- Python 的thread模块是比较底层的模块,Python的threading模块是对thread做了一些包装,可以更加方便的
被使用;
1. 使用threading
模块
# 示例一: 单线程执行
import time
def say_happy():
print("Happy Birthday!")
time.sleep(2)
if __name__ == "__main__":
for i in range(5):
say_happy()
# 示例二: 多线程执行
import threading
import time
def say_happy():
print("Happy Birthday!")
time.sleep(2)
if __name__ == "__main__":
for i in range(5):
t = threading.Thread(target=say_happy)
t.start() # 启动线程,即让线程开始执行
# 示例三: 第二种创建多线程的方式
import threading
import time
class MyThread(threading.Thread):
def __init__(self, num):
threading.Thread.__init__(self)
self.num = num
def run(self): # 定义每个线程要运行的函数
print('running on number: %s' % self.num)
time.sleep(3)
if __name__ == '__main__':
t1 = MyThread(1)
t2 = MyThread(2)
t3 = MyThread(3)
t1.start()
t2.start()
t3.start()
2. 多线程之间共享全局变量
# 示例:
from threading import Thread
import time
g_num = 100
def work1():
global g_num
for i in range(3):
g_num += 1
print("=== in work1, g_num is %d ===" % g_num)
def work2():
global g_num
print("=== in work2, g_num is %d ===" % g_num)
print("=== 线程创建之前g_num is %d ===" % g_num)
t1 = Thread(target=work1)
t1.start()
# 延时一会儿, 保证t1线程中的事情做完
time.sleep(1)
t2 = Thread(target=work2)
t2.start()
# 输出:
# === 线程创建之前g_num is 100 ===
# === in work1, g_num is 103 ===
# === in work2, g_num is 103 ===
# 示例二: 多线程共享变量的问题
from threading import Thread
import time
g_num = 0
def test1():
global g_num
for i in range(1000000):
g_num += 1
print("=== test1 === g_num=%d" % g_num)
def test2():
global g_num
for i in range(1000000):
g_num += 1
print("=== test2 === g_num=%d" % g_num)
p1 = Thread(target=test1)
p1.start()
# time.sleep(3)
p2 = Thread(target=test2)
p2.start()
print("=== g_num=%d" % g_num)
# 输出:
# === g_num=281358
# === test1 === g_num=1477304
# === test2 === g_num=1531631
# 示例三: 列表当作实参传递到线程中
from threading import Thread
import time
def work1(nums):
nums.append(44)
print("=== in work1 ===", nums)
def work2(nums):
# 延时一会儿,保证t1线程中的事情做完
# 这里的参数,不需要使用 global 关键字
time.sleep(1)
print("=== in work2 ===", nums)
g_nums = [11, 22, 33]
t1 = Thread(target=work1, args=(g_nums,))
t1.start()
t2 = Thread(target=work2, args=(g_nums,))
t2.start()
3. 互斥锁
# 示例:
from threading import Thread, Lock
import time
g_num = 0
def test1():
global g_num
# 上锁
mutex.acquire()
for i in range(1000000):
g_num += 1
# 释放锁
mutex.release()
print("=== test1 === g_num=%d" % g_num)
def test2():
global g_num
mutex.acquire()
for i in range(1000000):
g_num += 1
# 释放锁
mutex.release()
print("=== test2 === g_num=%d" % g_num)
# 创建一把互斥锁
mutex = Lock()
p1 = Thread(target=test1)
p1.start()
p2 = Thread(target=test2)
p2.start()
# 输出:
# === test1 === g_num=1000000
# === test2 === g_num=2000000
4. 死锁
# 示例
import threading
import time
class MyThread1(threading.Thread):
def run(self):
if mutexA.acquire():
print(self.name + "=== do1 === up ===")
time.sleep(1)
if mutexB.acquire():
print(self.name + "=== do1 === down ===")
mutexB.release()
mutexA.release()
class MyThread2(threading.Thread):
def run(self):
if mutexB.acquire():
print(self.name + "=== do2 === up ===")
time.sleep(1)
if mutexA.acquire():
print(self.name + "=== do2 === down ===")
mutexA.release()
mutexB.release()
mutexA = threading.Lock()
mutexB = threading.Lock()
# lock = threading.RLock() # 递归锁(可重用锁)
if __name__ == '__main__':
t1 = MyThread1()
t2 = MyThread2()
t1.start()
t2.start()
5. 信号量(Semaphore)
- 信号量用来控制线程并发数的,BoundedSemaphore或Semaphore管理一个内置的计数器,每当调用
