The Python Podcast.__init__

The Python Podcast.__init__

The podcast about Python and the people who make it great

25 May 2020

Dependency Management Improvements In Pip's Resolver - E264

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Dependency management in Python has taken a long and winding path, which has led to the current dominance of Pip. One of the remaining shortcomings is the lack of a robust mechanism for resolving the package and version constraints that are necessary to produce a working system. Thankfully, the Python Software Foundation has funded an effort to upgrade the dependency resolution algorithm and user experience of Pip. In this episode the engineers working on these improvements, Pradyun Gedam, Tzu-Ping Chung, and Paul Moore, discuss the history of Pip, the challenges of dependency management in Python, and the benefits that surrounding projects will gain from a more robust resolution algorithm. This is an exciting development for the Python ecosystem, so listen now and then provide feedback on how the new resolver is working for you.


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  • Your host as usual is Tobias Macey and today I’m interviewing Tzu-ping Chung, Pradyun Gedam, and Paul Moore about their work to improve the dependency resolution capabilities of Pip and its user experience


  • Introductions
  • How did you get introduced to Python?
  • Can you start by describing the focus of the work that you are doing?
    • What is the scope of the work, and what is the established criteria for when it is considered complete?
  • What is your history with working on the Pip source code and what interests you most about this project?
  • What are the main sources or manifestations of technical debt that exist in Pip as of today?
    • How does it currently handle dependency resolution?
  • What are some of the workarounds that developers have had to resort to in the absence of a robust dependency resolver in Pip?
  • How is the new dependency resolver implemented?
    • How has your initial design evolved or shifted as you have gotten further along in its implementation?
  • What are the pieces of information that the resolver will rely on for determining which packages and versions to install? (e.g. will it install setuptools > 45.x in a Python 2 virtualenv?)
  • What are the new capabilities in Pip that will be enabled by this upgrade to the dependency resolver?
  • What projects or features in the encompassing ecosystem will be unblocked with the introduction of this upgrade?
  • What are some of the changes that users will need to make to adopt the updated Pip?
  • How do you anticipate the changes in Pip impacting the viability or adoption of Python and its ecosystem within different communities or industries?
  • What are some of the additional changes or improvements that you would like to see in Pip or other core elements of the Python landscape?
  • What are some of the most interesting, unexpected, or challenging lessons that you have learned while working on these updates to Pip?

Keep In Touch


Closing Announcements

  • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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