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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* 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
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark
import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
import scala.collection.mutable
import scala.collection.mutable.ArrayBuffer
import scala.math.log10
import scala.reflect.ClassTag
import scala.util.hashing.byteswap32
import org.apache.spark.rdd.{PartitionPruningRDD, RDD}
import org.apache.spark.serializer.JavaSerializer
import org.apache.spark.util.{CollectionsUtils, Utils}
import org.apache.spark.util.random.SamplingUtils
/**
* An object that defines how the elements in a key-value pair RDD are partitioned by key.
* Maps each key to a partition ID, from 0 to `numPartitions - 1`.
*
* Note that, partitioner must be deterministic, i.e. it must return the same partition id given
* the same partition key.
*/
abstract class Partitioner extends Serializable {
def numPartitions: Int
def getPartition(key: Any): Int
}
object Partitioner {
/**
* Choose a partitioner to use for a cogroup-like operation between a number of RDDs.
*
* If spark.default.parallelism is set, we'll use the value of SparkContext defaultParallelism
* as the default partitions number, otherwise we'll use the max number of upstream partitions.
*
* When available, we choose the partitioner from rdds with maximum number of partitions. If this
* partitioner is eligible (number of partitions within an order of maximum number of partitions
* in rdds), or has partition number higher than default partitions number - we use this
* partitioner.
*
* Otherwise, we'll use a new HashPartitioner with the default partitions number.
*
* Unless spark.default.parallelism is set, the number of partitions will be the same as the
* number of partitions in the largest upstream RDD, as this should be least likely to cause
* out-of-memory errors.
*
* We use two method parameters (rdd, others) to enforce callers passing at least 1 RDD.
*/
def defaultPartitioner(rdd: RDD[_], others: RDD[_]*): Partitioner = {
val rdds = (Seq(rdd) ++ others)
val hasPartitioner = rdds.filter(_.partitioner.exists(_.numPartitions > 0))
val hasMaxPartitioner: Option[RDD[_]] = if (hasPartitioner.nonEmpty) {
Some(hasPartitioner.maxBy(_.partitions.length))
} else {
None
}
val defaultNumPartitions = if (rdd.context.conf.contains("spark.default.parallelism")) {
rdd.context.defaultParallelism
} else {
rdds.map(_.partitions.length).max
}
// If the existing max partitioner is an eligible one, or its partitions number is larger
// than the default number of partitions, use the existing partitioner.
if (hasMaxPartitioner.nonEmpty && (isEligiblePartitioner(hasMaxPartitioner.get, rdds) ||
defaultNumPartitions < hasMaxPartitioner.get.getNumPartitions)) {
hasMaxPartitioner.get.partitioner.get
} else {
new HashPartitioner(defaultNumPartitions)
}
}
/**
* Returns true if the number of partitions of the RDD is either greater than or is less than and
* within a single order of magnitude of the max number of upstream partitions, otherwise returns
* false.
*/
private def isEligiblePartitioner(
hasMaxPartitioner: RDD[_],
rdds: Seq[RDD[_]]): Boolean = {
val maxPartitions = rdds.map(_.partitions.length).max
log10(maxPartitions) - log10(hasMaxPartitioner.getNumPartitions) < 1
}
}
/**
* A [[org.apache.spark.Partitioner]] that implements hash-based partitioning using
* Java's `Object.hashCode`.
*
* Java arrays have hashCodes that are based on the arrays' identities rather than their contents,
* so attempting to partition an RDD[Array[_]] or RDD[(Array[_], _)] using a HashPartitioner will
* produce an unexpected or incorrect result.
*/
class HashPartitioner(partitions: Int) extends Partitioner {
require(partitions >= 0, s"Number of partitions ($partitions) cannot be negative.")
def numPartitions: Int = partitions
def getPartition(key: Any): Int = key match {
case null => 0
case _ => Utils.nonNegativeMod(key.hashCode, numPartitions)
}
override def equals(other: Any): Boolean = other match {
case h: HashPartitioner =>
h.numPartitions == numPartitions
case _ =>
false
}
override def hashCode: Int = numPartitions
}
/**
* A [[org.apache.spark.Partitioner]] that partitions sortable records by range into roughly
* equal ranges. The ranges are determined by sampling the content of the RDD passed in.
