Project 2: Commit Graph Visualizer — Render the DAG Structure

A tool that reads a Git repository and generates a visual graph showing commits, branches, merges, and tags—revealing the true DAG structure that underlies Git history.

Quick Reference

Attribute Value
Difficulty Intermediate
Time Estimate 1-2 weeks
Main Programming Language Python
Alternative Programming Languages Go, Rust, JavaScript (D3.js for visualization)
Coolness Level Level 4: Hardcore Tech Flex
Business Potential 2. The “Micro-SaaS / Pro Tool”
Prerequisites Project 1 completed, basic graph theory, understanding of topological sort
Key Topics Graph Theory Basics, Git References, Merge Commit Structure

1. Learning Objectives

By completing this project, you will:

  1. Implement a working version of: A tool that reads a Git repository and generates a visual graph showing commits, branches, merges, and tags—revealing the true DAG structure that underlies Git history..
  2. Explain the core Git workflow tradeoff this project is designed to surface.
  3. Design deterministic checks so results can be verified and reproduced.
  4. Document operational failure modes and safe recovery actions.

2. All Theory Needed (Per-Concept Breakdown)

Graph Theory Basics

Fundamentals This concept matters in this project because your implementation will fail or become non-deterministic without a precise model of Graph Theory Basics. You should define what the concept controls, what invariants must hold, and which actions are safe versus destructive. Treat this concept as a production concern, not a tutorial checkbox.

Deep Dive into the concept When applying Graph Theory Basics in this project, reason in three passes: data shape, state transitions, and enforcement. First, identify which artifacts are authoritative (commit objects, refs, metadata, policy config, CI status, or scan findings). Second, map how those artifacts change when your tool runs. Third, define failure behavior explicitly. In Git tooling, silent partial success is dangerous: you need either complete success with evidence or an explicit failure state with remediation guidance. Also account for scale behavior. A workflow that works on a toy repo may fail on large history depth, concurrent updates, or mixed branch policies. Include trace logs for every irreversible action, and separate simulation mode from write mode. For interview readiness, be able to explain how this concept protects delivery speed while reducing operational risk.

How this fit on projects In this project, Graph Theory Basics is directly used in design decisions, implementation constraints, and verification criteria.

Definitions & key terms

  • Graph Theory Basics invariant: A condition that must remain true before and after every operation.
  • Safety boundary: The point where actions become destructive unless guarded.
  • Verification signal: Evidence proving the action behaved as expected.

Mental model diagram

Input state -> Validate invariant -> Apply change -> Verify output -> Record evidence

How it works

  1. Capture current state and constraints.
  2. Evaluate whether Graph Theory Basics preconditions are satisfied.
  3. Execute the minimal safe transition.
  4. Verify postconditions and publish an auditable result.

Failure modes: stale state, partial writes, race conditions, ambiguous output contracts.

Minimal concrete example

Plan -> dry-run -> execute -> verify -> rollback/forward-fix decision

Common misconceptions

  • Assuming local success implies team-safe behavior.
  • Treating policy violations as warnings instead of merge blockers.
  • Skipping deterministic verification because the output appears correct.

Check-your-understanding questions

  1. Which invariant is most likely to break first under concurrency?
  2. What output proves your tool handled an edge case correctly?
  3. Where should enforcement happen: local hook, CI, or protected branch gate?

Check-your-understanding answers

  1. The invariant tied to mutable refs or policy-dependent merge eligibility.
  2. A deterministic transcript showing both success and controlled failure behavior.
  3. Layered enforcement: fast local checks plus non-bypassable server-side gates.

Real-world applications

  • Change-management tooling for fast-moving teams.
  • Incident-safe release workflows with traceable rollback paths.
  • Compliance-ready source-control automation.

Where you’ll apply it This project and its immediate adjacent projects in this sprint.

References

  • https://git-scm.com/docs
  • https://dora.dev/capabilities/trunk-based-development/

Key insights Graph Theory Basics is only valuable when its invariants are encoded into tooling and checks.

Summary Mastering Graph Theory Basics here gives you transferable patterns for larger workflow systems.

Homework/Exercises to practice the concept

  1. Write one failing scenario and expected detection output.
  2. Define one invariant and one explicit violation test.

Solutions to the homework/exercises

  1. Use a stale branch or invalid metadata case and assert deterministic error reporting.
  2. Invariant: protected branch must not accept unchecked changes; violation test: bypass attempt should fail fast.

