参考博客: www.cnblogs.com/yuanchenqi/articles/5733873.html
并发:一段时间内做一些事情
并行:同时做多件事情
线程是操作系统能够进行运算调度的基本单位,一个线程就是一个指令集
IO 密集型任务或函数 计算密集型任务函数
t1 = threading.Thread( target=foo, args=( , ))
t1.start()
# _author: lily
# _date: 2019/1/29 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)
t1.start()
t2.start()
# _author: lily
# _date: 2019/1/28 import threading
import time def music(func):
for i in range(2):
print('listening to music %s.%s' % (func, time.ctime()))
time.sleep(1)
print('end listening %s' % time.ctime()) def move(func):
for i in range(2):
print('watching at the %s.%s' % (func, time.ctime()))
time.sleep(5)
print('end watching %s' % time.ctime()) threads = []
t1 = threading.Thread(target=music, args=('七里香', ))
threads.append(t1)
t2 = threading.Thread(target=move, args=('阿甘正传', ))
threads.append(t2)
if __name__ == '__main__':
for t in threads:
t.start()
print('all over %s' % time.ctime())
GIL: 全局解释器锁。 对于一个进程,在同一时刻,python解释器中只允许一个线程运行。
结论:在 python里,如果是 io 密集型,可以用多线程
计算密集型,改 C。
守护线程: t.setDaemon(True) 当主线程结束之后就认为程序执行完毕,不会等待 t 线程执行完毕。
得到当前线程: print(threading.current_thread())
得到当前活着的线程: print(threading.active_count())
同步锁:
原因:1. 线程共享同一资源,且进行 IO 阻塞时,对资源的操作容易被覆盖
- 使用 join 就会造成船串行,失去了多线程的意义
使用:r = threading.Lock()
同步锁与GIL关系:
没有GIL ,使用同步锁,可以达到一样得效果。
# _author: lily
# _date: 2019/1/29 import time
import threading num = 100 def add():
global num
# num -= 1 r.acquire()
temp = num
# time.sleep(0.0000001)
print('ok')
num = temp - 1
r.release() thread_list = [] r = threading.Lock()
for i in range(100):
t = threading.Thread(target=add)
t.start()
thread_list.append(t) for thd in thread_list:
thd.join() print('final num: ', num)
线程死锁和递归锁:
lock = threading.Lock()
lock = threading.RLock()
# _author: lily
# _date: 2019/1/29
import threading
import time class MyThread(threading.Thread):
def __init__(self, name):
threading.Thread.__init__(self)
self.name = name def run(self):
self.do_a()
self.do_b() def do_a(self):
# lock_a.acquire()
my_lock.acquire()
print('do_a: thread %s get lock A' % self.name)
time.sleep(3)
my_lock.acquire()
print('do_a: thread %s get lock B' % self.name)
# lock_b.release()
# lock_a.release()
my_lock.release()
my_lock.release() def do_b(self):
# lock_b.acquire()
my_lock.acquire()
print('do_b: thread %s get lock B' % self.name)
time.sleep(2)
# lock_a.acquire()
my_lock.acquire()
print('do_b: thread %s get lock A' % self.name)
# lock_a.release()
# lock_b.release()
my_lock.release()
my_lock.release() # lock_a = threading.Lock()
# lock_b = threading.Lock()
my_lock = threading.RLock()
thread_list = [] for i in range(5):
t = MyThread(i)
thread_list.append(t)
t.start() for t in thread_list:
t.join()