473 lines
16 KiB
Scala
473 lines
16 KiB
Scala
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.Random
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import java.util.Date
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import java.util.{HashMap => JHashMap}
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import scala.collection.mutable.ArrayBuffer
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import scala.collection.Map
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import scala.collection.mutable.HashMap
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import scala.collection.JavaConversions.mapAsScalaMap
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import org.apache.hadoop.io.BytesWritable
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import org.apache.hadoop.io.NullWritable
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import org.apache.hadoop.io.Text
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import org.apache.hadoop.io.Writable
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import org.apache.hadoop.mapred.FileOutputCommitter
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import org.apache.hadoop.mapred.HadoopWriter
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import org.apache.hadoop.mapred.JobConf
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import org.apache.hadoop.mapred.OutputCommitter
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import org.apache.hadoop.mapred.OutputFormat
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import org.apache.hadoop.mapred.SequenceFileOutputFormat
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import org.apache.hadoop.mapred.TextOutputFormat
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import it.unimi.dsi.fastutil.objects.{Object2LongOpenHashMap => OLMap}
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import spark.partial.BoundedDouble
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import spark.partial.CountEvaluator
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import spark.partial.GroupedCountEvaluator
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import spark.partial.PartialResult
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import spark.storage.StorageLevel
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import SparkContext._
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/**
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* A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. Represents an immutable,
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* partitioned collection of elements that can be operated on in parallel.
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*
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* Each RDD is characterized by five main properties:
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* - A list of splits (partitions)
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* - A function for computing each split
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* - A list of dependencies on other RDDs
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* - Optionally, a Partitioner for key-value RDDs (e.g. to say that the RDD is hash-partitioned)
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* - Optionally, a list of preferred locations to compute each split on (e.g. block locations for
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* HDFS)
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*
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* All the scheduling and execution in Spark is done based on these methods, allowing each RDD to
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* implement its own way of computing itself.
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*
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* This class also contains transformation methods available on all RDDs (e.g. map and filter). In
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* addition, PairRDDFunctions contains extra methods available on RDDs of key-value pairs, and
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* SequenceFileRDDFunctions contains extra methods for saving RDDs to Hadoop SequenceFiles.
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*/
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abstract class RDD[T: ClassManifest](@transient sc: SparkContext) extends Serializable {
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// Methods that must be implemented by subclasses
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def splits: Array[Split]
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def compute(split: Split): Iterator[T]
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@transient val dependencies: List[Dependency[_]]
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// Record user function generating this RDD
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val origin = getOriginDescription
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// Optionally overridden by subclasses to specify how they are partitioned
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val partitioner: Option[Partitioner] = None
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// Optionally overridden by subclasses to specify placement preferences
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def preferredLocations(split: Split): Seq[String] = Nil
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def context = sc
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def elementClassManifest: ClassManifest[T] = classManifest[T]
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// Get a unique ID for this RDD
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val id = sc.newRddId()
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// Variables relating to persistence
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private var storageLevel: StorageLevel = StorageLevel.NONE
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// Change this RDD's storage level
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def persist(newLevel: StorageLevel): RDD[T] = {
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// TODO: Handle changes of StorageLevel
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if (storageLevel != StorageLevel.NONE && newLevel != storageLevel) {
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throw new UnsupportedOperationException(
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"Cannot change storage level of an RDD after it was already assigned a level")
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}
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storageLevel = newLevel
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this
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}
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// Turn on the default caching level for this RDD
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def persist(): RDD[T] = persist(StorageLevel.MEMORY_ONLY_DESER)
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// Turn on the default caching level for this RDD
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def cache(): RDD[T] = persist()
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def getStorageLevel = storageLevel
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def checkpoint(level: StorageLevel = StorageLevel.DISK_AND_MEMORY_DESER_2): RDD[T] = {
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if (!level.useDisk && level.replication < 2) {
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throw new Exception("Cannot checkpoint without using disk or replication (level requested was " + level + ")")
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}
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// This is a hack. Ideally this should re-use the code used by the CacheTracker
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// to generate the key.
