dongjoon-hyun opened a new pull request, #489:
URL: https://github.com/apache/spark-connect-swift/pull/489
### What changes were proposed in this pull request?
This PR adds `SparkSession.parseDDL` which parses a DDL-formatted schema
string into the public `StructType` model using the server-side parser.
```swift
let schema = try await spark.parseDDL("id BIGINT NOT NULL, name STRING")
// StructType(fields: [
// StructField(name: "id", dataType: .long, nullable: false),
// StructField(name: "name", dataType: .string),
// ])
```
Internally, it reuses the existing `AnalyzePlan` `ddlParse` round-trip
(already used by `createDataFrame` and `DataFrameReader.schema`) and converts
the returned protobuf `DataType` via the `StructType` initializer added in
SPARK-58647. A non-struct top-level type (e.g. `array<int>`) throws
`SparkConnectError.InvalidType`, and an invalid DDL string throws
`SparkConnectError.ParseSyntaxError` through the existing `ErrorInfo`-based
error conversion.
Note that this follows the PySpark Connect behavior where `DataType.fromDDL`
delegates to the server-side parser (`SparkSession._parse_ddl` via the
`ddl_parse` analyze method), instead of implementing a client-local SQL type
parser.
### Why are the changes needed?
For feature parity with Scala (`StructType.fromDDL`) and PySpark
(`DataType.fromDDL`), so users can obtain a traversable `StructType` from a DDL
string. This is the missing inverse of `StructType.toDDL` added in SPARK-58648.
### Does this PR introduce _any_ user-facing change?
No. This adds a new API 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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