python多进程详解
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2023-12-24 13:21:39
对于python多进程的包multiprocessing作了一个详细的介绍。 ......
python多进程
序.multiprocessing
python中的多线程其实并不是真正的多线程，如果想要充分地使用多核cpu的资源，在python中大部分情况需要使用多进程。python提供了非常好用的多进程包multiprocessing，只需要定义一个函数，python会完成其他所有事情。借助这个包，可以轻松完成从单进程到并发执行的转换。multiprocessing支持子进程、通信和共享数据、执行不同形式的同步，提供了process、queue、pipe、lock等组件。
一、process
process介绍
创建进程的类：process([group [, target [, name [, args [, kwargs]]]]])，target表示调用对象，args表示调用对象的位置参数元组。kwargs表示调用对象的字典。name为别名。group实质上不使用。
方法：is_alive()、join([timeout])、run()、start()、terminate()。其中，process以start()启动某个进程。
属性：authkey、daemon（要通过start()设置）、exitcode(进程在运行时为none、如果为–n，表示被信号n结束）、name、pid。其中daemon是父进程终止后自动终止，且自己不能产生新进程，必须在start()之前设置。
例1.1：创建函数并将其作为单个进程
import multiprocessing
import time
def worker(interval):
n = 5
while n > 0:
print("the time is {0}".format(time.ctime()))
time.sleep(interval)
n -= 1
if __name__ == "__main__":
p = multiprocessing.process(target = worker, args = (3,))
p.start()
print("p.pid:", p.pid)
print("p.name:", p.name)
print("p.is_alive:", p.is_alive())
------------------------------------------------
>>> p.pid: 1004
>>> p.name: process-1
>>> p.is_alive: true
>>> the time is mon jul 29 21:31:11 2019
>>> the time is mon jul 29 21:31:14 2019
>>> the time is mon jul 29 21:31:17 2019
>>> the time is mon jul 29 21:31:20 2019
>>> the time is mon jul 29 21:31:23 2019
例1.2：创建函数并将其作为多个进程
import multiprocessing
import time
def worker_1(interval):
print("worker_1")
time.sleep(interval)
print("end worker_1")
def worker_2(interval):
print("worker_2")
time.sleep(interval)
print("end worker_2")
def worker_3(interval):
print("worker_3")
time.sleep(interval)
print("end worker_3")
if __name__ == "__main__":
p1 = multiprocessing.process(target = worker_1, args = (2,))
p2 = multiprocessing.process(target = worker_2, args = (3,))
p3 = multiprocessing.process(target = worker_3, args = (4,))
p1.start()
p2.start()
p3.start()
print("the number of cpu is:" + str(multiprocessing.cpu_count()))
for p in multiprocessing.active_children():
print("child p.name:" + p.name + "\tp.id" + str(p.pid))
print("end")
------------------------------------------------
>>> the number of cpu is:8
>>> child p.name:process-3 p.id18208
>>> child p.name:process-2 p.id1404
>>> child p.name:process-1 p.id11684
>>> end
>>> worker_1
>>> worker_2
>>> worker_3
>>> end worker_1
>>> end worker_2
>>> end worker_3
例1.3：将进程定义为类
import multiprocessing
import time
class clockprocess(multiprocessing.process):
def __init__(self, interval):
multiprocessing.process.__init__(self)
self.interval = interval
def run(self):
n = 5
while n > 0:
print("the time is {0}".format(time.ctime()))
time.sleep(self.interval)
n -= 1
if __name__ == '__main__':
p = clockprocess(3)
p.start()
------------------------------------------------
>>> the time is mon jul 29 21:43:07 2019
>>> the time is mon jul 29 21:43:10 2019
>>> the time is mon jul 29 21:43:13 2019
>>> the time is mon jul 29 21:43:16 2019
>>> the time is mon jul 29 21:43:19 2019
注：进程p调用start()时，自动调用run()
例1.4：daemon程序对比结果
1.4-1 不加daemon属性
import multiprocessing
import time
def worker(interval):
print("work start:{0}".format(time.ctime()));
time.sleep(interval)
print("work end:{0}".format(time.ctime()));
if __name__ == "__main__":
p = multiprocessing.process(target = worker, args = (3,))
p.start()
print("end!")
------------------------------------------------
>>> end!
