Troubleshooting Python Request Timeouts

Feb 3, 2024 ยท 3 min read

Making HTTP requests is essential in many Python programs. However, you may occasionally run into issues with requests unexpectedly timing out. This can lead to failed API calls, scraping jobs, or webhooks.

In this guide, we'll explore some of the common causes and solutions for Python request timeouts.

Why Requests Time Out

There are a few key reasons why a requests call may time out in Python:

  • Slow network connection - A poor internet connection can lead to delays in sending or receiving data from the server. This may trigger timeouts on requests.
  • Overloaded API server - If the API or website you are querying has high traffic or is experiencing issues, it may be slow to respond to requests.
  • Timeout value too short - Most Python request libraries allow you to set a timeout value in seconds. If this is too short for the API response time, timeouts may happen.
  • Connection issues - Any problems with connecting to the server such as DNS failures, unstable connections, or proxy problems can also lead to timeouts.
  • Handling Timeouts

    When you run into timeouts, there are some steps you can take to troubleshoot and improve the reliability:

  • Check connectivity - Confirm that you have a stable internet connection without proxy or DNS issues. Retry from different networks if possible.
  • Increase the timeout - Try raising the timeout duration (e.g. to 30+ seconds) to accommodate slower responses.
  • requests.get('https://api.example.com', timeout=30) 
  • Implement retries - Add retry logic with exponential backoff to retry timed out requests 2-3 times.
  • import requests
    from tenacity import retry, stop_after_attempt
    
    @retry(stop=stop_after_attempt(3))
    def make_request():
        response = requests.get('https://api.example.com', timeout=30)
        response.raise_for_status()
        return response
  • Handle exceptions - Wrap your requests in try/except blocks and handle ConnectTimeout or ReadTimeout errors. You can log and retry these failures.
  • Assess for overload - Check if the API has rate limits or is experiencing high traffic. If so, reduce request frequency or switch to another provider.
  • Avoiding Timeouts

    To help avoid timeouts, here are some key best practices:

  • Monitor requests and log any timeouts for analysis
  • Stress test remote APIs you rely on to understand failure modes
  • Implement circuit breakers to stop requests when failures exceed a threshold
  • Cache API response data to reduce calls during peak loads
  • With timeouts, the most important thing is having robust handling and retries in place. This ensures one slow API response doesn't break your entire application.

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