序. multiprocessing
python中的多線程其實並不是真正的多線程，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。藉助這個包，可以輕鬆完成從單進程到併發執行的轉換。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。
1. Process
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爲別名。group實質上不使用。
方法：is_alive()、join([timeout])、run()、start()、terminate()。其中，Process以start()啓動某個進程。
屬性：authkey、daemon（要通過start()設置）、exitcode(進程在運行時爲None、如果爲–N，表示被信號N結束）、name、pid。其中daemon是父進程終止後自動終止，且自己不能產生新進程，必須在start()之前設置。
例1.1：創建函數並將其作爲單個進程
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()結果
p.pid: 8736 p.name: Process-1 p.is_alive: True The time is Tue Apr 21 20:55:12 2015 The time is Tue Apr 21 20:55:15 2015 The time is Tue Apr 21 20:55:18 2015 The time is Tue Apr 21 20:55:21 2015 The time is Tue Apr 21 20:55:24 2015
例1.2：創建函數並將其作爲多個進程
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!!!!!!!!!!!!!!!!!"結果
The number of CPU is:4 child p.name:Process-3 p.id7992 child p.name:Process-2 p.id4204 child p.name:Process-1 p.id6380 END!!!!!!!!!!!!!!!!! worker_1 worker_3 worker_2 end worker_1 end worker_2 end worker_3
例1.3：將進程定義爲類
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()注：進程p調用start()時，自動調用run()
結果
the time is Tue Apr 21 20:31:30 2015 the time is Tue Apr 21 20:31:33 2015 the time is Tue Apr 21 20:31:36 2015 the time is Tue Apr 21 20:31:39 2015 the time is Tue Apr 21 20:31:42 2015
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!"結果
end! work start:Tue Apr 21 21:29:10 2015 work end:Tue Apr 21 21:29:13 2015
#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!"結果
end!#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!"結果
work start:Tue Apr 21 22:16:32 2015 work end:Tue Apr 21 22:16:35 2015 end!
2. 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衝突。
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"結果（輸出文件）
Lockd acquired via withLockd acquired via withLockd acquired via withLockd acquired via withLockd acquired via withLockd acquired via withLockd acquired via withLockd acquired via withLockd acquired via withLock acquired directlyLock acquired directlyLock acquired directlyLock acquired directlyLock acquired directlyLock acquired directlyLock acquired directlyLock acquired directlyLock acquired directly3. 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()
結果
Process-1acquire Process-1release Process-2acquire Process-3acquire Process-2release Process-5acquire Process-3release Process-4acquire Process-5release Process-4release
4. 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")結果
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
5. 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爲True（默認值），並且timeout爲正值，該方法會阻塞timeout指定的時間，直到該隊列有剩餘的空間。如果超時，會拋出Queue.Full異常。如果blocked爲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並且刪除一個元素。同樣，get方法有兩個可選參數：blocked和timeout。如果blocked爲True（默認值），並且timeout爲正值，那麼在等待時間內沒有取到任何元素，會拋出Queue.Empty異常。如果blocked爲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，如果隊列爲空，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()
結果
1
6. 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參數爲True(默認值)，那麼這個管道是全雙工模式，也就是說conn1和conn2均可收發。duplex爲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，那麼recv方法會拋出EOFError。
import multiprocessing import time def proc1(pipe): while True: for i in xrange(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()
結果
7. 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，並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，那麼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，那麼該請求就會等待，直到池中有進程結束，纔會創建新的進程來它。
例7.1：使用進程池（非阻塞）
#coding: utf-8 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 xrange(4): msg = "hello %d" %(i) pool.apply_async(func, (msg, )) #維持執行的進程總數爲processes，當一個進程執行完畢後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後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."
一次執行結果
mMsg: hark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~ello 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之後使用。
執行說明：創建一個進程池pool，並設定進程的數量爲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指定進程數爲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後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"後。因爲爲非阻塞，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循環後直接輸出“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）
#coding: utf-8 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 xrange(4): msg = "hello %d" %(i) pool.apply(func, (msg, )) #維持執行的進程總數爲processes，當一個進程執行完畢後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後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."
一次執行的結果
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，並關注結果
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 xrange(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."
一次執行結果
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
#coding: utf-8 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後，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()執行後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.'
一次執行結果
parent process 7704 Waiting for all subprocesses done... Run task Lee-6948 Run task Marlon-2896 Run task Allen-7304 Run task Frank-3052 Task Lee, runs 1.59 seconds. Task Marlon runs 8.48 seconds. Task Frank runs 15.68 seconds. Task Allen runs 18.08 seconds. All subprocesses done.