Git References

Fundamentals This concept matters in this project because your implementation will fail or become non-deterministic without a precise model of Git References. You should define what the concept controls, what invariants must hold, and which actions are safe versus destructive. Treat this concept as a production concern, not a tutorial checkbox.

Deep Dive into the concept When applying Git References in this project, reason in three passes: data shape, state transitions, and enforcement. First, identify which artifacts are authoritative (commit objects, refs, metadata, policy config, CI status, or scan findings). Second, map how those artifacts change when your tool runs. Third, define failure behavior explicitly. In Git tooling, silent partial success is dangerous: you need either complete success with evidence or an explicit failure state with remediation guidance. Also account for scale behavior. A workflow that works on a toy repo may fail on large history depth, concurrent updates, or mixed branch policies. Include trace logs for every irreversible action, and separate simulation mode from write mode. For interview readiness, be able to explain how this concept protects delivery speed while reducing operational risk.

How this fit on projects In this project, Git References is directly used in design decisions, implementation constraints, and verification criteria.

Definitions & key terms

  • Git References invariant: A condition that must remain true before and after every operation.
  • Safety boundary: The point where actions become destructive unless guarded.
  • Verification signal: Evidence proving the action behaved as expected.

Mental model diagram

Input state -> Validate invariant -> Apply change -> Verify output -> Record evidence

How it works

  1. Capture current state and constraints.
  2. Evaluate whether Git References preconditions are satisfied.
  3. Execute the minimal safe transition.
  4. Verify postconditions and publish an auditable result.

Failure modes: stale state, partial writes, race conditions, ambiguous output contracts.

Minimal concrete example

Plan -> dry-run -> execute -> verify -> rollback/forward-fix decision

Common misconceptions

  • Assuming local success implies team-safe behavior.
  • Treating policy violations as warnings instead of merge blockers.
  • Skipping deterministic verification because the output appears correct.

Check-your-understanding questions

  1. Which invariant is most likely to break first under concurrency?
  2. What output proves your tool handled an edge case correctly?
  3. Where should enforcement happen: local hook, CI, or protected branch gate?

Check-your-understanding answers

  1. The invariant tied to mutable refs or policy-dependent merge eligibility.
  2. A deterministic transcript showing both success and controlled failure behavior.
  3. Layered enforcement: fast local checks plus non-bypassable server-side gates.

Real-world applications

  • Change-management tooling for fast-moving teams.
  • Incident-safe release workflows with traceable rollback paths.
  • Compliance-ready source-control automation.

Where you’ll apply it This project and its immediate adjacent projects in this sprint.

References

  • https://git-scm.com/docs
  • https://dora.dev/capabilities/trunk-based-development/

Key insights Git References is only valuable when its invariants are encoded into tooling and checks.

Summary Mastering Git References here gives you transferable patterns for larger workflow systems.

Homework/Exercises to practice the concept

  1. Write one failing scenario and expected detection output.
  2. Define one invariant and one explicit violation test.

Solutions to the homework/exercises

  1. Use a stale branch or invalid metadata case and assert deterministic error reporting.
  2. Invariant: protected branch must not accept unchecked changes; violation test: bypass attempt should fail fast.

Merge Commit Structure

Fundamentals This concept matters in this project because your implementation will fail or become non-deterministic without a precise model of Merge Commit Structure. You should define what the concept controls, what invariants must hold, and which actions are safe versus destructive. Treat this concept as a production concern, not a tutorial checkbox.

Deep Dive into the concept When applying Merge Commit Structure in this project, reason in three passes: data shape, state transitions, and enforcement. First, identify which artifacts are authoritative (commit objects, refs, metadata, policy config, CI status, or scan findings). Second, map how those artifacts change when your tool runs. Third, define failure behavior explicitly. In Git tooling, silent partial success is dangerous: you need either complete success with evidence or an explicit failure state with remediation guidance. Also account for scale behavior. A workflow that works on a toy repo may fail on large history depth, concurrent updates, or mixed branch policies. Include trace logs for every irreversible action, and separate simulation mode from write mode. For interview readiness, be able to explain how this concept protects delivery speed while reducing operational risk.

How this fit on projects In this project, Merge Commit Structure is directly used in design decisions, implementation constraints, and verification criteria.

Definitions & key terms

  • Merge Commit Structure invariant: A condition that must remain true before and after every operation.
  • Safety boundary: The point where actions become destructive unless guarded.
  • Verification signal: Evidence proving the action behaved as expected.