acquire()
时-1,调用release()
时,+1; - 计数器不能小于0,当计数器为0时,
acquire()
将阻塞线程至同步锁定状态,直至其他线程调用release()
; -
BoundedSemaphore
与Semaphore
的唯一区别在于前者将在调用release
时,检查计数器的值是否超过了计
数器的初始值,如果超过了将抛出一个异常;
# 示例:
import threading
import time
class myThread(threading.Thread):
def run(self):
if semaphore.acquire():
print(self.name)
time.sleep(5)
semaphore.release()
if __name__ == '__main__':
semaphore = threading.BoundedSemaphore(5) # 创建信号量锁
thrs = []
for i in range(100):
thrs.append(myThread())
for t in thrs:
t.start()
6. 条件变量同步(Condition)
- 有一类线程需要满足条件之后,才能够继续执行,Python提供了
threading.Condition
对象用于条件变量线程的支
持,它除了能提供RLock()
或Lock()
的方法外,还提供了wait()
,notify()
,notifyAll()
方法; -
wait()
: 条件不满足时调用,线程会释放锁并进入等待阻塞; -
notify()
: 条件创造后调用,通知等待池激活一个线程; -
notifyAll()
: 条件创造后调用,通知等待池激活所有线程;
# 示例一:
lock_con = threading.Condition([Lock/Rlock]): 锁参数是可选选项,不传入锁,对象自动创建一个 RLock()
# 示例二:
import threading
import time
from random import randint
class Producer(threading.Thread):
def run(self):
global L
while True:
val = randint(0, 100)
print('生产者', self.name, ':Append' + str(val), L)
if lock_con.acquire():
L.append(val)
lock_con.notify()
lock_con.release()
time.sleep(3)
class Customer(threading.Thread):
def run(self):
global L
while True:
lock_con.acquire()
if len(L) == 0:
lock_con.wait()
print('消费者', self.name,':Delete'+str(L[0]), L)
del L[0]
lock_con.release()
time.sleep(0.25)
if __name__ == '__main__':
L = []
lock_con = threading.Condition()
threads = []
for i in range(5):
threads.append(Producer())
threads.append(Customer())
for t in threads:
t.start()
for t in threads:
t.join()
7. 同步条件(Event)
- 条件同步和条件变量同步差不多意思,只是少了锁功能,因为条件同步设计于不访问共享资源的条件环境。
-
event = threading.Event()
: 条件环境对象,初始值为False; -
event.isSet()
: 返回event的状态值; -
event.wait()
: 如果 event.isSet() == False
将阻塞线程; -
event.set()
: 设置event的状态值为True,所有阻塞池的线程激活进入就绪状态,等待操作系统调度; -
event.clear()
: 恢复event的状态值为False;
# 示例一:
import threading
import time
class Boss(threading.Thread):
def run(self):
print('Boss: 今晚大家都要加班到22:00')
event.isSet() or event.set()
time.sleep(5)
print('Boss: <22:00>可以下班了')
event.isSet() or event.set()
class Worker(threading.Thread):
def run(self):
event.wait()
print('Worker: 哎.....命苦啊!')
time.sleep(0.25)
event.clear()
event.wait()
print('Worker: Oh Yeah!')
if __name__ == '__main__':
event = threading.Event()
threads = []
for i in range(5):
threads.append(Worker())
threads.append(Boss())
for t in threads:
t.start()
for t in threads:
t.join()
# 示例二: 红绿灯
import threading
import time
import random
def light():
if not event.isSet():
event.set() # 绿灯状态
count = 0
while True:
if count < 10:
print('\033[42;1m--green light on---\033[0m')
elif count < 13:
print('\033[43;1m--yellow light on---\033[0m')
elif count < 20:
if event.isSet():
event.clear()
print('\033[41;1m--red light on---\033[0m')
else:
count = 0
event.set() #打开绿灯
time.sleep(1)
count += 1
def car(n):
while 1:
time.sleep(random.randrange(10))
if event.isSet(): # 绿灯
print('car [%s] is running...' % n)
else:
print('car [%s] is waiting for the red light...' %n)
if __name__ == '__main__':
event = threading.Event()
Light = threading.Thread(target = light)
Light.start()
for i in range(3):
t = threading.Thread(target = car, args = (i,))
t.start()
8. 生产者与消费者模式
# 示例:
from queue import Queue
class Producer(threading.Thread):
def run(self):
global queue
count = 0
while True:
if queue.qsize() < 1000:
for i in range(100):
count = count + 1
msg = "生成产品" + str(count)
queue.put(msg)
