dongjoon-hyun opened a new pull request, #488:
URL: https://github.com/apache/spark-connect-swift/pull/488
### What changes were proposed in this pull request?
This PR adds `StructType` overloads to the four schema-taking APIs which
previously accepted only DDL strings, as a follow-up of #487 ([SPARK-58647]).
- `DataFrameReader.schema(_ schema: StructType)`
- `DataStreamReader.schema(_ schema: StructType)`
- `SparkSession.createDataFrame(_ data: [[Sendable?]], _ schema: StructType)`
- `DataFrame.to(_ schema: StructType)`
Following the Scala client implementation, the reader and `createDataFrame`
overloads convert the schema via the newly added public
`StructType.toDDL`/`StructField.toDDL` (Catalyst `toDDL` format with
backtick-quoting and `NOT NULL`) and reuse the existing DDL-string paths, while
`DataFrame.to` converts the model directly to the protobuf `DataType` (like
`DataTypeProtoConverter.toConnectProtoType`) and requires no server round-trip.
```swift
let schema = StructType(fields: [
StructField(name: "id", dataType: .integer, nullable: false),
StructField(name: "name", dataType: .string),
])
let df = try await spark.createDataFrame([[1, "Alice"], [2, nil]], schema)
```
### Why are the changes needed?
For feature parity with the Scala/PySpark clients where all schema-taking
APIs accept a `StructType` object, so users can build and pass schemas
programmatically instead of assembling DDL strings by hand.
### Does this PR introduce _any_ user-facing change?
No. This adds new APIs (`StructType` overloads and `toDDL`) without changing
existing behavior.
### 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 Fable 5
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