Mental model diagram

Input state -> Validate invariant -> Apply change -> Verify output -> Record evidence

How it works

  1. Capture current state and constraints.
  2. Evaluate whether Merge Commit Structure preconditions are satisfied.
  3. Execute the minimal safe transition.
  4. Verify postconditions and publish an auditable result.

Failure modes: stale state, partial writes, race conditions, ambiguous output contracts.

Minimal concrete example

Plan -> dry-run -> execute -> verify -> rollback/forward-fix decision

Common misconceptions

  • Assuming local success implies team-safe behavior.
  • Treating policy violations as warnings instead of merge blockers.
  • Skipping deterministic verification because the output appears correct.

Check-your-understanding questions

  1. Which invariant is most likely to break first under concurrency?
  2. What output proves your tool handled an edge case correctly?
  3. Where should enforcement happen: local hook, CI, or protected branch gate?

Check-your-understanding answers

  1. The invariant tied to mutable refs or policy-dependent merge eligibility.
  2. A deterministic transcript showing both success and controlled failure behavior.
  3. Layered enforcement: fast local checks plus non-bypassable server-side gates.

Real-world applications

  • Change-management tooling for fast-moving teams.
  • Incident-safe release workflows with traceable rollback paths.
  • Compliance-ready source-control automation.

Where you’ll apply it This project and its immediate adjacent projects in this sprint.

References

  • https://git-scm.com/docs
  • https://dora.dev/capabilities/trunk-based-development/

Key insights Merge Commit Structure is only valuable when its invariants are encoded into tooling and checks.

Summary Mastering Merge Commit Structure here gives you transferable patterns for larger workflow systems.

Homework/Exercises to practice the concept

  1. Write one failing scenario and expected detection output.
  2. Define one invariant and one explicit violation test.

Solutions to the homework/exercises

  1. Use a stale branch or invalid metadata case and assert deterministic error reporting.
  2. Invariant: protected branch must not accept unchecked changes; violation test: bypass attempt should fail fast.

3. Project Specification

3.1 What You Will Build

A tool that reads a Git repository and generates a visual graph showing commits, branches, merges, and tags—revealing the true DAG structure that underlies Git history.

3.2 Functional Requirements

  1. Scope control: Deliver a deterministic and testable implementation.
  2. Correctness: Preserve Git invariants and policy constraints.

3.3 Non-Functional Requirements

  • Performance: Deterministic execution with documented runtime behavior on representative history sizes.
  • Reliability: Repeated runs on the same input produce identical outputs.
  • Usability: Clear CLI or report output for both success and failure cases.

3.4 Example Usage / Output

You’ll have a tool that generates visual representations of Git history, revealing the true graph structure:

Example Output:

$ ./git-graph --repo /path/to/repo --output graph.png

Analyzing repository...
Found 156 commits across 8 branches
Detected 12 merge commits
Generating graph...

Saved to: graph.png

ASCII preview:
    * 3b18e51 (HEAD -> main) Merge feature-auth
    |\
    | * a1b2c3d Add password hashing
    | * 5e6f7g8 Add login form
    |/
    * 9h0i1j2 Update README
    * k3l4m5n Initial commit

$ ./git-graph --repo /path/to/repo --format svg --show-refs

[Opens browser with interactive SVG showing]:
- Commit nodes colored by author
- Branch labels at their current positions
- Merge commits highlighted
- Clickable nodes showing commit details

3.5 Data Formats / Schemas / Protocols

Describe input repository assumptions, output report shape, and any policy/config schema consumed by the tool.

3.6 Edge Cases

  • Empty repository or shallow clone state.
  • Detached HEAD or rewritten history during execution.
  • Invalid metadata/policy configuration.

3.7 Real World Outcome

You’ll have a tool that generates visual representations of Git history, revealing the true graph structure:

Example Output:

$ ./git-graph --repo /path/to/repo --output graph.png

Analyzing repository...
Found 156 commits across 8 branches
Detected 12 merge commits
Generating graph...

