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package spark
import java.util.{Date, HashMap => JHashMap}
import java.text.SimpleDateFormat
import scala.collection.Map
import scala.collection.mutable.ArrayBuffer
import scala.collection.mutable.HashMap
import scala.collection.JavaConversions._
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.Path
import org.apache.hadoop.mapred.FileOutputCommitter
import org.apache.hadoop.mapred.FileOutputFormat
import org.apache.hadoop.mapred.HadoopWriter
import org.apache.hadoop.mapred.JobConf
import org.apache.hadoop.mapred.OutputFormat
import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat}
import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat, RecordWriter => NewRecordWriter, Job => NewAPIHadoopJob, HadoopMapReduceUtil, TaskAttemptID, TaskAttemptContext}
import spark.partial.BoundedDouble
import spark.partial.PartialResult
import spark.rdd._
import spark.SparkContext._
/**
* Extra functions available on RDDs of (key, value) pairs through an implicit conversion.
* Import `spark.SparkContext._` at the top of your program to use these functions.
*/
class PairRDDFunctions[K: ClassManifest, V: ClassManifest](
self: RDD[(K, V)])
extends Logging
with HadoopMapReduceUtil
with Serializable {
/**
* Generic function to combine the elements for each key using a custom set of aggregation
* functions. Turns an RDD[(K, V)] into a result of type RDD[(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, Seq[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: V => C,
mergeValue: (C, V) => C,
mergeCombiners: (C, C) => C,
partitioner: Partitioner,
mapSideCombine: Boolean = true): RDD[(K, C)] = {
if (getKeyClass().isArray) {
if (mapSideCombine) {
throw new SparkException("Cannot use map-side combining with array keys.")
}
if (partitioner.isInstanceOf[HashPartitioner]) {
throw new SparkException("Default partitioner cannot partition array keys.")
}
}
val aggregator =
new Aggregator[K, V, C](createCombiner, mergeValue, mergeCombiners)
if (Option(partitioner) == self.partitioner) {
self.mapPartitions(aggregator.combineValuesByKey(_), true)
} else if (mapSideCombine) {
val mapSideCombined = self.mapPartitions(aggregator.combineValuesByKey(_), true)
val partitioned = new ShuffledRDD[K, C](mapSideCombined, partitioner)
partitioned.mapPartitions(aggregator.combineCombinersByKey(_), true)
} else {
// Don't apply map-side combiner.
// A sanity check to make sure mergeCombiners is not defined.
assert(mergeCombiners == null)
val values = new ShuffledRDD[K, V](self, partitioner)
values.mapPartitions(aggregator.combineValuesByKey(_), true)
}
}
/**
* Simplified version of combineByKey that hash-partitions the output RDD.
*/
def combineByKey[C](createCombiner: V => C,
mergeValue: (C, V) => C,
mergeCombiners: (C, C) => C,
numSplits: Int): RDD[(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: (V, V) => V): RDD[(K, V)] = {
combineByKey[V]((v: V) => v, func, func, partitioner)
}
/**
* 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: (V, V) => V): Map[K, V] = {
if (getKeyClass().isArray) {
throw new SparkException("reduceByKeyLocally() does not support array keys")
}
def reducePartition(iter: Iterator[(K, V)]): Iterator[JHashMap[K, V]] = {
val map = new JHashMap[K, V]
for ((k, v) <- iter) {
val old = map.get(k)
map.put(k, if (old == null) v else func(old, v))
}
Iterator(map)
}
def mergeMaps(m1: JHashMap[K, V], m2: JHashMap[K, V]): JHashMap[K, V] = {
for ((k, v) <- m2) {
val old = m1.get(k)
m1.put(k, if (old == null) v else func(old, v))
}
return m1
}
self.mapPartitions(reducePartition).reduce(mergeMaps)
}
/** Alias for reduceByKeyLocally */
def reduceByKeyToDriver(func: (V, V) => V): Map[K, V] = reduceByKeyLocally(func)
/** Count the number of elements for each key, and return the result to the master as a Map. */
def countByKey(): Map[K, Long] = self.map(_._1).countByValue()
/**
* (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[Map[K, BoundedDouble]] = {
