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package spark.api.java
import java.util.{List => JList}
import java.util.Comparator
import scala.Tuple2
import scala.collection.JavaConversions._
import org.apache.hadoop.mapred.JobConf
import org.apache.hadoop.mapred.OutputFormat
import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat}
import org.apache.hadoop.conf.Configuration
import spark.api.java.function.{Function2 => JFunction2}
import spark.api.java.function.{Function => JFunction}
import spark.partial.BoundedDouble
import spark.partial.PartialResult
import spark.OrderedRDDFunctions
import spark.storage.StorageLevel
import spark.HashPartitioner
import spark.Partitioner
import spark.RDD
import spark.SparkContext.rddToPairRDDFunctions
class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kManifest: ClassManifest[K],
implicit val vManifest: ClassManifest[V]) extends JavaRDDLike[(K, V), JavaPairRDD[K, V]] {
override def wrapRDD(rdd: RDD[(K, V)]): JavaPairRDD[K, V] = JavaPairRDD.fromRDD(rdd)
override val classManifest: ClassManifest[(K, V)] =
implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[Tuple2[K, V]]]
import JavaPairRDD._
// Common RDD functions
/** Persist this RDD with the default storage level (`MEMORY_ONLY`). */
def cache(): JavaPairRDD[K, V] = new JavaPairRDD[K, V](rdd.cache())
/**
* Set this RDD's storage level to persist its values across operations after the first time
* it is computed. Can only be called once on each RDD.
*/
def persist(newLevel: StorageLevel): JavaPairRDD[K, V] =
new JavaPairRDD[K, V](rdd.persist(newLevel))
// Transformations (return a new RDD)
/**
* Return a new RDD containing the distinct elements in this RDD.
*/
def distinct(): JavaPairRDD[K, V] = new JavaPairRDD[K, V](rdd.distinct())
/**
* Return a new RDD containing the distinct elements in this RDD.
*/
def distinct(numSplits: Int): JavaPairRDD[K, V] = new JavaPairRDD[K, V](rdd.distinct(numSplits))
/**
* Return a new RDD containing only the elements that satisfy a predicate.
*/
def filter(f: Function[(K, V), java.lang.Boolean]): JavaPairRDD[K, V] =
new JavaPairRDD[K, V](rdd.filter(x => f(x).booleanValue()))
/**
* Return a sampled subset of this RDD.
*/
def sample(withReplacement: Boolean, fraction: Double, seed: Int): JavaPairRDD[K, V] =
new JavaPairRDD[K, V](rdd.sample(withReplacement, fraction, seed))
/**
* Return the union of this RDD and another one. Any identical elements will appear multiple
* times (use `.distinct()` to eliminate them).
*/
def union(other: JavaPairRDD[K, V]): JavaPairRDD[K, V] =
new JavaPairRDD[K, V](rdd.union(other.rdd))
// first() has to be overridden here so that the generated method has the signature
// 'public scala.Tuple2 first()'; if the trait's definition is used,
// then the method has the signature 'public java.lang.Object first()',
// causing NoSuchMethodErrors at runtime.
override def first(): (K, V) = rdd.first()
// Pair RDD functions
/**
* Generic function to combine the elements for each key using a custom set of aggregation
* functions. Turns a JavaPairRDD[(K, V)] into a result of type JavaPairRDD[(K, C)], for a
* "combined type" C * Note that V and C can be different -- for example, one might group an
* RDD of type (Int, Int) into an RDD of type (Int, List[Int]). Users provide three
* functions:
*
* - `createCombiner`, which turns a V into a C (e.g., creates a one-element list)
* - `mergeValue`, to merge a V into a C (e.g., adds it to the end of a list)
* - `mergeCombiners`, to combine two C's into a single one.
*
* In addition, users can control the partitioning of the output RDD, and whether to perform
* map-side aggregation (if a mapper can produce multiple items with the same key).
*/
def combineByKey[C](createCombiner: Function[V, C],
mergeValue: JFunction2[C, V, C],
mergeCombiners: JFunction2[C, C, C],
partitioner: Partitioner): JavaPairRDD[K, C] = {
implicit val cm: ClassManifest[C] =
implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[C]]
fromRDD(rdd.combineByKey(
createCombiner,
mergeValue,
mergeCombiners,
partitioner
))
}
/**
* Simplified version of combineByKey that hash-partitions the output RDD.
