[SPARK-14116][SQL] Implements buildReader() for ORC data source
## What changes were proposed in this pull request? This PR implements `FileFormat.buildReader()` for our ORC data source. It also fixed several minor styling issues related to `HadoopFsRelation` planning code path. Note that `OrcNewInputFormat` doesn't rely on `OrcNewSplit` for creating `OrcRecordReader`s, plain `FileSplit` is just fine. That's why we can simply create the record reader with the help of `OrcNewInputFormat` and `FileSplit`. ## How was this patch tested? Existing test cases should do the work Author: Cheng Lian <lian@databricks.com> Closes #11936 from liancheng/spark-14116-build-reader-for-orc.
This commit is contained in:
Cheng Lian
Yin Huai
parent
8989d3a396
commit
b547de8a60
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@ -208,9 +208,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
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val bucketedRDD = new UnionRDD(t.sqlContext.sparkContext,
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(0 until spec.numBuckets).map { bucketId =>
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bucketedDataMap.get(bucketId).getOrElse {
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t.sqlContext.emptyResult: RDD[InternalRow]
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}
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bucketedDataMap.getOrElse(bucketId, t.sqlContext.emptyResult: RDD[InternalRow])
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})
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bucketedRDD
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}
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@ -387,7 +385,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
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result.setColumn(resultIdx, input.column(inputIdx))
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inputIdx += 1
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} else {
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require(partitionColumnSchema.fields.filter(_.name.equals(attr.name)).length == 1)
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require(partitionColumnSchema.fields.count(_.name == attr.name) == 1)
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var partitionIdx = 0
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partitionColumnSchema.fields.foreach { f => {
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if (f.name.equals(attr.name)) {
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@ -32,7 +32,7 @@ case class PartitionedFile(
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filePath: String,
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start: Long,
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length: Long) {
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override def toString(): String = {
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override def toString: String = {
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s"path: $filePath, range: $start-${start + length}, partition values: $partitionValues"
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}
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}
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@ -44,7 +44,7 @@ case class PartitionedFile(
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*
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* TODO: This currently does not take locality information about the files into account.
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*/
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case class FilePartition(val index: Int, files: Seq[PartitionedFile]) extends Partition
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case class FilePartition(index: Int, files: Seq[PartitionedFile]) extends Partition
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class FileScanRDD(
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@transient val sqlContext: SQLContext,
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@ -29,7 +29,6 @@ import org.apache.spark.sql.catalyst.planning.PhysicalOperation
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import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
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import org.apache.spark.sql.execution.{DataSourceScan, SparkPlan}
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import org.apache.spark.sql.sources._
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import org.apache.spark.sql.types._
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/**
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* A strategy for planning scans over collections of files that might be partitioned or bucketed
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@ -56,9 +55,10 @@ import org.apache.spark.sql.types._
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*/
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private[sql] object FileSourceStrategy extends Strategy with Logging {
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def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
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case PhysicalOperation(projects, filters, l@LogicalRelation(files: HadoopFsRelation, _, _))
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case PhysicalOperation(projects, filters, l @ LogicalRelation(files: HadoopFsRelation, _, _))
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if (files.fileFormat.toString == "TestFileFormat" ||
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files.fileFormat.isInstanceOf[parquet.DefaultSource]) &&
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files.fileFormat.isInstanceOf[parquet.DefaultSource] ||
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files.fileFormat.toString == "ORC") &&
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files.sqlContext.conf.parquetFileScan =>
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// Filters on this relation fall into four categories based on where we can use them to avoid
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// reading unneeded data:
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@ -81,10 +81,10 @@ private[sql] object FileSourceStrategy extends Strategy with Logging {
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val bucketColumns =
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AttributeSet(
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files.bucketSpec
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.map(_.bucketColumnNames)
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.getOrElse(Nil)
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.map(l.resolveQuoted(_, files.sqlContext.conf.resolver)
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.getOrElse(sys.error(""))))
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.map(_.bucketColumnNames)
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.getOrElse(Nil)
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.map(l.resolveQuoted(_, files.sqlContext.conf.resolver)
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.getOrElse(sys.error(""))))
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// Partition keys are not available in the statistics of the files.