time.sleep(0.5)
class Consumer(threading.Thread):
def run(self):
global queue
while True:
if queue.qsize() > 100:
for i in range(3):
msg = self.name + "消费了" + queue.get()
print(msg)
time.sleep(1)
if __name__ == '__main__':
queue = Queue()
for i in range(500):
queue.put("初始产品"+str(i))
for i in range(2):
p = Producer()
p.start()
for i in range(5):
c = Consumer()
c.start()
9. ThreadLocal 对象在线程中的使用
# 示例:
import threading
# 创建全局 ThreadLocal 对象
local_school = threading.local()
def process_student():
# 获取当前线程关联的student
std = local_school.student
print("Hello, %s (in %s)" % (std, threading.current_thread().name))
def process_thread(name):
# 绑定 ThreadLocal 的 student
local_school.student = name
process_student()
t1 = threading.Thread(target=process_thread, args=("zhangsan",), name="Thread-A")
t2 = threading.Thread(target=process_thread, args=("lisi",), name="Thread-B")
t1.start()
t2.start()
t1.join()
t2.join()
# 输出:
# Hello, zhangsan (in Thread-A)
# Hello, lisi (in Thread-B)
10. 异步
# 示例:
from multiprocessing import Pool
import time
import os
def test():
print("=== 进程池中的进程 === pid=%d, ppid=%d==" % (os.getpid(), os.getppid()))
for i in range(3):
print("=== %d ===" % i)
time.sleep(1)
return "hehe"
def test2(args):
print("=== callback func == pid=%d" % os.getpid())
print("=== callback func == args=%s" % args)
pool = Pool(3)
pool.apply_async(func=test, callback=test2)
time.sleep(5)
print("=== 主进程 = pid=%d ===" % os.getpid())
11. Join 和 Daemon
- join完成的工作就是线程同步,即主线程任务结束之后,进入阻塞状态,一直等待其他的子线程执行结束之后,主线程才终止;
- 将线程声明为守护进程,必须在
start()
方法调用之前设置,这个方法基本和join是相反的; - 当我们在程序运行中,执行一个主线程,如果主线程又创建一个子线程,主线程和子线程就兵分两路,分别运行,那么当
主线程完成想退出时,会检验子线程是否完成。如果子线程未完成,则主线程会等待子线程完成后再退出。 - 但是,有时候我们需要的是只要主线程完成了,不管子线程是否完成,都要和主线程一起退出,这时,就可以使用
setDaemon
方法;
# 示例:
import threading
from time import ctime, sleep
import time
def music(func):
for i in range(2):
print('Begin listening to %s. %s' % (func, ctime()))
sleep(2)
print('end listening %s' % ctime())
def movie(func):
for i in range(2):
print('Begin watching at the %s! %s' % (func, ctime()))
sleep(3)
print('end watching %s'% ctime())
threads = []
t1 = threading.Thread(target = music, args = ('七里香',))
threads.append(t1)
t2 = threading.Thread(target = movie, args = ('阿甘正传',))
threads.append(t2)
if __name__ == '__main__':
for t in threads:
t.setDaemon(True)
t.start()
print(threading.current_thread()) # 打印当前线程名称
print(threading.active_count()) #
print('all over %s', % ctime)
12. 线程中的队列(Queue)
-
Queue([maxsize])
:FIFO队列模式,[maxsize]定义队列容量; 缺省,即无穷大; -
LifoQueue([maxsize])
:LIFO队列模式; -
PriorityQueue([maxsize])
:优先级队列模式,使用此队列时,项目应该是(priority,data)
的形式;
# 示例一:
import queue
q = queue.Queue(maxsize = 10)
# 存入一个值
q.put(item, block=True, timeout=None)
# block 为可选参数,如果队列满了的话,put(item),调用者将被阻塞, 而put(item,0)会报Full异常;
# 取出一个值
q.get(item, block=False, timeout=None)
# block 为可选参数,如果队列为空, get(item,0)会报Empty异常;
# 常用方法:
# q.qsize(): 返回队列大小;
# q.full(): 如果队列满了,返回True,否则返回False;
# q.empty(): 如果队列为空,返回True,否则返回False;
# 示例二:
import threading
import queue
from time import sleep
from random import randint
class Production(threading.Thread):
def run(self):
while True:
r = randint(0, 100)
q.put(r)
print('生产出来%s号包子' % r)
sleep(1)
class Process(threading.Thread):
def run(self):
while True:
re = q.get()
print('吃掉%s号包子' % re)
if __name__ == '__main__':
q = queue.Queue(10)
threads = [Production(), Production(), Production(), Process()]
for t in threads:
t.start()