*/
def combineByKey[C](createCombiner: JFunction[V, C],
mergeValue: JFunction2[C, V, C],
mergeCombiners: JFunction2[C, C, C],
numSplits: Int): JavaPairRDD[K, C] =
combineByKey(createCombiner, mergeValue, mergeCombiners, new HashPartitioner(numSplits))
/**
* Merge the values for each key using an associative reduce function. This will also perform
* the merging locally on each mapper before sending results to a reducer, similarly to a
* "combiner" in MapReduce.
*/
def reduceByKey(partitioner: Partitioner, func: JFunction2[V, V, V]): JavaPairRDD[K, V] =
fromRDD(rdd.reduceByKey(partitioner, func))
/**
* Merge the values for each key using an associative reduce function, but return the results
* immediately to the master as a Map. This will also perform the merging locally on each mapper
* before sending results to a reducer, similarly to a "combiner" in MapReduce.
*/
def reduceByKeyLocally(func: JFunction2[V, V, V]): java.util.Map[K, V] =
mapAsJavaMap(rdd.reduceByKeyLocally(func))
/** Count the number of elements for each key, and return the result to the master as a Map. */
def countByKey(): java.util.Map[K, Long] = mapAsJavaMap(rdd.countByKey())
/**
* (Experimental) Approximate version of countByKey that can return a partial result if it does
* not finish within a timeout.
*/
def countByKeyApprox(timeout: Long): PartialResult[java.util.Map[K, BoundedDouble]] =
rdd.countByKeyApprox(timeout).map(mapAsJavaMap)
/**
* (Experimental) Approximate version of countByKey that can return a partial result if it does
* not finish within a timeout.
*/
def countByKeyApprox(timeout: Long, confidence: Double = 0.95)
: PartialResult[java.util.Map[K, BoundedDouble]] =
rdd.countByKeyApprox(timeout, confidence).map(mapAsJavaMap)
/**
* Merge the values for each key using an associative reduce function. This will also perform
* the merging locally on each mapper before sending results to a reducer, similarly to a
* "combiner" in MapReduce. Output will be hash-partitioned with numSplits splits.
*/
def reduceByKey(func: JFunction2[V, V, V], numSplits: Int): JavaPairRDD[K, V] =
fromRDD(rdd.reduceByKey(func, numSplits))
/**
* Group the values for each key in the RDD into a single sequence. Allows controlling the
* partitioning of the resulting key-value pair RDD by passing a Partitioner.
*/
def groupByKey(partitioner: Partitioner): JavaPairRDD[K, JList[V]] =
fromRDD(groupByResultToJava(rdd.groupByKey(partitioner)))
/**
* Group the values for each key in the RDD into a single sequence. Hash-partitions the
* resulting RDD with into `numSplits` partitions.
*/
def groupByKey(numSplits: Int): JavaPairRDD[K, JList[V]] =
fromRDD(groupByResultToJava(rdd.groupByKey(numSplits)))
/**
* Return a copy of the RDD partitioned using the specified partitioner. If `mapSideCombine`
* is true, Spark will group values of the same key together on the map side before the
* repartitioning, to only send each key over the network once. If a large number of
* duplicated keys are expected, and the size of the keys are large, `mapSideCombine` should
* be set to true.
*/
def partitionBy(partitioner: Partitioner): JavaPairRDD[K, V] =
fromRDD(rdd.partitionBy(partitioner))
/**
* Merge the values for each key using an associative reduce function. This will also perform
* the merging locally on each mapper before sending results to a reducer, similarly to a
* "combiner" in MapReduce.
*/
def join[W](other: JavaPairRDD[K, W], partitioner: Partitioner): JavaPairRDD[K, (V, W)] =
fromRDD(rdd.join(other, partitioner))
/**
* Perform a left outer join of `this` and `other`. For each element (k, v) in `this`, the
* resulting RDD will either contain all pairs (k, (v, Some(w))) for w in `other`, or the
* pair (k, (v, None)) if no elements in `other` have key k. Uses the given Partitioner to
* partition the output RDD.
