Python的并行（持续更新）
0. 参考：
- 《Python并行编程 中文版》https://python-parallel-programmning-cookbook.readthedocs.io/zh_CN/latest/index.html
1. 线程和进程：
- 进程可以包含多个并行运行的线程；
- 通常，操作系统创建和管理线程比进程更省CPU资源；
- 线程用于一些小任务，进程用于繁重的任务；
- 同一个进程下的线程共享地址空间和其他资源，进程之间相互独立；
2. 在Python中使用线程:
2.1 多线程简介：
- 线程是独立的处理流程，可以和系统的其他线程并行或并发地执行。
- 多线程可以共享数据和资源，利用所谓的共享内存空间。
- 每一个线程基本上包含3个元素：程序计数器，寄存器和栈。
- 线程的状态大体上可以分为
ready, running, blocked。- 多线程编程一般使用共享内容空间进行线程间的通讯，这就使管理内容空间成为多线程编程的重点和难点。
- 线程的典型应用是应用软件的并行化。
- 相比于进程，使用线程的优势主要是性能。
2.2 threading 库实现多线程:
1. 如何定义一个线程 threading.Thread():
class threading.Thread(group=None, ## 一般设置为 None ，这是为以后的一些特性预留的
target=None, ## 当线程启动的时候要执行的函数
name=None, ## 线程的名字，默认会分配一个唯一名字 Thread-N
args=(), ## 使用 tuple 类型给 target 传递参数
kwargs={}) ## 使用 dict 类型给 target 传递参数
举例：
import threading
def function(i):
print("function called by thread %i\n" % i)
return
#threads = []
for i in range(5):
t = threading.Thread(target=function, args=(i,)) ## 用 function 函数初始化一个 Thread 对象 t，并将参数 i 传入；
#threads.append(t)
t.start() ## 线程被创建后不会马上执行，需要手动调用 .start() 方法执行线程
t.join() ## 阻塞调用 t 线程的主线程，t 线程执行结束，主线程才会继续执行
运行结果：
function called by thread 0
function called by thread 1
function called by thread 2
function called by thread 3
function called by thread 4
2. 如何确定当前线程 threading.current_thread().name：
- 通常一个服务进程中有多个线程服务，负责不同的操作，所以对于线程的命名是很重要的；
- Python中每一个线程在被
Thread被创建时都有一个默认的名字（可以修改）；
举例:
import threading
import time
def first_func():
print(threading.current_thread().name + str(" is Starting"))
time.sleep(2)
print(threading.current_thread().name + str("is Exiting"))
return
def second_func():
print(threading.current_thread().name + str(" is Starting"))
time.sleep(2)
print(threading.current_thread().name + str("is Exiting"))
return
def third_func():
print(threading.current_thread().name + str(" is Starting"))
time.sleep(2)
print(threading.current_thread().name + str("is Exiting"))
return
if __name__ == "__main__":
t1 = threading.Thread(name="first_func", target=first_func)
t2 = threading.Thread(name="second_func", target=second_func)
t3 = threading.Thread(target=third_func)
t1.start()
t2.start()
t3.start()
t1.join()
t2.join()
t3.join()
运行结果：
first_func is Starting
second_func is Starting
Thread-36 (third_func) is Starting
first_funcis Exiting
second_funcis Exiting
Thread-36 (third_func)is Exiting
从上面运行结果可以看出，如果不用 name= 参数指定线程名称的话，那么线程名称将使用默认值。
3. 如何实现一个线程 threading:
使用
threading模块实现一个线程，需要3步：
- 定义一个
Thread类的子类；- 重写
__init__(self, [,args])方法；- 重写
run(self, [,args])方法实现一个线程；
举例：
import threading
#import _thread
import time
#exitFlag = 0
class myThread(threading.Thread): ## 定义一个 threading 子类，继承 threading.Thread 父类
def __init__(self, threadID, name, counter): ## 重写 __init__() 方法，并添加额外的参数
threading.Thread.__init__(self) ## 初始化继承自Thread类的属性，使子类对象能够正确地继承和使用父类的属性和方法
self.threadID = threadID ## 子类额外的属性
self.name = name
self.counter = counter
def run(self):
print("Starting " + self.name)
print_time(self.name, self.counter, 5)
print("Exiting " + self.name)
def print_time(threadName, delay, counter):
while counter:
##if exitFlag: ## 当 exitFlag != 0时，执行 _thread.exit()，线程退出 (但是在该段代码中，exitFlag的值没有被改变，所以不会执行 _thread.exit()，所以可以直接注释掉)
