Java Code Examples for org.apache.spark.sql.types.DataTypes#FloatType

private DataType getDataType(int type) {
    switch (type) {
        case LiteralType.BOOLEAN:
            return DataTypes.BooleanType;
        case LiteralType.STRING:
            return DataTypes.StringType;
        case LiteralType.FLOAT:
            return DataTypes.FloatType;
        case LiteralType.DOUBLE:
            return DataTypes.DoubleType;
        case LiteralType.INTEGER:
            return DataTypes.IntegerType;
        case LiteralType.LONG:
            return DataTypes.LongType;
        case LiteralType.DATETIME:
            // datetime not supported due to timezone issues with java.sql.Timestamp
            // check the InstanceAggregator for more info
            return DataTypes.StringType;
    }
    throw new NotImplementedException("Not able to write literal type " + type);
}
 

/**
 * Convert a datavec schema to a
 * struct type in spark
 *
 * @param schema the schema to convert
 * @return the datavec struct type
 */
public static StructType fromSchema(Schema schema) {
    StructField[] structFields = new StructField[schema.numColumns()];
    for (int i = 0; i < structFields.length; i++) {
        switch (schema.getColumnTypes().get(i)) {
            case Double:
                structFields[i] = new StructField(schema.getName(i), DataTypes.DoubleType, false, Metadata.empty());
                break;
            case Integer:
                structFields[i] =
                                new StructField(schema.getName(i), DataTypes.IntegerType, false, Metadata.empty());
                break;
            case Long:
                structFields[i] = new StructField(schema.getName(i), DataTypes.LongType, false, Metadata.empty());
                break;
            case Float:
                structFields[i] = new StructField(schema.getName(i), DataTypes.FloatType, false, Metadata.empty());
                break;
            default:
                throw new IllegalStateException(
                                "This api should not be used with strings , binary data or ndarrays. This is only for columnar data");
        }
    }
    return new StructType(structFields);
}
 

/**
 * Convert a datavec schema to a
 * struct type in spark
 *
 * @param schema the schema to convert
 * @return the datavec struct type
 */
public static StructType fromSchema(Schema schema) {
    StructField[] structFields = new StructField[schema.numColumns()];
    for (int i = 0; i < structFields.length; i++) {
        switch (schema.getColumnTypes().get(i)) {
            case Double:
                structFields[i] = new StructField(schema.getName(i), DataTypes.DoubleType, false, Metadata.empty());
                break;
            case Integer:
                structFields[i] =
                                new StructField(schema.getName(i), DataTypes.IntegerType, false, Metadata.empty());
                break;
            case Long:
                structFields[i] = new StructField(schema.getName(i), DataTypes.LongType, false, Metadata.empty());
                break;
            case Float:
                structFields[i] = new StructField(schema.getName(i), DataTypes.FloatType, false, Metadata.empty());
                break;
            default:
                throw new IllegalStateException(
                                "This api should not be used with strings , binary data or ndarrays. This is only for columnar data");
        }
    }
    return new StructType(structFields);
}
 

private static AttributeDescriptor attrDescFromStructField(
    final AttributeTypeBuilder attrBuilder,
    final StructField field) {
  if (field.name().equals("geom")) {
    return attrBuilder.binding(Geometry.class).nillable(false).buildDescriptor("geom");
  }
  if (field.dataType() == DataTypes.StringType) {
    return attrBuilder.binding(String.class).buildDescriptor(field.name());
  } else if (field.dataType() == DataTypes.DoubleType) {
    return attrBuilder.binding(Double.class).buildDescriptor(field.name());
  } else if (field.dataType() == DataTypes.FloatType) {
    return attrBuilder.binding(Float.class).buildDescriptor(field.name());
  } else if (field.dataType() == DataTypes.LongType) {
    return attrBuilder.binding(Long.class).buildDescriptor(field.name());
  } else if (field.dataType() == DataTypes.IntegerType) {
    return attrBuilder.binding(Integer.class).buildDescriptor(field.name());
  } else if (field.dataType() == DataTypes.BooleanType) {
    return attrBuilder.binding(Boolean.class).buildDescriptor(field.name());
  } else if (field.dataType() == DataTypes.TimestampType) {
    return attrBuilder.binding(Date.class).buildDescriptor(field.name());
  }

  return null;
}
 

static
public DataType translateDataType(org.dmg.pmml.DataType dataType){

	switch(dataType){
		case STRING:
			return DataTypes.StringType;
		case INTEGER:
			return DataTypes.IntegerType;
		case FLOAT:
			return DataTypes.FloatType;
		case DOUBLE:
			return DataTypes.DoubleType;
		case BOOLEAN:
			return DataTypes.BooleanType;
		default:
			throw new IllegalArgumentException();
	}
}
 

