Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/19872#discussion_r154642230
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/python/AggregateInPandasExec.scala
---
@@ -0,0 +1,135 @@
+/*
+ * 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 java.io.File
+
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.{SparkEnv, 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, JoinedRow, SortOrder, UnsafeProjection, UnsafeRow}
+import org.apache.spark.sql.catalyst.plans.physical.{AllTuples,
ClusteredDistribution, Distribution, Partitioning}
+import org.apache.spark.sql.execution.{GroupedIterator, SparkPlan,
UnaryExecNode}
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.util.Utils
+
+case class AggregateInPandasExec(
+ groupingAttributes: Seq[Attribute],
+ func: Seq[Expression],
+ output: Seq[Attribute],
+ child: SparkPlan)
+ extends UnaryExecNode {
+ private val udfs = func.map(expr => expr.asInstanceOf[PythonUDF])
+
+ override def outputPartitioning: Partitioning = child.outputPartitioning
+
+ override def producedAttributes: AttributeSet = AttributeSet(output)
+
+ override def requiredChildDistribution: Seq[Distribution] = {
+ if (groupingAttributes.isEmpty) {
+ AllTuples :: Nil
+ } else {
+ ClusteredDistribution(groupingAttributes) :: Nil
+ }
+ }
+
+ private def collectFunctions(udf: PythonUDF): (ChainedPythonFunctions,
Seq[Expression]) = {
+ udf.children match {
+ case Seq(u: PythonUDF) =>
+ val (chained, children) = collectFunctions(u)
+ (ChainedPythonFunctions(chained.funcs ++ Seq(udf.func)), children)
+ case children =>
+ // There should not be any other UDFs, or the children can't be
evaluated directly.
+ assert(children.forall(_.find(_.isInstanceOf[PythonUDF]).isEmpty))
+ (ChainedPythonFunctions(Seq(udf.func)), udf.children)
+ }
+ }
+
+ 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 argOffsets = Array((0 until (child.output.length -
groupingAttributes.length)).toArray)
+ val schema = StructType(child.schema.drop(groupingAttributes.length))
+ val sessionLocalTimeZone = conf.sessionLocalTimeZone
+ val pandasRespectSessionTimeZone = conf.pandasRespectSessionTimeZone
+
+ val (pyFuncs, inputs) = udfs.map(collectFunctions).unzip
+
+ val allInputs = new ArrayBuffer[Expression]
+
+ val argOffsets = inputs.map { input =>
+ input.map { e =>
+ allInputs += e
--- End diff --
indentation nit
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