Python 併發總結，多線程，多進程，異步IO

1 測量函數運行時間

import time 
 def profile(func): 
     def wrapper(*args, **kwargs): 
         import time 
         start = time.time() 
         func(*args, **kwargs) 
         end   = time.time() 
         print 'COST: {}'.format(end - start) 
     return wrapper 
   
 @profile 
 def fib(n): 
     if n<= 2: 
         return 1 
     return fib(n-1) + fib(n-2) 
   
 fib(35) 
  

 

2 啟動多個線程，並等待完成   2.1 使用threading.enumerate()

import threading 
 for i in range(2): 
     t = threading.Thread(target=fib, args=(35,)) 
     t.start() 
 main_thread = threading.currentThread() 
   
 for t in threading.enumerate(): 
     if t is main_thread: 
         continue 
     t.join()

 

2.2 先保存啟動的線程

threads = [] 
 for i in range(5): 
     t = Thread(target=foo, args=(i,)) 
     threads.append(t) 
     t.start() 
 for t in threads: 
     t.join()

  3 使用信號量，限制同時能有幾個線程訪問臨界區

from threading import Semaphore 
 import time 
   
 sema = Semaphore(3) 
   
 def foo(tid): 
     with sema: 
         print('{} acquire sema'.format(tid)) 
         wt = random() * 2 
         time.sleep(wt) 
         print('{} release sema'.format(tid)) 
  

 

4 鎖，相當於信號量為1的情況

from threading import Thread Lock 
 value = 0 
 lock = Lock() 
 def getlock(): 
     global lock 
     with lock: 
         new = value + 1 
         time.sleep(0.001) 
         value = new

 

  5 可重入鎖RLock     acquire() 可以不被阻塞的被同一個線程調用多次，release()需要和acquire()調用次數匹配才能釋放鎖 6 條件 Condition 一個線程發出信號，另一個線程等待信號 常用於生產者-消費者模型

import time 
 import threading 
   
 def consumer(cond): 
     t = threading.currentThread() 
     with cond: 
         cond.wait() 
         print("{}: Resource is available to sonsumer".format(t.name)) 
   
 def producer(cond): 
     t = threading.currentThread() 
     with cond: 
         print("{}: Making resource available".format(t.name)) 
         cond.notifyAll() 
   
 condition = threading.Condition() 
 c1 = threading.Thread(name='c1', target=consumer, args=(condition,)) 
 c2 = threading.Thread(name='c2', target=consumer, args=(condition,)) 
 p = threading.Thread(name='p', target=producer, args=(condition,)) 
   
 c1.start() 
 c2.start() 
 p.start()

 

  7 事件 Event 感覺和Condition 差不多

import time 
 import threading 
 from random import randint 
   
 TIMEOUT = 2 
   
 def consumer(event, l): 
     t = threading.currentThread() 
     while 1: 
         event_is_set = event.wait(TIMEOUT) 
         if event_is_set: 
             try: 
                 integer = l.pop() 
                 print '{} popped from list by {}'.format(integer, t.name) 
                 event.clear()  # 重置事件狀態 
             except IndexError:  # 為了讓剛啟動時容錯 
                 pass 
   
 def producer(event, l): 
     t = threading.currentThread() 
     while 1: 
         integer = randint(10, 100) 
         l.append(integer) 
         print '{} appended to list by {}'.format(integer, t.name) 
         event.set()  # 設置事件 
         time.sleep(1) 
   
 event = threading.Event() 
 l = [] 
   
 threads = [] 
   
 for name in ('consumer1', 'consumer2'): 
     t = threading.Thread(name=name, target=consumer, args=(event, l)) 
     t.start() 
     threads.append(t) 
   
 p = threading.Thread(name='producer1', target=producer, args=(event, l)) 
 p.start() 
 threads.append(p) 
   
   
 for t in threads: 
     t.join()

 

  8 線程隊列  線程隊列有task_done() 和 join() 標準庫里的例子 往隊列內放結束標誌，注意do_work阻塞可能無法結束，需要用超時

import queue 
 def worker(): 
     while True: 
         item = q.get() 
         if item is None: 
             break 
         do_work(item) 
         q.task_done() 
 q = queue.Queue() 
 threads = [] 
 for i in range(num_worker_threads): 
     t = threading.Thread(target=worker) 
     t.start() 
     threads.append(t) 
 for item in source(): 
     q.put(item) 
 q.join() 
 for i in range(num_worker_threads): 
     q.put(None) 
 for t in threads: 
     t.join()

 

