Concurrent Programming Python 05 / deadlocks / recursive lock / semaphore / GIL lock / process pool / thread pool

Concurrent Programming Python 05 / deadlocks / recursive lock / semaphore / GIL lock / process pool / thread pool

1. Recalling yesterday

#生产者消费者模型.
#    生产者: 产生数据,
#    消费者: 接收数据并做下一步处理
#    容器: 队列.
#进程, 线程:
#   进程就是资源单位,线程就是执行单位.
#进程与线程的区别:
#   线程: 开销小,速度快,同一个进程下的线程资源内存级别共享.
#   进程: 开销巨大,速度慢, 不同进程的数据内存级别不共享.
#join: 阻塞,
#   t1.join()  阻塞.
#   print('主')
#getname, setname  .name
#activeCount() 线程的数量
守护线程: 如果守护线程的生命周期小于其他线程,则他肯定先结束,否则等待其他非守护线程和主线程结束之后结束.

#互斥锁,锁

2. deadlock with recursive lock

#递归锁可以解决死锁现象,业务需要多个锁时,先要考虑递归锁

2.1 deadlock

# from threading import Thread
# from threading import Lock
# import time
#
# lock_A = Lock()
# lock_B = Lock()
#
#
# class MyThread(Thread):
#
#     def run(self):
#         self.f1()
#         self.f2()
#
#
#     def f1(self):
#
#         lock_A.acquire()
#         print(f'{self.name}拿到了A锁')
#
#         lock_B.acquire()
#         print(f'{self.name}拿到了B锁')
#
#         lock_B.release()
#
#         lock_A.release()
#
#     def f2(self):
#
#         lock_B.acquire()
#         print(f'{self.name}拿到了B锁')
#
#         time.sleep(0.1)
#         lock_A.acquire()
#         print(f'{self.name}拿到了A锁')
#
#         lock_A.release()
#         lock_B.release()
#
# if __name__ == '__main__':
#
#     for i in range(3):
#         t = MyThread()
#         t.start()

2.2 recursive lock

递归锁有一个计数的功能, 原数字为0,上一次锁,计数+1,释放一次锁,计数-1,
只要递归锁上面的数字不为零,其他线程就不能抢锁.

# from threading import Thread
# from threading import RLock
# import time
#
# lock_A = lock_B = RLock()

# 递归锁有一个计数的功能, 原数字为0,上一次锁,计数+1,释放一次锁,计数-1,
# 只要递归锁上面的数字不为零,其他线程就不能抢锁.
# class MyThread(Thread):
#
#     def run(self):
#         self.f1()
#         self.f2()
#
#
#     def f1(self):
#
#         lock_A.acquire()
#         print(f'{self.name}拿到了A锁')
#
#         lock_B.acquire()
#         print(f'{self.name}拿到了B锁')
#
#         lock_B.release()
#
#         lock_A.release()
#
#     def f2(self):
#
#         lock_B.acquire()
#         print(f'{self.name}拿到了B锁')
#
#         time.sleep(0.1)
#         lock_A.acquire()
#         print(f'{self.name}拿到了A锁')
#
#         lock_A.release()
#
#         lock_B.release()
#
# if __name__ == '__main__':
#
#     for i in range(3):
#         t = MyThread()
#         t.start()

3. Semaphore

也是一种锁,控制并发数量

# from threading import Thread, Semaphore, current_thread
# import time
# import random
# sem = Semaphore(5)
#
# def task():
#     sem.acquire()
#
#     print(f'{current_thread().name} 厕所ing')
#     time.sleep(random.randint(1,3))
#
#     sem.release()
#
#
# if __name__ == '__main__':
#     for i in range(20):
#         t = Thread(target=task,)
#         t.start()

4.GIL Global Interpreter Lock

4.1 Background

#理论上来说:单个进程的多线程可以利用多核.

#但是,开发Cpython解释器的程序员,给进入解释器的线程加了锁.

Why lock 4.2

1. 当时都是单核时代,而且cpu价格非常贵.
2. 如果不加全局解释器锁, 开发Cpython解释器的程序员就会在源码内部各种主动加锁,解锁,非常麻烦,各种死锁现象等等.他为了省事儿,直接进入解释器时给线程加一个锁.
   优点: 保证了Cpython解释器的数据资源的安全.
   缺点: 单个进程的多线程不能利用多核.
#Jpython没有GIL锁.
#pypy也没有GIL锁.
#现在多核时代, 我将Cpython的GIL锁去掉行么?
#因为Cpython解释器所有的业务逻辑都是围绕着单个线程实现的,去掉这个GIL锁,几乎不可能.

单个进程的多线程可以并发,但是不能利用多核,不能并行.
多个进程可以并发,并行.

