例如,如果您有一个线程工作池,并且每个线程都需要访问其自己的资源(例如网络或数据库连接),则线程本地存储很有用。请注意,该threading
模块使用常规的线程概念(可以访问进程全局数据),但是由于全局解释器锁定,它们并不是太有用。不同的multiprocessing
模块会为每个模块创建一个新的子流程,因此任何全局变量都将是线程局部的。
这是一个简单的示例:
import threading
from threading import current_thread
threadLocal = threading.local()
def hi():
initialized = getattr(threadLocal, 'initialized', None)
if initialized is None:
print("Nice to meet you", current_thread().name)
threadLocal.initialized = True
else:
print("Welcome back", current_thread().name)
hi(); hi()
这将打印出:
Nice to meet you MainThread
Welcome back MainThread
一件很容易被忽略的重要事情:一个threading.local()
对象只需要创建一次,而不是每个线程一次或每个函数调用一次。的global
或class
水平的理想地点。
这就是为什么:threading.local()
每次调用它时都会实际上创建一个新实例(就像任何工厂或类调用一样),因此threading.local()
多次调用会不断覆盖原始对象,这很可能不是您想要的。当任何线程访问现有threadLocal
变量(或任何被调用的变量)时,它将获得该变量的私有视图。
这将无法正常工作:
import threading
from threading import current_thread
def wont_work():
threadLocal = threading.local() #oops, this creates a new dict each time!
initialized = getattr(threadLocal, 'initialized', None)
if initialized is None:
print("First time for", current_thread().name)
threadLocal.initialized = True
else:
print("Welcome back", current_thread().name)
wont_work(); wont_work()
将产生以下输出:
First time for MainThread
First time for MainThread
因为multiprocessing
模块为每个线程创建一个新进程,所以所有全局变量都是线程局部的。
考虑以下示例,其中processed
计数器是线程本地存储的示例:
from multiprocessing import Pool
from random import random
from time import sleep
import os
processed=0
def f(x):
sleep(random())
global processed
processed += 1
print("Processed by %s: %s" % (os.getpid(), processed))
return x*x
if __name__ == '__main__':
pool = Pool(processes=4)
print(pool.map(f, range(10)))
Processed by 7636: 1
Processed by 9144: 1
Processed by 5252: 1
Processed by 7636: 2
Processed by 6248: 1
Processed by 5252: 2
Processed by 6248: 2
Processed by 9144: 2
Processed by 7636: 3
Processed by 5252: 3
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
…当然,线程ID以及每个线程ID和每个命令的计数会因运行而异。