  9 優先級隊列 PriorityQueue

import threading 
 from random import randint 
 from queue import PriorityQueue 
   
 q = PriorityQueue() 
   
 def double(n): 
     return n * 2 
   
 def producer(): 
     count = 0 
     while 1: 
         if count > 5: 
             break 
         pri = randint(0, 100) 
         print('put :{}'.format(pri)) 
         q.put((pri, double, pri))  # (priority, func, args) 
         count += 1 
   
 def consumer(): 
     while 1: 
         if q.empty(): 
             break 
         pri, task, arg = q.get() 
         print('[PRI:{}] {} * 2 = {}'.format(pri, arg, task(arg))) 
         q.task_done() 
         time.sleep(0.1) 
   
 t = threading.Thread(target=producer) 
 t.start() 
 time.sleep(1) 
 t = threading.Thread(target=consumer) 
 t.start()

 

  10 線程池 當線程執行相同的任務時用線程池 10.1 multiprocessing.pool 中的線程池

from multiprocessing.pool import ThreadPool 
 pool = ThreadPool(5) 
 pool.map(lambda x: x**2, range(5))

 

10.2 multiprocessing.dummy

from multiprocessing.dummy import Pool

 

10.3 concurrent.futures.ThreadPoolExecutor

from concurrent.futures improt ThreadPoolExecutor 
 from concurrent.futures import as_completed 
 import urllib.request 
   
 URLS = ['http://www.baidu.com', 'http://www.hao123.com'] 
   
 def load_url(url, timeout): 
     with urllib.request.urlopen(url, timeout=timeout) as conn: 
         return conn.read() 
   
 with ThreadPoolExecutor(max_workers=5) as executor: 
     future_to_url = {executor.submit(load_url, url, 60): url for url in URLS} 
     for future in as_completed(future_to_url): 
         url = future_to_url[future] 
         try: 
             data = future.result() 
         execpt Exception as exc: 
             print("%r generated an exception: %s" % (url, exc)) 
         else: 
             print("%r page is %d bytes" % (url, len(data))) 
  

 

11 啟動多進程，等待多個進程結束

import multiprocessing 
 jobs = [] 
 for i in range(2): 
     p = multiprocessing.Process(target=fib, args=(12,)) 
     p.start() 
     jobs.append(p) 
 for p in jobs: 
     p.join() 
  

 

12 進程池 12.1 multiprocessing.Pool

from multiprocessing import Pool 
 pool = Pool(2) 
 pool.map(fib, [36] * 2)

 

  12.2 concurrent.futures.ProcessPoolExecutor

from concurrent.futures import ProcessPoolExecutor 
 import math 
   
 PRIMES = [ 112272535095293, 112582705942171] 
   
 def is_prime(n): 
     if n < 2: 
         return False 
     if n == 2: 
         return True 
     if n % 2 == 0: 
         return False 
     sqrt_n = int(math.floor(math.sqrt(n))) 
     for i in range(3, sqrt_n + 1, 2): 
         if n % i == 0: 
             return False 
     return True 
   
 if __name__ == "__main__": 
     with ProcessPoolExecutor() as executor: 
         for number, prime in zip(PRIMES, executor.map(is_prime, PRIMES)): 
             print("%d is prime: %s" % (number, prime)) 
  

 

  13 asyncio   13.1 最基本的示例,單個任務

import asyncio 
   
 async def hello(): 
     print("Hello world!") 
     await asyncio.sleep(1) 
     print("Hello again") 
   
 loop = asyncio.get_event_loop() 
 loop.run_until_complete(hello()) 
 loop.close()

 

13.2 最基本的示例,多個任務

import asyncio 
   
 async def hello(): 
     print("Hello world!") 
     await asyncio.sleep(1) 
     print("Hello again") 
   
 loop = asyncio.get_event_loop() 
 tasks = [hello(), hello()] 
 loop.run_until_complete(asyncio.wait(tasks)) 
 loop.close()

 

  13.3 結合httpx 執行多個任務並接收返回結果 httpx 接口和 requests基本一致

import asyncio 
 import httpx 
   
   
 async def get_url(): 
     r = await httpx.get("http://www.baidu.com") 
     return r.status_code 
   
   
 loop = asyncio.get_event_loop() 
 tasks = [get_url() for i in range(10)] 
 results = loop.run_until_complete(asyncio.gather(*tasks)) 
 loop.close() 
   
   
 for num, result in zip(range(10), results): 
     print(num, result) 
  

 

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Orignal From: Python 併發總結，多線程，多進程，異步IO