How does OpenSearch Serverless Collection determine which index strategy/index type to use while data is being ingested to one of the collections?

Choosing a collection type

OpenSearch Serverless supports three primary collection types:

Time series – The log analytics segment that focuses on analyzing large volumes of semi-structured, machine-generated data in real-time for operational, security, user behavior, and business insights.

Search – Full-text search that powers applications in your internal networks (content management systems, legal documents) and internet-facing applications, such as ecommerce website search and content search.

Vector search – Semantic search on vector embeddings that simplifies vector data management and powers machine learning (ML) augmented search experiences and generative AI applications, such as chatbots, personal assistants, and fraud detection.

You choose a collection type when you first create a collection.

The collection type that you choose depends on the kind of data that you plan to ingest into the collection, and how you plan to query it. You can't change the collection type after you create it.

The collection types have the following notable differences:

• For search and vector search collections, all data is stored in hot storage to ensure fast query response times. Time series collections use a combination of hot and warm storage, where the most recent data is kept in hot storage to optimize query response times for more frequently accessed data.

• For time series and vector search collections, you can't index by custom document ID or update by upsert requests. This operation is reserved for search use cases. You can update by document ID instead. For more information, see Supported OpenSearch API operations and permissions.

• For search and time series collections, you can't use k-NN type indexes.

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Hope this helps!