Python 多线程抓取图片效率对比

目的:

是学习python 多线程的工作原理，及通过抓取400张图片这种IO密集型应用来查看多线程效率对比

import requests
import urlparse
import os
import time
import threading
import Queue

path = '/home/lidongwei/scrapy/owan_img_urls.txt'
#path = '/home/lidongwei/scrapy/cc.txt'
fetch_img_save_path = '/home/lidongwei/scrapy/owan_imgs/'

# 读取保存再文件里面400个urls
with open(path) as f :
  urls = f.readlines()

urls = urls[:400]
# 使用Queue来线程通信，因为队列是线程安全的（就是默认这个队列已经有锁）
q = Queue.Queue()
for url in urls:
  q.put(url)

start = time.time()

def fetch_img_func(q):
  while True:
    try:
      # 不阻塞的读取队列数据
      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)
      # 保存下载的图片
      with open(save_img_path, 'wb') as fs:
        for chunk in res.iter_content(1024):
          fs.write(chunk)
        print 'save %s pic ' % i

# 可以开多个线程测试不同效果
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)

实验结果

400图片

4线程 Done 12.443133831
3线程 Done 12.9201757908 
2线程 Done 32.8628299236
1线程 Done 54.6115460396 

总结

Python 自带GIL 大锁， 没有真正意义上的多线程并行执行。GIL 大锁会在线程阻塞的时候释放，此时等待的线程就可以激活工作，这样如此类推，大大提高IO阻塞型应用的效率。

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