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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.
*/
package org.apache.spark.sql.execution
import scala.collection.mutable.ArrayBuffer
import org.apache.commons.lang3.StringUtils
import org.apache.hadoop.fs.{BlockLocation, FileStatus, LocatedFileStatus, Path}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.catalyst.{InternalRow, TableIdentifier}
import org.apache.spark.sql.catalyst.catalog.BucketSpec
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext
import org.apache.spark.sql.catalyst.plans.QueryPlan
import org.apache.spark.sql.catalyst.plans.physical.{HashPartitioning, Partitioning, UnknownPartitioning}
import org.apache.spark.sql.execution.datasources._
import org.apache.spark.sql.execution.datasources.parquet.{ParquetFileFormat => ParquetSource}
import org.apache.spark.sql.execution.metric.SQLMetrics
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.sources.BaseRelation
import org.apache.spark.sql.types.StructType
import org.apache.spark.util.Utils
trait DataSourceScanExec extends LeafExecNode with CodegenSupport {
val relation: BaseRelation
val metastoreTableIdentifier: Option[TableIdentifier]
protected val nodeNamePrefix: String = ""
override val nodeName: String = {
s"Scan $relation ${metastoreTableIdentifier.map(_.unquotedString).getOrElse("")}"
}
override def simpleString: String = {
val metadataEntries = metadata.toSeq.sorted.map {
case (key, value) =>
key + ": " + StringUtils.abbreviate(redact(value), 100)
}
val metadataStr = Utils.truncatedString(metadataEntries, " ", ", ", "")
s"$nodeNamePrefix$nodeName${Utils.truncatedString(output, "[", ",", "]")}$metadataStr"
}
override def verboseString: String = redact(super.verboseString)
override def treeString(verbose: Boolean, addSuffix: Boolean): String = {
redact(super.treeString(verbose, addSuffix))
}
/**
* Shorthand for calling redactString() without specifying redacting rules
*/
private def redact(text: String): String = {
Utils.redact(SparkSession.getActiveSession.map(_.sparkContext.conf).orNull, text)
}
}
/** Physical plan node for scanning data from a relation. */
case class RowDataSourceScanExec(
output: Seq[Attribute],
rdd: RDD[InternalRow],
@transient relation: BaseRelation,
override val outputPartitioning: Partitioning,
override val metadata: Map[String, String],
override val metastoreTableIdentifier: Option[TableIdentifier])
extends DataSourceScanExec {
override lazy val metrics =
Map("numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows"))
val outputUnsafeRows = relation match {
case r: HadoopFsRelation if r.fileFormat.isInstanceOf[ParquetSource] =>
!SparkSession.getActiveSession.get.sessionState.conf.getConf(
SQLConf.PARQUET_VECTORIZED_READER_ENABLED)
case _: HadoopFsRelation => true
case _ => false
}
protected override def doExecute(): RDD[InternalRow] = {
val unsafeRow = if (outputUnsafeRows) {
rdd
} else {
rdd.mapPartitionsWithIndexInternal { (index, iter) =>
val proj = UnsafeProjection.create(schema)
proj.initialize(index)
iter.map(proj)
}
}
val numOutputRows = longMetric("numOutputRows")
unsafeRow.map { r =>
numOutputRows += 1
r
}
}
override def inputRDDs(): Seq[RDD[InternalRow]] = {
rdd :: Nil
}
override protected def doProduce(ctx: CodegenContext): String = {
val numOutputRows = metricTerm(ctx, "numOutputRows")
// PhysicalRDD always just has one input
val input = ctx.freshName("input")
ctx.addMutableState("scala.collection.Iterator", input, s"$input = inputs[0];")
val exprRows = output.zipWithIndex.map{ case (a, i) =>
BoundReference(i, a.dataType, a.nullable)
}
val row = ctx.freshName("row")
ctx.INPUT_ROW = row
ctx.currentVars = null
val columnsRowInput = exprRows.map(_.genCode(ctx))
val inputRow = if (outputUnsafeRows) row else null
s"""
|while ($input.hasNext()) {
| InternalRow $row = (InternalRow) $input.next();
| $numOutputRows.add(1);
| ${consume(ctx, columnsRowInput, inputRow).trim}
| if (shouldStop()) return;
|}
""".stripMargin
}
// Only care about `relation` and `metadata` when canonicalizing.
override def preCanonicalized: SparkPlan =
copy(rdd = null, outputPartitioning = null, metastoreTableIdentifier = None)
}
/**
* Physical plan node for scanning data from HadoopFsRelations.
*
* @param relation The file-based relation to scan.
* @param output Output attributes of the scan, including data attributes and partition attributes.
* @param requiredSchema Required schema of the underlying relation, excluding partition columns.
* @param partitionFilters Predicates to use for partition pruning.
* @param dataFilters Filters on non-partition columns.