5.GIL difference between Lock locks

相同点: 都是同种锁,互斥锁.
不同点:
    GIL锁全局解释器锁,保护解释器内部的资源数据的安全. 
    GIL锁 上锁,释放无需手动操作.
    自己代码中定义的互斥锁保护进程中的资源数据的安全.
    自己定义的互斥锁必须自己手动上锁,释放锁.

6. IO intensive verification calculation intensive efficiency

6.1 IO-intensive

# IO密集型: 单个进程的多线程并发 vs 多个进程的并发并行

# def task():
#     count = 0
#     time.sleep(random.randint(1,3))
#     count += 1

# if __name__ == '__main__':

# 多进程的并发,并行
#     start_time = time.time()
#     l1 = []
#     for i in range(50):
#         p = Process(target=task,)
#         l1.append(p)
#         p.start()
#
#     for p in l1:
#         p.join()
#
#     print(f'执行效率:{time.time()- start_time}')  #  8.000000000

# 多线程的并发
#     start_time = time.time()
#     l1 = []
#     for i in range(50):
#         p = Thread(target=task,)
#         l1.append(p)
#         p.start()
#
#     for p in l1:
#         p.join()
#
#     print(f'执行效率:{time.time()- start_time}')  # 3.0294392108917236

对于IO密集型: 单个进程的多线程的并发效率高.

6.2 compute-intensive

#from threading import Thread
#from multiprocessing import Process
#import time
#import random
# # 计算密集型: 单个进程的多线程并发 vs 多个进程的并发并行
#
# def task():
#     count = 0
#     for i in range(10000000):
#         count += 1
#
#
# if __name__ == '__main__':
#
#     # 多进程的并发,并行
#     # start_time = time.time()
#     # l1 = []
#     # for i in range(4):
#     #     p = Process(target=task,)
#     #     l1.append(p)
#     #     p.start()
#     #
#     # for p in l1:
#     #     p.join()
#     #
#     # print(f'执行效率:{time.time()- start_time}')  # 3.1402080059051514
#
#     # 多线程的并发
#     # start_time = time.time()
#     # l1 = []
#     # for i in range(4):
#     #     p = Thread(target=task,)
#     #     l1.append(p)
#     #     p.start()
#     #
#     # for p in l1:
#     #     p.join()
#     #
#     # print(f'执行效率:{time.time()- start_time}')  # 4.5913777351379395

总结: 计算密集型: 多进程的并发并行效率高.

7. multithread socket communication

#无论是多线程还是多进程,如果按照上面的写法,来一个客户端请求,我就开一个线程,来一个请求开一个线程,

#应该是这样: 你的计算机允许范围内,开启的线程进程数量越多越好.

7.1 server

# import socket
# from threading import Thread
#
# def communicate(conn,addr):
#     while 1:
#         try:
#             from_client_data = conn.recv(1024)
#             print(f'来自客户端{addr[1]}的消息: {from_client_data.decode("utf-8")}')
#             to_client_data = input('>>>').strip()
#             conn.send(to_client_data.encode('utf-8'))
#         except Exception:
#             break
#     conn.close()
#
# def _accept():
#     server = socket.socket()
#     server.bind(('127.0.0.1', 8848))
#     server.listen(5)
#
#     while 1:
#         conn, addr = server.accept()
#         t = Thread(target=communicate,args=(conn,addr))
#         t.start()
#
# if __name__ == '__main__':
#     _accept()

7.2 Client

# import socket
# client = socket.socket()
# client.connect(('127.0.0.1',8848))
#
# while 1:
#     try:
#         to_server_data = input('>>>').strip()
#         client.send(to_server_data.encode('utf-8'))
#
#         from_server_data = client.recv(1024)
#         print(f'来自服务端的消息: {from_server_data.decode("utf-8")}')
#
#     except Exception:
#         break
# client.close()

8. The process pool, thread pool

#线程池: 一个容器,这个容器限制住你开启线程的数量,比如4个,第一次肯定只能并发的处理4个任务,只要有任务完成,线程马上就会接下一个任务.

以时间换空间.

# from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
# import os
# import time
# import random
# 
# # print(os.cpu_count())
# def task(n):
#     print(f'{os.getpid()} 接客')
#     time.sleep(random.randint(1,3))
# 
# 
# if __name__ == '__main__':

    # 开启进程池  (并行(并行+并发))
    # p = ProcessPoolExecutor()  # 默认不写,进程池里面的进程数与cpu个数相等
    #
    # # p.submit(task,1)
    # # p.submit(task,1)
    # # p.submit(task,1)
    # # p.submit(task,1)
    # # p.submit(task,1)
    # # p.submit(task,1)
    # # p.submit(task,1)
    # for i in range(20):
    #     p.submit(task,i)
    #
    # 开启线程池  (并发)
    # t = ThreadPoolExecutor()  # 默认不写, cpu个数*5 线程数
    # t = ThreadPoolExecutor(100)  # 100个线程

    # for i in range(20):
    #     t.submit(task,i)