Spark之键值RDD转换(转载)
1.mapValus(fun):对[K,V]型数据中的V值map操作
(例1):对每个的的年龄加2
输出:
(mobin,24)
(kpop,22)
(lufei,25)
(RDD依赖图:红色块表示一个RDD区,黑色块表示该分区集合,下同)
2.flatMapValues(fun):对[K,V]型数据中的V值flatmap操作
(例2):
输出:
(mobin,22)
(mobin,male)
(kpop,20)
(kpop,male)
(lufei,23)
(lufei,male)
如果是mapValues会输出:
(mobin,List(22, male))
(kpop,List(20, male))
(lufei,List(23, male))
(RDD依赖图)
3.comineByKey(createCombiner,mergeValue,mergeCombiners,partitioner,mapSideCombine)
comineByKey(createCombiner,mergeValue,mergeCombiners,numPartitions)
comineByKey(createCombiner,mergeValue,mergeCombiners)
createCombiner:在第一次遇到Key时创建组合器函数,将RDD数据集中的V类型值转换C类型值(V => C),
如例3:
mergeValue:合并值函数,再次遇到相同的Key时,将createCombiner道理的C类型值与这次传入的V类型值合并成一个C类型值(C,V)=>C,
如例3:
mergeCombiners:合并组合器函数,将C类型值两两合并成一个C类型值
如例3:
partitioner:使用已有的或自定义的分区函数,默认是HashPartitioner
mapSideCombine:是否在map端进行Combine操作,默认为true
注意前三个函数的参数类型要对应;第一次遇到Key时调用createCombiner,再次遇到相同的Key时调用mergeValue合并值
(例3):统计男性和女生的个数,并以(性别,(名字,名字....),个数)的形式输出
输出:
(male,(List(Lufei, Kpop, Mobin),3))
(female,(List(Amy, Lucy),2))
过程分解:
Partition1:
K="male" --> ("male","Mobin") --> createCombiner("Mobin") => peo1 = ( List("Mobin") , 1 )
K="male" --> ("male","Kpop") --> mergeValue(peo1,"Kpop") => peo2 = ( "Kpop" :: peo1_1 , 1 + 1 ) //Key相同调用mergeValue函数对值进行合并
K="female" --> ("female","Lucy") --> createCombiner("Lucy") => peo3 = ( List("Lucy") , 1 )
Partition2:
K="male" --> ("male","Lufei") --> createCombiner("Lufei") => peo4 = ( List("Lufei") , 1 )
K="female" --> ("female","Amy") --> createCombiner("Amy") => peo5 = ( List("Amy") , 1 )
Merger Partition:
K="male" --> mergeCombiners(peo2,peo4) => (List(Lufei,Kpop,Mobin))
K="female" --> mergeCombiners(peo3,peo5) => (List(Amy,Lucy))
(RDD依赖图)
4.foldByKey(zeroValue)(func)
foldByKey(zeroValue,partitioner)(func)
foldByKey(zeroValue,numPartitiones)(func)
foldByKey函数是通过调用CombineByKey函数实现的
zeroVale:对V进行初始化,实际上是通过CombineByKey的createCombiner实现的 V => (zeroValue,V),再通过func函数映射成新的值,即func(zeroValue,V),如例4可看作对每个V先进行 V=> 2 + V
func: Value将通过func函数按Key值进行合并(实际上是通过CombineByKey的mergeValue,mergeCombiners函数实现的,只不过在这里,这两个函数是相同的)
例4:
//省略
输出:
(Amy,2)
(Mobin,4)
(Lucy,6)
先对每个V都加2,再对相同Key的value值相加。
5.reduceByKey(func,numPartitions):按Key进行分组,使用给定的func函数聚合value值, numPartitions设置分区数,提高作业并行度
例5
//省略
输出:
(A,5)
(A,4)
(RDD依赖图)
6.groupByKey(numPartitions):按Key进行分组,返回[K,Iterable[V]],numPartitions设置分区数,提高作业并行度
例6:
//省略
输出:
(B,CompactBuffer(2, 3))
(A,CompactBuffer(1, 2))
以上foldByKey,reduceByKey,groupByKey函数最终都是通过调用combineByKey函数实现的