*
* @note The actual number of partitions created by the RangePartitioner might not be the same
* as the `partitions` parameter, in the case where the number of sampled records is less than
* the value of `partitions`.
*/
class RangePartitioner[K : Ordering : ClassTag, V](
partitions: Int,
rdd: RDD[_ <: Product2[K, V]],
private var ascending: Boolean = true,
val samplePointsPerPartitionHint: Int = 20)
extends Partitioner {
// A constructor declared in order to maintain backward compatibility for Java, when we add the
// 4th constructor parameter samplePointsPerPartitionHint. See SPARK-22160.
// This is added to make sure from a bytecode point of view, there is still a 3-arg ctor.
def this(partitions: Int, rdd: RDD[_ <: Product2[K, V]], ascending: Boolean) = {
this(partitions, rdd, ascending, samplePointsPerPartitionHint = 20)
}
// We allow partitions = 0, which happens when sorting an empty RDD under the default settings.
require(partitions >= 0, s"Number of partitions cannot be negative but found $partitions.")
require(samplePointsPerPartitionHint > 0,
s"Sample points per partition must be greater than 0 but found $samplePointsPerPartitionHint")
private var ordering = implicitly[Ordering[K]]
// An array of upper bounds for the first (partitions - 1) partitions
private var rangeBounds: Array[K] = {
if (partitions <= 1) {
Array.empty
} else {
// This is the sample size we need to have roughly balanced output partitions, capped at 1M.
// Cast to double to avoid overflowing ints or longs
val sampleSize = math.min(samplePointsPerPartitionHint.toDouble * partitions, 1e6)
// Assume the input partitions are roughly balanced and over-sample a little bit.
val sampleSizePerPartition = math.ceil(3.0 * sampleSize / rdd.partitions.length).toInt
val (numItems, sketched) = RangePartitioner.sketch(rdd.map(_._1), sampleSizePerPartition)
if (numItems == 0L) {
Array.empty
} else {
// If a partition contains much more than the average number of items, we re-sample from it
// to ensure that enough items are collected from that partition.
val fraction = math.min(sampleSize / math.max(numItems, 1L), 1.0)
val candidates = ArrayBuffer.empty[(K, Float)]
val imbalancedPartitions = mutable.Set.empty[Int]
sketched.foreach { case (idx, n, sample) =>
if (fraction * n > sampleSizePerPartition) {
imbalancedPartitions += idx
} else {
// The weight is 1 over the sampling probability.
val weight = (n.toDouble / sample.length).toFloat
for (key <- sample) {
candidates += ((key, weight))
}
}
}
if (imbalancedPartitions.nonEmpty) {
// Re-sample imbalanced partitions with the desired sampling probability.
val imbalanced = new PartitionPruningRDD(rdd.map(_._1), imbalancedPartitions.contains)
val seed = byteswap32(-rdd.id - 1)
val reSampled = imbalanced.sample(withReplacement = false, fraction, seed).collect()
val weight = (1.0 / fraction).toFloat
candidates ++= reSampled.map(x => (x, weight))
}
RangePartitioner.determineBounds(candidates, math.min(partitions, candidates.size))
}
}
}
def numPartitions: Int = rangeBounds.length + 1
private var binarySearch: ((Array[K], K) => Int) = CollectionsUtils.makeBinarySearch[K]
def getPartition(key: Any): Int = {
val k = key.asInstanceOf[K]
var partition = 0
if (rangeBounds.length <= 128) {
// If we have less than 128 partitions naive search
while (partition < rangeBounds.length && ordering.gt(k, rangeBounds(partition))) {
partition += 1
}
} else {
// Determine which binary search method to use only once.
