spark RDD 源码
spark RDD 代码
文件路径：/core/src/main/scala/org/apache/spark/rdd/RDD.scala
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* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
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*
* http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.spark.rdd
import java.util.Random
import scala.collection.{mutable, Map}
import scala.collection.mutable.ArrayBuffer
import scala.io.Codec
import scala.language.implicitConversions
import scala.ref.WeakReference
import scala.reflect.{classTag, ClassTag}
import com.clearspring.analytics.stream.cardinality.HyperLogLogPlus
import org.apache.hadoop.io.{BytesWritable, NullWritable, Text}
import org.apache.hadoop.io.compress.CompressionCodec
import org.apache.hadoop.mapred.TextOutputFormat
import org.apache.spark._
import org.apache.spark.Partitioner._
import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
import org.apache.spark.api.java.JavaRDD
import org.apache.spark.errors.SparkCoreErrors
import org.apache.spark.internal.Logging
import org.apache.spark.internal.config._
import org.apache.spark.internal.config.RDD_LIMIT_SCALE_UP_FACTOR
import org.apache.spark.partial.BoundedDouble
import org.apache.spark.partial.CountEvaluator
import org.apache.spark.partial.GroupedCountEvaluator
import org.apache.spark.partial.PartialResult
import org.apache.spark.resource.ResourceProfile
import org.apache.spark.storage.{RDDBlockId, StorageLevel}
import org.apache.spark.util.Utils
import org.apache.spark.util.collection.{ExternalAppendOnlyMap, OpenHashMap,
Utils => collectionUtils}
import org.apache.spark.util.random.{BernoulliCellSampler, BernoulliSampler, PoissonSampler,
SamplingUtils, XORShiftRandom}
/**
* A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. Represents an immutable,
* partitioned collection of elements that can be operated on in parallel. This class contains the
* basic operations available on all RDDs, such as `map`, `filter`, and `persist`. In addition,
* [[org.apache.spark.rdd.PairRDDFunctions]] contains operations available only on RDDs of key-value
* pairs, such as `groupByKey` and `join`;
* [[org.apache.spark.rdd.DoubleRDDFunctions]] contains operations available only on RDDs of
* Doubles; and
* [[org.apache.spark.rdd.SequenceFileRDDFunctions]] contains operations available on RDDs that
* can be saved as SequenceFiles.
* All operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)])
* through implicit.
*
* Internally, each RDD is characterized by five main properties:
*
* - A list of partitions
* - A function for computing each split
* - A list of dependencies on other RDDs
* - Optionally, a Partitioner for key-value RDDs (e.g. to say that the RDD is hash-partitioned)
* - Optionally, a list of preferred locations to compute each split on (e.g. block locations for
* an HDFS file)
*
* All of the scheduling and execution in Spark is done based on these methods, allowing each RDD
* to implement its own way of computing itself. Indeed, users can implement custom RDDs (e.g. for
* reading data from a new storage system) by overriding these functions. Please refer to the
* <a href="http://people.csail.mit.edu/matei/papers/2012/nsdi_spark.pdf">Spark paper</a>
* for more details on RDD internals.
*/
abstract class RDD[T: ClassTag](
@transient private var _sc: SparkContext,
@transient private var deps: Seq[Dependency[_]]
) extends Serializable with Logging {
if (classOf[RDD[_]].isAssignableFrom(elementClassTag.runtimeClass)) {
// This is a warning instead of an exception in order to avoid breaking user programs that
// might have defined nested RDDs without running jobs with them.
logWarning("Spark does not support nested RDDs (see SPARK-5063)")
}
private def sc: SparkContext = {
if (_sc == null) {
throw SparkCoreErrors.rddLacksSparkContextError()
}
_sc
}
/** Construct an RDD with just a one-to-one dependency on one parent */
def this(@transient oneParent: RDD[_]) =
this(oneParent.context, List(new OneToOneDependency(oneParent)))
private[spark] def conf = sc.conf
// =======================================================================
// Methods that should be implemented by subclasses of RDD
// =======================================================================
/**
* :: DeveloperApi ::
* Implemented by subclasses to compute a given partition.
*/
@DeveloperApi
def compute(split: Partition, context: TaskContext): Iterator[T]
/**
* Implemented by subclasses to return the set of partitions in this RDD. This method will only
* be called once, so it is safe to implement a time-consuming computation in it.
*
* The partitions in this array must satisfy the following property:
* `rdd.partitions.zipWithIndex.forall { case (partition, index) => partition.index == index }`
*/
protected def getPartitions: Array[Partition]
/**
* Implemented by subclasses to return how this RDD depends on parent RDDs. This method will only
* be called once, so it is safe to implement a time-consuming computation in it.
*/
protected def getDependencies: Seq[Dependency[_]] = deps
/**
* Optionally overridden by subclasses to specify placement preferences.
*/
protected def getPreferredLocations(split: Partition): Seq[String] = Nil
/** Optionally overridden by subclasses to specify how they are partitioned. */
@transient val partitioner: Option[Partitioner] = None
// =======================================================================
// Methods and fields available on all RDDs
// =======================================================================
/** The SparkContext that created this RDD. */
def sparkContext: SparkContext = sc
/** A unique ID for this RDD (within its SparkContext). */
val id: Int = sc.newRddId()
/** A friendly name for this RDD */
@transient var name: String = _
/** Assign a name to this RDD */
def setName(_name: String): this.type = {
name = _name
this
}
/**
* Mark this RDD for persisting using the specified level.
*
* @param newLevel the target storage level
* @param allowOverride whether to override any existing level with the new one
*/
private def persist(newLevel: StorageLevel, allowOverride: Boolean): this.type = {
// TODO: Handle changes of StorageLevel
if (storageLevel != StorageLevel.NONE && newLevel != storageLevel && !allowOverride) {
throw SparkCoreErrors.cannotChangeStorageLevelError()
}
// If this is the first time this RDD is marked for persisting, register it
// with the SparkContext for cleanups and accounting. Do this only once.
if (storageLevel == StorageLevel.NONE) {
sc.cleaner.foreach(_.registerRDDForCleanup(this))
sc.persistRDD(this)
}
storageLevel = newLevel
this
}
/**
* Set this RDD's storage level to persist its values across operations after the first time
* it is computed. This can only be used to assign a new storage level if the RDD does not
* have a storage level set yet. Local checkpointing is an exception.
*/
def persist(newLevel: StorageLevel): this.type = {
if (isLocallyCheckpointed) {
// This means the user previously called localCheckpoint(), which should have already
// marked this RDD for persisting. Here we should override the old storage level with
// one that is explicitly requested by the user (after adapting it to use disk).
persist(LocalRDDCheckpointData.transformStorageLevel(newLevel), allowOverride = true)
} else {
persist(newLevel, allowOverride = false)
}
}
/**
* Persist this RDD with the default storage level (`MEMORY_ONLY`).
*/
def persist(): this.type = persist(StorageLevel.MEMORY_ONLY)
/**
* Persist this RDD with the default storage level (`MEMORY_ONLY`).
*/
def cache(): this.type = persist()
/**
* Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
*
* @param blocking Whether to block until all blocks are deleted (default: false)
* @return This RDD.
*/
def unpersist(blocking: Boolean = false): this.type = {
logInfo(s"Removing RDD $id from persistence list")
sc.unpersistRDD(id, blocking)
storageLevel = StorageLevel.NONE
this
}
/** Get the RDD's current storage level, or StorageLevel.NONE if none is set. */
def getStorageLevel: StorageLevel = storageLevel
/**
* Lock for all mutable state of this RDD (persistence, partitions, dependencies, etc.). We do
* not use `this` because RDDs are user-visible, so users might have added their own locking on
* RDDs; sharing that could lead to a deadlock.
*
* One thread might hold the lock on many of these, for a chain of RDD dependencies; but
* because DAGs are acyclic, and we only ever hold locks for one path in that DAG, there is no
* chance of deadlock.
*
* Executors may reference the shared fields (though they should never mutate them,
* that only happens on the driver).
*/
private val stateLock = new Serializable {}
// Our dependencies and partitions will be gotten by calling subclass's methods below, and will
// be overwritten when we're checkpointed
@volatile private var dependencies_ : Seq[Dependency[_]] = _
// When we overwrite the dependencies we keep a weak reference to the old dependencies
// for user controlled cleanup.
@volatile @transient private var legacyDependencies: WeakReference[Seq[Dependency[_]]] = _
@volatile @transient private var partitions_ : Array[Partition] = _
/** An Option holding our checkpoint RDD, if we are checkpointed */
private def checkpointRDD: Option[CheckpointRDD[T]] = checkpointData.flatMap(_.checkpointRDD)
/**
* Get the list of dependencies of this RDD, taking into account whether the
* RDD is checkpointed or not.
*/
final def dependencies: Seq[Dependency[_]] = {
checkpointRDD.map(r => List(new OneToOneDependency(r))).getOrElse {
if (dependencies_ == null) {
stateLock.synchronized {
if (dependencies_ == null) {
dependencies_ = getDependencies
}
}
}
dependencies_
}
}
/**
* Get the list of dependencies of this RDD ignoring checkpointing.
*/
final private def internalDependencies: Option[Seq[Dependency[_]]] = {
if (legacyDependencies != null) {
legacyDependencies.get
} else if (dependencies_ != null) {
Some(dependencies_)
} else {
// This case should be infrequent.
stateLock.synchronized {
if (dependencies_ == null) {
dependencies_ = getDependencies
}
Some(dependencies_)
}
}
}
/**
* Get the array of partitions of this RDD, taking into account whether the
* RDD is checkpointed or not.
*/
final def partitions: Array[Partition] = {
checkpointRDD.map(_.partitions).getOrElse {
if (partitions_ == null) {
stateLock.synchronized {
if (partitions_ == null) {
partitions_ = getPartitions
partitions_.zipWithIndex.foreach { case (partition, index) =>
require(partition.index == index,
s"partitions($index).partition == ${partition.index}, but it should equal $index")
}
}
}
}
partitions_
}
}
/**
* Returns the number of partitions of this RDD.
