2018-11-17 Spark算子练习常用Transformation(即转换，延迟加载)通过并行化scala集合创建RDD查看该rdd的分区数量union求并集，注意类型要一致intersecti

常用Transformation(即转换，延迟加载)

通过并行化scala集合创建RDD

val rdd1 = sc.parallelize(Array(1,2,3,4,5,6,7,8))

查看该rdd的分区数量

rdd1.partitions.length

val rdd1 = sc.parallelize(List(5,6,4,7,3,8,2,9,1,10)) val rdd2 = sc.parallelize(List(5,6,4,7,3,8,2,9,1,10)).map(*2).sortBy(x=>x,true) val rdd3 = rdd2.filter(>10) val rdd2 = sc.parallelize(List(5,6,4,7,3,8,2,9,1,10)).map(*2).sortBy(x=>x+"",true) val rdd2 = sc.parallelize(List(5,6,4,7,3,8,2,9,1,10)).map(*2).sortBy(x=>x.toString,true)

val rdd4 = sc.parallelize(Array("a b c", "d e f", "h i j")) rdd4.flatMap(_.split(' ')).collect

val rdd5 = sc.parallelize(List(List("a b c", "a b b"),List("e f g", "a f g"), List("h i j", "a a b")))

List("a b c", "a b b") =List("a","b",))

rdd5.flatMap(.flatMap(.split(" "))).collect

union求并集，注意类型要一致

val rdd6 = sc.parallelize(List(5,6,4,7)) val rdd7 = sc.parallelize(List(1,2,3,4)) val rdd8 = rdd6.union(rdd7) rdd8.distinct.sortBy(x=>x).collect

intersection求交集

val rdd9 = rdd6.intersection(rdd7)

val rdd1 = sc.parallelize(List(("tom", 1), ("jerry", 2), ("kitty", 3))) val rdd2 = sc.parallelize(List(("jerry", 9), ("tom", 8), ("shuke", 7), ("tom", 2)))

join(连接)

val rdd3 = rdd1.join(rdd2) val rdd3 = rdd1.leftOuterJoin(rdd2) val rdd3 = rdd1.rightOuterJoin(rdd2)

groupByKey

val rdd3 = rdd1 union rdd2 rdd3.groupByKey //(tom,CompactBuffer(1, 8, 2)) rdd3.groupByKey.map(x=>(x._1,x.2.sum)) groupByKey.mapValues(.sum).collect Array((tom,CompactBuffer(1, 8, 2)), (jerry,CompactBuffer(9, 2)), (shuke,CompactBuffer(7)), (kitty,CompactBuffer(3)))

WordCount

sc.textFile("/root/words.txt").flatMap(x=>x.split(" ")).map((,1)).reduceByKey(+).sortBy(.2,false).collect sc.textFile("/root/words.txt").flatMap(x=>x.split(" ")).map((,1)).groupByKey.map(t=>(t._1, t._2.sum)).collect

cogroup

val rdd1 = sc.parallelize(List(("tom", 1), ("tom", 2), ("jerry", 3), ("kitty", 2))) val rdd2 = sc.parallelize(List(("jerry", 2), ("tom", 1), ("shuke", 2))) val rdd3 = rdd1.cogroup(rdd2) val rdd4 = rdd3.map(t=>(t._1, t._2._1.sum + t._2._2.sum))

cartesian笛卡尔积

val rdd1 = sc.parallelize(List("tom", "jerry")) val rdd2 = sc.parallelize(List("tom", "kitty", "shuke")) val rdd3 = rdd1.cartesian(rdd2)

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spark action

val rdd1 = sc.parallelize(List(1,2,3,4,5), 2)

collect

rdd1.collect

reduce

val r = rdd1.reduce(+)

count

rdd1.count

top

rdd1.top(2)

take

rdd1.take(2)

first(similer to take(1))

rdd1.first

takeOrdered

rdd1.takeOrdered(3)

http://homepage.cs.latrobe.edu.au/zhe/ZhenHeSparkRDDAPIExamples.html

mapPartitionsWithIndex val func = (index: Int, iter: Iterator[(String)]) => { iter.map(x => "[partID:" + index + ", val: " + x + "]") }

mapPartitionsWithIndex val func = (index: Int, iter: Iterator[Int]) => { iter.map(x => "[partID:" + index + ", val: " + x + "]") } val rdd1 = sc.parallelize(List(1,2,3,4,5,6,7,8,9), 2) rdd1.mapPartitionsWithIndex(func).collect

aggregate

def func1(index: Int, iter: Iterator[(Int)]) : Iterator[String] = { iter.toList.map(x => "[partID:" + index + ", val: " + x + "]").iterator } val rdd1 = sc.parallelize(List(1,2,3,4,5,6,7,8,9), 2) rdd1.mapPartitionsWithIndex(func1).collect rdd1.aggregate(0)(math.max(_, _), _ + ) rdd1.aggregate(5)(math.max(, _), _ + _)

