[SPARK-2523] [SQL] Hadoop table scan bug fixing

In HiveTableScan.scala, ObjectInspector was created for all of the partition based records, which probably causes ClassCastException if the object inspector is not identical among table & partitions.

This is the follow up with:
apache#1408
apache#1390

I've run a micro benchmark in my local with 15000000 records totally, and got the result as below:

With This Patch  |  Partition-Based Table  |  Non-Partition-Based Table
------------ | ------------- | -------------
No  |  1927 ms  |  1885 ms
Yes  | 1541 ms  |  1524 ms

It showed this patch will also improve the performance.

PS:  the benchmark code is also attached. (thanks liancheng )
```
package org.apache.spark.sql.hive

import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.sql._

object HiveTableScanPrepare extends App {
  case class Record(key: String, value: String)

  val sparkContext = new SparkContext(
    new SparkConf()
      .setMaster("local")
      .setAppName(getClass.getSimpleName.stripSuffix("$")))

  val hiveContext = new LocalHiveContext(sparkContext)

  val rdd = sparkContext.parallelize((1 to 3000000).map(i => Record(s"$i", s"val_$i")))

  import hiveContext._

  hql("SHOW TABLES")
  hql("DROP TABLE if exists part_scan_test")
  hql("DROP TABLE if exists scan_test")
  hql("DROP TABLE if exists records")
  rdd.registerAsTable("records")

  hql("""CREATE TABLE part_scan_test (key STRING, value STRING) PARTITIONED BY (part1 string, part2 STRING)
                 | ROW FORMAT SERDE
                 | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
                 | STORED AS RCFILE
               """.stripMargin)
  hql("""CREATE TABLE scan_test (key STRING, value STRING)
                 | ROW FORMAT SERDE
                 | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
                 | STORED AS RCFILE
               """.stripMargin)

  for (part1 <- 2000 until 2001) {
    for (part2 <- 1 to 5) {
      hql(s"""from records
                 | insert into table part_scan_test PARTITION (part1='$part1', part2='2010-01-$part2')
                 | select key, value
               """.stripMargin)
      hql(s"""from records
                 | insert into table scan_test select key, value
               """.stripMargin)
    }
  }
}

object HiveTableScanTest extends App {
  val sparkContext = new SparkContext(
    new SparkConf()
      .setMaster("local")
      .setAppName(getClass.getSimpleName.stripSuffix("$")))

  val hiveContext = new LocalHiveContext(sparkContext)

  import hiveContext._

  hql("SHOW TABLES")
  val part_scan_test = hql("select key, value from part_scan_test")
  val scan_test = hql("select key, value from scan_test")

  val r_part_scan_test = (0 to 5).map(i => benchmark(part_scan_test))
  val r_scan_test = (0 to 5).map(i => benchmark(scan_test))
  println("Scanning Partition-Based Table")
  r_part_scan_test.foreach(printResult)
  println("Scanning Non-Partition-Based Table")
  r_scan_test.foreach(printResult)

  def printResult(result: (Long, Long)) {
    println(s"Duration: ${result._1} ms Result: ${result._2}")
  }

  def benchmark(srdd: SchemaRDD) = {
    val begin = System.currentTimeMillis()
    val result = srdd.count()
    val end = System.currentTimeMillis()
    ((end - begin), result)
  }
}
```

Author: Cheng Hao <hao.cheng@intel.com>

Closes apache#1439 from chenghao-intel/hadoop_table_scan and squashes the following commits:

888968f [Cheng Hao] Fix issues in code style
27540ba [Cheng Hao] Fix the TableScan Bug while partition serde differs
40a24a7 [Cheng Hao] Add Unit Test

sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala

@@ -24,20 +24,25 @@ 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.StructObjectInspector
+
 import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf}
 
 import org.apache.spark.SerializableWritable
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.rdd.{EmptyRDD, HadoopRDD, RDD, UnionRDD}
 
+import org.apache.spark.sql.catalyst.expressions.{Attribute, Row, GenericMutableRow, Literal, Cast}
+import org.apache.spark.sql.catalyst.types.DataType
+
 /**
  * A trait for subclasses that handle table scans.
  */
 private[hive] sealed trait TableReader {
-  def makeRDDForTable(hiveTable: HiveTable): RDD[_]
+  def makeRDDForTable(hiveTable: HiveTable): RDD[Row]
 
-  def makeRDDForPartitionedTable(partitions: Seq[HivePartition]): RDD[_]
+  def makeRDDForPartitionedTable(partitions: Seq[HivePartition]): RDD[Row]
 }
 