*/
@Since("1.6.0")
final def getNumPartitions: Int = partitions.length
/**
* Get the preferred locations of a partition, taking into account whether the
* RDD is checkpointed.
*/
final def preferredLocations(split: Partition): Seq[String] = {
checkpointRDD.map(_.getPreferredLocations(split)).getOrElse {
getPreferredLocations(split)
}
}
/**
* Internal method to this RDD; will read from cache if applicable, or otherwise compute it.
* This should ''not'' be called by users directly, but is available for implementers of custom
* subclasses of RDD.
*/
final def iterator(split: Partition, context: TaskContext): Iterator[T] = {
if (storageLevel != StorageLevel.NONE) {
getOrCompute(split, context)
} else {
computeOrReadCheckpoint(split, context)
}
}
/**
* Return the ancestors of the given RDD that are related to it only through a sequence of
* narrow dependencies. This traverses the given RDD's dependency tree using DFS, but maintains
* no ordering on the RDDs returned.
*/
private[spark] def getNarrowAncestors: Seq[RDD[_]] = {
val ancestors = new mutable.HashSet[RDD[_]]
def visit(rdd: RDD[_]): Unit = {
val narrowDependencies = rdd.dependencies.filter(_.isInstanceOf[NarrowDependency[_]])
val narrowParents = narrowDependencies.map(_.rdd)
val narrowParentsNotVisited = narrowParents.filterNot(ancestors.contains)
narrowParentsNotVisited.foreach { parent =>
ancestors.add(parent)
visit(parent)
}
}
visit(this)
// In case there is a cycle, do not include the root itself
ancestors.filterNot(_ == this).toSeq
}
/**
* Compute an RDD partition or read it from a checkpoint if the RDD is checkpointing.
*/
private[spark] def computeOrReadCheckpoint(split: Partition, context: TaskContext): Iterator[T] =
{
if (isCheckpointedAndMaterialized) {
firstParent[T].iterator(split, context)
} else {
compute(split, context)
}
}
/**
* Gets or computes an RDD partition. Used by RDD.iterator() when an RDD is cached.
*/
private[spark] def getOrCompute(partition: Partition, context: TaskContext): Iterator[T] = {
val blockId = RDDBlockId(id, partition.index)
var readCachedBlock = true
// This method is called on executors, so we need call SparkEnv.get instead of sc.env.
SparkEnv.get.blockManager.getOrElseUpdate(blockId, storageLevel, elementClassTag, () => {
readCachedBlock = false
computeOrReadCheckpoint(partition, context)
}) match {
// Block hit.
case Left(blockResult) =>
if (readCachedBlock) {
val existingMetrics = context.taskMetrics().inputMetrics
existingMetrics.incBytesRead(blockResult.bytes)
new InterruptibleIterator[T](context, blockResult.data.asInstanceOf[Iterator[T]]) {
override def next(): T = {
existingMetrics.incRecordsRead(1)
delegate.next()
}
}
} else {
new InterruptibleIterator(context, blockResult.data.asInstanceOf[Iterator[T]])
}
// Need to compute the block.
case Right(iter) =>
new InterruptibleIterator(context, iter)
}
}
/**
* Execute a block of code in a scope such that all new RDDs created in this body will
* be part of the same scope. For more detail, see {{org.apache.spark.rdd.RDDOperationScope}}.
*
* Note: Return statements are NOT allowed in the given body.
*/
private[spark] def withScope[U](body: => U): U = RDDOperationScope.withScope[U](sc)(body)
// Transformations (return a new RDD)
/**
* Return a new RDD by applying a function to all elements of this RDD.
*/
def map[U: ClassTag](f: T => U): RDD[U] = withScope {
val cleanF = sc.clean(f)
new MapPartitionsRDD[U, T](this, (_, _, iter) => iter.map(cleanF))
}
/**
* Return a new RDD by first applying a function to all elements of this
* RDD, and then flattening the results.
*/
def flatMap[U: ClassTag](f: T => TraversableOnce[U]): RDD[U] = withScope {
val cleanF = sc.clean(f)
new MapPartitionsRDD[U, T](this, (_, _, iter) => iter.flatMap(cleanF))
}
/**
* Return a new RDD containing only the elements that satisfy a predicate.
*/
def filter(f: T => Boolean): RDD[T] = withScope {
val cleanF = sc.clean(f)
new MapPartitionsRDD[T, T](
this,
(_, _, iter) => iter.filter(cleanF),
preservesPartitioning = true)
}
/**
* Return a new RDD containing the distinct elements in this RDD.
*/
def distinct(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = withScope {
def removeDuplicatesInPartition(partition: Iterator[T]): Iterator[T] = {
// Create an instance of external append only map which ignores values.
val map = new ExternalAppendOnlyMap[T, Null, Null](
createCombiner = _ => null,
mergeValue = (a, b) => a,
mergeCombiners = (a, b) => a)
map.insertAll(partition.map(_ -> null))
map.iterator.map(_._1)
}
partitioner match {
case Some(_) if numPartitions == partitions.length =>
mapPartitions(removeDuplicatesInPartition, preservesPartitioning = true)
case _ => map(x => (x, null)).reduceByKey((x, _) => x, numPartitions).map(_._1)
}
}
/**
* Return a new RDD containing the distinct elements in this RDD.
*/
def distinct(): RDD[T] = withScope {
distinct(partitions.length)
}
/**
* Return a new RDD that has exactly numPartitions partitions.
*
* Can increase or decrease the level of parallelism in this RDD. Internally, this uses
* a shuffle to redistribute data.
*
* If you are decreasing the number of partitions in this RDD, consider using `coalesce`,
* which can avoid performing a shuffle.
*/
def repartition(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = withScope {
coalesce(numPartitions, shuffle = true)
}
/**
* Return a new RDD that is reduced into `numPartitions` partitions.
*
* This results in a narrow dependency, e.g. if you go from 1000 partitions
* to 100 partitions, there will not be a shuffle, instead each of the 100
* new partitions will claim 10 of the current partitions. If a larger number
* of partitions is requested, it will stay at the current number of partitions.
*
* However, if you're doing a drastic coalesce, e.g. to numPartitions = 1,
* this may result in your computation taking place on fewer nodes than
* you like (e.g. one node in the case of numPartitions = 1). To avoid this,
* you can pass shuffle = true. This will add a shuffle step, but means the
* current upstream partitions will be executed in parallel (per whatever
* the current partitioning is).
*
* @note With shuffle = true, you can actually coalesce to a larger number
* of partitions. This is useful if you have a small number of partitions,
* say 100, potentially with a few partitions being abnormally large. Calling
* coalesce(1000, shuffle = true) will result in 1000 partitions with the
* data distributed using a hash partitioner. The optional partition coalescer
* passed in must be serializable.
*/
def coalesce(numPartitions: Int, shuffle: Boolean = false,
partitionCoalescer: Option[PartitionCoalescer] = Option.empty)
(implicit ord: Ordering[T] = null)
: RDD[T] = withScope {
require(numPartitions > 0, s"Number of partitions ($numPartitions) must be positive.")
if (shuffle) {
/** Distributes elements evenly across output partitions, starting from a random partition. */
val distributePartition = (index: Int, items: Iterator[T]) => {
var position = new XORShiftRandom(index).nextInt(numPartitions)
items.map { t =>
// Note that the hash code of the key will just be the key itself. The HashPartitioner
// will mod it with the number of total partitions.
position = position + 1
(position, t)
}
} : Iterator[(Int, T)]
// include a shuffle step so that our upstream tasks are still distributed
new CoalescedRDD(
new ShuffledRDD[Int, T, T](
mapPartitionsWithIndexInternal(distributePartition, isOrderSensitive = true),
new HashPartitioner(numPartitions)),
numPartitions,
partitionCoalescer).values
} else {
new CoalescedRDD(this, numPartitions, partitionCoalescer)
}
}
/**
* Return a sampled subset of this RDD.
*
* @param withReplacement can elements be sampled multiple times (replaced when sampled out)
* @param fraction expected size of the sample as a fraction of this RDD's size
* without replacement: probability that each element is chosen; fraction must be [0, 1]
* with replacement: expected number of times each element is chosen; fraction must be greater
* than or equal to 0
* @param seed seed for the random number generator
*
* @note This is NOT guaranteed to provide exactly the fraction of the count
* of the given [[RDD]].
*/
def sample(
withReplacement: Boolean,
fraction: Double,
seed: Long = Utils.random.nextLong): RDD[T] = {
require(fraction >= 0,
s"Fraction must be nonnegative, but got ${fraction}")
withScope {
if (withReplacement) {
new PartitionwiseSampledRDD[T, T](this, new PoissonSampler[T](fraction), true, seed)
} else {
new PartitionwiseSampledRDD[T, T](this, new BernoulliSampler[T](fraction), true, seed)
}
}
}
/**
* Randomly splits this RDD with the provided weights.
*
* @param weights weights for splits, will be normalized if they don't sum to 1
* @param seed random seed
*
* @return split RDDs in an array
*/
def randomSplit(
weights: Array[Double],
seed: Long = Utils.random.nextLong): Array[RDD[T]] = {
require(weights.forall(_ >= 0),
s"Weights must be nonnegative, but got ${weights.mkString("[", ",", "]")}")
require(weights.sum > 0,
s"Sum of weights must be positive, but got ${weights.mkString("[", ",", "]")}")
withScope {
val sum = weights.sum
val normalizedCumWeights = weights.map(_ / sum).scanLeft(0.0d)(_ + _)
normalizedCumWeights.sliding(2).map { x =>
randomSampleWithRange(x(0), x(1), seed)
}.toArray
}
}
/**
* Internal method exposed for Random Splits in DataFrames. Samples an RDD given a probability
* range.
