Python线程同步（1）
概念
线程同步，线程间协同，通过某种技术，让一个线程访问某些数据时，其他线程不能访问这些数据，直到该线程完成对数据的操作。
不同操作系统实现拘束有所不同，有临界区（Critical Section）,互斥量（Mutex）、信号量（Semaphore）、事件event等。
Event（重要）
event事件，是线程间通信机制中最简单的实现，使用一个内部的标记flag，通过flag的true或false的变化来进行操作。
set()：标记设置为true
clear()：标记设置为false
is_set()：标记是否为true
wait(timeout = none)：设置等待标记为true的时长，none为无限等待，等到返回true，未等到超时了返回false.
需求
老板雇佣了一个工人，让他生产一个杯子，老板一直等着这个工人，知道生产了10个杯子。
from threading import Event,Thread import logging import time FORMAT = "%(asctime)s %(threadNname)s %(thread)d %(message)s" logging.basicConfig(format = FORMAT,level = logging.INFO) def boss(event:Event): logging.info("i'm boss,waiting for u") #等待 event.wait() logging.info("good job") def worker(event:Event,count = 10): logging.info("i'm working for u") cups = [] while True: logging.info("make 1") time.sleep(0.5) cups.append(1) if len(cups)>= count: #通知 event.set() break logging.info("i finished my job. cups = {}".format(cups)) event = Event() w = Thread(target = worker,args = (event,)) b = Thread(target = boss,args = (event,)) w.start() b.start()
结果为：
2019-11-25 17:13:57,577 Thread-1 9760 i'm working for u
2019-11-25 17:13:57,577 Thread-1 9760 make 1
2019-11-25 17:13:57,577 Thread-2 10592 i'm boss,waiting for u.
2019-11-25 17:13:58,077 Thread-1 9760 make 1
2019-11-25 17:13:58,577 Thread-1 9760 make 1
2019-11-25 17:13:59,077 Thread-1 9760 make 1
2019-11-25 17:13:59,577 Thread-1 9760 make 1
2019-11-25 17:14:00,077 Thread-1 9760 make 1
2019-11-25 17:14:00,577 Thread-1 9760 make 1
2019-11-25 17:14:01,077 Thread-1 9760 make 1
2019-11-25 17:14:01,577 Thread-1 9760 make 1
2019-11-25 17:14:02,077 Thread-1 9760 make 1
2019-11-25 17:14:02,577 Thread-1 9760 i finished my job. cups = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
2019-11-25 17:14:02,577 Thread-2 10592 good job
总结
使用同一个Event对象的标记flag，谁wait就是等到flag变为true，或等到超时返回false，不限制等待的个数。
wait的作用
from threading import Event,Thread import logging logging.basicConfig(level = logging.INFO) def do(event:Event,interval:int): while not event.wait(interval):#条件中使用，返回true或false logging.info("do sth") e = Event() Thread(target = do,args = (e,3)).start() e.wait(10)#也可以使用time.sleep(10) e.set() print("main exit")
Event的wait优于time.sleep，它会更快的切换到其他线程，提高并发效率。
Event练习
实现Timer，延时执行的线程
延时计算add(x,y)
思路
Timer的构造函数中参数得有哪些？
如何实现start启动一个线程执行函数
如何cancel取消待执行任务
思路实现
from threading import Event,Thread import logging logging.basicConfig(level = logging.INFO) def add(x:int,y:int): logging.info(x+y) class Timer(): def __init__(self,interval,fn,*args,**kwargs): pass def start(self): pass def cancel(self): pass
完整实现
from threading import Event,Thread import logging import datetime logging.basicConfig(level = logging.INFO) def add(x:int,y:int): logging.info(x+y) class Timer(): def __init__(self,interval,fn,*args,**kwargs): self.interval = interval self.fn = fn self.args = args self.kwargs = kwargs self.event = Event() def start(self): Thread(target = self.__run).start() def cancel(self): self.event.set() def __run(self): start = datetime.datetime.now() logging.info("waiting") self.event.wait(self.interval) if not self.event.is_set(): self.fn(*self.args,**self.kwargs) delta = (datetime.datetime.now() - start).total_seconds() logging.info("finished {} ".format(delta)) self.event.set() t = Timer(10,add,4,50) t.start() e = Event() e.wait(4) t.cancel() print("============")