Saved to: graph.png

ASCII preview:
    * 3b18e51 (HEAD -> main) Merge feature-auth
    |\
    | * a1b2c3d Add password hashing
    | * 5e6f7g8 Add login form
    |/
    * 9h0i1j2 Update README
    * k3l4m5n Initial commit

$ ./git-graph --repo /path/to/repo --format svg --show-refs

[Opens browser with interactive SVG showing]:
- Commit nodes colored by author
- Branch labels at their current positions
- Merge commits highlighted
- Clickable nodes showing commit details

4. Solution Architecture

4.1 High-Level Design

Inputs -> Validation -> Core Engine -> Output Formatter -> Verification Report

4.2 Key Components

Component Responsibility Key Decisions
Input loader Discover commits/refs/config inputs Deterministic ordering and clear failure messages
Core engine Compute project-specific logic Separate read-only simulation from mutating actions
Reporter Produce user-facing output and evidence Include machine-readable and human-readable forms

4.4 Data Structures (No Full Code)

ProjectState { refs, commits, policy, findings, metrics }
Result { status, evidence, warnings, next_actions }

4.4 Algorithm Overview

  1. Collect state from repository and configuration.
  2. Evaluate invariants and policy preconditions.
  3. Execute core transformation or analysis logic.
  4. Verify postconditions and emit deterministic report.

Complexity Analysis:

  • Time: O(history + affected scope)
  • Space: O(active graph window + report size)

5. Implementation Guide

5.1 Development Environment Setup

Use the environment defined in the main guide. Pin tool versions and fixture data to keep outputs reproducible.

5.2 Project Structure

project-root/
├── fixtures/
├── src/
├── tests/
├── docs/
└── README.md

5.3 The Core Question You’re Answering

“What does ‘rewriting history’ actually mean, and why is the commit graph a DAG, not a tree?”

Before you write any code, sit with this question. A DAG (Directed Acyclic Graph) allows multiple parents (merges) and multiple children (branches), but no cycles. Understanding this structure explains why you can’t have a commit that’s its own ancestor.

5.4 Concepts You Must Understand First

Stop and research these before coding:

  1. Graph Theory Basics
    • What’s the difference between a DAG and a tree?
    • What’s topological sorting and why does it matter for Git?
    • How do you detect cycles in a graph?
    • Book Reference: “Grokking Algorithms” Ch. 6 — Bhargava
  2. Git References
    • What’s the difference between a branch, a tag, and HEAD?
    • What does “detached HEAD” mean?
    • Where are refs stored in .git/?
    • Book Reference: “Pro Git” Ch. 10.3 — Chacon
  3. Merge Commit Structure
    • How does a merge commit differ from a regular commit?
    • What are the first and second parents of a merge?
    • What does git log --first-parent show?
    • Book Reference: “Pro Git” Ch. 3.2 — Chacon

5.5 Questions to Guide Your Design

Before implementing, think through these:

  1. Graph Construction
    • How will you build the graph in memory?
    • What data structure represents a commit node?
    • How do you handle the fact that Git stores parents, not children?
  2. Layout Algorithm
    • How do you position nodes so branches don’t overlap?
    • How do you handle very long linear histories?
    • Should time flow top-to-bottom or left-to-right?
  3. Branch Assignment
    • A commit can be reachable from multiple branches—how do you show this?
    • How do you determine the “main line” for display?

5.6 Thinking Exercise

Trace a Merge Manually

Create a test repository and trace what happens:

git init test-repo && cd test-repo
echo "initial" > file.txt && git add . && git commit -m "A"
git checkout -b feature
echo "feature" >> file.txt && git commit -am "B"
echo "more feature" >> file.txt && git commit -am "C"
git checkout main
echo "main work" >> file.txt && git commit -am "D"
git merge feature -m "E: Merge feature"

Questions while tracing:

  • Draw the DAG on paper. How many parents does commit E have?
  • If you now run git log --oneline, what order do you see?
  • What about git log --oneline --first-parent?
  • Look at .git/refs/heads/ — what files exist?

5.7 The Interview Questions They’ll Ask

Prepare to answer these:

  1. “Explain the difference between merge and rebase using a graph diagram.”
  2. “What happens to the commit graph when you force push after a rebase?”
  3. “How would you find the common ancestor of two branches programmatically?”
  4. “Why does git log show commits in the order it does?”
  5. “What’s the complexity of finding if commit A is an ancestor of commit B?”

5.8 Hints in Layers

Hint 1: Starting Point Read all refs from .git/refs/ (branches and tags) and HEAD. Each points to a commit SHA.

Hint 2: Graph Building Starting from each ref, walk parent pointers recursively. Store visited commits to avoid cycles (though Git guarantees no cycles).

Hint 3: Layout Assign each branch a “lane” (column). Commits on the same branch go in the same lane. Merge commits connect lanes.

Hint 4: Tools Graphviz’s DOT format is simple: "sha1" -> "sha2" for edges. Let Graphviz handle layout with dot -Tpng.