* @param lb lower bound to use for the Bernoulli sampler
* @param ub upper bound to use for the Bernoulli sampler
* @param seed the seed for the Random number generator
* @return A random sub-sample of the RDD without replacement.
*/
private[spark] def randomSampleWithRange(lb: Double, ub: Double, seed: Long): RDD[T] = {
this.mapPartitionsWithIndex( { (index, partition) =>
val sampler = new BernoulliCellSampler[T](lb, ub)
sampler.setSeed(seed + index)
sampler.sample(partition)
}, isOrderSensitive = true, preservesPartitioning = true)
}
/**
* Return a fixed-size sampled subset of this RDD in an array
*
* @param withReplacement whether sampling is done with replacement
* @param num size of the returned sample
* @param seed seed for the random number generator
* @return sample of specified size in an array
*
* @note this method should only be used if the resulting array is expected to be small, as
* all the data is loaded into the driver's memory.
*/
def takeSample(
withReplacement: Boolean,
num: Int,
seed: Long = Utils.random.nextLong): Array[T] = withScope {
val numStDev = 10.0
require(num >= 0, "Negative number of elements requested")
require(num <= (Int.MaxValue - (numStDev * math.sqrt(Int.MaxValue)).toInt),
"Cannot support a sample size > Int.MaxValue - " +
s"$numStDev * math.sqrt(Int.MaxValue)")
if (num == 0) {
new Array[T](0)
} else {
val initialCount = this.count()
if (initialCount == 0) {
new Array[T](0)
} else {
val rand = new Random(seed)
if (!withReplacement && num >= initialCount) {
Utils.randomizeInPlace(this.collect(), rand)
} else {
val fraction = SamplingUtils.computeFractionForSampleSize(num, initialCount,
withReplacement)
var samples = this.sample(withReplacement, fraction, rand.nextInt()).collect()
// If the first sample didn't turn out large enough, keep trying to take samples;
// this shouldn't happen often because we use a big multiplier for the initial size
var numIters = 0
while (samples.length < num) {
logWarning(s"Needed to re-sample due to insufficient sample size. Repeat #$numIters")
samples = this.sample(withReplacement, fraction, rand.nextInt()).collect()
numIters += 1
}
Utils.randomizeInPlace(samples, rand).take(num)
}
}
}
}
/**
* Return the union of this RDD and another one. Any identical elements will appear multiple
* times (use `.distinct()` to eliminate them).
*/
def union(other: RDD[T]): RDD[T] = withScope {
sc.union(this, other)
}
/**
* Return the union of this RDD and another one. Any identical elements will appear multiple
* times (use `.distinct()` to eliminate them).
*/
def ++(other: RDD[T]): RDD[T] = withScope {
this.union(other)
}
/**
* Return this RDD sorted by the given key function.
*/
def sortBy[K](
f: (T) => K,
ascending: Boolean = true,
numPartitions: Int = this.partitions.length)
(implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[T] = withScope {
this.keyBy[K](f)
.sortByKey(ascending, numPartitions)
.values
}
/**
* Return the intersection of this RDD and another one. The output will not contain any duplicate
* elements, even if the input RDDs did.
*
* @note This method performs a shuffle internally.
*/
def intersection(other: RDD[T]): RDD[T] = withScope {
this.map(v => (v, null)).cogroup(other.map(v => (v, null)))
.filter { case (_, (leftGroup, rightGroup)) => leftGroup.nonEmpty && rightGroup.nonEmpty }
.keys
}
/**
* Return the intersection of this RDD and another one. The output will not contain any duplicate
* elements, even if the input RDDs did.
*
* @note This method performs a shuffle internally.
*
* @param partitioner Partitioner to use for the resulting RDD
*/
def intersection(
other: RDD[T],
partitioner: Partitioner)(implicit ord: Ordering[T] = null): RDD[T] = withScope {
this.map(v => (v, null)).cogroup(other.map(v => (v, null)), partitioner)
.filter { case (_, (leftGroup, rightGroup)) => leftGroup.nonEmpty && rightGroup.nonEmpty }
.keys
}
/**
* Return the intersection of this RDD and another one. The output will not contain any duplicate
* elements, even if the input RDDs did. Performs a hash partition across the cluster
*
* @note This method performs a shuffle internally.
*
* @param numPartitions How many partitions to use in the resulting RDD
*/
def intersection(other: RDD[T], numPartitions: Int): RDD[T] = withScope {
intersection(other, new HashPartitioner(numPartitions))
}
/**
* Return an RDD created by coalescing all elements within each partition into an array.
*/
def glom(): RDD[Array[T]] = withScope {
new MapPartitionsRDD[Array[T], T](this, (_, _, iter) => Iterator(iter.toArray))
}
/**
* Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of
* elements (a, b) where a is in `this` and b is in `other`.
*/
def cartesian[U: ClassTag](other: RDD[U]): RDD[(T, U)] = withScope {
new CartesianRDD(sc, this, other)
}
/**
* Return an RDD of grouped items. Each group consists of a key and a sequence of elements
* mapping to that key. 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 groupBy[K](f: T => K)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] = withScope {
groupBy[K](f, defaultPartitioner(this))
}
/**
* Return an RDD of grouped elements. Each group consists of a key and a sequence of elements
* mapping to that key. 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 groupBy[K](
f: T => K,
numPartitions: Int)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] = withScope {
groupBy(f, new HashPartitioner(numPartitions))
}
/**
* Return an RDD of grouped items. Each group consists of a key and a sequence of elements
* mapping to that key. 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 groupBy[K](f: T => K, p: Partitioner)(implicit kt: ClassTag[K], ord: Ordering[K] = null)
: RDD[(K, Iterable[T])] = withScope {
val cleanF = sc.clean(f)
this.map(t => (cleanF(t), t)).groupByKey(p)
}
/**
* Return an RDD created by piping elements to a forked external process.
*/
def pipe(command: String): RDD[String] = withScope {
// Similar to Runtime.exec(), if we are given a single string, split it into words
// using a standard StringTokenizer (i.e. by spaces)
pipe(PipedRDD.tokenize(command))
}
/**
* Return an RDD created by piping elements to a forked external process.
*/
def pipe(command: String, env: Map[String, String]): RDD[String] = withScope {
// Similar to Runtime.exec(), if we are given a single string, split it into words
// using a standard StringTokenizer (i.e. by spaces)
pipe(PipedRDD.tokenize(command), env)
}
/**
* Return an RDD created by piping elements to a forked external process. The resulting RDD
* is computed by executing the given process once per partition. All elements
* of each input partition are written to a process's stdin as lines of input separated
* by a newline. The resulting partition consists of the process's stdout output, with
* each line of stdout resulting in one element of the output partition. A process is invoked
* even for empty partitions.
*
* The print behavior can be customized by providing two functions.
*
* @param command command to run in forked process.
* @param env environment variables to set.
* @param printPipeContext Before piping elements, this function is called as an opportunity
* to pipe context data. Print line function (like out.println) will be
* passed as printPipeContext's parameter.
* @param printRDDElement Use this function to customize how to pipe elements. This function
* will be called with each RDD element as the 1st parameter, and the
* print line function (like out.println()) as the 2nd parameter.
* An example of pipe the RDD data of groupBy() in a streaming way,
* instead of constructing a huge String to concat all the elements:
* {{{
* def printRDDElement(record:(String, Seq[String]), f:String=>Unit) =
* for (e <- record._2) {f(e)}
* }}}
* @param separateWorkingDir Use separate working directories for each task.
* @param bufferSize Buffer size for the stdin writer for the piped process.
* @param encoding Char encoding used for interacting (via stdin, stdout and stderr) with
* the piped process
* @return the result RDD
*/
def pipe(
command: Seq[String],
env: Map[String, String] = Map(),
printPipeContext: (String => Unit) => Unit = null,
printRDDElement: (T, String => Unit) => Unit = null,
separateWorkingDir: Boolean = false,
bufferSize: Int = 8192,
encoding: String = Codec.defaultCharsetCodec.name): RDD[String] = withScope {
new PipedRDD(this, command, env,
if (printPipeContext ne null) sc.clean(printPipeContext) else null,
if (printRDDElement ne null) sc.clean(printRDDElement) else null,
separateWorkingDir,
bufferSize,
encoding)
}
/**
* Return a new RDD by applying a function to each partition of this RDD.
*
* `preservesPartitioning` indicates whether the input function preserves the partitioner, which
* should be `false` unless this is a pair RDD and the input function doesn't modify the keys.
*/
def mapPartitions[U: ClassTag](
f: Iterator[T] => Iterator[U],
preservesPartitioning: Boolean = false): RDD[U] = withScope {
val cleanedF = sc.clean(f)
new MapPartitionsRDD(
this,
(_: TaskContext, _: Int, iter: Iterator[T]) => cleanedF(iter),
preservesPartitioning)
}
/**
* [performance] Spark's internal mapPartitionsWithIndex method that skips closure cleaning.
* It is a performance API to be used carefully only if we are sure that the RDD elements are
* serializable and don't require closure cleaning.
*
* @param preservesPartitioning indicates whether the input function preserves the partitioner,
* which should be `false` unless this is a pair RDD and the input
* function doesn't modify the keys.
* @param isOrderSensitive whether or not the function is order-sensitive. If it's order
* sensitive, it may return totally different result when the input order
* is changed. Mostly stateful functions are order-sensitive.
*/
private[spark] def mapPartitionsWithIndexInternal[U: ClassTag](
f: (Int, Iterator[T]) => Iterator[U],
preservesPartitioning: Boolean = false,
isOrderSensitive: Boolean = false): RDD[U] = withScope {
new MapPartitionsRDD(
this,
(_: TaskContext, index: Int, iter: Iterator[T]) => f(index, iter),
preservesPartitioning = preservesPartitioning,
isOrderSensitive = isOrderSensitive)
}
/**
* [performance] Spark's internal mapPartitions method that skips closure cleaning.
