Microsoft has announced that Cosmos DB, its distributed multi-model database, is being improved with more elasticity and support for vector search for MongoDB. 

Cosmos DB is a globally distributed, multi-model database service that lets you scale throughput and storage independently across any number of Azure's geographic regions. It indexes all data, and the multi-model service supports document, key-value, graph and column-family data models. Cosmos DB has wire-compatible APIs for MongoDB, Apache Cassandra and Apache Gremlin, along with a native SQL dialect.

The elasticity is being improved with Burst capacity, which uses the idle throughput capacity of a database or container to handle traffic spikes. The Cosmos DB team says that databases and containers using standard provisioned throughput can be set to use burst capacity and will be able to maintain performance during short bursts, when requests exceed the throughput limit. This gives customers a temporary cushion if they've under-provisioned.

Another performance-related improvement is support for hierarchical partition keys. This enables up to three partition keys to be used instead of one to improve data distribution and achieve greater scale.

A preview was also announced at Build of Materialized views for Azure Cosmos DB for NoSQL. This lets users create and maintain secondary views of their data in containers that are used to serve queries that would be too expensive to serve with an existing container. Materialized Views can be used to create and maintain data between two containers, allowing both to work efficiently, optimizing costs and saving time.

Vector Search in Azure Cosmos DB for MongoDB vCore was also announced at Build. This lets customers integrate AI-based applications, including those built on Azure OpenAI, with their data stored in Azure Cosmos DB, and store and work with high dimensional vector data directly in Azure Cosmos DB for MongoDB vCore. This feature should reduce the need to transfer data to more expensive alternatives for vector search.

More Information

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