README.md

parallelism

Unlock advanced task scheduling in Python with parallelism. Seamlessly coordinate parallel and concurrent execution, optimizing performance while ensuring code integrity. Embrace expert scheduling techniques to elevate your programming to new heights of efficiency and responsiveness.

Installation

You can install this package using pip:

pip install parallelism

Basic Usage

Explore an effortless approach to task creation and management. Follow these steps to get started:

1. Import the necessary classes and functions in your Python code.

# Built-in modules
from multiprocessing import Process
from threading import Thread

# Third-party libraries
from parallelism import scheduled_task, task_scheduler

2. Define your task functions. These user-defined functions will be executed in parallel and concurrently.

def func(*args, **kwargs):
    if not args and not kwargs:
        raise ValueError('Missing *args or **kwargs')
    return args, kwargs

3. Create task instances using the scheduled_task function, specifying the execution unit (Process or Thread), task name, function, and provide any required positional or keyword arguments.

task1 = scheduled_task(Process, 'task1', func, args=(1, 2, 3), continual=True)
task2 = scheduled_task(Process, 'task2', func, kwargs={'a': 10, 'b': 20}, continual=True)
task3 = scheduled_task(Thread, 'task3', func, continual=True)

4. Schedule tasks using the task_scheduler function, Specify the tasks to be executed along with the desired number of processes and threads.

result = task_scheduler(tasks=(task1, task2, task3), processes=2, threads=4)

5. Access task execution details and results through the result object, providing insights into execution times, elapsed times, exceptions, and return values:

>>> result.execution_time
{
    'task1': datetime.datetime(%Y, %m, %d, %H, %M, %S, %f),
    'task2': datetime.datetime(%Y, %m, %d, %H, %M, %S, %f),
    'task3': datetime.datetime(%Y, %m, %d, %H, %M, %S, %f),
}
>>> result.elapsed_time
{
    'task1': float(...),
    'task2': float(...),
    'task3': float(...),
}
>>> result.raise_exception
{
    'task3': ValueError('Missing *args or **kwargs'),
}
>>> result.return_value
{
    'task1': ((1, 2, 3), {}),
    'task2': ((), {'a': 10, 'b': 20}),
}

For more comprehensive documentation and advanced usage, please refer to the full API Documentation.