A vector index from Microsoft Research for approximate nearest-neighbour search. Azure Cosmos DB offers it as diskANN (vectors of up to 4,096 dimensions) and Azure Database for PostgreSQL as pg_diskann, and it works best once a collection holds more than roughly 50,000 vectors.
Read more: Microsoft Learn
In the Ultra Transcenders books
Each book explains DiskANN in context, with comparison tables and the common traps.
Terms in this definition
- Vector index
An entry in a Cosmos DB indexing policy, of type flat, quantizedFlat or diskANN, that makes VectorDistance searches faster. It can't be changed once created and needs the vector search feature turned on.
- Cosmos DB
NoSQL database distributed globally, offering writes in multiple regions, automatic indexing and latency below 10 ms.
- Azure Database for PostgreSQL
Azure's managed PostgreSQL. It grows by scaling up and by adding read replicas rather than multi-master writes; to spread writes horizontally you use its elastic clusters feature, based on Citus.
Related terms
- ANN
Short for approximate nearest neighbour: a vector search that finds most of the closest matches rather than guaranteeing every one, trading completeness for speed and lower cost. DiskANN and HNSW indexes work this way, whereas flat search is exact.