第2章 RDD编程（2.3）

第2章 RDD编程（2.3）

2.3 TransFormation

2.3.1 map(func)

返回一个新的RDD，该RDD由每一个输入元素经过func函数转换后组成

scala> var source  = sc.parallelize(1 to 10)

source: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[8] at parallelize at <console>:24

 

scala> source.collect()

res7: Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

 

scala> val mapadd = source.map(_ * 2)

mapadd: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[9] at map at <console>:26

 

scala> mapadd.collect()

res8: Array[Int] = Array(2, 4, 6, 8, 10, 12, 14, 16, 18, 20)

2.3.2 mapPartitions(func) 尽量使用mapPartitions

类似于map，但独立地在RDD的每一个分片上运行，因此在类型为T的RDD上运行时，func的函数类型必须是Iterator[T] => Iterator[U]。假设有N个元素，有M个分区，那么map的函数的将被调用N次,而mapPartitions被调用M次,一个函数一次处理所有分区

scala> val rdd = sc.parallelize(List(("kpop","female"),("zorro","male"),("mobin","male"),("lucy","female")))

rdd: org.apache.spark.rdd.RDD[(String, String)] = ParallelCollectionRDD[16] at parallelize at <console>:24

 

scala> :paste

// Entering paste mode (ctrl-D to finish)

def partitionsFun(iter : Iterator[(String,String)]) : Iterator[String] = {

  var woman = List[String]()

  while (iter.hasNext){

    val next = iter.next()

    next match {

       case (_,"female") => woman = next._1 :: woman

       case _ =>

    }

  }

  woman.iterator

}

// Exiting paste mode, now interpreting.

 

partitionsFun: (iter: Iterator[(String, String)])Iterator[String]

 

scala> val result = rdd.mapPartitions(partitionsFun)

result: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[17] at mapPartitions at <console>:28

 

scala> result.collect()

res13: Array[String] = Array(kpop, lucy)

2.3.3 glom

将每一个分区形成一个数组，形成新的RDD类型时RDD[Array[T]]

scala> val rdd = sc.parallelize(1 to 16,4)

rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[65] at parallelize at <console>:24

 

scala> rdd.glom().collect()

res25: Array[Array[Int]] = Array(Array(1, 2, 3, 4), Array(5, 6, 7, 8), Array(9, 10, 11, 12), Array(13, 14, 15, 16))

2.3.4 flatMap(func) map后再扁平化

类似于map，但是每一个输入元素可以被映射为0或多个输出元素（所以func应该返回一个序列，而不是单一元素）

scala> val sourceFlat = sc.parallelize(1 to 5)

sourceFlat: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[12] at parallelize at <console>:24

 

scala> sourceFlat.collect()

res11: Array[Int] = Array(1, 2, 3, 4, 5)

 

scala> val flatMap = sourceFlat.flatMap(1 to _)

flatMap: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[13] at flatMap at <console>:26

 

scala> flatMap.collect()

res12: Array[Int] = Array(1, 1, 2, 1, 2, 3, 1, 2, 3, 4, 1, 2, 3, 4, 5)

2.3.5 filter(func)

返回一个新的RDD，该RDD由经过func函数计算后返回值为true的输入元素组成

scala> var sourceFilter = sc.parallelize(Array("xiaoming","xiaojiang","xiaohe","dazhi"))

sourceFilter: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[10] at parallelize at <console>:24

 

scala> val filter = sourceFilter.filter(_.contains("xiao"))

filter: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[11] at filter at <console>:26

 

scala> sourceFilter.collect()

res9: Array[String] = Array(xiaoming, xiaojiang, xiaohe, dazhi)

 

scala> filter.collect()

res10: Array[String] = Array(xiaoming, xiaojiang, xiaohe)

2.3.6 mapPartitionsWithIndex(func)

类似于mapPartitions，但func带有一个整数参数表示分片的索引值，因此在类型为T的RDD上运行时，func的函数类型必须是(Int, Interator[T]) => Iterator[U]

scala> val rdd = sc.parallelize(List(("kpop","female"),("zorro","male"),("mobin","male"),("lucy","female")))

rdd: org.apache.spark.rdd.RDD[(String, String)] = ParallelCollectionRDD[18] at parallelize at <console>:24

