Metadata-Version: 2.1
Name: Pebble
Version: 4.5.3
Summary: Threading and multiprocessing eye-candy.
Home-page: https://github.com/noxdafox/pebble
Author: Matteo Cafasso
Author-email: noxdafox@gmail.com
License: LGPL
Keywords: thread process pool decorator
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)
License-File: LICENSE

Pebble
======

Pebble provides a neat API to manage threads and processes within an application.

:Source: https://github.com/noxdafox/pebble
:Documentation: https://pebble.readthedocs.io
:Download: https://pypi.python.org/pypi/pebble

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   :target: https://pebble.readthedocs.io
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Examples
--------

Run a job in a separate thread and wait for its results.

.. code:: python

    from pebble import concurrent

    @concurrent.thread
    def function(foo, bar=0):
        return foo + bar

    future = function(1, bar=2)

    result = future.result()  # blocks until results are ready

Run a function with a timeout of ten seconds and deal with errors.

.. code:: python

    from pebble import concurrent
    from concurrent.futures import TimeoutError

    @concurrent.process(timeout=10)
    def function(foo, bar=0):
        return foo + bar

    future = function(1, bar=2)

    try:
        result = future.result()  # blocks until results are ready
    except TimeoutError as error:
        print("Function took longer than %d seconds" % error.args[1])
    except Exception as error:
        print("Function raised %s" % error)
        print(error.traceback)  # traceback of the function

Pools support workers restart, timeout for long running tasks and more.

.. code:: python

    from pebble import ProcessPool
    from concurrent.futures import TimeoutError

    def function(foo, bar=0):
    	return foo + bar

    def task_done(future):
        try:
            result = future.result()  # blocks until results are ready
        except TimeoutError as error:
            print("Function took longer than %d seconds" % error.args[1])
        except Exception as error:
            print("Function raised %s" % error)
            print(error.traceback)  # traceback of the function

    with ProcessPool(max_workers=5, max_tasks=10) as pool:
        for i in range(0, 10):
            future = pool.schedule(function, args=[i], timeout=3)
            future.add_done_callback(task_done)


