HNSW (Hierarchical Navigable Small World) is an approximate nearest neighbor algorithm optimized for high-dimensional vector search — providing sub-millisecond query times on millions of vectors through a multi-layer graph structure, making it the foundation of modern vector databases.

What Is HNSW?

• Type: Approximate nearest neighbor (ANN) search algorithm.
• Structure: Multi-layer graph with skip-list-like hierarchy.
• Speed: Sub-millisecond queries on millions of vectors.
• Accuracy: 95-99% recall with proper tuning.
• Usage: Core algorithm in Qdrant, Milvus, Pinecone, FAISS.

Why HNSW Matters

• Speed: 100-1000× faster than brute-force search.
• Scalability: Handles billions of vectors efficiently.
• Accuracy: High recall rates for production use.
• Memory-Efficient: Optimized graph structure.
• Industry Standard: Used by all major vector databases.

How It Works

1. Build Phase: Insert vectors into multi-layer graph. 2. Layers: Top layers have few nodes (long jumps), bottom layers dense (fine search). 3. Search: Start at top layer, greedily descend to find nearest neighbors. 4. Result: Fast approximate nearest neighbors with tunable accuracy.

Key Parameters

• M: Number of connections per node (higher = more accurate, slower).
• ef_construction: Build-time search depth.
• ef_search: Query-time search depth.

HNSW is the backbone of semantic search — enabling real-time similarity search at scale.