 

scala> :paste

// Entering paste mode (ctrl-D to finish)

def partitionsFun(index : Int, iter : Iterator[(String,String)]) : Iterator[String] = {

  var woman = List[String]()

  while (iter.hasNext){

    val next = iter.next()

    next match {

       case (_,"female") => woman = "["+index+"]"+next._1 :: woman

       case _ =>

    }

  }

  woman.iterator

}

// Exiting paste mode, now interpreting.

 

partitionsFun: (index: Int, iter: Iterator[(String, String)])Iterator[String]

 

scala> val result = rdd.mapPartitionsWithIndex(partitionsFun)

result: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[19] at mapPartitionsWithIndex at <console>:28

 

scala> result.collect()

res14: Array[String] = Array([0]kpop, [3]lucy)

2.3.7 sample(withReplacement, fraction, seed)

以指定的随机种子随机抽样出数量为fraction的数据，withReplacement表示是抽出的数据是否放回，true为有放回的抽样，false为无放回的抽样，seed用于指定随机数生成器种子。例子从RDD中随机且有放回的抽出50%的数据，随机种子值为3（即可能以1 2 3的其中一个起始值）

scala> val rdd = sc.parallelize(1 to 10)

rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[20] at parallelize at <console>:24

 

scala> rdd.collect()

res15: Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

 

scala> var sample1 = rdd.sample(true,0.4,2)

sample1: org.apache.spark.rdd.RDD[Int] = PartitionwiseSampledRDD[21] at sample at <console>:26

 

scala> sample1.collect()

res16: Array[Int] = Array(1, 2, 2, 7, 7, 8, 9)

 

scala> var sample2 = rdd.sample(false,0.2,3)

sample2: org.apache.spark.rdd.RDD[Int] = PartitionwiseSampledRDD[22] at sample at <console>:26

 

scala> sample2.collect()

res17: Array[Int] = Array(1, 9)

2.3.8 distinct([numTasks]))

对源RDD进行去重后返回一个新的RDD. 默认情况下，只有8个并行任务来操作，但是可以传入一个可选的numTasks参数改变它。

scala> val distinctRdd = sc.parallelize(List(1,2,1,5,2,9,6,1))

distinctRdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[34] at parallelize at <console>:24

 

scala> val unionRDD = distinctRdd.distinct()

unionRDD: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[37] at distinct at <console>:26

 

scala> unionRDD.collect()

[Stage 16:> (0 + 4) [Stage 16:=============================>                            (2 + 2)                                                                             res20: Array[Int] = Array(1, 9, 5, 6, 2)

 

scala> val unionRDD = distinctRdd.distinct(2)

unionRDD: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[40] at distinct at <console>:26

 

scala> unionRDD.collect()

res21: Array[Int] = Array(6, 2, 1, 9, 5)

2.3.9 partitionBy

对RDD进行分区操作，如果原有的partionRDD和现有的partionRDD是一致的话就不进行分区， 否则会生成ShuffleRDD。

scala> val rdd = sc.parallelize(Array((1,"aaa"),(2,"bbb"),(3,"ccc"),(4,"ddd")),4)

rdd: org.apache.spark.rdd.RDD[(Int, String)] = ParallelCollectionRDD[44] at parallelize at <console>:24

 

scala> rdd.partitions.size

res24: Int = 4

 

scala> var rdd2 = rdd.partitionBy(new org.apache.spark.HashPartitioner(2))

rdd2: org.apache.spark.rdd.RDD[(Int, String)] = ShuffledRDD[45] at partitionBy at <console>:26

 

scala> rdd2.partitions.size

res25: Int = 2

2.3.10 coalesce(numPartitions) 

与repartition的区别: repartition(numPartitions:Int):RDD[T]和coalesce(numPartitions:Int，shuffle:Boolean=false):RDD[T] repartition只是coalesce接口中shuffle为true的实现.

