Project 10: Distributed Vector Search Cluster

Build a distributed vector search system with sharding, routing, and aggregation.

Quick Reference

Attribute Value
Difficulty Level 5: Expert
Time Estimate 3-4 weeks
Language Python
Prerequisites Networking basics, vector search
Key Topics sharding, routing, aggregation, distributed systems

1. Learning Objectives

By completing this project, you will:

  1. Shard vectors across multiple nodes.
  2. Route queries to relevant shards.
  3. Aggregate top-k results from shards.
  4. Handle node failures and retries.
  5. Measure throughput and latency under load.

2. Theoretical Foundation

2.1 Distributed ANN

Distributed search scales by sharding data and merging results, but introduces consistency and latency challenges.

3. Project Specification

3.1 What You Will Build

A small cluster that distributes vector data across nodes and returns merged top-k results.

3.2 Functional Requirements

  1. Sharding strategy (hash or range).
  2. Query router to send queries to shards.
  3. Aggregator to merge top-k results.
  4. Health checks and retry logic.
  5. Load testing for throughput and latency.

3.3 Non-Functional Requirements

  • Deterministic tests for routing and aggregation.
  • Graceful degradation on node failure.
  • Metrics collection per node.

4. Solution Architecture

4.1 Components

Component Responsibility
Shard Node Store vectors and handle queries
Router Distribute queries
Aggregator Merge top-k results
Health Monitor Detect node failures

5. Implementation Guide

5.1 Project Structure

LEARN_VECTOR_DATABASES/P10-distributed-cluster/
├── src/
│   ├── shard.py
│   ├── router.py
│   ├── aggregator.py
│   ├── health.py
│   └── load_test.py

5.2 Implementation Phases

Phase 1: Sharding + routing (8-12h)

  • Implement shard nodes and router.
  • Checkpoint: queries reach correct shards.

Phase 2: Aggregation (8-12h)

  • Merge results across shards.
  • Checkpoint: global top-k correct.

Phase 3: Resilience + load (8-12h)

  • Add retries and health checks.
  • Run load tests.
  • Checkpoint: latency and throughput reported.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit routing correct shard selection
Integration aggregation top-k merged correctly
Regression failures retries on node down

6.2 Critical Test Cases

  1. Node failure triggers retry.
  2. Aggregator returns correct global top-k.
  3. Load test reports p95/p99 latency.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Hot shards uneven load adjust shard strategy
Slow aggregation high latency optimize merge step
Missing results shard timeout add partial results handling

8. Extensions & Challenges

Beginner

  • Add simple replication.
  • Add shard metadata registry.

Intermediate

  • Add vector index selection per shard.
  • Add cache at router.

Advanced

  • Add dynamic resharding.
  • Add consensus for metadata changes.

9. Real-World Connections

  • Production vector DBs use distributed search for scale.
  • Search SLAs depend on routing and aggregation efficiency.

10. Resources

  • Distributed search papers
  • Vector database architecture references

11. Self-Assessment Checklist

  • I can shard and route vector queries.
  • I can aggregate top-k results.
  • I can handle node failures gracefully.

12. Submission / Completion Criteria

Minimum Completion:

  • Sharding + routing + aggregation

Full Completion:

  • Failure handling + load tests

Excellence:

  • Dynamic resharding
  • Replication or consensus layer

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