allisonwang-db commented on code in PR #41316: URL: https://github.com/apache/spark/pull/41316#discussion_r1223912761
########## sql/core/src/main/scala/org/apache/spark/sql/execution/python/BatchEvalPythonUDTFExec.scala: ########## @@ -0,0 +1,163 @@ +/* + * 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.JavaConverters._ +import scala.collection.mutable.ArrayBuffer + +import net.razorvine.pickle.Unpickler + +import org.apache.spark.{ContextAwareIterator, 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.util.GenericArrayData +import org.apache.spark.sql.execution.{SparkPlan, UnaryExecNode} +import org.apache.spark.sql.types.{DataType, StructField, StructType} +import org.apache.spark.util.Utils + +/** + * A physical plan that evaluates a [[PythonUDTF]]. This is similar to [[BatchEvalPythonExec]]. + * + * @param udtf the user-defined Python function + * @param requiredChildOutput the required output of the child plan. It's used for omitting data + * generation that will be discarded next by a projection. + * @param resultAttrs the output schema of the Python UDTF. + * @param child the child plan + */ +case class BatchEvalPythonUDTFExec( + udtf: PythonUDTF, + requiredChildOutput: Seq[Attribute], + resultAttrs: Seq[Attribute], + child: SparkPlan) + extends UnaryExecNode with PythonSQLMetrics { + + override def output: Seq[Attribute] = requiredChildOutput ++ resultAttrs + + override def producedAttributes: AttributeSet = AttributeSet(resultAttrs) + + protected override def doExecute(): RDD[InternalRow] = { + val inputRDD = child.execute().map(_.copy()) + + inputRDD.mapPartitions { iter => + val context = TaskContext.get() + val contextAwareIterator = new ContextAwareIterator(context, iter) + + // 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)), child.output.length) + context.addTaskCompletionListener[Unit] { ctx => + queue.close() + } + + val inputs = Seq(udtf.children) + + // 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 = MutableProjection.create(allInputs.toSeq, child.output) + projection.initialize(context.partitionId()) + val schema = StructType(dataTypes.zipWithIndex.map { case (dt, i) => + StructField(s"_$i", dt) + }.toArray) + + // Add rows to the queue to join later with the result. + // Also keep track of the number rows added to the queue. + var count = 0L + val projectedRowIter = contextAwareIterator.map { inputRow => + queue.add(inputRow.asInstanceOf[UnsafeRow]) + count += 1 + projection(inputRow) + } + + val outputRowIterator = evaluate(udtf, argOffsets, projectedRowIter, schema, context) + + val pruneChildForResult: InternalRow => InternalRow = + if (child.outputSet == AttributeSet(requiredChildOutput)) { + identity + } else { + UnsafeProjection.create(requiredChildOutput, child.output) + } + + val joined = new JoinedRow + val resultProj = UnsafeProjection.create(output, output) + + outputRowIterator.flatMap { outputRows => + if (count > 0) { + val left = queue.remove() + count -= 1 + joined.withLeft(pruneChildForResult(left)) + } + outputRows.map(r => resultProj(joined.withRight(r))) + } Review Comment: To allow `terminate` to yield more rows, we need to keep track of the number of elements in the queue, and when there are no more input rows, we join the rows generated by `terminate` with the last input row from a partition (same as how Hive UDTFs). I am using a counter here to see if all input rows have been consumed. cc @cloud-fan @HyukjinKwon please let me know if there are better ways to do this. -- 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: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
