spark编程模型（十四）之RDD键值转换操作（Transformation Operation）——groupByKey、reduceByKey、reduceByKeyLocally

groupByKey

• def groupByKey(): RDD[(K, Iterable[V])]

• def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])]

• def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])]

• 该函数用于将RDD[K,V]中每个K对应的V值，合并到一个集合Iterable[V]中

• 参数numPartitions用于指定分区数

• 参数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[89] 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)))
  

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

• 参数numPartitions用于指定分区数

• 参数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[91] at makeRDD at :21
     
    scala> rdd1.partitions.size
    res82: Int = 15
     
    scala> var rdd2 = rdd1.reduceByKey((x,y) => x + y)
    rdd2: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[94] at reduceByKey at :23
     
    scala> rdd2.collect
    res85: Array[(String, Int)] = Array((A,2), (B,3), (C,1))
     
    scala> rdd2.partitions.size
    res86: Int = 15
     
    scala> var rdd2 = rdd1.reduceByKey(new org.apache.spark.HashPartitioner(2),(x,y) => x + y)
    rdd2: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[95] at reduceByKey at :23
     
    scala> rdd2.collect
    res87: Array[(String, Int)] = Array((B,3), (A,2), (C,1))
     
    scala> rdd2.partitions.size
    res88: Int = 2
  

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]

    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[91] at makeRDD at :21
  
    scala> rdd1.reduceByKeyLocally((x,y) => x + y)
    res90: scala.collection.Map[String,Int] = Map(B -> 3, A -> 2, C -> 1)