 
@@ -46,7 +51,10 @@ private[hive] sealed trait TableReader {
  * data warehouse directory.
  */
 private[hive]
-class HadoopTableReader(@transient _tableDesc: TableDesc, @transient sc: HiveContext)
+class HadoopTableReader(
+    @transient attributes: Seq[Attribute],
+    @transient relation: MetastoreRelation,
+    @transient sc: HiveContext)
   extends TableReader {
 
   // Choose the minimum number of splits. If mapred.map.tasks is set, then use that unless
@@ -63,10 +71,10 @@ class HadoopTableReader(@transient _tableDesc: TableDesc, @transient sc: HiveCon
 
   def hiveConf = _broadcastedHiveConf.value.value
 
-  override def makeRDDForTable(hiveTable: HiveTable): RDD[_] =
+  override def makeRDDForTable(hiveTable: HiveTable): RDD[Row] =
     makeRDDForTable(
       hiveTable,
-      _tableDesc.getDeserializerClass.asInstanceOf[Class[Deserializer]],
+      relation.tableDesc.getDeserializerClass.asInstanceOf[Class[Deserializer]],
       filterOpt = None)
 
   /**
@@ -81,14 +89,14 @@ class HadoopTableReader(@transient _tableDesc: TableDesc, @transient sc: HiveCon
   def makeRDDForTable(
       hiveTable: HiveTable,
       deserializerClass: Class[_ <: Deserializer],
-      filterOpt: Option[PathFilter]): RDD[_] = {
+      filterOpt: Option[PathFilter]): RDD[Row] = {
 
     assert(!hiveTable.isPartitioned, """makeRDDForTable() cannot be called on a partitioned table,
       since input formats may differ across partitions. Use makeRDDForTablePartitions() instead.""")
 
     // Create local references to member variables, so that the entire `this` object won't be
     // serialized in the closure below.
-    val tableDesc = _tableDesc
+    val tableDesc = relation.tableDesc
     val broadcastedHiveConf = _broadcastedHiveConf
 
     val tablePath = hiveTable.getPath
@@ -99,23 +107,20 @@ class HadoopTableReader(@transient _tableDesc: TableDesc, @transient sc: HiveCon
       .asInstanceOf[java.lang.Class[InputFormat[Writable, Writable]]]
     val hadoopRDD = createHadoopRdd(tableDesc, inputPathStr, ifc)
 
+    val attrsWithIndex = attributes.zipWithIndex
+    val mutableRow = new GenericMutableRow(attrsWithIndex.length)
     val deserializedHadoopRDD = hadoopRDD.mapPartitions { iter =>
       val hconf = broadcastedHiveConf.value.value
       val deserializer = deserializerClass.newInstance()
       deserializer.initialize(hconf, tableDesc.getProperties)
 
-      // Deserialize each Writable to get the row value.
-      iter.map {
-        case v: Writable => deserializer.deserialize(v)
-        case value =>
-          sys.error(s"Unable to deserialize non-Writable: $value of ${value.getClass.getName}")
-      }
+      HadoopTableReader.fillObject(iter, deserializer, attrsWithIndex, mutableRow)
     }
 
     deserializedHadoopRDD
   }
 
-  override def makeRDDForPartitionedTable(partitions: Seq[HivePartition]): RDD[_] = {
+  override def makeRDDForPartitionedTable(partitions: Seq[HivePartition]): RDD[Row] = {
     val partitionToDeserializer = partitions.map(part =>
       (part, part.getDeserializer.getClass.asInstanceOf[Class[Deserializer]])).toMap
     makeRDDForPartitionedTable(partitionToDeserializer, filterOpt = None)
@@ -132,9 +137,9 @@ class HadoopTableReader(@transient _tableDesc: TableDesc, @transient sc: HiveCon
    *     subdirectory of each partition being read. If None, then all files are accepted.
    */
   def makeRDDForPartitionedTable(
-      partitionToDeserializer: Map[HivePartition, Class[_ <: Deserializer]],
-      filterOpt: Option[PathFilter]): RDD[_] = {
-
+      partitionToDeserializer: Map[HivePartition,
+      Class[_ <: Deserializer]],
+      filterOpt: Option[PathFilter]): RDD[Row] = {
     val hivePartitionRDDs = partitionToDeserializer.map { case (partition, partDeserializer) =>
       val partDesc = Utilities.getPartitionDesc(partition)
       val partPath = partition.getPartitionPath
@@ -156,33 +161,42 @@ class HadoopTableReader(@transient _tableDesc: TableDesc, @transient sc: HiveCon
       }
 