7.sortByKey(accending,numPartitions):返回以Key排序的(K,V)键值对组成的RDD,accending为true时表示升序,为false时表示降序,numPartitions设置分区数,提高作业并行度
例7:
//省略sc
输出:
(A,1)
(A,2)
(B,2)
(B,3)
8.cogroup(otherDataSet,numPartitions):对两个RDD(如:(K,V)和(K,W))相同Key的元素先分别做聚合,最后返回(K,Iterator<V>,Iterator<W>)形式的RDD,numPartitions设置分区数,提高作业并行度
例8:
//省略
输出:
(B,(CompactBuffer(2, 3),CompactBuffer(B1, B2)))
(A,(CompactBuffer(1, 2),CompactBuffer(A1, A2)))
(RDD依赖图)
9.join(otherDataSet,numPartitions):对两个RDD先进行cogroup操作形成新的RDD,再对每个Key下的元素进行笛卡尔积,numPartitions设置分区数,提高作业并行度
例9
//省略
输出:
(B,(2,B1))
(B,(2,B2))
(B,(3,B1))
(B,(3,B2))
(A,(1,A1))
(A,(1,A2))
(A,(2,A1))
(A,(2,A2)
(RDD依赖图)
10.LeftOutJoin(otherDataSet,numPartitions):左外连接,包含左RDD的所有数据,如果右边没有与之匹配的用None表示,numPartitions设置分区数,提高作业并行度
例10:
//省略
输出:
(B,(2,Some(B1)))
(B,(2,Some(B2)))
(B,(3,Some(B1)))
(B,(3,Some(B2)))
(C,(1,None))
(A,(1,Some(A1)))
(A,(1,Some(A2)))
(A,(2,Some(A1)))
(A,(2,Some(A2)))
11.RightOutJoin(otherDataSet, numPartitions):右外连接,包含右RDD的所有数据,如果左边没有与之匹配的用None表示,numPartitions设置分区数,提高作业并行度
例11:
//省略
输出:
(B,(Some(2),B1))
(B,(Some(2),B2))
(B,(Some(3),B1))
(B,(Some(3),B2))
(C,(None,C1))
(A,(Some(1),A1))
(A,(Some(1),A2))
(A,(Some(2),A1))
(A,(Some(2),A2))
(例1):对每个的的年龄加2
object MapValues { def main(args: Array[String]) { val conf = new SparkConf().setMaster("local").setAppName("map") val sc = new SparkContext(conf) val list = List(("mobin",22),("kpop",20),("lufei",23)) val rdd = sc.parallelize(list) val mapValuesRDD = rdd.mapValues(_+2) mapValuesRDD.foreach(println) } }
(mobin,24)
(kpop,22)
(lufei,25)
(RDD依赖图:红色块表示一个RDD区,黑色块表示该分区集合,下同)
2.flatMapValues(fun):对[K,V]型数据中的V值flatmap操作
(例2):
//省略<br>val list = List(("mobin",22),("kpop",20),("lufei",23)) val rdd = sc.parallelize(list) val mapValuesRDD = rdd.flatMapValues(x => Seq(x,"male")) mapValuesRDD.foreach(println)
输出:
(mobin,22)
(mobin,male)
(kpop,20)
(kpop,male)
(lufei,23)
(lufei,male)
如果是mapValues会输出:
(mobin,List(22, male))
(kpop,List(20, male))
(lufei,List(23, male))
(RDD依赖图)
3.comineByKey(createCombiner,mergeValue,mergeCombiners,partitioner,mapSideCombine)
comineByKey(createCombiner,mergeValue,mergeCombiners,numPartitions)
comineByKey(createCombiner,mergeValue,mergeCombiners)
createCombiner:在第一次遇到Key时创建组合器函数,将RDD数据集中的V类型值转换C类型值(V => C),
如例3:
mergeValue:合并值函数,再次遇到相同的Key时,将createCombiner道理的C类型值与这次传入的V类型值合并成一个C类型值(C,V)=>C,
如例3:
mergeCombiners:合并组合器函数,将C类型值两两合并成一个C类型值
如例3:
partitioner:使用已有的或自定义的分区函数,默认是HashPartitioner
mapSideCombine:是否在map端进行Combine操作,默认为true
注意前三个函数的参数类型要对应;第一次遇到Key时调用createCombiner,再次遇到相同的Key时调用mergeValue合并值
(例3):统计男性和女生的个数,并以(性别,(名字,名字....),个数)的形式输出