>>> work start:tue jul 29 21:29:10 2019
>>> work end:tue jul 29 21:29:13 2019
1.4-2 加上daemon属性
import multiprocessing
import time
def worker(interval):
print("work start:{0}".format(time.ctime()));
time.sleep(interval)
print("work end:{0}".format(time.ctime()));
if __name__ == "__main__":
p = multiprocessing.process(target = worker, args = (3,))
p.daemon = true
p.start()
print("end!")
------------------------------------------------
>>> end!
注：因子进程设置了daemon属性，主进程结束，它们就随着结束了。
1.4-3 设置daemon执行完结束的方法
import multiprocessing
import time
def worker(interval):
print("work start:{0}".format(time.ctime()));
time.sleep(interval)
print("work end:{0}".format(time.ctime()));
if __name__ == "__main__":
p = multiprocessing.process(target = worker, args = (3,))
p.daemon = true
p.start()
p.join()
print("end!")
------------------------------------------------
>>> work start:tue jul 29 22:16:32 2019
>>> work end:tue jul 29 22:16:35 2019
>>> end!
二、lock
当多个进程需要访问共享资源的时候，lock可以用来避免访问的冲突。
import multiprocessing
import sys
def worker_with(lock, f):
with lock:
fs = open(f, 'a+')
n = 10
while n > 1:
fs.write("lockd acquired via with\n")
n -= 1
fs.close()
def worker_no_with(lock, f):
lock.acquire()
try:
fs = open(f, 'a+')
n = 10
while n > 1:
fs.write("lock acquired directly\n")
n -= 1
fs.close()
finally:
lock.release()
if __name__ == "__main__":
lock = multiprocessing.lock()
f = "file.txt"
w = multiprocessing.process(target = worker_with, args=(lock, f))
nw = multiprocessing.process(target = worker_no_with, args=(lock, f))
w.start()
nw.start()
print("end")
------------------------------------------------
>>> lockd acquired via with
>>> lockd acquired via with
>>> lockd acquired via with
>>> lockd acquired via with
>>> lockd acquired via with
>>> lockd acquired via with
>>> lockd acquired via with
>>> lockd acquired via with
>>> lockd acquired via with
>>> lock acquired directly
>>> lock acquired directly
>>> lock acquired directly
>>> lock acquired directly
>>> lock acquired directly
>>> lock acquired directly
>>> lock acquired directly
>>> lock acquired directly
>>> lock acquired directly
三、semaphore
semaphore用来控制对共享资源的访问数量，例如池的最大连接数。
import multiprocessing
import time
def worker(s, i):
s.acquire()
print(multiprocessing.current_process().name + "acquire");
time.sleep(i)
print(multiprocessing.current_process().name + "release\n");
s.release()
if __name__ == "__main__":
s = multiprocessing.semaphore(2)
for i in range(5):
p = multiprocessing.process(target = worker, args=(s, i*2))
p.start()
------------------------------------------------
>>> process-1acquire
>>> process-1release
>>>
>>> process-2acquire
>>> process-3acquire
>>> process-2release
>>>
>>> process-5acquire
>>> process-3release
>>>
>>> process-4acquire
>>> process-5release
>>>
>>> process-4release
四、event
event用来实现进程间同步通信。
import multiprocessing
import time
def wait_for_event(e):
print("wait_for_event: starting")
e.wait()
print("wairt_for_event: e.is_set()->" + str(e.is_set()))
def wait_for_event_timeout(e, t):
print("wait_for_event_timeout:starting")
e.wait(t)
print("wait_for_event_timeout:e.is_set->" + str(e.is_set()))
if __name__ == "__main__":
e = multiprocessing.event()
w1 = multiprocessing.process(name = "block",
target = wait_for_event,
args = (e,))
w2 = multiprocessing.process(name = "non-block",
target = wait_for_event_timeout,
args = (e, 2))
w1.start()
