Python 线程与进程

线程是操作系统能够进行运算调度的最小单位。它被包含在进程之中，是进程中的实际运作单位。一条线程指的是进程中一个单一顺序的控制流，一个进程中可以并发多个线程，每条线程并行执行不同的任务。

使用 threading 模块

方法一：

import threading
import time

def foo(n):
    print('foo %s'%n)
    time.sleep(1)
    print('end foo')


def bar(n):
    print('bar %s'%n)
    time.sleep(2)
    print('end bar')

t1 = threading.Thread(target=foo, args=(1,))
t2 = threading.Thread(target=bar, args=(2,))
t1.start()
t2.start()

print('........in the main..........')

运行结果：
foo 1
bar 2
........in the main..........
end foo
end bar

方法二：

import time, threading

class MyThread(threading.Thread):

    def __init__(self, num):
        threading.Thread.__init__(self)
        self.num = num

    def run(self):             #定义线程要运行的函数
        print("running on number:%s" % self.num)
        time.sleep(3)

if __name__ == '__main__':
    t1 = MyThread(1)
    t2 = MyThread(2)
    t1.start()
    t2.start()

运行结果:
running on number:1
running on number:2

join 方法使得主线程等待子线程完成才继续

import threading
import time

begin = time.time()

def foo(n):
    print('foo %s'%n)
    time.sleep(1)
    print('end foo')


def bar(n):
    print('bar %s'%n)
    time.sleep(2)
    print('end bar')

t1 = threading.Thread(target=foo, args=(1,))
t2 = threading.Thread(target=bar, args=(2,))
t1.start()
t2.start()


t1.join()
t2.join()
print('........in the main..........')

运行结果:
foo 1
bar 2
end foo
end bar
........in the main..........

在计算密集型任务中串行与多线程进行对比

import threading, time

begin = time.time()

def add(n):
    sum = 0
    for i in range(n):
        sum += i
    print(sum)

add(100000000)
add(200000000)
end = time.time()
print(end-begin)

运行结果：
4999999950000000
19999999900000000
17.66856598854065


import threading, time

begin = time.time()

def add(n):
    sum = 0
    for i in range(n):
        sum += i
    print(sum)

t1 = threading.Thread(target=add, args=(100000000,))
t1.start()
t2 = threading.Thread(target=add, args=(200000000,))
t2.start()
t1.join()
t2.join()
end = time.time()
print(end-begin)

运行结果：
4999999950000000
19999999900000000
21.088160276412964

# 结果为串行运行比多线程运行更快

Cpython 中有 GIL (Global Interpreter Lock，全局解释器锁)，所以在同一时刻，只能有一个线程进入调度。如果任务是IO密集型的，可以使用多线程；如果任务是计算密集型的，最优方法是改成 C。

---

setDaemon()

调用该方法只要是主线程完成，不管子线程是否完成都要和主线程一起退出。

threading.currentThread()

返回当前的线程变量。

threading.active_count()

返回正在运行的线程数量。

import threading, time
from time import ctime,sleep

def music(func):
    print(threading.current_thread())
    for i in range(2):
        print("Begin listening to %s. %s" %(func, ctime()))
        sleep(2)
        print("end listening %s" %ctime())


def movie(func):
    print(threading.current_thread())
    for i in range(2):
        print("Begin watching at the %s %s" %{func, ctime()})
        sleep(4)
        print("end watching %s" %ctime())


threads = []
t1 = threading.Thread(target=music, args=('klvchen',))
threads.append(t1)
t2 = threading.Thread(target=movie, args=('lili',))
threads.append(t2)


if __name__ == '__main__':
    for t in threads:
        t.setDaemon(True)
        t.start()

    print(threading.current_thread())
    print(threading.active_count())
    print("all over %s" %ctime())

运行结果：
<Thread(Thread-1, started daemon 5856)>
Begin listening to klvchen. Wed Jul 11 23:43:51 2018
<Thread(Thread-2, started daemon 9124)>
<_MainThread(MainThread, started 9444)>
3
all over Wed Jul 11 23:43:51 2018