self.map(_._1).countByValueApprox(timeout, confidence)
}
/**
* 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: (V, V) => V, numSplits: Int): RDD[(K, V)] = {
reduceByKey(new HashPartitioner(numSplits), func)
}
/**
* 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): RDD[(K, Seq[V])] = {
def createCombiner(v: V) = ArrayBuffer(v)
def mergeValue(buf: ArrayBuffer[V], v: V) = buf += v
def mergeCombiners(b1: ArrayBuffer[V], b2: ArrayBuffer[V]) = b1 ++= b2
val bufs = combineByKey[ArrayBuffer[V]](
createCombiner _, mergeValue _, mergeCombiners _, partitioner)
bufs.asInstanceOf[RDD[(K, Seq[V])]]
}
/**
* 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): RDD[(K, Seq[V])] = {
groupByKey(new HashPartitioner(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, mapSideCombine: Boolean = false): RDD[(K, V)] = {
if (getKeyClass().isArray) {
if (mapSideCombine) {
throw new SparkException("Cannot use map-side combining with array keys.")
}
if (partitioner.isInstanceOf[HashPartitioner]) {
throw new SparkException("Default partitioner cannot partition array keys.")
}
}
if (mapSideCombine) {
def createCombiner(v: V) = ArrayBuffer(v)
def mergeValue(buf: ArrayBuffer[V], v: V) = buf += v
def mergeCombiners(b1: ArrayBuffer[V], b2: ArrayBuffer[V]) = b1 ++= b2
val bufs = combineByKey[ArrayBuffer[V]](
createCombiner _, mergeValue _, mergeCombiners _, partitioner)
bufs.flatMapValues(buf => buf)
} else {
new ShuffledRDD[K, V](self, partitioner)
}
}
/**
* 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`. Uses the given Partitioner to partition the output RDD.
*/
def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] = {
this.cogroup(other, partitioner).flatMapValues {
case (vs, ws) =>
for (v <- vs.iterator; w <- ws.iterator) yield (v, w)
}
}
/**
* 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: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, Option[W]))] = {
this.cogroup(other, partitioner).flatMapValues {
case (vs, ws) =>
if (ws.isEmpty) {
vs.iterator.map(v => (v, None))
} else {
for (v <- vs.iterator; w <- ws.iterator) yield (v, Some(w))
}
}
}
/**
* 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: RDD[(K, W)], partitioner: Partitioner)
: RDD[(K, (Option[V], W))] = {
this.cogroup(other, partitioner).flatMapValues {
case (vs, ws) =>
if (vs.isEmpty) {
ws.iterator.map(w => (None, w))
} else {
for (v <- vs.iterator; w <- ws.iterator) yield (Some(v), w)
}
}
}
/**
* Simplified version of combineByKey that hash-partitions the resulting RDD using the default
* parallelism level.
*/
def combineByKey[C](createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiners: (C, C) => C)
: RDD[(K, C)] = {
combineByKey(createCombiner, mergeValue, mergeCombiners, defaultPartitioner(self))
}
/**
* 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: (V, V) => V): RDD[(K, V)] = {
reduceByKey(defaultPartitioner(self), 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(): RDD[(K, Seq[V])] = {
groupByKey(defaultPartitioner(self))
}
/**
* 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: RDD[(K, W)]): RDD[(K, (V, W))] = {
join(other, defaultPartitioner(self, 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: RDD[(K, W)], numSplits: Int): RDD[(K, (V, W))] = {
join(other, new HashPartitioner(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: RDD[(K, W)]): RDD[(K, (V, Option[W]))] = {
leftOuterJoin(other, defaultPartitioner(self, 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: RDD[(K, W)], numSplits: Int): RDD[(K, (V, Option[W]))] = {
leftOuterJoin(other, new HashPartitioner(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: RDD[(K, W)]): RDD[(K, (Option[V], W))] = {
rightOuterJoin(other, defaultPartitioner(self, 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: RDD[(K, W)], numSplits: Int): RDD[(K, (Option[V], W))] = {
rightOuterJoin(other, new HashPartitioner(numSplits))
}
/**
* Return the key-value pairs in this RDD to the master as a Map.
