[SPARK-11787][SPARK-11883][SQL][FOLLOW-UP] Cleanup for this patch.

This mainly moves SqlNewHadoopRDD to the sql package. There is some state that is
shared between core and I've left that in core. This allows some other associated
minor cleanup.

Author: Nong Li <nong@databricks.com>

Closes #9845 from nongli/spark-11787.

3 files changed, 44 insertions, 320 deletions

diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
index 7db5834687..f37c95bedc 100644
--- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
@@ -215,8 +215,8 @@ class HadoopRDD[K, V](
 
       // Sets the thread local variable for the file's name
       split.inputSplit.value match {
-        case fs: FileSplit => SqlNewHadoopRDD.setInputFileName(fs.getPath.toString)
-        case _ => SqlNewHadoopRDD.unsetInputFileName()
+        case fs: FileSplit => SqlNewHadoopRDDState.setInputFileName(fs.getPath.toString)
+        case _ => SqlNewHadoopRDDState.unsetInputFileName()
       }
 
       // Find a function that will return the FileSystem bytes read by this thread. Do this before
@@ -256,7 +256,7 @@ class HadoopRDD[K, V](
 
       override def close() {
         if (reader != null) {
-          SqlNewHadoopRDD.unsetInputFileName()
+          SqlNewHadoopRDDState.unsetInputFileName()
           // Close the reader and release it. Note: it's very important that we don't close the
           // reader more than once, since that exposes us to MAPREDUCE-5918 when running against
           // Hadoop 1.x and older Hadoop 2.x releases. That bug can lead to non-deterministic
diff --git a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
deleted file mode 100644
index 4d176332b6..0000000000
--- a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
+++ /dev/null
@@ -1,317 +0,0 @@
-/*
- * 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.rdd
-
-import java.text.SimpleDateFormat
-import java.util.Date
-
-import org.apache.hadoop.conf.{Configurable, Configuration}
-import org.apache.hadoop.io.Writable
-import org.apache.hadoop.mapreduce._
-import org.apache.hadoop.mapreduce.lib.input.{CombineFileSplit, FileSplit}
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.executor.DataReadMethod
-import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
-import org.apache.spark.storage.StorageLevel
-import org.apache.spark.unsafe.types.UTF8String
-import org.apache.spark.util.{Utils, SerializableConfiguration, ShutdownHookManager}
-import org.apache.spark.{Partition => SparkPartition, _}
-
-import scala.reflect.ClassTag
-
-
-private[spark] class SqlNewHadoopPartition(
-    rddId: Int,
-    val index: Int,
-    rawSplit: InputSplit with Writable)
-  extends SparkPartition {
-
-  val serializableHadoopSplit = new SerializableWritable(rawSplit)
-
-  override def hashCode(): Int = 41 * (41 + rddId) + index
-}
-
-/**
- * An RDD that provides core functionality for reading data stored in Hadoop (e.g., files in HDFS,
- * sources in HBase, or S3), using the new MapReduce API (`org.apache.hadoop.mapreduce`).
- * It is based on [[org.apache.spark.rdd.NewHadoopRDD]]. It has three additions.
- * 1. A shared broadcast Hadoop Configuration.
- * 2. An optional closure `initDriverSideJobFuncOpt` that set configurations at the driver side
- *    to the shared Hadoop Configuration.
- * 3. An optional closure `initLocalJobFuncOpt` that set configurations at both the driver side
- *    and the executor side to the shared Hadoop Configuration.
- *
- * Note: This is RDD is basically a cloned version of [[org.apache.spark.rdd.NewHadoopRDD]] with
- * changes based on [[org.apache.spark.rdd.HadoopRDD]].
- */
-private[spark] class SqlNewHadoopRDD[V: ClassTag](
-    sc : SparkContext,
-    broadcastedConf: Broadcast[SerializableConfiguration],
-    @transient private val initDriverSideJobFuncOpt: Option[Job => Unit],
-    initLocalJobFuncOpt: Option[Job => Unit],
-    inputFormatClass: Class[_ <: InputFormat[Void, V]],
-    valueClass: Class[V])
-  extends RDD[V](sc, Nil)
-  with SparkHadoopMapReduceUtil
-  with Logging {
-
-  protected def getJob(): Job = {
-    val conf: Configuration = broadcastedConf.value.value
-    // "new Job" will make a copy of the conf. Then, it is
-    // safe to mutate conf properties with initLocalJobFuncOpt
-    // and initDriverSideJobFuncOpt.
-    val newJob = new Job(conf)
-    initLocalJobFuncOpt.map(f => f(newJob))
-    newJob
-  }
-
-  def getConf(isDriverSide: Boolean): Configuration = {
-    val job = getJob()
-    if (isDriverSide) {
-      initDriverSideJobFuncOpt.map(f => f(job))
-    }
-    SparkHadoopUtil.get.getConfigurationFromJobContext(job)
-  }
-
-  private val jobTrackerId: String = {
-    val formatter = new SimpleDateFormat("yyyyMMddHHmm")
-    formatter.format(new Date())
-  }
-
-  @transient protected val jobId = new JobID(jobTrackerId, id)
-
-  // If true, enable using the custom RecordReader for parquet. This only works for