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def getSplitKey(split: Split) = "rdd:%d:%d".format(this.id, split.index)
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persist(level)
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sc.runJob(this, (iter: Iterator[T]) => {} )
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val p = this.partitioner
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new BlockRDD[T](sc, splits.map(getSplitKey).toArray) {
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override val partitioner = p
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}
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}
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// Read this RDD; will read from cache if applicable, or otherwise compute
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final def iterator(split: Split): Iterator[T] = {
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if (storageLevel != StorageLevel.NONE) {
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SparkEnv.get.cacheTracker.getOrCompute[T](this, split, storageLevel)
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} else {
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compute(split)
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}
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}
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// Describe which spark and user functions generated this RDD. Only works if called from
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// constructor.
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def getOriginDescription : String = {
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val trace = Thread.currentThread().getStackTrace().filter( el =>
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(!el.getMethodName().contains("getStackTrace")))
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// Keep crawling up the stack trace until we find the first function not inside of the spark
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// package. We track the last (shallowest) contiguous Spark method. This might be an RDD
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// transformation, a SparkContext function (such as parallelize), or anything else that leads
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// to instantiation of an RDD. We also track the first (deepest) user method, file, and line.
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var lastSparkMethod = "<not_found>"
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var firstUserMethod = "<not_found>"
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var firstUserFile = "<not_found>"
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var firstUserLine = -1
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var finished = false
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for (el <- trace) {
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if (!finished) {
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if (el.getClassName().contains("spark") && !el.getClassName().startsWith("spark.examples")) {
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lastSparkMethod = el.getMethodName()
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}
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else {
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firstUserMethod = el.getMethodName()
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firstUserLine = el.getLineNumber()
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firstUserFile = el.getFileName()
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finished = true
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}
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}
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}
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"%s at: %s (%s:%s)".format(lastSparkMethod, firstUserMethod, firstUserFile, firstUserLine)
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}
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// Transformations (return a new RDD)
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def map[U: ClassManifest](f: T => U): RDD[U] = new MappedRDD(this, sc.clean(f))
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def flatMap[U: ClassManifest](f: T => TraversableOnce[U]): RDD[U] =
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new FlatMappedRDD(this, sc.clean(f))
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def filter(f: T => Boolean): RDD[T] = new FilteredRDD(this, sc.clean(f))
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def distinct(): RDD[T] = map(x => (x, "")).reduceByKey((x, y) => x).map(_._1)
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def sample(withReplacement: Boolean, fraction: Double, seed: Int): RDD[T] =
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new SampledRDD(this, withReplacement, fraction, seed)
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def takeSample(withReplacement: Boolean, num: Int, seed: Int): Array[T] = {
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var fraction = 0.0
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var total = 0
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var multiplier = 3.0
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var initialCount = count()
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var maxSelected = 0
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if (initialCount > Integer.MAX_VALUE - 1) {
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maxSelected = Integer.MAX_VALUE - 1
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} else {
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maxSelected = initialCount.toInt
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}
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if (num > initialCount) {
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total = maxSelected
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fraction = math.min(multiplier * (maxSelected + 1) / initialCount, 1.0)
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} else if (num < 0) {
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throw(new IllegalArgumentException("Negative number of elements requested"))
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} else {
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fraction = math.min(multiplier * (num + 1) / initialCount, 1.0)
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total = num
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}
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val rand = new Random(seed)
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var samples = this.sample(withReplacement, fraction, rand.nextInt).collect()
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while (samples.length < total) {
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samples = this.sample(withReplacement, fraction, rand.nextInt).collect()
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}
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Utils.randomizeInPlace(samples, rand).take(total)
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}
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def union(other: RDD[T]): RDD[T] = new UnionRDD(sc, Array(this, other))
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def ++(other: RDD[T]): RDD[T] = this.union(other)
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def glom(): RDD[Array[T]] = new GlommedRDD(this)
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def cartesian[U: ClassManifest](other: RDD[U]): RDD[(T, U)] = new CartesianRDD(sc, this, other)
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def groupBy[K: ClassManifest](f: T => K, numSplits: Int): RDD[(K, Seq[T])] = {
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val cleanF = sc.clean(f)
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this.map(t => (cleanF(t), t)).groupByKey(numSplits)
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}
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def groupBy[K: ClassManifest](f: T => K): RDD[(K, Seq[T])] = groupBy[K](f, sc.defaultParallelism)
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def pipe(command: String): RDD[String] = new PipedRDD(this, command)
