Introduction
Handling failed requests is a critical aspect of building robust applications in Python. Requests may fail due to network issues, server errors, or other transient problems. Having a solid retry strategy helps make applications more fault-tolerant and improves overall reliability.
In this comprehensive guide, we'll cover everything you need to know about retrying failed requests in Python using the powerful Requests library.
Overview of Retrying Failed Requests
The goal of retrying failed requests is to repeat the request in case of failure to maximize the chance of it succeeding. This prevents failures from bringing down the entire application.
Some common cases where retrying requests helps include:
By retrying with some delay, the underlying cause of failure may be resolved before the next attempt.
Common Reasons for Request Failures
Some typical reasons requests fail:
Having proper retry logic helps handle many of these cases gracefully.
Implementing Retry Logic
The Requests library makes it easy to retry failed requests with some handy components.
Core Components
Retry Object
The
HTTPAdapter
The
Session Object
The
Number of Retries
Deciding the number of retry attempts involves balancing reliability and performance. Some factors to consider:
Infinite retries with backoff can be used for critical requests. But safeguards should prevent endless futile retries.
Retry Conditions
The Retry object allows controlling exactly when a retry is triggered via:
Delay and Backoff
Adding a delay between retries can improve reliability:
Putting It Together
Basic Retry Pattern
retries = Retry(total=3, backoff_factor=1)
adapter = HTTPAdapter(max_retries=retries)
session = requests.Session()
session.mount('https://', adapter)
Full Configuration Example
More advanced configuration:
retries = Retry(
total=10,
backoff_factor=2,
status_forcelist=[500, 502, 503],
method_whitelist=['GET','POST']
)
adapter = HTTPAdapter(max_retries=retries)
session = requests.Session()
session.mount('<https://api.example.com>', adapter)
This does ten total retries with exponential backoff for the specified server and request methods.
Advanced Usage
Timeouts
Setting timeouts prevents hanging requests. They can be combined with retries.
# Timeout if response takes over 3 seconds
timeout = Timeout(connect=3.0, read=3.0)
adapter = HTTPAdapter(max_retries=retries, timeout=timeout)
This times out a request but allows retrying.
Logging
Debugging failed requests is easier with logging enabled:
# Log details for all requests
import logging
logging.basicConfig(level=logging.DEBUG)
The Requests log includes the request URL, parameters, response code, and timing.
Testing
Mocking API responses allows testing retry logic during development without hitting real endpoints:
import responses
@responses.activate
def test_retries():
responses.add(responses.GET, '<https://api.example.com/users>', status=500)
resp = session.get('<https://api.example.com/users>')
# Assert retries happened as expected
Responses provides a simple way to simulate API responses when testing.
Common Issues
Avoiding Infinite Loops
Infinite retry loops can happen if there are no limits and the issue persists. Some ways to prevent this:
Handling Side Effects
For non-idempotent requests like POST, retries can end up duplicating effects. Ways to address:
Watching for Stale Data
Retrying means multiple requests for the same resource. Ensure:
Conclusion
That covers the key aspects around retrying failed requests in Python! Here are some key takeaways:
Overall, having solid retry capabilities makes applications much more reliable. And the Python Requests library provides excellent tools to build this in a customizable way.
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import requests
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'https': PROXY_URL
}
requests.get('<http://example.com>', proxies=proxies)
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