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[NSE-245]Adding columnar RDD cache support (#246)
* Adding columnar RDD cache support Signed-off-by: Chendi Xue <[email protected]> * Directly save reference, only convert to Array[Byte] when calling by BlockManager Signed-off-by: Chendi Xue <[email protected]> * Add DeAllocator to construction to make sure this instance will be released once it be deleted by JVM Signed-off-by: Chendi Xue <[email protected]> * Delete cache by adding a release in InMemoryRelation Since unpersist only delete RDD object, seems our deAllocator wasn't being called along Now we added a release function in InMemoryRelation clearCache() func, may need to think a new way for 3.1.0 Signed-off-by: Chendi Xue <[email protected]>
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native-sql-engine/core/src/main/scala/com/intel/oap/execution/ColumnarInMemoryRelation.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 com.intel.oap.execution | ||
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| import java.io._ | ||
| import org.apache.commons.lang3.StringUtils | ||
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| import com.intel.oap.expression._ | ||
| import com.intel.oap.vectorized.ArrowWritableColumnVector | ||
| import com.intel.oap.vectorized.CloseableColumnBatchIterator | ||
| import org.apache.arrow.memory.ArrowBuf | ||
| import org.apache.spark.TaskContext | ||
| import org.apache.spark.network.util.JavaUtils | ||
| import org.apache.spark.rdd.RDD | ||
| import org.apache.spark.sql.catalyst.InternalRow | ||
| import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation | ||
| import org.apache.spark.sql.catalyst.expressions._ | ||
| import org.apache.spark.sql.catalyst.plans.{logical, QueryPlan} | ||
| import org.apache.spark.sql.catalyst.plans.logical.{ColumnStat, LogicalPlan, Statistics} | ||
| import org.apache.spark.sql.catalyst.util.truncatedString | ||
| import org.apache.spark.sql.columnar.{ | ||
| CachedBatch, | ||
| CachedBatchSerializer, | ||
| SimpleMetricsCachedBatch, | ||
| SimpleMetricsCachedBatchSerializer | ||
| } | ||
| import org.apache.spark.sql.execution.SparkPlan | ||
| import org.apache.spark.sql.execution.vectorized.{WritableColumnVector} | ||
| import org.apache.spark.sql.internal.{SQLConf, StaticSQLConf} | ||
| import org.apache.spark.sql.types._ | ||
| import org.apache.spark.sql.vectorized.{ColumnarBatch, ColumnVector} | ||
| import org.apache.spark.storage.StorageLevel | ||
| import org.apache.spark.util.{LongAccumulator, Utils} | ||
| import scala.collection.JavaConverters._ | ||
| import scala.collection.mutable.ArrayBuffer | ||
| import sun.misc.Cleaner | ||
|
|
||
| private class Deallocator(var arrowColumnarBatch: Array[ColumnarBatch]) extends Runnable { | ||
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| override def run(): Unit = { | ||
| try { | ||
| Option(arrowColumnarBatch) match { | ||
| case Some(buffer) => | ||
| //System.out.println(s"ArrowCachedBatch released in DeAllocator, First buffer name is ${buffer(0)}") | ||
| buffer.foreach(_.close) | ||
| case other => | ||
| } | ||
| } catch { | ||
| case e: Exception => | ||
| // We should suppress all possible errors in Cleaner to prevent JVM from being shut down | ||
| //System.err.println("ArrowCachedBatch-Deallocator: Error running deallocator") | ||
| e.printStackTrace() | ||
| } | ||
| } | ||
| } | ||
|
|
||
| /** | ||
| * The default implementation of CachedBatch. | ||
| * | ||
| * @param numRows The total number of rows in this batch | ||