5.9 Books That Will Help

Topic Book Chapter
Graph algorithms “Grokking Algorithms” by Bhargava Ch. 6
Git internal refs “Pro Git” by Chacon Ch. 10.3
DAG visualization “Graph Drawing” by Tamassia Ch. 9

5.10 Implementation Phases

Phase 1: Foundation (1-2 sessions)

  • Define fixtures, expected outputs, and invariant checks.
  • Build read-only analysis path.

Phase 2: Core Functionality (2-4 sessions)

  • Implement project-specific core logic and deterministic reporting.
  • Add policy and edge-case handling.

Phase 3: Polish and Edge Cases (1-2 sessions)

  • Add failure demos, performance notes, and usability improvements.
  • Finalize docs and validation transcripts.

5.11 Key Implementation Decisions

Decision Options Recommendation Rationale
Execution mode direct write vs dry-run+write dry-run+write Safer and easier to debug
Output contract free text vs structured+text structured+text Better automation and readability
Enforcement location local only vs local+CI local+CI Prevents bypass in shared branches

6. Testing Strategy

6.1 Test Categories

  • Unit tests for parsing and policy logic.
  • Integration tests on fixture repositories.
  • Edge-case tests for stale refs, malformed metadata, and large histories.

6.2 Critical Test Cases

  1. Deterministic golden-path scenario.
  2. Policy violation hard-fail scenario.
  3. Recovery path after partial or conflicting state.

6.3 Test Data

Use fixed repository fixtures with known commit graphs and expected outputs stored under version control.

7. Common Pitfalls & Debugging

Problem 1: “Output looks correct but history or metadata is inconsistent”

  • Why: Validation happens after mutation, not before.
  • Fix: Add a preflight invariant check and a post-write verification step.
  • Quick test: Run the same command twice on the same fixture and verify identical results.

Problem 2: “Tool works on small repo but times out on larger history”

  • Why: Full traversal is performed where selective traversal is possible.
  • Fix: Cache intermediate graph lookups and scope analysis to affected commits/paths.
  • Quick test: Compare runtime on small and large fixtures with a clear budget target.

Problem 3: “Policy check can be bypassed by local-only behavior”

  • Why: Enforcement is advisory, not server-authoritative.
  • Fix: Mirror critical checks in CI and protected branch rules.
  • Quick test: Attempt merge with failing policy in CI and confirm hard block.

8. Extensions & Challenges

8.1 Beginner Extensions

  • Add richer error messages with remediation hints.
  • Add fixture generation helpers for repeatable demos.

8.2 Intermediate Extensions

  • Add performance instrumentation and budget assertions.
  • Add policy configuration profiles by repository type.

8.3 Advanced Extensions

  • Add distributed execution support for large repositories.
  • Add signed evidence exports for compliance workflows.

9. Real-World Connections

9.1 Industry Applications

  • Internal developer portals.
  • Enterprise repository governance systems.
  • Release safety and incident diagnostics tooling.
  • Git core: https://git-scm.com/
  • GitHub CLI: https://github.com/cli/cli
  • pre-commit framework: https://pre-commit.com/

9.3 Interview Relevance

This project prepares you for architecture and debugging interviews that focus on merge policy, CI gates, and workflow reliability tradeoffs.

10. Resources

10.1 Essential Reading

  • Pro Git (Internals and Workflows chapters)
  • Software Engineering at Google (Version control and build chapters)
  • Accelerate (delivery performance practices)

10.2 Video Resources

  • Git internals talks from Git Merge conference archives.
  • DORA and delivery metrics conference sessions.

10.3 Tools and Documentation

  • https://git-scm.com/docs
  • https://docs.github.com/
  • https://dora.dev/

11. Self-Assessment Checklist

11.1 Understanding

  • I can explain the primary invariant this project enforces.
  • I can explain one failure mode and one safe recovery path.

11.2 Implementation

  • Functional requirements are met on deterministic fixtures.
  • Critical edge cases are tested and documented.

11.3 Growth

  • I can describe tradeoffs in an interview setting.
  • I documented what I would change in a production version.

12. Submission / Completion Criteria

Minimum Viable Completion:

  • Deterministic golden-path output exists.
  • One failure scenario is handled with clear output.
  • Core workflow objective is demonstrably met.

Full Completion:

  • Minimum criteria plus policy validation, structured reporting, and edge-case coverage.

Excellence:

  • Full completion plus measurable performance budget and production-hardening notes.