dongjoon-hyun opened a new pull request, #448:
URL: https://github.com/apache/spark-connect-swift/pull/448
### What changes were proposed in this pull request?
This PR aims to add shortcut aggregation methods to `GroupedData`:
- `count()` — counts the number of records for each group with a `count`
result column.
- `sum(_ colNames: String...)`
- `avg(_ colNames: String...)`
- `mean(_ colNames: String...)` (an alias of `avg`)
- `min(_ colNames: String...)`
- `max(_ colNames: String...)`
The semantics follow the Scala Spark Connect client's
`RelationalGroupedDataset`:
`count()` is built as `count(lit(1)).alias("count")`, and the
column-name-based
methods map each name via the corresponding `Column` function without
client-side
numeric-type validation (delegated to the server, like the Connect Scala
client).
All methods reuse the existing `Aggregate` relation path, so they work
identically
for `groupBy`, `rollup`, and `cube`.
```swift
let df = try await spark.sql(DEALER_TABLE).groupBy("city").count()
let df2 = try await spark.sql(DEALER_TABLE).rollup("city").sum("quantity")
```
### Why are the changes needed?
Previously, `GroupedData` only provided `agg`. These shortcut methods provide
feature parity with PySpark's `GroupedData` and the Scala client's
`RelationalGroupedDataset` for the most common aggregations.
### Does this PR introduce _any_ user-facing change?
No. This is a new API addition.
### How was this patch tested?
Pass the CIs with newly added test cases.
### Was this patch authored or co-authored using generative AI tooling?
Generated-by: Claude Fable 5
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