目的:
是學習python 多線程的工作原理，及通過抓取400張圖片這種IO密集型應用來查看多線程效率對比
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import requestsimport urlparseimport osimport timeimport threadingimport Queuepath = '/home/lidongwei/scrapy/owan_img_urls.txt'#path = '/home/lidongwei/scrapy/cc.txt'fetch_img_save_path = '/home/lidongwei/scrapy/owan_imgs/'# 讀取保存再文件里面400個urlswith open(path) as f : urls = f.readlines()urls = urls[:400]# 使用Queue來線程通信，因為隊列是線程安全的（就是默認這個隊列已經有鎖）q = Queue.Queue()for url in urls: q.put(url)start = time.time()def fetch_img_func(q): while True: try: # 不阻塞的讀取隊列數據 url = q.get_nowait() i = q.qsize() except Exception, e: print e break; print 'Current Thread Name Runing %s ... 11' % threading.currentThread().name url = url.strip() img_path = urlparse.urlparse(url).path ext = os.path.splitext(img_path)[1] print 'handle %s pic... pic url %s ' % (i, url) res = requests.get(url, stream=True) if res.status_code == 200: save_img_path = '%s%s%s' % (fetch_img_save_path, i, ext) # 保存下載的圖片 with open(save_img_path, 'wb') as fs: for chunk in res.iter_content(1024): fs.write(chunk) print 'save %s pic ' % i# 可以開多個線程測試不同效果t1 = threading.Thread(target=fetch_img_func, args=(q, ), name="child_thread_1")#t2 = threading.Thread(target=fetch_img_func, args=(q, ), name="child_thread_2")#t3 = threading.Thread(target=fetch_img_func, args=(q, ), name="child_thread_3")#t4 = threading.Thread(target=fetch_img_func, args=(q, ), name="child_thread_4")t1.start()#t2.start()#t3.start()#t4.start()t1.join()#t2.join()#t3.join()#t4.join()end = time.time()print 'Done %s ' % (end-start) |
實驗結果
400圖片
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4線程 Done 12.4431338313線程 Done 12.92017579082線程 Done 32.86282992361線程 Done 54.6115460396 |
總結
Python 自帶GIL 大鎖， 沒有真正意義上的多線程并行執行。GIL 大鎖會在線程阻塞的時候釋放，此時等待的線程就可以激活工作，這樣如此類推，大大提高IO阻塞型應用的效率。