*/
def collectAsMap(): Map[K, V] = HashMap(self.collect(): _*)
/**
* 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: V => U): RDD[(K, U)] = {
val cleanF = self.context.clean(f)
new MappedValuesRDD(self, cleanF)
}
/**
* 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: V => TraversableOnce[U]): RDD[(K, U)] = {
val cleanF = self.context.clean(f)
new FlatMappedValuesRDD(self, cleanF)
}
/**
* 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: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (Seq[V], Seq[W]))] = {
if (partitioner.isInstanceOf[HashPartitioner] && getKeyClass().isArray) {
throw new SparkException("Default partitioner cannot partition array keys.")
}
val cg = new CoGroupedRDD[K](
Seq(self.asInstanceOf[RDD[(_, _)]], other.asInstanceOf[RDD[(_, _)]]),
partitioner)
val prfs = new PairRDDFunctions[K, Seq[Seq[_]]](cg)(classManifest[K], Manifests.seqSeqManifest)
prfs.mapValues {
case Seq(vs, ws) =>
(vs.asInstanceOf[Seq[V]], ws.asInstanceOf[Seq[W]])
}
}
/**
* 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: RDD[(K, W1)], other2: RDD[(K, W2)], partitioner: Partitioner)
: RDD[(K, (Seq[V], Seq[W1], Seq[W2]))] = {
if (partitioner.isInstanceOf[HashPartitioner] && getKeyClass().isArray) {
throw new SparkException("Default partitioner cannot partition array keys.")
}
val cg = new CoGroupedRDD[K](
Seq(self.asInstanceOf[RDD[(_, _)]],
other1.asInstanceOf[RDD[(_, _)]],
other2.asInstanceOf[RDD[(_, _)]]),
partitioner)
val prfs = new PairRDDFunctions[K, Seq[Seq[_]]](cg)(classManifest[K], Manifests.seqSeqManifest)
prfs.mapValues {
case Seq(vs, w1s, w2s) =>
(vs.asInstanceOf[Seq[V]], w1s.asInstanceOf[Seq[W1]], w2s.asInstanceOf[Seq[W2]])
}
}
/**
* 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: RDD[(K, W)]): RDD[(K, (Seq[V], Seq[W]))] = {
cogroup(other, defaultPartitioner(self, 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: RDD[(K, W1)], other2: RDD[(K, W2)])
: RDD[(K, (Seq[V], Seq[W1], Seq[W2]))] = {
cogroup(other1, other2, defaultPartitioner(self, 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: RDD[(K, W)], numSplits: Int): RDD[(K, (Seq[V], Seq[W]))] = {
cogroup(other, new HashPartitioner(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: RDD[(K, W1)], other2: RDD[(K, W2)], numSplits: Int)
: RDD[(K, (Seq[V], Seq[W1], Seq[W2]))] = {
cogroup(other1, other2, new HashPartitioner(numSplits))
}
/** Alias for cogroup. */
def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Seq[V], Seq[W]))] = {
cogroup(other, defaultPartitioner(self, other))
}
/** Alias for cogroup. */
def groupWith[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)])
: RDD[(K, (Seq[V], Seq[W1], Seq[W2]))] = {
cogroup(other1, other2, defaultPartitioner(self, other1, other2))
}
/**
* Choose a partitioner to use for a cogroup-like operation between a number of RDDs. If any of
* the RDDs already has a partitioner, choose that one, otherwise use a default HashPartitioner.
