我有一个Spark数据框,如下所示 -
val myDF = Seq(
(1,"A",100,0,0),
(1,"E",200,0,0),
(1,"",300,1,49),
(2,"A",200,0,0),
(2,"C",300,0,0),
(2,"D",100,0,0)
).toDF("visitor","channel","timestamp","purchase_flag","amount")
scala> myDF.show
+-------+-------+---------+-------------+------+
|visitor|channel|timestamp|purchase_flag|amount|
+-------+-------+---------+-------------+------+
| 1| A| 100| 0| 0|
| 1| E| 200| 0| 0|
| 1| | 300| 1| 49|
| 2| A| 200| 0| 0|
| 2| C| 300| 0| 0|
| 2| D| 100| 0| 0|
+-------+-------+---------+-------------+------+
我想为来自myDF
的每个访问者创建一个序列数据框,该访问者跟踪按timestamp
维度排序的访问者的购买路径。
输出数据框应如下所示(->
可以是任何分隔符) -
+-------+---------------------+
|visitor|channel sequence |
+-------+---------------------+
| 1| A->E->purchase |
| 2| D->A->C->no_purchase|
+-------+---------------------+
为清楚起见,访问者2
已展示给频道D
,然后A
,然后C
;他没有购买。
因此,序列将形成为D->A-C->no_purchase
。
注意:购买时,频道值为blank
,purchase_flag
设为1.
我想在Spark中使用Scala UDF
来执行此操作,以便在其他数据集上重新应用该方法。
答案 0 :(得分:2)
以下是使用udf
函数
val myDF = Seq(
(1,"A",100,0,0),
(1,"E",200,0,0),
(1,"",300,1,49),
(2,"A",200,0,0),
(2,"C",300,0,0),
(2,"D",100,0,0)
).toDF("visitor","channel","timestamp","purchase_flag","amount")
import org.apache.spark.sql.functions._
def sequenceUdf = udf((struct: Seq[Row], purchased: Seq[Int])=> struct.map(row => (row.getAs[String]("channel"), row.getAs[Int]("timestamp"))).sortBy(_._2).map(_._1).filterNot(_ == "").mkString("->")+{if(purchased.contains(1)) "->purchase" else "->no_purchase"})
myDF.groupBy("visitor").agg(collect_list(struct("channel", "timestamp")).as("struct"), collect_list("purchase_flag").as("purchased"))
.select(col("visitor"), sequenceUdf(col("struct"), col("purchased")).as("channel sequence"))
.show(false)
应该给你
+-------+--------------------+
|visitor|channel sequence |
+-------+--------------------+
|1 |A->E->purchase |
|2 |D->A->C->no_purchase|
+-------+--------------------+
你可以尽可能多地使用它。这只是一个关于你应该如何进行的演示