@Test
public void testRangeDataTypes() throws Exception {
  Config config = ConfigUtils.configFromResource("/dq/dq-range-rules.conf").getConfig("steps");
  StructType schema = new StructType(new StructField[] {
    new StructField("fa", DataTypes.LongType, false, Metadata.empty()),
    new StructField("fi", DataTypes.IntegerType, false, Metadata.empty()),
    new StructField("fl", DataTypes.LongType, false, Metadata.empty()),
    new StructField("ff", DataTypes.FloatType, false, Metadata.empty()),
    new StructField("fe", DataTypes.DoubleType, false, Metadata.empty()),
    new StructField("fd", DataTypes.createDecimalType(), false, Metadata.empty())
  });
  Row row = new RowWithSchema(schema, new Long(2), 2, new Long(2), new Float(2.0), 2.0, new BigDecimal("2.0"));
    
  ConfigObject rro =  config.getObject("dq1.deriver.rules") ;
  for ( String rulename : rro.keySet() ) {
    Config rrc = rro.toConfig().getConfig(rulename);
    RangeRowRule rrr = new RangeRowRule() ;
    rrr.configure(rrc);
    rrr.configureName(rulename);
    assertTrue("Row should pass rule " + rulename, rrr.check(row));
  }
}
 

@Test
public void testAgeRangeFloat() {
  StructType schema = new StructType(new StructField[] {
      new StructField("name", DataTypes.StringType, false, Metadata.empty()),
      new StructField("nickname", DataTypes.StringType, false, Metadata.empty()),
      new StructField("age", DataTypes.FloatType, false, Metadata.empty()),
      new StructField("candycrushscore", DataTypes.createDecimalType(), false, Metadata.empty())
  });

  Map<String, Object> configMap = new HashMap<>();
  configMap.put(RangeRowRule.FIELDS_CONFIG, Lists.newArrayList("age"));
  configMap.put(RangeRowRule.FIELD_TYPE_CONFIG, "float");
  configMap.put(RangeRowRule.RANGE_CONFIG, Lists.newArrayList(0.1,105.0));
  Config config = ConfigFactory.parseMap(configMap);

  RangeRowRule rule = new RangeRowRule();
  assertNoValidationFailures(rule, config);
  rule.configure(config);
  rule.configureName("agerange");

  Row row1 = new RowWithSchema(schema, "Ian", "Ian", 34.0f, new BigDecimal("0.00"));
  assertTrue("Row should pass rule", rule.check(row1));

  Row row2 = new RowWithSchema(schema, "Webster1", "Websta1", 110.0f, new BigDecimal("450.10"));
  assertFalse("Row should not pass rule", rule.check(row2));

  Row row3 = new RowWithSchema(schema, "", "Ian1", 110.0f, new BigDecimal("450.10"));
  assertFalse("Row should not pass rule", rule.check(row3));

  Row row4 = new RowWithSchema(schema, "First Last", "Ian Last", 100.0f, new BigDecimal("450.10"));
  assertTrue("Row should pass rule", rule.check(row4));
}
 

@Test
public void setPartitionValueTest() {
    FloatColumnBlock longColumnBlock = new FloatColumnBlock(null, DataTypes.FloatType);
    longColumnBlock.setPartitionValue("45.23",1000);
    for (int i = 0; i< 1000; i++) {
        Assert.assertEquals(45.23f,longColumnBlock.getTestObject(i));
    }
}
 

