queue-automator 0.1.0

A simple wrapper to build queue multiprocessing pipelines

QueueAutomator

A Python library that wraps the multiprocessing package. It provides a simple to use API to build queue-based multiprocessing pipelines.

instalation

Run pip install queue-automator (use python >=3.6 )

Summary

QueueAutomator provides a clean decorator API to get started with multiprocessing and queues.

• This library offers an easy interface to parallelize consecutive tasks that take a long time to finish.

• As it is build on top of the native python-multiprocessing library, you can run compute intensive tasks without locking the main process

• All the code that manages queues, spawning and joining processes is already implemented, so you can focus on the task at hand.

How it works:

Example

Using only 1 worker function

from queue_automator import QueueAutomator
from time import sleep

# Create an instance of QueueAutomator()
automator = QueueAutomator()

# Register a worker function (if input_queue_name and output_queue_name are not provided
# they will default to 'input' and 'output' respectively). 'input' and 'output'
# are necessary  to mark the start and ending of your pipeline

@automator.register_as_worker_function(process_count=2)
def do_work(item: int) -> int:
    sleep(2)
    result = item*2
    print(f'{item} times two {result}')
    return result

if __name__ == '__main__':
    input_data = range(30)

    # Always set your input data before calling .run()
    automator.set_input_data(input_data)
    results = automator.run()
    print(results)

Using more than 1 worker function

from queue_automator import QueueAutomator
from time import sleep

automator = QueueAutomator()

@automator.register_as_worker_function(output_queue_name='square_queue', process_count=2)
def do_work(item: int) -> int:
    sleep(2)
    result = item*2
    print(f'{item} times two {result}')
    return result

@automator.register_as_worker_function(input_queue_name='square_queue', output_queue_name='cube_queue', process_count=2)
def do_work_2(item: int) -> int:
    sleep(2)
    result = item**2
    print(f'{item} squared {result}')
    return result

@automator.register_as_worker_function(input_queue_name='cube_queue', process_count=2)
def do_work_3(item: int) -> int:
    sleep(2)
    result = item**3
    print(f'{item} cubed {result}')
    return result

# Note that the first and last functions in the pipeline do not need to
# declare the input and output queue names respectively.

if __name__ == '__main__':
    input_data = range(30)

    # Always set your input data before calling .run()
    automator.set_input_data(input_data)
    results = automator.run()
    print(results)

Using the MultiprocessingMaybe interface

from queue_automator.maybe import MultiprocessMaybe


def do_work(item: int) -> int:
    sleep(2)
    result = item*2
    print(f'{item} times two {result}')
    return result


def do_work_2(item: int) -> int:
    sleep(2)
    result = item**2
    print(f'{item} squared {result}')
    return result


def do_work_3(item: int) -> int:
    sleep(2)
    result = item**3
    print(f'{item} cubed {result}')
    return result


if __name__ == '__main__':
    result = MultiprocessMaybe() \
        .insert(range(10)) \
        .then(do_work) \
        .insert(range(10, 20)) \
        .then(do_work_2) \
        .insert(range(20, 30)) \
        .maybe(do_work_3, default=0)

    print(result)

Cautions

As with anything, this is not a silver bullet that gets rid of all problems using python multiprocessing

There are some caveats when using this library:

• Launching processes in python is an expensive operation, as it spawns a separate instance of the interpreter. The performance gains could be offset by the time it takes to spawn a process

• Try to keep the number of processes in line with your CPU cores, spawning a ton of them could result in slower performance overall.

• The input objects of every worker function need to be serializable or pickable. This is a limitation of python multiprocessing. If you are dealing with complex objects try to convert them to a suitable format before processing, or implement the __reduce__, __repr__ or __dict__ methods in your classes.

• It is important that you try to keep your worker functions pure, which means that they should not have side effects.

• The .run() method should be called from your main entry point or a function that is called at your main entry point, (this is another limitation of python's multiprocessing)

• Try to optimize the number of process depending of how long a task takes, prioritize longer running tasks.