Project 2: Build Your Own Package Manager

A functional package manager that can install, remove, track dependencies, and upgrade software packages.

Quick Reference

Attribute Value
Primary Language See main guide
Alternative Languages N/A
Difficulty Level 3: Advanced
Time Estimate 2-3 weeks
Knowledge Area Systems Administration / Algorithms
Tooling Package Management
Prerequisites Comfortable with C or Rust, basic data structures

What You Will Build

A functional package manager that can install, remove, track dependencies, and upgrade software packages.

Why It Matters

This project builds core skills that appear repeatedly in real-world systems and tooling.

Core Challenges

  • Designing a package format (tarball + metadata) (maps to packaging)
  • Implementing dependency resolution algorithm (maps to graph algorithms)
  • Handling file conflicts and ownership tracking (maps to filesystem management)
  • Building a repository index and fetching packages (maps to networking/HTTP)
  • Implementing atomic install/rollback (maps to transactions)

Key Concepts

  • Dependency resolution: “Grokking Algorithms” Chapter 6 (Graphs) - Aditya Bhargava
  • Database design for packages: “Designing Data-Intensive Applications” Chapter 2 - Martin Kleppmann
  • Filesystem transactions: “Operating Systems: Three Easy Pieces” Chapter 42 - Arpaci-Dusseau
  • Archive formats: man tar, man ar, and studying .deb/.rpm internals

Real-World Outcome

Deliver a working demo with observable output that proves the feature is correct.

Implementation Guide

  1. Reproduce the simplest happy-path scenario.
  2. Build the smallest working version of the core feature.
  3. Add input validation and error handling.
  4. Add instrumentation/logging to confirm behavior.
  5. Refactor into clean modules with tests.

Milestones

  • Milestone 1: Minimal working program that runs end-to-end.
  • Milestone 2: Correct outputs for typical inputs.
  • Milestone 3: Robust handling of edge cases.
  • Milestone 4: Clean structure and documented usage.

Validation Checklist

  • Output matches the real-world outcome example
  • Handles invalid inputs safely
  • Provides clear errors and exit codes
  • Repeatable results across runs

References

  • Main guide: LINUX_DISTRIBUTION_BUILDING_LEARNING_PROJECTS.md
  • “Designing Data-Intensive Applications” by Martin Kleppmann