package example

import org.apache.spark.rdd.RDD

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

  //需求：统计出每一个省份广告被点击次数的TOP3

  object Example {

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

      //1.初始化spark配置信息并建立与spark的连接

      val sparkConf = new SparkConf().setMaster("local[*]").setAppName("example")

      val sc = new SparkContext(sparkConf)

      //2.读取数据生成RDD：TS，Province，City，User，AD

      val line = sc.textFile("E:/idea/spark3/in/agent.log")

      //3.按照最小粒度聚合：((Province,AD),1)

      val provinceAdToOne = line.map { x =>

        val fields: Array[String] = x.split(" ")

        ((fields(1), fields(4)), 1)

      }

      //4.计算每个省中每个广告被点击的总数：((Province,AD),sum)

      val provinceAdToSum: RDD[((String, String), Int)] = provinceAdToOne.reduceByKey(_ + _)

      //5.将省份作为key，广告加点击数为value：(Province,(AD,sum))

      val provinceToAdSum: RDD[(String, (String, Int))] = provinceAdToSum.map(x => (x._1._1, (x._1._2, x._2)))

      //6.将同一个省份的所有广告进行聚合(Province,List((AD1,sum1),(AD2,sum2)...))

      val provinceGroup: RDD[(String, Iterable[(String, Int)])] = provinceToAdSum.groupByKey()

      //7.对同一个省份所有广告的集合进行排序并取前3条，排序规则为广告点击总数

      val provinceAdTop3: RDD[(String, List[(String, Int)])] = provinceGroup.mapValues { x =>

        x.toList.sortWith((x, y) => x._2 > y._2).take(3)

      }

      //8.将数据拉取到Driver端并打印

      provinceAdTop3.saveAsTextFile("E:/idea/spark3/out/example")

      //9.关闭与spark的连接

      sc.stop()

    }

  }