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ScaNN for AlloyDB: The first PostgreSQL vector search index that works well from millions to billion of vectors
- ScaNN for AlloyDB is the first Postgres-based vector search extension designed to handle vector indexes of various sizes efficiently, offering fast index builds, transactional updates, small memory usage, and speedy and precise search capabilities.
- AlloyDB users engage in complex semantic search and AI tasks with 100 million to over 1 billion vectors, often turning to the pgvector HNSW graph algorithm for large vector search indexes.
- Although pgvector HNSW excels in small dataset query performance, it faces challenges with speed, cost, and performance for very large datasets, leading to the development of ScaNN for AlloyDB.
- ScaNN for AlloyDB, integrating Google's ScaNN vector search technology, offers a cost-effective solution with smaller memory usage, improved latency, and compatibility with pgvector.
- In benchmarks, ScaNN for AlloyDB demonstrated significantly lower index build costs and faster latency compared to other PostgreSQL systems, especially for large datasets that do not fit in main memory.
- The comparison between ScaNN for AlloyDB and pgvector HNSW underlined the superior performance and lower memory footprint of ScaNN, providing faster search and insert latencies even for extensive datasets.
- Key differences lie in the data organization and algorithms, where ScaNN's tree-based structure offers cache-friendly, sequential access, outperforming HNSW's random access for larger datasets.
- ScaNN for AlloyDB's market-leading features make it a competitive option for vector search applications, showcasing better search performance, low memory usage, and cost-effectiveness.
- The ScaNN for AlloyDB extension is available in AlloyDB, empowering users to leverage efficient vector search capabilities for diverse applications, whether for large or small datasets.
- To delve deeper into the ScaNN for AlloyDB index, users can refer to the introduction and whitepaper provided, enabling them to explore and implement the cutting-edge ScaNN algorithm in their projects.
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