http://homepage.cs.latrobe.edu.au/zhe/ZhenHeSparkRDDAPIExamples.html
map是对每个元素操作, mapPartitions是对其中的每个partition操作
mapPartitionsWithIndex : 把每个partition中的分区号和对应的值拿出来, 看源码
val func = (index: Int, iter: Iterator[(Int)]) => {
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(func).collect
aggregate(初始值+目标值)+(第二个分区初始值+目标值)…
#先得到分区的加和再汇总时使用
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
###是action操作, 第一个参数是初始值, 二:是2个函数[每个函数都是2个参数(第一个参数:先对个个分区进行合并, 第二个:对个个分区合并后的结果再进行合并), 输出一个参数]
###0 + (0+1+2+3+4 + 0+5+6+7+8+9)
rdd1.aggregate(0)(+, +)
#(0+4)+(0+9)
rdd1.aggregate(0)(math.max(_, _), _ + )
###5和1比, 得5再和234比得5 --> 5和6789比,得9 --> (5+5) + (5+9)
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("|")( + _, _ + )
rdd2.aggregate("=")( + _, _ + _)
rdd3.mapPartitionsWithIndex(func2).collect
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)
#结果为24或42因为并行计算
rdd3.aggregate("")((x,y) => math.min(x.length, y.length).toString, (x,y) => x + y)
#结果为11
rdd4.mapPartitionsWithIndex(func2).collect
val rdd4 = sc.parallelize(List(“12”,“23”,“345”,""),2)
#"“的length toString为0,0的长度再和x的length相比得到最小长度为1
rdd4.aggregate(”")((x,y) => math.min(x.length, y.length).toString, (x,y) => x + y)
rdd4.aggregate("")((x,y) => math.max(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
#把Key相同的进行加和
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.toList.map(x => “[partID:” + index + ", val: " + x + “]”).iterator
}
pairRDD.mapPartitionsWithIndex(func2).collect
pairRDD.aggregateByKey(0)(_ + _, _ + ).collect
pairRDD.aggregateByKey(0)(math.max(, _), _ + ).collect
pairRDD.aggregateByKey(100)(math.max(, _), _ + _).collect
checkpoint (检查点)(转换动作)
//设置共享存储位置,如果rdd找不到,程序自己先检查cache然后检查checkpoint
//一般把一些比较重要东西checkpoint (如计算出的结果也就是reduceByKey执行后,强烈建议使用前将数据持久化到内存)
//必须在action之前执行
//也就是rdd先cache,再checkpoint ,再collect
//做非常复杂的运算时候数据有可能丢失时使用(即时cache也有可能丢失)
sc.setCheckpointDir(“hdfs://node-1.itcast.cn:9000/ck”)
sc.setCheckpointDir(“file:///root/ck”)
val rdd = sc.textFile(“hdfs://node-1.itcast.cn:9000/wc”).flatMap(.split(" ")).map((, 1)).reduceByKey(+)
val rdd = sc.textFile(“file:///root//wc.txt”).flatMap(.split(" ")).map((, 1)).reduceByKey(+)
rdd.checkpoint
rdd.isCheckpointed
rdd.count
rdd.isCheckpointed
rdd.getCheckpointFile
coalesce, repartition
#合并为N个分区
val rdd1 = sc.parallelize(1 to 10, 10)
val rdd2 = rdd1.coalesce(2, false)
rdd2.partitions.length
collectAsMap : Map(b -> 2, a -> 1)
#把一个collect专为map
val rdd = sc.parallelize(List((“a”, 1), (“b”, 2)))
rdd.collectAsMap
combineByKey : 和reduceByKey是相同的效果
###第一个参数x:原封不动取出来, 第二个参数:是函数, 局部运算, 第三个:是函数, 对局部运算后的结果再做运算
###每个分区中每个key中value中的第一个值, (hello,1)(hello,1)(good,1)–>(hello(1,1),good(1))–>x就相当于hello的第一个1, good中的1
val rdd1 = sc.textFile(“hdfs://master:9000/wordcount/input/”).flatMap(.split(" ")).map((, 1))
#a和b先局部求和(分区内部求和),m和n在分区外部求和
val rdd2 = rdd1.combineByKey(x => x, (a: Int, b: Int) => a + b, (m: Int, n: Int) => m + n)
rdd1.collect
rdd2.collect
###当input下有3个文件时(有3个block块, 不是有3个文件就有3个block, ), 每个会多加3个10
#a+b是计算各个分区中相同key的值,后面mn是计算所有分区中相同key的值
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)
#zip把两个rdd合并为一个array
val rdd6 = rdd5.zip(rdd4)
#x :+ y在一个list后面追加元素,这一步是在每个分区中计算得到list,后面mn是合并各个分区的list
val rdd7 = rdd6.combineByKey(List(_), (x: List[String], y: String) => x :+ y, (m: List[String], n: List[String]) => m ++ n)
countByKey (统计每个key的数量)(countByValue统计每个kv的数量)
val rdd1 = sc.parallelize(List((“a”, 1), (“b”, 2), (“b”, 2), (“c”, 2), (“c”, 1)))
rdd1.countByKey
rdd1.countByValue
filterByRange(把b和d中间的字母取出来)
val rdd1 = sc.parallelize(List((“e”, 5), (“c”, 3), (“d”, 4), (“c”, 2), (“a”, 1)))
val rdd2 = rdd1.filterByRange(“b”, “d”)
rdd2.collect
flatMapValues : Array((a,1), (a,2), (b,3), (b,4))(把一个array做成多个array)
val rdd3 = sc.parallelize(List((“a”, “1 2”), (“b”, “3 4”)))
val rdd4 = rdd3.flatMapValues(_.split(" "))
rdd4.collect
foldByKey(该函数用于RDD[K,V]根据K将V做折叠、合并处理，其中的参数zeroValue表示先根据映射函数将zeroValue应用于V,进行初始化V,再将映射函数应用于初始化后的V.)
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.itcast.cn:9000/wc”).flatMap(.split(" ")).map((, 1))
val rdd = sc.textFile(“file:///root//wc.txt”).flatMap(.split(" ")).map((, 1)).reduceByKey(+)
rdd.foldByKey(0)(+)
foreachPartition(把每个分区拿出来操作,不会产生新的rdd)
val rdd1 = sc.parallelize(List(1, 2, 3, 4, 5, 6, 7, 8, 9), 3)
rdd1.foreachPartition(x => println(x.reduce(_ + _)))
#结果为 6 15 24
keyBy : 以传入的参数做key
val rdd1 = sc.parallelize(List(“dog”, “salmon”, “salmon”, “rat”, “elephant”), 3)
val rdd2 = rdd1.keyBy(_.length)
rdd2.collect
keys values(得到数据的key和value)
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
(与map方法类似，map是对rdd中的每一个元素进行操作，而mapPartitions(foreachPartition)则是对rdd中的每个分区的迭代器进行操作。如果在map过程中需要频繁创建额外的对象(例如将rdd中的数据通过jdbc写入数据库,map需要为每个元素创建一个链接而mapPartition为每个partition创建一个链接),则mapPartitions效率比map高的多)
val a = sc.parallelize(1 to 9, 3)
def doubleFunc(iter: Iterator[Int]) : Iterator[(Int,Int)] = {
var res = List(Int,Int)
while (iter.hasNext)
{
val cur = iter.next;
res .::= (cur,cur*2)
}
res.iterator
}
val result = a.mapPartitions(doubleFunc)
println(result.collect().mkString)