/**
 * Convert the DataVec sequence schema to a StructType for Spark, for example for use in
 * {@link #toDataFrameSequence(Schema, JavaRDD)}}
 * <b>Note</b>: as per {@link #toDataFrameSequence(Schema, JavaRDD)}}, the StructType has two additional columns added to it:<br>
 * - Column 0: Sequence UUID (name: {@link #SEQUENCE_UUID_COLUMN}) - a UUID for the original sequence<br>
 * - Column 1: Sequence index (name: {@link #SEQUENCE_INDEX_COLUMN} - an index (integer, starting at 0) for the position
 * of this record in the original time series.<br>
 * These two columns are required if the data is to be converted back into a sequence at a later point, for example
 * using {@link #toRecordsSequence(Dataset<Row>)}
 *
 * @param schema Schema to convert
 * @return StructType for the schema
 */
public static StructType fromSchemaSequence(Schema schema) {
    StructField[] structFields = new StructField[schema.numColumns() + 2];

    structFields[0] = new StructField(SEQUENCE_UUID_COLUMN, DataTypes.StringType, false, Metadata.empty());
    structFields[1] = new StructField(SEQUENCE_INDEX_COLUMN, DataTypes.IntegerType, false, Metadata.empty());

    for (int i = 0; i < schema.numColumns(); i++) {
        switch (schema.getColumnTypes().get(i)) {
            case Double:
                structFields[i + 2] =
                                new StructField(schema.getName(i), DataTypes.DoubleType, false, Metadata.empty());
                break;
            case Integer:
                structFields[i + 2] =
                                new StructField(schema.getName(i), DataTypes.IntegerType, false, Metadata.empty());
                break;
            case Long:
                structFields[i + 2] =
                                new StructField(schema.getName(i), DataTypes.LongType, false, Metadata.empty());
                break;
            case Float:
                structFields[i + 2] =
                                new StructField(schema.getName(i), DataTypes.FloatType, false, Metadata.empty());
                break;
            default:
                throw new IllegalStateException(
                                "This api should not be used with strings , binary data or ndarrays. This is only for columnar data");
        }
    }
    return new StructType(structFields);
}
 

private static SimpleFeatureDataType attrDescToDataType(final AttributeDescriptor attrDesc) {
  boolean isGeom = false;
  DataType dataTypeOut = DataTypes.NullType;

  if (attrDesc.getType().getBinding().equals(String.class)) {

    dataTypeOut = DataTypes.StringType;
  } else if (attrDesc.getType().getBinding().equals(Double.class)) {
    dataTypeOut = DataTypes.DoubleType;
  } else if (attrDesc.getType().getBinding().equals(Float.class)) {
    dataTypeOut = DataTypes.FloatType;
  } else if (attrDesc.getType().getBinding().equals(Long.class)) {
    dataTypeOut = DataTypes.LongType;
  } else if (attrDesc.getType().getBinding().equals(Integer.class)) {
    dataTypeOut = DataTypes.IntegerType;
  } else if (attrDesc.getType().getBinding().equals(Boolean.class)) {
    dataTypeOut = DataTypes.BooleanType;
  } else if (attrDesc.getType().getBinding().equals(Date.class)) {
    dataTypeOut = DataTypes.TimestampType;
  }

  // Custom geometry types get WKB encoding
  else if (Geometry.class.isAssignableFrom(attrDesc.getType().getBinding())) {
    dataTypeOut = GeoWaveSpatialEncoders.geometryUDT;
    isGeom = true;
  }

  return new SimpleFeatureDataType(dataTypeOut, isGeom);
}
 

public static List<StructField> indexrSchemaToSparkSchema(SegmentSchema schema) {
    List<StructField> fields = new ArrayList<>();
    for (ColumnSchema cs : schema.getColumns()) {
        DataType dataType;
        switch (cs.getSqlType()) {
            case INT:
                dataType = DataTypes.IntegerType;
                break;
            case BIGINT:
                dataType = DataTypes.LongType;
                break;
            case FLOAT:
                dataType = DataTypes.FloatType;
                break;
            case DOUBLE:
                dataType = DataTypes.DoubleType;
                break;
            case VARCHAR:
                dataType = DataTypes.StringType;
                break;
            case DATE:
                dataType = DataTypes.DateType;
                break;
            case DATETIME:
                dataType = DataTypes.TimestampType;
                break;
            default:
                throw new IllegalStateException("Unsupported type: " + cs.getSqlType());
        }
        fields.add(new StructField(cs.getName(), dataType, scala.Boolean.box(false), Metadata.empty()));
    }
    return fields;
}
 