*/
private[spark] def mapPartitionsInternal[U: ClassTag](
f: Iterator[T] => Iterator[U],
preservesPartitioning: Boolean = false): RDD[U] = withScope {
new MapPartitionsRDD(
this,
(_: TaskContext, _: Int, iter: Iterator[T]) => f(iter),
preservesPartitioning)
}
/**
* Return a new RDD by applying a function to each partition of this RDD, while tracking the index
* of the original partition.
*
* `preservesPartitioning` indicates whether the input function preserves the partitioner, which
* should be `false` unless this is a pair RDD and the input function doesn't modify the keys.
*/
def mapPartitionsWithIndex[U: ClassTag](
f: (Int, Iterator[T]) => Iterator[U],
preservesPartitioning: Boolean = false): RDD[U] = withScope {
val cleanedF = sc.clean(f)
new MapPartitionsRDD(
this,
(_: TaskContext, index: Int, iter: Iterator[T]) => cleanedF(index, iter),
preservesPartitioning)
}
/**
* Return a new RDD by applying a function to each partition of this RDD, while tracking the index
* of the original partition.
*
* `preservesPartitioning` indicates whether the input function preserves the partitioner, which
* should be `false` unless this is a pair RDD and the input function doesn't modify the keys.
*
* `isOrderSensitive` indicates whether the function is order-sensitive. If it is order
* sensitive, it may return totally different result when the input order
* is changed. Mostly stateful functions are order-sensitive.
*/
private[spark] def mapPartitionsWithIndex[U: ClassTag](
f: (Int, Iterator[T]) => Iterator[U],
preservesPartitioning: Boolean,
isOrderSensitive: Boolean): RDD[U] = withScope {
val cleanedF = sc.clean(f)
new MapPartitionsRDD(
this,
(_: TaskContext, index: Int, iter: Iterator[T]) => cleanedF(index, iter),
preservesPartitioning,
isOrderSensitive = isOrderSensitive)
}
/**
* Zips this RDD with another one, returning key-value pairs with the first element in each RDD,
* second element in each RDD, etc. Assumes that the two RDDs have the *same number of
* partitions* and the *same number of elements in each partition* (e.g. one was made through
* a map on the other).
*/
def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)] = withScope {
zipPartitions(other, preservesPartitioning = false) { (thisIter, otherIter) =>
new Iterator[(T, U)] {
def hasNext: Boolean = (thisIter.hasNext, otherIter.hasNext) match {
case (true, true) => true
case (false, false) => false
case _ => throw SparkCoreErrors.canOnlyZipRDDsWithSamePartitionSizeError()
}
def next(): (T, U) = (thisIter.next(), otherIter.next())
}
}
}
/**
* Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by
* applying a function to the zipped partitions. Assumes that all the RDDs have the
* *same number of partitions*, but does *not* require them to have the same number
* of elements in each partition.
*/
def zipPartitions[B: ClassTag, V: ClassTag]
(rdd2: RDD[B], preservesPartitioning: Boolean)
(f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = withScope {
new ZippedPartitionsRDD2(sc, sc.clean(f), this, rdd2, preservesPartitioning)
}
def zipPartitions[B: ClassTag, V: ClassTag]
(rdd2: RDD[B])
(f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = withScope {
zipPartitions(rdd2, preservesPartitioning = false)(f)
}
def zipPartitions[B: ClassTag, C: ClassTag, V: ClassTag]
(rdd2: RDD[B], rdd3: RDD[C], preservesPartitioning: Boolean)
(f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = withScope {
new ZippedPartitionsRDD3(sc, sc.clean(f), this, rdd2, rdd3, preservesPartitioning)
}
def zipPartitions[B: ClassTag, C: ClassTag, V: ClassTag]
(rdd2: RDD[B], rdd3: RDD[C])
(f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = withScope {
zipPartitions(rdd2, rdd3, preservesPartitioning = false)(f)
}
def zipPartitions[B: ClassTag, C: ClassTag, D: ClassTag, V: ClassTag]
(rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D], preservesPartitioning: Boolean)
(f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = withScope {
new ZippedPartitionsRDD4(sc, sc.clean(f), this, rdd2, rdd3, rdd4, preservesPartitioning)
}
def zipPartitions[B: ClassTag, C: ClassTag, D: ClassTag, V: ClassTag]
(rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D])
(f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = withScope {
zipPartitions(rdd2, rdd3, rdd4, preservesPartitioning = false)(f)
}
// Actions (launch a job to return a value to the user program)
/**
* Applies a function f to all elements of this RDD.
*/
def foreach(f: T => Unit): Unit = withScope {
val cleanF = sc.clean(f)
sc.runJob(this, (iter: Iterator[T]) => iter.foreach(cleanF))
}
/**
* Applies a function f to each partition of this RDD.
*/
def foreachPartition(f: Iterator[T] => Unit): Unit = withScope {
val cleanF = sc.clean(f)
sc.runJob(this, (iter: Iterator[T]) => cleanF(iter))
}
/**
* Return an array that contains all of the elements in this RDD.
*
* @note This method should only be used if the resulting array is expected to be small, as
* all the data is loaded into the driver's memory.
*/
def collect(): Array[T] = withScope {
val results = sc.runJob(this, (iter: Iterator[T]) => iter.toArray)
Array.concat(results: _*)
}
/**
* Return an iterator that contains all of the elements in this RDD.
*
* The iterator will consume as much memory as the largest partition in this RDD.
*
* @note This results in multiple Spark jobs, and if the input RDD is the result
* of a wide transformation (e.g. join with different partitioners), to avoid
* recomputing the input RDD should be cached first.
*/
def toLocalIterator: Iterator[T] = withScope {
def collectPartition(p: Int): Array[T] = {
sc.runJob(this, (iter: Iterator[T]) => iter.toArray, Seq(p)).head
}
partitions.indices.iterator.flatMap(i => collectPartition(i))
}
/**
* Return an RDD that contains all matching values by applying `f`.
*/
def collect[U: ClassTag](f: PartialFunction[T, U]): RDD[U] = withScope {
val cleanF = sc.clean(f)
filter(cleanF.isDefinedAt).map(cleanF)
}
/**
* Return an RDD with the elements from `this` that are not in `other`.
*
* Uses `this` partitioner/partition size, because even if `other` is huge, the resulting
* RDD will be <= us.
*/
def subtract(other: RDD[T]): RDD[T] = withScope {
subtract(other, partitioner.getOrElse(new HashPartitioner(partitions.length)))
}
/**
* Return an RDD with the elements from `this` that are not in `other`.
*/
def subtract(other: RDD[T], numPartitions: Int): RDD[T] = withScope {
subtract(other, new HashPartitioner(numPartitions))
}
/**
* Return an RDD with the elements from `this` that are not in `other`.
*/
def subtract(
other: RDD[T],
p: Partitioner)(implicit ord: Ordering[T] = null): RDD[T] = withScope {
if (partitioner == Some(p)) {
// Our partitioner knows how to handle T (which, since we have a partitioner, is
// really (K, V)) so make a new Partitioner that will de-tuple our fake tuples
val p2 = new Partitioner() {
override def numPartitions: Int = p.numPartitions
override def getPartition(k: Any): Int = p.getPartition(k.asInstanceOf[(Any, _)]._1)
}
// Unfortunately, since we're making a new p2, we'll get ShuffleDependencies
// anyway, and when calling .keys, will not have a partitioner set, even though
// the SubtractedRDD will, thanks to p2's de-tupled partitioning, already be
// partitioned by the right/real keys (e.g. p).
this.map(x => (x, null)).subtractByKey(other.map((_, null)), p2).keys
} else {
this.map(x => (x, null)).subtractByKey(other.map((_, null)), p).keys
}
}
/**
* Reduces the elements of this RDD using the specified commutative and
* associative binary operator.
*/
def reduce(f: (T, T) => T): T = withScope {
val cleanF = sc.clean(f)
val reducePartition: Iterator[T] => Option[T] = iter => {
if (iter.hasNext) {
Some(iter.reduceLeft(cleanF))
} else {
None
}
}
var jobResult: Option[T] = None
val mergeResult = (_: Int, taskResult: Option[T]) => {
if (taskResult.isDefined) {
jobResult = jobResult match {
case Some(value) => Some(f(value, taskResult.get))
case None => taskResult
}
}
}
sc.runJob(this, reducePartition, mergeResult)
// Get the final result out of our Option, or throw an exception if the RDD was empty
jobResult.getOrElse(throw SparkCoreErrors.emptyCollectionError())
}
/**
* Reduces the elements of this RDD in a multi-level tree pattern.
*
* @param depth suggested depth of the tree (default: 2)
* @see [[org.apache.spark.rdd.RDD#reduce]]
*/
def treeReduce(f: (T, T) => T, depth: Int = 2): T = withScope {
require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.")
val cleanF = context.clean(f)
val reducePartition: Iterator[T] => Option[T] = iter => {
if (iter.hasNext) {
Some(iter.reduceLeft(cleanF))
} else {
None
}
}
val partiallyReduced = mapPartitions(it => Iterator(reducePartition(it)))
val op: (Option[T], Option[T]) => Option[T] = (c, x) => {
if (c.isDefined && x.isDefined) {
Some(cleanF(c.get, x.get))
} else if (c.isDefined) {
c
} else if (x.isDefined) {
x
} else {
None
}
}
partiallyReduced.treeAggregate(Option.empty[T])(op, op, depth)
.getOrElse(throw SparkCoreErrors.emptyCollectionError())
}
/**
* Aggregate the elements of each partition, and then the results for all the partitions, using a
* given associative function and a neutral "zero value". The function
* op(t1, t2) is allowed to modify t1 and return it as its result value to avoid object
* allocation; however, it should not modify t2.
