amaliujia commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109166322
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/Dataset.scala:
##########
@@ -1035,6 +1035,29 @@ class Dataset[T] private[sql] (val session:
SparkSession, private[sql] val plan:
}
}
+ /**
+ * Groups the Dataset using the specified columns, so we can run aggregation
on them. See
+ * [[RelationalGroupedDataset]] for all the available aggregate functions.
+ *
+ * {{{
+ * // Compute the average for all numeric columns grouped by department.
+ * ds.groupBy($"department").avg()
+ *
+ * // Compute the max age and average salary, grouped by department and
gender.
+ * ds.groupBy($"department", $"gender").agg(Map(
+ * "salary" -> "avg",
+ * "age" -> "max"
+ * ))
+ * }}}
+ *
+ * @group untypedrel
+ * @since 3.4.0
+ */
+ @scala.annotation.varargs
+ def groupBy(cols: Column*): RelationalGroupedDataset = {
+ RelationalGroupedDataset(toDF(), cols.map(_.expr))
Review Comment:
Yeah I guess the major thing probably is because this is not a public API.
How about let me follow up in future PRs on what is the final class
signature for RelationalGroupedDataset? There are a lot more API to add in this
class.
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
##########
@@ -0,0 +1,162 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import java.util.Locale
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.connect.proto
+
+/**
+ * A set of methods for aggregations on a `DataFrame`, created by
[[Dataset#groupBy groupBy]],
+ * [[Dataset#cube cube]] or [[Dataset#rollup rollup]] (and also `pivot`).
+ *
+ * The main method is the `agg` function, which has multiple variants. This
class also contains
+ * some first-order statistics such as `mean`, `sum` for convenience.
+ *
+ * @note
+ * This class was named `GroupedData` in Spark 1.x.
+ *
+ * @since 3.4.0
+ */
+class RelationalGroupedDataset protected[sql] (
+ private[sql] val df: DataFrame,
+ private[sql] val groupingExprs: Seq[proto.Expression]) {
+
+ private[this] def toDF(aggExprs: Seq[proto.Expression]): DataFrame = {
+ // TODO: support other GroupByType such as Rollup, Cube, Pivot.
+ df.session.newDataset { builder =>
+ builder.getAggregateBuilder
+ .setGroupType(proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
+ .setInput(df.plan.getRoot)
+ .addAllGroupingExpressions(groupingExprs.asJava)
+ .addAllAggregateExpressions(aggExprs.asJava)
+ }
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying the column names and
aggregate methods. The
+ * resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for
each department
+ * df.groupBy("department").agg(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * )
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame =
{
+ toDF((aggExpr +: aggExprs).map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ })
+ }
+
+ /**
+ * (Scala-specific) Compute aggregates by specifying a map from column name
to aggregate
+ * methods. The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for
each department
+ * df.groupBy("department").agg(Map(
+ * "age" -> "max",
+ * "expense" -> "sum"
+ * ))
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: Map[String, String]): DataFrame = {
+ toDF(exprs.map { case (colName, expr) =>
+ strToExpr(expr)(df(colName).expr)
+ }.toSeq)
+ }
+
+ /**
+ * (Java-specific) Compute aggregates by specifying a map from column name
to aggregate methods.
+ * The resulting `DataFrame` will also contain the grouping columns.
+ *
+ * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`.
+ * {{{
+ * // Selects the age of the oldest employee and the aggregate expense for
each department
+ * import com.google.common.collect.ImmutableMap;
+ * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense",
"sum"));
+ * }}}
+ *
+ * @since 3.4.0
+ */
+ def agg(exprs: java.util.Map[String, String]): DataFrame = {
+ agg(exprs.asScala.toMap)
+ }
+
+ private[this] def strToExpr(expr: String): (proto.Expression =>
proto.Expression) = {
Review Comment:
Sure, removed this indirection.
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