可以在dataframe中创建包含要运行的查询的新列。然后您可以选择查询列并将其转换为数组并在其上循环以获得最终的Dataframe，然后对其执行任何操作，如将其保存为表、Parquet文件、csv文件等。如果要单独保存每个表数据，则必须在下面的for循环中编写代码才能完成此操作。

//source data
val df = Seq(("Schemaname","Table1","Column_A"),("Schemaname","Table2","Column_A"),("Schemaname","Table3","Column_A"),("Schemaname","Table4","Column_A"),("Schemaname","Table5","Column_A"),("Schemaname","Table6","Column_A"))
.toDF("country_table_schema","country_table_name","country_table_column_name")
//add a column for the query that gets generated for each row
import org.apache.spark.sql.functions._
 val df1 = df.withColumn("fulltableName",concat_ws(".",$"country_table_schema",$"country_table_name"))
.withColumn("Query",concat_ws("",concat(lit("("),lit(" Select * from ")) , $"fulltableName" , lit("where column_name = "), concat($"country_table_column_name", lit(") a"))))
.drop("fulltableName")
import org.apache.spark.sql.DataFrame
//convert it to array. I am using collect here but if you have large volume don't use collect otherwise it would crash your driver.
val queryArray = df1.select("Query").rdd.collect()
val rowscount = queryArray.length
//create an array of dataframe to hold the details of each query output.
var QueryArrayDF: Array[DataFrame] = new Array[DataFrame](rowscount)
//loop through the query and create dataframe and add that to the array of dataframe
for(i <- 0 to rowscount - 1){
       val df = (spark.read
                  .format("jdbc")
                  .option("url", url)
                  .option("ssl","true")
                  .option("sslfactory",sslfactory)
                  .option("user", user)
                  .option("password", pw)
                  .option("driver", driver)
                  .option("query",queryArray(i).toString().replace("[","").replace("]",""))
                  .load())
      QueryArrayDF(i) = df
    }
// now let's combine the dataframes, if we have more than one
        var CombinedDF = QueryArrayDF(0)
        for (i <- 1 to QueryArrayDF.length - 1) {
          CombinedDF = CombinedDF.union(QueryArrayDF(i))
        }

现在您可以根据需要保存组合的Dataframe。

我对代码做了一些调整（基本上结合了我之前使用的代码和@nikunkakadiya共享的代码中的几行），它对我很有用。共享代码以供参考-

val sql="select inf_schema.table_name, inf_schema.table_schema, country_table_column_name from information_schema.tables as inf_schema left join (SELECT table_schema as country_table_schema ,table_name as country_table_name, column_name as country_table_column_name FROM information_schema.columns WHERE table_schema = '<Schema>' AND columns.column_name = 'Column_A') as country on inf_schema.table_schema = country.country_table_schema and inf_schema.table_name = country.country_table_name WHERE inf_schema.table_schema='<Schema>'"
var dfTbl = (spark.read
      .format("jdbc")
      .option("url", url)
      .option("ssl","true")
      .option("sslfactory",sslfactory)
      .option("user", user)
      .option("password", pw)
      .option("driver", driver)
      .option("query",sql)
      .load())
val df = dfTbl.select(dfTbl.col("table_name")).where(dfTbl.col("country_table_column_name").isNotNull)
println(df)
import org.apache.spark.sql.DataFrame
val df2 = (df.select("table_name").collect())
val rows = df2.length
for (i <- 0 to rows - 1){
      println(df2(i).mkString(","))     
      val sql2 = "select * from <Schema>."+df2(i).mkString(",") + " where Column_A = '<Column_Value>'"
  println(sql2)
  var df_f = (spark.read
                .format("jdbc")
                .option("url", url)
                .option("ssl","true")
                .option("sslfactory",sslfactory)
                .option("user", user)
                .option("password", pw)
                .option("driver", driver)
                .option("query",sql2)
                .load())
   var File_withCountry = df_f
        .coalesce(1)
        .write
        .format("com.databricks.spark.csv")
        .option("header","true")
        .option("delimiter", "~")
        .mode(SaveMode.Overwrite)
        .option("encoding", "UTF-8")
        .csv("wasbs://<Container>@<StorageAccount>.blob.core.windows.net/<TargetDirectory>/"+ df2(i).mkString(",") )
   val partition_path = dbutils.fs.ls("wasbs://<Container>@<StorageAccount>.blob.core.windows.net/<TargetDirectory>/"+ df2(i).mkString(",")).filter(file=>file.name.startsWith("part"))(0).path
dbutils.fs.cp(partition_path,"wasbs://<Container>@<StorageAccount>.blob.core.windows.net/<TargetDirectory>/"+ df2(i).mkString(",")+".csv")
dbutils.fs.rm (partition_path, recurse = true)
}

如果有什么问题，请告诉我。
谢谢你们的支持。真的很感激。干杯！