缩减分区数，用于大数据集过滤后，提高小数据集的执行效率。

scala> val rdd = sc.parallelize(1 to 16,4)

rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[54] at parallelize at <console>:24

 

scala> rdd.partitions.size

res20: Int = 4

 

scala> val coalesceRDD = rdd.coalesce(3)

coalesceRDD: org.apache.spark.rdd.RDD[Int] = CoalescedRDD[55] at coalesce at <console>:26

 

scala> coalesceRDD.partitions.size

res21: Int = 3

2.3.11 repartition(numPartitions) 

根据分区数，从新通过网络随机洗牌所有数据。

scala> val rdd = sc.parallelize(1 to 16,4)

rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[56] at parallelize at <console>:24

 

scala> rdd.partitions.size

res22: Int = 4

 

scala> val rerdd = rdd.repartition(2)

rerdd: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[60] at repartition at <console>:26

 

scala> rerdd.partitions.size

res23: Int = 2

 

scala> val rerdd = rdd.repartition(4)

rerdd: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[64] at repartition at <console>:26

 

scala> rerdd.partitions.size

res24: Int = 4

2.3.12 repartitionAndSortWithinPartitions(partitioner) 

repartitionAndSortWithinPartitions函数是repartition函数的变种，与repartition函数不同的是，repartitionAndSortWithinPartitions在给定的partitioner内部进行排序，性能比repartition要高。 

2.3.13 sortBy(func,[ascending], [numTasks])

用func先对数据进行处理，按照处理后的数据比较结果排序。

scala> val rdd = sc.parallelize(List(1,2,3,4))

rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[21] at parallelize at <console>:24

 

scala> rdd.sortBy(x => x).collect()

res11: Array[Int] = Array(1, 2, 3, 4)

 

scala> rdd.sortBy(x => x%3).collect()

res12: Array[Int] = Array(3, 4, 1, 2)

2.3.14 union(otherDataset)

对源RDD和参数RDD求并集后返回一个新的RDD  不去重

scala> val rdd1 = sc.parallelize(1 to 5)

rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[23] at parallelize at <console>:24

 

scala> val rdd2 = sc.parallelize(5 to 10)

rdd2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[24] at parallelize at <console>:24

 

scala> val rdd3 = rdd1.union(rdd2)

rdd3: org.apache.spark.rdd.RDD[Int] = UnionRDD[25] at union at <console>:28

 

scala> rdd3.collect()

res18: Array[Int] = Array(1, 2, 3, 4, 5, 5, 6, 7, 8, 9, 10)

2.3.15 subtract (otherDataset)

计算差的一种函数，去除两个RDD中相同的元素，不同的RDD将保留下来 

scala> val rdd = sc.parallelize(3 to 8)

rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[70] at parallelize at <console>:24

 

scala> val rdd1 = sc.parallelize(1 to 5)

rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[71] at parallelize at <console>:24

 

scala> rdd.subtract(rdd1).collect()

res27: Array[Int] = Array(8, 6, 7)

2.3.16 intersection(otherDataset)

对源RDD和参数RDD求交集后返回一个新的RDD

scala> val rdd1 = sc.parallelize(1 to 7)

rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[26] at parallelize at <console>:24

 

scala> val rdd2 = sc.parallelize(5 to 10)

rdd2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[27] at parallelize at <console>:24

 

scala> val rdd3 = rdd1.intersection(rdd2)

rdd3: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[33] at intersection at <console>:28

 

scala> rdd3.collect()

res19: Array[Int] = Array(5, 6, 7)

2.3.17 cartesian(otherDataset)

笛卡尔积

scala> val rdd1 = sc.parallelize(1 to 3)

rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[47] at parallelize at <console>:24

 

scala> val rdd2 = sc.parallelize(2 to 5)

rdd2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[48] at parallelize at <console>:24

 

scala> rdd1.cartesian(rdd2).collect()

res17: Array[(Int, Int)] = Array((1,2), (1,3), (1,4), (1,5), (2,2), (2,3), (2,4), (2,5), (3,2), (3,3), (3,4), (3,5))

