Improving Performance of Python Requests with Threading

Feb 3, 2024 ยท 2 min read

The Python requests library provides a simple interface for making HTTP requests in Python. However, each request occurs sequentially within a single thread by default. For APIs or web scraping tasks that require sending many requests, this can be slow and inefficient.

This is where utilizing threading can help speed things up by allowing multiple requests to be sent concurrently. Here's an example:

import requests
import threading

def request(url):
    resp = requests.get(url)
    print(f"{url}: {resp.status_code}")

urls = ["" for i in range(10)]

for url in urls:
    t = threading.Thread(target=request, args=(url,)) 

This creates a separate thread for each request rather than performing them sequentially. The Thread class launches the request function and handles the concurrency automatically.

Some tips when using threads with requests:

  • Use a thread pool instead of unlimited threads to limit resource usage
  • Handle exceptions properly in the thread functions
  • Watch out for race conditions when threads access shared data
  • Use locks or queues if coordination is required between threads
  • Threading can provide substantial speed improvements, but it also complexifies the code. An alternative is to use the excellent grequests library which provides an asychronous version of requests using gevent.

    Overall, threading is a straightforward way to improve the performance of Python requests:

  • Easy to parallelize requests without async/await
  • Perfect for I/O-bound tasks like downloading or processing web pages
  • Beware of shared data between threads causing race conditions
  • With some care taken to manage the concurrency, threading can allow requests-based code to maximize network and CPU utilization for demanding workflows.

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