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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.hive
import java.util.Properties
import scala.collection.JavaConverters._
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.{Path, PathFilter}
import org.apache.hadoop.hive.metastore.api.hive_metastoreConstants._
import org.apache.hadoop.hive.ql.exec.Utilities
import org.apache.hadoop.hive.ql.metadata.{Partition => HivePartition, Table => HiveTable}
import org.apache.hadoop.hive.ql.plan.TableDesc
import org.apache.hadoop.hive.serde2.Deserializer
import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspectorConverters, StructObjectInspector}
import org.apache.hadoop.hive.serde2.objectinspector.primitive._
import org.apache.hadoop.io.Writable
import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf}
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.deploy.SparkHadoopUtil
import org.apache.spark.internal.Logging
import org.apache.spark.rdd.{EmptyRDD, HadoopRDD, RDD, UnionRDD}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.analysis.CastSupport
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.util.DateTimeUtils
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.unsafe.types.UTF8String
import org.apache.spark.util.{SerializableConfiguration, Utils}
/**
* A trait for subclasses that handle table scans.
*/
private[hive] sealed trait TableReader {
def makeRDDForTable(hiveTable: HiveTable): RDD[InternalRow]
def makeRDDForPartitionedTable(partitions: Seq[HivePartition]): RDD[InternalRow]
}
/**
* Helper class for scanning tables stored in Hadoop - e.g., to read Hive tables that reside in the
* data warehouse directory.
*/
private[hive]
class HadoopTableReader(
@transient private val attributes: Seq[Attribute],
@transient private val partitionKeys: Seq[Attribute],
@transient private val tableDesc: TableDesc,
@transient private val sparkSession: SparkSession,
hadoopConf: Configuration)
extends TableReader with CastSupport with Logging {
// Hadoop honors "mapreduce.job.maps" as hint,
// but will ignore when mapreduce.jobtracker.address is "local".
// https://hadoop.apache.org/docs/r2.6.5/hadoop-mapreduce-client/hadoop-mapreduce-client-core/
// mapred-default.xml
//
// In order keep consistency with Hive, we will let it be 0 in local mode also.
private val _minSplitsPerRDD = if (sparkSession.sparkContext.isLocal) {
0 // will splitted based on block by default.
} else {
math.max(hadoopConf.getInt("mapreduce.job.maps", 1),
sparkSession.sparkContext.defaultMinPartitions)
}
SparkHadoopUtil.get.appendS3AndSparkHadoopConfigurations(
sparkSession.sparkContext.conf, hadoopConf)
private val _broadcastedHadoopConf =
sparkSession.sparkContext.broadcast(new SerializableConfiguration(hadoopConf))
override def conf: SQLConf = sparkSession.sessionState.conf
override def makeRDDForTable(hiveTable: HiveTable): RDD[InternalRow] =
makeRDDForTable(
hiveTable,
Utils.classForName(tableDesc.getSerdeClassName).asInstanceOf[Class[Deserializer]],
filterOpt = None)
/**
* Creates a Hadoop RDD to read data from the target table's data directory. Returns a transformed
* RDD that contains deserialized rows.
*
* @param hiveTable Hive metadata for the table being scanned.
* @param deserializerClass Class of the SerDe used to deserialize Writables read from Hadoop.
* @param filterOpt If defined, then the filter is used to reject files contained in the data
* directory being read. If None, then all files are accepted.
*/
def makeRDDForTable(
hiveTable: HiveTable,
deserializerClass: Class[_ <: Deserializer],
filterOpt: Option[PathFilter]): RDD[InternalRow] = {
assert(!hiveTable.isPartitioned,
"makeRDDForTable() cannot be called on a partitioned table, since input formats may " +
"differ across partitions. Use makeRDDForPartitionedTable() instead.")
// Create local references to member variables, so that the entire `this` object won't be
// serialized in the closure below.
val localTableDesc = tableDesc
val broadcastedHadoopConf = _broadcastedHadoopConf
val tablePath = hiveTable.getPath
val inputPathStr = applyFilterIfNeeded(tablePath, filterOpt)
// logDebug("Table input: %s".format(tablePath))
val ifc = hiveTable.getInputFormatClass
.asInstanceOf[java.lang.Class[InputFormat[Writable, Writable]]]
val hadoopRDD = createHadoopRdd(localTableDesc, inputPathStr, ifc)
val attrsWithIndex = attributes.zipWithIndex
val mutableRow = new SpecificInternalRow(attributes.map(_.dataType))
val deserializedHadoopRDD = hadoopRDD.mapPartitions { iter =>
val hconf = broadcastedHadoopConf.value.value
val deserializer = deserializerClass.newInstance()
DeserializerLock.synchronized {
deserializer.initialize(hconf, localTableDesc.getProperties)
}
HadoopTableReader.fillObject(iter, deserializer, attrsWithIndex, mutableRow, deserializer)
}
deserializedHadoopRDD
}
override def makeRDDForPartitionedTable(partitions: Seq[HivePartition]): RDD[InternalRow] = {
val partitionToDeserializer = partitions.map(part =>
(part, part.getDeserializer.getClass.asInstanceOf[Class[Deserializer]])).toMap
makeRDDForPartitionedTable(partitionToDeserializer, filterOpt = None)
}
/**
* Create a HadoopRDD for every partition key specified in the query. Note that for on-disk Hive
* tables, a data directory is created for each partition corresponding to keys specified using
* 'PARTITION BY'.
