dongjoon-hyun opened a new pull request, #532:
URL: https://github.com/apache/spark-connect-swift/pull/532

   ### What changes were proposed in this pull request?
   
   This PR aims to support 7 vector functions (11 overloads).
   
   | Function | Since | Signature |
   | --- | --- | --- |
   | `vector_cosine_similarity` | 4.3.0 | `(Column, Column)` |
   | `vector_inner_product` | 4.3.0 | `(Column, Column)` |
   | `vector_l2_distance` | 4.3.0 | `(Column, Column)` |
   | `vector_norm` | 4.3.0 | `(Column)`, `(Column, Float \| Column)` |
   | `vector_normalize` | 4.3.0 | `(Column)`, `(Column, Float \| Column)` |
   | `vector_avg` | 4.3.0 | `(Column)` |
   | `vector_sum` | 4.3.0 | `(Column)` |
   
   The five scalar functions live in a new `VectorFunctions.swift`. The two 
aggregate functions,
   `vector_avg` and `vector_sum`, are added to the existing 
`AggregateFunctions.swift`, following
   the `schema_of_variant_agg` precedent, while their tests stay with the rest 
of the family in
   `VectorFunctionsTests.swift`.
   
   Despite the name, these functions take no dedicated `VECTOR` type. They 
operate on `ARRAY<FLOAT>`
   columns, and all the vectors involved must have the same dimension.
   
   The optional `degree` of `vector_norm` and `vector_normalize` is expressed 
as a Swift overload,
   mirroring the two Scala overloads. In addition to the `Column` overload, a 
`Float` overload is
   provided because the server requires exactly a `FLOAT` for this argument:
   
   ```
   SELECT vector_norm(ARRAY(3.0f, 4.0f), CAST(1.0 AS DOUBLE))
   [DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE] ... The second parameter requires 
the "FLOAT" type,
   however "CAST(1.0 AS DOUBLE)" has the type "DOUBLE".
   ```
   
   Since `lit(Double)` produces a `double` literal in this client, a `Double` 
overload would compile
   and then fail at analysis time on the server. The `Float` overload keeps 
`vector_norm(v, 2.0)`
   correct by construction, the same reasoning behind the `Int32` srid of 
`st_setsrid`.
   
   ### Why are the changes needed?
   
   To support the vector functions of Apache Spark 4.3.0 and improve the API 
coverage.
   
   ### Does this PR introduce _any_ user-facing change?
   
   No, this PR only adds new functions.
   
   ### How was this patch tested?
   
   Pass the CIs with the newly added test suite, `VectorFunctionsTests`.
   
   ### Was this patch authored or co-authored using generative AI tooling?
   
   Generated-by: Claude Opus 5


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