解决方案：

val targetTableSchemaArray = spark.catalog.listColumns(dbName, tableName).
      select("name", "dataType", "isPartition", "isBucket").
      rdd.map(catalog => {
      val name = catalog.getAs("name").toString
      val typeName = catalog.getAs("dataType").toString
      val isPartition = catalog.getAs("isPartition").toString.toBoolean
      val isBucket = catalog.getAs("isBucket").toString.toBoolean

      (name, typeName, isPartition, isBucket)
    }).collect()

    targetTableSchemaArray.foreach(x => {
      val name = x._1
      val typeName = x._2
      //判读字段名称和字段类型是否和目标表一致，如果不一致，抛出异常
      val sourcetypeName = sourceSchemaMap.getOrElse(name, "None")

      sourcetypeName match {
        case "None" => throw new Exception(s"Source table not exist ${name} column")
        case typeName => println("yes")
        case _ => throw new Exception(s"Inconsistent table structure types ,details：spark -> ${name}:${sourcetypeName} \t hive -> ${name}:${typeName} ")
      }
    })