我想在sparksql表中转置多列
我发现这个解决方案只适用于两列，我想知道如何使用具有三列的zip函数 varA, varB and varC. ```
import org.apache.spark.sql.functions.{udf, explode}

val zip = udf((xs: Seq[Long], ys: Seq[Long]) => xs.zip(ys))

df.withColumn("vars", explode(zip($"varA", $"varB"))).select(
$"userId", $"someString",
$"vars._1".alias("varA"), $"vars._2".alias("varB")).show

这是我的Dataframe架构：

root |-- owningcustomerid: string (nullable = true) |-- event_stoptime: string (nullable = true) |-- balancename: string (nullable = false) |-- chargedvalue: string (nullable = false) |-- newbalance: string (nullable = false)

我试过这个代码：

val zip = udf((xs: Seq[String], ys: Seq[String], zs: Seq[String]) => (xs, ys, zs).zipped.toSeq)

df.printSchema

val df4=df.withColumn("vars", explode(zip($"balancename", $"chargedvalue",$"newbalance"))).select(
$"owningcustomerid", $"event_stoptime",
$"vars._1".alias("balancename"), $"vars._2".alias("chargedvalue"),$"vars._2".alias("newbalance"))

我有个错误：

cannot resolve 'UDF(balancename, chargedvalue, newbalance)' due to data type mismatch: argument 1 requires array type, however, 'balancename' is of string type. argument 2 requires array type, however, 'chargedvalue' is of string type. argument 3 requires array type, however, 'newbalance' is of string type.;;

'项目[owningcustomerid#1085，事件ŧ停止时间ŧ1086，余额名称ŧ1159，chargedvalueŧ1160，newbalanceŧ1161，分解（udf（balancenameŧ1159，chargedvalueŧ1160，newbalanceŧ1161））作为变量ŧ1167]