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Synopsis: This paper provides insights into the performance characteristics of IVF-HNSW, IVFFlat, and SCAAN indexing techniques for vector data in PostgreSQL. By understanding the trade-offs in accuracy, speed, and resource consumption, users can select the optimal indexing strategy for their specific application needs. In this session we also talk about a comparative analysis of three leading vector index types—IVF-HNSW, IVFFlat, and SCAAN—within the PostgreSQL database for efficient high-dimensional data retrieval.

Details:

The increasing use of vector embeddings for representing high-dimensional data in applications such as NLP, image retrieval, and recommendation systems has led to a demand for efficient vector indexing techniques. PostgreSQL, a popular relational database, is incorporating support for various vector indexing methods, enabling rapid Approximate Nearest Neighbor (ANN) searches.

Postgres as a vector database grows in importance for applications like AI, machine learning, and recommendation systems, optimizing search performance is critical. We evaluate these three index type across multiple dimensions: search accuracy, query latency and scalability. Our findings highlight the strengths and weaknesses of each approach, offering guidelines for selecting the appropriate index based on specific application requirements.