Github user ueshin commented on a diff in the pull request:
https://github.com/apache/spark/pull/18732#discussion_r141807573
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/python/FlatMapGroupsInPandasExec.scala
---
@@ -0,0 +1,95 @@
+/*
+ * 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.execution.python
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.TaskContext
+import org.apache.spark.api.python.{ChainedPythonFunctions, PythonEvalType}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Ascending, Attribute,
AttributeSet, Expression, NamedExpression, SortOrder, UnsafeProjection}
+import
org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution,
Distribution, Partitioning}
+import org.apache.spark.sql.execution.{GroupedIterator, SparkPlan,
UnaryExecNode}
+
+case class FlatMapGroupsInPandasExec(
+ grouping: Seq[Expression],
+ func: Expression,
+ override val output: Seq[Attribute],
+ override val child: SparkPlan
+) extends UnaryExecNode {
+
+ val groupingAttributes: Seq[Attribute] = grouping.map {
+ case ne: NamedExpression => ne.toAttribute
+ }
+
+ private val pandasFunction = func.asInstanceOf[PythonUDF].func
+
+ override def outputPartitioning: Partitioning = child.outputPartitioning
+
+ override def producedAttributes: AttributeSet = AttributeSet(output)
+
+ override def requiredChildDistribution: Seq[Distribution] =
+ ClusteredDistribution(groupingAttributes) :: Nil
+
+ override def requiredChildOrdering: Seq[Seq[SortOrder]] =
+ Seq(groupingAttributes.map(SortOrder(_, Ascending)))
+
+ override protected def doExecute(): RDD[InternalRow] = {
+ val inputRDD = child.execute()
+
+ val bufferSize = inputRDD.conf.getInt("spark.buffer.size", 65536)
+ val reuseWorker =
inputRDD.conf.getBoolean("spark.python.worker.reuse", defaultValue = true)
+ val chainedFunc = Seq(ChainedPythonFunctions(Seq(pandasFunction)))
+ val argOffsets = Array((0 until child.schema.length).toArray)
+
+ inputRDD.mapPartitionsInternal { iter =>
+ val grouped = GroupedIterator(iter, groupingAttributes, child.output)
--- End diff --
We should use `grouping` instead of `groupingAttributes` here?
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