Migrating from Python 2 to Python 3 - Case Studies

Migrating from Python 2 to Python 3

Python is a popular programming language that is widely used in different industries, such as web development, data science, artificial intelligence, and more. Python 2, which was released in 2000, had a large user base and was supported for many years. However, Python 2 reached its end-of-life on January 1, 2020, meaning that it is no longer being maintained and updated. This has led many companies and organizations to migrate their codebase from Python 2 to Python 3.

A Challenging Task

Migrating from Python 2 to Python 3 can be a challenging task, as there are significant differences between the two versions. The most significant difference is the way that Python 3 handles strings, which can cause compatibility issues with existing code. However, with proper planning and execution, the migration can be completed successfully, leading to a more secure and efficient codebase.

Case Studies in Migrating to Python 3

In this article, we will discuss case studies of companies that have migrated from Python 2 to Python 3. These case studies will provide insights into the challenges faced during the migration process and the strategies used to overcome them.

Case Study 1: Dropbox

Dropbox is a file hosting service that had a large Python 2 codebase. In 2016, Dropbox announced that it would be migrating its codebase to Python 3. The migration was completed in 2018 and involved over 4 million lines of code.

Custom Library Called 'Py3c'

To begin the migration process, Dropbox first created a custom library called "Py3c" that would allow them to write code that was compatible with both Python 2 and Python 3. They then used automated tools to identify and fix issues in their codebase. The automated tools were used to replace deprecated code and update syntax that was no longer supported in Python 3.

Manually Updated Some of the Code

However, the automated tools were not able to fix all of the compatibility issues, so Dropbox had to manually update some of the code. This was a time-consuming process, as it involved identifying code that was causing issues and modifying it to work with Python 3.

Comprehensive Testing

To ensure that the migration was successful, Dropbox also created a comprehensive testing suite that would validate the new code. This testing suite included thousands of tests that were run on a regular basis to ensure that the code was functioning as expected.

Case Study 2: Instagram

Instagram is a social media platform that also had a large Python 2 codebase. In 2017, Instagram began its migration to Python 3. The migration was completed in 2019 and involved over 6 million lines of code.

Also Used a Custom Library

Like Dropbox, Instagram used automated tools to identify and fix compatibility issues in their codebase. They also used a custom library called "six" that allowed them to write code that was compatible with both Python 2 and Python 3.

Unicode Strings

However, Instagram encountered some challenges during the migration process. One of the challenges was the way that Python 3 handles Unicode strings. Instagram had to modify some of their code to handle these differences, which required significant changes to their codebase.

Ran Over 60,000 Tests

To ensure that the migration was successful, Instagram also created a comprehensive testing suite that would validate the new code. This testing suite included over 60,000 tests that were run on a regular basis to ensure that the code was functioning as expected.

Case Study 3: Pinterest

Pinterest is a social media platform that also had a significant Python 2 codebase. In 2018, Pinterest began its migration to Python 3. The migration was completed in 2019 and involved over 7 million lines of code.

Used a Custom Library and an Automated Tool

To begin the migration process, Pinterest used a tool called "futurize" that would automatically update their codebase to be compatible with Python 3. They also used a custom library called "six" that allowed them to write code that was compatible with both Python 2 and Python 3.

Also Encountered Problems With Strings

However, Pinterest also encountered some challenges during the migration process. One of the challenges was the way that Python 3 handles strings. Pinterest had to modify some of their code to handle these differences, which required significant changes to their codebase.

Ran Over 40,000 Tests

To ensure that the migration was successful, Pinterest created a comprehensive testing suite that would validate the new code. This testing suite included over 40,000 tests that were run on a regular basis to ensure that the code was functioning as expected.

Lessons Learned

From these case studies, there are several lessons that can be learned about migrating from Python 2 to Python 3:

Proper planning is essential

Before beginning the migration process, it is important to plan out the process and identify potential challenges that may arise. This will help ensure that the migration process is smooth and efficient.

Automated tools are helpful but not sufficient

While automated tools can help identify and fix compatibility issues, they are not always able to fix all of the issues. Manual updates may still be required to ensure that the code is compatible with Python 3.

Custom libraries can be helpful

Creating custom libraries can help ensure that the code is compatible with both Python 2 and Python 3. This can be especially helpful if the codebase is large and complex.

Comprehensive testing is necessary

A comprehensive testing suite is necessary to ensure that the new code is functioning as expected. This testing suite should include both unit tests and integration tests.

Conclusion

Migrating from Python 2 to Python 3 can be a challenging task, but it is necessary to ensure that the codebase is secure and efficient. By learning from the experiences of companies like Dropbox, Instagram, and Pinterest, it is possible to identify potential challenges and develop strategies to overcome them. Proper planning, the use of automated tools and custom libraries, and comprehensive testing are all essential components of a successful migration process.

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