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val dataFilters = filters.filter(_.references.intersect(partitionSet).isEmpty)
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@ -101,8 +101,8 @@ private[sql] object FileSourceStrategy extends Strategy with Logging {
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val readDataColumns =
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dataColumns
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.filter(requiredAttributes.contains)
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.filterNot(partitionColumns.contains)
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.filter(requiredAttributes.contains)
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.filterNot(partitionColumns.contains)
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val prunedDataSchema = readDataColumns.toStructType
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logInfo(s"Pruned Data Schema: ${prunedDataSchema.simpleString(5)}")
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@ -120,13 +120,12 @@ private[sql] object FileSourceStrategy extends Strategy with Logging {
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case Some(bucketing) if files.sqlContext.conf.bucketingEnabled =>
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logInfo(s"Planning with ${bucketing.numBuckets} buckets")
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val bucketed =
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selectedPartitions
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.flatMap { p =>
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p.files.map(f => PartitionedFile(p.values, f.getPath.toUri.toString, 0, f.getLen))
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}.groupBy { f =>
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selectedPartitions.flatMap { p =>
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p.files.map(f => PartitionedFile(p.values, f.getPath.toUri.toString, 0, f.getLen))
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}.groupBy { f =>
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BucketingUtils
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.getBucketId(new Path(f.filePath).getName)
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.getOrElse(sys.error(s"Invalid bucket file ${f.filePath}"))
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.getBucketId(new Path(f.filePath).getName)
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.getOrElse(sys.error(s"Invalid bucket file ${f.filePath}"))
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}
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(0 until bucketing.numBuckets).map { bucketId =>
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@ -321,11 +321,11 @@ private[sql] class DefaultSource
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// Try to push down filters when filter push-down is enabled.
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val pushed = if (sqlContext.getConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED.key).toBoolean) {
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filters
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// Collects all converted Parquet filter predicates. Notice that not all predicates can be
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// converted (`ParquetFilters.createFilter` returns an `Option`). That's why a `flatMap`
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// is used here.
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.flatMap(ParquetFilters.createFilter(dataSchema, _))
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.reduceOption(FilterApi.and)
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// Collects all converted Parquet filter predicates. Notice that not all predicates can be
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// converted (`ParquetFilters.createFilter` returns an `Option`). That's why a `flatMap`
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// is used here.
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.flatMap(ParquetFilters.createFilter(dataSchema, _))
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.reduceOption(FilterApi.and)
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} else {
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None
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}
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@ -17,7 +17,6 @@
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package org.apache.spark.sql.execution.datasources.text
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import com.google.common.base.Objects
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import org.apache.hadoop.fs.{FileStatus, Path}
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import org.apache.hadoop.io.{LongWritable, NullWritable, Text}
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import org.apache.hadoop.mapred.{JobConf, TextInputFormat}
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@ -24,7 +24,6 @@ import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector
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import org.apache.spark.deploy.SparkHadoopUtil
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import org.apache.spark.internal.Logging
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import org.apache.spark.sql.AnalysisException
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import org.apache.spark.sql.hive.HiveMetastoreTypes
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import org.apache.spark.sql.types.StructType
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@ -92,7 +91,6 @@ private[orc] object OrcFileOperator extends Logging {
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// TODO: Check if the paths coming in are already qualified and simplify.