w2.start()
time.sleep(3)
e.set()
print("main: event is set")
------------------------------------------------
>>> wait_for_event: starting
>>> wait_for_event_timeout:starting
>>> wait_for_event_timeout:e.is_set->false
>>> main: event is set
>>> wairt_for_event: e.is_set()->true
五、queue
queue是多进程安全的队列，可以使用queue实现多进程之间的数据传递。put方法用以插入数据到队列中，put方法还有两个可选参数：blocked和timeout。如果blocked为true（默认值），并且timeout为正值，该方法会阻塞timeout指定的时间，直到该队列有剩余的空间。如果超时，会抛出queue.full异常。如果blocked为false，但该queue已满，会立即抛出queue.full异常。
get方法可以从队列读取并且删除一个元素。同样，get方法有两个可选参数：blocked和timeout。如果blocked为true（默认值），并且timeout为正值，那么在等待时间内没有取到任何元素，会抛出queue.empty异常。如果blocked为false，有两种情况存在，如果queue有一个值可用，则立即返回该值，否则，如果队列为空，则立即抛出queue.empty异常。queue的一段示例代码：
import multiprocessing
def writer_proc(q):
try:
q.put(1, block = false)
except:
pass
def reader_proc(q):
try:
print(q.get(block = false))
except:
pass
if __name__ == "__main__":
q = multiprocessing.queue()
writer = multiprocessing.process(target=writer_proc, args=(q,))
writer.start()
reader = multiprocessing.process(target=reader_proc, args=(q,))
reader.start()
reader.join()
writer.join()
------------------------------------------------
>>> 1
六、pipe
pipe方法返回(conn1, conn2)代表一个管道的两个端。pipe方法有duplex参数，如果duplex参数为true(默认值)，那么这个管道是全双工模式，也就是说conn1和conn2均可收发。duplex为false，conn1只负责接受消息，conn2只负责发送消息。
send和recv方法分别是发送和接受消息的方法。例如，在全双工模式下，可以调用conn1.send发送消息，conn1.recv接收消息。如果没有消息可接收，recv方法会一直阻塞。如果管道已经被关闭，那么recv方法会抛出eoferror。
import multiprocessing
import time
def proc1(pipe):
while true:
for i in range(10000):
print("send: %s" %(i))
pipe.send(i)
time.sleep(1)
def proc2(pipe):
while true:
print("proc2 rev:", pipe.recv())
time.sleep(1)
def proc3(pipe):
while true:
print("proc3 rev:", pipe.recv())
time.sleep(1)
if __name__ == "__main__":
pipe = multiprocessing.pipe()
p1 = multiprocessing.process(target=proc1, args=(pipe[0],))
p2 = multiprocessing.process(target=proc2, args=(pipe[1],))
# p3 = multiprocessing.process(target=proc3, args=(pipe[1],))
p1.start()
p2.start()
# p3.start()
p1.join()
p2.join()
# p3.join()
------------------------------------------------
>>> send: 0
>>> roc2 rev: 0
>>> send: 1
>>> proc2 rev: 1
>>> send: 2
>>> proc2 rev: 2
>>> send: 3
>>> proc2 rev: 3
>>> send: 4
>>> proc2 rev: 4
>>> send: 5
>>> proc2 rev: 5
>>> send: 6
>>> proc2 rev: 6
>>> send: 7
>>> proc2 rev: 7
>>> send: 8
>>> proc2 rev: 8
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七、pool
在利用python进行系统管理的时候，特别是同时操作多个文件目录，或者远程控制多台主机，并行操作可以节约大量的时间。当被操作对象数目不大时，可以直接利用multiprocessing中的process动态成生多个进程，十几个还好，但如果是上百个，上千个目标，手动的去限制进程数量却又太过繁琐，此时可以发挥进程池的功效。
pool可以提供指定数量的进程，供用户调用，当有新的请求提交到pool中时，如果池还没有满，那么就会创建一个新的进程用来执行该请求；但如果池中的进程数已经达到规定最大值，那么该请求就会等待，直到池中有进程结束，才会创建新的进程来它。
例7.1：使用进程池（非阻塞）
import multiprocessing
import time
def func(msg):
print("msg:", msg)
time.sleep(3)
print("end")
if __name__ == "__main__":
pool = multiprocessing.pool(processes = 3)
for i in range(4):
msg = "hello %d" %(i)
pool.apply_async(func, (msg, )) #维持执行的进程总数为processes，当一个进程执行完毕后会添加新的进程进去
print("mark~ mark~ mark~~~~~~~~~~~~~~~~~~~~~~")
pool.close()
pool.join() #调用join之前，先调用close函数，否则会出错。执行完close后不会有新的进程加入到pool,join函数等待所有子进程结束
print("sub-process(es) done.")
------------------------------------------------
>>> mark~ mark~ mark~~~~~~~~~~~~~~~~~~~~~~
>>> msg: hello 0
>>> msg: hello 1
>>> msg: hello 2
>>> end
>>> msg: hello 3
>>> end
>>> end
>>> end
>>> sub-process(es) done.
