Project 3: BuildKit Cache and Reproducibility Lab

Design a build pipeline that is both fast and deterministic under ephemeral CI conditions.

Quick Reference

Attribute Value
Difficulty Intermediate
Time Estimate 6-10 hours
Main Programming Language Dockerfile + shell
Alternative Programming Languages Make, Python, Go
Coolness Level Level 2 - Practical High ROI
Business Potential 4. Cost and Velocity Win
Prerequisites OCI layers, CI pipeline basics
Key Topics BuildKit graph, cache invalidation, reproducible builds

1. Learning Objectives

  1. Benchmark cold vs warm build behavior.
  2. Reduce rebuild scope by stage design.
  3. Export/import cache for ephemeral runners.
  4. Validate reproducible output digests.

2. All Theory Needed (Per-Concept Breakdown)

2.1 Build Graph and Cache Invalidation

Fundamentals

BuildKit treats builds as dependency graphs. Cache reuse is possible when inputs for a node are unchanged.

Deep Dive into the concept

Stage ordering is a high-leverage decision. Put stable dependencies before fast-changing source files, otherwise every edit invalidates large downstream segments. Cache strategy must match runner lifecycle: local cache helps persistent builders, registry cache helps ephemeral CI. Determinism requires stable toolchain versions and controlled build metadata.

How this fit on projects

  • Core for P03 and used by P10 release flow.

Definitions & key terms

  • cache key, invalidation boundary, warm build, deterministic artifact.

Mental model diagram

source + deps -> stage A -> stage B -> stage C
change in A invalidates B/C; change in C invalidates only C

How it works

  1. Parse build graph.
  2. Resolve cache keys.
  3. Recompute only invalid nodes.
  4. Export cache metadata for future runs.

Invariants: identical node inputs should reuse cached output. Failure modes: hidden non-deterministic steps, broad invalidation.

Minimal concrete example

Run 1 (cold): 4m12s
Run 2 (warm): 1m03s
Run 3 (warm + source edit): 1m28s

Common misconceptions

  • “Fast build implies reproducible build.” -> not always.

Check-your-understanding questions

  1. Why do early-stage changes cost more?
  2. Why use external cache in ephemeral CI?

Check-your-understanding answers

  1. They invalidate all dependent stages.
  2. Local runner cache disappears between jobs.

Real-world applications

  • CI cost reduction and developer feedback loops.

Where you’ll apply it

  • P03, P10.

References

  • Docker BuildKit docs
  • Docker cache optimization docs

Key insights

  • The best cache optimization is structural, not cosmetic.

Summary

  • Build graph design drives both speed and repeatability.

Homework/Exercises to practice the concept

  1. Draw cache invalidation map for your pipeline.
  2. Compare local vs registry cache effectiveness.

Solutions to the homework/exercises

  1. Annotate each stage with volatile inputs.
  2. Collect build duration and hit ratio across 10 runs.

2.2 Reproducibility Controls

Fundamentals

Reproducibility means same inputs produce same digest outputs.

Deep Dive into the concept

Sources of nondeterminism include timestamps, unpinned dependencies, and environment variance. Controlled builds pin base images, package indices, and compiler versions. A reproducibility gate compares digest outputs across repeated builds and fails if drift exceeds policy.

How this fit on projects

  • P03 and capstone governance in P10.

Definitions & key terms

  • hermetic build, pinned dependency, provenance metadata.

Mental model diagram

stable inputs + pinned toolchain + normalized metadata -> stable digest

How it works

  1. Pin dependencies.
  2. Normalize metadata.
  3. Build repeatedly.
  4. Compare digest outputs.

Invariants: controlled inputs produce controlled outputs. Failure modes: hidden time/randomness dependency.

Minimal concrete example

Build 1 digest: sha256:AAA
Build 2 digest: sha256:AAA
Build 3 digest: sha256:AAA

Common misconceptions

  • “Pinned base image is enough.” -> dependency chain still matters.

Check-your-understanding questions

  1. What hidden input often breaks reproducibility?
  2. Why is reproducibility useful for security, not just performance?

Check-your-understanding answers

  1. Timestamps and unpinned package indexes.
  2. It improves provenance confidence and auditability.

3. Project Specification

3.1 What You Will Build

A benchmark harness that runs cold/warm builds, captures cache metrics, and verifies reproducibility.

3.2 Functional Requirements

  1. Execute benchmark scenarios.
  2. Capture build duration and cache hit indicators.
  3. Export digest comparison report.
  4. Recommend structural optimizations.

3.3 Non-Functional Requirements

  • Performance: benchmark overhead under 5%.
  • Reliability: identical benchmark input yields stable report format.
  • Usability: one command to run all scenarios.

3.4 Example Usage / Output

$ ./build-lab run --all
$ ./build-lab report --format markdown

3.5 Data Formats / Schemas / Protocols

  • Benchmark scenario schema
  • Result event schema (duration, hit ratio, digest)

3.6 Edge Cases

  • Cache backend unavailable.
  • Base image digest changed mid-run.
  • Network latency spikes during dependency fetch.

3.7 Real World Outcome

3.7.1 How to Run (Copy/Paste)

$ ./build-lab run --scenario cold
$ ./build-lab run --scenario warm
$ ./build-lab compare --last 2

3.7.2 Golden Path Demo (Deterministic)

  • Baseline cold run.
  • Two warm runs with unchanged inputs.
  • One warm run after source-only edit.

3.7.3 If CLI: exact terminal transcript

$ ./build-lab report
cold_duration: 252s
warm_duration: 63s
cache_hit_ratio: 0.78
digest_stability: PASS
recommended_actions:
  - isolate dependency install stage
  - export cache to registry backend

4. Solution Architecture

4.1 High-Level Design

scenario runner -> build executor -> metrics collector -> report generator

4.2 Key Components

Component Responsibility Key Decisions
Runner execute scenarios deterministic ordering
Executor invoke builds stable parameterization
Metrics collect timings/hits comparable output shape
Reporter recommendations evidence-based optimization

4.4 Data Structures (No Full Code)

BuildScenario:
- name
- input_mutation
- cache_mode

BuildResult:
- duration_sec
- cache_hit_ratio
- digest

4.4 Algorithm Overview

  1. Run scenario matrix.
  2. Collect metrics.
  3. Compare digests and timings.
  4. Generate optimization report.

5. Implementation Guide

5.1 Development Environment Setup

# BuildKit-enabled environment and registry cache endpoint

5.2 Project Structure

build-lab/
  scenarios/
  scripts/
  reports/

5.3 The Core Question You’re Answering

“How can we make builds predictably fast without sacrificing artifact determinism?”

5.4 Concepts You Must Understand First

  • build graph invalidation
  • external cache backends
  • reproducibility checks

5.5 Questions to Guide Your Design

  1. Which stage should absorb volatile changes?
  2. Which metric best represents developer feedback speed?

5.6 Milestones

  1. Baseline benchmark.
  2. Cache strategy implementation.
  3. Reproducibility gate.
  4. Optimization report.

5.7 Validation and Testing

  • repeated run variance checks
  • cache backend failure injection
  • digest drift alerts

5.8 Common Pitfalls and Recovery

  • optimizing for one runner type only
  • missing reproducibility gates

5.9 Definition of Done

  • Cold/warm benchmark complete.
  • Cache strategy implemented and measured.
  • Reproducibility report generated.
  • Recommendations documented with evidence.