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。
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。
import multiprocessingimport timedef worker(interval): n = 5 while n > 0: print("The time is {0}".format(time.ctime())) time.sleep(interval) n -= 1if __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: 4976p.name: Process-1p.is_alive: TrueThe time is Sat Mar 04 09:40:02 2017The time is Sat Mar 04 09:40:05 2017The time is Sat Mar 04 09:40:08 2017The time is Sat Mar 04 09:40:11 2017The time is Sat Mar 04 09:40:14 2017創建函數并將其作為多個進程
import multiprocessingimport timedef 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:4child p.name:Process-3 p.id10676child p.name:Process-1 p.id4628child p.name:Process-2 p.id8028END!!!!!!!!!!!!!!!!!worker_1worker_2worker_3end worker_1end worker_2end worker_3將進程定義為類
import multiprocessingimport timeclass 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 -= 1if __name__ == '__main__': p = ClockProcess(3) p.start() 注：進程p調用start()時，自動調用run()
the time is Sat Mar 04 09:44:17 2017the time is Sat Mar 04 09:44:20 2017the time is Sat Mar 04 09:44:23 2017the time is Sat Mar 04 09:44:26 2017the time is Sat Mar 04 09:44:29 2017daemon程序對比結果不加daemon屬性
import multiprocessingimport timedef 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() p注：因子進程設置了daemon屬性，主進程結束，它們就隨著結束了。rint "end!"end!work start:Sat Mar 04 09:46:20 2017work end:Sat Mar 04 09:46:23 2017加上daemon屬性
import multiprocessingimport timedef 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。設置daemon執行完結束的方法
import multiprocessingimport timedef 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:Sat Mar 04 09:48:20 2017work end:Sat Mar 04 09:48:23 2017end!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沖突。import multiprocessingimport sysdef 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 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 multiprocessingimport timedef 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-1acquirePProcess-1releaserocess-3acquireProcess-4acquireProcess-3releaseProcess-5acquireProcess-4releaseProcess-2acquireProcess-2releaseProcess-5release4. Event
Event用來實現進程間同步通信。import multiprocessingimport timedef 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: startingwait_for_event_timeout:startingwait_for_event_timeout:e.is_set->Falsewmairt_for_event: e.is_set()->Trueain: event is set5. 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 multiprocessingdef writer_proc(q): try: q.put(1, block = False) except: pass def reader_proc(q): try: print q.get(block = False) except: passif __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()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 multiprocessingimport timedef 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()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，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）
#coding: utf-8import multiprocessingimport timedef 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."一次執行結果
| 12345678910 | mMsg: hark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~ello 0 msg: hello 1msg: hello 2endmsg: hello 3endendendSub-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）
#coding: utf-8import multiprocessingimport timedef 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."一次執行的結果
| 12345678910 | msg: hello 0endmsg: hello 1endmsg: hello 2endmsg: hello 3endMark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~Sub-process(es) done. |
例7.3：使用進程池，并關注結果
import multiprocessingimport timedef func(msg): print "msg:", msg time.sleep(3) print "end" return "done" + msgif __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
| 12345678910 | msg: hello 0msg: hello 1msg: hello 2endendend::: donehello 0::: donehello 1::: donehello 2Sub-process(es) done. |
例7.4：使用多個進程池
#coding: utf-8import multiprocessingimport os, time, randomdef 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.'一次執行結果
| 123456789101112131415 | parent process 7704 Waiting for all subprocesses done...Run task Lee-6948 Run task Marlon-2896 Run task Allen-7304 Run task Frank-3052Task 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. |
新聞熱點
疑難解答
