在Apache-Spark数据帧中求和距离

Question

以下代码给出了每列中具有三个值的数据帧,如下所示.

import org.graphframes._
    import org.apache.spark.sql.DataFrame
    val v = sqlContext.createDataFrame(List(
      ("1", "Al"),
      ("2", "B"),
      ("3", "C"),
      ("4", "D"),
      ("5", "E")
    )).toDF("id", "name")

    val e = sqlContext.createDataFrame(List(
      ("1", "3", 5),
      ("1", "2", 8),
      ("2", "3", 6),
      ("2", "4", 7),
      ("2", "1", 8),
      ("3", "1", 5),
      ("3", "2", 6),
      ("4", "2", 7),
      ("4", "5", 8),
      ("5", "4", 8)
    )).toDF("src", "dst", "property")
val g = GraphFrame(v, e)
val paths: DataFrame = g.bfs.fromExpr("id = '1'").toExpr("id = '5'").run()
paths.show()
val df=paths
df.select(df.columns.filter(_.startsWith("e")).map(df(_)) : _*).show

以上代码的OutPut如下:

    +-------+-------+-------+                                                       
    |     e0|     e1|     e2|
    +-------+-------+-------+
    |[1,2,8]|[2,4,7]|[4,5,8]|
    +-------+-------+-------+

在上面的输出中,我们可以看到每列有三个值,它们可以解释如下.

e0 : 
source 1, Destination 2 and distance 8  

e1:
source 2, Destination 4 and distance 7

e2:
source 4, Destination 5 and distance 8

基本上e0,e1和e3是边缘.我想总结每列的第三个元素,即添加每个边的距离以获得总距离.我怎样才能做到这一点？

Answer 1

它可以这样做:

val total = df.columns.filter(_.startsWith("e"))
 .map(c => col(s"$c.property")) // or col(c).getItem("property")
 .reduce(_ + _)

df.withColumn("total", total)