*/
def leftOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner)
: JavaPairRDD[K, (V, Option[W])] =
fromRDD(rdd.leftOuterJoin(other, partitioner))
/**
* Perform a right outer join of `this` and `other`. For each element (k, w) in `other`, the
* resulting RDD will either contain all pairs (k, (Some(v), w)) for v in `this`, or the
* pair (k, (None, w)) if no elements in `this` have key k. Uses the given Partitioner to
* partition the output RDD.
*/
def rightOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner)
: JavaPairRDD[K, (Option[V], W)] =
fromRDD(rdd.rightOuterJoin(other, partitioner))
/**
* Simplified version of combineByKey that hash-partitions the resulting RDD using the default
* parallelism level.
*/
def combineByKey[C](createCombiner: JFunction[V, C],
mergeValue: JFunction2[C, V, C],
mergeCombiners: JFunction2[C, C, C]): JavaPairRDD[K, C] = {
implicit val cm: ClassManifest[C] =
implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[C]]
fromRDD(combineByKey(createCombiner, mergeValue, mergeCombiners))
}
/**
* Merge the values for each key using an associative reduce function. This will also perform
* the merging locally on each mapper before sending results to a reducer, similarly to a
* "combiner" in MapReduce. Output will be hash-partitioned with the default parallelism level.
*/
def reduceByKey(func: JFunction2[V, V, V]): JavaPairRDD[K, V] = {
val partitioner = rdd.defaultPartitioner(rdd)
fromRDD(reduceByKey(partitioner, func))
}
/**
* Group the values for each key in the RDD into a single sequence. Hash-partitions the
* resulting RDD with the default parallelism level.
*/
def groupByKey(): JavaPairRDD[K, JList[V]] =
fromRDD(groupByResultToJava(rdd.groupByKey()))
/**
* Return an RDD containing all pairs of elements with matching keys in `this` and `other`. Each
* pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and
* (k, v2) is in `other`. Performs a hash join across the cluster.
*/
def join[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (V, W)] =
fromRDD(rdd.join(other))
/**
* Return an RDD containing all pairs of elements with matching keys in `this` and `other`. Each
* pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and
* (k, v2) is in `other`. Performs a hash join across the cluster.
*/
def join[W](other: JavaPairRDD[K, W], numSplits: Int): JavaPairRDD[K, (V, W)] =
fromRDD(rdd.join(other, numSplits))
/**
* Perform a left outer join of `this` and `other`. For each element (k, v) in `this`, the
* resulting RDD will either contain all pairs (k, (v, Some(w))) for w in `other`, or the
* pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output
* using the default level of parallelism.
*/
def leftOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (V, Option[W])] =
fromRDD(rdd.leftOuterJoin(other))
/**
* Perform a left outer join of `this` and `other`. For each element (k, v) in `this`, the
* resulting RDD will either contain all pairs (k, (v, Some(w))) for w in `other`, or the
* pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output
* into `numSplits` partitions.
*/
def leftOuterJoin[W](other: JavaPairRDD[K, W], numSplits: Int): JavaPairRDD[K, (V, Option[W])] =
fromRDD(rdd.leftOuterJoin(other, numSplits))
/**
* Perform a right outer join of `this` and `other`. For each element (k, w) in `other`, the
* resulting RDD will either contain all pairs (k, (Some(v), w)) for v in `this`, or the
* pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting
* RDD using the default parallelism level.
*/
def rightOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Option[V], W)] =
fromRDD(rdd.rightOuterJoin(other))
/**
* Perform a right outer join of `this` and `other`. For each element (k, w) in `other`, the
* resulting RDD will either contain all pairs (k, (Some(v), w)) for v in `this`, or the
* pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting
* RDD into the given number of partitions.
*/
def rightOuterJoin[W](other: JavaPairRDD[K, W], numSplits: Int): JavaPairRDD[K, (Option[V], W)] =
fromRDD(rdd.rightOuterJoin(other, numSplits))
/**
* Return the key-value pairs in this RDD to the master as a Map.
