Guidance on Maintaining Uptime When Python Libraries Are Yanked or Deprecated in Azure Functions

Question

I need guidance or best practices for maintaining 100% uptime and smooth deployment for Python Azure Functions in scenarios where a Python library becomes:

• Deprecated
• Yanked (removed from Pypi)
• Incompatible due to versioning constraints

We recently encountered an issue where our Azure Function deployment failed because a version in requirements.txt referenced a non-existent/yanked version

Scenario:
We have an Azure Function App written in Python 3.10 that uses Flask and Flask-Cors. During a routine deployment, the Function App failed to start with the following error:

• ImportError: cannot import name 'url_quote' from 'werkzeug.urls'

and the issue was "url_quote" function was removed in a newer version of werkzeug 2.1.2, yet Flask-Cors (which indirectly depends on it) was trying to import it.

However, Azure Functions environment pulled a newer version of Werkzeug (e.g., 2.1 or later) which no longer contained url_quote. This mismatch caused the app to fail at import time.

Answer 1

Thank you for reaching out with your concerns regarding maintaining 100% uptime and smooth deployment for your Python Azure Functions, especially in scenarios where a Python library becomes deprecated, yanked, or incompatible due to versioning constraints.

Balancing Effort and Control in Dependency Management

When specifying versions of dependencies in your requirements.txt, it's crucial to strike a balance between effort and control. While pinning exact versions of dependencies can provide stability and predictability, it also requires diligent maintenance to ensure that your application remains secure and up-to-date. Conversely, allowing more flexibility in versioning can reduce maintenance overhead but may introduce risks of incompatibility or unexpected behavior.

[!WARNING] When selecting a strategy for controlling versioning of dependencies, it's important to remember that many library developers adopt semantic versioning schema without actually following it's "rules". This erodes your ability to be confident that minor or revision number changes will not contain breaking changes

Strategy for Maintaining Uptime

Given the critical nature of maintaining uptime, I recommend adopting a strategy that favors control. Here are some best practices to consider:

• Pin Exact Versions: Specify exact versions of your dependencies in requirements.txt to ensure that your application always uses the same versions that have been tested and verified. For example:

Flask==2.0.3
Flask-Cors==3.0.10
Werkzeug==2.0.3

• Use a Dependency Lock File: In addition to requirements.txt, use a dependency lock file (e.g., pip freeze > requirements.lock) to capture the exact versions of all dependencies, including transitive dependencies. This ensures that the same versions are installed across different environments.
• Regularly Update Dependencies: Schedule regular intervals to review and update your dependencies. This proactive approach helps you stay ahead of potential deprecations or yanked versions. Use tools like pip-tools or pipenv to manage and update dependencies systematically.
• Implement CI/CD Pipelines: Integrate continuous integration and continuous deployment (CI/CD) pipelines to automate testing and deployment processes. This allows you to catch issues early and ensure that updates do not break your application.
• Monitor Dependency Health: Use tools like Dependabot or Snyk to monitor the health of your dependencies. These tools can alert you to vulnerabilities, deprecated packages, and other issues, allowing you to take timely action.
• Fallback Mechanism: Implement a fallback mechanism to handle scenarios where a dependency version becomes unavailable. For instance, maintain a local cache of critical dependencies or use a private PyPI repository to host your own copies of dependencies.

Addressing the Specific Issue

Regarding the specific issue you encountered with the url_quote function being removed in Werkzeug 2.1.2, here are some steps to mitigate such problems:

• Pin the Werkzeug Version: Ensure that your requirements.txt specifies a version of Werkzeug that is compatible with Flask-Cors. For example:

Werkzeug==2.0.3

• Test Dependency Updates: Before updating any dependency, test the new version in a staging environment to verify compatibility with your application.
• Review Release Notes: Always review the release notes of your dependencies to understand any breaking changes or deprecations that may affect your application.

By adopting these practices, you can achieve a higher level of control over your dependencies, thereby maintaining uptime and ensuring smooth deployments.

Answer 2

Hi Sibte Abbas Rizvi,

As suggested by Kyle Burns, pinning exact versions, using a dependency lock file, and setting up CI/CD pipelines will help maintain stability and prevent issues with outdated or incompatible libraries. These steps will ensure smoother deployments and reduce risks.

For further clarification, please refer to these links as well.

https://stackoverflow.com/questions/77213053/why-did-flask-start-failing-with-importerror-cannot-import-name-url-quote-fr

https://github.com/Sanster/IOPaint/issues/389

Check out these helpful links for further details:

• Azure Functions Python developer guide
• Troubleshoot Python errors

Hope this helps you maintain uptime for your Azure Functions! If you have any further questions or need clarification, just let me know.