Spark中分区器直接决定了RDD中分区的个数、RDD中每条数据经过Shuffle过程属于哪个分区和Reduce的个数
注意：
(1)只有Key-Value类型的RDD才有分区函数，非Key-Value类型的RDD无分区函数，但是也是有分区的
(2)每个RDD的分区ID范围：0~numPartitions-1，决定这个值是属于那个分区的。
分区方式的优劣
HashPartitioner分区弊端：
可能导致每个分区中数据量的不均匀，极端情况下会导致某些分区拥有RDD的全部数据(HashCode为负数时，为了避免小于0，spark做了以下处理)。
/* Calculates 'x' modulo 'mod', takes to consideration sign of x,
* i.e. if 'x' is negative, than 'x' % 'mod' is negative too
* so function return (x % mod) + mod in that case.
*/
def nonNegativeMod(x: Int, mod: Int): Int = {
val rawMod = x % mod
rawMod + (if (rawMod < 0) mod else 0)
}
RangePartitioner分区优势：尽量保证每个分区中数据量的均匀，而且分区与分区之间是有序的，一个分区中的元素肯定都是比另一个分区内的元素小或者大；
但是分区内的元素是不能保证顺序的。简单的说就是将一定范围内的数映射到某一个分区内。
一、三种分区方式介绍
1、默认分区方式(实际上是HashPartitioner)
/**
* Choose a partitioner to use for a cogroup-like operation between a number of RDDs.
*
* If any of the RDDs already has a partitioner, choose that one.
*
* Otherwise, we use a default HashPartitioner. For the number of partitions, if
* spark.default.parallelism is set, then we'll use the value from SparkContext
* defaultParallelism, otherwise we'll use the max number of upstream partitions.
*
* 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 bySize = (Seq(rdd) ++ others).sortBy(_.partitions.size).reverse
for (r <- bysize="" if="" r="" partitioner="" isdefined="" r="" partitioner="" get="" numpartitions=""> 0) {
return r.partitioner.get
}
if (rdd.context.conf.contains("spark.default.parallelism")) {
new HashPartitioner(rdd.context.defaultParallelism)
} else {
new HashPartitioner(bySize.head.partitions.size)
}
}
2、HashPartitioner分区
HashPartitioner分区的原理：对于给定的key，计算其hashCode，并除于分区的个数取余，如果余数小于0，则用余数+分区的个数，最后返回的值就是这个key所属的分区ID。实现如下：
/**
* 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
}
3、RangePartitioner分区
RangePartitioner作用：将一定范围内的数映射到某一个分区内，在实现中，分界的算法尤为重要。算法对应的函数是rangeBounds。
代码如下：
/**
* 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 that 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)" extends="" partitioner="" 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.")
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.
val sampleSize = math.min(20.0 * partitions, 1e6)
// Assume the input partitions are roughly balanced and over-sample a little bit.
val sampleSizePerPartition = math.ceil(3.0 * sampleSize / rdd.partitions.size).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.size).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, partitions)
}
}
}
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]]()
}
}
}
}
使用的分区算法是：水塘抽样，参考---》https://www.iteblog.com/archives/1525.html
二、自定义分区
需要继承org.apache.spark.Partitioner类,实现如下：
import org.apache.spark.Partitioner
/**
* Created by Jeff Yang on 2017/3/30
* Update date:
* Time: 18:03
* Describle :
* Result of Test:
* Command:
* Email: highfei2011@126.com
*/
class MySparkPartition(numParts: Int) extends Partitioner {
override def numPartitions: Int = numParts
/**
* 可以自定义分区算法
* @param key
* @return
*/
override def getPartition(key: Any): Int = {
val domain = new java.net.URL(key.toString).getHost()
val code = (domain.hashCode % numPartitions)
if (code < 0) {
code + numPartitions
} else {
code
}
}
override def equals(other: Any): Boolean = other match {
case mypartition: MySparkPartition =>
mypartition.numPartitions == numPartitions
case _ =>
false
}
override def hashCode: Int = numPartitions
}
/**
*
*
* def numPartitions：这个方法需要返回你想要创建分区的个数；
* def getPartition：这个函数需要对输入的key做计算，然后返回该key的分区ID，范围一定是0到numPartitions-1；
* equals()：这个是Java标准的判断相等的函数，之所以要求用户实现这个函数是因为Spark内部会比较两个RDD的分区是否一样。
*
*
*
* /
三、使用分区
创建了自定义分区后，使用方式如下：
import org.apache.spark.{SparkConf, SparkContext}
/**
* Created by Jeff Yang on 2017/3/30
* Update date:
* Time: 18:47
* Describle :使用自定义的分区器
* Result of Test:
* Command:
* Email: highfei2011@126.com
*/
object UseMyPartitioner {
def main(args: Array[String]) {
val conf=new SparkConf()
.setMaster("local[2]")
.setAppName("TestMyParttioner")
.set("spark.app.id","test-partition-id")
val sc=new SparkContext(conf)
//读取hdfs文件
val lines=sc.textFile("hdfs://hadoop2:8020/user/test/word.txt")
val splitMap=lines.flatMap(line=>line.split("\t")).map(word=>(word,2))//注意：RDD一定要是key-value
//保存
splitMap.partitionBy(new MySparkPartition(3)).saveAsTextFile("F:/partrion/test")
sc.stop()
}
}
