如果我是你的话，我会在提取查询中做一些更改，或者让bi团队做一些努力：p在提取时动态添加和强制转换字段，但是你所要求的任何方式都是可能的。
您可以从现有列中添加列，如下所示。检查
addColsTosampleDF dataframe . 我希望下面的评论将足以理解，如果你有任何问题，请随时添加在评论，我会编辑我的答案。

scala> import org.apache.spark.sql.functions._
import org.apache.spark.sql.functions._

scala> import org.apache.spark.sql.{DataFrame, Row, SparkSession}
import org.apache.spark.sql.{DataFrame, Row, SparkSession}

scala> val ss = SparkSession.builder().appName("TEST").getOrCreate()
18/08/07 15:51:42 WARN SparkSession$Builder: Using an existing SparkSession; some configuration may not take effect.
ss: org.apache.spark.sql.SparkSession = org.apache.spark.sql.SparkSession@6de4071b

//Sample dataframe with int, double and string fields
scala> val sampleDf = Seq((100, 1.0, "row1"),(1,10.12,"col_float")).toDF("col1", "col2", "col3")
sampleDf: org.apache.spark.sql.DataFrame = [col1: int, col2: double ... 1 more field]

scala> sampleDf.printSchema
root
 |-- col1: integer (nullable = false)
 |-- col2: double (nullable = false)
 |-- col3: string (nullable = true)

//Adding columns col1_string from col1 and col2_doubletostring from col2 with casting and alias
scala> val addColsTosampleDF = sampleDf.
select(sampleDf.col("col1"),
sampleDf.col("col2"),
sampleDf.col("col3"),
sampleDf.col("col1").cast("string").alias("col1_string"),
sampleDf.col("col2").cast("string").alias("col2_doubletostring"))
addColsTosampleDF: org.apache.spark.sql.DataFrame = [col1: int, col2: double ... 3 more fields]

//Schema with added columns
scala> addColsTosampleDF.printSchema
root
 |-- col1: integer (nullable = false)
 |-- col2: double (nullable = false)
 |-- col3: string (nullable = true)
 |-- col1_string: string (nullable = false)
 |-- col2_doubletostring: string (nullable = false)

 scala> addColsTosampleDF.show()
+----+-----+---------+-----------+-------------------+
|col1| col2|     col3|col1_string|col2_doubletostring|
+----+-----+---------+-----------+-------------------+
| 100|  1.0|     row1|        100|                1.0|
|   1|10.12|col_float|          1|              10.12|
+----+-----+---------+-----------+-------------------+

你可以这样做 query 从所有 columns 就像下面一样

import org.apache.spark.sql.types.StringType

//Input: 

scala> df.show
+----+-----+--------+--------+
|  id| name|  salary|   bonus|
+----+-----+--------+--------+
|1001|Alice| 8000.25|1233.385|
|1002|  Bob|7526.365| 1856.69|
+----+-----+--------+--------+

scala> df.printSchema
root
 |-- id: integer (nullable = false)
 |-- name: string (nullable = true)
 |-- salary: double (nullable = false)
 |-- bonus: double (nullable = false)

//solution approach:
val query=df.columns.toList.map(cl=>if(cl=="salary" || cl=="bonus") col(cl).cast(StringType).as(cl+"_text") else col(cl))

//Output: 

scala> df.select(query:_*).printSchema
root
 |-- id: integer (nullable = false)
 |-- name: string (nullable = true)
 |-- salary_text: string (nullable = false)
 |-- bonus_text: string (nullable = false)

scala> df.select(query:_*).show
+----+-----+-----------+----------+
|  id| name|salary_text|bonus_text|
+----+-----+-----------+----------+
|1001|Alice|    8000.25|  1233.385|
|1002|  Bob|   7526.365|   1856.69|
+----+-----+-----------+----------+