public static Schema convertToSchema()

in paimon-format/src/main/java/org/apache/paimon/format/avro/AvroSchemaConverter.java [69:199]
125 lines of code
43 McCabe index (conditional complexity)

    public static Schema convertToSchema(DataType dataType, String rowName) {
        int precision;
        boolean nullable = dataType.isNullable();
        switch (dataType.getTypeRoot()) {
            case BOOLEAN:
                Schema bool = SchemaBuilder.builder().booleanType();
                return nullable ? nullableSchema(bool) : bool;
            case TINYINT:
            case SMALLINT:
            case INTEGER:
                Schema integer = SchemaBuilder.builder().intType();
                return nullable ? nullableSchema(integer) : integer;
            case BIGINT:
                Schema bigint = SchemaBuilder.builder().longType();
                return nullable ? nullableSchema(bigint) : bigint;
            case FLOAT:
                Schema f = SchemaBuilder.builder().floatType();
                return nullable ? nullableSchema(f) : f;
            case DOUBLE:
                Schema d = SchemaBuilder.builder().doubleType();
                return nullable ? nullableSchema(d) : d;
            case CHAR:
            case VARCHAR:
                Schema str = SchemaBuilder.builder().stringType();
                return nullable ? nullableSchema(str) : str;
            case BINARY:
            case VARBINARY:
                Schema binary = SchemaBuilder.builder().bytesType();
                return nullable ? nullableSchema(binary) : binary;
            case TIMESTAMP_WITHOUT_TIME_ZONE:
                // use long to represents Timestamp
                final TimestampType timestampType = (TimestampType) dataType;
                precision = timestampType.getPrecision();
                org.apache.avro.LogicalType avroLogicalType;
                if (precision <= 3) {
                    avroLogicalType = LogicalTypes.timestampMillis();
                } else if (precision <= 6) {
                    avroLogicalType = LogicalTypes.timestampMicros();
                } else {
                    throw new IllegalArgumentException(
                            "Avro does not support TIMESTAMP type "
                                    + "with precision: "
                                    + precision
                                    + ", it only supports precision less than 6.");
                }
                Schema timestamp = avroLogicalType.addToSchema(SchemaBuilder.builder().longType());
                return nullable ? nullableSchema(timestamp) : timestamp;
            case TIMESTAMP_WITH_LOCAL_TIME_ZONE:
                final LocalZonedTimestampType localTimestampType =
                        (LocalZonedTimestampType) dataType;
                precision = localTimestampType.getPrecision();
                org.apache.avro.LogicalType localTimestampLogicalType;
                if (precision <= 3) {
                    localTimestampLogicalType = LogicalTypes.localTimestampMillis();
                } else if (precision <= 6) {
                    localTimestampLogicalType = LogicalTypes.localTimestampMicros();
                } else {
                    throw new IllegalArgumentException(
                            "Avro does not support TIMESTAMP type "
                                    + "with precision: "
                                    + precision
                                    + ", it only supports precision less than 6.");
                }
                Schema localTimestampSchema =
                        localTimestampLogicalType.addToSchema(SchemaBuilder.builder().longType());
                return nullable ? nullableSchema(localTimestampSchema) : localTimestampSchema;
            case DATE:
                // use int to represents Date
                Schema date = LogicalTypes.date().addToSchema(SchemaBuilder.builder().intType());
                return nullable ? nullableSchema(date) : date;
            case TIME_WITHOUT_TIME_ZONE:
                precision = ((TimeType) dataType).getPrecision();
                if (precision > 3) {
                    throw new IllegalArgumentException(
                            "Avro does not support TIME type with precision: "
                                    + precision
                                    + ", it only supports precision less than 3.");
                }
                // use int to represents Time, we only support millisecond when deserialization
                Schema time =
                        LogicalTypes.timeMillis().addToSchema(SchemaBuilder.builder().intType());
                return nullable ? nullableSchema(time) : time;
            case DECIMAL:
                DecimalType decimalType = (DecimalType) dataType;
                // store BigDecimal as byte[]
                Schema decimal =
                        LogicalTypes.decimal(decimalType.getPrecision(), decimalType.getScale())
                                .addToSchema(SchemaBuilder.builder().bytesType());
                return nullable ? nullableSchema(decimal) : decimal;
            case ROW:
                RowType rowType = (RowType) dataType;
                List<String> fieldNames = rowType.getFieldNames();
                // we have to make sure the record name is different in a Schema
                SchemaBuilder.FieldAssembler<Schema> builder =
                        SchemaBuilder.builder().record(rowName).fields();
                for (int i = 0; i < rowType.getFieldCount(); i++) {
                    String fieldName = fieldNames.get(i);
                    DataType fieldType = rowType.getTypeAt(i);
                    SchemaBuilder.GenericDefault<Schema> fieldBuilder =
                            builder.name(fieldName)
                                    .type(convertToSchema(fieldType, rowName + "_" + fieldName));

                    if (fieldType.isNullable()) {
                        builder = fieldBuilder.withDefault(null);
                    } else {
                        builder = fieldBuilder.noDefault();
                    }
                }
                Schema record = builder.endRecord();
                return nullable ? nullableSchema(record) : record;
            case MULTISET:
            case MAP:
                Schema map =
                        SchemaBuilder.builder()
                                .map()
                                .values(
                                        convertToSchema(
                                                extractValueTypeToAvroMap(dataType), rowName));
                return nullable ? nullableSchema(map) : map;
            case ARRAY:
                ArrayType arrayType = (ArrayType) dataType;
                Schema array =
                        SchemaBuilder.builder()
                                .array()
                                .items(convertToSchema(arrayType.getElementType(), rowName));
                return nullable ? nullableSchema(array) : array;
            default:
                throw new UnsupportedOperationException(
                        "Unsupported to derive Schema for type: " + dataType);
        }
    }