WorQ - Python task queue

WorQ is a Python task queue that uses a worker pool to execute tasks in parallel. Workers can run in a single process, multiple processes on a single machine, or many processes on many machines. It ships with two backend options (memory and redis) and two worker pool implementations (multi-process and threaded). Task results can be monitored, waited on, or passed as arguments to another task.

WorQ has two main components:

• TaskQueue
• WorkerPool

WorQ ships with more than one implementation of each of these components.

• worq.queue.memory.TaskQueue - an in-memory (process local) task queue.
• worq.queue.redis.TaskQueue - a Redis-backed task queue that can scale to multiple servers.
• worq.pool.thread.WorkerPool - a multi-thread worker pool.
• worq.pool.process.WorkerPool - a multi-process worker pool.

These components can be mixed and matched as desired to meet the needs of your application. For example, an in-memory task queue can be used with a multi- process worker pool to to execute truely concurrent Python tasks on a single multi-core machine.

An example with Redis and a multi-process worker pool

Create the following files.

tasks.py:

import logging
from worq import get_broker, TaskSpace

ts = TaskSpace(__name__)

def init(url):
    logging.basicConfig(level=logging.DEBUG)
    broker = get_broker(url)
    broker.expose(ts)
    return broker

@ts.task
def num(value):
    return int(value)

@ts.task
def add(values):
    return sum(values)

pool.py:

#!/usr/bin/env python
import sys
from worq.pool.process import WorkerPool
from tasks import init

def main(url, **kw):
    broker = init(url)
    pool = WorkerPool(broker, init, workers=2)
    pool.start(**kw)
    return pool

if __name__ == '__main__':
    main(sys.argv[-1])

main.py:

#!/usr/bin/env python
import sys
import logging
from worq import get_queue

def main(url):
    logging.basicConfig(level=logging.DEBUG)
    q = get_queue(url)

    # enqueue tasks to be executed in parallel
    nums = [q.tasks.num(x) for x in range(10)]

    # process the results when they are ready
    result = q.tasks.add(nums)

    # wait for the final result
    result.wait(timeout=30)

    print('0 + 1 + ... + 9 = {}'.format(result.value))

if __name__ == '__main__':
    main(sys.argv[-1])

Make sure Redis is accepting connections on port 6379. It is recommended, but not required, that you setup a virtualenv. Then, in a terminal window:

$ pip install "WorQ[redis]"
$ python pool.py redis://localhost:6379/0

And in a second terminal window:

$ python main.py redis://localhost:6379/0

Tasks may also be queued in in memory rather than using Redis. In this case the queue must reside in the same process that initiates tasks, but the work can still be done in separate processes. For example:

Example with memory queue and a multi-process worker pool

In addition to the three files from the previous example, create the following:

mem.py:

#!/usr/bin/env python
import main
import pool

if __name__ == "__main__":
    url = "memory://"
    p = pool.main(url, timeout=2, handle_sigterm=False)
    try:
        main.main(url)
    finally:
        p.stop()

Then, in a terminal window:

$ python mem.py

See :ref:`examples.py` for more things that can be done with WorQ.

Links

• Documentation: http://worq.readthedocs.org/
• Source: https://github.com/millerdev/WorQ/
• PyPI: http://pypi.python.org/pypi/WorQ

Running the tests

WorQ development is mostly done using TDD. Tests are important to verify that new code works. You may want to run the tests if you want to contribute to WorQ or simply just want to hack. Setup a virtualenv and run these commands where you have checked out the WorQ source code:

$ pip install nose
$ nosetests

The tests for some components (e.g., redis TaskQueue) are disabled unless the necessary requirements are available. For example, by default the tests look for redis at redis://localhost:16379/0 (note non-standard port; you may customize this url with the WORQ_TEST_REDIS_URL environment variable).

Change Log

v1.1.1, 2018-03-20

• Add example using memory queue
• Fix python 3 compatibility

v1.1.0, 2014-03-29

• Add support for Python 3

v1.0.2, 2012-09-07

• Allow clearing entire Queue with del queue[:].
• Raise DuplicateTask (rather than the more generic TaskFailure) when trying to enqueue a task with an id matching that of another task in the queue.

v1.0.1, 2012-09-06

• Better support for managing more than one process.WorkerPool with a single pool manager process.
• Queue can be created with default task options.
• Can now check the approximate number of tasks in the queue with len(queue).
• Allow passing a completed Deferred as an argument to another task.
• Fix redis leaks.

v1.0.0, 2012-09-02 -- Initial release.