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val origPath = new Path(pathStr)
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val fs = origPath.getFileSystem(conf)
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val path = origPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
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val paths = SparkHadoopUtil.get.listLeafStatuses(fs, origPath)
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.filterNot(_.isDirectory)
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.map(_.getPath)
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@ -17,6 +17,7 @@
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package org.apache.spark.sql.hive.orc
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import java.net.URI
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import java.util.Properties
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import org.apache.hadoop.conf.Configuration
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@ -24,12 +25,13 @@ import org.apache.hadoop.fs.{FileStatus, Path}
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import org.apache.hadoop.hive.conf.HiveConf.ConfVars
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import org.apache.hadoop.hive.ql.io.orc._
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import org.apache.hadoop.hive.ql.io.orc.OrcFile.OrcTableProperties
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import org.apache.hadoop.hive.serde2.objectinspector.SettableStructObjectInspector
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import org.apache.hadoop.hive.serde2.objectinspector.{SettableStructObjectInspector, StructObjectInspector}
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import org.apache.hadoop.hive.serde2.typeinfo.{StructTypeInfo, TypeInfoUtils}
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import org.apache.hadoop.io.{NullWritable, Writable}
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import org.apache.hadoop.mapred.{InputFormat => MapRedInputFormat, JobConf, OutputFormat => MapRedOutputFormat, RecordWriter, Reporter}
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import org.apache.hadoop.mapreduce.{Job, TaskAttemptContext}
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import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
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import org.apache.hadoop.mapreduce._
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import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat, FileSplit}
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import org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
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import org.apache.spark.broadcast.Broadcast
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import org.apache.spark.internal.Logging
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@ -37,6 +39,7 @@ import org.apache.spark.rdd.{HadoopRDD, RDD}
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import org.apache.spark.sql.{Row, SQLContext}
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import org.apache.spark.sql.catalyst.InternalRow
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import org.apache.spark.sql.catalyst.expressions._
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import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection
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import org.apache.spark.sql.execution.datasources._
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import org.apache.spark.sql.hive.{HiveInspectors, HiveMetastoreTypes, HiveShim}
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import org.apache.spark.sql.sources.{Filter, _}
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@ -44,7 +47,8 @@ import org.apache.spark.sql.types.StructType
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import org.apache.spark.util.SerializableConfiguration
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import org.apache.spark.util.collection.BitSet
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private[sql] class DefaultSource extends FileFormat with DataSourceRegister {
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private[sql] class DefaultSource
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extends FileFormat with DataSourceRegister with Serializable {
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override def shortName(): String = "orc"
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@ -55,7 +59,9 @@ private[sql] class DefaultSource extends FileFormat with DataSourceRegister {
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options: Map[String, String],
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files: Seq[FileStatus]): Option[StructType] = {
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OrcFileOperator.readSchema(
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files.map(_.getPath.toUri.toString), Some(sqlContext.sparkContext.hadoopConfiguration))
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files.map(_.getPath.toUri.toString),
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Some(sqlContext.sparkContext.hadoopConfiguration)
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)
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}
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override def prepareWrite(
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@ -80,8 +86,8 @@ private[sql] class DefaultSource extends FileFormat with DataSourceRegister {
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job.getConfiguration.set(
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OrcTableProperties.COMPRESSION.getPropName,
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OrcRelation
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.shortOrcCompressionCodecNames
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.getOrElse(codecName, CompressionKind.NONE).name())
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.shortOrcCompressionCodecNames
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.getOrElse(codecName, CompressionKind.NONE).name())
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}
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job.getConfiguration match {
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@ -117,6 +123,68 @@ private[sql] class DefaultSource extends FileFormat with DataSourceRegister {
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val output = StructType(requiredColumns.map(dataSchema(_))).toAttributes
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OrcTableScan(sqlContext, output, filters, inputFiles).execute()
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}
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override def buildReader(
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sqlContext: SQLContext,
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partitionSchema: StructType,
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dataSchema: StructType,
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filters: Seq[Filter],
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options: Map[String, String]): (PartitionedFile) => Iterator[InternalRow] = {
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val orcConf = new Configuration(sqlContext.sparkContext.hadoopConfiguration)
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if (sqlContext.conf.orcFilterPushDown) {
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// Sets pushed predicates
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OrcFilters.createFilter(filters.toArray).foreach { f =>
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orcConf.set(OrcTableScan.SARG_PUSHDOWN, f.toKryo)
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orcConf.setBoolean(ConfVars.HIVEOPTINDEXFILTER.varname, true)
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}
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}
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val broadcastedConf = sqlContext.sparkContext.broadcast(new SerializableConfiguration(orcConf))
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(file: PartitionedFile) => {
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val conf = broadcastedConf.value.value
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// SPARK-8501: Empty ORC files always have an empty schema stored in their footer. In this
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// case, `OrcFileOperator.readSchema` returns `None`, and we can simply return an empty
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// iterator.
