Qdrant vector database adds tiered multitenancy

Qdrant has released Qdrant 1.16, an update of the Qdrant open source vector database that introduces tiered multitenancy, a capability intended to help isolate heavy-traffic tenants, boost performance, and scale search workloads more efficiently.

Announced November 19, Qdrant 1.16 also offers ACORN, a search algorithm that improves the quality of filtered vector search in cases of multiple filters with weak selectivity, Qdrant said. To upgrade, users can go to Qdrant Cloud, then go to the Cluster Details screen and select Qdrant 1.16 from the dropdown menu.

With tiered multitenancy, users get an improved approach to multitenancy that enables the combining of small and large tenants in a single collection, with the ability to promote growing tenants to dedicated shards, Qdrant said. Multitenancy is a common requirement for SaaS applications, where multiple customers, or tenants, share a database instance. When an instance is shared between multiple users in Qdrant, vectors may need to be partitioned by the user. The main principles behind tiered multitenancy are user-defined sharding, fallback shards, and tenant promotion, Qdrant said. User-defined sharding enables users to create named shards within a collection, allowing large tenants to be isolated in their own shards. Fallback shards are a routing mechanism that allows Qdrant to route a request to a dedicated shard or shared fallback shard. Tenant promotion is a mechanism that allows tenants to be changed from a shared fallback shard to their own dedicated shard when they have grown large enough.

ACORN stands for ANN (Approximate Nearest Neighbor) Constraint-Optimized Retrieval Network. This capability offers improved vector search in cases of multiple filters with weak selectivity, according to Qdrant. With ACORN enabled, Qdrant not only traverses direct neighbors in Qdrant’s HNSW (Hierarchical Navigable Small World) graph-based infrastructure but also examines neighbors of neighbors if direct neighbors have been filtered out. This improves search accuracy but at the expense of performance, especially when multiple low-selectivity filters are applied. Because ACORN is slower (approximately 2x to 10x slower in typical scenarios) but improves recall (i.e. accuracy) for restrictive filters, tuning this parameter is about deciding when the accuracy improvement justifies the performance cost, the company said. Qdrant has published a decision matrix on when to use ACORN.

Qdrant 1.16 also features a revamped UI intended to offer a fresh new look and an improved user experience. The new design incudes a welcome page that offers quick access to tutorials and reference documentation as well as redesigned Point, Visualize, and Graph views in the Collections Manager. This redesign makes it easier to work with data by presenting it in a more-compact format. In the tutorials, code snippets now are executed inline, thus freeing up screen space for better usability, Qdrant said.

Also featured in Qdrant 1.16 is a new HNSW index storage mode, which enables more efficient disk-based vector search; a conditional update API, which facilitates easier migration of embedding models to a new version; and improved full-text search capabilities, with a new text_any condition and ASCII folding support.