Python monorepos: what, why and how

This talk will describe the monorepo codebase architecture, explain why you might want to use it for your Python code, and what kind of tooling you need to work effectively in it.

As organizations and repos grow, we have to choose how to manage codebases in a scalable way. We have two architectural alternatives:

- Multirepo: split the codebase into increasing numbers of small repos, along team or project boundaries.
- Monorepo: Maintain one large repository containing code for many projects and libraries, with multiple teams collaborating across it.

In this talk we'll discuss the pros and cons of monorepos for Python codebases, and the kinds of tooling and processes we can use to make working in a Python monorepo effective.

Transcript

1. Python Monorepos:
   What, Why and How
   Benjy Weinberger
   Maintainer, Pants Build
   PyCon Israel 2021
   

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2. About me
   ● 25 years' experience as a
   Software Engineer.
   ● Worked at Check Point, Google,
   Twitter, Foursquare.
   ● Maintainer of the Pants OSS project.
   ● Co-founder of Toolchain.
   

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3. Overview
   1. What is a monorepo
   2. Why would I want one?
   3. Tooling for a Python monorepo
   

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4. 1. What is a monorepo?
   

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5. A common codebase characteristic
   They
   g r o w
   over time.
   

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6. A common consequence of growth
   Builds get harder: slower, less manageable
   

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7. Two ways to scale your codebase
   Multi-repo vs. Monorepo
   

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8. Multi-repo
   Split the codebase into growing numbers of small
   repos, along team or project boundaries.
   

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9. Monorepo
   A monorepo is a unified codebase containing
   code for multiple projects that share underlying
   dependencies, data models, functionality, tooling
   and processes.
   

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10. monorepo != monolithic server
    Monorepos are often great for microservices.
    

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11. 2. Why should I want a monorepo?
    

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12. Multi-repo kinda sounds better at first
    More decentralized. More bottom-up.
    I can do my own thing in my own repo.
    

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13. But, for some core problems...
    Multi-repo doesn't solve them.
    It hides them.
    And it creates new ones.
    

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14. The hardest codebase problems are...
    Managing
    Changes
    😤
    Managing
    Dependencies
    😡
    
    

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15. Multi-repo relies on publishing
    For code from repo A to be consumed by other repos,
    it must publish an artifact, such as an sdist or wheel.
    A-1.2.0
    

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16. Multi-repo relies on versioning
    When repo A makes a change, it has to re-publish
    under a new version.
    A-1.3.0
    A-1.2.0
    

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17. Say repo B depends on repo A
    It does so at a specific version:
    B-4.1.0 A-1.2.0
    

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18. When repo B needs a change in repo A
    Modify A, publish it at a new version, and consume
    that new version in a new version of B.
    Now, you have two choices...
    B-4.2.0 A-1.3.0
    

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19. Change management: virtuous choice
    1. Find all the consumers of repo A
    2. Ensure that they still work at A-1.3.0
    3. Make changes as needed until tests pass
    4. Repeat - recursively! - for all repos you changed
    

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20. Change management: lazy choice
    Don't worry about the other consumers of repo A.
    After all, they're safely pinned to A-1.2.0.
    Let them deal with the problems when they upgrade.
    But...
    

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21. Dependency hell
    This causes a huge dependency resolution problem.
    C-1.8.0
    B-4.2.0 A-1.3.0
    A-1.2.0
    
    

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22. But in a monorepo
    There is no versioning or publishing.
    All the consumers are right there in the same repo.
    Breakages are immediately visible.
    

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23. Monorepos are more flexible
    Easier to refactor
    Easier to debug
    Easier to discover and reuse code
    Unified change history
    

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24. Your codebase ➜ your organization
    Balkanized codebase ➜ balkanized org
    Unified codebase ➜ unified org
    

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25. 3. Tooling for a Python monorepo
    

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26. Build Performance At Scale
    Standard Python tools not designed for monorepos.
    Small changes trigger full rebuilds.
    As your codebase grows, so do your build times.
    

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27. How to speed things up
    Do less work
    ● Fine-grained invalidation
    ● Caching
    Do more work at once
    ● Concurrency
    ● Remote execution
    

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28. What kind of tooling has these features?
    To work effectively, you need a build system
    designed for monorepos.
    It sits on top of existing standard tooling, and
    orchestrates them for you.
    

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29. Examples of such tools include
    Pants
    Bazel
    Buck
    

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30. How do these tools work?
    ● Goal-based command interface
    ● Build graph metadata
    ● Extensible workflow with no side-effects
    

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31. Goals
    A monorepo build system typically supports
    requesting goals on specific inputs.
    $ pants test src/python/foo/bar/test.py
    $ pants package src/python/foo/**
    $ pants lint fmt --changed-since=HEAD
    

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32. Code dependencies
    A monorepo build system requires extra metadata to
    describe the build graph: the units of code and the
    dependencies between them.
    

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33. Task dependencies
    A monorepo build system maintains the rule graph:
    The units of work and the dependencies between
    them.
    Custom rules can be plugged in, for extensibility.
    

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34. Build workflow
    Code dependencies + task dependencies = workflow.
    Workflow recursively maps initial inputs to final
    outputs - the goals the user requested.
    Workflow is side effect-free.
    

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35. The explicitly-modelled workflow enables
    ● fine-grained invalidation
    ● caching
    ● concurrency
    ● remote execution
    Which is what makes builds scale with your
    codebase!
    

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36. Summary
    ● Monorepos are an effective codebase
    architecture
    ● They require appropriate tooling for performance
    and reliability at scale
    ● This tooling exists!
    

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37. Thanks for attending!
    I'll be happy to take any questions.
    You can also find us on https://www.pantsbuild.org/
    

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