* @param metastoreTableIdentifier identifier for the table in the metastore.
*/
case class FileSourceScanExec(
@transient relation: HadoopFsRelation,
output: Seq[Attribute],
requiredSchema: StructType,
partitionFilters: Seq[Expression],
dataFilters: Seq[Expression],
override val metastoreTableIdentifier: Option[TableIdentifier])
extends DataSourceScanExec with ColumnarBatchScan {
val supportsBatch: Boolean = relation.fileFormat.supportBatch(
relation.sparkSession, StructType.fromAttributes(output))
val needsUnsafeRowConversion: Boolean = if (relation.fileFormat.isInstanceOf[ParquetSource]) {
SparkSession.getActiveSession.get.sessionState.conf.parquetVectorizedReaderEnabled
} else {
false
}
@transient private lazy val selectedPartitions: Seq[PartitionDirectory] = {
val optimizerMetadataTimeNs = relation.location.metadataOpsTimeNs.getOrElse(0L)
val startTime = System.nanoTime()
val ret = relation.location.listFiles(partitionFilters, dataFilters)
val timeTakenMs = ((System.nanoTime() - startTime) + optimizerMetadataTimeNs) / 1000 / 1000
metrics("numFiles").add(ret.map(_.files.size.toLong).sum)
metrics("metadataTime").add(timeTakenMs)
val executionId = sparkContext.getLocalProperty(SQLExecution.EXECUTION_ID_KEY)
SQLMetrics.postDriverMetricUpdates(sparkContext, executionId,
metrics("numFiles") :: metrics("metadataTime") :: Nil)
ret
}
override val (outputPartitioning, outputOrdering): (Partitioning, Seq[SortOrder]) = {
val bucketSpec = if (relation.sparkSession.sessionState.conf.bucketingEnabled) {
relation.bucketSpec
} else {
None
}
bucketSpec match {
case Some(spec) =>
// For bucketed columns:
// -----------------------
// `HashPartitioning` would be used only when:
// 1. ALL the bucketing columns are being read from the table
//
// For sorted columns:
// ---------------------
// Sort ordering should be used when ALL these criteria's match:
// 1. `HashPartitioning` is being used
// 2. A prefix (or all) of the sort columns are being read from the table.
//
// Sort ordering would be over the prefix subset of `sort columns` being read
// from the table.
// eg.
// Assume (col0, col2, col3) are the columns read from the table
// If sort columns are (col0, col1), then sort ordering would be considered as (col0)
// If sort columns are (col1, col0), then sort ordering would be empty as per rule #2
// above
def toAttribute(colName: String): Option[Attribute] =
output.find(_.name == colName)
val bucketColumns = spec.bucketColumnNames.flatMap(n => toAttribute(n))
if (bucketColumns.size == spec.bucketColumnNames.size) {
val partitioning = HashPartitioning(bucketColumns, spec.numBuckets)
val sortColumns =
spec.sortColumnNames.map(x => toAttribute(x)).takeWhile(x => x.isDefined).map(_.get)
val sortOrder = if (sortColumns.nonEmpty) {
// In case of bucketing, its possible to have multiple files belonging to the
// same bucket in a given relation. Each of these files are locally sorted
// but those files combined together are not globally sorted. Given that,
// the RDD partition will not be sorted even if the relation has sort columns set
// Current solution is to check if all the buckets have a single file in it
val files = selectedPartitions.flatMap(partition => partition.files)
val bucketToFilesGrouping =
files.map(_.getPath.getName).groupBy(file => BucketingUtils.getBucketId(file))
val singleFilePartitions = bucketToFilesGrouping.forall(p => p._2.length <= 1)
if (singleFilePartitions) {
// TODO Currently Spark does not support writing columns sorting in descending order
// so using Ascending order. This can be fixed in future
sortColumns.map(attribute => SortOrder(attribute, Ascending))
} else {
Nil
}
} else {
Nil
}
(partitioning, sortOrder)
} else {
(UnknownPartitioning(0), Nil)
}
case _ =>
(UnknownPartitioning(0), Nil)
}
}
@transient
private val pushedDownFilters = dataFilters.flatMap(DataSourceStrategy.translateFilter)
logInfo(s"Pushed Filters: ${pushedDownFilters.mkString(",")}")
// These metadata values make scan plans uniquely identifiable for equality checking.
