使用dataframe解决spark TopN问题：分组、排序、取TopN

标签：jdb   its   window   import   rownumber   分组   top   yield   tool   

package com.profile.main
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._

import com.profile.tools.{DateTools, JdbcTools, LogTools, SparkTools}
import com.dhd.comment.Constant
import com.profile.comment.Comments
 /**
  * 测试类 //使用dataframe解决spark TopN问题：分组、排序、取TopN
  * @author 
  * date 2017-09-27 14:55
  */
object Test {

   def main(args: Array[String]): Unit = {
     val sc=SparkTools.getSparkContext
     val sqlContext = new org.apache.spark.sql.SQLContext(sc)
     import sqlContext.implicits._

     val df = sc.parallelize(Seq(
       (0,"cat26",30.9), (0,"cat13",22.1), (0,"cat95",19.6), (0,"cat105",1.3),
       (1,"cat67",28.5), (1,"cat4",26.8), (1,"cat13",12.6), (1,"cat23",5.3),
       (2,"cat56",39.6), (2,"cat40",29.7), (2,"cat187",27.9), (2,"cat68",9.8),
       (3,"cat8",35.6))).toDF("Hour", "Category", "TotalValue")

     df.show
     /*
     +----+--------+----------+
     |Hour|Category|TotalValue|
     +----+--------+----------+
     |   0|   cat26|      30.9|
     |   0|   cat13|      22.1|
     |   0|   cat95|      19.6|
     |   0|  cat105|       1.3|
     |   1|   cat67|      28.5|
     |   1|    cat4|      26.8|
     |   1|   cat13|      12.6|
     |   1|   cat23|       5.3|
     |   2|   cat56|      39.6|
     |   2|   cat40|      29.7|
     |   2|  cat187|      27.9|
     |   2|   cat68|       9.8|
     |   3|    cat8|      35.6|
     +----+--------+----------+
     */

/*     val w = Window.partitionBy($"Hour").orderBy($"TotalValue".desc)
     //取Top1
     val dfTop1 = df.withColumn("rn", rowNumber.over(w)).where($"rn" === 1).drop("rn")
     //注意：row_number()在spark1.x版本中为rowNumber(),在2.x版本为row_number()
     //取Top3
     val dfTop3 = df.withColumn("rn", rowNumber.over(w)).where($"rn" <= 3).drop("rn")

     dfTop1.show*/
     /*
     +----+--------+----------+
     |Hour|Category|TotalValue|
     +----+--------+----------+
     |   1|   cat67|      28.5|
     |   3|    cat8|      35.6|
     |   2|   cat56|      39.6|
     |   0|   cat26|      30.9|
     +----+--------+----------+
     */
//     dfTop3.show
     /*
     +----+--------+----------+
     |Hour|Category|TotalValue|
     +----+--------+----------+
     |   1|   cat67|      28.5|
     |   1|    cat4|      26.8|
     |   1|   cat13|      12.6|
     |   3|    cat8|      35.6|
     |   2|   cat56|      39.6|
     |   2|   cat40|      29.7|
     |   2|  cat187|      27.9|
     |   0|   cat26|      30.9|
     |   0|   cat13|      22.1|
     |   0|   cat95|      19.6|
     +----+--------+----------+
     */


     //使用RDD解决spark TopN问题：分组、排序、取TopN

     val rdd1 = sc.parallelize(Seq(
       (0,"cat26",30.9), (0,"cat13",22.1), (0,"cat95",19.6), (0,"cat105",1.3),
       (1,"cat67",28.5), (1,"cat4",26.8), (1,"cat13",12.6), (1,"cat23",5.3),
       (2,"cat56",39.6), (2,"cat40",29.7), (2,"cat187",27.9), (2,"cat68",9.8),
       (3,"cat8",35.6)))

     val rdd2 = rdd1.map(x => (x._1,(x._2, x._3))).groupByKey()
     /*
     rdd2.collect
     res9: Array[(Int, Iterable[(String, Double)])] = Array((0,CompactBuffer((cat26,30.9), (cat13,22.1), (cat95,19.6), (cat105,1.3))),
                                                            (1,CompactBuffer((cat67,28.5), (cat4,26.8), (cat13,12.6), (cat23,5.3))),
                                  (2,CompactBuffer((cat56,39.6), (cat40,29.7), (cat187,27.9), (cat68,9.8))),
                                  (3,CompactBuffer((cat8,35.6))))

     */
     val N_value = 1  //取前3

     val rdd3 = rdd2.map( x => {
       val i2 = x._2.toBuffer
       val i2_2 = i2.sortBy(_._2)
       if (i2_2.length > N_value) i2_2.remove(0, (i2_2.length - N_value))
       (x._1, i2_2.toIterable)
     })

     /*
      rdd3.collect
     res8: Array[(Int, Iterable[(String, Double)])] = Array((0,ArrayBuffer((cat95,19.6), (cat13,22.1), (cat26,30.9))),
                                                            (1,ArrayBuffer((cat13,12.6), (cat4,26.8), (cat67,28.5))),
                                  (2,ArrayBuffer((cat187,27.9), (cat40,29.7), (cat56,39.6))),
                                  (3,ArrayBuffer((cat8,35.6))))
     */

     val rdd4 = rdd3.flatMap(x => {
       val y = x._2
       for (w <- y) yield (x._1, w._1, w._2)
     })

     rdd4.collect
     /*
     res3: Array[(Int, String, Double)] = Array((0,cat95,19.6), (0,cat13,22.1), (0,cat26,30.9),
                                                (1,cat13,12.6), (1,cat4,26.8), (1,cat67,28.5),
                            (2,cat187,27.9), (2,cat40,29.7), (2,cat56,39.6),
                            (3,cat8,35.6))
     */

     rdd4.toDF("Hour", "Category", "TotalValue").show
    /* +----+--------+----------+
     |Hour|Category|TotalValue|
     +----+--------+----------+
     |   0|   cat95|      19.6|
     |   0|   cat13|      22.1|
     |   0|   cat26|      30.9|
     |   2|  cat187|      27.9|
     |   2|   cat40|      29.7|
     |   2|   cat56|      39.6|
     |   1|   cat13|      12.6|
     |   1|    cat4|      26.8|
     |   1|   cat67|      28.5|
     |   3|    cat8|      35.6|
     +----+--------+----------+*/

   }

}

使用dataframe解决spark TopN问题：分组、排序、取TopN

标签：jdb   its   window   import   rownumber   分组   top   yield   tool   

原文地址：http://www.cnblogs.com/hd-zg/p/7874291.html