| * @param buffers The buffers for serialized columns | ||
| * @param stats The stat of columns | ||
| */ | ||
| case class ArrowCachedBatch( | ||
| var numRows: Int, | ||
| var buffer: Array[ColumnarBatch], | ||
| stats: InternalRow) | ||
| extends SimpleMetricsCachedBatch | ||
| with Externalizable { | ||
| if (buffer != null) { | ||
| //System.out.println(s"ArrowCachedBatch constructed First buffer name is ${buffer(0)}") | ||
| Cleaner.create(this, new Deallocator(buffer)) | ||
| } | ||
| def this() = { | ||
| this(0, null, null) | ||
| } | ||
| def release() = { | ||
| //System.out.println(s"ArrowCachedBatch released by clear cache, First buffer name is ${buffer(0)}") | ||
| buffer.foreach(_.close) | ||
| } | ||
| lazy val estimatedSize: Long = { | ||
| var size: Long = 0 | ||
| buffer.foreach(batch => { | ||
| size += ConverterUtils.calcuateEstimatedSize(batch) | ||
| }) | ||
| //System.out.println(s"ArrowCachedBatch${buffer(0)} estimated size is ${size}") | ||
| size | ||
| } | ||
| override def sizeInBytes: Long = estimatedSize | ||
| override def writeExternal(out: ObjectOutput): Unit = { | ||
| // System.out.println(s"writeExternal for $this") | ||
| val rawArrowData = ConverterUtils.convertToNetty(buffer) | ||
| out.writeObject(rawArrowData) | ||
| buffer.foreach(_.close) | ||
| } | ||
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| override def readExternal(in: ObjectInput): Unit = { | ||
| numRows = 0 | ||
| val rawArrowData = in.readObject().asInstanceOf[Array[Byte]] | ||
| buffer = ConverterUtils.convertFromNetty(null, new ByteArrayInputStream(rawArrowData)).toArray | ||
| //System.out.println(s"ArrowCachedBatch constructed by deserilizer, First buffer name is ${buffer(0)}") | ||
| Cleaner.create(this, new Deallocator(buffer)) | ||
| } | ||
| } | ||
|
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| /** | ||
| * The default implementation of CachedBatchSerializer. | ||
| */ | ||
| class ArrowColumnarCachedBatchSerializer extends SimpleMetricsCachedBatchSerializer { | ||
| override def supportsColumnarInput(schema: Seq[Attribute]): Boolean = true | ||
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| override def convertColumnarBatchToCachedBatch( | ||
| input: RDD[ColumnarBatch], | ||
| schema: Seq[Attribute], | ||
| storageLevel: StorageLevel, | ||
| conf: SQLConf): RDD[CachedBatch] = { | ||
| val batchSize = conf.columnBatchSize | ||
| val useCompression = conf.useCompression | ||
| convertForCacheInternal(input, schema, batchSize, useCompression) | ||
| } | ||
|
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| override def convertInternalRowToCachedBatch( | ||
| input: RDD[InternalRow], | ||
| schema: Seq[Attribute], | ||
| storageLevel: StorageLevel, | ||
| conf: SQLConf): RDD[CachedBatch] = | ||
| throw new IllegalStateException("InternalRow input is not supported") | ||
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| def convertForCacheInternal( | ||
| input: RDD[ColumnarBatch], | ||
| output: Seq[Attribute], | ||
| batchSize: Int, | ||
| useCompression: Boolean): RDD[CachedBatch] = { | ||
| input.mapPartitions { iter => | ||
| var processed = false | ||
| new Iterator[ArrowCachedBatch] { | ||
| def next(): ArrowCachedBatch = { | ||
| processed = true | ||
| var _numRows: Int = 0 | ||
| val _input = new ArrayBuffer[ColumnarBatch]() | ||
| while (iter.hasNext) { | ||
| val batch = iter.next | ||
| if (batch.numRows > 0) { | ||
| (0 until batch.numCols).foreach(i => | ||
| batch.column(i).asInstanceOf[ArrowWritableColumnVector].retain()) | ||
| _numRows += batch.numRows | ||