partition = binarySearch(rangeBounds, k)
// binarySearch either returns the match location or -[insertion point]-1
if (partition < 0) {
partition = -partition-1
}
if (partition > rangeBounds.length) {
partition = rangeBounds.length
}
}
if (ascending) {
partition
} else {
rangeBounds.length - partition
}
}
override def equals(other: Any): Boolean = other match {
case r: RangePartitioner[_, _] =>
r.rangeBounds.sameElements(rangeBounds) && r.ascending == ascending
case _ =>
false
}
override def hashCode(): Int = {
val prime = 31
var result = 1
var i = 0
while (i < rangeBounds.length) {
result = prime * result + rangeBounds(i).hashCode
i += 1
}
result = prime * result + ascending.hashCode
result
}
@throws(classOf[IOException])
private def writeObject(out: ObjectOutputStream): Unit = Utils.tryOrIOException {
val sfactory = SparkEnv.get.serializer
sfactory match {
case js: JavaSerializer => out.defaultWriteObject()
case _ =>
out.writeBoolean(ascending)
out.writeObject(ordering)
out.writeObject(binarySearch)
val ser = sfactory.newInstance()
Utils.serializeViaNestedStream(out, ser) { stream =>
stream.writeObject(scala.reflect.classTag[Array[K]])
stream.writeObject(rangeBounds)
}
}
}
@throws(classOf[IOException])
private def readObject(in: ObjectInputStream): Unit = Utils.tryOrIOException {
val sfactory = SparkEnv.get.serializer
sfactory match {
case js: JavaSerializer => in.defaultReadObject()
case _ =>
ascending = in.readBoolean()
ordering = in.readObject().asInstanceOf[Ordering[K]]
binarySearch = in.readObject().asInstanceOf[(Array[K], K) => Int]
val ser = sfactory.newInstance()
Utils.deserializeViaNestedStream(in, ser) { ds =>
implicit val classTag = ds.readObject[ClassTag[Array[K]]]()
rangeBounds = ds.readObject[Array[K]]()
}
}
}
}
private[spark] object RangePartitioner {
/**
* Sketches the input RDD via reservoir sampling on each partition.
*
* @param rdd the input RDD to sketch
* @param sampleSizePerPartition max sample size per partition
* @return (total number of items, an array of (partitionId, number of items, sample))
*/
def sketch[K : ClassTag](
rdd: RDD[K],
sampleSizePerPartition: Int): (Long, Array[(Int, Long, Array[K])]) = {
val shift = rdd.id
// val classTagK = classTag[K] // to avoid serializing the entire partitioner object
val sketched = rdd.mapPartitionsWithIndex { (idx, iter) =>
val seed = byteswap32(idx ^ (shift << 16))
val (sample, n) = SamplingUtils.reservoirSampleAndCount(
iter, sampleSizePerPartition, seed)
Iterator((idx, n, sample))
}.collect()
val numItems = sketched.map(_._2).sum
(numItems, sketched)
}
/**
* Determines the bounds for range partitioning from candidates with weights indicating how many
* items each represents. Usually this is 1 over the probability used to sample this candidate.
*
* @param candidates unordered candidates with weights
* @param partitions number of partitions
* @return selected bounds
*/
def determineBounds[K : Ordering : ClassTag](
candidates: ArrayBuffer[(K, Float)],
partitions: Int): Array[K] = {
val ordering = implicitly[Ordering[K]]
val ordered = candidates.sortBy(_._1)
val numCandidates = ordered.size
val sumWeights = ordered.map(_._2.toDouble).sum
val step = sumWeights / partitions
var cumWeight = 0.0
var target = step
val bounds = ArrayBuffer.empty[K]
var i = 0
var j = 0
var previousBound = Option.empty[K]
while ((i < numCandidates) && (j < partitions - 1)) {
val (key, weight) = ordered(i)
cumWeight += weight
if (cumWeight >= target) {
// Skip duplicate values.
if (previousBound.isEmpty || ordering.gt(key, previousBound.get)) {
bounds += key
target += step
j += 1
previousBound = Some(key)
}
}
i += 1
}
bounds.toArray
}
}