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def pipe(command: Seq[String]): RDD[String] = new PipedRDD(this, command)
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def pipe(command: Seq[String], env: Map[String, String]): RDD[String] =
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new PipedRDD(this, command, env)
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def mapPartitions[U: ClassManifest](f: Iterator[T] => Iterator[U]): RDD[U] =
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new MapPartitionsRDD(this, sc.clean(f))
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def mapPartitionsWithSplit[U: ClassManifest](f: (Int, Iterator[T]) => Iterator[U]): RDD[U] =
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new MapPartitionsWithSplitRDD(this, sc.clean(f))
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// Actions (launch a job to return a value to the user program)
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def foreach(f: T => Unit) {
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val cleanF = sc.clean(f)
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sc.runJob(this, (iter: Iterator[T]) => iter.foreach(cleanF))
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}
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def collect(): Array[T] = {
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val results = sc.runJob(this, (iter: Iterator[T]) => iter.toArray)
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Array.concat(results: _*)
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}
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def toArray(): Array[T] = collect()
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def reduce(f: (T, T) => T): T = {
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val cleanF = sc.clean(f)
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val reducePartition: Iterator[T] => Option[T] = iter => {
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if (iter.hasNext) {
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Some(iter.reduceLeft(cleanF))
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}else {
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None
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}
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}
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val options = sc.runJob(this, reducePartition)
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val results = new ArrayBuffer[T]
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for (opt <- options; elem <- opt) {
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results += elem
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}
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if (results.size == 0) {
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throw new UnsupportedOperationException("empty collection")
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} else {
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return results.reduceLeft(cleanF)
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}
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}
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/**
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* Aggregate the elements of each partition, and then the results for all the partitions, using a
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* given associative function and a neutral "zero value". The function op(t1, t2) is allowed to
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* modify t1 and return it as its result value to avoid object allocation; however, it should not
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* modify t2.
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*/
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def fold(zeroValue: T)(op: (T, T) => T): T = {
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val cleanOp = sc.clean(op)
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val results = sc.runJob(this, (iter: Iterator[T]) => iter.fold(zeroValue)(cleanOp))
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return results.fold(zeroValue)(cleanOp)
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}
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/**
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* Aggregate the elements of each partition, and then the results for all the partitions, using
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* given combine functions and a neutral "zero value". This function can return a different result
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* type, U, than the type of this RDD, T. Thus, we need one operation for merging a T into an U
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* and one operation for merging two U's, as in scala.TraversableOnce. Both of these functions are
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* allowed to modify and return their first argument instead of creating a new U to avoid memory
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* allocation.
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*/
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def aggregate[U: ClassManifest](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U): U = {
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val cleanSeqOp = sc.clean(seqOp)
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val cleanCombOp = sc.clean(combOp)
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val results = sc.runJob(this,
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(iter: Iterator[T]) => iter.aggregate(zeroValue)(cleanSeqOp, cleanCombOp))
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return results.fold(zeroValue)(cleanCombOp)
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}
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def count(): Long = {
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sc.runJob(this, (iter: Iterator[T]) => {
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var result = 0L
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while (iter.hasNext) {
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result += 1L
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iter.next
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}
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result
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}).sum
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}
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/**
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* Approximate version of count() that returns a potentially incomplete result after a timeout.
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*/
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def countApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = {
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val countElements: (TaskContext, Iterator[T]) => Long = { (ctx, iter) =>
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var result = 0L
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while (iter.hasNext) {
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result += 1L
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iter.next
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}
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result
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}
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val evaluator = new CountEvaluator(splits.size, confidence)
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sc.runApproximateJob(this, countElements, evaluator, timeout)
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}
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/**
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* Count elements equal to each value, returning a map of (value, count) pairs. The final combine
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* step happens locally on the master, equivalent to running a single reduce task.
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*
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* TODO: This should perhaps be distributed by default.
