一、RDD 分区器简介
- Spark 分区器的父类是 Partitioner 抽象类
- 分区器直接决定了 RDD 中分区的个数、RDD 中每条数据经过 Shuffle 后进入哪个分区，进而决定了 Reduce 的个数
- 只有 Key-Value 类型的 RDD 才有分区器，非 Key-Value 类型的 RDD 分区的值是 None
- 每个 RDD 的分区索引的范围：0~(numPartitions - 1)
二、HashPartitioner
默认的分区器，对于给定的 key，计算其 hashCode 并除以分区个数取余获得数据所在的分区索引
class HashPartitioner(partitions: Int) extends Partitioner {require(partitions >= 0, s"Number of partitions ($partitions) cannot be negative.")def numPartitions: Int = partitionsdef getPartition(key: Any): Int = key match {case null => 0case _ => Utils.nonNegativeMod(key.hashCode, numPartitions)}override def equals(other: Any): Boolean = other match {case h: HashPartitioner => h.numPartitions == numPartitionscase _ => false}override def hashCode: Int = numPartitions
}
三、RangePartitioner
将一定范围内的数据映射到一个分区中，尽量保证每个分区数据均匀，而且分区间有序
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) partitionsprivate var rangeBounds: Array[K] = {...}def numPartitions: Int = rangeBounds.length + 1private var binarySearch: ((Array[K], K) => Int) = CollectionsUtils.makeBinarySearch[K]def getPartition(key: Any): Int = {val k = key.asInstanceOf[K]var partition = 0if (rangeBounds.length <= 128) {// If we have less than 128 partitions naive searchwhile(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]-1if (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 {...}override def hashCode(): Int = {...}@throws(classOf[IOException])private def writeObject(out: ObjectOutputStream): Unit = Utils.tryOrIOException {...}@throws(classOf[IOException])private def readObject(in: ObjectInputStream): Unit = Utils.tryOrIOException {...}
}
四、自定义 Partitioner
/**1.继承 Partitioner 抽象类2.重写 numPartitions: Int 和 getPartition(key: Any): Int 方法
*/
object TestRDDPartitioner {def main(args: Array[String]): Unit = {val conf = new SparkConf().setMaster("local[*]").setAppName("partition")val sc = new SparkContext(conf)val rdd = sc.makeRDD(List(("nba", "xxxxxxxxxxx"),("cba", "xxxxxxxxxxx"),("nba", "xxxxxxxxxxx"),("ncaa", "xxxxxxxxxxx"),("cuba", "xxxxxxxxxxx")))val partRdd = rdd.partitionBy(new MyPartitioner)partRdd.saveAsTextFile("output")}
}class MyPartitioner extends Partitioner {// 重写返回分区数量的方法override def numPartitions: Int = 3// 重写根据数据的key返回数据所在的分区索引的方法override def getPartition(key: Any): Int = {key match {case "nba" => 0case "cba" => 1case _ => 2}}}
)
)
