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[SPARK-44287][SQL] Use PartitionEvaluator API in RowToColumnarExec & …
…ColumnarToRowExec SQL operators ### What changes were proposed in this pull request? SQL operators `RowToColumnarExec` & `ColumnarToRowExec` are updated to use the `PartitionEvaluator` API to do execution. ### Why are the changes needed? To avoid the use of lambda during distributed execution. Ref: SPARK-43061 for more details. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Updated 2 test cases, once all the SQL operators are migrated, the flag `spark.sql.execution.useTaskEvaluator` will be enabled by default to avoid running the tests with and without this TaskEvaluator Closes #41839 from vinodkc/br_refactorToEvaluatorFactory1. Authored-by: Vinod KC <vinod.kc.in@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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sql/core/src/main/scala/org/apache/spark/sql/execution/ColumnarEvaluatorFactory.scala
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.sql.execution | ||
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| import scala.collection.JavaConverters._ | ||
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| import org.apache.spark.{PartitionEvaluator, PartitionEvaluatorFactory, TaskContext} | ||
| import org.apache.spark.sql.catalyst.InternalRow | ||
| import org.apache.spark.sql.catalyst.expressions.{Attribute, UnsafeProjection} | ||
| import org.apache.spark.sql.execution.metric.SQLMetric | ||
| import org.apache.spark.sql.execution.vectorized.{OffHeapColumnVector, OnHeapColumnVector, WritableColumnVector} | ||
| import org.apache.spark.sql.types.StructType | ||
| import org.apache.spark.sql.vectorized.ColumnarBatch | ||
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| class ColumnarToRowEvaluatorFactory( | ||
| childOutput: Seq[Attribute], | ||
| numOutputRows: SQLMetric, | ||
| numInputBatches: SQLMetric) | ||
| extends PartitionEvaluatorFactory[ColumnarBatch, InternalRow] { | ||
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| override def createEvaluator(): PartitionEvaluator[ColumnarBatch, InternalRow] = { | ||
| new ColumnarToRowEvaluator | ||
| } | ||
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| private class ColumnarToRowEvaluator extends PartitionEvaluator[ColumnarBatch, InternalRow] { | ||
| override def eval( | ||
| partitionIndex: Int, | ||
| inputs: Iterator[ColumnarBatch]*): Iterator[InternalRow] = { | ||
| assert(inputs.length == 1) | ||
| val toUnsafe = UnsafeProjection.create(childOutput, childOutput) | ||
| inputs.head.flatMap { input => | ||
| numInputBatches += 1 | ||
| numOutputRows += input.numRows() | ||
| input.rowIterator().asScala.map(toUnsafe) | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| class RowToColumnarEvaluatorFactory( | ||
| enableOffHeapColumnVector: Boolean, | ||
| numRows: Int, | ||
| schema: StructType, | ||
| numInputRows: SQLMetric, | ||
| numOutputBatches: SQLMetric) | ||
| extends PartitionEvaluatorFactory[InternalRow, ColumnarBatch] { | ||
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| override def createEvaluator(): PartitionEvaluator[InternalRow, ColumnarBatch] = { | ||
| new RowToColumnarEvaluator | ||
| } | ||
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| private class RowToColumnarEvaluator extends PartitionEvaluator[InternalRow, ColumnarBatch] { | ||
| override def eval( | ||
| partitionIndex: Int, | ||
| inputs: Iterator[InternalRow]*): Iterator[ColumnarBatch] = { | ||
| assert(inputs.length == 1) | ||
| val rowIterator = inputs.head | ||
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| if (rowIterator.hasNext) { | ||
| new Iterator[ColumnarBatch] { | ||
| private val converters = new RowToColumnConverter(schema) | ||
| private val vectors: Seq[WritableColumnVector] = if (enableOffHeapColumnVector) { | ||
| OffHeapColumnVector.allocateColumns(numRows, schema) | ||
| } else { | ||
| OnHeapColumnVector.allocateColumns(numRows, schema) | ||
| } | ||
| private val cb: ColumnarBatch = new ColumnarBatch(vectors.toArray) | ||
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| TaskContext.get().addTaskCompletionListener[Unit] { _ => | ||
| cb.close() | ||
| } | ||
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| override def hasNext: Boolean = { | ||
| rowIterator.hasNext | ||
| } | ||
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| override def next(): ColumnarBatch = { | ||
| cb.setNumRows(0) | ||
| vectors.foreach(_.reset()) | ||
| var rowCount = 0 | ||
| while (rowCount < numRows && rowIterator.hasNext) { | ||
| val row = rowIterator.next() | ||
| converters.convert(row, vectors.toArray) | ||
| rowCount += 1 | ||
| } | ||
| cb.setNumRows(rowCount) | ||
| numInputRows += rowCount | ||
| numOutputBatches += 1 | ||
| cb | ||
| } | ||
| } | ||
| } else { | ||
| Iterator.empty | ||
| } | ||
| } | ||
| } | ||
| } |
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