Project 9: Vector Database Storage Engine

Build a storage engine that manages vector data, metadata, and index persistence reliably.

Quick Reference

Attribute Value
Difficulty Level 4: Expert
Time Estimate 2-3 weeks
Language Python
Prerequisites Storage systems, indexing basics
Key Topics storage layout, persistence, WAL

1. Learning Objectives

By completing this project, you will:

  1. Design storage layouts for vectors and metadata.
  2. Implement write-ahead logging for durability.
  3. Support index rebuilds from persisted data.
  4. Handle compaction and cleanup.
  5. Benchmark load and save performance.

2. Theoretical Foundation

2.1 Storage Engines

Storage engines manage durability, consistency, and performance under writes and reads.

3. Project Specification

3.1 What You Will Build

A storage layer that persists vectors, metadata, and index snapshots with durability guarantees.

3.2 Functional Requirements

  1. On-disk format for vectors and metadata.
  2. Write-ahead log for durability.
  3. Snapshot/recovery from WAL.
  4. Compaction for old data.
  5. Benchmarking for load/save latency.

3.3 Non-Functional Requirements

  • Crash safety with recovery tests.
  • Versioned formats for upgrades.
  • Clear metrics for IO performance.

4. Solution Architecture

4.1 Components

Component Responsibility
WAL Append-only log
Storage Persist vectors and metadata
Snapshotter Create checkpoint snapshots
Recovery Restore from WAL

5. Implementation Guide

5.1 Project Structure

LEARN_VECTOR_DATABASES/P09-storage-engine/
├── src/
│   ├── wal.py
│   ├── storage.py
│   ├── snapshot.py
│   ├── recovery.py
│   └── benchmark.py

5.2 Implementation Phases

Phase 1: Storage format (6-10h)

  • Design on-disk layout.
  • Checkpoint: vectors persisted and reloaded.

Phase 2: WAL + recovery (8-12h)

  • Implement WAL and replay logic.
  • Checkpoint: crash recovery passes.

Phase 3: Compaction + benchmarks (6-10h)

  • Add compaction and performance tests.
  • Checkpoint: benchmark report produced.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit WAL log append correctness
Integration recovery crash simulation
Regression benchmarks stable performance

6.2 Critical Test Cases

  1. WAL replay restores all data.
  2. Snapshot reduces recovery time.
  3. Compaction reclaims space.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Corrupt data load failures checksums + versioning
Slow recovery long restart add snapshots
WAL bloat huge logs compaction + pruning

8. Extensions & Challenges

Beginner

  • Add CRC checksums.
  • Add metadata indexing.

Intermediate

  • Add background compaction.
  • Add compression.

Advanced

  • Add multi-version concurrency control.
  • Add replication logs.

9. Real-World Connections

  • Vector DBs need reliable storage engines.
  • Durability guarantees matter for production data.

10. Resources

  • Storage engine design docs
  • WAL references

11. Self-Assessment Checklist

  • I can design durable storage layouts.
  • I can implement WAL recovery.
  • I can benchmark storage performance.

12. Submission / Completion Criteria

Minimum Completion:

  • Persistent storage + WAL

Full Completion:

  • Recovery tests + compaction

Excellence:

  • MVCC or replication logs

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/LEARN_VECTOR_DATABASES.md.