*
* @param partitionToDeserializer Mapping from a Hive Partition metadata object to the SerDe
* class to use to deserialize input Writables from the corresponding partition.
* @param filterOpt If defined, then the filter is used to reject files contained in the data
* subdirectory of each partition being read. If None, then all files are accepted.
*/
def makeRDDForPartitionedTable(
partitionToDeserializer: Map[HivePartition, Class[_ <: Deserializer]],
filterOpt: Option[PathFilter]): RDD[InternalRow] = {
// SPARK-5068:get FileStatus and do the filtering locally when the path is not exists
def verifyPartitionPath(
partitionToDeserializer: Map[HivePartition, Class[_ <: Deserializer]]):
Map[HivePartition, Class[_ <: Deserializer]] = {
if (!sparkSession.sessionState.conf.verifyPartitionPath) {
partitionToDeserializer
} else {
val existPathSet = collection.mutable.Set[String]()
val pathPatternSet = collection.mutable.Set[String]()
partitionToDeserializer.filter {
case (partition, partDeserializer) =>
def updateExistPathSetByPathPattern(pathPatternStr: String) {
val pathPattern = new Path(pathPatternStr)
val fs = pathPattern.getFileSystem(hadoopConf)
val matches = fs.globStatus(pathPattern)
matches.foreach(fileStatus => existPathSet += fileStatus.getPath.toString)
}
// convert /demo/data/year/month/day to /demo/data/*/*/*/
def getPathPatternByPath(parNum: Int, tempPath: Path): String = {
var path = tempPath
for (i <- (1 to parNum)) path = path.getParent
val tails = (1 to parNum).map(_ => "*").mkString("/", "/", "/")
path.toString + tails
}
val partPath = partition.getDataLocation
val partNum = Utilities.getPartitionDesc(partition).getPartSpec.size()
val pathPatternStr = getPathPatternByPath(partNum, partPath)
if (!pathPatternSet.contains(pathPatternStr)) {
pathPatternSet += pathPatternStr
updateExistPathSetByPathPattern(pathPatternStr)
}
existPathSet.contains(partPath.toString)
}
}
}
val hivePartitionRDDs = verifyPartitionPath(partitionToDeserializer)
.map { case (partition, partDeserializer) =>
val partDesc = Utilities.getPartitionDesc(partition)
val partPath = partition.getDataLocation
val inputPathStr = applyFilterIfNeeded(partPath, filterOpt)
val ifc = partDesc.getInputFileFormatClass
.asInstanceOf[java.lang.Class[InputFormat[Writable, Writable]]]
// Get partition field info
val partSpec = partDesc.getPartSpec
val partProps = partDesc.getProperties
val partColsDelimited: String = partProps.getProperty(META_TABLE_PARTITION_COLUMNS)
// Partitioning columns are delimited by "/"
val partCols = partColsDelimited.trim().split("/").toSeq
// 'partValues[i]' contains the value for the partitioning column at 'partCols[i]'.
val partValues = if (partSpec == null) {
Array.fill(partCols.size)(new String)
} else {
partCols.map(col => new String(partSpec.get(col))).toArray
}
val broadcastedHiveConf = _broadcastedHadoopConf
val localDeserializer = partDeserializer
val mutableRow = new SpecificInternalRow(attributes.map(_.dataType))
// Splits all attributes into two groups, partition key attributes and those that are not.
// Attached indices indicate the position of each attribute in the output schema.
val (partitionKeyAttrs, nonPartitionKeyAttrs) =
attributes.zipWithIndex.partition { case (attr, _) =>
partitionKeys.contains(attr)
}
def fillPartitionKeys(rawPartValues: Array[String], row: InternalRow): Unit = {
partitionKeyAttrs.foreach { case (attr, ordinal) =>
val partOrdinal = partitionKeys.indexOf(attr)
row(ordinal) = cast(Literal(rawPartValues(partOrdinal)), attr.dataType).eval(null)
}
}
// Fill all partition keys to the given MutableRow object
fillPartitionKeys(partValues, mutableRow)
val tableProperties = tableDesc.getProperties
// Create local references so that the outer object isn't serialized.
