Project 7: Disk-Based Vector Index (DiskANN-style)

Build a disk-backed vector index that scales beyond memory with caching and streaming search.

Quick Reference

Attribute Value
Difficulty Level 4: Expert
Time Estimate 2-3 weeks
Language Python
Prerequisites HNSW/IVF basics, storage concepts
Key Topics disk IO, caching, graph search

1. Learning Objectives

By completing this project, you will:

  1. Store vector indexes on disk.
  2. Implement cache-aware search.
  3. Measure IO vs latency tradeoffs.
  4. Handle large datasets beyond RAM.
  5. Compare disk-based vs in-memory search.

2. Theoretical Foundation

2.1 DiskANN Concepts

Disk-based indexes store graph structures on disk and rely on caching to reduce IO during search.

3. Project Specification

3.1 What You Will Build

A disk-backed ANN index with a small in-memory cache and streaming search.

3.2 Functional Requirements

  1. Disk storage format for graph and vectors.
  2. Cache layer for hot nodes.
  3. Search algorithm aware of IO cost.
  4. Benchmarking for latency and cache hit rate.
  5. Failure handling for missing pages.

3.3 Non-Functional Requirements

  • Deterministic tests with fixed datasets.
  • Clear metrics for IO and latency.
  • Configurable cache size.

4. Solution Architecture

4.1 Components

Component Responsibility
Disk Store Persist vectors/graph
Cache Store hot nodes
Searcher Disk-aware traversal
Metrics IO + latency reports

5. Implementation Guide

5.1 Project Structure

LEARN_VECTOR_DATABASES/P07-disk-index/
├── src/
│   ├── storage.py
│   ├── cache.py
│   ├── search.py
│   └── benchmark.py

5.2 Implementation Phases

Phase 1: Disk format (6-10h)

  • Serialize vectors and graph to disk.
  • Checkpoint: index reloads successfully.

Phase 2: Cache + search (8-12h)

  • Implement cache-aware traversal.
  • Checkpoint: cache hit rate recorded.

Phase 3: Evaluation (6-8h)

  • Benchmark IO and latency.
  • Checkpoint: report shows disk vs memory.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit storage read/write integrity
Integration search correct results
Regression cache stable hit rates

6.2 Critical Test Cases

  1. Disk index reloads without corruption.
  2. Cache reduces IO for repeated queries.
  3. Search results match in-memory baseline.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Excessive IO slow queries tune cache or prefetch
Corrupt index crashes add checksum and versioning
Cache thrash low hit rate adjust cache policy

8. Extensions & Challenges

Beginner

  • Add compression for stored vectors.
  • Add prefetch hints.

Intermediate

  • Add async IO for search.
  • Add log-structured storage.

Advanced

  • Implement DiskANN-style graph pruning.
  • Compare with production systems.

9. Real-World Connections

  • Large vector DBs rely on disk-backed indexes.
  • Cost optimization depends on IO efficiency.

10. Resources

  • DiskANN papers
  • Storage system references

11. Self-Assessment Checklist

  • I can build a disk-backed index.
  • I can measure IO latency tradeoffs.
  • I can tune cache policy.

12. Submission / Completion Criteria

Minimum Completion:

  • Disk-backed index with search

Full Completion:

  • Cache layer + benchmarks

Excellence:

  • Async IO + graph pruning
  • Comparison with production systems

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