hagerf commented on a change in pull request #26087: [SPARK-29427][SQL] Create
KeyValueGroupedDataset from existing columns in DataFrame
URL: https://github.com/apache/spark/pull/26087#discussion_r333936540
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
##########
@@ -1725,6 +1725,30 @@ class Dataset[T] private[sql](
withGroupingKey.newColumns)
}
+ /**
+ * Returns a [[KeyValueGroupedDataset]] where the data is grouped by a row
of given
+ * columns.
+ *
+ * @group typedrel
+ * @since 3.0.0
+ */
+ @scala.annotation.varargs
+ def groupByRelationKey(
+ col1: String,
+ cols: String*): KeyValueGroupedDataset[Row, Row] = {
+ val colNames: Seq[String] = col1 +: cols
+ val keyAttrs = colNames.map(colName => resolve(colName).toAttribute)
+ val keySchema = StructType.fromAttributes(keyAttrs)
+ val keyEncoder = RowEncoder(keySchema)
+ val valEncoder = RowEncoder(schema)
Review comment:
I don't see why we couldn't keep the encoder that is already present in the
Dataset. With the current approach we lose that type information. I'd prefer
doing this:
```
def groupByRelationKey(
col1: String,
cols: String*): KeyValueGroupedDataset[Row, T] = {
val colNames: Seq[String] = col1 +: cols
val keyAttrs = colNames.map(colName => resolve(colName).toAttribute)
val keySchema = StructType.fromAttributes(keyAttrs)
val keyEncoder = RowEncoder(keySchema)
new KeyValueGroupedDataset(
keyEncoder,
encoder,
queryExecution,
logicalPlan.output,
keyAttrs)
}
```
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