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[SPARK-20822][SQL] Generate code to build table cache using ColumnarBatch and to get value from ColumnVector #18066
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220 changes: 220 additions & 0 deletions
220
sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/GeneratedColumnarBatch.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.columnar | ||
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| import org.apache.spark.sql.catalyst.InternalRow | ||
| import org.apache.spark.sql.catalyst.expressions.Attribute | ||
| import org.apache.spark.sql.catalyst.expressions.codegen._ | ||
| import org.apache.spark.sql.execution.vectorized.ColumnarBatch | ||
| import org.apache.spark.sql.types._ | ||
| import org.apache.spark.storage.StorageLevel | ||
| import org.apache.spark.storage.StorageLevel._ | ||
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| /** | ||
| * A helper class to expose the scala iterator to Java. | ||
| */ | ||
| abstract class ColumnarBatchIterator extends Iterator[ColumnarBatch] | ||
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| /** | ||
| * Generate code to batch [[InternalRow]]s into [[ColumnarBatch]]es. | ||
| */ | ||
| class GenerateColumnarBatch( | ||
| schema: StructType, | ||
| batchSize: Int, | ||
| storageLevel: StorageLevel) | ||
| extends CodeGenerator[Iterator[InternalRow], Iterator[CachedColumnarBatch]] { | ||
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| protected def canonicalize(in: Iterator[InternalRow]): Iterator[InternalRow] = in | ||
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| protected def bind( | ||
| in: Iterator[InternalRow], inputSchema: Seq[Attribute]): Iterator[InternalRow] = { | ||
| in | ||
| } | ||
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| protected def create(rowIterator: Iterator[InternalRow]): Iterator[CachedColumnarBatch] = { | ||
| import scala.collection.JavaConverters._ | ||
| val ctx = newCodeGenContext() | ||
| val columnStatsCls = classOf[ColumnStats].getName | ||
| val rowVar = ctx.freshName("row") | ||
| val batchVar = ctx.freshName("columnarBatch") | ||
| val rowNumVar = ctx.freshName("rowNum") | ||
| val numBytesVar = ctx.freshName("bytesInBatch") | ||
| ctx.addMutableState("long", numBytesVar, s"$numBytesVar = 0;") | ||
| val rowIterVar = ctx.addReferenceObj( | ||
| "rowIterator", rowIterator.asJava, classOf[java.util.Iterator[_]].getName) | ||
| val schemas = StructType( | ||
| schema.fields.map(s => StructField(s.name, | ||
| s.dataType match { | ||
| case udt: UserDefinedType[_] => udt.sqlType | ||
| case other => other | ||
| }, s.nullable)) | ||
| ) | ||
| val schemaVar = ctx.addReferenceObj("schema", schemas, classOf[StructType].getName) | ||
| val maxNumBytes = ColumnBuilder.MAX_BATCH_SIZE_IN_BYTE | ||
| val numColumns = schema.fields.length | ||
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| val colStatVars = (0 to numColumns - 1).map(i => ctx.freshName("colStat" + i)) | ||
| val colStatCode = ctx.splitExpressions( | ||
| (schemas.fields zip colStatVars).zipWithIndex.map { | ||
| case ((field, varName), i) => | ||
| val columnStatsCls = field.dataType match { | ||
| case IntegerType => classOf[IntColumnStats].getName | ||
| case DoubleType => classOf[DoubleColumnStats].getName | ||
| case others => throw new UnsupportedOperationException(s"$others is not supported yet") | ||
| } | ||
| ctx.addMutableState(columnStatsCls, varName, "") | ||
| s"$varName = new $columnStatsCls(); statsArray[$i] = $varName;\n" | ||
| }, | ||
| "apply", | ||
| Seq.empty | ||
| ) | ||
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| val populateColumnVectorsCode = ctx.splitExpressions( | ||
| (schemas.fields zip colStatVars).zipWithIndex.map { | ||
| case ((field, colStatVar), i) => | ||
| GenerateColumnarBatch.putColumnCode(ctx, field.dataType, field.nullable, | ||
| batchVar, rowVar, rowNumVar, colStatVar, i, numBytesVar).trim + "\n" | ||
| }, | ||
| "apply", | ||
| Seq(("InternalRow", rowVar), ("ColumnarBatch", batchVar), ("int", rowNumVar)) | ||
| ) | ||
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| val code = s""" | ||
| import org.apache.spark.memory.MemoryMode; | ||
| import org.apache.spark.sql.catalyst.InternalRow; | ||
| import org.apache.spark.sql.execution.columnar.CachedColumnarBatch; | ||
| import org.apache.spark.sql.execution.columnar.GenerateColumnarBatch; | ||
| import org.apache.spark.sql.execution.vectorized.ColumnarBatch; | ||
| import org.apache.spark.sql.execution.vectorized.ColumnVector; | ||