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val maybePhysicalSchema = OrcFileOperator.readSchema(Seq(file.filePath), Some(conf))
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if (maybePhysicalSchema.isEmpty) {
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Iterator.empty
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} else {
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val physicalSchema = maybePhysicalSchema.get
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OrcRelation.setRequiredColumns(conf, physicalSchema, dataSchema)
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val orcRecordReader = {
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val job = Job.getInstance(conf)
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FileInputFormat.setInputPaths(job, file.filePath)
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val inputFormat = new OrcNewInputFormat
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val fileSplit = new FileSplit(
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new Path(new URI(file.filePath)), file.start, file.length, Array.empty
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)
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val attemptId = new TaskAttemptID(new TaskID(new JobID(), TaskType.MAP, 0), 0)
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val hadoopAttemptContext = new TaskAttemptContextImpl(conf, attemptId)
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inputFormat.createRecordReader(fileSplit, hadoopAttemptContext)
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}
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// Unwraps `OrcStruct`s to `UnsafeRow`s
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val unsafeRowIterator = OrcRelation.unwrapOrcStructs(
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file.filePath, conf, dataSchema, new RecordReaderIterator[OrcStruct](orcRecordReader)
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)
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// Appends partition values
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val fullOutput = dataSchema.toAttributes ++ partitionSchema.toAttributes
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val joinedRow = new JoinedRow()
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val appendPartitionColumns = GenerateUnsafeProjection.generate(fullOutput, fullOutput)
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unsafeRowIterator.map { dataRow =>
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appendPartitionColumns(joinedRow(dataRow, file.partitionValues))
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}
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}
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}
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}
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}
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private[orc] class OrcOutputWriter(
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@ -225,55 +293,6 @@ private[orc] case class OrcTableScan(
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extends Logging
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with HiveInspectors {
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private def addColumnIds(
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dataSchema: StructType,
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output: Seq[Attribute],
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conf: Configuration): Unit = {
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val ids = output.map(a => dataSchema.fieldIndex(a.name): Integer)
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val (sortedIds, sortedNames) = ids.zip(attributes.map(_.name)).sorted.unzip
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HiveShim.appendReadColumns(conf, sortedIds, sortedNames)
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}
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// Transform all given raw `Writable`s into `InternalRow`s.
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private def fillObject(
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path: String,
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conf: Configuration,
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iterator: Iterator[Writable],
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nonPartitionKeyAttrs: Seq[Attribute]): Iterator[InternalRow] = {
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val deserializer = new OrcSerde
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val maybeStructOI = OrcFileOperator.getObjectInspector(path, Some(conf))
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val mutableRow = new SpecificMutableRow(attributes.map(_.dataType))
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val unsafeProjection = UnsafeProjection.create(StructType.fromAttributes(nonPartitionKeyAttrs))
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// SPARK-8501: ORC writes an empty schema ("struct<>") to an ORC file if the file contains zero
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// rows, and thus couldn't give a proper ObjectInspector. In this case we just return an empty
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// partition since we know that this file is empty.
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maybeStructOI.map { soi =>
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val (fieldRefs, fieldOrdinals) = nonPartitionKeyAttrs.zipWithIndex.map {
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case (attr, ordinal) =>
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soi.getStructFieldRef(attr.name) -> ordinal
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}.unzip
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val unwrappers = fieldRefs.map(unwrapperFor)
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// Map each tuple to a row object
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iterator.map { value =>
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val raw = deserializer.deserialize(value)
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var i = 0
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while (i < fieldRefs.length) {
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val fieldValue = soi.getStructFieldData(raw, fieldRefs(i))
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if (fieldValue == null) {
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mutableRow.setNullAt(fieldOrdinals(i))
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} else {
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unwrappers(i)(fieldValue, mutableRow, fieldOrdinals(i))
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}
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i += 1
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}
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unsafeProjection(mutableRow)
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}
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}.getOrElse {
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Iterator.empty
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}
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}
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def execute(): RDD[InternalRow] = {
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val job = Job.getInstance(sqlContext.sparkContext.hadoopConfiguration)
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val conf = job.getConfiguration
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@ -291,10 +310,10 @@ private[orc] case class OrcTableScan(
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val orcFormat = new DefaultSource
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val dataSchema =
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orcFormat
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.inferSchema(sqlContext, Map.empty, inputPaths)
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.getOrElse(sys.error("Failed to read schema from target ORC files."))