或者
from threading import Event,Thread import logging import datetime logging.basicConfig(level = logging.INFO) def add(x:int,y:int): logging.info(x+y) class Timer(): def __init__(self,interval,fn,*args,**kwargs): self.interval = interval self.fn = fn self.args = args self.kwargs = kwargs self.event = Event() def start(self): Thread(target = self.__run).start() def cancel(self): self.event.set() def __run(self): start = datetime.datetime.now() logging.info("waiting") if not self.event.wait(self.interval): self.fn(*self.args,**self.kwargs) delta = (datetime.datetime.now() - start).total_seconds() logging.info("finished {} ".format(delta)) self.event.set() t = Timer(10,add,4,50) t.start() e = Event() e.wait(4) t.cancel() print("============")
lock
锁，凡是存在共享资源争抢的地方都可以使用锁，从而保证只有一个使用者可以完全使用这个资源。
需求：
订单要求生产1000个杯子， 组织10个工人生产。
import logging import threading logging.basicConfig(level=logging.INFO) #10个人生产100个杯子 cups = [] def worker(task = 100): while True: count = len(cups) logging.info(count) if count>=task: break cups.append(1) logging.info("{} make 1".format(threading.current_thread().name)) logging.info("{}".format(cups)) for _ in range(10): threading.Thread(target = worker,args=(100,)).start()
从结果截图可以看出，最后的结果是104，并不是100，因为线程都在访问cups，也就是都在访问cups，这就导致了多生产。也就是接近临界点的时候，都在生产。
import threading from threading import Thread,Lock import logging import time FORMAT = "%(asctime)s %(threadName)s %(thread)d %(message)s" logging.basicConfig(format = FORMAT,level = logging.INFO) cups = [] FORMAT = "%(asctime)s %(threadName)s %(thread)d %(message)s" logging.basicConfig(format = FORMAT,level = logging.INFO) cups = [] def worker(count = 10): logging.info("i'm working for u") while len(cups)<count: time.sleep(0.0001)#为了看出线程切换效果 cups.append(1) logging.info("i finished . cups = {}".format(len(cups))) for _ in range(10): Thread(target = worker,args = (1000,)).start()
从上例的运行结果来看，多线程调度，导致了判断失效，多生产了杯子。如何修改？加锁
lock
锁，一旦线程获得锁，其他视图获取锁的线程将被阻塞。
import threading lock = threading.Lock() lock.acquire()#拿到锁 print("get locker") lock.release() print("release locker") 结果为： get locker release locker
上面拿到锁了，然后打印，再然后释放锁，再打印。
import threading lock = threading.Lock() lock.acquire()#拿到锁 print("get locker1") lock.acquire() print("get locker2") lock.release() print("release locker") 结果为： get locker1
上面拿到锁了，没有释放，又在继续拿锁，就被阻塞咯。等待释放。不能在往后面执行。
import logging import threading logging.basicConfig(level=logging.INFO) #10个人生产100个杯子 cups = [] lock = threading.Lock() def worker(lock:threading.Lock ,task =100): while True: lock.acquire() count = len(cups) lock.release() logging.info(count) if count>=task: break lock.acquire() cups.append(1) lock.release() logging.info("{} make 1".format(threading.current_thread().name)) logging.info("{}".format(cups)) for _ in range(10): threading.Thread(target = worker,args=(lock,100)).start()
上面这样的代码，最后的执行结果还是不正确，因为从业务上来说是不正确的。
import logging import threading logging.basicConfig(level=logging.INFO) #10个人生产100个杯子 cups = [] lock = threading.Lock() def worker(lock:threading.Lock ,task =100): while True: lock.acquire() count = len(cups) logging.info(count) if count>=task: break cups.append(1) lock.release() logging.info("{} make 1".format(threading.current_thread().name)) logging.info("{}".format(cups)) for _ in range(10): threading.Thread(target = worker,args=(lock,100)).start()
