spark.rdd.HadoopRDD.scala Maven / Gradle / Ivy

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package spark.rdd

import java.io.EOFException
import java.util.NoSuchElementException

import org.apache.hadoop.io.LongWritable
import org.apache.hadoop.io.NullWritable
import org.apache.hadoop.io.Text
import org.apache.hadoop.mapred.FileInputFormat
import org.apache.hadoop.mapred.InputFormat
import org.apache.hadoop.mapred.InputSplit
import org.apache.hadoop.mapred.JobConf
import org.apache.hadoop.mapred.TextInputFormat
import org.apache.hadoop.mapred.RecordReader
import org.apache.hadoop.mapred.Reporter
import org.apache.hadoop.util.ReflectionUtils

import spark.{Dependency, Logging, Partition, RDD, SerializableWritable, SparkContext, TaskContext}


/**
 * A Spark split class that wraps around a Hadoop InputSplit.
 */
private[spark] class HadoopPartition(rddId: Int, idx: Int, @transient s: InputSplit)
  extends Partition {
  
  val inputSplit = new SerializableWritable[InputSplit](s)

  override def hashCode(): Int = (41 * (41 + rddId) + idx).toInt

  override val index: Int = idx
}

/**
 * An RDD that reads a Hadoop dataset as specified by a JobConf (e.g. files in HDFS, the local file
 * system, or S3, tables in HBase, etc).
 */
class HadoopRDD[K, V](
    sc: SparkContext,
    @transient conf: JobConf,
    inputFormatClass: Class[_ <: InputFormat[K, V]],
    keyClass: Class[K],
    valueClass: Class[V],
    minSplits: Int)
  extends RDD[(K, V)](sc, Nil) with Logging {

  // A Hadoop JobConf can be about 10 KB, which is pretty big, so broadcast it
  private val confBroadcast = sc.broadcast(new SerializableWritable(conf))

  override def getPartitions: Array[Partition] = {
    val inputFormat = createInputFormat(conf)
    val inputSplits = inputFormat.getSplits(conf, minSplits)
    val array = new Array[Partition](inputSplits.size)
    for (i <- 0 until inputSplits.size) {
      array(i) = new HadoopPartition(id, i, inputSplits(i))
    }
    array
  }

  def createInputFormat(conf: JobConf): InputFormat[K, V] = {
    ReflectionUtils.newInstance(inputFormatClass.asInstanceOf[Class[_]], conf)
      .asInstanceOf[InputFormat[K, V]]
  }

  override def compute(theSplit: Partition, context: TaskContext) = new Iterator[(K, V)] {
    val split = theSplit.asInstanceOf[HadoopPartition]
    var reader: RecordReader[K, V] = null

    val conf = confBroadcast.value.value
    val fmt = createInputFormat(conf)
    reader = fmt.getRecordReader(split.inputSplit.value, conf, Reporter.NULL)

    // Register an on-task-completion callback to close the input stream.
    context.addOnCompleteCallback{ () => close() }

    val key: K = reader.createKey()
    val value: V = reader.createValue()
    var gotNext = false
    var finished = false

    override def hasNext: Boolean = {
      if (!gotNext) {
        try {
          finished = !reader.next(key, value)
        } catch {
          case eof: EOFException =>
            finished = true
        }
        gotNext = true
      }
      !finished
    }

    override def next: (K, V) = {
      if (!gotNext) {
        finished = !reader.next(key, value)
      }
      if (finished) {
        throw new NoSuchElementException("End of stream")
      }
      gotNext = false
      (key, value)
    }

    private def close() {
      try {
        reader.close()
      } catch {
        case e: Exception => logWarning("Exception in RecordReader.close()", e)
      }
    }
  }

  override def getPreferredLocations(split: Partition): Seq[String] = {
    // TODO: Filtering out "localhost" in case of file:// URLs
    val hadoopSplit = split.asInstanceOf[HadoopPartition]
    hadoopSplit.inputSplit.value.getLocations.filter(_ != "localhost")
  }

  override def checkpoint() {
    // Do nothing. Hadoop RDD should not be checkpointed.
  }
}