## _thread.exit()
time.sleep(delay)
print("%s: %s" % (threadName, time.ctime(time.time())))
counter -= 1
## 创建线程
thread1 = myThread(1, "Thread-1", 1)
thread2 = myThread(2, "Thread-2", 2)
## 开启线程
thread1.start()
thread2.start()
## .join()
thread1.join()
thread2.join()
print("Exiting Main Thread")
运行结果：
Starting Thread-1
Starting Thread-2
Thread-1: Wed Jun 21 11:12:09 2023
Thread-2: Wed Jun 21 11:12:10 2023
Thread-1: Wed Jun 21 11:12:10 2023
Thread-1: Wed Jun 21 11:12:11 2023
Thread-2: Wed Jun 21 11:12:12 2023
Thread-1: Wed Jun 21 11:12:12 2023
Thread-1: Wed Jun 21 11:12:13 2023
Exiting Thread-1
Thread-2: Wed Jun 21 11:12:14 2023
Thread-2: Wed Jun 21 11:12:16 2023
Thread-2: Wed Jun 21 11:12:18 2023
Exiting Thread-2
Exiting Main Thread
由于 thread1 的 sleep 时间比 thread2 的时间短，所以 thread2 会执行更久一些，退出也就更晚一些。
4. 使用 Lock 进行线程同步 (lock()):
- 并发线程中，多个线程对共享内存进行操作，并且至少有一个可以改变数据。这种情况下如果没有同步机制，那么多个线程之间就会产生竞争，从而导致代码无效或出错。
- 解决多线程竞争问题的最简单的方法就是用锁 (Lock)。当一个线程需要访问共享内存时，它必须先获得 Lock 之后才能访问；当该线程对共享资源使用完成后，必须释放 Lock，然后其他线程在拿到 Lock 进行访问资源。因此，为了避免多线程竞争的出现，必须保证：同一时刻只能允许一个线程访问共享内存。
- 在实际使用中，该方法经常会导致一种 死锁 现象，原因是不同线程互相拿着对方需要的 Lock，导致死锁的发生。
详见：https://python-parallel-programmning-cookbook.readthedocs.io/zh_CN/latest/chapter2/06_Thread_synchronization_with_Lock_and_Rlock.html
举例:
import threading
shared_resource_with_lock = 0
shared_resource_with_no_lock = 0
COUNT = 100000
shared_resource_lock = threading.Lock() ## 锁
## 有锁的情况
def increment_with_lock():
global shared_resource_with_lock ## shared_resource_with_lock 即最外面的 shared_resource_with_lock，这样写就不需要再通过函数的参数引入 shared_resource_with_lock 了
for _ in range(COUNT):
shared_resource_lock.acquire() ## 获取 锁
shared_resource_with_lock += 1
shared_resource_lock.release() ## 释放 锁
def decrement_with_lock():
global shared_resource_with_lock
for _ in range(COUNT):
shared_resource_lock.acquire()
shared_resource_with_lock -= 1
shared_resource_lock.release()
## 没有锁的情况
def increment_without_lock():
global shared_resource_with_no_lock
for _ in range(COUNT):
shared_resource_with_no_lock += 1
def decrement_without_lock():
global shared_resource_with_no_lock
for _ in range(COUNT):
shared_resource_with_no_lock -= 1
if __name__ == "__main__":
t1 = threading.Thread(target=increment_with_lock)
t2 = threading.Thread(target=decrement_with_lock)
t3 = threading.Thread(target=increment_without_lock)
t4 = threading.Thread(target=decrement_without_lock)
## 开启线程
t1.start()
t2.start()
t3.start()
t4.start()
## .join()
t1.join()
t2.join()
t3.join()
t4.join()
print ("the value of shared variable with lock management is %s" % shared_resource_with_lock)
print ("the value of shared variable with race condition is %s" % shared_resource_with_no_lock)
运行结果：
the value of shared variable with lock management is 0
the value of shared variable with race condition is 0
尽管在上面的结果中，没锁的情况下得到的结果也是正确的，但是执行多次，总会出现错误的结果；而有锁的情况下，执行多次，结果一定是正确的。
尽管理论上用锁的策略可以避免多线程中的竞争问题，但是可能会对程序的其他方面产生负面影响。此外，锁的策略经常会导致不必要的开销，也会限制程序的可扩展性和可读性。更重要的是，有时候需要对多进程共享的内存分配优先级，使用锁可能和这种优先级冲突。从实践的经验来看，使用锁的应用将对debug带来不小的麻烦。所以，最好使用其他可选的方法确保同步读取共享内存，避免竞争条件。
5. 使用RLock进行线程同步:
- 为了保证 “只有拿到锁的线程才能释放锁”，那么应该使用
RLock()对象；- 和
Lock()一样，RLock()也有acquire()和release()两种方法；RLock()有三个特点：
1). 谁拿到谁释放。如果线程A拿到锁，线程B无法释放这个锁，只有A可以释放；
2). 同一线程可以多次拿到该锁，即可以acquire多次；
3).acquire多少次就必须release多少次，只有最后一次release才能改变RLock的状态为unlocked）;
举例：
import threading
import time
class Box(object):
lock = threading.RLock()
def __init__(self):
self.total_items = 0
def execute(self, n):
Box.lock.acquire()
self.total_items += n
Box.lock.release()
def add(self):
Box.lock.acquire()
self.execute(1)
Box.lock.release()
def remove(self):
Box.lock.acquire()
self.execute(-1)
Box.lock.release()
def adder(box, items):
while items > 0:
print("adding 1 item in the box")
box.add()
time.sleep(1)
items -= 1
def remover(box, items):
while items > 0:
print("removing 1 item in the box")
box.remove()
time.sleep(1)
items -= 1
if __name__ == "__main__":
items = 5
print("putting %s items in the box"% items)
box = Box()
t1 = threading.Thread(target=adder, args=(box, items))
t2 = threading.Thread(target=remover, args=(box, items))
t1.start()
t2.start()
t1.join()
t2.join()
print("%s items still remain in the box " % box.total_items)
运行结果：
putting 5 items in the box
adding 1 item in the box
removing 1 item in the box
adding 1 item in the box
removing 1 item in the box
removing 1 item in the box
adding 1 item in the box
removing 1 item in the box
adding 1 item in the box
adding 1 item in the box
removing 1 item in the box
0 items still remain in the box
Box类的execute()方法包含RLock，adder()和remover()方法也包含RLock，就是说无论是调用Box还是adder()或者remover()，每个线程的每一步都有拿到资源、释放资源的过程。
6. 使用信号量进行线程同步：
- 信号量是由操作系统管理的一种抽象数据类型，用于多线程中同步对共享资源的使用；
- 信号量是一个内部数据，用于表明当前共享资源可以有多少并发读取；
- 在
Threading中，信号量的操作有两个函数：acquire()和release()；
举例：
import threading
import time
import random
semaphore = threading.Semaphore(0) ## 可以理解为一个内置的计数器，当调用 acquire 方法时候内置计数器 -1，对应着申请资源；调用 release 方法时候内置计数器+1，对应着释放可用资源。
print("init semaphore %s" % semaphore._value)
def consumer():
print("consumer is waiting.")
semaphore.acquire()
print("consumer notify: consumed item number %s" % item)
print("consumer semaphore %s" % semaphore._value)
def producer():
global item
time.sleep(10)
item = random.randint(0, 1000)
print("producer notify : produced item number %s" % item)
semaphore.release()
print("producer semaphore %s" % semaphore._value)
if __name__ == "__main__":
for _ in range(0, 5):
t1 = threading.Thread(target=producer)
t2 = threading.Thread(target=consumer)
t1.start()
t2.start()
t1.join()
t2.join()
print("program terminated")
运行结果：
init semaphore 0
consumer is waiting.
producer notify : produced item number 756
producer semaphore 1
consumer notify: consumed item number 756
consumer semaphore 0
consumer is waiting.
producer notify : produced item number 948
producer semaphore 1
consumer notify: consumed item number 948
consumer semaphore 0
consumer is waiting.
producer notify : produced item number 597
producer semaphore 1
consumer notify: consumed item number 597
consumer semaphore 0
consumer is waiting.
producer notify : produced item number 239
producer semaphore 1
consumer notify: consumed item number 239
consumer semaphore 0
consumer is waiting.