-  // a subset of the types (no complex types).
-  protected val enableUnsafeRowParquetReader: Boolean =
-      sc.conf.getBoolean("spark.parquet.enableUnsafeRowRecordReader", true)
-
-  override def getPartitions: Array[SparkPartition] = {
-    val conf = getConf(isDriverSide = true)
-    val inputFormat = inputFormatClass.newInstance
-    inputFormat match {
-      case configurable: Configurable =>
-        configurable.setConf(conf)
-      case _ =>
-    }
-    val jobContext = newJobContext(conf, jobId)
-    val rawSplits = inputFormat.getSplits(jobContext).toArray
-    val result = new Array[SparkPartition](rawSplits.size)
-    for (i <- 0 until rawSplits.size) {
-      result(i) =
-        new SqlNewHadoopPartition(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable])
-    }
-    result
-  }
-
-  override def compute(
-      theSplit: SparkPartition,
-      context: TaskContext): Iterator[V] = {
-    val iter = new Iterator[V] {
-      val split = theSplit.asInstanceOf[SqlNewHadoopPartition]
-      logInfo("Input split: " + split.serializableHadoopSplit)
-      val conf = getConf(isDriverSide = false)
-
-      val inputMetrics = context.taskMetrics
-        .getInputMetricsForReadMethod(DataReadMethod.Hadoop)
-
-      // Sets the thread local variable for the file's name
-      split.serializableHadoopSplit.value match {
-        case fs: FileSplit => SqlNewHadoopRDD.setInputFileName(fs.getPath.toString)
-        case _ => SqlNewHadoopRDD.unsetInputFileName()
-      }
-
-      // Find a function that will return the FileSystem bytes read by this thread. Do this before
-      // creating RecordReader, because RecordReader's constructor might read some bytes
-      val bytesReadCallback = inputMetrics.bytesReadCallback.orElse {
-        split.serializableHadoopSplit.value match {
-          case _: FileSplit | _: CombineFileSplit =>
-            SparkHadoopUtil.get.getFSBytesReadOnThreadCallback()
-          case _ => None
-        }
-      }
-      inputMetrics.setBytesReadCallback(bytesReadCallback)
-
-      val attemptId = newTaskAttemptID(jobTrackerId, id, isMap = true, split.index, 0)
-      val hadoopAttemptContext = newTaskAttemptContext(conf, attemptId)
-      val format = inputFormatClass.newInstance
-      format match {
-        case configurable: Configurable =>
-          configurable.setConf(conf)
-        case _ =>
-      }
-      private[this] var reader: RecordReader[Void, V] = null
-
-      /**
-        * If the format is ParquetInputFormat, try to create the optimized RecordReader. If this
-        * fails (for example, unsupported schema), try with the normal reader.
-        * TODO: plumb this through a different way?
-        */
-      if (enableUnsafeRowParquetReader &&
-          format.getClass.getName == "org.apache.parquet.hadoop.ParquetInputFormat") {
-        // TODO: move this class to sql.execution and remove this.
-        reader = Utils.classForName(
-          "org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader")
-          .newInstance().asInstanceOf[RecordReader[Void, V]]
-        try {
-          reader.initialize(split.serializableHadoopSplit.value, hadoopAttemptContext)
-        } catch {
-          case e: Exception => reader = null
-        }
-      }
-
-      if (reader == null) {
-        reader = format.createRecordReader(
-          split.serializableHadoopSplit.value, hadoopAttemptContext)
-        reader.initialize(split.serializableHadoopSplit.value, hadoopAttemptContext)
-      }
-
-      // Register an on-task-completion callback to close the input stream.
-      context.addTaskCompletionListener(context => close())
-
-      private[this] var havePair = false
-      private[this] var finished = false
-
-      override def hasNext: Boolean = {
-        if (context.isInterrupted) {
-          throw new TaskKilledException
-        }
-        if (!finished && !havePair) {
-          finished = !reader.nextKeyValue
-          if (finished) {
-            // Close and release the reader here; close() will also be called when the task
-            // completes, but for tasks that read from many files, it helps to release the
-            // resources early.
-            close()
-          }
-          havePair = !finished
-        }
-        !finished
-      }
-
-      override def next(): V = {
-        if (!hasNext) {
-          throw new java.util.NoSuchElementException("End of stream")
-        }
-        havePair = false
-        if (!finished) {
-          inputMetrics.incRecordsRead(1)
-        }
-        reader.getCurrentValue
-      }
-
-      private def close() {
-        if (reader != null) {
-          SqlNewHadoopRDD.unsetInputFileName()
-          // Close the reader and release it. Note: it's very important that we don't close the
-          // reader more than once, since that exposes us to MAPREDUCE-5918 when running against
