Spark经典案例解析-优快云博客

案例—WordCount

实现步骤
1）创建spark的项目
在scala中创建项目导入spark相关的jar包

2）开发spark相关代码

代码示例：

import org.apache.spark.SparkConf import org.apache.spark.SparkContext

object WordCountDriver {

def main(args: Array[String]): Unit = {

val conf=new SparkConf().setMaster("spark://hadoop01:7077").setAppName("wordcount") val sc=new SparkContext(conf)

val data=sc.textFile("hdfs://hadoop01:9000/words.txt", 2)
val result=data.flatMap { x => x.split(" ") }.map { x => (x,1) }.reduceByKey(_+_)

result.saveAsTextFile("hdfs://hadoop01:9000/wcresult")
}
}

3）将写好的项目打成jar，上传到服务器，进入bin目录
执行：spark-submit --class cn.tedu.WordCountDriver /home/software/spark/conf/wc.jar

案例二求平均值

案例文件：
1 16
2 74
3 51
4 35
5 44
6 95
7 5
8 29
10 60
11 13
12 99
13 7
14 26

正确答案：42

代码示例一：

import org.apache.spark.SparkConf import org.apache.spark.SparkContext

object AverageDriver {       
def main(args: Array[String]): Unit = {
val conf=new SparkConf().setMaster("local").setAppName("AverageDriver") val sc=new SparkContext(conf)
val data=sc.textFile("d://average.txt")   
    val ageData=data.map { line=>{line.split(" ")(1).toInt}}    
val ageSum=ageData.reduce(_+_) val pepopleCount=data.count()
val average=ageSum/pepopleCount   
    println(average)
}
}

代码示例二：

import org.apache.spark.SparkConf import org.apache.spark.SparkContext

object AverageDriver {   
def main(args: Array[String]): Unit = {
val conf=new SparkConf().setMaster("local").setAppName("AverageDriver") val sc=new SparkContext(conf)
val data=sc.textFile("d://average.txt",3)   
val ageData=data.map { line=>{line.split(" ")(1).toInt}}  
val ageSum=ageData.mapPartitions{it=>{ val result=List[Int]()
var i=0 while(it.hasNext){ i+=it.next()
}
result.::(i).iterator   
    }}.reduce(_+_)
    val pepopleCount=data.count()       
val average=ageSum/pepopleCount   
    println(average)
}
}

案例—求最大值和最小值
2018年3月2日 17:43

案例文件：
1 M 174
2 F 165
3 M 172
4 M 180
5 F 160
6 F 162
7 M 172
8 M 191
9 F 175
10 F 167

代码示例一：

import org.apache.spark.SparkConf import org.apache.spark.SparkContext

object MaxMinDriver {
    def main(args: Array[String]): Unit = {
	    val conf=new SparkConf().setMaster("local").setAppName("MaxMin") val sc=new SparkContext(conf)
	    val data=sc.textFile("d://MaxMin.txt")
	    val manData=data.filter { x => x.contains("M") }.map { x => x.split(" ")(2).toInt} val girlData=data.filter { x => x.contains("F") }.map { x => x.split(" ")(2).toInt}
	    println("Man Max is:"+manData.max()+"Man min is:"+manData.min())
    }
}

代码示例二：

import org.apache.spark.SparkConf import org.apache.spark.SparkContext
object MaxMinDriver {
    def main(args: Array[String]): Unit = {
    val conf=new SparkConf().setMaster("local").setAppName("MaxMin") val sc=new SparkContext(conf)
    val data=sc.textFile("d://MaxMin.txt")
    val manMax=data.filter { line => line.contains("M") }. sortBy({line=>line.split(" ")(2)},false).first().mkString
    val manMin=data.filter { line => line.contains("M") }. sortBy({line=>line.split(" ")(2)},true).first.mkString
    println(manMax+"\n"+manMin)
    }
}

代码示例三：

import org.apache.spark.SparkConf import org.apache.spark.SparkContext
object MaxMinDriver {
    def main(args: Array[String]): Unit = {
	    val conf=new SparkConf().setMaster("spark://hadoop01:7077").setAppName("MaxMin") 
	    val sc=new SparkContext(conf)
	    val data=sc.textFile("hdfs://hadoop01:9000/MaxMin.txt",3)
	    val manMax=data.filter { line => line.contains("M") }. sortBy({line=>line.split(" ")(2)},false).first.mkString
	    val manMin=data.filter { line => line.contains("M") }. sortBy({line=>line.split(" ")(2)},true).first.mkString
	    val result=sc.makeRDD(Array(manMax,manMin))
    
    //--spark输出文件时，默认是有几个Task,就会生成几个结果文件，
    //--所以如果想控制文件个数，控制分区数(task)即可result.coalesce(1,true).saveAsTextFile("hdfs://hadoop01:9000/MaxMinResult")
    }
}

案例TopK

案例说明
Top K算法有两步，一是统计词频，二是找出词频最高的前K个词。

文件数据 topk.txt:
hello world bye world hello hadoop bye hadoop hello world java web hadoop scala java hive hadoop hive redis hbase hello hbase java redis

代码示例:

import org.apache.spark.SparkConf import org.apache.spark.SparkContext

object TopkDriver {       
    def main(args: Array[String]): Unit = {
	    val conf =new SparkConf().setMaster("local").setAppName("topk") val sc=new SparkContext(conf)    
	    val data=sc.textFile("e://topk.txt",2)       
	    val count=data.flatMap { x => x.split(" ") }
	    .map { x => (x,1) }.reduceByKey(_+_)       
	    val orderingDesc = Ordering.by [(String, Int), Int](_._2) val topk=count.top(3)(orderingDesc)
	    //val topk=count.top(3)(Ordering.by{case (word,count)=>count}) topk.foreach{println}
    }
}

应用场景
Top K的示例模型可以应用在求过去一段时间消费次数最多的消费者、访问最频繁的IP地址和最近、更新、最频繁的微博等应用场景。

案例二次排序

文件数据：
aa 12
bb 32
aa 3
cc 43
dd 23
cc 5
cc 8
bb 33
bb 12

要求：先按第一例升序排序，再按第二列降序排序

自定义排序类代码：
class SecondarySortKey(val first:String,val second:Int) extends Ordered[SecondarySortKey] with Serializable {

def compare(other:SecondarySortKey):Int={ var comp=this.first.compareTo(other.first) if(comp==0){ other.second.compareTo(this.second)
}else{ comp
}
}
}

Driver代码：

import org.apache.spark.SparkConf import org.apache.spark.SparkContext
    
object SsortDriver {   
   def main(args: Array[String]): Unit = {    
	       val conf=new SparkConf().setMaster("local").setAppName("ssort") val sc=new SparkContext(conf)
	       val data=sc.textFile("d://ssort.txt",3)       
	       val ssortData=data.map { line =>{
	        (new SecondarySortKey(line.split(" ")(0),line.split(" ")(1).toInt),line)
	        }
  		  }       
 		   val result=ssortData.sortByKey(true) result.foreach(println)
        }
    }