RDD的转换（Transformation）操作
文章目录
- 1.map(func)
- 2. mapPartitions(func) 尽量使用mapPartitions
- 3.glom
- 4. flatMap(func) map后再扁平化
- 5.filter(func)
- 6.mapPartitionsWithIndex(func)
- 7.sample(withReplacement, fraction, seed)
- 8.distinct([numTasks]))
- 9.partitionBy
- 10.coalesce(numPartitions)
- 11. repartition(numPartitions)
- 12.repartitionAndSortWithinPartitions(partitioner)
- 13.sortBy(func,[ascending], [numTasks])
- 14.union(otherDataset)
- 15.subtract (otherDataset)
- 16.intersection(otherDataset)
- 17.cartesian(otherDataset)
- 18.pipe(command, [envVars])
- 19.join(otherDataset, [numTasks])
- 20.cogroup(otherDataset, [numTasks])
- 21.reduceByKey(func, [numTasks])
- 22.groupByKey
- 23.combineByKey[C]
- 24.aggregateByKey
- 25.foldByKey
- 26.sortByKey([ascending], [numTasks])
- 27. mapValues
算子的学习思想：查看源码，主要看输入的是什么类型，需要什么类型的输出，然后给出合适的函数来执行操作。
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
Map算子是对于输入的数据进行定义函数的操作，可以返回其他类型的参数，Map操作应用于传入的每一个数据。
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. mapPartitions(func) 尽量使用mapPartitions
要求输入是一个迭代器的类型，返回的也是一个迭代器的类型
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)
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))
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)
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)
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],里面的Int类型 的参数是分区号
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)
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)
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)
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
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。
shuffle ,当不用shuffle的时候在每个Executor内执行，shuffle是跨进程的通信
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
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
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。
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。底层调用的是SortByKey()
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)
union(并) substract(差) intersection（交集） cartesian（笛卡尔积）
参数都是另外的一个数据集
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)
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)
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)
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))
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
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)))
20.cogroup(otherDataset, [numTasks])
在类型为(K,V)和(K,W)的RDD上调用，返回一个(K,(Iterable,Iterable))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))))
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))
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))
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))
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))
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))
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))
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|||))
注意：下换线_ 的省略问题
下换线有如下两种的转换过程
- 1.eta-conversion 简化操作
- 2.eta-extension 扩展操作
这里是根据括号有一个就近原则
(_*2 + 1) 扩展之后的结果是x => x * 2 + 1
(_*2)+1 扩展之后的结果是(x => x * 2) + 1