[GitHub] [spark] amaliujia commented on a diff in pull request #40057: [SPARK-42468][CONNECT] Implement agg by (String, String)*

["amaliujia (via GitHub)" <gi...@apache.org>]

amaliujia commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109157262


##########
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) = {
+    val exprToFunc: (proto.Expression => proto.Expression) = {
+      val builder = proto.Expression.newBuilder()
+
+      (inputExpr: proto.Expression) =>
+        expr.toLowerCase(Locale.ROOT) match {
+          // We special handle a few cases that have alias that are not in function registry.
+          case "avg" | "average" | "mean" =>
+            builder.getUnresolvedFunctionBuilder
+              .setFunctionName("avg")
+              .addArguments(inputExpr)
+              .setIsDistinct(false)
+          case "stddev" | "std" =>
+            builder.getUnresolvedFunctionBuilder
+              .setFunctionName("stddev")
+              .addArguments(inputExpr)
+              .setIsDistinct(false)
+          // Also special handle count because we need to take care count(*).
+          case "count" | "size" =>
+            // Turn count(*) into count(1)
+            inputExpr match {
+              case s if s.hasUnresolvedStar =>
+                val exprBuilder = proto.Expression.newBuilder
+                exprBuilder.getLiteralBuilder.setInteger(1)
+                builder.getUnresolvedFunctionBuilder
+                  .setFunctionName("count")
+                  .addArguments(exprBuilder)
+                  .setIsDistinct(false)
+              case _ =>
+                builder.getUnresolvedFunctionBuilder
+                  .setFunctionName("count")
+                  .addArguments(inputExpr)
+                  .setIsDistinct(false)
+            }
+          case name =>
+            builder.getUnresolvedFunctionBuilder
+              .setFunctionName(name)
+              .addArguments(inputExpr)
+              .setIsDistinct(false)
+        }
+        builder.build()
+    }
+    (inputExpr: proto.Expression) => exprToFunc(inputExpr)
+  }
+}
+
+private[sql] object RelationalGroupedDataset {
+  def apply(df: DataFrame, groupingExprs: Seq[proto.Expression]): RelationalGroupedDataset = {

Review Comment:
   I think without this, I will need to `new class()` to invoke the class constructor. But existing codebase does not do that but offering the object with an apply implementation. So I am following that.



-- 
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org

For queries about this service, please contact Infrastructure at:
users@infra.apache.org


---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org
For additional commands, e-mail: reviews-help@spark.apache.org