Spark常用Actions算子

介绍以下Actions算子：
foreach
foreachPatition
reduce
collect
count
first
take
takeSample
top
takeOrdered
saveAsTextFile
saveAsSequenceFile
saveAsObjectFile
countByKey
countByValue
aggregate

(1) foreach、foreachPatition

foreach：遍历RDD中的元素
foreachPatition：按照分区遍历RDD中的元素

    val arr = Array(1,2,3,4,5,6)
    val rdd = sc.makeRDD(arr,2)
    
    rdd.foreach(x => {
      println("===========")
      println(x)
    })
    /*
    ===========
    1
    ===========
    2
    ===========
    3
    ===========
    4
    ===========
    5
    ===========
    6
     */
    
    rdd.foreachPartition(x => {
      println("===========")
      while(x.hasNext) {
        println(x.next())
      }
    })
    /*
    ===========
    1
    2
    3
    ===========
    4
    5
    6
     */
    
    }

(2) reduce：按照指定规则聚合RDD中的元素

val numArr = Array(1,2,3,4,5)
val rdd = sc.parallelize(numArr)
val sum = rdd.reduce(_+_)
println(sum)
/*
15
*/

(3) collect：计算结果拉取回Driver端

val numArr = Array((1,1),(1,2),(1,3),(2,1),(2,2),(2,3))
val rdd = sc.parallelize(numArr)
val sum = rdd.reduceByKey(_+_)

sum.collect().foreach(println)
/*
(1,6)
(2,6)
 */

(4) count、countByKey、countByValue

count：统计RDD中元素个数
countByKey：统计每个Key中的元素的个数
countByValue：统计每个value的个数

// -- count
val arr = Array("Tom","Jack","Tony","Bob","Kate")
val rdd = sc.makeRDD(arr)
println(rdd.count())
/*
5
 */

// -- countByKey
val rdd = sc.parallelize(Array(
        ("销售部","Tom"), ("销售部","Jack"),("销售部","Bob"),("销售部","Terry"),
        ("后勤部","Jack"),("后勤部","Selina"),("后勤部","Hebe"),
        ("人力部","Ella"),("人力部","Harry"),
        ("开发部","Allen")
    ))
val result = rdd.countByKey();
result.foreach(println)
/*
(后勤部,3)
(开发部,1)
(销售部,4)
(人力部,2)

// -- countByValue
val rdd = sc.parallelize(Array(
      "Tom","Jed","Tom",
      "Tom","Jed","Jed",
      "Tom","Tony","Jed"
    ))
val result = rdd.countByValue();
result.foreach(println)
/*
(Tom,4)
(Tony,1)
(Jed,4)
*/

(5) first、take、takeSample

take(n)：取RDD中前n条数据
first：= take(1)
takeSample(withReplacement,num,[seed])：随机抽取RDD中的元素

withReplacement : 是否是放回式抽样  
    true代表如果抽中A元素，之后还可以抽取A元素
    false代表如果抽中了A元素，之后都不在抽取A元素  
fraction : 抽样的比例  
seed : 抽样算法的随机数种子，不同的数值代表不同的抽样规则，可以手动设置，默认为long的随机数


val arr = Array(("Tom",88),("Bob",92),("Allen",86),("Kate",100),("Sandy",97))
val rdd = sc.makeRDD(arr)

// 排序后去前三个
rdd.sortBy(_._2,false).take(3).foreach(println)
/*
(Kate,100)
(Sandy,97)
(Bob,92)
 */

// 排序后取top1
rdd.sortBy(_._2,false).take(1).foreach(println) // (Kate,100)
println(rdd.sortBy(_._2,false).first()) // (Kate,100)

// 随机抽取2个元素
rdd.takeSample(false, 2).foreach(println)

(6) top、takeOrdered

top(n)：从RDD中，按照默认（降序）或者指定的排序规则，返回前n个元素
takeOrdered(n)：从RDD中，按照默认（升序）或者指定的排序规则，返回前n个元素

var rdd = sc.makeRDD(Array(10, 4, 2, 12, 3))

rdd.top(3).foreach(println) // 12 10 4(降序取)

rdd.takeOrdered(3).foreach(println) // 2 3 4(升序取)

(7) saveAsTextFile、saveAsSequenceFile 、saveAsObjectFile

saveAsTextFile：把结果文件保存为textFile
saveAsSequenceFile：把结果文件保存为SequenceFile
saveAsObjectFile：把结果文件保存为ObjectFile

    val line = sc.textFile("hdfs://repo:9000/user/spark/wordcount/input/wordcount.txt")
    line.flatMap(_.split(" "))
      .map((_,1))
      .reduceByKey(_+_)
      .sortBy(_._2,false)
      // .foreach(t => println(t._1 + " " + t._2))
      .saveAsTextFile("hdfs://repo:9000/user/spark/wordcount/output/")
    ```