threaded 4.0.0

Decorators for running functions in Thread/ThreadPool/IOLoop

threaded

threaded is a set of decorators, which wrap functions in:

• concurrent.futures.ThreadPool

• threading.Thread

• asyncio.Task in Python 3.

• gevent.threadpool.ThreadPool if gevent is installed.

Why? Because copy-paste of loop.create_task, threading.Thread and thread_pool.submit is boring, especially if target functions is used by this way only.

Pros:

• Free software: Apache license

• Open Source: https://github.com/python-useful-helpers/threaded

• PyPI packaged: https://pypi.python.org/pypi/threaded

• Tested: see bages on top

• Support multiple Python versions:

Python 3.4
Python 3.5
Python 3.6
Python 3.7
PyPy3 3.5+

Note

For python 2.7/PyPy you can use versions 1.x.x

Decorators:

• ThreadPooled - native concurrent.futures.ThreadPool.

• threadpooled is alias for ThreadPooled.

• Threaded - wrap in threading.Thread.

• threaded is alias for Threaded.

• AsyncIOTask - wrap in asyncio.Task. Uses the same API, as ThreadPooled.

• asynciotask is alias for AsyncIOTask.

Note

gevent is not in default package requirements.

Usage

ThreadPooled

Mostly it is required decorator: submit function to ThreadPoolExecutor on call.

Note

API quite differs between Python 3 and Python 2.7. See API section below.

threaded.ThreadPooled.configure(max_workers=3)

Note

By default, if executor is not configured - it configures with default parameters: max_workers=CPU_COUNT * 5

@threaded.ThreadPooled
def func():
    pass

concurrent.futures.wait([func()])

Python 3.5+ usage with asyncio:

Note

if loop_getter is not callable, loop_getter_need_context is ignored.

loop = asyncio.get_event_loop()
@threaded.ThreadPooled(loop_getter=loop, loop_getter_need_context=False)
def func():
    pass

loop.run_until_complete(asyncio.wait_for(func(), timeout))

Python 3.5+ usage with asyncio and loop extraction from call arguments:

loop_getter = lambda tgt_loop: tgt_loop
@threaded.ThreadPooled(loop_getter=loop_getter, loop_getter_need_context=True)  # loop_getter_need_context is required
def func(*args, **kwargs):
    pass

loop = asyncio.get_event_loop()
loop.run_until_complete(asyncio.wait_for(func(loop), timeout))

During application shutdown, pool can be stopped (while it will be recreated automatically, if some component will request).

threaded.ThreadPooled.shutdown()

Threaded

Classic threading.Thread. Useful for running until close and self-closing threads without return.

Usage example:

@threaded.Threaded
def func(*args, **kwargs):
    pass

thread = func()
thread.start()
thread.join()

Without arguments, thread name will use pattern: 'Threaded: ' + func.__name__

Note

If func.__name__ is not accessible, str(hash(func)) will be used instead.

Override name can be don via corresponding argument:

@threaded.Threaded(name='Function in thread')
def func(*args, **kwargs):
    pass

Thread can be daemonized automatically:

@threaded.Threaded(daemon=True)
def func(*args, **kwargs):
    pass

Also, if no any addition manipulations expected before thread start, it can be started automatically before return:

@threaded.Threaded(started=True)
def func(*args, **kwargs):
    pass

AsyncIOTask

Wrap in asyncio.Task.

usage with asyncio:

@threaded.AsyncIOTask
def func():
    pass

loop = asyncio.get_event_loop()
loop.run_until_complete(asyncio.wait_for(func(), timeout))

Provide event loop directly:

Note

if loop_getter is not callable, loop_getter_need_context is ignored.

loop = asyncio.get_event_loop()
@threaded.AsyncIOTask(loop_getter=loop)
def func():
    pass

loop.run_until_complete(asyncio.wait_for(func(), timeout))

Usage with loop extraction from call arguments:

loop_getter = lambda tgt_loop: tgt_loop
@threaded.AsyncIOTask(loop_getter=loop_getter, loop_getter_need_context=True)
def func(*args, **kwargs):
    pass

loop = asyncio.get_event_loop()
loop.run_until_complete(asyncio.wait_for(func(loop), timeout))

Testing

The main test mechanism for the package threaded is using tox. Available environments can be collected via tox -l

CI systems

For code checking several CI systems is used in parallel:

1. Travis CI: is used for checking: PEP8, pylint, bandit, installation possibility and unit tests. Also it’s publishes coverage on coveralls.

2. coveralls: is used for coverage display.

3. Azure CI: is used for functional tests on Windows.

CD system

Travis CI: is used for package delivery on PyPI.