Python urllib2.open is slow, need a better way to read several urls

Question

Hey guys. As the title suggests, I'm working on a site written in python and it makes several calls to the urllib2 module to read websites. I then parse them with BeautifulSoup.

As I have to read 5-10 sites, the page takes a while to load.

I'm just wondering if there's a way to read the sites all at once? Or anytricks to make it faster, like should I close the urllib2.urlopen after each read, or keep it open?

Added: also, if I were to just switch over to php, would that be faster for fetching and Parsi g HTML and XML files from other sites? I just want it to load faster, as opposed to the ~20 seconds it currently takes

Answer 1

1) Are you opening the same site many times, or many different site? If many different sites, I think urllib2 is good. If doing the same site over and over again, I have had some personal luck with urllib3 http://code.google.com/p/urllib3/

2) BeautifulSoup is easy to use, but is pretty slow. If you do have to use it, make sure to decompose your tags to get rid of memory leaks.. or it will likely lead to memory issues (did for me).

What do your memory and cpu look like? If you are maxing your CPU, make sure you are using real heavyweight threads, so you can run on more than 1 core.

Answer 2

Edit: Please take a look at Wai's post for a better version of this code. Note that there is nothing wrong with this code and it will work properly, despite the comments below.

The speed of reading web pages is probably bounded by your Internet connection, not Python.

You could use threads to load them all at once.

import thread, time, urllib
websites = {}
def read_url(url):
  websites[url] = urllib.open(url).read()

for url in urls_to_load: thread.start_new(read_url, (url,))
while websites.keys() != urls_to_load: time.sleep(0.1)

# Now websites will contain the contents of all the web pages in urls_to_load

Answer 3

How about using pycurl?

You can apt-get it by

$ sudo apt-get python-pycurl

Answer 4

As a general rule, a given construct in any language is not slow until it is measured.

In Python, not only do timings often run counter to intuition but the tools for measuring execution time are exceptionally good.

Answer 5

Scrapy might be useful for you. If you don't need all of its functionality, you might just use twisted's twisted.web.client.getPage instead. Asynchronous IO in one thread is going to be way more performant and easy to debug than anything that uses multiple threads and blocking IO.

Answer 6

Why is Dumb Guy's answer got -1? He is using old modules alright. But he is the first guy that propose a right approach using threads and provided a working example.

I'm rewriting his code using modern Python modules like threading and Queue.

import threading, urllib2
import Queue

urls_to_load = [
'http://stackoverflow.com/',
'http://slashdot.org/',
'http://www.archive.org/',
'http://www.yahoo.co.jp/',
]

def read_url(url, queue):
    data = urllib2.urlopen(url).read()
    print('Fetched %s from %s' % (len(data), url))
    queue.put(data)

def fetch_parallel():
    result = Queue.Queue()
    threads = [threading.Thread(target=read_url, args = (url,result)) for url in urls_to_load]
    for t in threads:
        t.start()
    for t in threads:
        t.join()
    return result

def fetch_sequencial():
    result = Queue.Queue()
    for url in urls_to_load:
        read_url(url,result)
    return result

Best time for find_sequencial() is 2s. Best time for fetch_parallel() is 0.9s.

Also it is incorrect to say thread is useless in Python because of GIL. This is one of those case when thread is useful in Python because the the threads are blocked on I/O. As you can see in my result the parallel case is 2 times faster.