*
* This behaves somewhat differently from fold operations implemented for non-distributed
* collections in functional languages like Scala. This fold operation may be applied to
* partitions individually, and then fold those results into the final result, rather than
* apply the fold to each element sequentially in some defined ordering. For functions
* that are not commutative, the result may differ from that of a fold applied to a
* non-distributed collection.
*
* @param zeroValue the initial value for the accumulated result of each partition for the `op`
* operator, and also the initial value for the combine results from different
* partitions for the `op` operator - this will typically be the neutral
* element (e.g. `Nil` for list concatenation or `0` for summation)
* @param op an operator used to both accumulate results within a partition and combine results
* from different partitions
*/
def fold(zeroValue: T)(op: (T, T) => T): T = withScope {
// Clone the zero value since we will also be serializing it as part of tasks
var jobResult = Utils.clone(zeroValue, sc.env.closureSerializer.newInstance())
val cleanOp = sc.clean(op)
val foldPartition = (iter: Iterator[T]) => iter.fold(zeroValue)(cleanOp)
val mergeResult = (_: Int, taskResult: T) => jobResult = op(jobResult, taskResult)
sc.runJob(this, foldPartition, mergeResult)
jobResult
}
/**
* Aggregate the elements of each partition, and then the results for all the partitions, using
* given combine functions and a neutral "zero value". This function can return a different result
* type, U, than the type of this RDD, T. Thus, we need one operation for merging a T into an U
* and one operation for merging two U's, as in scala.TraversableOnce. Both of these functions are
* allowed to modify and return their first argument instead of creating a new U to avoid memory
* allocation.
*
* @param zeroValue the initial value for the accumulated result of each partition for the
* `seqOp` operator, and also the initial value for the combine results from
* different partitions for the `combOp` operator - this will typically be the
* neutral element (e.g. `Nil` for list concatenation or `0` for summation)
* @param seqOp an operator used to accumulate results within a partition
* @param combOp an associative operator used to combine results from different partitions
*/
def aggregate[U: ClassTag](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U): U = withScope {
// Clone the zero value since we will also be serializing it as part of tasks
var jobResult = Utils.clone(zeroValue, sc.env.serializer.newInstance())
val cleanSeqOp = sc.clean(seqOp)
val cleanCombOp = sc.clean(combOp)
val aggregatePartition = (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp)
val mergeResult = (_: Int, taskResult: U) => jobResult = combOp(jobResult, taskResult)
sc.runJob(this, aggregatePartition, mergeResult)
jobResult
}
/**
* Aggregates the elements of this RDD in a multi-level tree pattern.
* This method is semantically identical to [[org.apache.spark.rdd.RDD#aggregate]].
*
* @param depth suggested depth of the tree (default: 2)
*/
def treeAggregate[U: ClassTag](zeroValue: U)(
seqOp: (U, T) => U,
combOp: (U, U) => U,
depth: Int = 2): U = withScope {
treeAggregate(zeroValue, seqOp, combOp, depth, finalAggregateOnExecutor = false)
}
/**
* [[org.apache.spark.rdd.RDD#treeAggregate]] with a parameter to do the final
* aggregation on the executor
*
* @param finalAggregateOnExecutor do final aggregation on executor
*/
def treeAggregate[U: ClassTag](
zeroValue: U,
seqOp: (U, T) => U,
combOp: (U, U) => U,
depth: Int,
finalAggregateOnExecutor: Boolean): U = withScope {
require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.")
if (partitions.length == 0) {
Utils.clone(zeroValue, context.env.closureSerializer.newInstance())
} else {
val cleanSeqOp = context.clean(seqOp)
val cleanCombOp = context.clean(combOp)
val aggregatePartition =
(it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp)
var partiallyAggregated: RDD[U] = mapPartitions(it => Iterator(aggregatePartition(it)))
var numPartitions = partiallyAggregated.partitions.length
val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2)
// If creating an extra level doesn't help reduce
// the wall-clock time, we stop tree aggregation.
// Don't trigger TreeAggregation when it doesn't save wall-clock time
while (numPartitions > scale + math.ceil(numPartitions.toDouble / scale)) {
numPartitions /= scale
val curNumPartitions = numPartitions
partiallyAggregated = partiallyAggregated.mapPartitionsWithIndex {
(i, iter) => iter.map((i % curNumPartitions, _))
}.foldByKey(zeroValue, new HashPartitioner(curNumPartitions))(cleanCombOp).values
}
if (finalAggregateOnExecutor && partiallyAggregated.partitions.length > 1) {
// map the partially aggregated rdd into a key-value rdd
// do the computation in the single executor with one partition
// get the new RDD[U]
partiallyAggregated = partiallyAggregated
.map(v => (0.toByte, v))
.foldByKey(zeroValue, new ConstantPartitioner)(cleanCombOp)
.values
}
val copiedZeroValue = Utils.clone(zeroValue, sc.env.closureSerializer.newInstance())
partiallyAggregated.fold(copiedZeroValue)(cleanCombOp)
}
}
/**
* Return the number of elements in the RDD.
*/
def count(): Long = sc.runJob(this, Utils.getIteratorSize _).sum
/**
* Approximate version of count() that returns a potentially incomplete result
* within a timeout, even if not all tasks have finished.
*
* 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 countApprox(
timeout: Long,
confidence: Double = 0.95): PartialResult[BoundedDouble] = withScope {
require(0.0 <= confidence && confidence <= 1.0, s"confidence ($confidence) must be in [0,1]")
val countElements: (TaskContext, Iterator[T]) => Long = { (_, iter) =>
var result = 0L
while (iter.hasNext) {
result += 1L
iter.next()
}
result
}
val evaluator = new CountEvaluator(partitions.length, confidence)
sc.runApproximateJob(this, countElements, evaluator, timeout)
}
/**
* Return the count of each unique value in this RDD as a local map of (value, count) pairs.
*
* @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.map(x => (x, 1L)).reduceByKey(_ + _)
* }}}
*
* , which returns an RDD[T, Long] instead of a map.
*/
def countByValue()(implicit ord: Ordering[T] = null): Map[T, Long] = withScope {
map(value => (value, null)).countByKey()
}
/**
* Approximate version of countByValue().
*
* @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 countByValueApprox(timeout: Long, confidence: Double = 0.95)
(implicit ord: Ordering[T] = null)
: PartialResult[Map[T, BoundedDouble]] = withScope {
require(0.0 <= confidence && confidence <= 1.0, s"confidence ($confidence) must be in [0,1]")
if (elementClassTag.runtimeClass.isArray) {
throw SparkCoreErrors.countByValueApproxNotSupportArraysError()
}
val countPartition: (TaskContext, Iterator[T]) => OpenHashMap[T, Long] = { (_, iter) =>
val map = new OpenHashMap[T, Long]
iter.foreach {
t => map.changeValue(t, 1L, _ + 1L)
}
map
}
val evaluator = new GroupedCountEvaluator[T](partitions.length, confidence)
sc.runApproximateJob(this, countPartition, evaluator, timeout)
}
/**
* Return approximate number of distinct elements in the RDD.
*
* The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
* Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
* <a href="https://doi.org/10.1145/2452376.2452456">here</a>.
*
* The relative accuracy is approximately `1.054 / sqrt(2^p)`. Setting a nonzero (`sp` is greater
* than `p`) would trigger sparse representation of registers, which may reduce the memory
* consumption and increase accuracy when the cardinality is small.
*
* @param p The precision value for the normal set.
* `p` must be a value between 4 and `sp` if `sp` is not zero (32 max).
* @param sp The precision value for the sparse set, between 0 and 32.
* If `sp` equals 0, the sparse representation is skipped.
*/
def countApproxDistinct(p: Int, sp: Int): Long = withScope {
require(p >= 4, s"p ($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 ($sp)")
val zeroCounter = new HyperLogLogPlus(p, sp)
aggregate(zeroCounter)(
(hll: HyperLogLogPlus, v: T) => {
hll.offer(v)
hll
},
(h1: HyperLogLogPlus, h2: HyperLogLogPlus) => {
h1.addAll(h2)
h1
}).cardinality()
}
/**
* Return approximate number of distinct elements in the RDD.
*
* The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice:
* Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available
* <a href="https://doi.org/10.1145/2452376.2452456">here</a>.
*
* @param relativeSD Relative accuracy. Smaller values create counters that require more space.
* It must be greater than 0.000017.
*/
def countApproxDistinct(relativeSD: Double = 0.05): Long = withScope {
require(relativeSD > 0.000017, s"accuracy ($relativeSD) must be greater than 0.000017")
val p = math.ceil(2.0 * math.log(1.054 / relativeSD) / math.log(2)).toInt
countApproxDistinct(if (p < 4) 4 else p, 0)
}
/**
* Zips this RDD with its element indices. The ordering is first based on the partition index
* and then the ordering of items within each partition. So the first item in the first
* partition gets index 0, and the last item in the last partition receives the largest index.
*
* This is similar to Scala's zipWithIndex but it uses Long instead of Int as the index type.
* This method needs to trigger a spark job when this RDD contains more than one partitions.
*
* @note Some RDDs, such as those returned by groupBy(), do not guarantee order of
* elements in a partition. The index assigned to each element is therefore not guaranteed,
* and may even change if the RDD is reevaluated. If a fixed ordering is required to guarantee
* the same index assignments, you should sort the RDD with sortByKey() or save it to a file.
*/
def zipWithIndex(): RDD[(T, Long)] = withScope {
new ZippedWithIndexRDD(this)
}
/**
* Zips this RDD with generated unique Long ids. Items in the kth partition will get ids k, n+k,
* 2*n+k, ..., where n is the number of partitions. So there may exist gaps, but this method
* won't trigger a spark job, which is different from [[org.apache.spark.rdd.RDD#zipWithIndex]].
*
* @note Some RDDs, such as those returned by groupBy(), do not guarantee order of
* elements in a partition. The unique ID assigned to each element is therefore not guaranteed,
* and may even change if the RDD is reevaluated. If a fixed ordering is required to guarantee
* the same index assignments, you should sort the RDD with sortByKey() or save it to a file.
*/
def zipWithUniqueId(): RDD[(T, Long)] = withScope {
val n = this.partitions.length.toLong
this.mapPartitionsWithIndex { case (k, iter) =>
Utils.getIteratorZipWithIndex(iter, 0L).map { case (item, i) =>
(item, i * n + k)
}
}
}
/**
* Take the first num elements of the RDD. It works by first scanning one partition, and use the
* results from that partition to estimate the number of additional partitions needed to satisfy
* the limit.
*
* @note This method should only be used if the resulting array is expected to be small, as
* all the data is loaded into the driver's memory.
*
* @note Due to complications in the internal implementation, this method will raise
* an exception if called on an RDD of `Nothing` or `Null`.
*/
def take(num: Int): Array[T] = withScope {
val scaleUpFactor = Math.max(conf.get(RDD_LIMIT_SCALE_UP_FACTOR), 2)
if (num == 0) {
new Array[T](0)
} else {
val buf = new ArrayBuffer[T]
val totalParts = this.partitions.length
var partsScanned = 0
while (buf.size < num && partsScanned < totalParts) {
// The number of partitions to try in this iteration. It is ok for this number to be
// greater than totalParts because we actually cap it at totalParts in runJob.
var numPartsToTry = conf.get(RDD_LIMIT_INITIAL_NUM_PARTITIONS)
val left = num - buf.size
if (partsScanned > 0) {
// If we didn't find any rows after the previous iteration, multiply by
// limitScaleUpFactor and retry. Otherwise, interpolate the number of partitions we need
// to try, but overestimate it by 50%. We also cap the estimation in the end.
if (buf.isEmpty) {
numPartsToTry = partsScanned * scaleUpFactor
} else {
// As left > 0, numPartsToTry is always >= 1
numPartsToTry = Math.ceil(1.5 * left * partsScanned / buf.size).toInt
numPartsToTry = Math.min(numPartsToTry, partsScanned * scaleUpFactor)
}
}
val p = partsScanned.until(math.min(partsScanned + numPartsToTry, totalParts).toInt)
val res = sc.runJob(this, (it: Iterator[T]) => it.take(left).toArray, p)
res.foreach(buf ++= _.take(num - buf.size))
partsScanned += p.size
}
buf.toArray
}
}
/**
* Return the first element in this RDD.
*/
def first(): T = withScope {
take(1) match {
case Array(t) => t
case _ => throw SparkCoreErrors.emptyCollectionError()
}
}
/**
* Returns the top k (largest) elements from this RDD as defined by the specified
* implicit Ordering[T] and maintains the ordering. This does the opposite of
* [[takeOrdered]]. For example:
* {{{
* sc.parallelize(Seq(10, 4, 2, 12, 3)).top(1)
* // returns Array(12)
*
* sc.parallelize(Seq(2, 3, 4, 5, 6)).top(2)
* // returns Array(6, 5)
* }}}
*
* @note This method should only be used if the resulting array is expected to be small, as
* all the data is loaded into the driver's memory.
*
* @param num k, the number of top elements to return
* @param ord the implicit ordering for T
* @return an array of top elements
*/
def top(num: Int)(implicit ord: Ordering[T]): Array[T] = withScope {
takeOrdered(num)(ord.reverse)
}
/**
* Returns the first k (smallest) elements from this RDD as defined by the specified
* implicit Ordering[T] and maintains the ordering. This does the opposite of [[top]].
* For example:
* {{{
* sc.parallelize(Seq(10, 4, 2, 12, 3)).takeOrdered(1)
* // returns Array(2)
*
* sc.parallelize(Seq(2, 3, 4, 5, 6)).takeOrdered(2)
* // returns Array(2, 3)
* }}}
*
* @note This method should only be used if the resulting array is expected to be small, as
* all the data is loaded into the driver's memory.
*
* @param num k, the number of elements to return
* @param ord the implicit ordering for T
* @return an array of top elements
*/
def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = withScope {
if (num == 0 || this.getNumPartitions == 0) {
Array.empty
} else {
this.mapPartitionsWithIndex { case (pid, iter) =>
if (iter.nonEmpty) {
// Priority keeps the largest elements, so let's reverse the ordering.
Iterator.single(collectionUtils.takeOrdered(iter, num)(ord).toArray)
} else if (pid == 0) {
// make sure partition 0 always returns an array to avoid reduce on empty RDD
Iterator.single(Array.empty[T])
} else {
Iterator.empty
}
}.reduce { (array1, array2) =>
val size = math.min(num, array1.length + array2.length)
val array = Array.ofDim[T](size)
collectionUtils.mergeOrdered[T](Seq(array1, array2))(ord).copyToArray(array, 0, size)
array
}
}
}
/**
* Returns the max of this RDD as defined by the implicit Ordering[T].
* @return the maximum element of the RDD
* */
def max()(implicit ord: Ordering[T]): T = withScope {
this.reduce(ord.max)
}
/**
* Returns the min of this RDD as defined by the implicit Ordering[T].
* @return the minimum element of the RDD
* */
def min()(implicit ord: Ordering[T]): T = withScope {
this.reduce(ord.min)
}
/**
* @note Due to complications in the internal implementation, this method will raise an
* exception if called on an RDD of `Nothing` or `Null`. This may be come up in practice
* because, for example, the type of `parallelize(Seq())` is `RDD[Nothing]`.
* (`parallelize(Seq())` should be avoided anyway in favor of `parallelize(Seq[T]())`.)
* @return true if and only if the RDD contains no elements at all. Note that an RDD
* may be empty even when it has at least 1 partition.
*/
def isEmpty(): Boolean = withScope {
partitions.length == 0 || take(1).length == 0
}
/**
* Save this RDD as a text file, using string representations of elements.
*/
def saveAsTextFile(path: String): Unit = withScope {
saveAsTextFile(path, null)
}
/**
* Save this RDD as a compressed text file, using string representations of elements.
*/
def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]): Unit = withScope {
this.mapPartitions { iter =>
val text = new Text()
iter.map { x =>
require(x != null, "text files do not allow null rows")
text.set(x.toString)
(NullWritable.get(), text)
}
}.saveAsHadoopFile[TextOutputFormat[NullWritable, Text]](path, codec)
}
/**
* Save this RDD as a SequenceFile of serialized objects.
*/
def saveAsObjectFile(path: String): Unit = withScope {
this.mapPartitions(iter => iter.grouped(10).map(_.toArray))
.map(x => (NullWritable.get(), new BytesWritable(Utils.serialize(x))))
.saveAsSequenceFile(path)
}
/**
* Creates tuples of the elements in this RDD by applying `f`.
*/
def keyBy[K](f: T => K): RDD[(K, T)] = withScope {
val cleanedF = sc.clean(f)
map(x => (cleanedF(x), x))
}
/** A private method for tests, to look at the contents of each partition */
private[spark] def collectPartitions(): Array[Array[T]] = withScope {
sc.runJob(this, (iter: Iterator[T]) => iter.toArray)
}
/**
* Mark this RDD for checkpointing. It will be saved to a file inside the checkpoint
* directory set with `SparkContext#setCheckpointDir` and all references to its parent
* RDDs will be removed. This function must be called before any job has been
* executed on this RDD. It is strongly recommended that this RDD is persisted in
* memory, otherwise saving it on a file will require recomputation.
*/
def checkpoint(): Unit = RDDCheckpointData.synchronized {
// NOTE: we use a global lock here due to complexities downstream with ensuring
// children RDD partitions point to the correct parent partitions. In the future
// we should revisit this consideration.
if (context.checkpointDir.isEmpty) {
throw SparkCoreErrors.checkpointDirectoryHasNotBeenSetInSparkContextError()
} else if (checkpointData.isEmpty) {
checkpointData = Some(new ReliableRDDCheckpointData(this))
}
}
/**
* Mark this RDD for local checkpointing using Spark's existing caching layer.
*
* This method is for users who wish to truncate RDD lineages while skipping the expensive
* step of replicating the materialized data in a reliable distributed file system. This is
* useful for RDDs with long lineages that need to be truncated periodically (e.g. GraphX).
*
* Local checkpointing sacrifices fault-tolerance for performance. In particular, checkpointed
* data is written to ephemeral local storage in the executors instead of to a reliable,
* fault-tolerant storage. The effect is that if an executor fails during the computation,
* the checkpointed data may no longer be accessible, causing an irrecoverable job failure.
*
* This is NOT safe to use with dynamic allocation, which removes executors along
* with their cached blocks. If you must use both features, you are advised to set
* `spark.dynamicAllocation.cachedExecutorIdleTimeout` to a high value.
*
* The checkpoint directory set through `SparkContext#setCheckpointDir` is not used.
*/
def localCheckpoint(): this.type = RDDCheckpointData.synchronized {
if (conf.get(DYN_ALLOCATION_ENABLED) &&
conf.contains(DYN_ALLOCATION_CACHED_EXECUTOR_IDLE_TIMEOUT)) {
logWarning("Local checkpointing is NOT safe to use with dynamic allocation, " +
"which removes executors along with their cached blocks. If you must use both " +
"features, you are advised to set `spark.dynamicAllocation.cachedExecutorIdleTimeout` " +
"to a high value. E.g. If you plan to use the RDD for 1 hour, set the timeout to " +
"at least 1 hour.")
}
// Note: At this point we do not actually know whether the user will call persist() on
// this RDD later, so we must explicitly call it here ourselves to ensure the cached
// blocks are registered for cleanup later in the SparkContext.