object CombineByKey { def main(args: Array[String]) { val conf = new SparkConf().setMaster("local").setAppName("combinByKey") val sc = new SparkContext(conf) val people = List(("male", "Mobin"), ("male", "Kpop"), ("female", "Lucy"), ("male", "Lufei"), ("female", "Amy")) val rdd = sc.parallelize(people) val combinByKeyRDD = rdd.combineByKey( (x: String) => (List(x), 1), (peo: (List[String], Int), x : String) => (x :: peo._1, peo._2 + 1), (sex1: (List[String], Int), sex2: (List[String], Int)) => (sex1._1 ::: sex2._1, sex1._2 + sex2._2)) combinByKeyRDD.foreach(println) sc.stop() } }
输出:
(male,(List(Lufei, Kpop, Mobin),3))
(female,(List(Amy, Lucy),2))
过程分解:
Partition1:
K="male" --> ("male","Mobin") --> createCombiner("Mobin") => peo1 = ( List("Mobin") , 1 )
K="male" --> ("male","Kpop") --> mergeValue(peo1,"Kpop") => peo2 = ( "Kpop" :: peo1_1 , 1 + 1 ) //Key相同调用mergeValue函数对值进行合并
K="female" --> ("female","Lucy") --> createCombiner("Lucy") => peo3 = ( List("Lucy") , 1 )
Partition2:
K="male" --> ("male","Lufei") --> createCombiner("Lufei") => peo4 = ( List("Lufei") , 1 )
K="female" --> ("female","Amy") --> createCombiner("Amy") => peo5 = ( List("Amy") , 1 )
Merger Partition:
K="male" --> mergeCombiners(peo2,peo4) => (List(Lufei,Kpop,Mobin))
K="female" --> mergeCombiners(peo3,peo5) => (List(Amy,Lucy))
(RDD依赖图)
4.foldByKey(zeroValue)(func)
foldByKey(zeroValue,partitioner)(func)
foldByKey(zeroValue,numPartitiones)(func)
foldByKey函数是通过调用CombineByKey函数实现的
zeroVale:对V进行初始化,实际上是通过CombineByKey的createCombiner实现的 V => (zeroValue,V),再通过func函数映射成新的值,即func(zeroValue,V),如例4可看作对每个V先进行 V=> 2 + V
func: Value将通过func函数按Key值进行合并(实际上是通过CombineByKey的mergeValue,mergeCombiners函数实现的,只不过在这里,这两个函数是相同的)
例4:
//省略
val people = List(("Mobin", 2), ("Mobin", 1), ("Lucy", 2), ("Amy", 1), ("Lucy", 3)) val rdd = sc.parallelize(people) val foldByKeyRDD = rdd.foldByKey(2)(_+_) foldByKeyRDD.foreach(println)
输出:
(Amy,2)
(Mobin,4)
(Lucy,6)
先对每个V都加2,再对相同Key的value值相加。
5.reduceByKey(func,numPartitions):按Key进行分组,使用给定的func函数聚合value值, numPartitions设置分区数,提高作业并行度
例5
//省略
val arr = List(("A",3),("A",2),("B",1),("B",3)) val rdd = sc.parallelize(arr) val reduceByKeyRDD = rdd.reduceByKey(_ +_) reduceByKeyRDD.foreach(println) sc.stop
输出:
(A,5)
(A,4)
(RDD依赖图)
6.groupByKey(numPartitions):按Key进行分组,返回[K,Iterable[V]],numPartitions设置分区数,提高作业并行度
例6:
//省略
val arr = List(("A",1),("B",2),("A",2),("B",3)) val rdd = sc.parallelize(arr) val groupByKeyRDD = rdd.groupByKey() groupByKeyRDD.foreach(println) sc.stop
输出:
(B,CompactBuffer(2, 3))
(A,CompactBuffer(1, 2))
