url[future]
        if future.exception() is not None:
            print('%r generated an exception: %s' % (url, future.exception()))
        else:
            print('%r page is %d bytes' % (url, len(future.result())))

Interface
---------

The proposed package provides two core classes: `Executor` and `Future`.
An `Executor` receives asynchronous work requests (in terms of a callable and
its arguments) and returns a `Future` to represent the execution of that
work request.

Executor
''''''''

`Executor` is an abstract class that provides methods to execute calls
asynchronously.

`submit(fn, *args, **kwargs)`

Schedules the callable to be executed as fn(*\*args*, *\*\*kwargs*) and returns
a `Future` instance representing the execution of the function.

`map(func, *iterables, timeout=None)`

Equivalent to map(*func*, *\*iterables*) but executed asynchronously and
possibly out-of-order. The returned iterator raises a `TimeoutError` if
`__next__()` is called and the result isn't available after *timeout* seconds
from the original call to `map()`. If *timeout* is not specified or
``None`` then 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.

`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` and made after shutdown will raise
`RuntimeError`.

If wait is `True` then the executor will not return until all the pending
futures are done executing and the resources associated with the executor
have been freed.

`__enter__()`
`__exit__(exc_type, exc_val, exc_tb)`

When using an executor as a context manager, `__exit__` will call
`Executor.shutdown(wait=True)`.


ProcessPoolExecutor
'''''''''''''''''''

The `ProcessPoolExecutor` class is an `Executor` subclass that uses a pool of
processes to execute calls asynchronously. The callable objects and arguments
passed to `ProcessPoolExecutor.submit` must be serializeable according to the
same limitations as the multiprocessing module.

`__init__(max_workers)`

Executes calls asynchronously using a pool of a most *max_workers*
processes. If *max_workers* is ``None`` or not given then as many worker
processes will be created as the machine has processors.

ThreadPoolExecutor
''''''''''''''''''

The `ThreadPoolExecutor` class is an `Executor` subclass that uses a pool of
threads to execute calls asynchronously.

`__init__(max_workers)`

Executes calls asynchronously using a pool of at most *max_workers* threads.

Future Objects
''''''''''''''

The `Future` class encapsulates the asynchronous execution of a function
or method call. `Future` instances are returned by `Executor.submit`.

`cancel()`

Attempt to cancel the call. If the call is currently being executed then
it cannot be cancelled and 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.

`Future.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. If *timeout*
is not specified or ``None`` then there is no limit to the wait time.

If the future is cancelled before completing then `CancelledError` will
be raised.

If the call raised then 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.
If *timeout* is not specified or ``None`` then 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 then ``None`` is returned.

Module Functions
''''''''''''''''

`wait(fs, timeout=None, return_when=ALL_COMPLETED)`

Wait for the `Future` instances in the given sequence to complete. Returns a
named 2-tuple of sets. The first set, named "finished", contains the futures
that completed (finished  or were cancelled) before the wait completed. The
second set, named "not_finished", contains uncompleted futures.

This method should always be called using keyword arguments, which are:

*fs* is the sequence of Future instances that should be waited on.

*timeout* can be used to control the maximum number of seconds to wait before
returning. If timeout is not specified or None then there is no limit to the
wait time.

*return_when* indicates when the method should return. It must be one of the
following constants:

============================= ==================================================
 Constant                      Description
============================= ==================================================
`FIRST_COMPLETED`             The method will return when any future finishes or
                              is cancelled.
`FIRST_EXCEPTION`             The method will return when any future finishes by
                              raising an exception. If not future raises an
                              exception then it is equivalent to ALL_COMPLETED.
`ALL_COMPLETED`               The method will return when all calls finish.
============================= ==================================================

`as_completed(fs, timeout=None)`

Returns an iterator over the Future instances given by *fs* that yields futures
as they complete (finished or were cancelled). Any futures that completed
before `as_completed()` was 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()`. If
*timeout* is not specified or `None` then there is no limit to the wait time.

=========
Rationale
=========

The proposed design of this module was heavily influenced by the the Java
java.util.concurrent package [1]_. The conceptual basis of the module, as in
Java, is the Future class, which represents the progress and result of an
asynchronous computation. The Future class makes little commitment to the
evaluation mode being used e.g. it can be be used to represent lazy or eager
evaluation, for evaluation using threads, processes or remote procedure call.

Futures are created by concrete implementations of the Executor class
(called ExecutorService in Java). The reference implementation provides
classes that use either a process a thread pool to eagerly evaluate
computations.

Futures have already been seen in Python as part of a popular Python
cookbook recipe [2]_ and have discussed on the Python-3000 mailing list [3]_.

The proposed design is explicit i.e. it requires that clients be aware that
they are consuming Futures. It would be possible to design a module that
would return proxy objects (in the style of `weakref`) that could be used
transparently. It is possible to build a proxy implementation on top of
the proposed explicit mechanism.

The proposed design does not introduce any changes to Python language syntax
or semantics. Special syntax could be introduced [4]_ to mark function and
method calls as asynchronous. A proxy result would be returned while the
operation is eagerly evaluated asynchronously, and execution would only
block if the proxy object were used before the operation completed.

Anh Hai Trinh proposed a simpler but more limited API concept [5]_.

========================
Reference Implementation
========================

The reference implementation [6]_ contains a complete implementation of the
proposed design. It has been tested on Linux and Mac OS X.

==========
References
==========

.. [1]
   `java.util.concurrent` package documentation
   `http://java.sun.com/j2se/1.5.0/docs/api/java/util/concurrent/package-summary.html`

.. [2]
   Python Cookbook recipe 84317, "Easy threading with Futures"
   `http://code.activestate.com/recipes/84317/`

.. [3]
   `Python-3000` thread, "mechanism for handling asynchronous concurrency"
   `http://mail.python.org/pipermail/python-3000/2006-April/000960.html`

.. [4]
   `Python 3000` thread, "Futures in Python 3000 (was Re: mechanism for handling asynchronous concurrency)"
   `http://mail.python.org/pipermail/python-3000/2006-April/000970.html`

.. [5]
   A discussion of `stream`, a similar concept proposed by Anh Hai Trinh
   `http://www.mail-archive.com/stdlib-sig@python.org/msg00480.html`

.. [6]
   Reference `futures` implementation `http://code.google.com/p/pythonfutures`

=========
Copyright
=========

This document has been placed in the public domain.



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