265 lines
9.3 KiB
Scala
265 lines
9.3 KiB
Scala
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package spark
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import java.io.EOFException
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import java.net.URL
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import java.io.ObjectInputStream
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import java.util.concurrent.atomic.AtomicLong
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import java.util.HashSet
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import java.util.Random
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import java.util.Date
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import scala.collection.mutable.ArrayBuffer
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import scala.collection.mutable.Map
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import scala.collection.mutable.HashMap
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import org.apache.hadoop.mapred.JobConf
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import org.apache.hadoop.mapred.HadoopFileWriter
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import org.apache.hadoop.mapred.OutputFormat
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import org.apache.hadoop.mapred.TextOutputFormat
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import org.apache.hadoop.mapred.SequenceFileOutputFormat
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import org.apache.hadoop.mapred.OutputCommitter
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import org.apache.hadoop.mapred.FileOutputCommitter
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import org.apache.hadoop.io.Writable
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import org.apache.hadoop.io.NullWritable
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import org.apache.hadoop.io.BytesWritable
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import org.apache.hadoop.io.Text
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import SparkContext._
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/**
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* Extra functions available on RDDs of (key, value) pairs through an implicit conversion.
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*/
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@serializable
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class PairRDDFunctions[K: ClassManifest, V: ClassManifest](self: RDD[(K, V)]) extends Logging {
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def reduceByKeyToDriver(func: (V, V) => V): Map[K, V] = {
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def mergeMaps(m1: HashMap[K, V], m2: HashMap[K, V]): HashMap[K, V] = {
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for ((k, v) <- m2) {
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m1.get(k) match {
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case None => m1(k) = v
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case Some(w) => m1(k) = func(w, v)
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}
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}
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return m1
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}
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self.map(pair => HashMap(pair)).reduce(mergeMaps)
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}
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def combineByKey[C](createCombiner: V => C,
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mergeValue: (C, V) => C,
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mergeCombiners: (C, C) => C,
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numSplits: Int)
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: RDD[(K, C)] =
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{
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val aggregator = new Aggregator[K, V, C](createCombiner, mergeValue, mergeCombiners)
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val partitioner = new HashPartitioner(numSplits)
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new ShuffledRDD(self, aggregator, partitioner)
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}
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def reduceByKey(func: (V, V) => V, numSplits: Int): RDD[(K, V)] = {
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combineByKey[V]((v: V) => v, func, func, numSplits)
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}
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def groupByKey(numSplits: Int): RDD[(K, Seq[V])] = {
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def createCombiner(v: V) = ArrayBuffer(v)
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def mergeValue(buf: ArrayBuffer[V], v: V) = buf += v
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def mergeCombiners(b1: ArrayBuffer[V], b2: ArrayBuffer[V]) = b1 ++= b2
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val bufs = combineByKey[ArrayBuffer[V]](
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createCombiner _, mergeValue _, mergeCombiners _, numSplits)
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bufs.asInstanceOf[RDD[(K, Seq[V])]]
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}
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def join[W](other: RDD[(K, W)], numSplits: Int): RDD[(K, (V, W))] = {
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val vs: RDD[(K, Either[V, W])] = self.map { case (k, v) => (k, Left(v)) }
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val ws: RDD[(K, Either[V, W])] = other.map { case (k, w) => (k, Right(w)) }
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(vs ++ ws).groupByKey(numSplits).flatMap {
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case (k, seq) => {
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val vbuf = new ArrayBuffer[V]
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val wbuf = new ArrayBuffer[W]
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seq.foreach(_ match {
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case Left(v) => vbuf += v
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case Right(w) => wbuf += w
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})
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for (v <- vbuf; w <- wbuf) yield (k, (v, w))
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}
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}
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}
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def leftOuterJoin[W](other: RDD[(K, W)], numSplits: Int): RDD[(K, (V, Option[W]))] = {
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val vs: RDD[(K, Either[V, W])] = self.map { case (k, v) => (k, Left(v)) }
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val ws: RDD[(K, Either[V, W])] = other.map { case (k, w) => (k, Right(w)) }
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(vs ++ ws).groupByKey(numSplits).flatMap {
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case (k, seq) => {
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val vbuf = new ArrayBuffer[V]
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val wbuf = new ArrayBuffer[Option[W]]
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seq.foreach(_ match {
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case Left(v) => vbuf += v
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case Right(w) => wbuf += Some(w)
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})
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if (wbuf.isEmpty) {
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wbuf += None
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}
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for (v <- vbuf; w <- wbuf) yield (k, (v, w))
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}
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}
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}
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def rightOuterJoin[W](other: RDD[(K, W)], numSplits: Int): RDD[(K, (Option[V], W))] = {
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val vs: RDD[(K, Either[V, W])] = self.map { case (k, v) => (k, Left(v)) }
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val ws: RDD[(K, Either[V, W])] = other.map { case (k, w) => (k, Right(w)) }
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(vs ++ ws).groupByKey(numSplits).flatMap {
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case (k, seq) => {
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val vbuf = new ArrayBuffer[Option[V]]
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val wbuf = new ArrayBuffer[W]
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seq.foreach(_ match {
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case Left(v) => vbuf += Some(v)
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case Right(w) => wbuf += w
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})
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if (vbuf.isEmpty) {
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vbuf += None
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}
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for (v <- vbuf; w <- wbuf) yield (k, (v, w))
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}
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}
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}
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def combineByKey[C](createCombiner: V => C,
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mergeValue: (C, V) => C,
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mergeCombiners: (C, C) => C)
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: RDD[(K, C)] = {
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combineByKey(createCombiner, mergeValue, mergeCombiners, numCores)
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}
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def reduceByKey(func: (V, V) => V): RDD[(K, V)] = {