*/
def defaultPartitioner(rdds: RDD[_]*): Partitioner = {
for (r <- rdds if r.partitioner != None) {
return r.partitioner.get
}
return new HashPartitioner(self.context.defaultParallelism)
}
/**
* 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): Seq[V] = {
self.partitioner match {
case Some(p) =>
val index = p.getPartition(key)
def process(it: Iterator[(K, V)]): Seq[V] = {
val buf = new ArrayBuffer[V]
for ((k, v) <- it if k == key) {
buf += v
}
buf
}
val res = self.context.runJob(self, process _, Array(index), false)
res(0)
case None =>
self.filter(_._1 == key).map(_._2).collect()
}
}
/**
* Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class
* supporting the key and value types K and V in this RDD.
*/
def saveAsHadoopFile[F <: OutputFormat[K, V]](path: String)(implicit fm: ClassManifest[F]) {
saveAsHadoopFile(path, getKeyClass, getValueClass, fm.erasure.asInstanceOf[Class[F]])
}
/**
* Output the RDD to any Hadoop-supported file system, using a new Hadoop API `OutputFormat`
* (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.
*/
def saveAsNewAPIHadoopFile[F <: NewOutputFormat[K, V]](path: String)(implicit fm: ClassManifest[F]) {
saveAsNewAPIHadoopFile(path, getKeyClass, getValueClass, fm.erasure.asInstanceOf[Class[F]])
}
/**
* Output the RDD to any Hadoop-supported file system, using a new Hadoop API `OutputFormat`
* (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.
*/
def saveAsNewAPIHadoopFile(
path: String,
keyClass: Class[_],
valueClass: Class[_],
outputFormatClass: Class[_ <: NewOutputFormat[_, _]],
conf: Configuration = self.context.hadoopConfiguration) {
val job = new NewAPIHadoopJob(conf)
job.setOutputKeyClass(keyClass)
job.setOutputValueClass(valueClass)
val wrappedConf = new SerializableWritable(job.getConfiguration)
NewFileOutputFormat.setOutputPath(job, new Path(path))
val formatter = new SimpleDateFormat("yyyyMMddHHmm")
val jobtrackerID = formatter.format(new Date())
val stageId = self.id
def writeShard(context: spark.TaskContext, iter: Iterator[(K,V)]): Int = {
// Hadoop wants a 32-bit task attempt ID, so if ours is bigger than Int.MaxValue, roll it
// around by taking a mod. We expect that no task will be attempted 2 billion times.
val attemptNumber = (context.attemptId % Int.MaxValue).toInt
/* "reduce task" <split #> <attempt # = spark task #> */
val attemptId = new TaskAttemptID(jobtrackerID,
stageId, false, context.splitId, attemptNumber)
val hadoopContext = newTaskAttemptContext(wrappedConf.value, attemptId)
val format = outputFormatClass.newInstance
val committer = format.getOutputCommitter(hadoopContext)
committer.setupTask(hadoopContext)
val writer = format.getRecordWriter(hadoopContext).asInstanceOf[NewRecordWriter[K,V]]
while (iter.hasNext) {
val (k, v) = iter.next
writer.write(k, v)
}
writer.close(hadoopContext)
committer.commitTask(hadoopContext)
return 1
}
val jobFormat = outputFormatClass.newInstance
/* apparently we need a TaskAttemptID to construct an OutputCommitter;
* however we're only going to use this local OutputCommitter for
* setupJob/commitJob, so we just use a dummy "map" task.
*/
val jobAttemptId = new TaskAttemptID(jobtrackerID, stageId, true, 0, 0)
val jobTaskContext = newTaskAttemptContext(wrappedConf.value, jobAttemptId)
val jobCommitter = jobFormat.getOutputCommitter(jobTaskContext)
jobCommitter.setupJob(jobTaskContext)
val count = self.context.runJob(self, writeShard _).sum
jobCommitter.cleanupJob(jobTaskContext)
}
/**
* Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class
* supporting the key and value types K and V in this RDD.
