groupByKey reduceByKey

Spark算子：RDD键值转换操作(3)–groupByKey、reduceByKey、reduceByKeyLocally

1.groupByKey

def groupByKey():RDD[( K, Iterable[V]) ]
def groupByKey(numPartitions:Int):RDD[(K, Iterable[V])]
def groupByKey(partitioner:Partitioner):RDD[(K,Iterable[v])]

该函数将RDD [K，V]中每个K对应的V值，一个集合Iterable[V]中；

• numPartitions用于指定分区数；
• partitioner用于指定分区函数。

scala> var rdd1 = sc.makeRDD(Array(("A",0),("A",2),("B",1),("B",2),("C",1)))
rdd1: org.apache.spark.rdd.RDD[(String,Int)] = ParallelCollectionRDD[0] at makeRDD at :21
scala >rdd1.groupByKey().collect
res81: Array[(String, Iterable[Int])] = Array((A,CompactBuffer(0,2)),(B,compactBuffer(2,1)),(C,CompactBuffer(1)))

2.reduceByKey

def reduceBykey(func:(V,V)=>V):RDD[(K,V)]
def reduceByKey(func:(V,V)=>V),numPartitions:Int):RDD[(K,V)]
def reduceByKey(partitioner:Partitioner, func:(V,V)=>V):RDD[(K,V)]

该函数用于将RDD[K,V]中每个K对应的V值根据映射函数来运算。

• numPartitions用于指定分区数；
• 参数partitioner用于指定分区函数；

var rdd1 = sc.makeRDD(Array(("A",0),("A",2),("B",1),("B",2),("C",1)))

rdd1.partitions.size //Int = 15

var rdd2 = rdd1.reduceByKey((x,y)=>x+y) rdd2.collect //Array[(String,Int)] = Array((A,2),(B,3),(C,1)) rdd2.partitions.size // Int = 15 var rdd2 = rdd1.reduceByKey(new org.apache.spark.HashPartitioner(2),(x,y)=>x+y) rdd2.collect //Array[(String, Int)] = Array((B,3),(A,2),(C,1)) rdd2.partitions.size //Int =2

3.reduceByKeyLocally

def reduceByKeyLocally(func:(V,V)=>V):Map[K,V]

该函数将RDD[K,V]中每个K对应的V值根据映射函数来运算，运算结果映射到一个Map[K,V]中，而不是RDD[K,V].

var rdd1 = sc.makeRDD(Array(("A",0),("A",2),("B",1),("B",2),("C",1)))

rdd1.reduceByKeyLocally((x,y)=>x+y) //scala.collection.Map[String,Int] = Map(B->3,A->2,C->1)