这样写才是正确的。但是效率还是不太高。还有点问题！因为到中间的位置，被break了，这个时候根本没有释放锁。
看结果，程序根本就没有结束。直接阻塞咯。
import logging import threading logging.basicConfig(level=logging.INFO) #10个人生产100个杯子 cups = [] lock = threading.Lock() def worker(lock:threading.Lock ,task =100): while True: lock.acquire() count = len(cups) logging.info(count) if count>=task: lock.release() break cups.append(1) lock.release() logging.info("{} make 1".format(threading.current_thread().name)) logging.info("{}".format(cups)) for _ in range(10): threading.Thread(target = worker,args=(lock,100)).start()
这样才可以咯。
acquire(blocking = True,timeout = -1):默认阻塞，阻塞可以设置超时时间，非阻塞时，timeout禁止设置。成功获取锁，返回true，否则返回false。
release()：释放锁。可以从任何线程调用释放，已上锁的锁，会被重置为unlocked未上锁的锁上调用，抛runtimeerror异常。
上例的锁的实现
import threading from threading import Thread,Lock import logging import time FORMAT = "%(asctime)s %(threadName)s %(thread)d %(message)s" logging.basicConfig(format = FORMAT,level = logging.INFO) cups = [] lock = Lock() def worker(count = 10): logging.info("i'm working for u") flag = False while True: lock.acquire()#获取锁 if len(cups)>=count: flag = True #lock.release()#1 这里释放锁？ time.sleep(0.0001)#为了看出线程切换效果 if not flag: cups.append(1) #lock.release()#2 这里释放锁？ if flag: break logging.info("i finished . cups = {}".format(len(cups))) for _ in range(10): Thread(target = worker,args = (1000,)).start()
思考，上面的代码中，共有两处可以释放锁，放在何处合适？
假设位置1的lock.release()合适，分析如下：
有一个时刻，在某一个线程中len(cups)正好是999，flag= true，释放锁，正好线程被打断。另一个线程判断发现也是999，flag= true，可能线程被打断。可能另外一个线程也判断是999，flag也设置为true。这3个线程只要继续执行到cups.append(1),一定会导致cups的长度超过1000.
假设位置2的lock.release()合适，分析如下：
在某一个时刻，len(cups)正好是999，flag= true，其他线程试图访问这段代码的线程都阻塞获取不到锁，直到当前线程安全的增加了一个数据，然后释放锁，其他线程有一个抢到锁，但发现已经1000了，只好break打印退出。再其他线程 都一样，发现已经1000咯。都退出了。
所以位置2释放锁是正确的。
但是我们发现锁保证了数据完整性，但是性能下降很多。
上例中if flag：break是为了保证release方法被执行，否则，就出现了死锁，得到的锁永远没有释放锁。
计数器类，可以加，可以减。
import threading from threading import Thread,Lock import time class Counter: def __init__(self): self._val = 0 @property def value(self): return self._val def inc(self): self._val+=1 def dec(self): self._val-=1 def run(c:Counter,count = 100):#重复了100次，50次加，50次减，最后结果应该0. for _ in range(count): for i in range(-50,50): if i<0: c.dec() else: c.inc() c = Counter() c1 = 10 #线程数 c2 = 1000 for i in range(c1): Thread(target=run,args =(c,c2)).start() print(c.value)#这一句有问题，可能线程还没有启动，这一句就完了。可以让主线程睡一会。
c1 取10、100、1000看看
c2取10、100、1000看看。当是1000的时候，结果就不对了。而且应该注意，最后一句打印的结果不一定是最终结果，可能线程还在运行。
self._val+=1或self._val-=1在线程执行的时候，有可能被打断。
要加锁，怎么加？
加锁，解锁
一般来说，加锁就需要解锁，但是加锁后，解锁前，还要有一些代码执行，就有可能会抛异常，一旦出现异常，锁是无法释放，但是当前线程可能因为这个异常被终止了，这就产生了死锁。
加锁，解锁常用语句：
- 使用try……finally语句保证锁的释放。
- with上下文管理，锁对象支持上下文管理。
改造counter类，如下：
import threading from threading import Thread,Lock import time class Counter: def __init__(self): self._val = 0 self.__lock = Lock() @property def value(self): with self.__lock: return self._val def inc(self): try: self.__lock.acquire() self._val+=1 finally: self.__lock.release() def dec(self): with self.__lock: self._val -= 1 def run(c:Counter,count = 100): for _ in range(count): for i in range(-50,50): if i<0: c.dec() else: c.inc() c = Counter() c1 = 10 #线程数 c2 = 1000 for i in range(c1): Thread(target=run,args =(c,c2)).start() print(c.value)#这一句合适吗？