*/
def saveAsHadoopFile(
path: String,
keyClass: Class[_],
valueClass: Class[_],
outputFormatClass: Class[_ <: OutputFormat[_, _]],
conf: JobConf = new JobConf(self.context.hadoopConfiguration)) {
conf.setOutputKeyClass(keyClass)
conf.setOutputValueClass(valueClass)
// conf.setOutputFormat(outputFormatClass) // Doesn't work in Scala 2.9 due to what may be a generics bug
conf.set("mapred.output.format.class", outputFormatClass.getName)
conf.setOutputCommitter(classOf[FileOutputCommitter])
FileOutputFormat.setOutputPath(conf, HadoopWriter.createPathFromString(path, conf))
saveAsHadoopDataset(conf)
}
/**
* 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) {
val outputFormatClass = conf.getOutputFormat
val keyClass = conf.getOutputKeyClass
val valueClass = conf.getOutputValueClass
if (outputFormatClass == null) {
throw new SparkException("Output format class not set")
}
if (keyClass == null) {
throw new SparkException("Output key class not set")
}
if (valueClass == null) {
throw new SparkException("Output value class not set")
}
logInfo("Saving as hadoop file of type (" + keyClass.getSimpleName+ ", " + valueClass.getSimpleName+ ")")
val writer = new HadoopWriter(conf)
writer.preSetup()
def writeToFile(context: TaskContext, iter: Iterator[(K,V)]) {
// Hadoop wants a 32-bit task attempt ID, so if ours is bigger than Int.MaxValue, roll it
// around by taking a mod. We expect that no task will be attempted 2 billion times.
val attemptNumber = (context.attemptId % Int.MaxValue).toInt
writer.setup(context.stageId, context.splitId, attemptNumber)
writer.open()
var count = 0
while(iter.hasNext) {
val record = iter.next()
count += 1
writer.write(record._1.asInstanceOf[AnyRef], record._2.asInstanceOf[AnyRef])
}
writer.close()
writer.commit()
}
self.context.runJob(self, writeToFile _)
writer.cleanup()
}
/**
* Return an RDD with the keys of each tuple.
*/
def keys: RDD[K] = self.map(_._1)
/**
* Return an RDD with the values of each tuple.
*/
def values: RDD[V] = self.map(_._2)
private[spark] def getKeyClass() = implicitly[ClassManifest[K]].erasure
private[spark] def getValueClass() = implicitly[ClassManifest[V]].erasure
}
/**
* Extra functions available on RDDs of (key, value) pairs where the key is sortable through
* an implicit conversion. Import `spark.SparkContext._` at the top of your program to use these
* functions. They will work with any key type that has a `scala.math.Ordered` implementation.
*/
class OrderedRDDFunctions[K <% Ordered[K]: ClassManifest, V: ClassManifest](
self: RDD[(K, V)])
extends Logging
with Serializable {
/**
* 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 = true, numSplits: Int = self.splits.size): RDD[(K,V)] = {
val shuffled =
new ShuffledRDD[K, V](self, new RangePartitioner(numSplits, self, ascending))
shuffled.mapPartitions(iter => {
val buf = iter.toArray
if (ascending) {
buf.sortWith((x, y) => x._1 < y._1).iterator
} else {
buf.sortWith((x, y) => x._1 > y._1).iterator
}
}, true)
}
}
private[spark]
class MappedValuesRDD[K, V, U](prev: RDD[(K, V)], f: V => U) extends RDD[(K, U)](prev) {
override def getSplits = firstParent[(K, V)].splits
override val partitioner = firstParent[(K, V)].partitioner
override def compute(split: Split, context: TaskContext) =
firstParent[(K, V)].iterator(split, context).map{ case (k, v) => (k, f(v)) }
}
private[spark]
class FlatMappedValuesRDD[K, V, U](prev: RDD[(K, V)], f: V => TraversableOnce[U])
extends RDD[(K, U)](prev) {
override def getSplits = firstParent[(K, V)].splits
override val partitioner = firstParent[(K, V)].partitioner
override def compute(split: Split, context: TaskContext) = {
firstParent[(K, V)].iterator(split, context).flatMap { case (k, v) => f(v).map(x => (k, x)) }
}
}
private[spark] object Manifests {
val seqSeqManifest = classManifest[Seq[Seq[_]]]
}
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