val localTableDesc = tableDesc
createHadoopRdd(localTableDesc, inputPathStr, ifc).mapPartitions { iter =>
val hconf = broadcastedHiveConf.value.value
val deserializer = localDeserializer.newInstance()
// SPARK-13709: For SerDes like AvroSerDe, some essential information (e.g. Avro schema
// information) may be defined in table properties. Here we should merge table properties
// and partition properties before initializing the deserializer. Note that partition
// properties take a higher priority here. For example, a partition may have a different
// SerDe as the one defined in table properties.
val props = new Properties(tableProperties)
partProps.asScala.foreach {
case (key, value) => props.setProperty(key, value)
}
DeserializerLock.synchronized {
deserializer.initialize(hconf, props)
}
// get the table deserializer
val tableSerDe = localTableDesc.getDeserializerClass.newInstance()
DeserializerLock.synchronized {
tableSerDe.initialize(hconf, localTableDesc.getProperties)
}
// fill the non partition key attributes
HadoopTableReader.fillObject(iter, deserializer, nonPartitionKeyAttrs,
mutableRow, tableSerDe)
}
}.toSeq
// Even if we don't use any partitions, we still need an empty RDD
if (hivePartitionRDDs.size == 0) {
new EmptyRDD[InternalRow](sparkSession.sparkContext)
} else {
new UnionRDD(hivePartitionRDDs(0).context, hivePartitionRDDs)
}
}
/**
* If `filterOpt` is defined, then it will be used to filter files from `path`. These files are
* returned in a single, comma-separated string.
*/
private def applyFilterIfNeeded(path: Path, filterOpt: Option[PathFilter]): String = {
filterOpt match {
case Some(filter) =>
val fs = path.getFileSystem(hadoopConf)
val filteredFiles = fs.listStatus(path, filter).map(_.getPath.toString)
filteredFiles.mkString(",")
case None => path.toString
}
}
/**
* Creates a HadoopRDD based on the broadcasted HiveConf and other job properties that will be
* applied locally on each slave.
*/
private def createHadoopRdd(
tableDesc: TableDesc,
path: String,
inputFormatClass: Class[InputFormat[Writable, Writable]]): RDD[Writable] = {
val initializeJobConfFunc = HadoopTableReader.initializeLocalJobConfFunc(path, tableDesc) _
val rdd = new HadoopRDD(
sparkSession.sparkContext,
_broadcastedHadoopConf.asInstanceOf[Broadcast[SerializableConfiguration]],
Some(initializeJobConfFunc),
inputFormatClass,
classOf[Writable],
classOf[Writable],
_minSplitsPerRDD)
// Only take the value (skip the key) because Hive works only with values.
rdd.map(_._2)
}
}
private[hive] object HiveTableUtil {
// copied from PlanUtils.configureJobPropertiesForStorageHandler(tableDesc)
// that calls Hive.get() which tries to access metastore, but it's not valid in runtime
// it would be fixed in next version of hive but till then, we should use this instead
def configureJobPropertiesForStorageHandler(
tableDesc: TableDesc, conf: Configuration, input: Boolean) {
val property = tableDesc.getProperties.getProperty(META_TABLE_STORAGE)
val storageHandler =
org.apache.hadoop.hive.ql.metadata.HiveUtils.getStorageHandler(conf, property)
if (storageHandler != null) {
val jobProperties = new java.util.LinkedHashMap[String, String]
if (input) {
storageHandler.configureInputJobProperties(tableDesc, jobProperties)
} else {
storageHandler.configureOutputJobProperties(tableDesc, jobProperties)
}
if (!jobProperties.isEmpty) {
tableDesc.setJobProperties(jobProperties)
}
}
}
}
/**
* Object to synchronize on when calling org.apache.hadoop.hive.serde2.Deserializer#initialize.
*
* [SPARK-17398] org.apache.hive.hcatalog.data.JsonSerDe#initialize calls the non-thread-safe
* HCatRecordObjectInspectorFactory.getHCatRecordObjectInspector, the results of which are
* returned by JsonSerDe#getObjectInspector.
* To protect against this bug in Hive (HIVE-15773/HIVE-21752), we synchronize on this object
* when calling initialize on Deserializer instances that could be JsonSerDe instances.
*/
private[hive] object DeserializerLock
private[hive] object HadoopTableReader extends HiveInspectors with Logging {
/**
* Curried. After given an argument for 'path', the resulting JobConf => Unit closure is used to
* instantiate a HadoopRDD.
*/
def initializeLocalJobConfFunc(path: String, tableDesc: TableDesc)(jobConf: JobConf) {
FileInputFormat.setInputPaths(jobConf, Seq[Path](new Path(path)): _*)
if (tableDesc != null) {
HiveTableUtil.configureJobPropertiesForStorageHandler(tableDesc, jobConf, true)
Utilities.copyTableJobPropertiesToConf(tableDesc, jobConf)
}
val bufferSize = System.getProperty("spark.buffer.size", "65536")
jobConf.set("io.file.buffer.size", bufferSize)
}
/**
* Transform all given raw `Writable`s into `Row`s.