最后一句修改如下：
import threading from threading import Thread,Lock import time class Counter: def __init__(self): self._val = 0 self.__lock = Lock() @property def value(self): with self.__lock:#__enter__和__exit__ return self._val def inc(self): try: self.__lock.acquire() self._val+=1 finally: self.__lock.release() def dec(self): with self.__lock: self._val -= 1 def run(c:Counter,count = 100): for _ in range(count): for i in range(-50,50): if i<0: c.dec() else: c.inc() c = Counter() c1 = 10 #线程数 c2 = 1000 for i in range(c1): Thread(target=run,args =(c,c2)).start() while True: time.sleep(1) if threading.active_count() ==1: print(threading.enumerate()) print(c.value) break else: print(threading.enumerate())
print(v.value)这一句在主线程中，很早就执行完了。退出条件是，只剩下主线程的时候。
锁的应用场景
锁使用于访问和修改同一个共享资源的时候，即读写同一个资源的时候。
如果全部都是读取同一个共享资源需要锁吗？
不需要，因为这时可以认为共享资源是不可变的，每一次读取它都是一样的值，所以不用加锁。
使用锁的注意事项：
- 少用锁，必要时用锁，使用了锁，多线程访问被锁的资源时，就成了串行，要么排队执行，要么争抢执行。
- 举例，高速公路上车并行跑，可是到了省界只开放了一个收费口，过了这个口，车辆依然可以在多车道上一起跑。过收费口的时候，如果排队一辆辆过，加不加锁一样效率相当，但是一旦出现争抢，就必须加锁一辆辆过。
- 加锁时间越短越好，不需要就立即释放锁。
- 一定要避免死锁。
不使用锁，有了效率，但是结果是错的。
使用了锁，效率低下，但是结果是正确的。
所以，我们是为了效率要错误的结果呢？还是为了正确的结果，让计算机去计算？
非阻塞锁使用
import threading import time import logging FORMAT = "%(asctime)-15s\t [%(threadName)s,%(thread)8d] %(message)s" logging.basicConfig(level=logging.INFO,format = FORMAT) def worker(tasks): for task in tasks: time.sleep(0.001) if task.lock.acquire(False):#获取锁则返回true logging.info("{} {} begin to start".format(threading.current_thread(),task.name)) #适当的时机释放锁 else: logging.info(" {} {} is working".format(threading.current_thread(),task.name)) class Task: def __init__(self,name): self.name = name self.lock = threading.Lock() #构造10个任务 tasks = [Task("task - {}".format(x)) for x in range(10)] #启动5个线程 for i in range(5): threading.Thread(target=worker,name="worker - {}".format(i),args=(tasks,)).start()
可重入锁RLock
可重入锁，是线程相关的锁。
线程A获得可重复锁，并可以多次成功获取，不会阻塞，最后要在线程A中做和asquire次数相同的release.
import threading lock = threading.RLock() ret = lock.acquire() print(ret) ret = lock.acquire() print(ret)
结果为：
True
True
没有释放锁，后面还是拿到了锁。
import threading import time lock = threading.RLock() print(lock.acquire()) print("________________________") print(lock.acquire(blocking=False)) print(lock.acquire()) print(lock.acquire(timeout = 3.55)) print(lock.acquire(blocking=False)) #print(lock.acquire(blocking=False,timeout=10))#异常 lock.release() lock.release() lock.release() lock.release() print("main thread {}".format(threading.current_thread().ident)) print("locked in main thread {}".format(lock))#注意观察lock对象的信息 lock.release() #lock.release()#多了一次 print("========================") print() print(lock.acquire(blocking=False))#1次 #threading.Timer(3,lambda x:x.release(),args=(lock,)).start()#跨线程了，异常 lock.release() print("```````````````````````````````````") print() #测试多线程 print(lock.acquire()) def sub(l): print("{}:{}".format(threading.current_thread(),l.acquire()))#阻塞 print("{}:{}".format(threading.current_thread(), l.acquire(False))) print("lock in sub thread {}".format(lock)) l.release() print("sub 1") l.release() print("sub 2") #l.release()#多了一次 threading.Timer(2,sub, args=(lock,)).start()#传入同一个lock对象 print("+++++++++++++++++++++++++++++") print() print(lock.acquire()) lock.release() time.sleep(5) print("释放主线程锁") lock.release()
