[jira] [Commented] (PHOENIX-7998) Vector Indexes

["Andrew Kyle Purtell (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/PHOENIX-7998?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=18119536#comment-18119536 ] 

Andrew Kyle Purtell commented on PHOENIX-7998:
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The PR contains an extensive reviewer's guide: https://github.com/apache/phoenix/pull/2624

> Vector Indexes
> --------------
>
>                 Key: PHOENIX-7998
>                 URL: https://issues.apache.org/jira/browse/PHOENIX-7998
>             Project: Phoenix
>          Issue Type: New Feature
>          Components: core
>            Reporter: Andrew Kyle Purtell
>            Assignee: Andrew Kyle Purtell
>            Priority: Major
>
> Vector similarity search uses mathematical embeddings to identify records by semantic meaning and conceptual proximity rather than exact keyword matches. In a hybrid document database like Apache Phoenix, native vector search allows applications to execute unified queries that combine AI-driven semantic retrieval with relational filters and nested BSON document predicates. This eliminates the operational overhead, data replication, and synchronization lag of maintaining external vector databases while leveraging Phoenix's existing scale-out query parallelism.
> This design integrates native vector similarity search into Apache Phoenix across relational columns and BSON documents using fixed-dimension vector types, typed path extraction expressions, and standard SQL distance functions. Exact search pushes distance evaluation and bounded candidate heaps down to RegionServers for distributed top-N execution, while approximate retrieval maps Inverted File centroid posting lists into contiguous global index ranges traversed via multi-range skip scans. Server-side coprocessors maintain centroid assignments and verified-write consistency across relational mutations and atomic BSON updates, background compactions observe centroid drift to trigger asynchronous rebuilds, and the optimizer balances exact evaluation, covered index scanning, and adaptive probing within an extensible architecture.
> Design Document: https://docs.google.com/document/d/139E87IC2b6UG5T0UaxOK64qAjPt2u-ePB7DirWDm3v0/edit?tab=t.0



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