Question

我需要逻辑方面的帮助。我有这样的数据

tag,timestamp,listner,org,suborg,rssi                              
4,101,1901,4,3,0.60                                                                                                                                                                    
4,110,1901,4,3,0.90
4,104,1901,4,3,0.30
4,109,1901,4,3,0.40
4,111,1901,4,3,0.60                                                        
4,128,1901,4,3,0.40
4,129,1901,4,3,0.80
4,131,1901,4,3,0.60                                                                  
4,133,1901,4,3,0.30
4,143,1901,4,3,0.60                                                                  
4,147,1901,4,3,0.70
4,148,1901,4,3,0.40
4,149,1901,4,3,0.30
4,150,1901,4,3,0.90

我必须找到rssi列的平均值，它们距离最近的10秒内。

这是我的预期输出。

tagShortID,timestamp,listenerShortID,rootOrgID,subOrgID,rssi_Weight,rssi_Weight_avg
4,150,1901,4,3,0.9,0.58
4,149,1901,4,3,0.3,0.5
4,148,1901,4,3,0.4,0.56
4,147,1901,4,3,0.7,0.64
4,143,1901,4,3,0.6,0.44
4,133,1901,4,3,0.3,0.525
4,131,1901,4,3,0.6,0.6
4,129,1901,4,3,0.8,0.6
4,128,1901,4,3,0.4,0.4
4,111,1901,4,3,0.6,0.6
4,110,1901,4,3,0.9,0.9
4,109,1901,4,3,0.4,0.4
4,104,1901,4,3,0.3,0.3
4,101,1901,4,3,0.6,0.6

我试过这个

 df.withColumn("firstValue", first("Timestamp") over Window.orderBy($"Timestamp".desc).partitionBy("tagShortID",  "ListenerShortID"))
.filter($"firstValue".cast("long")-$"Timestamp".cast("long") <= 10)
.withColumn("count", count("Timestamp") over  Window.partitionBy("tagShortID", "ListenerShortID"))
.withColumn("RSSI_Weight", when($"count" >= 10, avg($"RSSI_Weight") over Window.orderBy("Timestamp").partitionBy("tagShortID", "ListenerShortID").rowsBetween(Long.MinValue, 0)) otherwise($"RSSI_Weight"))
.drop("firstValue", "count")
.show(30, false)

以上将检查高值时间戳，然后执行 - 10秒。但是我需要迭代每个时间戳并检查10秒。如果是，则平均值取rssi值。

任何帮助将不胜感激。

Answer 1

您可以在rdd中使用以下逻辑来获取所需的数据框

def avgCalc(buffer: Iterable[Array[String]], list: Array[String]) = {
  val currentTimeStamp = list(1).toLong
  var sum = 0.0
  var count = 0
  var check = false
  import scala.util.control.Breaks._
  breakable {
    for (array <- buffer) {
      val toCheckTimeStamp = array(1).toLong
      if (((currentTimeStamp - 10L) <= toCheckTimeStamp) && (currentTimeStamp >= toCheckTimeStamp)) {
        sum += array(5).toDouble
        count += 1
      }
      if ((currentTimeStamp - 10L) > toCheckTimeStamp) {
        check = true
        break
      }
    }
  }
  if (sum != 0.0 && check) list :+ sum / count
  else list :+ list(5).toDouble
}

import sqlContext.implicits._
val averageDF = sc.textFile("path to your csv file")
    .map(line => line.split(",").map(_.trim))
    .sortBy(array => array(1), false)
    .groupBy(array => (array(0), array(2)))
    .mapValues(buffer => {
      buffer.map(list => {
        avgCalc(buffer, list)
      })
    })
    .flatMap(x => x._2)
    .map(x => Jessi(x(0).toString, x(1).toString.toLong, x(2).toString, x(3).toString, x(4).toString, x(5).toString.toDouble, x(6).toString.toDouble))
    .toDF

averageDF.show

Jessi是案例类

case class Jessi(tagShortID: String, Timestamp: Long, ListenerShortID: String, rootOrgID: String, subOrgID: String, RSSI_Weight: Double, RSSI_Weight_avg: Double)

所以你应该有以下输出

+----------+---------+---------------+---------+--------+-----------+-------------------+
|tagShortID|Timestamp|ListenerShortID|rootOrgID|subOrgID|RSSI_Weight|RSSI_Weight_avg    |
+----------+---------+---------------+---------+--------+-----------+-------------------+
|4         |150      |1901           |4        |3       |0.9        |0.58               |
|4         |149      |1901           |4        |3       |0.3        |0.5                |
|4         |148      |1901           |4        |3       |0.4        |0.5666666666666668 |
|4         |147      |1901           |4        |3       |0.7        |0.6499999999999999 |
|4         |143      |1901           |4        |3       |0.6        |0.44999999999999996|
|4         |133      |1901           |4        |3       |0.3        |0.525              |
|4         |131      |1901           |4        |3       |0.6        |0.6                |
|4         |129      |1901           |4        |3       |0.8        |0.6000000000000001 |
|4         |128      |1901           |4        |3       |0.4        |0.4                |
|4         |111      |1901           |4        |3       |0.6        |0.6                |
|4         |110      |1901           |4        |3       |0.9        |0.9                |
|4         |109      |1901           |4        |3       |0.4        |0.4                |
|4         |104      |1901           |4        |3       |0.3        |0.3                |
|4         |101      |1901           |4        |3       |0.6        |0.6                |
+----------+---------+---------------+---------+--------+-----------+-------------------+

在scala中使用有限的记录范围进行迭代

1 个答案: