spark PairRDDFunctions 源码
spark PairRDDFunctions 代码
文件路径：/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
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* The ASF licenses this file to You under the Apache License, Version 2.0
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*
* http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.spark.rdd
import java.nio.ByteBuffer
import java.util.{HashMap => JHashMap}
import scala.collection.{mutable, Map}
import scala.collection.JavaConverters._
import scala.collection.mutable.ArrayBuffer
import scala.reflect.ClassTag
import com.clearspring.analytics.stream.cardinality.HyperLogLogPlus
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.io.SequenceFile.CompressionType
import org.apache.hadoop.io.compress.CompressionCodec
import org.apache.hadoop.mapred.{FileOutputCommitter, FileOutputFormat, JobConf, OutputFormat}
import org.apache.hadoop.mapreduce.{Job => NewAPIHadoopJob, OutputFormat => NewOutputFormat}
import org.apache.spark._
import org.apache.spark.Partitioner.defaultPartitioner
import org.apache.spark.errors.SparkCoreErrors
import org.apache.spark.internal.Logging
import org.apache.spark.internal.config.SPECULATION_ENABLED
import org.apache.spark.internal.io._
import org.apache.spark.partial.{BoundedDouble, PartialResult}
import org.apache.spark.serializer.Serializer
import org.apache.spark.util.{SerializableConfiguration, SerializableJobConf, Utils}
import org.apache.spark.util.collection.CompactBuffer
import org.apache.spark.util.random.StratifiedSamplingUtils
/**
* Extra functions available on RDDs of (key, value) pairs through an implicit conversion.
*/
class PairRDDFunctions[K, V](self: RDD[(K, V)])
(implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null)
extends Logging 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
*
* 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).
*
* @note 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]).
*/
def combineByKeyWithClassTag[C](
createCombiner: V => C,
mergeValue: (C, V) => C,
mergeCombiners: (C, C) => C,
partitioner: Partitioner,
mapSideCombine: Boolean = true,
serializer: Serializer = null)(implicit ct: ClassTag[C]): RDD[(K, C)] = self.withScope {
require(mergeCombiners != null, "mergeCombiners must be defined") // required as of Spark 0.9.0
if (keyClass.isArray) {
if (mapSideCombine) {
throw SparkCoreErrors.cannotUseMapSideCombiningWithArrayKeyError()
}
if (partitioner.isInstanceOf[HashPartitioner]) {
throw SparkCoreErrors.hashPartitionerCannotPartitionArrayKeyError()
}
}
val aggregator = new Aggregator[K, V, C](
self.context.clean(createCombiner),
self.context.clean(mergeValue),
self.context.clean(mergeCombiners))
if (self.partitioner == Some(partitioner)) {
self.mapPartitions(iter => {
val context = TaskContext.get()
new InterruptibleIterator(context, aggregator.combineValuesByKey(iter, context))
}, preservesPartitioning = true)
} else {
new ShuffledRDD[K, V, C](self, partitioner)
.setSerializer(serializer)
.setAggregator(aggregator)
.setMapSideCombine(mapSideCombine)
}
}
/**
* Generic function to combine the elements for each key using a custom set of aggregation
* functions. This method is here for backward compatibility. It does not provide combiner
* classtag information to the shuffle.
*
* @see `combineByKeyWithClassTag`
*/
def combineByKey[C](
createCombiner: V => C,
mergeValue: (C, V) => C,
mergeCombiners: (C, C) => C,
partitioner: Partitioner,
mapSideCombine: Boolean = true,
serializer: Serializer = null): RDD[(K, C)] = self.withScope {
combineByKeyWithClassTag(createCombiner, mergeValue, mergeCombiners,
partitioner, mapSideCombine, serializer)(null)
}
/**
* Simplified version of combineByKeyWithClassTag that hash-partitions the output RDD.
* This method is here for backward compatibility. It does not provide combiner
* classtag information to the shuffle.
*
* @see `combineByKeyWithClassTag`
*/
def combineByKey[C](
createCombiner: V => C,
mergeValue: (C, V) => C,
mergeCombiners: (C, C) => C,
numPartitions: Int): RDD[(K, C)] = self.withScope {
combineByKeyWithClassTag(createCombiner, mergeValue, mergeCombiners, numPartitions)(null)
}
/**
* Simplified version of combineByKeyWithClassTag that hash-partitions the output RDD.
*/
def combineByKeyWithClassTag[C](
createCombiner: V => C,
mergeValue: (C, V) => C,
mergeCombiners: (C, C) => C,
numPartitions: Int)(implicit ct: ClassTag[C]): RDD[(K, C)] = self.withScope {
combineByKeyWithClassTag(createCombiner, mergeValue, mergeCombiners,
new HashPartitioner(numPartitions))
}
/**
* Aggregate the values of each key, using given combine functions and a neutral "zero value".
* This function can return a different result type, U, than the type of the values in this RDD,
* V. Thus, we need one operation for merging a V into a U and one operation for merging two U's,
* as in scala.TraversableOnce. The former operation is used for merging values within a
* partition, and the latter is used for merging values between partitions. To avoid memory
* allocation, both of these functions are allowed to modify and return their first argument
* instead of creating a new U.
*/
def aggregateByKey[U: ClassTag](zeroValue: U, partitioner: Partitioner)(seqOp: (U, V) => U,
combOp: (U, U) => U): RDD[(K, U)] = self.withScope {
// Serialize the zero value to a byte array so that we can get a new clone of it on each key
val zeroBuffer = SparkEnv.get.serializer.newInstance().serialize(zeroValue)
val zeroArray = new Array[Byte](zeroBuffer.limit)
zeroBuffer.get(zeroArray)
lazy val cachedSerializer = SparkEnv.get.serializer.newInstance()
val createZero = () => cachedSerializer.deserialize[U](ByteBuffer.wrap(zeroArray))
// We will clean the combiner closure later in `combineByKey`
val cleanedSeqOp = self.context.clean(seqOp)
combineByKeyWithClassTag[U]((v: V) => cleanedSeqOp(createZero(), v),
cleanedSeqOp, combOp, partitioner)
}
/**
* Aggregate the values of each key, using given combine functions and a neutral "zero value".
* This function can return a different result type, U, than the type of the values in this RDD,
* V. Thus, we need one operation for merging a V into a U and one operation for merging two U's,
* as in scala.TraversableOnce. The former operation is used for merging values within a
* partition, and the latter is used for merging values between partitions. To avoid memory
* allocation, both of these functions are allowed to modify and return their first argument
* instead of creating a new U.
*/
def aggregateByKey[U: ClassTag](zeroValue: U, numPartitions: Int)(seqOp: (U, V) => U,
combOp: (U, U) => U): RDD[(K, U)] = self.withScope {
aggregateByKey(zeroValue, new HashPartitioner(numPartitions))(seqOp, combOp)
}
/**
* Aggregate the values of each key, using given combine functions and a neutral "zero value".
* This function can return a different result type, U, than the type of the values in this RDD,
* V. Thus, we need one operation for merging a V into a U and one operation for merging two U's,
* as in scala.TraversableOnce. The former operation is used for merging values within a
* partition, and the latter is used for merging values between partitions. To avoid memory
* allocation, both of these functions are allowed to modify and return their first argument
* instead of creating a new U.
*/
def aggregateByKey[U: ClassTag](zeroValue: U)(seqOp: (U, V) => U,
combOp: (U, U) => U): RDD[(K, U)] = self.withScope {
aggregateByKey(zeroValue, defaultPartitioner(self))(seqOp, combOp)
}
/**
* Merge the values for each key using an associative function and a neutral "zero value" which
* may be added to the result an arbitrary number of times, and must not change the result
* (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
*/
def foldByKey(
zeroValue: V,
partitioner: Partitioner)(func: (V, V) => V): RDD[(K, V)] = self.withScope {
// Serialize the zero value to a byte array so that we can get a new clone of it on each key
val zeroBuffer = SparkEnv.get.serializer.newInstance().serialize(zeroValue)
val zeroArray = new Array[Byte](zeroBuffer.limit)
zeroBuffer.get(zeroArray)
// When deserializing, use a lazy val to create just one instance of the serializer per task
lazy val cachedSerializer = SparkEnv.get.serializer.newInstance()
val createZero = () => cachedSerializer.deserialize[V](ByteBuffer.wrap(zeroArray))
val cleanedFunc = self.context.clean(func)
combineByKeyWithClassTag[V]((v: V) => cleanedFunc(createZero(), v),
cleanedFunc, cleanedFunc, partitioner)
}
/**
* Merge the values for each key using an associative function and a neutral "zero value" which
* may be added to the result an arbitrary number of times, and must not change the result
* (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
*/
def foldByKey(zeroValue: V, numPartitions: Int)(func: (V, V) => V): RDD[(K, V)] = self.withScope {
foldByKey(zeroValue, new HashPartitioner(numPartitions))(func)
}
/**
* Merge the values for each key using an associative function and a neutral "zero value" which
* may be added to the result an arbitrary number of times, and must not change the result
* (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
*/
def foldByKey(zeroValue: V)(func: (V, V) => V): RDD[(K, V)] = self.withScope {
foldByKey(zeroValue, defaultPartitioner(self))(func)
}
/**
* Return a subset of this RDD sampled by key (via stratified sampling).
*
* Create a sample of this RDD using variable sampling rates for different keys as specified by
* `fractions`, a key to sampling rate map, via simple random sampling with one pass over the
* RDD, to produce a sample of size that's approximately equal to the sum of
* math.ceil(numItems * samplingRate) over all key values.
*
* @param withReplacement whether to sample with or without replacement
* @param fractions map of specific keys to sampling rates
* @param seed seed for the random number generator
* @return RDD containing the sampled subset
*/
def sampleByKey(withReplacement: Boolean,
fractions: Map[K, Double],
seed: Long = Utils.random.nextLong): RDD[(K, V)] = self.withScope {
require(fractions.values.forall(v => v >= 0.0), "Negative sampling rates.")
val samplingFunc = if (withReplacement) {
StratifiedSamplingUtils.getPoissonSamplingFunction(self, fractions, false, seed)
} else {
StratifiedSamplingUtils.getBernoulliSamplingFunction(self, fractions, false, seed)
}
self.mapPartitionsWithIndex(samplingFunc, preservesPartitioning = true, isOrderSensitive = true)
}
/**
* Return a subset of this RDD sampled by key (via stratified sampling) containing exactly
* math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
*
* This method differs from [[sampleByKey]] in that we make additional passes over the RDD to
* create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate)
* over all key values with a 99.99% confidence. When sampling without replacement, we need one
* additional pass over the RDD to guarantee sample size; when sampling with replacement, we need
* two additional passes.
*
* @param withReplacement whether to sample with or without replacement
* @param fractions map of specific keys to sampling rates
* @param seed seed for the random number generator
* @return RDD containing the sampled subset
*/
def sampleByKeyExact(
withReplacement: Boolean,
fractions: Map[K, Double],
seed: Long = Utils.random.nextLong): RDD[(K, V)] = self.withScope {
require(fractions.values.forall(v => v >= 0.0), "Negative sampling rates.")
val samplingFunc = if (withReplacement) {
StratifiedSamplingUtils.getPoissonSamplingFunction(self, fractions, true, seed)
} else {
StratifiedSamplingUtils.getBernoulliSamplingFunction(self, fractions, true, seed)
}
self.mapPartitionsWithIndex(samplingFunc, preservesPartitioning = true, isOrderSensitive = true)
}
/**
* Merge the values for each key using an associative and commutative 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)] = self.withScope {
combineByKeyWithClassTag[V]((v: V) => v, func, func, partitioner)
}
/**
* Merge the values for each key using an associative and commutative 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 numPartitions partitions.
*/
def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = self.withScope {
reduceByKey(new HashPartitioner(numPartitions), func)
}
/**
* Merge the values for each key using an associative and commutative 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 existing partitioner/
* parallelism level.
*/
def reduceByKey(func: (V, V) => V): RDD[(K, V)] = self.withScope {
reduceByKey(defaultPartitioner(self), func)
}
/**
* Merge the values for each key using an associative and commutative 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] = self.withScope {
val cleanedF = self.sparkContext.clean(func)
if (keyClass.isArray) {
throw SparkCoreErrors.reduceByKeyLocallyNotSupportArrayKeysError()
}
val reducePartition = (iter: Iterator[(K, V)]) => {
val map = new JHashMap[K, V]
iter.foreach { pair =>
val old = map.get(pair._1)
map.put(pair._1, if (old == null) pair._2 else cleanedF(old, pair._2))
}
Iterator(map)
} : Iterator[JHashMap[K, V]]
val mergeMaps = (m1: JHashMap[K, V], m2: JHashMap[K, V]) => {
m2.asScala.foreach { pair =>
val old = m1.get(pair._1)
m1.put(pair._1, if (old == null) pair._2 else cleanedF(old, pair._2))
}
m1
} : JHashMap[K, V]
self.mapPartitions(reducePartition).reduce(mergeMaps).asScala
}
/**
* Count the number of elements for each key, collecting the results to a local Map.
*
* @note This method should only be used if the resulting map is expected to be small, as
* the whole thing is loaded into the driver's memory.
* To handle very large results, consider using rdd.mapValues(_ => 1L).reduceByKey(_ + _), which
* returns an RDD[T, Long] instead of a map.
*/
def countByKey(): Map[K, Long] = self.withScope {
self.mapValues(_ => 1L).reduceByKey(_ + _).collect().toMap
}
/**
* Approximate version of countByKey that can return a partial result if it does
* not finish within a timeout.
*
* The confidence is the probability that the error bounds of the result will
* contain the true value. That is, if countApprox were called repeatedly
* with confidence 0.9, we would expect 90% of the results to contain the
* true count. The confidence must be in the range [0,1] or an exception will
* be thrown.
*
* @param timeout maximum time to wait for the job, in milliseconds
* @param confidence the desired statistical confidence in the result
* @return a potentially incomplete result, with error bounds
*/
def countByKeyApprox(timeout: Long, confidence: Double = 0.95)
: PartialResult[Map[K, BoundedDouble]] = self.withScope {
self.map(_._1).countByValueApprox(timeout, confidence)
}
/**
* Return approximate number of distinct values for each key in this RDD.
*
* The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
* Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
* <a href="https://doi.org/10.1145/2452376.2452456">here</a>.
*
* The relative accuracy is approximately `1.054 / sqrt(2^p)`. Setting a nonzero (`sp` is
* greater than `p`) would trigger sparse representation of registers, which may reduce the
* memory consumption and increase accuracy when the cardinality is small.
*
* @param p The precision value for the normal set.
* `p` must be a value between 4 and `sp` if `sp` is not zero (32 max).
* @param sp The precision value for the sparse set, between 0 and 32.
* If `sp` equals 0, the sparse representation is skipped.
* @param partitioner Partitioner to use for the resulting RDD.
*/
def countApproxDistinctByKey(
p: Int,
sp: Int,
partitioner: Partitioner): RDD[(K, Long)] = self.withScope {
require(p >= 4, s"p ($p) must be >= 4")
require(sp <= 32, s"sp ($sp) must be <= 32")
require(sp == 0 || p <= sp, s"p ($p) cannot be greater than sp ($sp)")
val createHLL = (v: V) => {
val hll = new HyperLogLogPlus(p, sp)
hll.offer(v)
hll
}
val mergeValueHLL = (hll: HyperLogLogPlus, v: V) => {
hll.offer(v)
hll
}
val mergeHLL = (h1: HyperLogLogPlus, h2: HyperLogLogPlus) => {
h1.addAll(h2)
h1
}
combineByKeyWithClassTag(createHLL, mergeValueHLL, mergeHLL, partitioner)
.mapValues(_.cardinality())
}
/**
* Return approximate number of distinct values for each key in this RDD.
*
* The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
* Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
* <a href="https://doi.org/10.1145/2452376.2452456">here</a>.
*
* @param relativeSD Relative accuracy. Smaller values create counters that require more space.
* It must be greater than 0.000017.
* @param partitioner partitioner of the resulting RDD
*/
def countApproxDistinctByKey(
relativeSD: Double,
partitioner: Partitioner): RDD[(K, Long)] = self.withScope {
require(relativeSD > 0.000017, s"accuracy ($relativeSD) must be greater than 0.000017")
val p = math.ceil(2.0 * math.log(1.054 / relativeSD) / math.log(2)).toInt
assert(p <= 32)
countApproxDistinctByKey(if (p < 4) 4 else p, 0, partitioner)
}
/**
* Return approximate number of distinct values for each key in this RDD.
*
* The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
* Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
* <a href="https://doi.org/10.1145/2452376.2452456">here</a>.
*
* @param relativeSD Relative accuracy. Smaller values create counters that require more space.
* It must be greater than 0.000017.
* @param numPartitions number of partitions of the resulting RDD
*/
def countApproxDistinctByKey(
relativeSD: Double,
numPartitions: Int): RDD[(K, Long)] = self.withScope {
countApproxDistinctByKey(relativeSD, new HashPartitioner(numPartitions))
}
/**
* Return approximate number of distinct values for each key in this RDD.
*
* The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
* Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
* <a href="https://doi.org/10.1145/2452376.2452456">here</a>.
*
* @param relativeSD Relative accuracy. Smaller values create counters that require more space.
* It must be greater than 0.000017.
*/
def countApproxDistinctByKey(relativeSD: Double = 0.05): RDD[(K, Long)] = self.withScope {
countApproxDistinctByKey(relativeSD, defaultPartitioner(self))
}
/**
* 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.
* The ordering of elements within each group is not guaranteed, and may even differ
* each time the resulting RDD is evaluated.
*
* @note This operation may be very expensive. If you are grouping in order to perform an
* aggregation (such as a sum or average) over each key, using `PairRDDFunctions.aggregateByKey`
* or `PairRDDFunctions.reduceByKey` will provide much better performance.
*
* @note As currently implemented, groupByKey must be able to hold all the key-value pairs for any
* key in memory. If a key has too many values, it can result in an `OutOfMemoryError`.
*/
def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = self.withScope {
// groupByKey shouldn't use map side combine because map side combine does not
// reduce the amount of data shuffled and requires all map side data be inserted
// into a hash table, leading to more objects in the old gen.
val createCombiner = (v: V) => CompactBuffer(v)
val mergeValue = (buf: CompactBuffer[V], v: V) => buf += v
val mergeCombiners = (c1: CompactBuffer[V], c2: CompactBuffer[V]) => c1 ++= c2
val bufs = combineByKeyWithClassTag[CompactBuffer[V]](
createCombiner, mergeValue, mergeCombiners, partitioner, mapSideCombine = false)
bufs.asInstanceOf[RDD[(K, Iterable[V])]]
}
/**
* Group the values for each key in the RDD into a single sequence. Hash-partitions the
* resulting RDD with into `numPartitions` partitions. The ordering of elements within
* each group is not guaranteed, and may even differ each time the resulting RDD is evaluated.
*
* @note This operation may be very expensive. If you are grouping in order to perform an
* aggregation (such as a sum or average) over each key, using `PairRDDFunctions.aggregateByKey`
* or `PairRDDFunctions.reduceByKey` will provide much better performance.
*
* @note As currently implemented, groupByKey must be able to hold all the key-value pairs for any
* key in memory. If a key has too many values, it can result in an `OutOfMemoryError`.
*/
def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])] = self.withScope {
groupByKey(new HashPartitioner(numPartitions))
}
/**
* Return a copy of the RDD partitioned using the specified partitioner.
*/
def partitionBy(partitioner: Partitioner): RDD[(K, V)] = self.withScope {
if (keyClass.isArray && partitioner.isInstanceOf[HashPartitioner]) {
throw SparkCoreErrors.hashPartitionerCannotPartitionArrayKeyError()
}
if (self.partitioner == Some(partitioner)) {
self
} else {
new ShuffledRDD[K, V, 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))] = self.withScope {
this.cogroup(other, partitioner).flatMapValues( pair =>
for (v <- pair._1.iterator; w <- pair._2.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]))] = self.withScope {
this.cogroup(other, partitioner).flatMapValues { pair =>
if (pair._2.isEmpty) {
pair._1.iterator.map(v => (v, None))
} else {
for (v <- pair._1.iterator; w <- pair._2.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))] = self.withScope {
this.cogroup(other, partitioner).flatMapValues { pair =>
if (pair._1.isEmpty) {
pair._2.iterator.map(w => (None, w))
} else {
for (v <- pair._1.iterator; w <- pair._2.iterator) yield (Some(v), w)
}
}
}
/**
* Perform a full outer join of `this` and `other`. For each element (k, v) in `this`, the
* resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in `other`, or
* the pair (k, (Some(v), None)) if no elements in `other` have key k. Similarly, for each
* element (k, w) in `other`, the resulting RDD will either contain all pairs
* (k, (Some(v), Some(w))) for v in `this`, or the pair (k, (None, Some(w))) if no elements
* in `this` have key k. Uses the given Partitioner to partition the output RDD.
*/
def fullOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner)
: RDD[(K, (Option[V], Option[W]))] = self.withScope {
this.cogroup(other, partitioner).flatMapValues {
case (vs, Seq()) => vs.iterator.map(v => (Some(v), None))
case (Seq(), ws) => ws.iterator.map(w => (None, Some(w)))
case (vs, ws) => for (v <- vs.iterator; w <- ws.iterator) yield (Some(v), Some(w))
}
}
/**
* Simplified version of combineByKeyWithClassTag that hash-partitions the resulting RDD using the
* existing partitioner/parallelism level. This method is here for backward compatibility. It
* does not provide combiner classtag information to the shuffle.
*
* @see `combineByKeyWithClassTag`
*/
def combineByKey[C](
createCombiner: V => C,
mergeValue: (C, V) => C,
mergeCombiners: (C, C) => C): RDD[(K, C)] = self.withScope {
combineByKeyWithClassTag(createCombiner, mergeValue, mergeCombiners)(null)
}
/**
* Simplified version of combineByKeyWithClassTag that hash-partitions the resulting RDD using the
* existing partitioner/parallelism level.
*/
def combineByKeyWithClassTag[C](
createCombiner: V => C,
mergeValue: (C, V) => C,
mergeCombiners: (C, C) => C)(implicit ct: ClassTag[C]): RDD[(K, C)] = self.withScope {
combineByKeyWithClassTag(createCombiner, mergeValue, mergeCombiners, defaultPartitioner(self))
}
/**
* Group the values for each key in the RDD into a single sequence. Hash-partitions the
* resulting RDD with the existing partitioner/parallelism level. The ordering of elements
* within each group is not guaranteed, and may even differ each time the resulting RDD is
* evaluated.
*
* @note This operation may be very expensive. If you are grouping in order to perform an
* aggregation (such as a sum or average) over each key, using `PairRDDFunctions.aggregateByKey`
* or `PairRDDFunctions.reduceByKey` will provide much better performance.
*/
def groupByKey(): RDD[(K, Iterable[V])] = self.withScope {
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))] = self.withScope {
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)], numPartitions: Int): RDD[(K, (V, W))] = self.withScope {
join(other, new HashPartitioner(numPartitions))
}
/**
* 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 existing partitioner/parallelism level.
*/
def leftOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (V, Option[W]))] = self.withScope {
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 `numPartitions` partitions.
*/
def leftOuterJoin[W](
other: RDD[(K, W)],
numPartitions: Int): RDD[(K, (V, Option[W]))] = self.withScope {
leftOuterJoin(other, new HashPartitioner(numPartitions))
}
/**
* 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 existing partitioner/parallelism level.
*/
def rightOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], W))] = self.withScope {
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)],
numPartitions: Int): RDD[(K, (Option[V], W))] = self.withScope {
rightOuterJoin(other, new HashPartitioner(numPartitions))
}
/**
* Perform a full outer join of `this` and `other`. For each element (k, v) in `this`, the
* resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in `other`, or
* the pair (k, (Some(v), None)) if no elements in `other` have key k. Similarly, for each
* element (k, w) in `other`, the resulting RDD will either contain all pairs
* (k, (Some(v), Some(w))) for v in `this`, or the pair (k, (None, Some(w))) if no elements
* in `this` have key k. Hash-partitions the resulting RDD using the existing partitioner/
* parallelism level.
*/
def fullOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], Option[W]))] = self.withScope {
fullOuterJoin(other, defaultPartitioner(self, other))
}
/**
* Perform a full outer join of `this` and `other`. For each element (k, v) in `this`, the
* resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in `other`, or
* the pair (k, (Some(v), None)) if no elements in `other` have key k. Similarly, for each
* element (k, w) in `other`, the resulting RDD will either contain all pairs
* (k, (Some(v), Some(w))) for v in `this`, or the pair (k, (None, Some(w))) if no elements
* in `this` have key k. Hash-partitions the resulting RDD into the given number of partitions.
*/
def fullOuterJoin[W](
other: RDD[(K, W)],
numPartitions: Int): RDD[(K, (Option[V], Option[W]))] = self.withScope {
fullOuterJoin(other, new HashPartitioner(numPartitions))
}
/**
* Return the key-value pairs in this RDD to the master as a Map.
*
* Warning: this doesn't return a multimap (so if you have multiple values to the same key, only
* one value per key is preserved in the map returned)
*
* @note this method should only be used if the resulting data is expected to be small, as
* all the data is loaded into the driver's memory.
*/
def collectAsMap(): Map[K, V] = self.withScope {
val data = self.collect()
val map = new mutable.HashMap[K, V]
map.sizeHint(data.length)
data.foreach { pair => map.put(pair._1, pair._2) }
map
}
/**
* 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)] = self.withScope {
val cleanF = self.context.clean(f)
new MapPartitionsRDD[(K, U), (K, V)](self,
(context, pid, iter) => iter.map { case (k, v) => (k, cleanF(v)) },
preservesPartitioning = true)
}
/**
* 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)] = self.withScope {
val cleanF = self.context.clean(f)
new MapPartitionsRDD[(K, U), (K, V)](self,
(context, pid, iter) => iter.flatMap { case (k, v) =>
cleanF(v).map(x => (k, x))
},
preservesPartitioning = true)
}
/**
* For each key k in `this` or `other1` or `other2` or `other3`,
* return a resulting RDD that contains a tuple with the list of values
* for that key in `this`, `other1`, `other2` and `other3`.
*/
def cogroup[W1, W2, W3](other1: RDD[(K, W1)],
other2: RDD[(K, W2)],
other3: RDD[(K, W3)],
partitioner: Partitioner)
: RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope {
if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) {
throw SparkCoreErrors.hashPartitionerCannotPartitionArrayKeyError()
}
val cg = new CoGroupedRDD[K](Seq(self, other1, other2, other3), partitioner)
cg.mapValues { case Array(vs, w1s, w2s, w3s) =>
(vs.asInstanceOf[Iterable[V]],
w1s.asInstanceOf[Iterable[W1]],
w2s.asInstanceOf[Iterable[W2]],
w3s.asInstanceOf[Iterable[W3]])
}
}
/**
* 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, (Iterable[V], Iterable[W]))] = self.withScope {
if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) {
throw SparkCoreErrors.hashPartitionerCannotPartitionArrayKeyError()
}
val cg = new CoGroupedRDD[K](Seq(self, other), partitioner)
cg.mapValues { case Array(vs, w1s) =>
(vs.asInstanceOf[Iterable[V]], w1s.asInstanceOf[Iterable[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, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope {
if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) {
throw SparkCoreErrors.hashPartitionerCannotPartitionArrayKeyError()
}
val cg = new CoGroupedRDD[K](Seq(self, other1, other2), partitioner)
cg.mapValues { case Array(vs, w1s, w2s) =>
(vs.asInstanceOf[Iterable[V]],
w1s.asInstanceOf[Iterable[W1]],
w2s.asInstanceOf[Iterable[W2]])
}
}
/**
* For each key k in `this` or `other1` or `other2` or `other3`,
* return a resulting RDD that contains a tuple with the list of values
* for that key in `this`, `other1`, `other2` and `other3`.
*/
def cogroup[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)])
: RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope {
cogroup(other1, other2, other3, defaultPartitioner(self, other1, other2, other3))
}
/**
* 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, (Iterable[V], Iterable[W]))] = self.withScope {
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, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope {
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)],
numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope {
cogroup(other, new HashPartitioner(numPartitions))
}
/**
* 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)], numPartitions: Int)
: RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope {
cogroup(other1, other2, new HashPartitioner(numPartitions))
}
/**
* For each key k in `this` or `other1` or `other2` or `other3`,
* return a resulting RDD that contains a tuple with the list of values
* for that key in `this`, `other1`, `other2` and `other3`.
*/
def cogroup[W1, W2, W3](other1: RDD[(K, W1)],
other2: RDD[(K, W2)],
other3: RDD[(K, W3)],
numPartitions: Int)
: RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope {
cogroup(other1, other2, other3, new HashPartitioner(numPartitions))
}
/** Alias for cogroup. */
def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope {
cogroup(other, defaultPartitioner(self, other))
}
/** Alias for cogroup. */
def groupWith[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)])
: RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope {
cogroup(other1, other2, defaultPartitioner(self, other1, other2))
}
/** Alias for cogroup. */
def groupWith[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)])
: RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope {
cogroup(other1, other2, other3, defaultPartitioner(self, other1, other2, other3))
}
/**
* Return an RDD with the pairs from `this` whose keys are not in `other`.
*
* Uses `this` partitioner/partition size, because even if `other` is huge, the resulting
* RDD will be less than or equal to us.
*/
def subtractByKey[W: ClassTag](other: RDD[(K, W)]): RDD[(K, V)] = self.withScope {
subtractByKey(other, self.partitioner.getOrElse(new HashPartitioner(self.partitions.length)))
}
/**
* Return an RDD with the pairs from `this` whose keys are not in `other`.
*/
def subtractByKey[W: ClassTag](
other: RDD[(K, W)],
numPartitions: Int): RDD[(K, V)] = self.withScope {
subtractByKey(other, new HashPartitioner(numPartitions))
}
/**
* Return an RDD with the pairs from `this` whose keys are not in `other`.
*/
def subtractByKey[W: ClassTag](other: RDD[(K, W)], p: Partitioner): RDD[(K, V)] = self.withScope {
new SubtractedRDD[K, V, W](self, other, p)
}
/**
* 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.withScope {
self.partitioner match {
case Some(p) =>
val index = p.getPartition(key)
val process = (it: Iterator[(K, V)]) => {
val buf = new ArrayBuffer[V]
for (pair <- it if pair._1 == key) {
buf += pair._2
}
buf.toSeq
} : Seq[V]
val res = self.context.runJob(self, process, Array(index))
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: ClassTag[F]): Unit = self.withScope {
saveAsHadoopFile(path, keyClass, valueClass, fm.runtimeClass.asInstanceOf[Class[F]])
}
/**
* 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. Compress the result with the
* supplied codec.
*/
def saveAsHadoopFile[F <: OutputFormat[K, V]](
path: String,
codec: Class[_ <: CompressionCodec])(implicit fm: ClassTag[F]): Unit = self.withScope {
val runtimeClass = fm.runtimeClass
saveAsHadoopFile(path, keyClass, valueClass, runtimeClass.asInstanceOf[Class[F]], codec)
}
/**
* 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: ClassTag[F]): Unit = self.withScope {
saveAsNewAPIHadoopFile(path, keyClass, valueClass, fm.runtimeClass.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): Unit = self.withScope {
// Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
val hadoopConf = conf
val job = NewAPIHadoopJob.getInstance(hadoopConf)
job.setOutputKeyClass(keyClass)
job.setOutputValueClass(valueClass)
job.setOutputFormatClass(outputFormatClass)
val jobConfiguration = job.getConfiguration
jobConfiguration.set("mapreduce.output.fileoutputformat.outputdir", path)
saveAsNewAPIHadoopDataset(jobConfiguration)
}
/**
* 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. Compress with the supplied codec.
*/
def saveAsHadoopFile(
path: String,
keyClass: Class[_],
valueClass: Class[_],
outputFormatClass: Class[_ <: OutputFormat[_, _]],
codec: Class[_ <: CompressionCodec]): Unit = self.withScope {
saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass,
new JobConf(self.context.hadoopConfiguration), Option(codec))
}
/**
* 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.
*
* @note We should make sure our tasks are idempotent when speculation is enabled, i.e. do
* not use output committer that writes data directly.
* There is an example in https://issues.apache.org/jira/browse/SPARK-10063 to show the bad
* result of using direct output committer with speculation enabled.
*/
def saveAsHadoopFile(
path: String,
keyClass: Class[_],
valueClass: Class[_],
outputFormatClass: Class[_ <: OutputFormat[_, _]],
conf: JobConf = new JobConf(self.context.hadoopConfiguration),
codec: Option[Class[_ <: CompressionCodec]] = None): Unit = self.withScope {
// Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
val hadoopConf = conf
hadoopConf.setOutputKeyClass(keyClass)
hadoopConf.setOutputValueClass(valueClass)
conf.setOutputFormat(outputFormatClass)
for (c <- codec) {
hadoopConf.setCompressMapOutput(true)
hadoopConf.set("mapreduce.output.fileoutputformat.compress", "true")
hadoopConf.setMapOutputCompressorClass(c)
hadoopConf.set("mapreduce.output.fileoutputformat.compress.codec", c.getCanonicalName)
hadoopConf.set("mapreduce.output.fileoutputformat.compress.type",
CompressionType.BLOCK.toString)
}
// Use configured output committer if already set
if (conf.getOutputCommitter == null) {
hadoopConf.setOutputCommitter(classOf[FileOutputCommitter])
}
// When speculation is on and output committer class name contains "Direct", we should warn
// users that they may loss data if they are using a direct output committer.
val speculationEnabled = self.conf.get(SPECULATION_ENABLED)
val outputCommitterClass = hadoopConf.get("mapred.output.committer.class", "")
if (speculationEnabled && outputCommitterClass.contains("Direct")) {
val warningMessage =
s"$outputCommitterClass may be an output committer that writes data directly to " +
"the final location. Because speculation is enabled, this output committer may " +
"cause data loss (see the case in SPARK-10063). If possible, please use an output " +
"committer that does not have this behavior (e.g. FileOutputCommitter)."
logWarning(warningMessage)
}
FileOutputFormat.setOutputPath(hadoopConf,
SparkHadoopWriterUtils.createPathFromString(path, hadoopConf))
saveAsHadoopDataset(hadoopConf)
}
/**
* Output the RDD to any Hadoop-supported storage system with new Hadoop API, using a Hadoop
* Configuration object for that storage system. The Conf 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.
*
* @note We should make sure our tasks are idempotent when speculation is enabled, i.e. do
* not use output committer that writes data directly.
* There is an example in https://issues.apache.org/jira/browse/SPARK-10063 to show the bad
* result of using direct output committer with speculation enabled.
*/
def saveAsNewAPIHadoopDataset(conf: Configuration): Unit = self.withScope {
val config = new HadoopMapReduceWriteConfigUtil[K, V](new SerializableConfiguration(conf))
SparkHadoopWriter.write(
rdd = self,
config = config)
}
/**
* 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): Unit = self.withScope {
val config = new HadoopMapRedWriteConfigUtil[K, V](new SerializableJobConf(conf))
SparkHadoopWriter.write(
rdd = self,
config = config)
}
/**
* 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 keyClass: Class[_] = kt.runtimeClass
private[spark] def valueClass: Class[_] = vt.runtimeClass
private[spark] def keyOrdering: Option[Ordering[K]] = Option(ord)
}
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