override val metadata: Map[String, String] = {
def seqToString(seq: Seq[Any]) = seq.mkString("[", ", ", "]")
val location = relation.location
val locationDesc =
location.getClass.getSimpleName + seqToString(location.rootPaths)
val metadata =
Map(
"Format" -> relation.fileFormat.toString,
"ReadSchema" -> requiredSchema.catalogString,
"Batched" -> supportsBatch.toString,
"PartitionFilters" -> seqToString(partitionFilters),
"PushedFilters" -> seqToString(pushedDownFilters),
"Location" -> locationDesc)
val withOptPartitionCount =
relation.partitionSchemaOption.map { _ =>
metadata + ("PartitionCount" -> selectedPartitions.size.toString)
} getOrElse {
metadata
}
withOptPartitionCount
}
private lazy val inputRDD: RDD[InternalRow] = {
val readFile: (PartitionedFile) => Iterator[InternalRow] =
relation.fileFormat.buildReaderWithPartitionValues(
sparkSession = relation.sparkSession,
dataSchema = relation.dataSchema,
partitionSchema = relation.partitionSchema,
requiredSchema = requiredSchema,
filters = pushedDownFilters,
options = relation.options,
hadoopConf = relation.sparkSession.sessionState.newHadoopConfWithOptions(relation.options))
relation.bucketSpec match {
case Some(bucketing) if relation.sparkSession.sessionState.conf.bucketingEnabled =>
createBucketedReadRDD(bucketing, readFile, selectedPartitions, relation)
case _ =>
createNonBucketedReadRDD(readFile, selectedPartitions, relation)
}
}
override def inputRDDs(): Seq[RDD[InternalRow]] = {
inputRDD :: Nil
}
override lazy val metrics =
Map("numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows"),
"numFiles" -> SQLMetrics.createMetric(sparkContext, "number of files"),
"metadataTime" -> SQLMetrics.createMetric(sparkContext, "metadata time (ms)"),
"scanTime" -> SQLMetrics.createTimingMetric(sparkContext, "scan time"))
protected override def doExecute(): RDD[InternalRow] = {
if (supportsBatch) {
// in the case of fallback, this batched scan should never fail because of:
// 1) only primitive types are supported
// 2) the number of columns should be smaller than spark.sql.codegen.maxFields
WholeStageCodegenExec(this).execute()
} else {
val unsafeRows = {
val scan = inputRDD
if (needsUnsafeRowConversion) {
scan.mapPartitionsWithIndexInternal { (index, iter) =>
val proj = UnsafeProjection.create(schema)
proj.initialize(index)
iter.map(proj)
}
} else {
scan
}
}
val numOutputRows = longMetric("numOutputRows")
unsafeRows.map { r =>
numOutputRows += 1
r
}
}
}
override val nodeNamePrefix: String = "File"
override protected def doProduce(ctx: CodegenContext): String = {
if (supportsBatch) {
return super.doProduce(ctx)
}
val numOutputRows = metricTerm(ctx, "numOutputRows")
// PhysicalRDD always just has one input
val input = ctx.freshName("input")
ctx.addMutableState("scala.collection.Iterator", input, s"$input = inputs[0];")
val exprRows = output.zipWithIndex.map{ case (a, i) =>
BoundReference(i, a.dataType, a.nullable)
}
val row = ctx.freshName("row")
ctx.INPUT_ROW = row
ctx.currentVars = null
val columnsRowInput = exprRows.map(_.genCode(ctx))
val inputRow = if (needsUnsafeRowConversion) null else row
s"""
|while ($input.hasNext()) {
| InternalRow $row = (InternalRow) $input.next();
| $numOutputRows.add(1);
| ${consume(ctx, columnsRowInput, inputRow).trim}
| if (shouldStop()) return;
|}
""".stripMargin
}
/**
* Create an RDD for bucketed reads.
* The non-bucketed variant of this function is [[createNonBucketedReadRDD]].
*
* The algorithm is pretty simple: each RDD partition being returned should include all the files
* with the same bucket id from all the given Hive partitions.
*
* @param bucketSpec the bucketing spec.
* @param readFile a function to read each (part of a) file.
* @param selectedPartitions Hive-style partition that are part of the read.
* @param fsRelation [[HadoopFsRelation]] associated with the read.
*/
private def createBucketedReadRDD(
bucketSpec: BucketSpec,
readFile: (PartitionedFile) => Iterator[InternalRow],
selectedPartitions: Seq[PartitionDirectory],
fsRelation: HadoopFsRelation): RDD[InternalRow] = {
logInfo(s"Planning with ${bucketSpec.numBuckets} buckets")
val bucketed =
selectedPartitions.flatMap { p =>
p.files.map { f =>
val hosts = getBlockHosts(getBlockLocations(f), 0, f.getLen)
PartitionedFile(p.values, f.getPath.toUri.toString, 0, f.getLen, hosts)
}
}.groupBy { f =>
BucketingUtils
.getBucketId(new Path(f.filePath).getName)
.getOrElse(sys.error(s"Invalid bucket file ${f.filePath}"))
}
val filePartitions = Seq.tabulate(bucketSpec.numBuckets) { bucketId =>
FilePartition(bucketId, bucketed.getOrElse(bucketId, Nil))
}
new FileScanRDD(fsRelation.sparkSession, readFile, filePartitions)
}
/**
* Create an RDD for non-bucketed reads.
