pgvector Performance

The Problem with Vector Search

Your semantic search was lightning-fast with 10,000 documents. But at 1 million documents, queries are taking seconds, and your database CPU is pegged at 100%.

Symptoms You'll Recognise

• Database CPU spikes every time a user triggers a RAG or search feature.
• Sequential scan warnings in your query optimizer.
• Very slow response times that make your LLM integration feel broken.
• Memory exhaustion and out-of-memory crashes on your Postgres instance.

Why It Happens

Vector databases execute similarity searches across high-dimensional space. By default, this is a linear "exact" scan of every single row. Without proper indexing and maintenance (like running VACUUM correctly or sizing index building memory), the database chokes as the dataset grows.

How We Fix It

1. Index Optimization: We analyze your use case to deploy either IVFFlat (faster build) or HNSW (faster search, better recall) indexes, tuning lists and edge parameters.
2. Schema Design: We partition large embedding tables and fix data types to ensure minimal RAM usage and faster sequential loads.
3. Query Parameter Tuning: We dynamically adjust work_mem and maintanence_work_mem specifically for vector builds, ensuring optimal performance.
4. Embedding Pipeline Consolidation: We review how your embeddings are chunked, potentially reducing dimensions (e.g., matching models) or summarizing text prior to embedding.

Proof

Optimized a pgvector instance for a legal document SaaS, dropping typical nearest-neighbor search times from 4.2 seconds down to 18 milliseconds, averting a costly database upgrade.