Note the spark by spark achieve clickstream log analysis Case

1. Pv access

package cn.itcast

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

object PV {
  def main(args: Array[String]): Unit = {
        //todo：创建sparkconf，设置appName
        //todo:setMaster("local[2]")在本地模拟spark运行 这里的数字表示 使用2个线程
        val sparkConf: SparkConf = new SparkConf().setAppName("PV").setMaster("local[2]")
        //todo:创建SparkContext
        val sc: SparkContext = new SparkContext(sparkConf)
        //todo:读取数据
        val file: RDD[String] = sc.textFile("d:\\data\\access.log")
        //todo:将一行数据作为输入，输出("pv",1)
        val pvAndOne: RDD[(String, Int)] = file.map(x=>("pv",1))
        //todo:聚合输出
         val totalPV: RDD[(String, Int)] = pvAndOne.reduceByKey(_+_)
         totalPV.foreach(println)
         sc.stop()
  }
}

2. Visit uv

package cn.itcast

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

object UV {
  def main(args: Array[String]): Unit = {
    //todo:构建SparkConf和 SparkContext
    val sparkConf: SparkConf = new SparkConf().setAppName("UV").setMaster("local[2]")
    val sc: SparkContext = new SparkContext(sparkConf)
    //todo:读取数据
    val file: RDD[String] = sc.textFile("d:\\data\\access.log")
    //todo:对每一行分隔，获取IP地址
    val ips: RDD[(String)] = file.map(_.split(" ")).map(x=>x(0))
    //todo:对ip地址进行去重，最后输出格式 ("UV",1)
    val uvAndOne: RDD[(String, Int)] = ips.distinct().map(x=>("UV",1))
    //todo:聚合输出
    val totalUV: RDD[(String, Int)] = uvAndOne.reduceByKey(_+_)
    totalUV.foreach(println)
    //todo:数据结果保存
    totalUV.saveAsTextFile("d:\\data\\out")
    sc.stop()
  }
}

3. Visit topN

package cn.itcast

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

/**
  * 求访问的topN
  */
object TopN {
  def main(args: Array[String]): Unit = {
    val sparkConf: SparkConf = new SparkConf().setAppName("TopN").setMaster("local[2]")
    val sc: SparkContext = new SparkContext(sparkConf)
    sc.setLogLevel("WARN")
    //读取数据
    val file: RDD[String] = sc.textFile("d:\\data\\access.log")
    //将一行数据作为输入,输出(来源URL,1)
    val refUrlAndOne: RDD[(String, Int)] = file.map(_.split(" ")).filter(_.length>10).map(x=>(x(10),1))
    //聚合 排序-->降序
    val result: RDD[(String, Int)] = refUrlAndOne.reduceByKey(_+_).sortBy(_._2,false)
    //通过take取topN，这里是取前5名
    val finalResult: Array[(String, Int)] = result.take(5)
    println(finalResult.toBuffer)

    sc.stop()

  }

}