结果为：
True
________________________
True
True
True
True
main thread 12652
locked in main thread <locked _thread.RLock object owner=12652 count=1 at 0x000000000217B418>
========================
True
```````````````````````````````````
True
+++++++++++++++++++++++++++++
True
释放主线程锁
<Timer(Thread-1, started 15028)>:True
<Timer(Thread-1, started 15028)>:True
lock in sub thread <locked _thread.RLock object owner=15028 count=2 at 0x000000000217B418>
sub 1
sub 2
可重入锁，与线程相关，可在一个线程中获取锁，并可继续在同一个线程中不阻塞获取锁。当锁未释放完，其他线程获取锁就会阻塞，直到当前持有锁的线程释放完锁。
Condition
构造方法Condition(lock = None),可以传入一个lock或rlock对象，默认是rlock。
acquire（*args）:获取锁
wait(self,timeout = None):等待或超时
notify(n = 1):唤醒至多指定数目个数的等待的线程，没有等待的线程就没有任何操作。
notify_all():唤醒所有等待的线程
Conditin用于生产者，消费者模型，为了解决生产者消费者速度匹配问题。
先看一个例子，消费者消费速度大于生产者生产速度。
from threading import Thread,Event import logging import random FORMAT = "%(asctime)s %(threadName)s (thread)d %(message)s" logging.basicConfig(format = FORMAT,level=logging.INFO) #此例只是为了演示，不考虑线程安全问题。 class Dispatcher(): def __init__(self): self.data = None self.event = Event()#event只是为了使用方便，与逻辑无关 def produce(self,total): for _ in range(total): data = random.randint(0,100) logging.info(data) self.data = data self.event.wait(1) self.event.set() def consum(self): while not self.event.is_set(): data = self.data logging.info("recieved {}".format(data)) self.data = None self.event.wait(0.5) d = Dispatcher() p = Thread(target=d.produce,args=(10,),name="producer") c = Thread(target=d.consum,name="consumer") c.start() p.start()
结果为：
2019-11-26 16:39:02,354 consumer (thread)d recieved None
2019-11-26 16:39:02,355 producer (thread)d 100
2019-11-26 16:39:02,855 consumer (thread)d recieved 100
2019-11-26 16:39:03,369 producer (thread)d 37
2019-11-26 16:39:03,369 consumer (thread)d recieved 37
2019-11-26 16:39:03,869 consumer (thread)d recieved None
2019-11-26 16:39:04,383 producer (thread)d 20
2019-11-26 16:39:04,383 consumer (thread)d recieved None
2019-11-26 16:39:04,884 consumer (thread)d recieved None
2019-11-26 16:39:05,397 producer (thread)d 65
2019-11-26 16:39:05,397 consumer (thread)d recieved 65
2019-11-26 16:39:05,897 consumer (thread)d recieved None
2019-11-26 16:39:06,411 consumer (thread)d recieved None
2019-11-26 16:39:06,412 producer (thread)d 9
2019-11-26 16:39:06,912 consumer (thread)d recieved 9
2019-11-26 16:39:07,425 producer (thread)d 66
2019-11-26 16:39:07,425 consumer (thread)d recieved 66
2019-11-26 16:39:07,925 consumer (thread)d recieved None
2019-11-26 16:39:08,439 consumer (thread)d recieved None
2019-11-26 16:39:08,439 producer (thread)d 67
2019-11-26 16:39:08,939 consumer (thread)d recieved 67
2019-11-26 16:39:09,453 consumer (thread)d recieved None
2019-11-26 16:39:09,453 producer (thread)d 58
2019-11-26 16:39:09,953 consumer (thread)d recieved 58
2019-11-26 16:39:10,467 producer (thread)d 24
2019-11-26 16:39:10,467 consumer (thread)d recieved 24
2019-11-26 16:39:10,967 consumer (thread)d recieved None
2019-11-26 16:39:11,481 consumer (thread)d recieved None
2019-11-26 16:39:11,481 producer (thread)d 61
2019-11-26 16:39:11,981 consumer (thread)d recieved 61
这个例子采用了消费者主动消费，消费者浪费了大量时间，主动来查看有没有数据。