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*/
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def countByValue(): Map[T, Long] = {
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def countPartition(iter: Iterator[T]): Iterator[OLMap[T]] = {
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val map = new OLMap[T]
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while (iter.hasNext) {
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val v = iter.next()
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map.put(v, map.getLong(v) + 1L)
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}
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Iterator(map)
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}
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def mergeMaps(m1: OLMap[T], m2: OLMap[T]): OLMap[T] = {
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val iter = m2.object2LongEntrySet.fastIterator()
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while (iter.hasNext) {
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val entry = iter.next()
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m1.put(entry.getKey, m1.getLong(entry.getKey) + entry.getLongValue)
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}
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return m1
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}
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val myResult = mapPartitions(countPartition).reduce(mergeMaps)
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myResult.asInstanceOf[java.util.Map[T, Long]] // Will be wrapped as a Scala mutable Map
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}
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/**
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* Approximate version of countByValue().
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*/
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def countByValueApprox(
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timeout: Long,
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confidence: Double = 0.95
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): PartialResult[Map[T, BoundedDouble]] = {
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val countPartition: (TaskContext, Iterator[T]) => OLMap[T] = { (ctx, iter) =>
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val map = new OLMap[T]
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while (iter.hasNext) {
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val v = iter.next()
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map.put(v, map.getLong(v) + 1L)
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}
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map
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}
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val evaluator = new GroupedCountEvaluator[T](splits.size, confidence)
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sc.runApproximateJob(this, countPartition, evaluator, timeout)
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}
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/**
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* Take the first num elements of the RDD. This currently scans the partitions *one by one*, so
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* it will be slow if a lot of partitions are required. In that case, use collect() to get the
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* whole RDD instead.
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*/
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def take(num: Int): Array[T] = {
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if (num == 0) {
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return new Array[T](0)
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}
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val buf = new ArrayBuffer[T]
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var p = 0
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while (buf.size < num && p < splits.size) {
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val left = num - buf.size
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val res = sc.runJob(this, (it: Iterator[T]) => it.take(left).toArray, Array(p), true)
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buf ++= res(0)
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if (buf.size == num)
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return buf.toArray
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p += 1
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}
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return buf.toArray
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}
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def first(): T = take(1) match {
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case Array(t) => t
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case _ => throw new UnsupportedOperationException("empty collection")
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}
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def saveAsTextFile(path: String) {
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this.map(x => (NullWritable.get(), new Text(x.toString)))
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.saveAsHadoopFile[TextOutputFormat[NullWritable, Text]](path)
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}
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def saveAsObjectFile(path: String) {
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this.glom
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.map(x => (NullWritable.get(), new BytesWritable(Utils.serialize(x))))
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.saveAsSequenceFile(path)
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}
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/** A private method for tests, to look at the contents of each partition */
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private[spark] def collectPartitions(): Array[Array[T]] = {
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sc.runJob(this, (iter: Iterator[T]) => iter.toArray)
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}
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}
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class MappedRDD[U: ClassManifest, T: ClassManifest](
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prev: RDD[T],
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f: T => U)
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extends RDD[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 def compute(split: Split) = prev.iterator(split).map(f)
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}
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class FlatMappedRDD[U: ClassManifest, T: ClassManifest](
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prev: RDD[T],
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f: T => TraversableOnce[U])
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extends RDD[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 def compute(split: Split) = prev.iterator(split).flatMap(f)
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}
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class FilteredRDD[T: ClassManifest](prev: RDD[T], f: T => Boolean) extends RDD[T](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 def compute(split: Split) = prev.iterator(split).filter(f)
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}
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class GlommedRDD[T: ClassManifest](prev: RDD[T]) extends RDD[Array[T]](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 def compute(split: Split) = Array(prev.iterator(split).toArray).iterator
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}
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class MapPartitionsRDD[U: ClassManifest, T: ClassManifest](
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prev: RDD[T],
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f: Iterator[T] => Iterator[U])
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extends RDD[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 def compute(split: Split) = f(prev.iterator(split))
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}
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/**
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* A variant of the MapPartitionsRDD that passes the split index into the
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* closure. This can be used to generate or collect partition specific
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* information such as the number of tuples in a partition.
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*/
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class MapPartitionsWithSplitRDD[U: ClassManifest, T: ClassManifest](
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prev: RDD[T],
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f: (Int, Iterator[T]) => Iterator[U])
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extends RDD[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 def compute(split: Split) = f(split.index, prev.iterator(split))
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}
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