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Introducing vector search with UltraWarm in Amazon OpenSearch Service
- Amazon OpenSearch Service has been offering vector database capabilities for efficient vector similarity searches since 2019, supporting various use cases such as semantic search and RAG with large language models.
- OpenSearch Service offers UltraWarm and Cold tiers for cost-effective storage of less-active data, with UltraWarm being suitable for immutability-required scenarios like log analytics.
- Previously, UltraWarm and Cold tiers did not support k-NN indexes, leading to high costs for customers due to memory and storage constraints.
- To address cost concerns, k-NN indexes are now supported in both UltraWarm and Cold tiers, offering savings for less-active data scenarios.
- New capability allows enabling UltraWarm and Cold tiers for k-NN indexes from version 2.17, benefiting use cases like long-term semantic search and large-scale image similarity.
- For cost reduction, a balanced approach can be implemented by leveraging hot storage for frequently accessed data and UltraWarm for less-active data, resulting in significant savings.
- Multi-tiered storage strategy aids in managing growing datasets efficiently, automating data migration between tiers based on access patterns.
- The introduction of k-NN vector search in UltraWarm and Cold tiers offers a scalable solution for balancing performance and cost in vector search workloads.
- Best practices include optimizing data placement across tiers, using Index State Management for data lifecycle management, and monitoring cache hit rates for effective tiering.
- Authors of the article include Kunal Kotwani, Navneet Verma, and Sorabh Hamirwasia, who are software engineers at Amazon Web Services specializing in OpenSearch core and vector search technologies.
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