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reduceByKey(func, numCores)
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}
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def groupByKey(): RDD[(K, Seq[V])] = {
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groupByKey(numCores)
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}
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def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))] = {
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join(other, numCores)
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}
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def leftOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (V, Option[W]))] = {
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leftOuterJoin(other, numCores)
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}
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def rightOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], W))] = {
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rightOuterJoin(other, numCores)
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}
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def numCores = self.context.numCores
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def collectAsMap(): Map[K, V] = HashMap(self.collect(): _*)
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def mapValues[U](f: V => U): RDD[(K, U)] = {
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val cleanF = self.context.clean(f)
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new MappedValuesRDD(self, cleanF)
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}
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def flatMapValues[U](f: V => Traversable[U]): RDD[(K, U)] = {
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val cleanF = self.context.clean(f)
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new FlatMappedValuesRDD(self, cleanF)
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}
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def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Seq[V], Seq[W]))] = {
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val part = self.partitioner match {
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case Some(p) => p
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case None => new HashPartitioner(numCores)
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}
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new CoGroupedRDD[K](Seq(self.asInstanceOf[RDD[(_, _)]], other.asInstanceOf[RDD[(_, _)]]), part).map {
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case (k, Seq(vs, ws)) =>
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(k, (vs.asInstanceOf[Seq[V]], ws.asInstanceOf[Seq[W]]))
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}
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}
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def groupWith[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)])
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: RDD[(K, (Seq[V], Seq[W1], Seq[W2]))] = {
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val part = self.partitioner match {
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case Some(p) => p
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case None => new HashPartitioner(numCores)
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}
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new CoGroupedRDD[K](
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Seq(self.asInstanceOf[RDD[(_, _)]],
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other1.asInstanceOf[RDD[(_, _)]],
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other2.asInstanceOf[RDD[(_, _)]]),
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part).map {
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case (k, Seq(vs, w1s, w2s)) =>
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(k, (vs.asInstanceOf[Seq[V]], w1s.asInstanceOf[Seq[W1]], w2s.asInstanceOf[Seq[W2]]))
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}
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}
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def saveAsHadoopFile (path: String, jobConf: JobConf) {
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saveAsHadoopFile(path, jobConf.getOutputKeyClass, jobConf.getOutputValueClass, jobConf.getOutputFormat().getClass.asInstanceOf[Class[OutputFormat[AnyRef,AnyRef]]], jobConf.getOutputCommitter().getClass.asInstanceOf[Class[OutputCommitter]], jobConf)
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}
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def saveAsHadoopFile [F <: OutputFormat[K,V], C <: OutputCommitter] (path: String) (implicit fm: ClassManifest[F], cm: ClassManifest[C]) {
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saveAsHadoopFile(path, fm.erasure.asInstanceOf[Class[F]], cm.erasure.asInstanceOf[Class[C]])
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}
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def saveAsHadoopFile(path: String, outputFormatClass: Class[_ <: OutputFormat[K,V]], outputCommitterClass: Class[_ <: OutputCommitter]) {
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saveAsHadoopFile(path, implicitly[ClassManifest[K]].erasure, implicitly[ClassManifest[V]].erasure, outputFormatClass, outputCommitterClass)
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}
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def saveAsHadoopFile(path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_,_]], outputCommitterClass: Class[_ <: OutputCommitter]) {
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saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass, outputCommitterClass, null)
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}
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private def saveAsHadoopFile(path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_,_]], outputCommitterClass: Class[_ <: OutputCommitter], jobConf: JobConf) {
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logInfo ("Saving as hadoop file of type (" + keyClass.getSimpleName+ "," +valueClass.getSimpleName+ ")" )
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val writer = new HadoopFileWriter(path,
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keyClass,
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valueClass,
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outputFormatClass.asInstanceOf[Class[OutputFormat[AnyRef,AnyRef]]],
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outputCommitterClass.asInstanceOf[Class[OutputCommitter]],
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null)
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writer.preSetup()
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def writeToFile (context: TaskContext, iter: Iterator[(K,V)]): HadoopFileWriter = {
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writer.setup(context.stageId, context.splitId, context.attemptId)
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writer.open()
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var count = 0
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while(iter.hasNext) {
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val record = iter.next
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count += 1
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writer.write(record._1.asInstanceOf[AnyRef], record._2.asInstanceOf[AnyRef])
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}
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writer.close()
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return writer
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}
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self.context.runJob(self, writeToFile _ ).foreach(_.commit())
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writer.cleanup()
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}
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def getKeyClass() = implicitly[ClassManifest[K]].erasure
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def getValueClass() = implicitly[ClassManifest[V]].erasure
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}
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class MappedValuesRDD[K, V, U](
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prev: RDD[(K, V)], f: V => U)
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extends RDD[(K, U)](prev.context) {
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override def splits = prev.splits
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override val dependencies = List(new OneToOneDependency(prev))
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override val partitioner = prev.partitioner
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override def compute(split: Split) =
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prev.iterator(split).map{case (k, v) => (k, f(v))}
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}
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class FlatMappedValuesRDD[K, V, U](
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prev: RDD[(K, V)], f: V => Traversable[U])
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extends RDD[(K, U)](prev.context) {
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override def splits = prev.splits
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override val dependencies = List(new OneToOneDependency(prev))
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override val partitioner = prev.partitioner
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override def compute(split: Split) = {
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prev.iterator(split).toStream.flatMap {
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case (k, v) => f(v).map(x => (k, x))
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}.iterator
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}
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}
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