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)
###################################################################################################
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)})
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