以上foldByKey,reduceByKey,groupByKey函数最终都是通过调用combineByKey函数实现的
7.sortByKey(accending,numPartitions):返回以Key排序的(K,V)键值对组成的RDD,accending为true时表示升序,为false时表示降序,numPartitions设置分区数,提高作业并行度
例7:
//省略sc
val arr = List(("A",1),("B",2),("A",2),("B",3)) val rdd = sc.parallelize(arr) val sortByKeyRDD = rdd.sortByKey() sortByKeyRDD.foreach(println) sc.stop
输出:
(A,1)
(A,2)
(B,2)
(B,3)
8.cogroup(otherDataSet,numPartitions):对两个RDD(如:(K,V)和(K,W))相同Key的元素先分别做聚合,最后返回(K,Iterator<V>,Iterator<W>)形式的RDD,numPartitions设置分区数,提高作业并行度
例8:
//省略
val arr = List(("A", 1), ("B", 2), ("A", 2), ("B", 3)) val arr1 = List(("A", "A1"), ("B", "B1"), ("A", "A2"), ("B", "B2")) val rdd1 = sc.parallelize(arr, 3) val rdd2 = sc.parallelize(arr1, 3) val groupByKeyRDD = rdd1.cogroup(rdd2) groupByKeyRDD.foreach(println) sc.stop
输出:
(B,(CompactBuffer(2, 3),CompactBuffer(B1, B2)))
(A,(CompactBuffer(1, 2),CompactBuffer(A1, A2)))
(RDD依赖图)
9.join(otherDataSet,numPartitions):对两个RDD先进行cogroup操作形成新的RDD,再对每个Key下的元素进行笛卡尔积,numPartitions设置分区数,提高作业并行度
例9
//省略
val arr = List(("A", 1), ("B", 2), ("A", 2), ("B", 3)) val arr1 = List(("A", "A1"), ("B", "B1"), ("A", "A2"), ("B", "B2")) val rdd = sc.parallelize(arr, 3) val rdd1 = sc.parallelize(arr1, 3) val groupByKeyRDD = rdd.join(rdd1) groupByKeyRDD.foreach(println)
输出:
(B,(2,B1))
(B,(2,B2))
(B,(3,B1))
(B,(3,B2))
(A,(1,A1))
(A,(1,A2))
(A,(2,A1))
(A,(2,A2)
(RDD依赖图)
10.LeftOutJoin(otherDataSet,numPartitions):左外连接,包含左RDD的所有数据,如果右边没有与之匹配的用None表示,numPartitions设置分区数,提高作业并行度
例10:
//省略
val arr = List(("A", 1), ("B", 2), ("A", 2), ("B", 3),("C",1)) val arr1 = List(("A", "A1"), ("B", "B1"), ("A", "A2"), ("B", "B2")) val rdd = sc.parallelize(arr, 3) val rdd1 = sc.parallelize(arr1, 3) val leftOutJoinRDD = rdd.leftOuterJoin(rdd1) leftOutJoinRDD .foreach(println) sc.stop
输出:
(B,(2,Some(B1)))
(B,(2,Some(B2)))
(B,(3,Some(B1)))
(B,(3,Some(B2)))
(C,(1,None))
(A,(1,Some(A1)))
(A,(1,Some(A2)))
(A,(2,Some(A1)))
(A,(2,Some(A2)))
11.RightOutJoin(otherDataSet, numPartitions):右外连接,包含右RDD的所有数据,如果左边没有与之匹配的用None表示,numPartitions设置分区数,提高作业并行度
例11:
//省略
val arr = List(("A", 1), ("B", 2), ("A", 2), ("B", 3)) val arr1 = List(("A", "A1"), ("B", "B1"), ("A", "A2"), ("B", "B2"),("C","C1")) val rdd = sc.parallelize(arr, 3) val rdd1 = sc.parallelize(arr1, 3) val rightOutJoinRDD = rdd.rightOuterJoin(rdd1) rightOutJoinRDD.foreach(println) sc.stop
(B,(Some(2),B1))
(B,(Some(2),B2))
(B,(Some(3),B1))
(B,(Some(3),B2))
(C,(None,C1))
(A,(Some(1),A1))
(A,(Some(1),A2))
(A,(Some(2),A1))
(A,(Some(2),A2))
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