*/
def collectAsMap(): java.util.Map[K, V] = mapAsJavaMap(rdd.collectAsMap())
/**
* Pass each value in the key-value pair RDD through a map function without changing the keys;
* this also retains the original RDD's partitioning.
*/
def mapValues[U](f: Function[V, U]): JavaPairRDD[K, U] = {
implicit val cm: ClassManifest[U] =
implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[U]]
fromRDD(rdd.mapValues(f))
}
/**
* Pass each value in the key-value pair RDD through a flatMap function without changing the
* keys; this also retains the original RDD's partitioning.
*/
def flatMapValues[U](f: JFunction[V, java.lang.Iterable[U]]): JavaPairRDD[K, U] = {
import scala.collection.JavaConverters._
def fn = (x: V) => f.apply(x).asScala
implicit val cm: ClassManifest[U] =
implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[U]]
fromRDD(rdd.flatMapValues(fn))
}
/**
* For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
* list of values for that key in `this` as well as `other`.
*/
def cogroup[W](other: JavaPairRDD[K, W], partitioner: Partitioner)
: JavaPairRDD[K, (JList[V], JList[W])] =
fromRDD(cogroupResultToJava(rdd.cogroup(other, partitioner)))
/**
* For each key k in `this` or `other1` or `other2`, return a resulting RDD that contains a
* tuple with the list of values for that key in `this`, `other1` and `other2`.
*/
def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], partitioner: Partitioner)
: JavaPairRDD[K, (JList[V], JList[W1], JList[W2])] =
fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2, partitioner)))
/**
* For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
* list of values for that key in `this` as well as `other`.
*/
def cogroup[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (JList[V], JList[W])] =
fromRDD(cogroupResultToJava(rdd.cogroup(other)))
/**
* For each key k in `this` or `other1` or `other2`, return a resulting RDD that contains a
* tuple with the list of values for that key in `this`, `other1` and `other2`.
*/
def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2])
: JavaPairRDD[K, (JList[V], JList[W1], JList[W2])] =
fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2)))
/**
* For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
* list of values for that key in `this` as well as `other`.
*/
def cogroup[W](other: JavaPairRDD[K, W], numSplits: Int): JavaPairRDD[K, (JList[V], JList[W])]
= fromRDD(cogroupResultToJava(rdd.cogroup(other, numSplits)))
/**
* For each key k in `this` or `other1` or `other2`, return a resulting RDD that contains a
* tuple with the list of values for that key in `this`, `other1` and `other2`.
*/
def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], numSplits: Int)
: JavaPairRDD[K, (JList[V], JList[W1], JList[W2])] =
fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2, numSplits)))
/** Alias for cogroup. */
def groupWith[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (JList[V], JList[W])] =
fromRDD(cogroupResultToJava(rdd.groupWith(other)))
/** Alias for cogroup. */
def groupWith[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2])
: JavaPairRDD[K, (JList[V], JList[W1], JList[W2])] =
fromRDD(cogroupResult2ToJava(rdd.groupWith(other1, other2)))
/**
* Return the list of values in the RDD for key `key`. This operation is done efficiently if the
* RDD has a known partitioner by only searching the partition that the key maps to.
*/
def lookup(key: K): JList[V] = seqAsJavaList(rdd.lookup(key))
/** Output the RDD to any Hadoop-supported file system. */
def saveAsHadoopFile[F <: OutputFormat[_, _]](
path: String,
keyClass: Class[_],
valueClass: Class[_],
outputFormatClass: Class[F],
conf: JobConf) {
rdd.saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass, conf)
}
/** Output the RDD to any Hadoop-supported file system. */
def saveAsHadoopFile[F <: OutputFormat[_, _]](
path: String,
keyClass: Class[_],
valueClass: Class[_],
outputFormatClass: Class[F]) {
rdd.saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass)
}
/** Output the RDD to any Hadoop-supported file system. */
def saveAsNewAPIHadoopFile[F <: NewOutputFormat[_, _]](
path: String,
keyClass: Class[_],
valueClass: Class[_],
outputFormatClass: Class[F],
conf: Configuration) {
rdd.saveAsNewAPIHadoopFile(path, keyClass, valueClass, outputFormatClass, conf)
}
/** Output the RDD to any Hadoop-supported file system. */
def saveAsNewAPIHadoopFile[F <: NewOutputFormat[_, _]](
path: String,
keyClass: Class[_],
valueClass: Class[_],
outputFormatClass: Class[F]) {
rdd.saveAsNewAPIHadoopFile(path, keyClass, valueClass, outputFormatClass)
}
/**
* Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object for
* that storage system. The JobConf should set an OutputFormat and any output paths required
* (e.g. a table name to write to) in the same way as it would be configured for a Hadoop
* MapReduce job.