| _input += batch | ||
| } | ||
| } | ||
| // To avoid mem copy, we only save columnVector reference here | ||
| val res = ArrowCachedBatch(_numRows, _input.toArray, null) | ||
| // System.out.println(s"convertForCacheInternal cachedBatch is ${res}") | ||
| res | ||
| } | ||
|
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| def hasNext: Boolean = !processed | ||
| } | ||
| } | ||
| } | ||
|
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| override def convertCachedBatchToColumnarBatch( | ||
| input: RDD[CachedBatch], | ||
| cacheAttributes: Seq[Attribute], | ||
| selectedAttributes: Seq[Attribute], | ||
| conf: SQLConf): RDD[ColumnarBatch] = { | ||
| val columnIndices = | ||
| selectedAttributes.map(a => cacheAttributes.map(o => o.exprId).indexOf(a.exprId)).toArray | ||
| def createAndDecompressColumn(cachedIter: Iterator[CachedBatch]): Iterator[ColumnarBatch] = { | ||
| val res = new Iterator[ColumnarBatch] { | ||
| var iter: Iterator[ColumnarBatch] = null | ||
| if (cachedIter.hasNext) { | ||
| val cachedColumnarBatch: ArrowCachedBatch = | ||
| cachedIter.next.asInstanceOf[ArrowCachedBatch] | ||
| // System.out.println( | ||
| // s"convertCachedBatchToColumnarBatch cachedBatch is ${cachedColumnarBatch}") | ||
| val rawData = cachedColumnarBatch.buffer | ||
|
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| iter = new Iterator[ColumnarBatch] { | ||
| val numBatches = rawData.size | ||
| var batchIdx = 0 | ||
| override def hasNext: Boolean = batchIdx < numBatches | ||
| override def next(): ColumnarBatch = { | ||
| val vectors = columnIndices.map(i => rawData(batchIdx).column(i)) | ||
| vectors.foreach(v => v.asInstanceOf[ArrowWritableColumnVector].retain()) | ||
| val numRows = rawData(batchIdx).numRows | ||
| batchIdx += 1 | ||
| new ColumnarBatch(vectors, numRows) | ||
| } | ||
| } | ||
| } | ||
| def next(): ColumnarBatch = | ||
| if (iter != null) { | ||
| iter.next | ||
| } else { | ||
| val resultStructType = StructType(selectedAttributes.map(a => | ||
| StructField(a.name, a.dataType, a.nullable, a.metadata))) | ||
| val resultColumnVectors = | ||
| ArrowWritableColumnVector.allocateColumns(0, resultStructType).toArray | ||
| new ColumnarBatch(resultColumnVectors.map(_.asInstanceOf[ColumnVector]), 0) | ||
| } | ||
| def hasNext: Boolean = iter.hasNext | ||
| } | ||
| new CloseableColumnBatchIterator(res) | ||
| } | ||
| input.mapPartitions(createAndDecompressColumn) | ||
| } | ||
|
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| override def convertCachedBatchToInternalRow( | ||
| input: RDD[CachedBatch], | ||
| cacheAttributes: Seq[Attribute], | ||
| selectedAttributes: Seq[Attribute], | ||
| conf: SQLConf): RDD[InternalRow] = { | ||
| // Find the ordinals and data types of the requested columns. | ||
| val columnarBatchRdd = | ||
| convertCachedBatchToColumnarBatch(input, cacheAttributes, selectedAttributes, conf) | ||
| columnarBatchRdd.mapPartitions { batches => | ||
| val toUnsafe = UnsafeProjection.create(selectedAttributes, selectedAttributes) | ||
| batches.flatMap { batch => batch.rowIterator().asScala.map(toUnsafe) } | ||
| } | ||
| } | ||
|
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| override def supportsColumnarOutput(schema: StructType): Boolean = true | ||
|
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| override def vectorTypes(attributes: Seq[Attribute], conf: SQLConf): Option[Seq[String]] = | ||
| Option(Seq.fill(attributes.length)(classOf[ArrowWritableColumnVector].getName)) | ||
|
|
||
| } |
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