/**
 * Convert the DataVec sequence schema to a StructType for Spark, for example for use in
 * {@link #toDataFrameSequence(Schema, JavaRDD)}}
 * <b>Note</b>: as per {@link #toDataFrameSequence(Schema, JavaRDD)}}, the StructType has two additional columns added to it:<br>
 * - Column 0: Sequence UUID (name: {@link #SEQUENCE_UUID_COLUMN}) - a UUID for the original sequence<br>
 * - Column 1: Sequence index (name: {@link #SEQUENCE_INDEX_COLUMN} - an index (integer, starting at 0) for the position
 * of this record in the original time series.<br>
 * These two columns are required if the data is to be converted back into a sequence at a later point, for example
 * using {@link #toRecordsSequence(DataRowsFacade)}
 *
 * @param schema Schema to convert
 * @return StructType for the schema
 */
public static StructType fromSchemaSequence(Schema schema) {
    StructField[] structFields = new StructField[schema.numColumns() + 2];

    structFields[0] = new StructField(SEQUENCE_UUID_COLUMN, DataTypes.StringType, false, Metadata.empty());
    structFields[1] = new StructField(SEQUENCE_INDEX_COLUMN, DataTypes.IntegerType, false, Metadata.empty());

    for (int i = 0; i < schema.numColumns(); i++) {
        switch (schema.getColumnTypes().get(i)) {
            case Double:
                structFields[i + 2] =
                                new StructField(schema.getName(i), DataTypes.DoubleType, false, Metadata.empty());
                break;
            case Integer:
                structFields[i + 2] =
                                new StructField(schema.getName(i), DataTypes.IntegerType, false, Metadata.empty());
                break;
            case Long:
                structFields[i + 2] =
                                new StructField(schema.getName(i), DataTypes.LongType, false, Metadata.empty());
                break;
            case Float:
                structFields[i + 2] =
                                new StructField(schema.getName(i), DataTypes.FloatType, false, Metadata.empty());
                break;
            default:
                throw new IllegalStateException(
                                "This api should not be used with strings , binary data or ndarrays. This is only for columnar data");
        }
    }
    return new StructType(structFields);
}
 

private DataType sparkFromArrow(FieldType fieldType) {
  switch (fieldType.getType().getTypeID()) {
    case Null:
      return DataTypes.NullType;
    case Struct:
      throw new UnsupportedOperationException("have not implemented Struct type yet");
    case List:
      throw new UnsupportedOperationException("have not implemented List type yet");
    case FixedSizeList:
      throw new UnsupportedOperationException("have not implemented FixedSizeList type yet");
    case Union:
      throw new UnsupportedOperationException("have not implemented Union type yet");
    case Int:
      ArrowType.Int intType = (ArrowType.Int) fieldType.getType();
      int bitWidth = intType.getBitWidth();
      if (bitWidth == 8) {
        return DataTypes.ByteType;
      } else if (bitWidth == 16) {
        return DataTypes.ShortType;
      } else if (bitWidth == 32) {
        return DataTypes.IntegerType;
      } else if (bitWidth == 64) {
        return DataTypes.LongType;
      }
      throw new UnsupportedOperationException("unknown int type with bitwidth " + bitWidth);
    case FloatingPoint:
      ArrowType.FloatingPoint floatType = (ArrowType.FloatingPoint) fieldType.getType();
      FloatingPointPrecision precision = floatType.getPrecision();
      switch (precision) {
        case HALF:
        case SINGLE:
          return DataTypes.FloatType;
        case DOUBLE:
          return DataTypes.DoubleType;
      }
    case Utf8:
      return DataTypes.StringType;
    case Binary:
    case FixedSizeBinary:
      return DataTypes.BinaryType;
    case Bool:
      return DataTypes.BooleanType;
    case Decimal:
      throw new UnsupportedOperationException("have not implemented Decimal type yet");
    case Date:
      return DataTypes.DateType;
    case Time:
      return DataTypes.TimestampType; //note i don't know what this will do!
    case Timestamp:
      return DataTypes.TimestampType;
    case Interval:
      return DataTypes.CalendarIntervalType;
    case NONE:
      return DataTypes.NullType;
  }
  throw new IllegalStateException("Unexpected value: " + fieldType);
}
 

public static DataType getSparkDataType(String typeString) {
  DataType type;