2.3.18 pipe(command, [envVars])

管道，对于每个分区，都执行一个perl或者shell脚本，返回输出的RDD

Shell脚本

#!/bin/sh

echo "AA"

while read LINE; do

   echo ">>>"${LINE}

done

scala> val rdd = sc.parallelize(List("hi","Hello","how","are","you"),1)

rdd: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[50] at parallelize at <console>:24

 

scala> rdd.pipe("/home/bigdata/pipe.sh").collect()

res18: Array[String] = Array(AA, >>>hi, >>>Hello, >>>how, >>>are, >>>you)

 

scala> val rdd = sc.parallelize(List("hi","Hello","how","are","you"),2)

rdd: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[52] at parallelize at <console>:24

 

scala> rdd.pipe("/home/bigdata/pipe.sh").collect()

res19: Array[String] = Array(AA, >>>hi, >>>Hello, AA, >>>how, >>>are, >>>you)

 

pipe.sh:

#!/bin/sh

echo "AA"

while read LINE; do

   echo ">>>"${LINE}

done

2.3.19 join(otherDataset, [numTasks])

在类型为(K,V)和(K,W)的RDD上调用，返回一个相同key对应的所有元素对在一起的(K,(V,W))的RDD

scala> val rdd = sc.parallelize(Array((1,"a"),(2,"b"),(3,"c")))

rdd: org.apache.spark.rdd.RDD[(Int, String)] = ParallelCollectionRDD[32] at parallelize at <console>:24

 

scala> val rdd1 = sc.parallelize(Array((1,4),(2,5),(3,6)))

rdd1: org.apache.spark.rdd.RDD[(Int, Int)] = ParallelCollectionRDD[33] at parallelize at <console>:24

 

scala> rdd.join(rdd1).collect()

res13: Array[(Int, (String, Int))] = Array((1,(a,4)), (2,(b,5)), (3,(c,6)))

2.3.20 cogroup(otherDataset, [numTasks])

在类型为(K,V)和(K,W)的RDD上调用，返回一个(K,(Iterable<V>,Iterable<W>))类型的RDD

scala> val rdd = sc.parallelize(Array((1,"a"),(2,"b"),(3,"c")))

rdd: org.apache.spark.rdd.RDD[(Int, String)] = ParallelCollectionRDD[37] at parallelize at <console>:24

 

scala> val rdd1 = sc.parallelize(Array((1,4),(2,5),(3,6)))

rdd1: org.apache.spark.rdd.RDD[(Int, Int)] = ParallelCollectionRDD[38] at parallelize at <console>:24

 

scala> rdd.cogroup(rdd1).collect()

res14: Array[(Int, (Iterable[String], Iterable[Int]))] = Array((1,(CompactBuffer(a),CompactBuffer(4))), (2,(CompactBuffer(b),CompactBuffer(5))), (3,(CompactBuffer(c),CompactBuffer(6))))

 

scala> val rdd2 = sc.parallelize(Array((4,4),(2,5),(3,6)))

rdd2: org.apache.spark.rdd.RDD[(Int, Int)] = ParallelCollectionRDD[41] at parallelize at <console>:24

 

scala> rdd.cogroup(rdd2).collect()

res15: Array[(Int, (Iterable[String], Iterable[Int]))] = Array((4,(CompactBuffer(),CompactBuffer(4))), (1,(CompactBuffer(a),CompactBuffer())), (2,(CompactBuffer(b),CompactBuffer(5))), (3,(CompactBuffer(c),CompactBuffer(6))))

 

scala> val rdd3 = sc.parallelize(Array((1,"a"),(1,"d"),(2,"b"),(3,"c")))

rdd3: org.apache.spark.rdd.RDD[(Int, String)] = ParallelCollectionRDD[44] at parallelize at <console>:24

 

scala> rdd3.cogroup(rdd2).collect()

