python多進程

序.multiprocessing

python中的多線程其實并不是真正的多線程，如果想要充分地使用多核CPU的資源，在python中大部分情況需要使用多進程。Python提供了非常好用的多進程包multiprocessing，只需要定義一個函數，Python會完成其他所有事情。借助這個包，可以輕松完成從單進程到并發執行的轉換。multiprocessing支持子進程、通信和共享數據、執行不同形式的同步，提供了Process、Queue、Pipe、Lock等組件。

一、Process

process介紹

• 創建進程的類 ：Process([group [, target [, name [, args [, kwargs]]]]])，target表示調用對象，args表示調用對象的位置參數元組。kwargs表示調用對象的字典。name為別名。group實質上不使用。

• 方法 ：is_alive()、join([timeout])、run()、start()、terminate()。其中，Process以start()啟動某個進程。

• 屬性 ：authkey、daemon（要通過start()設置）、exitcode(進程在運行時為None、如果為–N，表示被信號N結束）、name、pid。其中daemon是父進程終止后自動終止，且自己不能產生新進程，必須在start()之前設置。

例1.1：創建函數并將其作為單個進程

          
            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：創建函數并將其作為多個進程

          
            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：將進程定義為類

          
            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 ：進程p調用start()時，自動調用run()

例1.4：daemon程序對比結果

1.4-1 不加daemon屬性

          
            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屬性

          
            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 ：因子進程設置了daemon屬性，主進程結束，它們就隨著結束了。

1.4-3 設置daemon執行完結束的方法

          
            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

當多個進程需要訪問共享資源的時候，Lock可以用來避免訪問的沖突。

          
            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用來控制對共享資源的訪問數量，例如池的最大連接數。

          
            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用來實現進程間同步通信。

          
            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是多進程安全的隊列，可以使用Queue實現多進程之間的數據傳遞。put方法用以插入數據到隊列中，put方法還有兩個可選參數：blocked和timeout。如果blocked為True（默認值），并且timeout為正值，該方法會阻塞timeout指定的時間，直到該隊列有剩余的空間。如果超時，會拋出Queue.Full異常。如果blocked為False，但該Queue已滿，會立即拋出Queue.Full異常。

get方法可以從隊列讀取并且刪除一個元素。同樣，get方法有兩個可選參數：blocked和timeout。如果blocked為True（默認值），并且timeout為正值，那么在等待時間內沒有取到任何元素，會拋出Queue.Empty異常。如果blocked為False，有兩種情況存在，如果Queue有一個值可用，則立即返回該值，否則，如果隊列為空，則立即拋出Queue.Empty異常。Queue的一段示例代碼：

          
            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)代表一個管道的兩個端。Pipe方法有duplex參數，如果duplex參數為True(默認值)，那么這個管道是全雙工模式，也就是說conn1和conn2均可收發。duplex為False，conn1只負責接受消息，conn2只負責發送消息。

send和recv方法分別是發送和接受消息的方法。例如，在全雙工模式下，可以調用conn1.send發送消息，conn1.recv接收消息。如果沒有消息可接收，recv方法會一直阻塞。如果管道已經被關閉，那么recv方法會拋出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
     .
     .
     .
     .
     .
     .
          
        

七、Pool

在利用Python進行系統管理的時候，特別是同時操作多個文件目錄，或者遠程控制多臺主機，并行操作可以節約大量的時間。當被操作對象數目不大時，可以直接利用multiprocessing中的Process動態成生多個進程，十幾個還好，但如果是上百個，上千個目標，手動的去限制進程數量卻又太過繁瑣，此時可以發揮進程池的功效。
Pool可以提供指定數量的進程，供用戶調用，當有新的請求提交到pool中時，如果池還沒有滿，那么就會創建一個新的進程用來執行該請求；但如果池中的進程數已經達到規定最大值，那么該請求就會等待，直到池中有進程結束，才會創建新的進程來它。

例7.1：使用進程池（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, ))   #維持執行的進程總數為processes，當一個進程執行完畢后會添加新的進程進去

    print("Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~")
    pool.close()
    pool.join()   #調用join之前，先調用close函數，否則會出錯。執行完close后不會有新的進程加入到pool,join函數等待所有子進程結束
    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.
          
        

函數解釋：

• apply_async(func[, args[, kwds[, callback]]]) 它是非阻塞，apply(func[, args[, kwds]])是阻塞的（理解區別，看例1例2結果區別）
• close() 關閉pool，使其不在接受新的任務。
• terminate() 結束工作進程，不在處理未完成的任務。
• join() 主進程阻塞，等待子進程的退出， join方法要在close或terminate之后使用。

執行說明：創建一個進程池pool，并設定進程的數量為3，xrange(4)會相繼產生四個對象[0, 1, 2, 4]，四個對象被提交到pool中，因pool指定進程數為3，所以0、1、2會直接送到進程中執行，當其中一個執行完事后才空出一個進程處理對象3，所以會出現輸出“msg: hello 3”出現在"end"后。因為為非阻塞，主函數會自己執行自個的，不搭理進程的執行，所以運行完for循環后直接輸出“mMsg: hark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~”，主程序在pool.join（）處等待各個進程的結束。

例7.2：使用進程池（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, ))   #維持執行的進程總數為processes，當一個進程執行完畢后會添加新的進程進去

    print("Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~")
    pool.close()
    pool.join()   #調用join之前，先調用close函數，否則會出錯。執行完close后不會有新的進程加入到pool,join函數等待所有子進程結束
    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：使用進程池，并關注結果

          
            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：使用多個進程池

          
            import multiprocessing
import os, time, random


def Lee():
    print("\nRun task Lee-%s" % (os.getpid()))  # os.getpid()獲取當前的進程的ID
    start = time.time()
    time.sleep(random.random() * 10)  # random.random()隨機生成0-1之間的小數
    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執行函數，apply執行函數,當有一個進程執行完畢后，會添加一個新的進程到pool中

    print('Waiting for all subprocesses done...')
    pool.close()
    pool.join()  # 調用join之前，一定要先調用close() 函數，否則會出錯, close()執行后不會有新的進程加入到pool,join函數等待素有子進程結束
    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.