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| public GeneratedColumnarBatchIterator generate(Object[] references) { | ||
| return new GeneratedColumnarBatchIterator(references); | ||
| } | ||
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| class GeneratedColumnarBatchIterator extends ${classOf[ColumnarBatchIterator].getName} { | ||
| private Object[] references; | ||
| ${ctx.declareMutableStates()} | ||
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| public GeneratedColumnarBatchIterator(Object[] references) { | ||
| this.references = references; | ||
| ${ctx.initMutableStates()} | ||
| } | ||
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| ${ctx.declareAddedFunctions()} | ||
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| $columnStatsCls[] statsArray = new $columnStatsCls[$numColumns]; | ||
| private void allocateColumnStats() { | ||
| ${colStatCode.trim} | ||
| } | ||
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| @Override | ||
| public boolean hasNext() { | ||
| return $rowIterVar.hasNext(); | ||
| } | ||
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| @Override | ||
| public CachedColumnarBatch next() { | ||
| ColumnarBatch $batchVar = | ||
| ColumnarBatch.allocate($schemaVar, MemoryMode.ON_HEAP, $batchSize); | ||
| allocateColumnStats(); | ||
| int $rowNumVar = 0; | ||
| $numBytesVar = 0; | ||
| while ($rowIterVar.hasNext() && $rowNumVar < $batchSize && $numBytesVar < $maxNumBytes) { | ||
| InternalRow $rowVar = (InternalRow) $rowIterVar.next(); | ||
| $populateColumnVectorsCode | ||
| $rowNumVar += 1; | ||
| } | ||
| $batchVar.setNumRows($rowNumVar); | ||
| return CachedColumnarBatch.apply( | ||
| $batchVar, GenerateColumnarBatch.generateStats(statsArray)); | ||
| } | ||
| } | ||
| """ | ||
| val formattedCode = CodeFormatter.stripOverlappingComments( | ||
| new CodeAndComment(code, ctx.getPlaceHolderToComments())) | ||
| CodeGenerator.compile(formattedCode).generate(ctx.references.toArray) | ||
| .asInstanceOf[Iterator[CachedColumnarBatch]] | ||
| } | ||
| } | ||
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| private[sql] object GenerateColumnarBatch { | ||
| def compressStorageLevel(storageLevel: StorageLevel, useCompression: Boolean): StorageLevel = { | ||
| if (!useCompression) return storageLevel | ||
| storageLevel match { | ||
| case MEMORY_ONLY => MEMORY_ONLY_SER | ||
| case MEMORY_ONLY_2 => MEMORY_ONLY_SER_2 | ||
| case MEMORY_AND_DISK => MEMORY_AND_DISK_SER | ||
| case MEMORY_AND_DISK_2 => MEMORY_AND_DISK_SER_2 | ||
| case sl => sl | ||
| } | ||
| } | ||
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| def isCompress(storageLevel: StorageLevel) : Boolean = { | ||
| (storageLevel == MEMORY_ONLY_SER || storageLevel == MEMORY_ONLY_SER_2 || | ||
| storageLevel == MEMORY_AND_DISK_SER || storageLevel == MEMORY_AND_DISK_SER_2) | ||
| } | ||
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| private val typeToName = Map[AbstractDataType, String]( | ||
| IntegerType -> "int", | ||
| DoubleType -> "double" | ||
| ) | ||
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| def putColumnCode(ctx: CodegenContext, dt: DataType, nullable: Boolean, batchVar: String, | ||
| rowVar: String, rowNumVar: String, | ||
| colStatVar: String, colNum: Int, numBytesVar: String): String = { | ||
| val colVar = s"$batchVar.column($colNum)" | ||
| val body = dt match { | ||
| case t if ctx.isPrimitiveType(dt) => | ||
| val typeName = GenerateColumnarBatch.typeToName(dt) | ||
| val put = "put" + typeName.capitalize | ||
| val get = "get" + typeName.capitalize | ||
| s""" | ||
| $typeName val = $rowVar.$get($colNum); | ||
| $colVar.$put($rowNumVar, val); | ||
| $numBytesVar += ${dt.defaultSize}; | ||
| $colStatVar.gatherValueStats(val); | ||
| """ | ||
| case _ => | ||
| throw new UnsupportedOperationException("Unsupported data type " + dt.simpleString); | ||
| } | ||
| if (nullable) { | ||
| s""" | ||
| if ($rowVar.isNullAt($colNum)) { | ||
| $colVar.putNull($rowNumVar); | ||
| $colStatVar.gatherNullStats(); | ||
| } else { | ||
| ${body.trim} | ||
| } | ||
| """ | ||
| } else { | ||
| s""" | ||
| { | ||
| ${body.trim} | ||
| } | ||
| """ | ||
| } | ||
| } | ||
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| def generateStats(columnStats: Array[ColumnStats]): InternalRow = { | ||
| val array = columnStats.flatMap(_.collectedStatistics) | ||
| InternalRow.fromSeq(array) | ||
| } | ||
| } | ||
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Hi, @kiszk .
Is there any reason having only two types,
intanddouble?The PR looks more general to me.
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As I described in the description, this is for ease of review.