       // Create local references so that the outer object isn't serialized.
-      val tableDesc = _tableDesc
+      val tableDesc = relation.tableDesc
       val broadcastedHiveConf = _broadcastedHiveConf
       val localDeserializer = partDeserializer
+      val mutableRow = new GenericMutableRow(attributes.length)
+
+      // split the attributes (output schema) into 2 categories:
+      // (partition keys, ordinal), (normal attributes, ordinal), the ordinal mean the 
+      // index of the attribute in the output Row.
+      val (partitionKeys, attrs) = attributes.zipWithIndex.partition(attr => {
+        relation.partitionKeys.indexOf(attr._1) >= 0
+      })
+
+      def fillPartitionKeys(parts: Array[String], row: GenericMutableRow) = {
+        partitionKeys.foreach { case (attr, ordinal) =>
+          // get partition key ordinal for a given attribute
+          val partOridinal = relation.partitionKeys.indexOf(attr)
+          row(ordinal) = Cast(Literal(parts(partOridinal)), attr.dataType).eval(null)
+        }
+      }
+      // fill the partition key for the given MutableRow Object
+      fillPartitionKeys(partValues, mutableRow)
 
       val hivePartitionRDD = createHadoopRdd(tableDesc, inputPathStr, ifc)
       hivePartitionRDD.mapPartitions { iter =>
         val hconf = broadcastedHiveConf.value.value
-        val rowWithPartArr = new Array[Object](2)
-
-        // The update and deserializer initialization are intentionally
-        // kept out of the below iter.map loop to save performance.
-        rowWithPartArr.update(1, partValues)
         val deserializer = localDeserializer.newInstance()
         deserializer.initialize(hconf, partProps)
 
-        // Map each tuple to a row object
-        iter.map { value =>
-          val deserializedRow = deserializer.deserialize(value)
-          rowWithPartArr.update(0, deserializedRow)
-          rowWithPartArr.asInstanceOf[Object]
-        }
+        // fill the non partition key attributes 
+        HadoopTableReader.fillObject(iter, deserializer, attrs, mutableRow)
       }
     }.toSeq
 
     // Even if we don't use any partitions, we still need an empty RDD
     if (hivePartitionRDDs.size == 0) {
-      new EmptyRDD[Object](sc.sparkContext)
+      new EmptyRDD[Row](sc.sparkContext)
     } else {
       new UnionRDD(hivePartitionRDDs(0).context, hivePartitionRDDs)
     }
@@ -225,10 +239,9 @@ class HadoopTableReader(@transient _tableDesc: TableDesc, @transient sc: HiveCon
     // Only take the value (skip the key) because Hive works only with values.
     rdd.map(_._2)
   }
-
 }
 
-private[hive] object HadoopTableReader {
+private[hive] object HadoopTableReader extends HiveInspectors {
   /**
    * Curried. After given an argument for 'path', the resulting JobConf => Unit closure is used to
    * instantiate a HadoopRDD.
@@ -241,4 +254,40 @@ private[hive] object HadoopTableReader {
     val bufferSize = System.getProperty("spark.buffer.size", "65536")
     jobConf.set("io.file.buffer.size", bufferSize)
   }
+
+  /**
+   * Transform the raw data(Writable object) into the Row object for an iterable input
+   * @param iter Iterable input which represented as Writable object
+   * @param deserializer Deserializer associated with the input writable object
+   * @param attrs Represents the row attribute names and its zero-based position in the MutableRow
+   * @param row reusable MutableRow object
+   * 
+   * @return Iterable Row object that transformed from the given iterable input.
+   */
+  def fillObject(
+      iter: Iterator[Writable],
+      deserializer: Deserializer,
+      attrs: Seq[(Attribute, Int)],
+      row: GenericMutableRow): Iterator[Row] = {
+    val soi = deserializer.getObjectInspector().asInstanceOf[StructObjectInspector]
+    // get the field references according to the attributes(output of the reader) required
+    val fieldRefs = attrs.map { case (attr, idx) => (soi.getStructFieldRef(attr.name), idx) }
+
+    // Map each tuple to a row object
+    iter.map { value =>
+      val raw = deserializer.deserialize(value)
+      var idx = 0;
+      while (idx < fieldRefs.length) {
+        val fieldRef = fieldRefs(idx)._1
+        val fieldIdx = fieldRefs(idx)._2
+        val fieldValue = soi.getStructFieldData(raw, fieldRef)
+
+        row(fieldIdx) = unwrapData(fieldValue, fieldRef.getFieldObjectInspector())
+
+        idx += 1
+      }
+
+      row: Row
+    }
+  }
 }

sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala

@@ -34,7 +34,6 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.types.{BooleanType, DataType}
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.hive._
-import org.apache.spark.util.MutablePair
 
 /**
  * :: DeveloperApi ::
@@ -50,8 +49,7 @@ case class HiveTableScan(
     relation: MetastoreRelation,
     partitionPruningPred: Option[Expression])(
     @transient val context: HiveContext)
-  extends LeafNode
-  with HiveInspectors {
+  extends LeafNode {
 
   require(partitionPruningPred.isEmpty || relation.hiveQlTable.isPartitioned,
     "Partition pruning predicates only supported for partitioned tables.")
@@ -67,42 +65,7 @@ case class HiveTableScan(
   }
 