  String prec_scale_regex_groups = "\\s*(decimal)\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*";
  Pattern prec_scale_regex_pattern = Pattern.compile(prec_scale_regex_groups);
  Matcher prec_scale_regex_matcher = prec_scale_regex_pattern.matcher(typeString);

  if (prec_scale_regex_matcher.matches()) {
    int precision = Integer.parseInt(prec_scale_regex_matcher.group(2)); 
    int scale = Integer.parseInt(prec_scale_regex_matcher.group(3)); 
    type = DataTypes.createDecimalType(precision, scale);
  }
  else {
    switch (typeString) {
      case DECIMAL:
        type = DataTypes.createDecimalType();
        break;
      case STRING:
        type = DataTypes.StringType;
        break;
      case FLOAT:
        type = DataTypes.FloatType;
        break;
      case DOUBLE:
        type = DataTypes.DoubleType;
        break;
      case BYTE:
        type = DataTypes.ByteType;
        break;
      case SHORT:
        type = DataTypes.ShortType;
        break;
      case INT:
        type = DataTypes.IntegerType;
        break;
      case LONG:
        type = DataTypes.LongType;
        break;
      case BOOLEAN:
        type = DataTypes.BooleanType;
        break;
      case BINARY:
        type = DataTypes.BinaryType;
        break;
      case DATE:
        type = DataTypes.DateType;
        break;
      case TIMESTAMP:
        type = DataTypes.TimestampType;
        break;
      default:
        throw new RuntimeException("Unsupported or unrecognized field type: " + typeString);
    } 
  }

  return type;
}
 

/**
 * Convert Avro Types into their associated DataType.
 *
 * @param schemaType Avro Schema.Type
 * @return DataType representation
 */
public static DataType dataTypeFor(Schema schemaType) {
  LOG.trace("Converting Schema[{}] to DataType", schemaType);

  // Unwrap "optional" unions to the base type
  boolean isOptional = isNullable(schemaType);

  if (isOptional) {
    // if only 2 items in the union, then "unwrap," otherwise, it's a full union and should be rendered as such
    if (schemaType.getTypes().size() == 2) {
      LOG.trace("Unwrapping simple 'optional' union for {}", schemaType);
      for (Schema s : schemaType.getTypes()) {
        if (s.getType().equals(NULL)) {
          continue;
        }
        // Unwrap
        schemaType = s;
        break;
      }
    }
  }

  // Convert supported LogicalTypes
  if (null != schemaType.getLogicalType()) {
    LogicalType logicalType = schemaType.getLogicalType();
    switch (logicalType.getName()) {
      case "date" :
        return DataTypes.DateType;
      case "timestamp-millis" :
        return DataTypes.TimestampType;
      case "decimal" :
        LogicalTypes.Decimal decimal = (LogicalTypes.Decimal) logicalType;
        return DataTypes.createDecimalType(decimal.getPrecision(), decimal.getScale());
      default:
        // Pass-thru
        LOG.warn("Unsupported LogicalType[{}], continuing with underlying base type", logicalType.getName());
    }
  }

  switch (schemaType.getType()) {
    case RECORD:
      // StructType
      List<StructField> structFieldList = Lists.newArrayListWithCapacity(schemaType.getFields().size());
      for (Field f : schemaType.getFields()) {
        structFieldList.add(DataTypes.createStructField(f.name(), dataTypeFor(f.schema()), isNullable(f.schema())));
      }
      return DataTypes.createStructType(structFieldList);
    case ARRAY:
      Schema elementType = schemaType.getElementType();
      return DataTypes.createArrayType(dataTypeFor(elementType), isNullable(elementType));
    case MAP:
      Schema valueType = schemaType.getValueType();
      return DataTypes.createMapType(DataTypes.StringType, dataTypeFor(valueType), isNullable(valueType));
    case UNION:
      // StructType of members
      List<StructField> unionFieldList = Lists.newArrayListWithCapacity(schemaType.getTypes().size());
      int m = 0;
      for (Schema u : schemaType.getTypes()) {
        unionFieldList.add(DataTypes.createStructField("member" + m++, dataTypeFor(u), isNullable(u)));
      }
      return DataTypes.createStructType(unionFieldList);
    case FIXED:
    case BYTES:
      return DataTypes.BinaryType;
    case ENUM:
    case STRING:
      return DataTypes.StringType;
    case INT:
      return DataTypes.IntegerType;
    case LONG:
      return DataTypes.LongType;
    case FLOAT:
      return DataTypes.FloatType;
    case DOUBLE:
      return DataTypes.DoubleType;
    case BOOLEAN:
      return DataTypes.BooleanType;
    case NULL:
      return DataTypes.NullType;
    default:
      throw new RuntimeException(String.format("Unrecognized or unsupported Avro Type conversion: %s", schemaType));
  }
}
 