val rdd2 = sc.parallelize(List("a","b","c","d","e","f"),2) def func2(index: Int, iter: Iterator[(String)]) : Iterator[String] = { iter.toList.map(x => "[partID:" + index + ", val: " + x + "]").iterator } rdd2.aggregate("")(_ + _, _ + ) rdd2.aggregate("=")( + _, _ + _)

val rdd3 = sc.parallelize(List("12","23","345","4567"),2) rdd3.aggregate("")((x,y) => math.max(x.length, y.length).toString, (x,y) => x + y)

val rdd4 = sc.parallelize(List("12","23","345",""),2) rdd4.aggregate("")((x,y) => math.min(x.length, y.length).toString, (x,y) => x + y)

val rdd5 = sc.parallelize(List("12","23","","345"),2) rdd5.aggregate("")((x,y) => math.min(x.length, y.length).toString, (x,y) => x + y)

aggregateByKey

val pairRDD = sc.parallelize(List( ("cat",2), ("cat", 5), ("mouse", 4),("cat", 12), ("dog", 12), ("mouse", 2)), 2) def func2(index: Int, iter: Iterator[(String, Int)]) : Iterator[String] = { iter.map(x => "[partID:" + index + ", val: " + x + "]") } pairRDD.mapPartitionsWithIndex(func2).collect pairRDD.aggregateByKey(0)(math.max(_, _), _ + ).collect pairRDD.aggregateByKey(100)(math.max(, _), _ + _).collect

checkpoint sc.setCheckpointDir("hdfs://node-1.edu360.cn:9000/ck") val rdd = sc.textFile("hdfs://node-1.edu360.cn:9000/wc").flatMap(.split(" ")).map((, 1)).reduceByKey(+) rdd.checkpoint rdd.isCheckpointed rdd.count rdd.isCheckpointed rdd.getCheckpointFile

coalesce, repartition val rdd1 = sc.parallelize(1 to 10, 10) val rdd2 = rdd1.coalesce(2, false) rdd2.partitions.length

collectAsMap val rdd = sc.parallelize(List(("a", 1), ("b", 2))) rdd.collectAsMap

combineByKey val rdd1 = sc.textFile("hdfs://node-1.edu360.cn:9000/wc").flatMap(.split(" ")).map((, 1)) val rdd2 = rdd1.combineByKey(x => x, (a: Int, b: Int) => a + b, (m: Int, n: Int) => m + n) rdd2.collect

val rdd3 = rdd1.combineByKey(x => x + 10, (a: Int, b: Int) => a + b, (m: Int, n: Int) => m + n) rdd3.collect

val rdd4 = sc.parallelize(List("dog","cat","gnu","salmon","rabbit","turkey","wolf","bear","bee"), 3) val rdd5 = sc.parallelize(List(1,1,2,2,2,1,2,2,2), 3) val rdd6 = rdd5.zip(rdd4) val rdd7 = rdd6.combineByKey(List(_), (x: List[String], y: String) => x :+ y, (m: List[String], n: List[String]) => m ++ n)

countByKey

val rdd1 = sc.parallelize(List(("a", 1), ("b", 2), ("b", 2), ("c", 2), ("c", 1))) rdd1.countByKey rdd1.countByValue

filterByRange

val rdd1 = sc.parallelize(List(("e", 5), ("c", 3), ("d", 4), ("c", 2), ("a", 1))) val rdd2 = rdd1.filterByRange("b", "d") rdd2.colllect

flatMapValues val a = sc.parallelize(List(("a", "1 2"), ("b", "3 4"))) rdd3.flatMapValues(_.split(" "))

foldByKey

val rdd1 = sc.parallelize(List("dog", "wolf", "cat", "bear"), 2) val rdd2 = rdd1.map(x => (x.length, x)) val rdd3 = rdd2.foldByKey("")(+)

val rdd = sc.textFile("hdfs://node-1.edu360.cn:9000/wc").flatMap(.split(" ")).map((, 1)) rdd.foldByKey(0)(+)

foreachPartition val rdd1 = sc.parallelize(List(1, 2, 3, 4, 5, 6, 7, 8, 9), 3) rdd1.foreachPartition(x => println(x.reduce(_ + _)))

keyBy val rdd1 = sc.parallelize(List("dog", "salmon", "salmon", "rat", "elephant"), 3) val rdd2 = rdd1.keyBy(_.length) rdd2.collect

keys values val rdd1 = sc.parallelize(List("dog", "tiger", "lion", "cat", "panther", "eagle"), 2) val rdd2 = rdd1.map(x => (x.length, x)) rdd2.keys.collect rdd2.values.collect

mapPartitions( it: Iterator => {it.map(x => x * 10)})