dongjoon-hyun opened a new pull request, #549:
URL: https://github.com/apache/spark-connect-swift/pull/549
### What changes were proposed in this pull request?
This PR aims to support `withField` and `dropFields` in `Column`.
```swift
// {"a":1,"b":2,"c":3} -> {"a":1,"b":20,"c":3}
df.select(col("s").withField("b", lit(20)))
// {"a":1,"b":2,"c":3} -> {"a":1}
df.select(col("s").dropFields("b", "c"))
// Nested fields: {"a":{"a":1,"b":2}} -> {"a":{"a":1}}
df.select(col("s").dropFields("a.b"))
```
Both methods are built on the existing `Expression.UpdateFields` proto,
following PySpark Connect.
- `withField` sets `struct_expression`, `field_name`, and `value_expression`.
- `dropFields` leaves `value_expression` unset, because the server treats an
unset value expression as a drop. Multiple field names are nested in order,
like `update_fields(update_fields(s, b), c)`.
`dropFields` requires at least one field name through its signature,
`dropFields(_ fieldName: String, _ fieldNames: String...)`, so an empty call is
rejected at compile time instead of at runtime.
### Why are the changes needed?
For feature parity with PySpark, which has supported `Column.withField` and
`Column.dropFields` since Apache Spark 3.1.0 and Spark Connect since 3.4.0.
These APIs let users add, replace, or drop fields of struct columns, including
nested fields.
### Does this PR introduce _any_ user-facing change?
Yes, this PR adds two new public APIs, `Column.withField` and
`Column.dropFields`.
### How was this patch tested?
Pass the CIs with the newly added test cases.
### Was this patch authored or co-authored using generative AI tooling?
Generated-by: Claude Opus 5
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