Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/19872#discussion_r162237228
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/python/AggregateInPandasExec.scala
---
@@ -0,0 +1,155 @@
+/*
+ * 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._
+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.{DataType, StructField, StructType}
+import org.apache.spark.util.Utils
+
+/**
+ * Physical node for aggregation with group aggregate Pandas UDF.
+ *
+ * This plan works by sending the necessary (projected) input grouped data
as Arrow record batches
+ * to the python worker, the python worker invokes the UDF and sends the
results to the executor,
+ * finally the executor evaluates any post-aggregation expressions and
join the result with the
+ * grouped key.
+ */
+case class AggregateInPandasExec(
+ groupingExpressions: Seq[NamedExpression],
+ udfExpressions: Seq[PythonUDF],
+ resultExpressions: Seq[NamedExpression],
+ child: SparkPlan)
+ extends UnaryExecNode {
+
+ override val output: Seq[Attribute] =
resultExpressions.map(_.toAttribute)
+
+ override def outputPartitioning: Partitioning = child.outputPartitioning
+
+ override def producedAttributes: AttributeSet = AttributeSet(output)
+
+ override def requiredChildDistribution: Seq[Distribution] = {
+ if (groupingExpressions.isEmpty) {
+ AllTuples :: Nil
+ } else {
+ ClusteredDistribution(groupingExpressions) :: 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(groupingExpressions.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 sessionLocalTimeZone = conf.sessionLocalTimeZone
+ val pandasRespectSessionTimeZone = conf.pandasRespectSessionTimeZone
+
+ val (pyFuncs, inputs) = udfExpressions.map(collectFunctions).unzip
+
+ // Filter child output attributes down to only those that are UDF
inputs.
+ // Also eliminate duplicate UDF inputs.
+ val allInputs = new ArrayBuffer[Expression]
+ val dataTypes = new ArrayBuffer[DataType]
+ val argOffsets = inputs.map { input =>
+ input.map { e =>
+ if (allInputs.exists(_.semanticEquals(e))) {
+ allInputs.indexWhere(_.semanticEquals(e))
+ } else {
+ allInputs += e
+ dataTypes += e.dataType
+ allInputs.length - 1
+ }
+ }.toArray
+ }.toArray
+
+ // Schema of input rows to the python runner
+ val aggInputSchema = StructType(dataTypes.zipWithIndex.map { case (dt,
i) =>
+ StructField(s"_$i", dt)
+ })
+
+ inputRDD.mapPartitionsInternal { iter =>
+ val prunedProj = UnsafeProjection.create(allInputs, child.output)
+
+ val grouped = if (groupingExpressions.isEmpty) {
+ // Use an empty unsafe row as a place holder for the grouping key
+ Iterator((new UnsafeRow(), iter))
+ } else {
+ GroupedIterator(iter, groupingExpressions, child.output)
+ }.map { case (key, rows) =>
+ (key, rows.map(prunedProj))
+ }
+
+ val context = TaskContext.get()
+
+ // The queue used to buffer input rows so we can drain it to
+ // combine input with output from Python.
+ val queue = HybridRowQueue(context.taskMemoryManager(),
+ new File(Utils.getLocalDir(SparkEnv.get.conf)),
groupingExpressions.length)
+ context.addTaskCompletionListener { _ =>
+ queue.close()
+ }
+
+ // Add rows to queue to join later with the result.
+ val projectedRowIter = grouped.map { case (groupingKey, rows) =>
+ queue.add(groupingKey.asInstanceOf[UnsafeRow])
+ rows
+ }
+
+ val columnarBatchIter = new ArrowPythonRunner(
+ pyFuncs, bufferSize, reuseWorker,
+ PythonEvalType.SQL_PANDAS_GROUP_AGG_UDF, argOffsets,
aggInputSchema,
+ sessionLocalTimeZone, pandasRespectSessionTimeZone)
+ .compute(projectedRowIter, context.partitionId(), context)
+
+ val joinedAttributes =
+ groupingExpressions.map(_.toAttribute) ++
udfExpressions.map(_.resultAttribute)
+ val joined = new JoinedRow
+ val resultProj = UnsafeProjection.create(resultExpressions,
joinedAttributes)
+
+ columnarBatchIter.map(_.rowIterator.next()).map { aggOutputRow =>
+ val leftRow = queue.remove()
--- End diff --
why we have to output the grouping columns? e.g. `select max(a) from t
group by b`, we don't need b in the result.
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