Timeout for python requests.get entire response

python

timeouts

requests

web-scraping

byAnton Shumikhin·Aug 17, 2024

Emphasizing on response time efficiency, this Python script uses the requests.get module with streamed downloads and time checks. Here, we fetch data in chunks and measure duration using time.time(). If the defined timeout is not maintained, the operation terminates and raises a TimeoutError. Here, speed is everything!

import requests
import time

def fetch_with_timeout(url, timeout_seconds):
    # Time us! 🦥 vs 🐆
    start = time.time()
    with requests.get(url, stream=True) as response:
        # "Houston, we have a... nevermind."
        response.raise_for_status()
        data = bytearray()
        for chunk in response.iter_content(chunk_size=8192):
            # "Horse faster! Times are a changin!"
            if time.time() - start > timeout_seconds:
                raise TimeoutError("Response time exceeded timeout.")
            data.extend(chunk)
    return data

# Usage
try:
    # Good ol' example domain, always up for some fetch_play.
    content = fetch_with_timeout('https://example.com', 10)  # 10 seconds
except TimeoutError as err:
    # "Time's up! Let's do the Time Warp again!"
    print(err)

NOTE: This remedy checks for every millisecond and aborts the download as soon as the allotted time crosses the threshold. No fuss, no wait!

The when and why of timeouts

Timeouts are the guardians of efficiency. They come in handy in various contexts:

In the unpredictable terrains of API responses.
With web scrapingon slow or sluggish websites.
During data streaming battles with connectivity issues.
In production landscapes where every resource is precious.

Working with timeouts

Understanding the subtle art of using timeouts in requests is essential. Here are some quick tips:

The timeout parameter controls connection time and read time. For a more precise command over these elements, use tuple syntax - (connect_timeout, read_timeout).
Use a try-except block to handle exceptions and prevent show-stopping crashes from timeout errors. Catch requests.exceptions.Timeout to tailor post-timeout actions.
Avoid None for the timeout parameter to prevent indefinite blocking. Always use a sensible, finite timeout value in tune with your requirements.

A walk on the Eventlet side

Need more power over timeouts? Eventlet, a nifty third-party library, has got you covered. It endows additional control over non-blocking I/O with greenlet:

import eventlet
eventlet.monkey_patch()

with eventlet.Timeout(10):
    response = requests.get('https://example.com')

With this snippet, a 10-second Eventlet timeout applies, wrapping around the request like a superhero cape!

Pro-level timeout tactics

Timeouts are the cornerstones of resilient network programming. Here’s how you can step up your timeout game:

Keep your requests library updated for the latest timeout techniques.
Test your custom timeout solutions in a sandbox before you let them play in real-world applications.
Bear in mind: timeout is all about server response time, not a measure of complete download time.
Keep an eye on the requests library's evolution through version control and change monitoring to adapt to new enhancements or deprecated methods.

Timeout application scenarios

As you maneuver timeouts in your code, various real-world contexts may guide your approach:

Monitoring systems, craving quick responses for timely alerts, employ tighter timeouts.
Office tools, integrating third-party services, might set lenient timeouts considering the lower risk factor.
IoT devices working with unstable networks would benefit from adaptive timeouts, fine-tuned to the connection quality.

Finding the sweet spot

Determining the optimal timeout value can be tricky. Here are some tips to get it right:

Start with a modest timeout, and increment as per your app's response.
Try dynamic timeouts, adjusting them based on time of day or system load.
Implement retries with exponential backoff to give temporary hiccups a chance to rectify themselves.