Skip to content

Executors that can automatically scale the number of workers up and down.

License

Notifications You must be signed in to change notification settings

leavers/flexexecutor

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

25 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Flexexecutor

Testing Package version Python

Flexexecutor provides executors that can automatically scale the number of workers up and down.

Overview

Flexexecutor implements several subclasses of concurrent.futures.Executor. Like the built-in ThreadPoolExecutor and ProcessPoolExecutor, they create multiple workers to execute tasks concurrently. Additionally, it can shut down idle workers to save resources. These subclasses are:

  • ThreadPoolExecutor - thread concurrency, same name as the built-in class and can be a in-place replacement.
  • AsyncPoolExecutor - coroutine concurrency. Note that in flexexecutor's implementation, all coroutines are executed in a dedicated worker thread.
  • ProcessPoolExecutor - flexexecutor directly imports the built-in implementation as it already has scaling capabilities.

Features

  • Supports various concurrency modes: threads, processes and coroutines.
  • Automatically shut down idle workers to save resources.
  • Single file design, keeps the code clean and easy for hackers to directly take away and add more features.

Installation

Flexexecutor is available on PyPI:

pip install flexexecutor

Usage

ThreadPoolExecutor

from flexexecutor import ThreadPoolExecutor


def task(i):
    import time

    print(f"task {i} started")
    time.sleep(1)

if __name__ == "__main__":
    with ThreadPoolExecutor(
        # 1024 is the default value of max_workers, since workers are closed if they are
        # idle for some time, you can set it to a big value to get better short-term
        # performance.
        max_workers=1024,
        # Timeout for idle workers.
        idle_timeout=60.0,
        # These parameters are given for compatibility with the built-in
        # `ThreadPoolExecutor`, I don't use them very often, do you?
        thread_name_prefix="Task",
        initializer=None,
        initargs=(),
    ) as executor:
        for i in range(1024):
            executor.submit(task, i)

AsyncPoolExecutor

from flexexecutor import AsyncPoolExecutor


async def task(i):
    import asyncio

    print(f"task {i} started")
    await asyncio.sleep(1)

if __name__ == "__main__":
    # AsyncPoolExecutor behaves just like ThreadPoolExecutor except it only accepts
    # coroutine functions.
    with AsyncPoolExecutor(
        # Default value of max_workers is huge, if you don't like it, set it smaller.
        max_workers=1024,
        # Idle timeout for the working thread.
        idle_timeout=60.0,
        # These parameters are given for compatibility with the built-in
        # `ThreadPoolExecutor`, I don't use them very often, do you?
        thread_name_prefix="Task",
        initializer=None,
        initargs=(),
    ) as executor:
        for i in range(1024):
            executor.submit(task, i)

ProcessPoolExecutor

ProcessPoolExecutor in flexexecutor is just the same as the built-in concurrent.futures.ProcessPoolExecutor, we just import it directly for convenience.

About

Executors that can automatically scale the number of workers up and down.

Topics

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages