Preview — Pro guide
You are seeing a portion of this guide. Sign in and upgrade to unlock the full article, quizzes, and interview answers.
Sections
Related Guides
Embeddings & Vector Databases: ANN Search at Scale
ML System Design
Two-Stage Retrieval & Ranking: The Architecture Behind Every Large-Scale Recommender
ML System Design
RAG Architecture: From Basics to Production
GenAI & Agents
Model Serving Architectures: Batch vs Real-Time, Shadow Deployments & Latency Budgets
ML System Design
Vector Search at Scale: HNSW, IVF-PQ, FAISS, and Production ANN Systems
Approximate Nearest Neighbor (ANN) search is the retrieval backbone of RAG, recommendation systems, semantic search, and visual similarity. Master HNSW graph construction, IVF-PQ compression, FAISS vs Qdrant vs pgvector selection, recall-latency tradeoffs, and hybrid dense+sparse search. Includes production sizing and indexing strategy for 1B+ vector corpora.
Why Exact Nearest Neighbor Search Doesn't Scale
Given a query vector q and a corpus of N vectors, exact nearest neighbor search computes cosine similarity between q and every vector: O(N × d) time where d is the embedding dimension. At N=1M and d=1536 (OpenAI embedding dimension), that's 1.5 billion multiply-add operations per query. At 1,000 queries/sec, that's 1.5 trillion operations/sec — requiring dedicated GPU compute for each query just to do retrieval.
Approximate Nearest Neighbor (ANN) search trades a small accuracy loss for orders-of-magnitude speedup. ANN algorithms organize the vector space during indexing so that query time searches only a small fraction of the corpus — typically 1-5% — while returning results within 95-99% of exact recall.
When ANN is appropriate: Recommendation systems, semantic search, RAG retrieval, visual similarity search, duplicate detection — any system where slightly suboptimal results are acceptable (they almost always are). When exact search is needed: Deduplication for compliance (medical, legal — no approximate matches), fraud detection where missing a near-duplicate costs money.