private StructType schemaFor(KuduTable table) {
  List<StructField> fields = Lists.newArrayList();

  for (ColumnSchema columnSchema : table.getSchema().getColumns()) {
    DataType fieldType;

    switch (columnSchema.getType()) {
      case DOUBLE:
        fieldType = DataTypes.DoubleType;
        break;
      case FLOAT:
        fieldType = DataTypes.FloatType;
        break;
      case INT8:
        fieldType = DataTypes.ByteType;
        break;
      case INT16:
        fieldType = DataTypes.ShortType;
        break;
      case INT32:
        fieldType = DataTypes.IntegerType;
        break;
      case INT64:
        fieldType = DataTypes.LongType;
        break;
      case STRING:
        fieldType = DataTypes.StringType;
        break;
      case BOOL:
        fieldType = DataTypes.BooleanType;
        break;
      case BINARY:
        fieldType = DataTypes.BinaryType;
        break;
      case UNIXTIME_MICROS:
        fieldType = DataTypes.TimestampType;
        break;
      case DECIMAL:
        int precision = columnSchema.getTypeAttributes().getPrecision();
        int scale = columnSchema.getTypeAttributes().getScale();
        fieldType = DataTypes.createDecimalType(precision, scale);
        break;
      default:
        throw new RuntimeException("Unsupported Kudu column type: " + columnSchema.getType());
    }

    fields.add(DataTypes.createStructField(columnSchema.getName(), fieldType, true));
  }

  return DataTypes.createStructType(fields);
}
 

private static DataType getType(String type) {
    DataType dataType = DataTypes.NullType;
    switch (type.toLowerCase()) {
        case "string":
            dataType = DataTypes.StringType;
            break;
        case "integer":
            dataType = DataTypes.IntegerType;
            break;
        case "long":
            dataType = DataTypes.LongType;
            break;
        case "double":
            dataType = DataTypes.DoubleType;
            break;
        case "float":
            dataType = DataTypes.FloatType;
            break;
        case "short":
            dataType = DataTypes.ShortType;
            break;
        case "date":
            dataType = DataTypes.DateType;
            break;
        case "timestamp":
            dataType = DataTypes.TimestampType;
            break;
        case "boolean":
            dataType = DataTypes.BooleanType;
            break;
        case "binary":
            dataType = DataTypes.BinaryType;
            break;
        case "byte":
            dataType = DataTypes.ByteType;
            break;
        default:
            throw new ConfigRuntimeException("Throw data type exception, unknown type: " + type);
    }
    return dataType;
}
 

static DataType getSparkType(Type type)
{
    if (type instanceof ParameterizedType && ((ParameterizedType) type).getRawType() == Map.class) {
        Type[] arguments = ((ParameterizedType) type).getActualTypeArguments();

        return DataTypes.createMapType(getSparkType(arguments[0]), getSparkType(arguments[1]));
    }
    else if (type instanceof ParameterizedType && ((ParameterizedType) type).getRawType() == List.class) {
        DataType dataType = getSparkType(((ParameterizedType) type).getActualTypeArguments()[0]);

        return DataTypes.createArrayType(dataType);
    }
    else {
        if (type == String.class) {
            return DataTypes.StringType;
        }
        else if (type == int.class || type == Integer.class) {
            return DataTypes.IntegerType;
        }
        else if (type == long.class || type == Long.class) {
            return DataTypes.LongType;
        }
        else if (type == boolean.class || type == Boolean.class) {
            return DataTypes.BooleanType;
        }
        else if (type == double.class || type == Double.class) {
            return DataTypes.DoubleType;
        }
        else if (type == float.class || type == Float.class) {
            return DataTypes.FloatType;
        }
        else if (type == byte.class || type == Byte.class) {
            return DataTypes.ByteType;
        }
        else if (type == Timestamp.class) {
            return DataTypes.TimestampType;
        }
        else if (type == Date.class) {
            return DataTypes.DateType;
        }
        else if (type == byte[].class || type == Byte[].class) {
            return DataTypes.BinaryType;
        }
        else {
            throw new IllegalArgumentException("this TYPE " + type + " have't support!");
        }
    }
}