private def asScalaValue()

in spark-doris-connector/spark-doris-connector-base/src/main/scala/org/apache/doris/spark/util/RowConvertors.scala [67:120]
54 lines of code
24 McCabe index (conditional complexity)

  private def asScalaValue(row: SpecializedGetters, dataType: DataType, ordinal: Int): Any = {
    if (row.isNullAt(ordinal)) null
    else {
      dataType match {
        case NullType => NULL_VALUE
        case BooleanType => row.getBoolean(ordinal)
        case ByteType => row.getByte(ordinal)
        case ShortType => row.getShort(ordinal)
        case IntegerType => row.getInt(ordinal)
        case LongType => row.getLong(ordinal)
        case FloatType => row.getFloat(ordinal)
        case DoubleType => row.getDouble(ordinal)
        case StringType => Option(row.getUTF8String(ordinal)).map(_.toString).getOrElse(NULL_VALUE)
        case TimestampType =>
          DateTimeUtils.toJavaTimestamp(row.getLong(ordinal)).toString
        case DateType => DateTimeUtils.toJavaDate(row.getInt(ordinal)).toString
        case BinaryType => row.getBinary(ordinal)
        case dt: DecimalType => row.getDecimal(ordinal, dt.precision, dt.scale).toJavaBigDecimal
        case at: ArrayType =>
          val arrayData = row.getArray(ordinal)
          if (arrayData == null) NULL_VALUE
          else {
            (0 until arrayData.numElements()).map(i => {
              if (arrayData.isNullAt(i)) null else asScalaValue(arrayData, at.elementType, i)
            }).mkString("[", ",", "]")
          }
        case mt: MapType =>
          val mapData = row.getMap(ordinal)
          if (mapData.numElements() == 0) "{}"
          else {
            val keys = mapData.keyArray()
            val values = mapData.valueArray()
            val map = mutable.HashMap[Any, Any]()
            var i = 0
            while (i < keys.numElements()) {
              map += asScalaValue(keys, mt.keyType, i) -> asScalaValue(values, mt.valueType, i)
              i += 1
            }
            MAPPER.writeValueAsString(map)
          }
        case st: StructType =>
          val structData = row.getStruct(ordinal, st.length)
          val map = new java.util.TreeMap[String, Any]()
          var i = 0
          while (i < structData.numFields) {
            val field = st.fields(i)
            map.put(field.name, asScalaValue(structData, field.dataType, i))
            i += 1
          }
          MAPPER.writeValueAsString(map)
        case _ => throw new Exception(s"Unsupported spark type: ${dataType.typeName}")
      }
    }
  }