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spark 例子groupByKey分组计算2

例子描述：

大概意思为，统计用户使用app的次数排名

原始数据：

000041b232,张三,FC:1A:11:5C:58:34,F8:E7:1E:1E:62:20,15097003,,2016/6/8 17:10,2016/6/8 17:10,690,6218,11=0|12=200,2016/7/5 11:11
000041b232,张三,FC:1A:11:5C:58:34,F8:E7:1E:1E:69:C0,15026002,,2016/6/8 17:10,2016/6/8 17:10,690,6218,11=0|12=200,2016/7/5 11:11
000041b232,张三,FC:1A:11:5C:58:34,F8:E7:1E:1E:62:20,15026002,,2016/6/8 17:10,2016/6/8 17:10,690,6218,11=0|12=200,2016/7/5 11:11
000041b744,张三,FC:1A:11:5C:58:34,F8:E7:1E:1E:62:20,15026002,,2016/6/8 17:10,2016/6/8 17:10,719,4174,6=2016-06-23 08:50:00|7=,2016/7/5 11:11
000041b22f,李四,FC:1A:11:5C:58:34,F8:E7:1E:1E:62:20,15097002,,2016/6/8 17:10,2016/6/8 17:10,856,367,7=,2016/7/5 11:11
000041b1bc,李四,FC:1A:11:5C:58:34,F8:E7:1E:1E:62:20,15026002,,2016/6/8 17:10,2016/6/8 17:10,937,2964,3=北京|4=上海,2016/7/5 11:11
000041cf18,赵六,7C:1D:D9:F4:BE:E0,F8:E7:1E:1E:62:20,15097002,,2016/6/8 17:10,2016/6/8 17:10,665,2669,5=2016-06-22 00:00:00,2016/7/5 11:11
000041b1bc,孙七,7C:1D:D9:F4:BE:E0,38:FF:36:2E:5B:A0,9003000,,2016/6/8 17:10,2016/6/8 17:10,530,245,,2016/7/5 11:11
000041b8f1,王五,FC:1A:11:5C:58:34,38:FF:36:2E:5B:A0,9007000,,2016/6/8 17:11,2016/6/8 17:11,626,6886,,2016/7/5 11:11
000041b8f1,周八,FC:1A:11:5C:58:34,38:FF:36:2E:5B:A0,16500000,,2016/6/8 17:11,2016/6/8 17:11,2532,646,,2016/7/5 11:11
000041966a,李四,FC:1A:11:5C:58:34,38:FF:36:2E:5B:A0,16501000,,2016/6/8 17:11,2016/6/8 17:11,690,454,,2016/7/5 11:11
000041966a,李四,FC:1A:11:5C:58:34,38:FF:36:2E:5B:A0,16501000,,2016/6/8 17:11,2016/6/8 17:11,690,454,,2016/7/5 11:11

结果数据：

周八,人人贷:1
孙七,支付宝:1
赵六,途牛机票:1
王五,快钱:1|天弘基金:1
李四,红岭创投:2|携程机票:1|携程酒店:1|途牛机票:1
张三,途牛酒店:5|携程机票:3

代码片段：

cxRDD0.map {
  lines =>
    val line = lines.split(",")//逗号分隔数据
    //想办法将数据拼成(数据,1)的映射,并且这个地方的数据要相同,可以理解取为用户,APPID,然后当成K,写个数字1当成V，这里使用的字典关联去取的数据
    (s"""${line((data_location.getOrElse("USR_NBR", "").toInt))},${buss_location.getOrElse(line((data_location.getOrElse("BUS_ID", "").toInt)), "").split(",", -1)(0)}""", 1)
}.reduceByKey(_ + _).map {//分组
  lines =>
    //将分组后的数据,以用户为K,其他为V拼成映射,便于后续分组
    (s"${lines._1.split(",")(0)}", s"${lines._1.split(",")(1)},${lines._2}")
}.groupByKey().map {//分组
  case (k, v) =>
    //对APPID数量 V 进行排序
    val app = v.map {
      x =>
        val a = x.split(",")
        //拆分APPID 与 数量,这里传递给下面的类型为映射
        (a(0), a(1))
        //使用sortWith对映射的第二位数字进行排序,需要转换成INT,因为传递过来都是字符
    }.toSeq.sortWith(_._2.toInt > _._2.toInt).map {
      app =>
        //格式化输出
        //V:V
        s"${app._1}:${app._2}"
    }
    //格式化输出
    //K,V
    //K,V1|V2......
    s"$k,${app.mkString("|")}"
}.foreach(println)

spark 例子groupByKey分组计算2

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原文地址：https://www.cnblogs.com/chendapao/p/9180994.html