-          // Hadoop 1.x and older Hadoop 2.x releases. That bug can lead to non-deterministic
-          // corruption issues when reading compressed input.
-          try {
-            reader.close()
-          } catch {
-            case e: Exception =>
-              if (!ShutdownHookManager.inShutdown()) {
-                logWarning("Exception in RecordReader.close()", e)
-              }
-          } finally {
-            reader = null
-          }
-          if (bytesReadCallback.isDefined) {
-            inputMetrics.updateBytesRead()
-          } else if (split.serializableHadoopSplit.value.isInstanceOf[FileSplit] ||
-                     split.serializableHadoopSplit.value.isInstanceOf[CombineFileSplit]) {
-            // If we can't get the bytes read from the FS stats, fall back to the split size,
-            // which may be inaccurate.
-            try {
-              inputMetrics.incBytesRead(split.serializableHadoopSplit.value.getLength)
-            } catch {
-              case e: java.io.IOException =>
-                logWarning("Unable to get input size to set InputMetrics for task", e)
-            }
-          }
-        }
-      }
-    }
-    iter
-  }
-
-  override def getPreferredLocations(hsplit: SparkPartition): Seq[String] = {
-    val split = hsplit.asInstanceOf[SqlNewHadoopPartition].serializableHadoopSplit.value
-    val locs = HadoopRDD.SPLIT_INFO_REFLECTIONS match {
-      case Some(c) =>
-        try {
-          val infos = c.newGetLocationInfo.invoke(split).asInstanceOf[Array[AnyRef]]
-          Some(HadoopRDD.convertSplitLocationInfo(infos))
-        } catch {
-          case e : Exception =>
-            logDebug("Failed to use InputSplit#getLocationInfo.", e)
-            None
-        }
-      case None => None
-    }
-    locs.getOrElse(split.getLocations.filter(_ != "localhost"))
-  }
-
-  override def persist(storageLevel: StorageLevel): this.type = {
-    if (storageLevel.deserialized) {
-      logWarning("Caching NewHadoopRDDs as deserialized objects usually leads to undesired" +
-        " behavior because Hadoop's RecordReader reuses the same Writable object for all records." +
-        " Use a map transformation to make copies of the records.")
-    }
-    super.persist(storageLevel)
-  }
-}
-
-private[spark] object SqlNewHadoopRDD {
-
-  /**
-   * The thread variable for the name of the current file being read. This is used by
-   * the InputFileName function in Spark SQL.
-   */
-  private[this] val inputFileName: ThreadLocal[UTF8String] = new ThreadLocal[UTF8String] {
-    override protected def initialValue(): UTF8String = UTF8String.fromString("")
-  }
-
-  def getInputFileName(): UTF8String = inputFileName.get()
-
-  private[spark] def setInputFileName(file: String) = inputFileName.set(UTF8String.fromString(file))
-
-  private[spark] def unsetInputFileName(): Unit = inputFileName.remove()
-
-  /**
-   * Analogous to [[org.apache.spark.rdd.MapPartitionsRDD]], but passes in an InputSplit to
-   * the given function rather than the index of the partition.
-   */
-  private[spark] class NewHadoopMapPartitionsWithSplitRDD[U: ClassTag, T: ClassTag](
-      prev: RDD[T],
-      f: (InputSplit, Iterator[T]) => Iterator[U],
-      preservesPartitioning: Boolean = false)
-    extends RDD[U](prev) {
-
-    override val partitioner = if (preservesPartitioning) firstParent[T].partitioner else None
-
-    override def getPartitions: Array[SparkPartition] = firstParent[T].partitions
-
-    override def compute(split: SparkPartition, context: TaskContext): Iterator[U] = {
-      val partition = split.asInstanceOf[SqlNewHadoopPartition]
-      val inputSplit = partition.serializableHadoopSplit.value
-      f(inputSplit, firstParent[T].iterator(split, context))
-    }
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDDState.scala b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDDState.scala
new file mode 100644
index 0000000000..3f15fff793
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDDState.scala
@@ -0,0 +1,41 @@
+/*
+ * 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.rdd
+
+import org.apache.spark.unsafe.types.UTF8String
+
+/**
+ * State for SqlNewHadoopRDD objects. This is split this way because of the package splits.
+ * TODO: Move/Combine this with org.apache.spark.sql.datasources.SqlNewHadoopRDD
+ */
+private[spark] object SqlNewHadoopRDDState {
+  /**
+   * The thread variable for the name of the current file being read. This is used by
+   * the InputFileName function in Spark SQL.
+   */
+  private[this] val inputFileName: ThreadLocal[UTF8String] = new ThreadLocal[UTF8String] {
+    override protected def initialValue(): UTF8String = UTF8String.fromString("")
+  }
+
+  def getInputFileName(): UTF8String = inputFileName.get()
+
+  private[spark] def setInputFileName(file: String) = inputFileName.set(UTF8String.fromString(file))
+
+  private[spark] def unsetInputFileName(): Unit = inputFileName.remove()
+
+}