Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/18659#discussion_r137412019
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/python/ArrowEvalPythonExec.scala
---
@@ -0,0 +1,127 @@
+/*
+ * 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, PythonRunner}
+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.execution.SparkPlan
+import org.apache.spark.sql.execution.arrow.{ArrowConverters, ArrowPayload}
+import org.apache.spark.sql.types.{DataType, StructField, StructType}
+import org.apache.spark.util.Utils
+
+
+/**
+ * A physical plan that evaluates a [[PythonUDF]],
+ */
+case class ArrowEvalPythonExec(udfs: Seq[PythonUDF], output:
Seq[Attribute], child: SparkPlan)
+ extends SparkPlan {
+
+ def children: Seq[SparkPlan] = child :: Nil
+
+ override def producedAttributes: AttributeSet =
AttributeSet(output.drop(child.output.length))
+
+ 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)
+ }
+ }
+
+ protected override def doExecute(): RDD[InternalRow] = {
+ val inputRDD = child.execute().map(_.copy())
+ val bufferSize = inputRDD.conf.getInt("spark.buffer.size", 65536)
+ val reuseWorker =
inputRDD.conf.getBoolean("spark.python.worker.reuse", defaultValue = true)
+
+ inputRDD.mapPartitions { iter =>
+
+ // The queue used to buffer input rows so we can drain it to
+ // combine input with output from Python.
+ val queue = HybridRowQueue(TaskContext.get().taskMemoryManager(),
+ new File(Utils.getLocalDir(SparkEnv.get.conf)),
child.output.length)
+ TaskContext.get().addTaskCompletionListener({ ctx =>
+ queue.close()
+ })
+
+ val (pyFuncs, inputs) = udfs.map(collectFunctions).unzip
+
+ // flatten all the arguments
+ 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
+ val projection = newMutableProjection(allInputs, child.output)
+ val schema = StructType(dataTypes.zipWithIndex.map { case (dt, i) =>
+ StructField(s"_$i", dt)
+ })
+
+ // Input iterator to Python: input rows are grouped so we send them
in batches to Python.
+ // For each row, add it to the queue.
+ val projectedRowIter = iter.map { inputRow =>
+ queue.add(inputRow.asInstanceOf[UnsafeRow])
+ projection(inputRow)
+ }
+
+ val context = TaskContext.get()
+
+ val inputIterator = ArrowConverters.toPayloadIterator(
+ projectedRowIter, schema, conf.arrowMaxRecordsPerBatch, context).
+ map(_.asPythonSerializable)
+
+ val schemaOut =
StructType.fromAttributes(output.drop(child.output.length).zipWithIndex.
+ map { case (attr, i) => attr.withName(s"_$i") })
+
+ // Output iterator for results from Python.
+ val outputIterator = new PythonRunner(
+ pyFuncs, bufferSize, reuseWorker, PythonEvalType.SQL_ARROW_UDF,
argOffsets).
+ compute(inputIterator, context.partitionId(), context)
+
+ val joined = new JoinedRow
+ val resultProj = UnsafeProjection.create(output, output)
+
+ val outputRowIterator = ArrowConverters.fromPayloadIterator(
+ outputIterator.map(new ArrowPayload(_)), context)
+
+ assert(schemaOut.equals(outputRowIterator.schema))
--- End diff --
@felixcheung , I think you had also brought up checking the return type
matches what was defined in the UDF. This is done here.
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