*/
def saveAsHadoopDataset(conf: JobConf) {
rdd.saveAsHadoopDataset(conf)
}
/**
* Sort the RDD by key, so that each partition contains a sorted range of the elements in
* ascending order. Calling `collect` or `save` on the resulting RDD will return or output an
* ordered list of records (in the `save` case, they will be written to multiple `part-X` files
* in the filesystem, in order of the keys).
*/
def sortByKey(): JavaPairRDD[K, V] = sortByKey(true)
/**
* Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
* `collect` or `save` on the resulting RDD will return or output an ordered list of records
* (in the `save` case, they will be written to multiple `part-X` files in the filesystem, in
* order of the keys).
*/
def sortByKey(ascending: Boolean): JavaPairRDD[K, V] = {
val comp = com.google.common.collect.Ordering.natural().asInstanceOf[Comparator[K]]
sortByKey(comp, true)
}
/**
* Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
* `collect` or `save` on the resulting RDD will return or output an ordered list of records
* (in the `save` case, they will be written to multiple `part-X` files in the filesystem, in
* order of the keys).
*/
def sortByKey(comp: Comparator[K]): JavaPairRDD[K, V] = sortByKey(comp, true)
/**
* Sort the RDD by key, so that each partition contains a sorted range of the elements. Calling
* `collect` or `save` on the resulting RDD will return or output an ordered list of records
* (in the `save` case, they will be written to multiple `part-X` files in the filesystem, in
* order of the keys).
*/
def sortByKey(comp: Comparator[K], ascending: Boolean): JavaPairRDD[K, V] = {
class KeyOrdering(val a: K) extends Ordered[K] {
override def compare(b: K) = comp.compare(a, b)
}
implicit def toOrdered(x: K): Ordered[K] = new KeyOrdering(x)
fromRDD(new OrderedRDDFunctions(rdd).sortByKey(ascending))
}
}
object JavaPairRDD {
def groupByResultToJava[K, T](rdd: RDD[(K, Seq[T])])(implicit kcm: ClassManifest[K],
vcm: ClassManifest[T]): RDD[(K, JList[T])] =
rddToPairRDDFunctions(rdd).mapValues(seqAsJavaList _)
def cogroupResultToJava[W, K, V](rdd: RDD[(K, (Seq[V], Seq[W]))])(implicit kcm: ClassManifest[K],
vcm: ClassManifest[V]): RDD[(K, (JList[V], JList[W]))] = rddToPairRDDFunctions(rdd).mapValues((x: (Seq[V],
Seq[W])) => (seqAsJavaList(x._1), seqAsJavaList(x._2)))
def cogroupResult2ToJava[W1, W2, K, V](rdd: RDD[(K, (Seq[V], Seq[W1],
Seq[W2]))])(implicit kcm: ClassManifest[K]) : RDD[(K, (JList[V], JList[W1],
JList[W2]))] = rddToPairRDDFunctions(rdd).mapValues(
(x: (Seq[V], Seq[W1], Seq[W2])) => (seqAsJavaList(x._1),
seqAsJavaList(x._2),
seqAsJavaList(x._3)))
def fromRDD[K: ClassManifest, V: ClassManifest](rdd: RDD[(K, V)]): JavaPairRDD[K, V] =
new JavaPairRDD[K, V](rdd)
implicit def toRDD[K, V](rdd: JavaPairRDD[K, V]): RDD[(K, V)] = rdd.rdd
}
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