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),6),"4",7),"1","5",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？
解决方法
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)