producer notify : produced item number 141
producer semaphore 1
consumer notify: consumed item number 141
consumer semaphore 0
program terminated
根据semaphore = threading.Semaphore(0)将信号量初始化为0，其目的在于同步两个或多个线程。
producer()执行完后，通过seaphore.release()释放资源，之后consumer()通过semaphore.acquire()拿到资源；
相应的信号量的计数器也会从初始化的0 --> 1 --> 多次重复。
信号量的一个特殊用法是互斥量，互斥量是初始值为1的信号量，可以实现数据、资源的互斥访问；
7. 使用条件进行线程同步：
- 条件：指的是程序状态的改变；
- 某些线程在等待某一条件发生，其他的线程会在该条件发生的时候进行通知。一旦条件发生，线程会拿到共享资源的唯一权限。
举例：
from threading import Thread, Condition
import time
items = []
condition = Condition() ## A condition variable allows one or more threads to wait until they are notified by another thread.
class consumer(Thread):
def __init__(self):
Thread.__init__(self)
def consume(self):
global condition
global items
condition.acquire()
if len(items) == 0:
condition.wait() ## Wait until notified or until a timeout occurs.
print("Consumer notify : no item to consume")
items.pop()
print("Consumer notify : consumed 1 item")
print("Consumer notify : items to consume are " + str(len(items)))
condition.notify() ## Wake up one or more threads waiting on this condition, if any.
condition.release()
def run(self):
for _ in range(0, 20):
time.sleep(2)
self.consume()
class producer(Thread):
def __init__(self):
Thread.__init__(self)
def produce(self):
global condition
global items
condition.acquire()
if len(items) == 10:
condition.wait()
print("Producer notify : items producted are " + str(len(items)))
print("Producer notify : stop the production!!")
items.append(1)
print("Producer notify : total items producted " + str(len(items)))
condition.notify()
condition.release()
def run(self):
for _ in range(0, 20):
time.sleep(1)
self.produce()
if __name__ == "__main__":
producer = producer()
consumer = consumer()
producer.start()
consumer.start()
producer.join()
consumer.join()
运行结果：
Producer notify : total items producted 1
Consumer notify : consumed 1 item
Consumer notify : items to consume are 0
Producer notify : total items producted 1
Producer notify : total items producted 2
Consumer notify : consumed 1 item
Consumer notify : items to consume are 1
Producer notify : total items producted 2
Producer notify : total items producted 3
Consumer notify : consumed 1 item
Consumer notify : items to consume are 2
Producer notify : total items producted 3
Producer notify : total items producted 4
Consumer notify : consumed 1 item
Consumer notify : items to consume are 3
Producer notify : total items producted 4
Producer notify : total items producted 5
Consumer notify : consumed 1 item
Consumer notify : items to consume are 4
Producer notify : total items producted 5
Producer notify : total items producted 6
Consumer notify : consumed 1 item
Consumer notify : items to consume are 5
Producer notify : total items producted 6
Producer notify : total items producted 7
Consumer notify : consumed 1 item
Consumer notify : items to consume are 6
Producer notify : total items producted 7
Producer notify : total items producted 8
Consumer notify : consumed 1 item
Consumer notify : items to consume are 7
Producer notify : total items producted 8
Producer notify : total items producted 9
Consumer notify : consumed 1 item
Consumer notify : items to consume are 8
Producer notify : total items producted 9
Producer notify : total items producted 10
Consumer notify : consumed 1 item
Consumer notify : items to consume are 9
Producer notify : total items producted 10
Consumer notify : consumed 1 item
Consumer notify : items to consume are 9
Consumer notify : consumed 1 item
Consumer notify : items to consume are 8
Consumer notify : consumed 1 item
Consumer notify : items to consume are 7
Consumer notify : consumed 1 item
Consumer notify : items to consume are 6
Consumer notify : consumed 1 item
Consumer notify : items to consume are 5
Consumer notify : consumed 1 item
Consumer notify : items to consume are 4
Consumer notify : consumed 1 item
Consumer notify : items to consume are 3
Consumer notify : consumed 1 item
Consumer notify : items to consume are 2
Consumer notify : consumed 1 item
Consumer notify : items to consume are 1
