thread-executor 0.1.2

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

Executor

Fast execute task with python and less mem ops

Why we need Thread Executor?

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

• First, threading module folk threads but python not commit late time. Then know your thread can run, but you don't know when? It's oke fast with small traffic but when server high load you will have some problem, high pressure on memory because when you create too many thread cause slowness. waste of time

• 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. It can put more pressure to 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 bring 2 importance field is: func and args and you can call them like 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 using when coding.

Disadvance?

• 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 Jobs in Queue that have not been processed will be lost.

Installtion

Now it's time you can install lib and experience

pip install thread-executor

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)