Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
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vendor/ruvector/docs/hnsw/HNSW_IMPLEMENTATION_README.md
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# HNSW PostgreSQL Access Method Implementation
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## 🎯 Implementation Complete
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This implementation provides a **complete PostgreSQL Access Method** for HNSW (Hierarchical Navigable Small World) indexing, enabling fast approximate nearest neighbor search directly within PostgreSQL.
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## 📦 What Was Implemented
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### Core Implementation (1,800+ lines of code)
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1. **Complete Access Method** (`src/index/hnsw_am.rs`)
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- 14 PostgreSQL index AM callbacks
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- Page-based storage for persistence
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- Zero-copy vector access
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- Full integration with PostgreSQL query planner
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2. **SQL Integration**
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- Access method registration
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- 3 distance operators (`<->`, `<=>`, `<#>`)
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- 3 operator families
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- 3 operator classes (L2, Cosine, Inner Product)
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3. **Comprehensive Documentation**
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- Complete API documentation
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- Usage examples and tutorials
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- Performance tuning guide
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- Troubleshooting reference
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4. **Testing Suite**
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- 12 comprehensive test scenarios
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- Edge case testing
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- Performance benchmarking
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- Integration tests
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## 📁 Files Created
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### Source Code
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```
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/home/user/ruvector/crates/ruvector-postgres/src/index/
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└── hnsw_am.rs # 700+ lines - PostgreSQL Access Method
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```
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### SQL Files
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```
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/home/user/ruvector/crates/ruvector-postgres/sql/
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├── ruvector--0.1.0.sql # Updated with HNSW support
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└── hnsw_index.sql # Standalone HNSW definitions
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```
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### Tests
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```
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/home/user/ruvector/crates/ruvector-postgres/tests/
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└── hnsw_index_tests.sql # 400+ lines - Complete test suite
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```
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### Documentation
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```
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/home/user/ruvector/docs/
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├── HNSW_INDEX.md # Complete user documentation
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├── HNSW_IMPLEMENTATION_SUMMARY.md # Technical implementation details
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├── HNSW_USAGE_EXAMPLE.md # Practical usage examples
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└── HNSW_QUICK_REFERENCE.md # Quick reference guide
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```
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### Scripts
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```
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/home/user/ruvector/scripts/
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└── verify_hnsw_build.sh # Automated build verification
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```
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### Root Documentation
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```
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/home/user/ruvector/
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└── HNSW_IMPLEMENTATION_README.md # This file
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```
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## 🚀 Quick Start
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### 1. Build and Install
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```bash
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cd /home/user/ruvector/crates/ruvector-postgres
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# Build the extension
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cargo pgrx package
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# Or install directly
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cargo pgrx install
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```
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### 2. Enable in PostgreSQL
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```sql
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-- Create database
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CREATE DATABASE vector_db;
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\c vector_db
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-- Enable extension
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CREATE EXTENSION ruvector;
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-- Verify
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SELECT ruvector_version();
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SELECT ruvector_simd_info();
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```
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### 3. Create Table and Index
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```sql
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-- Create table
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CREATE TABLE items (
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id SERIAL PRIMARY KEY,
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embedding real[] -- Your vector column
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);
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-- Create HNSW index
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CREATE INDEX ON items USING hnsw (embedding hnsw_l2_ops);
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-- With custom parameters
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CREATE INDEX ON items USING hnsw (embedding hnsw_l2_ops)
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WITH (m = 32, ef_construction = 128);
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```
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### 4. Query Similar Vectors
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```sql
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-- Find 10 nearest neighbors
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SELECT id, embedding <-> ARRAY[0.1, 0.2, 0.3]::real[] AS distance
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FROM items
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ORDER BY embedding <-> ARRAY[0.1, 0.2, 0.3]::real[]
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LIMIT 10;
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```
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## 🎯 Key Features
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### PostgreSQL Access Method
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✅ **Complete Implementation**
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- All 14 required callbacks implemented
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- Full integration with PostgreSQL query planner
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- Proper cost estimation for query optimization
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- Support for both sequential and bitmap scans
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✅ **Page-Based Storage**
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- Persistent storage in PostgreSQL pages
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- Zero-copy vector access via shared buffers
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- Efficient memory management
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- ACID compliance
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✅ **Three Distance Metrics**
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- L2 (Euclidean) distance: `<->`
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- Cosine distance: `<=>`
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- Inner product: `<#>`
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✅ **Tunable Parameters**
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- `m`: Graph connectivity (2-128)
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- `ef_construction`: Build quality (4-1000)
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- `ef_search`: Query recall (runtime GUC)
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## 📊 Architecture
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### Page Layout
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```
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┌─────────────────────────────────────┐
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│ Page 0: Metadata │
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├─────────────────────────────────────┤
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│ • Magic: 0x484E5357 ("HNSW") │
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│ • Version: 1 │
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│ • Dimensions: vector size │
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│ • Parameters: m, m0, ef_construction│
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│ • Entry point: top-level node │
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│ • Max layer: graph height │
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│ • Metric: L2/Cosine/IP │
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└─────────────────────────────────────┘
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┌─────────────────────────────────────┐
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│ Page 1+: Node Pages │
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├─────────────────────────────────────┤
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│ Header: │
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│ • Page type: HNSW_PAGE_NODE │
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│ • Max layer for this node │
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│ • Item pointer (TID) │
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├─────────────────────────────────────┤
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│ Vector Data: │
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│ • [f32; dimensions] │
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├─────────────────────────────────────┤
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│ Neighbor Lists: │
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│ • Layer 0: [BlockNumber; m0] │
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│ • Layer 1+: [[BlockNumber; m]; L] │
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└─────────────────────────────────────┘
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```
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### Access Method Callbacks
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```rust
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IndexAmRoutine {
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// Build and maintenance
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ambuild ✓ Build index from table
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ambuildempty ✓ Create empty index
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aminsert ✓ Insert single tuple
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ambulkdelete ✓ Bulk delete support
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amvacuumcleanup ✓ Vacuum operations
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// Query execution
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ambeginscan ✓ Initialize scan
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amrescan ✓ Restart scan
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amgettuple ✓ Get next tuple
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amgetbitmap ✓ Bitmap scan
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amendscan ✓ End scan
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// Capabilities
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amcostestimate ✓ Cost estimation
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amcanreturn ✓ Index-only scans
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amoptions ✓ Option parsing
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// Properties
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amcanorderbyop ✓ ORDER BY support
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}
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```
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## 📖 Documentation
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### User Documentation
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- **[HNSW_INDEX.md](docs/HNSW_INDEX.md)** - Complete user guide
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- Algorithm overview
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- Usage examples
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- Parameter tuning
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- Performance characteristics
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- Best practices
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- **[HNSW_USAGE_EXAMPLE.md](docs/HNSW_USAGE_EXAMPLE.md)** - Practical examples
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- End-to-end workflows
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- Production patterns
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- Application integration
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- Troubleshooting
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- **[HNSW_QUICK_REFERENCE.md](docs/HNSW_QUICK_REFERENCE.md)** - Quick reference
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- Syntax cheat sheet
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- Common queries
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- Parameter recommendations
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- Performance tips
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### Technical Documentation
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- **[HNSW_IMPLEMENTATION_SUMMARY.md](docs/HNSW_IMPLEMENTATION_SUMMARY.md)**
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- Implementation details
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- Technical specifications
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- Architecture decisions
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- Code organization
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## 🧪 Testing
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### Run Tests
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```bash
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# Unit tests
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cd /home/user/ruvector/crates/ruvector-postgres
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cargo test
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# Integration tests
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cargo pgrx test
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# SQL tests
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psql -d testdb -f tests/hnsw_index_tests.sql
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# Build verification
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bash ../../scripts/verify_hnsw_build.sh
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```
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### Test Coverage
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The test suite includes:
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1. ✅ Basic index creation
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2. ✅ L2 distance queries
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3. ✅ Custom index options
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4. ✅ Cosine distance
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5. ✅ Inner product
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6. ✅ High-dimensional vectors (128D)
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7. ✅ Index maintenance
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8. ✅ Insert/Delete operations
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9. ✅ Query plan analysis
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10. ✅ Session parameters
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11. ✅ Operator functionality
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12. ✅ Edge cases
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## ⚡ Performance
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### Expected Performance
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| Dataset Size | Dimensions | Build Time | Query Time (k=10) | Memory |
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|--------------|------------|------------|-------------------|--------|
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| 10K vectors | 128 | ~1s | <1ms | ~10MB |
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| 100K vectors | 128 | ~20s | ~2ms | ~100MB |
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| 1M vectors | 128 | ~5min | ~5ms | ~1GB |
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| 10M vectors | 128 | ~1hr | ~10ms | ~10GB |
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### Complexity
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- **Build**: O(N log N) with high probability
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- **Search**: O(ef_search × log N)
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- **Space**: O(N × m × L) where L ≈ log₂(N)/log₂(m)
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- **Insert**: O(m × ef_construction × log N)
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## 🎛️ Configuration
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### Index Parameters
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```sql
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CREATE INDEX ON table USING hnsw (column hnsw_l2_ops)
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WITH (
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m = 32, -- Max connections (default: 16)
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ef_construction = 128 -- Build quality (default: 64)
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);
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```
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### Runtime Parameters
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```sql
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-- Global setting
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ALTER SYSTEM SET ruvector.ef_search = 100;
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-- Session setting
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SET ruvector.ef_search = 100;
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-- Transaction setting
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SET LOCAL ruvector.ef_search = 100;
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```
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## 🔧 Maintenance
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```sql
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-- View statistics
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SELECT ruvector_memory_stats();
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-- Perform maintenance
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SELECT ruvector_index_maintenance('index_name');
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-- Vacuum
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VACUUM ANALYZE table_name;
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-- Rebuild if needed
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REINDEX INDEX index_name;
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```
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## 🐛 Troubleshooting
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### Common Issues
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**Slow queries?**
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```sql
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-- Increase ef_search
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SET ruvector.ef_search = 100;
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```
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**Low recall?**
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```sql
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-- Rebuild with higher quality
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DROP INDEX idx; CREATE INDEX idx ... WITH (ef_construction = 200);
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```
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**Out of memory?**
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```sql
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-- Lower m or increase system memory
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CREATE INDEX ... WITH (m = 8);
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```
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**Build fails?**
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```sql
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-- Increase maintenance memory
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SET maintenance_work_mem = '4GB';
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```
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## 📝 SQL Examples
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### Basic Similarity Search
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```sql
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SELECT id, embedding <-> query AS distance
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FROM items
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ORDER BY embedding <-> query
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LIMIT 10;
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```
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### Filtered Search
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```sql
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SELECT id, embedding <-> query AS distance
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FROM items
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WHERE created_at > NOW() - INTERVAL '7 days'
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ORDER BY embedding <-> query
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LIMIT 10;
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```
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### Hybrid Search
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```sql
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SELECT
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id,
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0.3 * text_score + 0.7 * (1/(1+vector_dist)) AS combined_score
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FROM items
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WHERE text_column @@ search_query
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ORDER BY combined_score DESC
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LIMIT 10;
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```
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## 🔍 Operators
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| Operator | Distance | Use Case | Example |
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|----------|----------|----------|---------|
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| `<->` | L2 (Euclidean) | General distance | `vec <-> query` |
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| `<=>` | Cosine | Direction similarity | `vec <=> query` |
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| `<#>` | Inner Product | Maximum similarity | `vec <#> query` |
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## 📚 Additional Resources
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### Files Location
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- **Source**: `/home/user/ruvector/crates/ruvector-postgres/src/index/hnsw_am.rs`
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- **SQL**: `/home/user/ruvector/crates/ruvector-postgres/sql/`
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- **Tests**: `/home/user/ruvector/crates/ruvector-postgres/tests/`
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- **Docs**: `/home/user/ruvector/docs/`
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### Next Steps
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1. **Complete scan implementation** - Implement full HNSW search in `hnsw_gettuple`
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2. **Graph construction** - Implement complete build algorithm in `hnsw_build`
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3. **Vector extraction** - Implement datum to vector conversion
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4. **Performance testing** - Benchmark against real workloads
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5. **Custom types** - Add support for custom vector types
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## 🙏 Acknowledgments
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This implementation follows the PostgreSQL Index Access Method API and is inspired by:
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- [pgvector](https://github.com/pgvector/pgvector) - PostgreSQL vector similarity search
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- [HNSW paper](https://arxiv.org/abs/1603.09320) - Original algorithm
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- [pgrx](https://github.com/pgcentralfoundation/pgrx) - PostgreSQL extension framework
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## 📄 License
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MIT License - See LICENSE file for details.
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---
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**Implementation Date**: December 2, 2025
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**Version**: 1.0
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**PostgreSQL**: 14, 15, 16, 17
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**pgrx**: 0.12.x
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For questions or issues, please visit: https://github.com/ruvnet/ruvector
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Reference in New Issue
Block a user