能否换成一种通知机制，有数据通知消费者来消费呢？
使用Condition对象
from threading import Thread, Event, Condition import logging import random FORMAT = "%(asctime)s %(threadName)s (thread)d %(message)s" logging.basicConfig(format=FORMAT, level=logging.INFO) # 此例只是为了演示，不考虑线程安全问题。 class Dispatcher(): def __init__(self): self.data = None self.event = Event() # event只是为了使用方便，与逻辑无关 self.cond = Condition() def produce(self, total): for _ in range(total): data = random.randint(0, 100) with self.cond: logging.info(data) self.data = data self.cond.notify_all() self.event.wait(1)#模拟产生数据速度 self.event.set() def consum(self): while not self.event.is_set(): with self.cond: self.cond.wait()#阻塞等通知 logging.info("recieved {}".format(self.data)) self.data = None self.event.wait(0.5) d = Dispatcher() p = Thread(target=d.produce, args=(10,), name="producer") c = Thread(target=d.consum, name="consumer") c.start() p.start()
结果为：
2019-11-26 16:40:26,151 producer (thread)d 23
2019-11-26 16:40:26,151 consumer (thread)d recieved 23
2019-11-26 16:40:27,158 producer (thread)d 71
2019-11-26 16:40:27,158 consumer (thread)d recieved 71
2019-11-26 16:40:28,172 producer (thread)d 35
2019-11-26 16:40:28,172 consumer (thread)d recieved 35
2019-11-26 16:40:29,186 producer (thread)d 15
2019-11-26 16:40:29,186 consumer (thread)d recieved 15
2019-11-26 16:40:30,200 producer (thread)d 26
2019-11-26 16:40:30,201 consumer (thread)d recieved 26
2019-11-26 16:40:31,215 producer (thread)d 42
2019-11-26 16:40:31,215 consumer (thread)d recieved 42
2019-11-26 16:40:32,229 producer (thread)d 63
2019-11-26 16:40:32,229 consumer (thread)d recieved 63
2019-11-26 16:40:33,243 producer (thread)d 50
2019-11-26 16:40:33,243 consumer (thread)d recieved 50
2019-11-26 16:40:34,257 producer (thread)d 50
2019-11-26 16:40:34,257 consumer (thread)d recieved 50
2019-11-26 16:40:35,271 producer (thread)d 50
2019-11-26 16:40:35,271 consumer (thread)d recieved 50
上例中，消费者等待数据等待，如果生产者准备好了会通知消费者消费，省得消费者反复来查看数据是否就绪。
如果是1个生产者，多个消费者怎么改？
from threading import Thread, Event, Condition import logging import random FORMAT = "%(asctime)s %(threadName)s (thread)d %(message)s" logging.basicConfig(format=FORMAT, level=logging.INFO) # 此例只是为了演示，不考虑线程安全问题。 class Dispatcher(): def __init__(self): self.data = None self.event = Event() # event只是为了使用方便，与逻辑无关 self.cond = Condition() def produce(self, total): for _ in range(total): data = random.randint(0, 100) with self.cond: logging.info(data) self.data = data self.cond.notify_all() self.event.wait(1)#模拟产生数据速度 self.event.set() def consum(self): while not self.event.is_set(): with self.cond: self.cond.wait()#阻塞等通知 logging.info("recieved {}".format(self.data)) self.event.wait(0.5)#模拟消费速度 d = Dispatcher() p = Thread(target=d.produce, args=(10,), name="producer") for i in range(5): c = Thread(target=d.consum,name="consumer-{}".format(i)) c.start() p.start()
2019-11-26 16:41:38,536 producer (thread)d 26
2019-11-26 16:41:38,536 consumer-4 (thread)d recieved 26
2019-11-26 16:41:38,536 consumer-3 (thread)d recieved 26
2019-11-26 16:41:38,536 consumer-0 (thread)d recieved 26
2019-11-26 16:41:38,537 consumer-2 (thread)d recieved 26
2019-11-26 16:41:38,537 consumer-1 (thread)d recieved 26
2019-11-26 16:41:39,543 producer (thread)d 80
2019-11-26 16:41:39,543 consumer-1 (thread)d recieved 80