函数解释：
- apply_async(func[, args[, kwds[, callback]]]) 它是非阻塞，apply(func[, args[, kwds]])是阻塞的（理解区别，看例1例2结果区别）
- close() 关闭pool，使其不在接受新的任务。
- terminate() 结束工作进程，不在处理未完成的任务。
- join() 主进程阻塞，等待子进程的退出， join方法要在close或terminate之后使用。
执行说明：创建一个进程池pool，并设定进程的数量为3，xrange(4)会相继产生四个对象[0, 1, 2, 4]，四个对象被提交到pool中，因pool指定进程数为3，所以0、1、2会直接送到进程中执行，当其中一个执行完事后才空出一个进程处理对象3，所以会出现输出“msg: hello 3”出现在"end"后。因为为非阻塞，主函数会自己执行自个的，不搭理进程的执行，所以运行完for循环后直接输出“mmsg: hark~ mark~ mark~~~~~~~~~~~~~~~~~~~~~~”，主程序在pool.join（）处等待各个进程的结束。
例7.2：使用进程池（阻塞）
import multiprocessing
import time
def func(msg):
print("msg:", msg)
time.sleep(3)
print("end")
if __name__ == "__main__":
pool = multiprocessing.pool(processes = 3)
for i in range(4):
msg = "hello %d" %(i)
pool.apply(func, (msg, )) #维持执行的进程总数为processes，当一个进程执行完毕后会添加新的进程进去
print("mark~ mark~ mark~~~~~~~~~~~~~~~~~~~~~~")
pool.close()
pool.join() #调用join之前，先调用close函数，否则会出错。执行完close后不会有新的进程加入到pool,join函数等待所有子进程结束
print("sub-process(es) done.")
------------------------------------------------
>>> msg: hello 0
>>> end
>>> msg: hello 1
>>> end
>>> msg: hello 2
>>> end
>>> msg: hello 3
>>> end
>>> mark~ mark~ mark~~~~~~~~~~~~~~~~~~~~~~
>>> sub-process(es) done.
例7.3：使用进程池，并关注结果
import multiprocessing
import time
def func(msg):
print("msg:", msg)
time.sleep(3)
print("end")
return "done" + msg
if __name__ == "__main__":
pool = multiprocessing.pool(processes=4)
result = []
for i in range(3):
msg = "hello %d" %(i)
result.append(pool.apply_async(func, (msg, )))
pool.close()
pool.join()
for res in result:
print(":::", res.get())
print("sub-process(es) done.")
------------------------------------------------
>>> msg: hello 0
>>> msg: hello 1
>>> msg: hello 2
>>> end
>>> end
>>> end
>>> ::: donehello 0
>>> ::: donehello 1
>>> ::: donehello 2
>>> sub-process(es) done.
例7.4：使用多个进程池
import multiprocessing
import os, time, random
def lee():
print("\nrun task lee-%s" % (os.getpid())) # os.getpid()获取当前的进程的id
start = time.time()
time.sleep(random.random() * 10) # random.random()随机生成0-1之间的小数
end = time.time()
print('task lee, runs %0.2f seconds.' % (end - start))
def marlon():
print("\nrun task marlon-%s" % (os.getpid()))
start = time.time()
time.sleep(random.random() * 40)
end = time.time()
print('task marlon runs %0.2f seconds.' % (end - start))
def allen():
print("\nrun task allen-%s" % (os.getpid()))
start = time.time()
time.sleep(random.random() * 30)
end = time.time()
print('task allen runs %0.2f seconds.' % (end - start))
def frank():
print("\nrun task frank-%s" % (os.getpid()))
start = time.time()
time.sleep(random.random() * 20)
end = time.time()
print('task frank runs %0.2f seconds.' % (end - start))
if __name__ == '__main__':
function_list = [lee, marlon, allen, frank]
print("parent process %s" % (os.getpid()))
pool = multiprocessing.pool(4)
for func in function_list:
pool.apply_async(func) # pool执行函数，apply执行函数,当有一个进程执行完毕后，会添加一个新的进程到pool中
print('waiting for all subprocesses done...')
pool.close()
pool.join() # 调用join之前，一定要先调用close() 函数，否则会出错, close()执行后不会有新的进程加入到pool,join函数等待素有子进程结束
print('all subprocesses done.')
------------------------------------------------
>>> parent process 9828
>>> waiting for all subprocesses done...
>>>
>>> run task lee-12948
>>>
>>> run task marlon-8948
>>>
>>> run task allen-18124
>>>
>>> run task frank-17404
>>> task frank runs 3.42 seconds.
>>> task lee, runs 6.69 seconds.
>>> task allen runs 8.38 seconds.
>>> task marlon runs 13.37 seconds.
>>> all subprocesses done.