我正在尝试以以下方式将数据从table:system\u releases从greenplum移动到hive：

val yearDF = spark.read.format("jdbc").option("url", "urltemplate;MaxNumericScale=30;MaxNumericPrecision=40;")
                                      .option("dbtable", s"(${execQuery}) as year2016")
                                      .option("user", "user")
                                      .option("password", "pwd")
                                      .option("partitionColumn","release_number")
                                      .option("lowerBound", 306)
                                      .option("upperBound", 500)
                                      .option("numPartitions",2)
                                      .load()

spark推断的Dataframeyeardf架构：

description:string
status_date:timestamp
time_zone:string
table_refresh_delay_min:decimal(38,30)
online_patching_enabled_flag:string
release_number:decimal(38,30)
change_number:decimal(38,30)
interface_queue_enabled_flag:string
rework_enabled_flag:string
smart_transfer_enabled_flag:string
patch_number:decimal(38,30)
threading_enabled_flag:string
drm_gl_source_name:string
reverted_flag:string
table_refresh_delay_min_text:string
release_number_text:string
change_number_text:string

我在配置单元上有相同的表，具有以下数据类型：

val hiveCols=string,status_date:timestamp,time_zone:string,table_refresh_delay_min:double,online_patching_enabled_flag:string,release_number:double,change_number:double,interface_queue_enabled_flag:string,rework_enabled_flag:string,smart_transfer_enabled_flag:string,patch_number:double,threading_enabled_flag:string,drm_gl_source_name:string,reverted_flag:string,table_refresh_delay_min_text:string,release_number_text:string,change_number_text:string

列： table_refresh_delay_min, release_number, change_number and patch_number 即使gp中的小数点不多，但给出的小数点也太多。所以我试着把它保存为csv文件，看看spark是如何读取数据的。例如，gp上的最大版本号是：306.00，但在我保存的csv文件dataframe:yeardf中，值是306.000000000000000000。
我尝试采用配置单元表模式，并将其转换为structtype，以便将其应用于yeardf，如下所示。

def convertDatatype(datatype: String): DataType = {
  val convert = datatype match {
    case "string"     => StringType
    case "bigint"     => LongType
    case "int"        => IntegerType
    case "double"     => DoubleType
    case "date"       => TimestampType
    case "boolean"    => BooleanType
    case "timestamp"  => TimestampType
  }
  convert
}

val schemaList        = hiveCols.split(",")
val schemaStructType  = new StructType(schemaList.map(col => col.split(":")).map(e => StructField(e(0), convertDatatype(e(1)), true)))
val newDF = spark.createDataFrame(yearDF.rdd, schemaStructType)
newDF.write.format("csv").save("hdfs/location")

但我得到了一个错误：

Caused by: java.lang.RuntimeException: java.math.BigDecimal is not a valid external type for schema of double
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.evalIfFalseExpr8$(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply_2$(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
    at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:287)
    ... 17 more

我试图以下面的方式将十进制列转换为doubletype，但仍然面临相同的异常。

val pattern = """DecimalType\(\d+,(\d+)\)""".r
  val df2 = dataDF.dtypes.
    collect{ case (dn, dt) if pattern.findFirstMatchIn(dt).map(_.group(1)).getOrElse("0") != "0" => dn }.
    foldLeft(dataDF)((accDF, c) => accDF.withColumn(c, col(c).cast("Double")))

   Caused by: java.lang.RuntimeException: java.math.BigDecimal is not a valid external type for schema of double
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.evalIfFalseExpr8$(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply_2$(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
    at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:287)
    ... 17 more

在尝试了以上两种方法之后，我已经没有想法了。有人能告诉我如何将Dataframe的列正确地转换为所需的数据类型吗？