* The bucketed variant of this function is [[createBucketedReadRDD]].
*
* @param readFile a function to read each (part of a) file.
* @param selectedPartitions Hive-style partition that are part of the read.
* @param fsRelation [[HadoopFsRelation]] associated with the read.
*/
private def createNonBucketedReadRDD(
readFile: (PartitionedFile) => Iterator[InternalRow],
selectedPartitions: Seq[PartitionDirectory],
fsRelation: HadoopFsRelation): RDD[InternalRow] = {
val defaultMaxSplitBytes =
fsRelation.sparkSession.sessionState.conf.filesMaxPartitionBytes
val openCostInBytes = fsRelation.sparkSession.sessionState.conf.filesOpenCostInBytes
val defaultParallelism = fsRelation.sparkSession.sparkContext.defaultParallelism
val totalBytes = selectedPartitions.flatMap(_.files.map(_.getLen + openCostInBytes)).sum
val bytesPerCore = totalBytes / defaultParallelism
val maxSplitBytes = Math.min(defaultMaxSplitBytes, Math.max(openCostInBytes, bytesPerCore))
logInfo(s"Planning scan with bin packing, max size: $maxSplitBytes bytes, " +
s"open cost is considered as scanning $openCostInBytes bytes.")
val splitFiles = selectedPartitions.flatMap { partition =>
partition.files.flatMap { file =>
val blockLocations = getBlockLocations(file)
if (fsRelation.fileFormat.isSplitable(
fsRelation.sparkSession, fsRelation.options, file.getPath)) {
(0L until file.getLen by maxSplitBytes).map { offset =>
val remaining = file.getLen - offset
val size = if (remaining > maxSplitBytes) maxSplitBytes else remaining
val hosts = getBlockHosts(blockLocations, offset, size)
PartitionedFile(
partition.values, file.getPath.toUri.toString, offset, size, hosts)
}
} else {
val hosts = getBlockHosts(blockLocations, 0, file.getLen)
Seq(PartitionedFile(
partition.values, file.getPath.toUri.toString, 0, file.getLen, hosts))
}
}
}.toArray.sortBy(_.length)(implicitly[Ordering[Long]].reverse)
val partitions = new ArrayBuffer[FilePartition]
val currentFiles = new ArrayBuffer[PartitionedFile]
var currentSize = 0L
/** Close the current partition and move to the next. */
def closePartition(): Unit = {
if (currentFiles.nonEmpty) {
val newPartition =
FilePartition(
partitions.size,
currentFiles.toArray.toSeq) // Copy to a new Array.
partitions += newPartition
}
currentFiles.clear()
currentSize = 0
}
// Assign files to partitions using "First Fit Decreasing" (FFD)
splitFiles.foreach { file =>
if (currentSize + file.length > maxSplitBytes) {
closePartition()
}
// Add the given file to the current partition.
currentSize += file.length + openCostInBytes
currentFiles += file
}
closePartition()
new FileScanRDD(fsRelation.sparkSession, readFile, partitions)
}
private def getBlockLocations(file: FileStatus): Array[BlockLocation] = file match {
case f: LocatedFileStatus => f.getBlockLocations
case f => Array.empty[BlockLocation]
}
// Given locations of all blocks of a single file, `blockLocations`, and an `(offset, length)`
// pair that represents a segment of the same file, find out the block that contains the largest
// fraction the segment, and returns location hosts of that block. If no such block can be found,
// returns an empty array.
private def getBlockHosts(
blockLocations: Array[BlockLocation], offset: Long, length: Long): Array[String] = {
val candidates = blockLocations.map {
// The fragment starts from a position within this block
case b if b.getOffset <= offset && offset < b.getOffset + b.getLength =>
b.getHosts -> (b.getOffset + b.getLength - offset).min(length)
// The fragment ends at a position within this block
case b if offset <= b.getOffset && offset + length < b.getLength =>
b.getHosts -> (offset + length - b.getOffset).min(length)
// The fragment fully contains this block
case b if offset <= b.getOffset && b.getOffset + b.getLength <= offset + length =>
b.getHosts -> b.getLength
// The fragment doesn't intersect with this block
case b =>
b.getHosts -> 0L
}.filter { case (hosts, size) =>
size > 0L
}
if (candidates.isEmpty) {
Array.empty[String]
} else {
val (hosts, _) = candidates.maxBy { case (_, size) => size }
hosts
}
}
override lazy val canonicalized: FileSourceScanExec = {
FileSourceScanExec(
relation,
output.map(QueryPlan.normalizeExprId(_, output)),
requiredSchema,
QueryPlan.normalizePredicates(partitionFilters, output),
QueryPlan.normalizePredicates(dataFilters, output),
None)
}
}
