rahil-c opened a new pull request, #18432:
URL: https://github.com/apache/hudi/pull/18432
Implements a new Spark TVF `hudi_vector_search` that performs exact
nearest-neighbor search over embeddings stored in a Hudi table using
brute-force KNN, without requiring a persistent vector index.
New files:
- HoodieVectorSearchTableValuedFunction: unresolved logical node holding raw
args; parses single-query (table, col, ARRAY(...), k [, metric]) and
batch-query (corpus, col, query_table, col, k [, metric]) modes; supports
cosine, l2/euclidean, dot_product distance metrics.
- HoodieVectorSearchPlanBuilder: builds the execution plan — single-query
mode uses withColumn + orderBy + limit(k); batch-query mode uses
crossJoin(broadcast(queries)) + window rank per _query_id; handles
Float/Double/Byte embedding types via cast to Double before UDF. Fixes
cross-join column ambiguity when corpus and query share column names by
renaming clashing query columns to _query_<colname> before the join.
Modified files:
- TableValuedFunctions: registers hudi_vector_search alongside existing Hudi
TVFs so all Spark adapters (3.3/3.4/3.5/4.0) inject it via
SparkSessionExtensions.
- HoodieSparkBaseAnalysis (ResolveReferences): resolves
HoodieVectorSearchTableValuedFunction — evaluates the query vector constant at
analysis time, resolves table by name or path, delegates to
HoodieVectorSearchPlanBuilder.
- TestHoodieVectorSearchFunction: end-to-end tests covering single/batch
query modes, all three distance metrics, DataFrame API, path-based and
view-based resolution, Float/Double embedding types, MOR tables, composability
(WHERE, subqueries), error handling, exact distance verification, and
same-column-name batch query regression test.
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### Contributor's checklist
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- [ ] Adequate tests were added if applicable
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