//
// If, however, the user has already called persist() on this RDD, then we must adapt
// the storage level he/she specified to one that is appropriate for local checkpointing
// (i.e. uses disk) to guarantee correctness.
if (storageLevel == StorageLevel.NONE) {
persist(LocalRDDCheckpointData.DEFAULT_STORAGE_LEVEL)
} else {
persist(LocalRDDCheckpointData.transformStorageLevel(storageLevel), allowOverride = true)
}
// If this RDD is already checkpointed and materialized, its lineage is already truncated.
// We must not override our `checkpointData` in this case because it is needed to recover
// the checkpointed data. If it is overridden, next time materializing on this RDD will
// cause error.
if (isCheckpointedAndMaterialized) {
logWarning("Not marking RDD for local checkpoint because it was already " +
"checkpointed and materialized")
} else {
// Lineage is not truncated yet, so just override any existing checkpoint data with ours
checkpointData match {
case Some(_: ReliableRDDCheckpointData[_]) => logWarning(
"RDD was already marked for reliable checkpointing: overriding with local checkpoint.")
case _ =>
}
checkpointData = Some(new LocalRDDCheckpointData(this))
}
this
}
/**
* Return whether this RDD is checkpointed and materialized, either reliably or locally.
*/
def isCheckpointed: Boolean = isCheckpointedAndMaterialized
/**
* Return whether this RDD is checkpointed and materialized, either reliably or locally.
* This is introduced as an alias for `isCheckpointed` to clarify the semantics of the
* return value. Exposed for testing.
*/
private[spark] def isCheckpointedAndMaterialized: Boolean =
checkpointData.exists(_.isCheckpointed)
/**
* Return whether this RDD is marked for local checkpointing.
* Exposed for testing.
*/
private[rdd] def isLocallyCheckpointed: Boolean = {
checkpointData match {
case Some(_: LocalRDDCheckpointData[T]) => true
case _ => false
}
}
/**
* Return whether this RDD is reliably checkpointed and materialized.
*/
private[rdd] def isReliablyCheckpointed: Boolean = {
checkpointData match {
case Some(reliable: ReliableRDDCheckpointData[_]) if reliable.isCheckpointed => true
case _ => false
}
}
/**
* Gets the name of the directory to which this RDD was checkpointed.
* This is not defined if the RDD is checkpointed locally.
*/
def getCheckpointFile: Option[String] = {
checkpointData match {
case Some(reliable: ReliableRDDCheckpointData[T]) => reliable.getCheckpointDir
case _ => None
}
}
/**
* Removes an RDD's shuffles and it's non-persisted ancestors.
* When running without a shuffle service, cleaning up shuffle files enables downscaling.
* If you use the RDD after this call, you should checkpoint and materialize it first.
* If you are uncertain of what you are doing, please do not use this feature.
* Additional techniques for mitigating orphaned shuffle files:
* * Tuning the driver GC to be more aggressive, so the regular context cleaner is triggered
* * Setting an appropriate TTL for shuffle files to be auto cleaned
*/
@DeveloperApi
@Since("3.1.0")
def cleanShuffleDependencies(blocking: Boolean = false): Unit = {
sc.cleaner.foreach { cleaner =>
/**
* Clean the shuffles & all of its parents.
*/
def cleanEagerly(dep: Dependency[_]): Unit = {
dep match {
case dependency: ShuffleDependency[_, _, _] =>
val shuffleId = dependency.shuffleId
cleaner.doCleanupShuffle(shuffleId, blocking)
case _ => // do nothing
}
val rdd = dep.rdd
val rddDepsOpt = rdd.internalDependencies
if (rdd.getStorageLevel == StorageLevel.NONE) {
rddDepsOpt.foreach(deps => deps.foreach(cleanEagerly))
}
}
internalDependencies.foreach(deps => deps.foreach(cleanEagerly))
}
}
/**
* :: Experimental ::
* Marks the current stage as a barrier stage, where Spark must launch all tasks together.
* In case of a task failure, instead of only restarting the failed task, Spark will abort the
* entire stage and re-launch all tasks for this stage.
* The barrier execution mode feature is experimental and it only handles limited scenarios.
* Please read the linked SPIP and design docs to understand the limitations and future plans.
* @return an [[RDDBarrier]] instance that provides actions within a barrier stage
* @see [[org.apache.spark.BarrierTaskContext]]
* @see <a href="https://jira.apache.org/jira/browse/SPARK-24374">SPIP: Barrier Execution Mode</a>
* @see <a href="https://jira.apache.org/jira/browse/SPARK-24582">Design Doc</a>
*/
@Experimental
@Since("2.4.0")
def barrier(): RDDBarrier[T] = withScope(new RDDBarrier[T](this))
/**
* Specify a ResourceProfile to use when calculating this RDD. This is only supported on
* certain cluster managers and currently requires dynamic allocation to be enabled.
* It will result in new executors with the resources specified being acquired to
* calculate the RDD.
*/
@Experimental
@Since("3.1.0")
def withResources(rp: ResourceProfile): this.type = {
resourceProfile = Option(rp)
sc.resourceProfileManager.addResourceProfile(resourceProfile.get)
this
}
/**
* Get the ResourceProfile specified with this RDD or null if it wasn't specified.
* @return the user specified ResourceProfile or null (for Java compatibility) if
* none was specified
*/
@Experimental
@Since("3.1.0")
def getResourceProfile(): ResourceProfile = resourceProfile.orNull
// =======================================================================
// Other internal methods and fields
// =======================================================================
private var storageLevel: StorageLevel = StorageLevel.NONE
@transient private var resourceProfile: Option[ResourceProfile] = None
/** User code that created this RDD (e.g. `textFile`, `parallelize`). */
@transient private[spark] val creationSite = sc.getCallSite()
/**
* The scope associated with the operation that created this RDD.
*
* This is more flexible than the call site and can be defined hierarchically. For more
* detail, see the documentation of {{RDDOperationScope}}. This scope is not defined if the
* user instantiates this RDD himself without using any Spark operations.
*/
@transient private[spark] val scope: Option[RDDOperationScope] = {
Option(sc.getLocalProperty(SparkContext.RDD_SCOPE_KEY)).map(RDDOperationScope.fromJson)
}
private[spark] def getCreationSite: String = Option(creationSite).map(_.shortForm).getOrElse("")
private[spark] def elementClassTag: ClassTag[T] = classTag[T]
private[spark] var checkpointData: Option[RDDCheckpointData[T]] = None
// Whether to checkpoint all ancestor RDDs that are marked for checkpointing. By default,
// we stop as soon as we find the first such RDD, an optimization that allows us to write
// less data but is not safe for all workloads. E.g. in streaming we may checkpoint both
// an RDD and its parent in every batch, in which case the parent may never be checkpointed
// and its lineage never truncated, leading to OOMs in the long run (SPARK-6847).
private val checkpointAllMarkedAncestors =
Option(sc.getLocalProperty(RDD.CHECKPOINT_ALL_MARKED_ANCESTORS)).exists(_.toBoolean)
/** Returns the first parent RDD */
protected[spark] def firstParent[U: ClassTag]: RDD[U] = {
dependencies.head.rdd.asInstanceOf[RDD[U]]
}
/** Returns the jth parent RDD: e.g. rdd.parent[T](0) is equivalent to rdd.firstParent[T] */
protected[spark] def parent[U: ClassTag](j: Int): RDD[U] = {
dependencies(j).rdd.asInstanceOf[RDD[U]]
}
/** The [[org.apache.spark.SparkContext]] that this RDD was created on. */
def context: SparkContext = sc
/**
* Private API for changing an RDD's ClassTag.
* Used for internal Java-Scala API compatibility.
*/
private[spark] def retag(cls: Class[T]): RDD[T] = {
val classTag: ClassTag[T] = ClassTag.apply(cls)
this.retag(classTag)
}
/**
* Private API for changing an RDD's ClassTag.
* Used for internal Java-Scala API compatibility.
*/
private[spark] def retag(implicit classTag: ClassTag[T]): RDD[T] = {
this.mapPartitions(identity, preservesPartitioning = true)(classTag)
}
// Avoid handling doCheckpoint multiple times to prevent excessive recursion
@transient private var doCheckpointCalled = false
/**
* Performs the checkpointing of this RDD by saving this. It is called after a job using this RDD
* has completed (therefore the RDD has been materialized and potentially stored in memory).
* doCheckpoint() is called recursively on the parent RDDs.
*/
private[spark] def doCheckpoint(): Unit = {
RDDOperationScope.withScope(sc, "checkpoint", allowNesting = false, ignoreParent = true) {
if (!doCheckpointCalled) {
doCheckpointCalled = true
if (checkpointData.isDefined) {
if (checkpointAllMarkedAncestors) {
// TODO We can collect all the RDDs that needs to be checkpointed, and then checkpoint
// them in parallel.
// Checkpoint parents first because our lineage will be truncated after we
// checkpoint ourselves
dependencies.foreach(_.rdd.doCheckpoint())
}
checkpointData.get.checkpoint()
} else {
dependencies.foreach(_.rdd.doCheckpoint())
}
}
}
}
/**
* Changes the dependencies of this RDD from its original parents to a new RDD (`newRDD`)
* created from the checkpoint file, and forget its old dependencies and partitions.
*/
private[spark] def markCheckpointed(): Unit = stateLock.synchronized {
legacyDependencies = new WeakReference(dependencies_)
clearDependencies()
partitions_ = null
deps = null // Forget the constructor argument for dependencies too
}
/**
* Clears the dependencies of this RDD. This method must ensure that all references
* to the original parent RDDs are removed to enable the parent RDDs to be garbage
* collected. Subclasses of RDD may override this method for implementing their own cleaning
* logic. See [[org.apache.spark.rdd.UnionRDD]] for an example.
*/
protected def clearDependencies(): Unit = stateLock.synchronized {
dependencies_ = null
}
/** A description of this RDD and its recursive dependencies for debugging. */
def toDebugString: String = {
// Get a debug description of an rdd without its children
def debugSelf(rdd: RDD[_]): Seq[String] = {
import Utils.bytesToString
val persistence = if (storageLevel != StorageLevel.NONE) storageLevel.description else ""
val storageInfo = rdd.context.getRDDStorageInfo(_.id == rdd.id).map(info =>
" CachedPartitions: %d; MemorySize: %s; DiskSize: %s".format(
info.numCachedPartitions, bytesToString(info.memSize), bytesToString(info.diskSize)))
s"$rdd [$persistence]" +: storageInfo
}
// Apply a different rule to the last child
def debugChildren(rdd: RDD[_], prefix: String): Seq[String] = {
val len = rdd.dependencies.length
len match {
case 0 => Seq.empty
case 1 =>
val d = rdd.dependencies.head
debugString(d.rdd, prefix, d.isInstanceOf[ShuffleDependency[_, _, _]], true)
case _ =>
val frontDeps = rdd.dependencies.take(len - 1)
val frontDepStrings = frontDeps.flatMap(
d => debugString(d.rdd, prefix, d.isInstanceOf[ShuffleDependency[_, _, _]]))
val lastDep = rdd.dependencies.last
val lastDepStrings =
debugString(lastDep.rdd, prefix, lastDep.isInstanceOf[ShuffleDependency[_, _, _]], true)
frontDepStrings ++ lastDepStrings
}
}
// The first RDD in the dependency stack has no parents, so no need for a +-
def firstDebugString(rdd: RDD[_]): Seq[String] = {
val partitionStr = "(" + rdd.partitions.length + ")"
val leftOffset = (partitionStr.length - 1) / 2
val nextPrefix = (" " * leftOffset) + "|" + (" " * (partitionStr.length - leftOffset))
debugSelf(rdd).zipWithIndex.map{
case (desc: String, 0) => s"$partitionStr $desc"
case (desc: String, _) => s"$nextPrefix $desc"
} ++ debugChildren(rdd, nextPrefix)
}
def shuffleDebugString(rdd: RDD[_], prefix: String = "", isLastChild: Boolean): Seq[String] = {
val partitionStr = "(" + rdd.partitions.length + ")"
val leftOffset = (partitionStr.length - 1) / 2
val thisPrefix = prefix.replaceAll("\\|\\s+$", "")
val nextPrefix = (
thisPrefix
+ (if (isLastChild) " " else "| ")
+ (" " * leftOffset) + "|" + (" " * (partitionStr.length - leftOffset)))
debugSelf(rdd).zipWithIndex.map{
case (desc: String, 0) => s"$thisPrefix+-$partitionStr $desc"
case (desc: String, _) => s"$nextPrefix$desc"
} ++ debugChildren(rdd, nextPrefix)
}
def debugString(
rdd: RDD[_],
prefix: String = "",
isShuffle: Boolean = true,
isLastChild: Boolean = false): Seq[String] = {
if (isShuffle) {
shuffleDebugString(rdd, prefix, isLastChild)
} else {
debugSelf(rdd).map(prefix + _) ++ debugChildren(rdd, prefix)
}
}
firstDebugString(this).mkString("\n")
}
override def toString: String = "%s%s[%d] at %s".format(
Option(name).map(_ + " ").getOrElse(""), getClass.getSimpleName, id, getCreationSite)
def toJavaRDD() : JavaRDD[T] = {
new JavaRDD(this)(elementClassTag)
}
/**
* Whether the RDD is in a barrier stage. Spark must launch all the tasks at the same time for a
* barrier stage.
*
* An RDD is in a barrier stage, if at least one of its parent RDD(s), or itself, are mapped from
* an [[RDDBarrier]]. This function always returns false for a [[ShuffledRDD]], since a
* [[ShuffledRDD]] indicates start of a new stage.
*
* A [[MapPartitionsRDD]] can be transformed from an [[RDDBarrier]], under that case the
* [[MapPartitionsRDD]] shall be marked as barrier.
*/
private[spark] def isBarrier(): Boolean = isBarrier_
// From performance concern, cache the value to avoid repeatedly compute `isBarrier()` on a long
// RDD chain.
@transient protected lazy val isBarrier_ : Boolean =
dependencies.filter(!_.isInstanceOf[ShuffleDependency[_, _, _]]).exists(_.rdd.isBarrier())
private final lazy val _outputDeterministicLevel: DeterministicLevel.Value =
getOutputDeterministicLevel
/**
* Returns the deterministic level of this RDD's output. Please refer to [[DeterministicLevel]]
* for the definition.
*
* By default, an reliably checkpointed RDD, or RDD without parents(root RDD) is DETERMINATE. For
* RDDs with parents, we will generate a deterministic level candidate per parent according to
* the dependency. The deterministic level of the current RDD is the deterministic level
* candidate that is deterministic least. Please override [[getOutputDeterministicLevel]] to
* provide custom logic of calculating output deterministic level.
*/
// TODO(SPARK-34612): make it public so users can set deterministic level to their custom RDDs.
// TODO: this can be per-partition. e.g. UnionRDD can have different deterministic level for
// different partitions.
private[spark] final def outputDeterministicLevel: DeterministicLevel.Value = {
if (isReliablyCheckpointed) {
DeterministicLevel.DETERMINATE
} else {
_outputDeterministicLevel
}
}
@DeveloperApi
protected def getOutputDeterministicLevel: DeterministicLevel.Value = {
val deterministicLevelCandidates = dependencies.map {
// The shuffle is not really happening, treat it like narrow dependency and assume the output
// deterministic level of current RDD is same as parent.
case dep: ShuffleDependency[_, _, _] if dep.rdd.partitioner.exists(_ == dep.partitioner) =>
dep.rdd.outputDeterministicLevel
case dep: ShuffleDependency[_, _, _] =>
if (dep.rdd.outputDeterministicLevel == DeterministicLevel.INDETERMINATE) {
// If map output was indeterminate, shuffle output will be indeterminate as well
DeterministicLevel.INDETERMINATE
} else if (dep.keyOrdering.isDefined && dep.aggregator.isDefined) {
// if aggregator specified (and so unique keys) and key ordering specified - then
// consistent ordering.
DeterministicLevel.DETERMINATE
} else {
// In Spark, the reducer fetches multiple remote shuffle blocks at the same time, and
// the arrival order of these shuffle blocks are totally random. Even if the parent map
// RDD is DETERMINATE, the reduce RDD is always UNORDERED.
DeterministicLevel.UNORDERED
}
// For narrow dependency, assume the output deterministic level of current RDD is same as
// parent.
case dep => dep.rdd.outputDeterministicLevel
}
if (deterministicLevelCandidates.isEmpty) {
// By default we assume the root RDD is determinate.
DeterministicLevel.DETERMINATE
} else {
deterministicLevelCandidates.maxBy(_.id)
}
}
}
/**
* Defines implicit functions that provide extra functionalities on RDDs of specific types.
*
* For example, [[RDD.rddToPairRDDFunctions]] converts an RDD into a [[PairRDDFunctions]] for
* key-value-pair RDDs, and enabling extra functionalities such as `PairRDDFunctions.reduceByKey`.
*/
object RDD {
private[spark] val CHECKPOINT_ALL_MARKED_ANCESTORS =
"spark.checkpoint.checkpointAllMarkedAncestors"
// The following implicit functions were in SparkContext before 1.3 and users had to
// `import SparkContext._` to enable them. Now we move them here to make the compiler find
// them automatically. However, we still keep the old functions in SparkContext for backward
// compatibility and forward to the following functions directly.
implicit def rddToPairRDDFunctions[K, V](rdd: RDD[(K, V)])
(implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null): PairRDDFunctions[K, V] = {
new PairRDDFunctions(rdd)
}
implicit def rddToAsyncRDDActions[T: ClassTag](rdd: RDD[T]): AsyncRDDActions[T] = {
new AsyncRDDActions(rdd)
}
implicit def rddToSequenceFileRDDFunctions[K, V](rdd: RDD[(K, V)])
(implicit kt: ClassTag[K], vt: ClassTag[V],
keyWritableFactory: WritableFactory[K],
valueWritableFactory: WritableFactory[V])
: SequenceFileRDDFunctions[K, V] = {
implicit val keyConverter = keyWritableFactory.convert
implicit val valueConverter = valueWritableFactory.convert
new SequenceFileRDDFunctions(rdd,
keyWritableFactory.writableClass(kt), valueWritableFactory.writableClass(vt))
}
implicit def rddToOrderedRDDFunctions[K : Ordering : ClassTag, V: ClassTag](rdd: RDD[(K, V)])
: OrderedRDDFunctions[K, V, (K, V)] = {
new OrderedRDDFunctions[K, V, (K, V)](rdd)
}
implicit def doubleRDDToDoubleRDDFunctions(rdd: RDD[Double]): DoubleRDDFunctions = {
new DoubleRDDFunctions(rdd)
}
implicit def numericRDDToDoubleRDDFunctions[T](rdd: RDD[T])(implicit num: Numeric[T])
: DoubleRDDFunctions = {
new DoubleRDDFunctions(rdd.map(x => num.toDouble(x)))
}
}
/**
* The deterministic level of RDD's output (i.e. what `RDD#compute` returns). This explains how
* the output will diff when Spark reruns the tasks for the RDD. There are 3 deterministic levels:
* 1. DETERMINATE: The RDD output is always the same data set in the same order after a rerun.
* 2. UNORDERED: The RDD output is always the same data set but the order can be different
* after a rerun.
* 3. INDETERMINATE. The RDD output can be different after a rerun.
*
* Note that, the output of an RDD usually relies on the parent RDDs. When the parent RDD's output
* is INDETERMINATE, it's very likely the RDD's output is also INDETERMINATE.
*/
private[spark] object DeterministicLevel extends Enumeration {
val DETERMINATE, UNORDERED, INDETERMINATE = Value
}
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