thread-executor 0.1.3

A Python library for executing tasks in parallel with threads and queues

Executor

Fast execute task with python threading and efficient mem ops

Installation

pip install thread-executor

Why do we need Thread Executor?

Python threading module is a great structure, it helps developers to folk a thread to run some background tasks. Python have Queue mechanism to connect thread's data. But, what is the problem??

• First, threading module create threads but number of threads that can be created is depended on the hardware. So there is a limit number of threads that can be created. It's fast and lightweight with small traffic but when server is high load you will have some problem, high pressure on memory because you can't create too many threads. can't create more threads

• Second, when you create and release threads many times, it'll increase memory and CPUs time of system. Sometime, developers did not handle exceptions and release thread cause thread leak(memory leak). It can put more pressure on the application. waste of resource

How to resolve problem??

This's my resolver.

• We create exact or dynamic number of threads. Then using Job - a unit bring data information to Worker to process. Workers don't need to release, and you only create 1 time or reset it when you update config.

• Job brings 2 importance fields: func and args and you can call them by func(*args) and get all the results and return on callback is optional.

• Your app doesn't need to create and release threads continuously

• Easy to access and use when coding.

Disadvantage?

• If you use callback then remembered to add try catch to handle thread leaked.
• If queue is full you need to wait for available queue slot. set max_queue_size=0 to avoid this.
• If you restart your app, all the Job in Queue that have not been processed will be lost.

Usage : Interface list

send(job: Job) -> None # Push a job to the queue
wait() -> None # wait for all jobs to be completed without blocking each other
scale_up(number_threads: int) -> None # scale up number of threads
scale_down(self, number_threads: int) -> None # scale down number of threads

Initial

from executor.safe_queue import Executor, Job

engine = Executor(number_threads=10, max_queue_size=0)

Send Simple Job

import time

def test_exec(*args, **kwargs):
    time.sleep(3)
    print(args)
    return [1, 2, 3]


def test_exec1(*args, **kwargs):
    print(kwargs)
    time.sleep(2)
    return {"a": 1, "b": 2, "c": 3}

engine.send(Job(func=test_exec, args=(1, 2), kwargs={}, callback=None, block=False))
engine.send(Job(func=test_exec1, args=(), kwargs={"time": 1}, callback=None, block=False))
engine.send(Job(func=test_exec1, args=(), kwargs={}, callback=None, block=False))
engine.send(Job(func=test_exec1, args=(), kwargs={}, callback=None, block=False))
engine.send(Job(func=test_exec1, args=(), kwargs={}, callback=None, block=False))
engine.wait()

Send Job with callback

def call_back(result):
    print(result)
    
for i in range(5):
    engine.send(Job(func=test_exec1, args=(), kwargs={"time": 1}, callback=call_back, block=False))
engine.wait()

Thread scale up/down

engine.scale_up(3)
engine.scale_down(3)