How can I optimize HTTP requests in Python for efficient data processing?

Optimizing HTTP Requests in Python

The need to send numerous HTTP requests swiftly in Python often arises, especially when dealing with large datasets. However, choosing the most efficient approach amidst the various concurrency and threading options in Python can be challenging. One viable solution lies in utilizing a simple yet effective method.

Efficient HTTP Request Implementation

The following code exemplifies a highly efficient implementation in Python (2.6 compatibility):

import urlparse
from threading import Thread
import httplib, sys
from Queue import Queue

concurrent = 200

def doWork():
    while True:
        url = q.get()
        status, url = getStatus(url)
        doSomethingWithResult(status, url)
        q.task_done()

def getStatus(ourl):
    try:
        url = urlparse(ourl)
        conn = httplib.HTTPConnection(url.netloc)
        conn.request("HEAD", url.path)
        res = conn.getresponse()
        return res.status, ourl
    except:
        return "error", ourl

def doSomethingWithResult(status, url):
    print status, url

q = Queue(concurrent * 2)
for i in range(concurrent):
    t = Thread(target=doWork)
    t.daemon = True
    t.start()
try:
    for url in open('urllist.txt'):
        q.put(url.strip())
    q.join()
except KeyboardInterrupt:
    sys.exit(1)

Explanation

• Multithreading: The code employs multithreading to execute tasks concurrently. Threads perform HTTP requests independently, reducing latency compared to sequential execution.
• Caching: By employing a queue (q), the code avoids unnecessary URL parsing and connection establishment for each thread, further enhancing efficiency.
• Thread Optimization: By setting daemon threads (t.daemon = True), the program will gracefully terminate if the main thread exits unexpectedly.
• HTTP HEAD Request: The "HEAD" method is utilized to retrieve only the status code without downloading the entire web page, minimizing bandwidth consumption.

This optimized solution outperforms traditional methods, utilizing a streamlined approach that balances resource usage and task execution speed.

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