如何在django视图中使用python多处理模块

Question

I have a simple function that go over a list of URLs, using GET to retrieve some information and update the DB ( PostgresSQL ) accordingly. The function works perfect. However, going over each URL one at a time talking too much time.

Using python, I'm able to do to following to parallel these tasks:

from multiprocessing import Pool

def updateDB(ip):
     code goes here...

if __name__ == '__main__':
    pool = Pool(processes=4)              # process per core
    pool.map(updateDB, ip)

This is working pretty well. However, I'm trying to find how do the same on django project. Currently I have a function (view) that go over each URL to get the information, and update the DB.

The only thing I could find is using Celery, but this seems to be a bit overpower for the simple task I want to perform.

Is there anything simple that i can do or do I have to use Celery?

Answer 1

Though using Celery may seem an overkill, it is a well-known way of doing asynchronous tasks. Essentially Django serves WSGI request-response cycle which knows nothing of multiprocessing or background tasks.

Here are alternative options:

• Django background tasks - might fit your case better.
• Redis queue

Answer 2

Currently I have a function (view) that go over each URL to get the information, and update the DB.

It means response time does not matter for you and instead of doing it in the background (asynchronously), you are OK with doing it in the foreground if your response time is cut by 4 (using 4 sub-processes/threads). If that is the case you can simply put your sample code in your view. Like

from multiprocessing import Pool

def updateDB(ip):
     code goes here...

def my_view(request):
    pool = Pool(processes=4)              # process per core
    pool.map(updateDB, ip)
    return HttpResponse("SUCCESS")

But, if you want to do it asynchronously in the background then you should use Celery or follow one of @BasicWolf's suggestions.

Answer 3

I will recommend to use gevent for multithreading solution instead of multiprocessing. Multiprocessing can cause problem in production environment where spawning new processes are restricted.

Example code:

from django.shortcuts import HttpResponse
from gevent.pool import Pool

def square(number):
    return number * number

def home(request):
    pool = Pool(50)
    numbers = [1, 3, 5]
    results = pool.map(square, numbers)
    return HttpResponse(results)