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.inferSchema(sqlContext, Map.empty, inputPaths)
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.getOrElse(sys.error("Failed to read schema from target ORC files."))
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// Sets requested columns
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addColumnIds(dataSchema, attributes, conf)
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OrcRelation.setRequiredColumns(conf, dataSchema, StructType.fromAttributes(attributes))
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if (inputPaths.isEmpty) {
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// the input path probably be pruned, return an empty RDD.
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@ -317,7 +336,12 @@ private[orc] case class OrcTableScan(
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rdd.mapPartitionsWithInputSplit { case (split: OrcSplit, iterator) =>
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val writableIterator = iterator.map(_._2)
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fillObject(split.getPath.toString, wrappedConf.value, writableIterator, attributes)
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OrcRelation.unwrapOrcStructs(
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split.getPath.toString,
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wrappedConf.value,
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StructType.fromAttributes(attributes),
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writableIterator
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)
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}
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}
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}
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@ -327,7 +351,7 @@ private[orc] object OrcTableScan {
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private[orc] val SARG_PUSHDOWN = "sarg.pushdown"
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}
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private[orc] object OrcRelation {
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private[orc] object OrcRelation extends HiveInspectors {
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// The ORC compression short names
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val shortOrcCompressionCodecNames = Map(
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"none" -> CompressionKind.NONE,
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@ -343,5 +367,47 @@ private[orc] object OrcRelation {
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CompressionKind.ZLIB.name -> ".zlib",
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CompressionKind.LZO.name -> ".lzo"
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)
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}
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def unwrapOrcStructs(
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filePath: String,
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conf: Configuration,
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dataSchema: StructType,
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iterator: Iterator[Writable]): Iterator[InternalRow] = {
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val deserializer = new OrcSerde
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val maybeStructOI = OrcFileOperator.getObjectInspector(filePath, Some(conf))
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val mutableRow = new SpecificMutableRow(dataSchema.map(_.dataType))
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val unsafeProjection = UnsafeProjection.create(dataSchema)
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|
||||
def unwrap(oi: StructObjectInspector): Iterator[InternalRow] = {
|
||||
val (fieldRefs, fieldOrdinals) = dataSchema.zipWithIndex.map {
|
||||
case (field, ordinal) => oi.getStructFieldRef(field.name) -> ordinal
|
||||
}.unzip
|
||||
|
||||
val unwrappers = fieldRefs.map(unwrapperFor)
|
||||
|
||||
iterator.map { value =>
|
||||
val raw = deserializer.deserialize(value)
|
||||
var i = 0
|
||||
while (i < fieldRefs.length) {
|
||||
val fieldValue = oi.getStructFieldData(raw, fieldRefs(i))
|
||||
if (fieldValue == null) {
|
||||
mutableRow.setNullAt(fieldOrdinals(i))
|
||||
} else {
|
||||
unwrappers(i)(fieldValue, mutableRow, fieldOrdinals(i))
|
||||
}
|
||||
i += 1
|
||||
}
|
||||
unsafeProjection(mutableRow)
|
||||
}
|
||||
}
|
||||
|
||||
maybeStructOI.map(unwrap).getOrElse(Iterator.empty)
|
||||
}
|
||||
|
||||
def setRequiredColumns(
|
||||
conf: Configuration, physicalSchema: StructType, requestedSchema: StructType): Unit = {
|
||||
val ids = requestedSchema.map(a => physicalSchema.fieldIndex(a.name): Integer)
|
||||
val (sortedIDs, sortedNames) = ids.zip(requestedSchema.fieldNames).sorted.unzip
|
||||
HiveShim.appendReadColumns(conf, sortedIDs, sortedNames)
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in a new issue