– Vector databases, such as pgvector, have seen a surge in popularity for semantic search and generative AI experiences. Developers use vector search for various applications, including product recommendations and chatbot enhancement.
– PostgreSQL is widely used by developers and pgvector is a popular extension for vector search. Support for the HNSW algorithm has been introduced, but some customers have reported issues with index build time and memory usage.
– Google has introduced the new ScaNN index for AlloyDB, providing faster vector queries, quicker index build times, and lower memory footprint compared to HNSW. This index is available in AlloyDB Omni and will be in the AlloyDB for PostgreSQL managed service soon.
Vector databases, such as pgvector which is a popular PostgreSQL extension, have experienced a surge in popularity over the past year. These databases are being used by developers for tasks ranging from product recommendations to image search and enhancing AI-powered chatbots with retrieval augmented generation. PostgreSQL, a widely used operational database, boasts a large user base and the introduction of support for the HNSW algorithm, which is a graph-based algorithm known for its query performance.
While the HNSW algorithm works well for many vector workloads, some customers have reported issues with large corpuses, index build time, memory usage, real-time updates, and query performance. To address these concerns, Google introduced the ScaNN index for AlloyDB, harnessing 12 years of research in approximate nearest neighbor algorithms to deliver up to 4x faster queries, 8x faster index build times, and a smaller memory footprint compared to HNSW in standard PostgreSQL.
Approximate Nearest Neighbor (ANN) search plays a crucial role in vector indexing, helping to find similar or relevant data efficiently by trading off accuracy for speed. Graph-based and tree-quantization-based algorithms are common types of ANN indices, with pgvector’s HNSW implementing a hierarchical graph algorithm. While these algorithms perform well, tree-quantization-based algorithms are known for lower memory footprints and quicker index build times, ultimately improving the scalability of KNN queries. The ScaNN index will be available in AlloyDB Omni and the AlloyDB for PostgreSQL managed service in Google Cloud soon.