[Stage 36:>                                                         (0 + 0)                                                                             res16: Array[(Int, (Iterable[String], Iterable[Int]))] = Array((4,(CompactBuffer(),CompactBuffer(4))), (1,(CompactBuffer(d, a),CompactBuffer())), (2,(CompactBuffer(b),CompactBuffer(5))), (3,(CompactBuffer(c),CompactBuffer(6))))

2.3.21 reduceByKey(func, [numTasks]) 

在一个(K,V)的RDD上调用，返回一个(K,V)的RDD，使用指定的reduce函数，将相同key的值聚合到一起，reduce任务的个数可以通过第二个可选的参数来设置。

scala> val rdd = sc.parallelize(List(("female",1),("male",5),("female",5),("male",2)))

rdd: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[46] at parallelize at <console>:24

 

scala> val reduce = rdd.reduceByKey((x,y) => x+y)

reduce: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[47] at reduceByKey at <console>:26

 

scala> reduce.collect()

res29: Array[(String, Int)] = Array((female,6), (male,7))

2.3.22 groupByKey

groupByKey也是对每个key进行操作，但只生成一个sequence。

scala> val words = Array("one", "two", "two", "three", "three", "three")

words: Array[String] = Array(one, two, two, three, three, three)

 

scala> val wordPairsRDD = sc.parallelize(words).map(word => (word, 1))

wordPairsRDD: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[4] at map at <console>:26

 

scala> val group = wordPairsRDD.groupByKey()

group: org.apache.spark.rdd.RDD[(String, Iterable[Int])] = ShuffledRDD[5] at groupByKey at <console>:28

 

scala> group.collect()

res1: Array[(String, Iterable[Int])] = Array((two,CompactBuffer(1, 1)), (one,CompactBuffer(1)), (three,CompactBuffer(1, 1, 1)))

 

scala> group.map(t => (t._1, t._2.sum))

res2: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[6] at map at <console>:31

 

scala> res2.collect()

res3: Array[(String, Int)] = Array((two,2), (one,1), (three,3))

 

scala> val map = group.map(t => (t._1, t._2.sum))

map: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[7] at map at <console>:30

 

scala> map.collect()

res4: Array[(String, Int)] = Array((two,2), (one,1), (three,3))

2.3.23 combineByKey[C]

(  createCombiner: V => C,  mergeValue: (C, V) => C,  mergeCombiners: (C, C) => C)

对相同K，把V合并成一个集合。

createCombiner: combineByKey() 会遍历分区中的所有元素，因此每个元素的键要么还没有遇到过，要么就 和之前的某个元素的键相同。如果这是一个新的元素,combineByKey() 会使用一个叫作 createCombiner() 的函数来创建 
那个键对应的累加器的初始值

mergeValue: 如果这是一个在处理当前分区之前已经遇到的键， 它会使用 mergeValue() 方法将该键的累加器对应的当前值与这个新的值进行合并

mergeCombiners: 由于每个分区都是独立处理的， 因此对于同一个键可以有多个累加器。如果有两个或者更多的分区都有对应同一个键的累加器， 就需要使用用户提供的 mergeCombiners() 方法将各个分区的结果进行合并。

scala> val scores = Array(("Fred", 88), ("Fred", 95), ("Fred", 91), ("Wilma", 93), ("Wilma", 95), ("Wilma", 98))

scores: Array[(String, Int)] = Array((Fred,88), (Fred,95), (Fred,91), (Wilma,93), (Wilma,95), (Wilma,98))

 

scala> val input = sc.parallelize(scores)

input: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[52] at parallelize at <console>:26

 

scala> val combine = input.combineByKey(

     |     (v)=>(v,1),

     |     (acc:(Int,Int),v)=>(acc._1+v,acc._2+1),

     |     (acc1:(Int,Int),acc2:(Int,Int))=>(acc1._1+acc2._1,acc1._2+acc2._2))

combine: org.apache.spark.rdd.RDD[(String, (Int, Int))] = ShuffledRDD[53] at combineByKey at <console>:28

 

scala> val result = combine.map{

     |     case (key,value) => (key,value._1/value._2.toDouble)}

result: org.apache.spark.rdd.RDD[(String, Double)] = MapPartitionsRDD[54] at map at <console>:30

