[GitHub] [spark] hagerf commented on a change in pull request #26087: [SPARK-29427][SQL] Create KeyValueGroupedDataset from existing columns in DataFrame

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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
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 @@ -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 this 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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