2019-11-26 16:41:39,544 consumer-4 (thread)d recieved 80
2019-11-26 16:41:39,544 consumer-2 (thread)d recieved 80
2019-11-26 16:41:39,544 consumer-3 (thread)d recieved 80
2019-11-26 16:41:39,544 consumer-0 (thread)d recieved 80
2019-11-26 16:41:40,557 producer (thread)d 17
2019-11-26 16:41:40,557 consumer-4 (thread)d recieved 17
2019-11-26 16:41:40,557 consumer-2 (thread)d recieved 17
2019-11-26 16:41:40,557 consumer-0 (thread)d recieved 17
2019-11-26 16:41:40,558 consumer-1 (thread)d recieved 17
2019-11-26 16:41:40,558 consumer-3 (thread)d recieved 17
2019-11-26 16:41:41,571 producer (thread)d 73
2019-11-26 16:41:41,571 consumer-1 (thread)d recieved 73
2019-11-26 16:41:41,572 consumer-3 (thread)d recieved 73
2019-11-26 16:41:41,572 consumer-0 (thread)d recieved 73
2019-11-26 16:41:41,573 consumer-2 (thread)d recieved 73
2019-11-26 16:41:41,573 consumer-4 (thread)d recieved 73
2019-11-26 16:41:42,585 producer (thread)d 4
2019-11-26 16:41:42,585 consumer-3 (thread)d recieved 4
2019-11-26 16:41:42,585 consumer-2 (thread)d recieved 4
2019-11-26 16:41:42,586 consumer-0 (thread)d recieved 4
2019-11-26 16:41:42,586 consumer-4 (thread)d recieved 4
2019-11-26 16:41:42,586 consumer-1 (thread)d recieved 4
2019-11-26 16:41:43,599 producer (thread)d 100
2019-11-26 16:41:43,599 consumer-0 (thread)d recieved 100
2019-11-26 16:41:43,599 consumer-4 (thread)d recieved 100
2019-11-26 16:41:43,599 consumer-2 (thread)d recieved 100
2019-11-26 16:41:43,600 consumer-1 (thread)d recieved 100
2019-11-26 16:41:43,600 consumer-3 (thread)d recieved 100
2019-11-26 16:41:44,613 producer (thread)d 68
2019-11-26 16:41:44,614 consumer-1 (thread)d recieved 68
2019-11-26 16:41:44,614 consumer-2 (thread)d recieved 68
2019-11-26 16:41:44,615 consumer-0 (thread)d recieved 68
2019-11-26 16:41:44,615 consumer-4 (thread)d recieved 68
2019-11-26 16:41:44,616 consumer-3 (thread)d recieved 68
2019-11-26 16:41:45,626 producer (thread)d 8
2019-11-26 16:41:45,626 consumer-0 (thread)d recieved 8
2019-11-26 16:41:45,626 consumer-1 (thread)d recieved 8
2019-11-26 16:41:45,626 consumer-2 (thread)d recieved 8
2019-11-26 16:41:45,627 consumer-3 (thread)d recieved 8
2019-11-26 16:41:45,627 consumer-4 (thread)d recieved 8
2019-11-26 16:41:46,640 producer (thread)d 15
2019-11-26 16:41:46,640 consumer-0 (thread)d recieved 15
2019-11-26 16:41:46,641 consumer-4 (thread)d recieved 15
2019-11-26 16:41:46,641 consumer-3 (thread)d recieved 15
2019-11-26 16:41:46,641 consumer-2 (thread)d recieved 15
2019-11-26 16:41:46,641 consumer-1 (thread)d recieved 15
2019-11-26 16:41:47,661 producer (thread)d 48
2019-11-26 16:41:47,662 consumer-2 (thread)d recieved 48
2019-11-26 16:41:47,662 consumer-1 (thread)d recieved 48
2019-11-26 16:41:47,663 consumer-3 (thread)d recieved 48
2019-11-26 16:41:47,663 consumer-4 (thread)d recieved 48
2019-11-26 16:41:47,663 consumer-0 (thread)d recieved 48
self.cond.notify_all()#发通知
修改为self.cond.notify(2)
试一试看看效果？
这个例子，可以看到实现了消息的一对多，这其实就是广播模式。
注：上例中，程序本身不是线程安全的，程序逻辑有很多瑕疵，但是可以很好的帮助理解condition的使用，和生产者消费者模型。
Condition总结
Condition用于生产者消费者模型中，解决生产者消费者速度匹配的问题。
采用了通知机制，非常有效率。
使用方式
使用Condition,必须先acquire，用完了要release，因为内部使用了锁，默认使用RLock锁，最好的方式是使用with上下文。
消费者wait，等待通知。
生产者生产好消息，对消费者发通知，可以使用notify或者notify_all方法。