 

scala> result.collect()

res33: Array[(String, Double)] = Array((Wilma,95.33333333333333), (Fred,91.33333333333333))

2.3.24 aggregateByKey

(zeroValue:U,[partitioner: Partitioner]) (seqOp: (U, V) => U,combOp: (U, U) => U)

在kv对的RDD中，，按key将value进行分组合并，合并时，将每个value和初始值作为seq函数的参数，进行计算，返回的结果作为一个新的kv对，然后再将结果按照key进行合并，最后将每个分组的value传递给combine函数进行计算（先将前两个value进行计算，将返回结果和下一个value传给combine函数，以此类推），将key与计算结果作为一个新的kv对输出。

seqOp函数用于在每一个分区中用初始值逐步迭代value，combOp函数用于合并每个分区中的结果。

scala> val rdd = sc.parallelize(List((1,3),(1,2),(1,4),(2,3),(3,6),(3,8)),3)

rdd: org.apache.spark.rdd.RDD[(Int, Int)] = ParallelCollectionRDD[12] at parallelize at <console>:24

 

scala> val agg = rdd.aggregateByKey(0)(math.max(_,_),_+_)

agg: org.apache.spark.rdd.RDD[(Int, Int)] = ShuffledRDD[13] at aggregateByKey at <console>:26

 

scala> agg.collect()

res7: Array[(Int, Int)] = Array((3,8), (1,7), (2,3))

 

scala> agg.partitions.size

res8: Int = 3

 

scala> val rdd = sc.parallelize(List((1,3),(1,2),(1,4),(2,3),(3,6),(3,8)),1)

rdd: org.apache.spark.rdd.RDD[(Int, Int)] = ParallelCollectionRDD[10] at parallelize at <console>:24

 

scala> val agg = rdd.aggregateByKey(0)(math.max(_,_),_+_).collect()

agg: Array[(Int, Int)] = Array((1,4), (3,8), (2,3))

2.3.25 foldByKey

(zeroValue: V)(func: (V, V) => V): RDD[(K, V)] 

aggregateByKey的简化操作，seqop和combop相同

scala> val rdd = sc.parallelize(List((1,3),(1,2),(1,4),(2,3),(3,6),(3,8)),3)

rdd: org.apache.spark.rdd.RDD[(Int, Int)] = ParallelCollectionRDD[91] at parallelize at <console>:24

 

scala> val agg = rdd.foldByKey(0)(_+_)

agg: org.apache.spark.rdd.RDD[(Int, Int)] = ShuffledRDD[92] at foldByKey at <console>:26

 

scala> agg.collect()

res61: Array[(Int, Int)] = Array((3,14), (1,9), (2,3))

2.3.26 sortByKey([ascending], [numTasks]) 

在一个(K,V)的RDD上调用，K必须实现Ordered接口，返回一个按照key进行排序的(K,V)的RDD

scala> val rdd = sc.parallelize(Array((3,"aa"),(6,"cc"),(2,"bb"),(1,"dd")))

rdd: org.apache.spark.rdd.RDD[(Int, String)] = ParallelCollectionRDD[14] at parallelize at <console>:24

 

scala> rdd.sortByKey(true).collect()

res9: Array[(Int, String)] = Array((1,dd), (2,bb), (3,aa), (6,cc))

 

scala> rdd.sortByKey(false).collect()

res10: Array[(Int, String)] = Array((6,cc), (3,aa), (2,bb), (1,dd))

2.3.27 mapValues

针对于(K,V)形式的类型只对V进行操作 

scala> val rdd3 = sc.parallelize(Array((1,"a"),(1,"d"),(2,"b"),(3,"c")))

rdd3: org.apache.spark.rdd.RDD[(Int, String)] = ParallelCollectionRDD[67] at parallelize at <console>:24

 

scala> rdd3.mapValues(_+"|||").collect()

res26: Array[(Int, String)] = Array((1,a|||), (1,d|||), (2,b|||), (3,c|||))