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A take on the concurrent.futures.thread.ThreadPoolExecutor that self-manages the number of threads

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CollapsingThreadPoolExecutor

The CollapsingThreadPoolExecutor is inspired by and compatible with the ThreadPoolExecutor from the "futures" module, it operates differently in that worker threads are handled with a stack which results in the same worker or workers doing all the work (and idle workers being destroyed).

How to install

$ pip install collapsing-thread-pool-executor

How to develop

Prerequisites

  • python3 w/ pip
  • python2 w/ pip
  • virtualenvwrapper
  • entr

Set up the environments

$ mkvirtualenv -p `which python2.7` collapsing-thread-pool-executor-py2
$ pip install .
$ pip install -r requirements.txt

$ mkvirtualenv -p `which python3` collapsing-thread-pool-executor-py3
$ pip install .
$ pip install -r requirements.txt

Watch the tests

# watch python2 tests in one window
$ workon collapsing-thread-pool-executor-py2
$ find ./ -name '*.py' | entr -c py.test -v --log-level=DEBUG collapsing_thread_pool_executor

# watch python3 tests in one window
$ workon collapsing-thread-pool-executor-py3
$ find ./ -name '*.py' | entr -c py.test -v --log-level=DEBUG collapsing_thread_pool_executor

Examples

The example below will execute some_task() 100 times; as some_task() should take a second to execute and as we've allocated 10 workers, the whole thing should take about 10 seconds.

import time

from collapsing_thread_pool_executor import CollapsingThreadPoolExecutor

def some_task():
    time.sleep(1)

# all arguments are optional
pool = CollapsingThreadPoolExecutor(
    workers=10,
    thread_name_prefix='SomePool',
    permitted_thread_age_in_seconds=60,
)

for i in range(0, 100):
    pool.submit(some_task)

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A take on the concurrent.futures.thread.ThreadPoolExecutor that self-manages the number of threads

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