   @transient
-  private[this] val hadoopReader = new HadoopTableReader(relation.tableDesc, context)
-
-  /**
-   * The hive object inspector for this table, which can be used to extract values from the
-   * serialized row representation.
-   */
-  @transient
-  private[this] lazy val objectInspector =
-    relation.tableDesc.getDeserializer.getObjectInspector.asInstanceOf[StructObjectInspector]
-
-  /**
-   * Functions that extract the requested attributes from the hive output.  Partitioned values are
-   * casted from string to its declared data type.
-   */
-  @transient
-  protected lazy val attributeFunctions: Seq[(Any, Array[String]) => Any] = {
-    attributes.map { a =>
-      val ordinal = relation.partitionKeys.indexOf(a)
-      if (ordinal >= 0) {
-        val dataType = relation.partitionKeys(ordinal).dataType
-        (_: Any, partitionKeys: Array[String]) => {
-          castFromString(partitionKeys(ordinal), dataType)
-        }
-      } else {
-        val ref = objectInspector.getAllStructFieldRefs
-          .find(_.getFieldName == a.name)
-          .getOrElse(sys.error(s"Can't find attribute $a"))
-        val fieldObjectInspector = ref.getFieldObjectInspector
-
-        (row: Any, _: Array[String]) => {
-          val data = objectInspector.getStructFieldData(row, ref)
-          unwrapData(data, fieldObjectInspector)
-        }
-      }
-    }
-  }
+  private[this] val hadoopReader = new HadoopTableReader(attributes, relation, context)
 
   private[this] def castFromString(value: String, dataType: DataType) = {
     Cast(Literal(value), dataType).eval(null)
@@ -114,6 +77,7 @@ case class HiveTableScan(
     val columnInternalNames = neededColumnIDs.map(HiveConf.getColumnInternalName(_)).mkString(",")
 
     if (attributes.size == relation.output.size) {
+      // SQLContext#pruneFilterProject guarantees no duplicated value in `attributes`
       ColumnProjectionUtils.setFullyReadColumns(hiveConf)
     } else {
       ColumnProjectionUtils.appendReadColumnIDs(hiveConf, neededColumnIDs)
@@ -140,12 +104,6 @@ case class HiveTableScan(
 
   addColumnMetadataToConf(context.hiveconf)
 
-  private def inputRdd = if (!relation.hiveQlTable.isPartitioned) {
-    hadoopReader.makeRDDForTable(relation.hiveQlTable)
-  } else {
-    hadoopReader.makeRDDForPartitionedTable(prunePartitions(relation.hiveQlPartitions))
-  }
-
   /**
    * Prunes partitions not involve the query plan.
    *
@@ -169,44 +127,10 @@ case class HiveTableScan(
     }
   }
 
-  override def execute() = {
-    inputRdd.mapPartitions { iterator =>
-      if (iterator.isEmpty) {
-        Iterator.empty
-      } else {
-        val mutableRow = new GenericMutableRow(attributes.length)
-        val mutablePair = new MutablePair[Any, Array[String]]()
-        val buffered = iterator.buffered
-
-        // NOTE (lian): Critical path of Hive table scan, unnecessary FP style code and pattern
-        // matching are avoided intentionally.
-        val rowsAndPartitionKeys = buffered.head match {
-          // With partition keys
-          case _: Array[Any] =>
-            buffered.map { case array: Array[Any] =>
-              val deserializedRow = array(0)
-              val partitionKeys = array(1).asInstanceOf[Array[String]]
-              mutablePair.update(deserializedRow, partitionKeys)
-            }
-
-          // Without partition keys
-          case _ =>
-            val emptyPartitionKeys = Array.empty[String]
-            buffered.map { deserializedRow =>
-              mutablePair.update(deserializedRow, emptyPartitionKeys)
-            }
-        }
-
-        rowsAndPartitionKeys.map { pair =>
-          var i = 0
-          while (i < attributes.length) {
-            mutableRow(i) = attributeFunctions(i)(pair._1, pair._2)
-            i += 1
-          }
-          mutableRow: Row
-        }
-      }
-    }
+  override def execute() = if (!relation.hiveQlTable.isPartitioned) {
+    hadoopReader.makeRDDForTable(relation.hiveQlTable)
+  } else {
+    hadoopReader.makeRDDForPartitionedTable(prunePartitions(relation.hiveQlPartitions))
   }
 
   override def output = attributes

sql/hive/src/test/resources/golden/partition_based_table_scan_with_different_serde-0-8caed2a6e80250a6d38a59388679c298

@@ -0,0 +1,2 @@
+100	100	2010-01-01
+200	200	2010-01-02