1.自定义一个集合，里面有多个字符串，按照每个元素的首字母进行分组

代码：

object StringDemo2 {
  def main(args: Array[String]): Unit = {
    //创建集合
    var list = ListBuffer("java","php","python","js")
    val sparkConf = new SparkConf().setAppName("test").setMaster("local[*]")
    val sc = new SparkContext(sparkConf)
    val rdd = sc.makeRDD(list)
    val rdd2 = rdd.groupBy(_.charAt(0))//每一个值的首字母
    rdd2.collect().foreach(println)
  }
 
}

运行结果：

(p,CompactBuffer(php, python))
(j,CompactBuffer(java, js))

2.统计日志中同一时间出现多少次日志

日志log.txt的数据

83.149.9.216 - - 17/05/2015:10:05:03 +0000 GET /presentations/logstash-monitorama-2013/images/kibana-search.png
83.149.9.216 - - 17/05/2015:10:05:43 +0000 GET /presentations/logstash-monitorama-2013/images/kibana-dashboard3.png
83.149.9.216 - - 17/05/2015:10:05:47 +0000 GET /presentations/logstash-monitorama-2013/plugin/highlight/highlight.js
83.149.9.216 - - 17/05/2015:10:05:12 +0000 GET /presentations/logstash-monitorama-2013/plugin/zoom-js/zoom.js
83.149.9.216 - - 17/05/2015:10:05:07 +0000 GET /presentations/logstash-monitorama-2013/plugin/notes/notes.js
83.149.9.216 - - 17/05/2015:10:05:34 +0000 GET /presentations/logstash-monitorama-2013/images/sad-medic.png
83.149.9.216 - - 17/05/2015:10:05:57 +0000 GET /presentations/logstash-monitorama-2013/css/fonts/Roboto-Bold.ttf

代码：

object reduceTest {
  def main(args: Array[String]): Unit = {
    val sparkConf = new SparkConf().setAppName("test").setMaster("local[*]")
    val sc = new SparkContext(sparkConf)
    val rdd = sc.textFile("input\\log.txt",1)
    val rdd2 = rdd.map(line=>{
      var data = line.split(" ")
      val simpleDateFormat = new SimpleDateFormat("dd/MM/yyyy:HH:mm:ss")
      var date = simpleDateFormat.parse(data(3))
      var simpleDateFormat2 = new SimpleDateFormat("yyyy:MM:dd:HH")
      var time = simpleDateFormat2.format(date)
      (time,1)
    }).groupBy(_._1)
    val rdd3 = rdd2.map({
      case (k,v)=>{
        (k,v.size)
      }
    })
 
    rdd3.saveAsTextFile("out\\out17")
  }
 
}

运行结果：

(2015:05:17:10,7)

3.完成统计相同字母组成的单词

text01.txt的数据

abc acb java
avaj bac
cba abc
jvaa php hpp
pph python thonpy

代码：

object Demo {
  def main(args: Array[String]): Unit = {
    val sparkConf = new SparkConf().setAppName("test").setMaster("local[*]")
    val sc = new SparkContext(sparkConf)
    val rdd = sc.textFile("input//text01.txt")
    rdd.flatMap(str=>{
      str.split(" ")
    }).map{
      str=>{
        val char = str.toCharArray
        java.util.Arrays.sort(char)
        val str2 =new String(char)
        (str2,str)
      }
    }.groupByKey().collect().foreach(println)
 
  }
 
}

运行结果：

(hpp,CompactBuffer(php, hpp, pph))
(hnopty,CompactBuffer(python, thonpy))
(aajv,CompactBuffer(java, avaj, jvaa))
(abc,CompactBuffer(abc, acb, bac, cba, abc))

4.使用Spark完成单词去重

text02.txt的数据

java php java
python php java
python mysql
hadoop hadoop
java php python
hadoop php

代码：

object Demo2 {
  def main(args: Array[String]): Unit = {
    val sparkConf = new SparkConf().setAppName("test").setMaster("local[*]")
    val sc = new SparkContext(sparkConf)
    val rdd = sc.textFile("input//text02.txt")
    rdd.flatMap(str=>str.split(" ")).distinct().collect().foreach(println)
 
  }
 
}

运行结果：

php
python
mysql
java
hadoop

5.使用Spark统计2005年1月和2月的平均气温值

text03.txt的数据

2005 01 02 04   -11
2005 01 02 05   -17
2005 01 02 06   -17
2005 01 02 07   -17
2005 01 02 08   -17
2005 01 02 09   -17
2005 01 02 10   -22
2005 01 02 11   -22
2005 01 02 12   -28
2005 01 02 13   -33
2005 02 02 14   -39
2005 02 02 15   -28
2005 02 02 16     0
2005 02 02 17    11
2005 02 02 18    17
2005 02 02 19    17
2005 02 02 20    22
2005 02 02 21    28
2005 02 02 22    28
2005 02 02 23    22

代码：

object Demo3 {
  def main(args: Array[String]): Unit = {
    val sparkConf = new SparkConf().setAppName("test").setMaster("local[*]")
    val sc = new SparkContext(sparkConf)
    val rdd = sc.textFile("input//text03.txt")
    var rdd2:RDD[(String,Int)] = rdd.map{
      line =>{
        var month = line.substring(5,7)
        var temp = line.substring(16,19).trim.toInt
        (month,temp)
      }
    }
    val rdd3:RDD[(String,Iterable[Int])] = rdd2.groupByKey()
    rdd3.mapValues(line=> {
      line.toList.sum/line.size
    }).collect().foreach(println)
 
  }
 
}

运行结果：

(02,7)
(01,-20)

6.使用Spark统计137 138 139开头的总流量

 text04.txt的数据

13726230503  81
13826544101  50
13926435656  30
13926251106  40
13826544101  2106
13826544101  1432
13719199419  300

代码：

object Demo4 {
  def main(args: Array[String]): Unit = {
    val sparkConf = new SparkConf().setAppName("test").setMaster("local[*]")
    val sc = new SparkContext(sparkConf)
    val rdd = sc.textFile("input//text04.txt")
    rdd.map { line => {
      val str = line.split("  ")
      (str(0).substring(0,3),str(1).toInt)
    }
    }.reduceByKey(_+_).collect().foreach(println)
  }
 
}

运行结果：

(138,3588)
(139,70)
(137,381)