*
* @param iterator Iterator of all `Writable`s to be transformed
* @param rawDeser The `Deserializer` associated with the input `Writable`
* @param nonPartitionKeyAttrs Attributes that should be filled together with their corresponding
* positions in the output schema
* @param mutableRow A reusable `MutableRow` that should be filled
* @param tableDeser Table Deserializer
* @return An `Iterator[Row]` transformed from `iterator`
*/
def fillObject(
iterator: Iterator[Writable],
rawDeser: Deserializer,
nonPartitionKeyAttrs: Seq[(Attribute, Int)],
mutableRow: InternalRow,
tableDeser: Deserializer): Iterator[InternalRow] = {
val soi = if (rawDeser.getObjectInspector.equals(tableDeser.getObjectInspector)) {
rawDeser.getObjectInspector.asInstanceOf[StructObjectInspector]
} else {
ObjectInspectorConverters.getConvertedOI(
rawDeser.getObjectInspector,
tableDeser.getObjectInspector).asInstanceOf[StructObjectInspector]
}
logDebug(soi.toString)
val (fieldRefs, fieldOrdinals) = nonPartitionKeyAttrs.map { case (attr, ordinal) =>
soi.getStructFieldRef(attr.name) -> ordinal
}.toArray.unzip
/**
* Builds specific unwrappers ahead of time according to object inspector
* types to avoid pattern matching and branching costs per row.
*/
val unwrappers: Seq[(Any, InternalRow, Int) => Unit] = fieldRefs.map {
_.getFieldObjectInspector match {
case oi: BooleanObjectInspector =>
(value: Any, row: InternalRow, ordinal: Int) => row.setBoolean(ordinal, oi.get(value))
case oi: ByteObjectInspector =>
(value: Any, row: InternalRow, ordinal: Int) => row.setByte(ordinal, oi.get(value))
case oi: ShortObjectInspector =>
(value: Any, row: InternalRow, ordinal: Int) => row.setShort(ordinal, oi.get(value))
case oi: IntObjectInspector =>
(value: Any, row: InternalRow, ordinal: Int) => row.setInt(ordinal, oi.get(value))
case oi: LongObjectInspector =>
(value: Any, row: InternalRow, ordinal: Int) => row.setLong(ordinal, oi.get(value))
case oi: FloatObjectInspector =>
(value: Any, row: InternalRow, ordinal: Int) => row.setFloat(ordinal, oi.get(value))
case oi: DoubleObjectInspector =>
(value: Any, row: InternalRow, ordinal: Int) => row.setDouble(ordinal, oi.get(value))
case oi: HiveVarcharObjectInspector =>
(value: Any, row: InternalRow, ordinal: Int) =>
row.update(ordinal, UTF8String.fromString(oi.getPrimitiveJavaObject(value).getValue))
case oi: HiveCharObjectInspector =>
(value: Any, row: InternalRow, ordinal: Int) =>
row.update(ordinal, UTF8String.fromString(oi.getPrimitiveJavaObject(value).getValue))
case oi: HiveDecimalObjectInspector =>
(value: Any, row: InternalRow, ordinal: Int) =>
row.update(ordinal, HiveShim.toCatalystDecimal(oi, value))
case oi: TimestampObjectInspector =>
(value: Any, row: InternalRow, ordinal: Int) =>
row.setLong(ordinal, DateTimeUtils.fromJavaTimestamp(oi.getPrimitiveJavaObject(value)))
case oi: DateObjectInspector =>
(value: Any, row: InternalRow, ordinal: Int) =>
row.setInt(ordinal, DateTimeUtils.fromJavaDate(oi.getPrimitiveJavaObject(value)))
case oi: BinaryObjectInspector =>
(value: Any, row: InternalRow, ordinal: Int) =>
row.update(ordinal, oi.getPrimitiveJavaObject(value))
case oi =>
val unwrapper = unwrapperFor(oi)
(value: Any, row: InternalRow, ordinal: Int) => row(ordinal) = unwrapper(value)
}
}
val converter = ObjectInspectorConverters.getConverter(rawDeser.getObjectInspector, soi)
// Map each tuple to a row object
iterator.map { value =>
val raw = converter.convert(rawDeser.deserialize(value))
var i = 0
val length = fieldRefs.length
while (i < length) {
val fieldValue = soi.getStructFieldData(raw, fieldRefs(i))
if (fieldValue == null) {
mutableRow.setNullAt(fieldOrdinals(i))
} else {
unwrappers(i)(fieldValue, mutableRow, fieldOrdinals(i))
}
i += 1
}
mutableRow: InternalRow
}
}
}
