Python Django 的多线程
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Multithreading for Python Django
提问by tomcounsell
Some functions should run asynchronously on the web server. Sending emails or data post-processing are typical use cases.
某些功能应在 Web 服务器上异步运行。发送电子邮件或数据后处理是典型的用例。
What is the best (or most pythonic) way write a decorator function to run a function asynchronously?
编写装饰器函数以异步运行函数的最佳（或最pythonic）方法是什么？
My setup is a common one: Python, Django, Gunicorn or Waitress, AWS EC2 standard Linux
我的设置很常见：Python、Django、Gunicorn 或 Waitress、AWS EC2 标准 Linux
For example, here's a start:
例如，这是一个开始：
from threading import Thread
def postpone(function):
def decorator(*args, **kwargs):
t = Thread(target = function, args=args, kwargs=kwargs)
t.daemon = True
t.start()
return decorator
desired usage:
所需用途：
@postpone
def foo():
pass #do stuff
采纳答案by tomcounsell
I've continued using this implementation at scale and in production with no issues.
我一直在规模和生产中继续使用这个实现，没有任何问题。
Decorator definition:
装饰器定义：
def start_new_thread(function):
def decorator(*args, **kwargs):
t = Thread(target = function, args=args, kwargs=kwargs)
t.daemon = True
t.start()
return decorator
Example usage:
用法示例：
@start_new_thread
def foo():
#do stuff
Over time, the stack has updated and transitioned without fail.
随着时间的推移，堆栈不断更新和转换。
Originally Python 2.4.7, Django 1.4, Gunicorn 0.17.2, now Python 3.6, Django 2.1, Waitress 1.1.
最初是 Python 2.4.7、Django 1.4、Gunicorn 0.17.2，现在是 Python 3.6、Django 2.1、Waitress 1.1。
If you are using any database transactions, Django will create a new connection and this needs to be manually closed:
如果您正在使用任何数据库事务，Django 将创建一个新连接，这需要手动关闭：
from django.db import connection
@postpone
def foo():
#do stuff
connection.close()
回答by Thomas Orozco
The most common way to do asynchronous processing in Django is to use Celeryand django-celery.
在 Django 中进行异步处理的最常见方法是使用Celery和django-celery.
回答by Glyn Hymanson
回答by bombs
tomcounsell's approach works well if there are not too many incoming jobs. If many long-lasting jobs are run in short period of time, therefore spawning a lot of threads, the main process will suffer. In this case, you can use a thread pool with a coroutine,
如果传入的工作不多，tomcounsell 的方法效果很好。如果在短时间内运行许多持久作业，从而产生大量线程，主进程将受到影响。在这种情况下，您可以使用带有协程的线程池，
# in my_utils.py
from concurrent.futures import ThreadPoolExecutor
MAX_THREADS = 10
def run_thread_pool():
"""
Note that this is not a normal function, but a coroutine.
All jobs are enqueued first before executed and there can be
no more than 10 threads that run at any time point.
"""
with ThreadPoolExecutor(max_workers=MAX_THREADS) as executor:
while True:
func, args, kwargs = yield
executor.submit(func, *args, **kwargs)
pool_wrapper = run_thread_pool()
# Advance the coroutine to the first yield (priming)
next(pool_wrapper)
from my_utils import pool_wrapper
def job(*args, **kwargs):
# do something
def handle(request):
# make args and kwargs
pool_wrapper.send((job, args, kwargs))
# return a response