Consumer notify : consumed 1 item
Consumer notify : items to consume are 0
整个过程有点绕，可以通过这个例子简单理解 使用 condition 进行线程同步 (https://blog.csdn.net/lzanze/article/details/105351064)
8. 使用事件进行线程同步：
事件：线程之间用于通讯的对象。
举例：
import time
from threading import Thread, Event
import random
items = []
event = Event()
class consumer(Thread):
def __init__(self, items, event):
Thread.__init__(self)
self.items = items
self.event = event
def run(self):
while True:
time.sleep(2)
self.event.wait() ## 线程会阻塞在这里，直到事件 event 被触发(set.evet.set())才会继续执行。
item = self.items.pop()
print("Consumer notify : %d popped from list by %s" % (item, self.name))
class producer(Thread):
def __init__(self, items, event):
Thread.__init__(self)
self.items = items
self.event = event
def run(self):
global item
for _ in range(10):
time.sleep(2)
item = random.randint(0, 256)
self.items.append(item) ## 将 item 添加到list末尾然后通过 self.event.set() 和 self.event.clear() 发出事件通知
print('Producer notify : item %d appended to list by %s' % (item, self.name))
print('Producer notify : event set by %s' % self.name)
self.event.set() ## set() 方法将内部变量设置为 True (is_set() == True)
print("Produce notify : event cleared by %s" % self.name)
#print("Produce set event label : ", self.event.is_set())
self.event.clear() ## clear() 方法将内部变量设置为 False (is_set() == False)
#print("Produce clear event label : ", self.event.is_set())
if __name__ == "__main__":
t1 = producer(items, event)
t2 = consumer(items, event)
t1.start()
t2.start()
t1.join()
t2.join()
运行结果（部分结果）：
Producer notify : item 140 appended to list by Thread-64
Producer notify : event set by Thread-64
Produce notify : event cleared by Thread-64
Consumer notify : 140 popped from list by Thread-65
Producer notify : item 42 appended to list by Thread-64
Producer notify : event set by Thread-64
Produce notify : event cleared by Thread-64
Producer notify : item 101 appended to list by Thread-64
Producer notify : event set by Thread-64
Produce notify : event cleared by Thread-64
Consumer notify : 101 popped from list by Thread-65
Producer notify : item 213 appended to list by Thread-64
Producer notify : event set by Thread-64
Produce notify : event cleared by Thread-64
Producer notify : item 31 appended to list by Thread-64
Producer notify : event set by Thread-64
Produce notify : event cleared by Thread-64
Consumer notify : 31 popped from list by Thread-65
Producer notify : item 235 appended to list by Thread-64
Producer notify : event set by Thread-64
Produce notify : event cleared by Thread-64
该脚本我自己在运行的时候，运行了20多分钟，没结束，所以直接停了（部分结果如上所示）。
9. 使用 with 语法：
- 当两个相关的操作需要在一部分代码块前后分别执行的时候，可以使用
with语法；- 使用
with语法可以在特定的地方分配和释放资源，因此，with语法也叫做“上下文管理器”；- 在
threading模块中，所有带有acquire()方法和release()方法的对象（包括Lock, RLock, Condition, Semaphore）都可以使用with语法；
举例：
import threading
import logging
logging.basicConfig(level=logging.DEBUG, format="(%(threadName)-10s) %(message)s", )
def threading_with(statement):
with statement:
logging.debug("%s acquired via with" % statement)
def threading_not_with(statement):
statement.acquire()
try:
logging.debug("%s acquired directly" % statement)
finally:
statement.release()
if __name__ == "__main__":
lock = threading.Lock()
rlock = threading.RLock()
condition = threading.Condition()
mutex = threading.Semaphore(1)
threading_synchronization_list = [lock, rlock, condition, mutex] ## 包含要测试的线程同步使用的对象
for statement in threading_synchronization_list:
t1 = threading.Thread(target=threading_with, args=(statement,))
t2 = threading.Thread(target=threading_not_with, args=(statement,))
t1.start()
t2.start()
t1.join()
t2.join()
运行结果：
(Thread-68 (threading_with)) acquired via with
(Thread-69 (threading_not_with)) acquired directly
(Thread-70 (threading_with)) acquired via with
(Thread-71 (threading_not_with)) acquired directly
(Thread-72 (threading_with)) , 0)> acquired via with
(Thread-73 (threading_not_with)) , 0)> acquired directly
(Thread-74 (threading_with)) acquired via with
(Thread-75 (threading_not_with)) acquired directly
上述结果展示了使用with和不用with的每一个函数以及用在了哪些地方(Lock, RLock, Condition, Semaphore)
10. 使用 queue 进行线程通信：
与
threading模块相比，queue操作更简单、更安全。
更新中……