如何分析spark lac 停留最长的两个地方

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package hgs.spark.othertestimport org.apache.spark.SparkConfimport org.apache.spark.SparkContextobject FindTheTop2 {  def main(args: Array[String]): Unit = {        val conf = new  SparkConf().setAppName("FindTheTop2").setMaster("local[3]")    val sc = new SparkContext(conf)    val rdd1 = sc.textFile("D:\\bs_log")      //rdd_phone_lac_time:(18688888888 16030401EAFB68F1E3CDF819735E1C66,-20160327082400,1), (18611132889 16030401EAFB68F1E3CDF819735E1C66,-20160327082500,1)    //先映射为上一行为例的map(K,V)    val rdd_phone_lac_time = rdd1.map(x=>{      val list = x.split(",")      if(Integer.parseInt(list(3))==1)      (list(0)+" "+list(2),-list(1).toLong)      else{       (list(0)+" "+list(2),list(1).toLong)         }     }    )    //根据rdd_phone_lac_time 的key进行reduce，将所有key相同的数据相加    val rdd_reduce_phone_lackey = rdd_phone_lac_time.reduceByKey((x,y)=>x+y)    //(18688888888,CompactBuffer((18688888888 16030401EAFB68F1E3CDF819735E1C66,87600), (18688888888 9F36407EAD0629FC166F14DDE7970F68,51200), (18688888888 CC0710CC94ECC657A8561DE549D940E0,1300)))    //取top2,mapValues对values操作，返回的是map(K,V),K是原始的K,V是操作后得到的V    val rdd_reduce_phone_lackey_groupyed = rdd_reduce_phone_lackey.groupBy(x=>x._1.split(" ")(0))    val rdd_top2 = rdd_reduce_phone_lackey_groupyed.mapValues(x=>{      x.toList.sortBy(_._2).reverse.take(2)        })    //(16030401EAFB68F1E3CDF819735E1C66,(18688888888,16030401EAFB68F1E3CDF819735E1C66,87600))    //下面需要与另一个map根据特定的字段例如16030401EAFB68F1E3CDF819735E1C66进行join，所以需要将‘18688888888 16030401EAFB68F1E3CDF819735E1C66’拆开，将第二个作为K，返回新的map    val rdd_result = rdd_top2.flatMap(x=>{         x._2.map(y=>{           val li = y._1.split(" ")           (li(1),(li(0),li(1),y._2))         })                })       //该文件中即是需要与上面的结果进行join     val lati_longti = sc.textFile("D:\\lac_info", 1)     //(9F36407EAD0629FC166F14DDE7970F68,(116.304864,40.050645))     //映射成如上一行的map     val rdd_coordinate = lati_longti.map(f=>{       val li = f.split(",")       (li(0),(li(0),li(1),li(2)))     })     //进行join     //rdd_coordinate 与rdd_result的结构类型已改是一样的，即K，V的类型对应，否则无法join     val join_resultWithcoordinate = rdd_coordinate.join(rdd_result)    // rdd_coordinate.to     //println(rdd_result.collect().length)     //保存文件    join_resultWithcoordinate.saveAsTextFile("d:\\dest")    sc.stop()      }}

样例数据

D:\\bs_log

18688888888,20160327082400,16030401EAFB68F1E3CDF819735E1C66,118611132889,20160327082500,16030401EAFB68F1E3CDF819735E1C66,118688888888,20160327170000,16030401EAFB68F1E3CDF819735E1C66,018611132889,20160327075000,9F36407EAD0629FC166F14DDE7970F68,118688888888,20160327075100,9F36407EAD0629FC166F14DDE7970F68,118611132889,20160327081000,9F36407EAD0629FC166F14DDE7970F68,018688888888,20160327081300,9F36407EAD0629FC166F14DDE7970F68,018688888888,20160327175000,9F36407EAD0629FC166F14DDE7970F68,118611132889,20160327182000,9F36407EAD0629FC166F14DDE7970F68,118688888888,20160327220000,9F36407EAD0629FC166F14DDE7970F68,018611132889,20160327230000,9F36407EAD0629FC166F14DDE7970F68,018611132889,20160327180000,16030401EAFB68F1E3CDF819735E1C66,018611132889,20160327081100,CC0710CC94ECC657A8561DE549D940E0,118688888888,20160327081200,CC0710CC94ECC657A8561DE549D940E0,118688888888,20160327081900,CC0710CC94ECC657A8561DE549D940E0,018611132889,20160327082000,CC0710CC94ECC657A8561DE549D940E0,018688888888,20160327171000,CC0710CC94ECC657A8561DE549D940E0,118688888888,20160327171600,CC0710CC94ECC657A8561DE549D940E0,018611132889,20160327180500,CC0710CC94ECC657A8561DE549D940E0,118611132889,20160327181500,CC0710CC94ECC657A8561DE549D940E0,0D:\\lac_info  9F36407EAD0629FC166F14DDE7970F68,116.304864,40.050645,6CC0710CC94ECC657A8561DE549D940E0,116.303955,40.041935,616030401EAFB68F1E3CDF819735E1C66,116.296302,40.032296,6数据结果：(16030401EAFB68F1E3CDF819735E1C66,((16030401EAFB68F1E3CDF819735E1C66,116.296302,40.032296),(18688888888,16030401EAFB68F1E3CDF819735E1C66,87600)))(16030401EAFB68F1E3CDF819735E1C66,((16030401EAFB68F1E3CDF819735E1C66,116.296302,40.032296),(18611132889,16030401EAFB68F1E3CDF819735E1C66,97500)))(9F36407EAD0629FC166F14DDE7970F68,((9F36407EAD0629FC166F14DDE7970F68,116.304864,40.050645),(18688888888,9F36407EAD0629FC166F14DDE7970F68,51200)))(9F36407EAD0629FC166F14DDE7970F68,((9F36407EAD0629FC166F14DDE7970F68,116.304864,40.050645),(18611132889,9F36407EAD0629FC166F14DDE7970F68,54000)))

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