org.apache.flink.api.common.serialization.DeserializationSchema Java Examples

@Override
public StreamTableSource<Row> createStreamTableSource(Map<String, String> properties) {
	final DescriptorProperties descriptorProperties = getValidatedProperties(properties);

	final String topic = descriptorProperties.getString(CONNECTOR_TOPIC);
	final DeserializationSchema<Row> deserializationSchema = getDeserializationSchema(properties);
	final StartupOptions startupOptions = getStartupOptions(descriptorProperties, topic);

	return createKafkaTableSource(
		descriptorProperties.getTableSchema(SCHEMA()),
		SchemaValidator.deriveProctimeAttribute(descriptorProperties),
		SchemaValidator.deriveRowtimeAttributes(descriptorProperties),
		SchemaValidator.deriveFieldMapping(
			descriptorProperties,
			Optional.of(deserializationSchema.getProducedType())),
		topic,
		getKafkaProperties(descriptorProperties),
		deserializationSchema,
		startupOptions.startupMode,
		startupOptions.specificOffsets);
}
 

/**
 * Creates a Kafka 0.11 {@link StreamTableSource}.
 *
 * @param schema                      Schema of the produced table.
 * @param proctimeAttribute           Field name of the processing time attribute.
 * @param rowtimeAttributeDescriptors Descriptor for a rowtime attribute
 * @param fieldMapping                Mapping for the fields of the table schema to
 *                                    fields of the physical returned type.
 * @param topic                       Kafka topic to consume.
 * @param properties                  Properties for the Kafka consumer.
 * @param deserializationSchema       Deserialization schema for decoding records from Kafka.
 * @param startupMode                 Startup mode for the contained consumer.
 * @param specificStartupOffsets      Specific startup offsets; only relevant when startup
 *                                    mode is {@link StartupMode#SPECIFIC_OFFSETS}.
 */
public Kafka011TableSource(
		TableSchema schema,
		Optional<String> proctimeAttribute,
		List<RowtimeAttributeDescriptor> rowtimeAttributeDescriptors,
		Optional<Map<String, String>> fieldMapping,
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema,
		StartupMode startupMode,
		Map<KafkaTopicPartition, Long> specificStartupOffsets) {

	super(
		schema,
		proctimeAttribute,
		rowtimeAttributeDescriptors,
		fieldMapping,
		topic,
		properties,
		deserializationSchema,
		startupMode,
		specificStartupOffsets);
}
 

/**
 * Creates a Kafka 0.9 {@link StreamTableSource}.
 *
 * @param schema                      Schema of the produced table.
 * @param proctimeAttribute           Field name of the processing time attribute.
 * @param rowtimeAttributeDescriptors Descriptor for a rowtime attribute
 * @param fieldMapping                Mapping for the fields of the table schema to
 *                                    fields of the physical returned type.
 * @param topic                       Kafka topic to consume.
 * @param properties                  Properties for the Kafka consumer.
 * @param deserializationSchema       Deserialization schema for decoding records from Kafka.
 * @param startupMode                 Startup mode for the contained consumer.
 * @param specificStartupOffsets      Specific startup offsets; only relevant when startup
 *                                    mode is {@link StartupMode#SPECIFIC_OFFSETS}.
 */
public Kafka09TableSource(
		TableSchema schema,
		Optional<String> proctimeAttribute,
		List<RowtimeAttributeDescriptor> rowtimeAttributeDescriptors,
		Optional<Map<String, String>> fieldMapping,
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema,
		StartupMode startupMode,
		Map<KafkaTopicPartition, Long> specificStartupOffsets) {

	super(
		schema,
		proctimeAttribute,
		rowtimeAttributeDescriptors,
		fieldMapping,
		topic,
		properties,
		deserializationSchema,
		startupMode,
		specificStartupOffsets);
}
 

@Override
protected KafkaTableSourceBase createKafkaTableSource(
		TableSchema schema,
		Optional<String> proctimeAttribute,
		List<RowtimeAttributeDescriptor> rowtimeAttributeDescriptors,
		Map<String, String> fieldMapping,
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema,
		StartupMode startupMode,
		Map<KafkaTopicPartition, Long> specificStartupOffsets) {

	return new Kafka010TableSource(
		schema,
		proctimeAttribute,
		rowtimeAttributeDescriptors,
		Optional.of(fieldMapping),
		topic,
		properties,
		deserializationSchema,
		startupMode,
		specificStartupOffsets);
}
 

@Override
public DynamicTableSource createDynamicTableSource(Context context) {
	FactoryUtil.TableFactoryHelper helper = FactoryUtil.createTableFactoryHelper(this, context);

	ReadableConfig tableOptions = helper.getOptions();

	String topic = tableOptions.get(TOPIC);
	DecodingFormat<DeserializationSchema<RowData>> decodingFormat = helper.discoverDecodingFormat(
			DeserializationFormatFactory.class,
			FactoryUtil.FORMAT);
	// Validate the option data type.
	helper.validateExcept(KafkaOptions.PROPERTIES_PREFIX);
	// Validate the option values.
	validateTableOptions(tableOptions);

	DataType producedDataType = context.getCatalogTable().getSchema().toPhysicalRowDataType();
	final KafkaOptions.StartupOptions startupOptions = getStartupOptions(tableOptions, topic);
	return createKafkaTableSource(
			producedDataType,
			topic,
			getKafkaProperties(context.getCatalogTable().getOptions()),
			decodingFormat,
			startupOptions.startupMode,
			startupOptions.specificOffsets,
			startupOptions.startupTimestampMillis);
}
 

@Override
protected KafkaTableSourceBase createKafkaTableSource(
		TableSchema schema,
		Optional<String> proctimeAttribute,
		List<RowtimeAttributeDescriptor> rowtimeAttributeDescriptors,
		Map<String, String> fieldMapping,
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema,
		StartupMode startupMode,
		Map<KafkaTopicPartition, Long> specificStartupOffsets,
		long startupTimestampMillis) {

	return new Kafka011TableSource(
		schema,
		proctimeAttribute,
		rowtimeAttributeDescriptors,
		Optional.of(fieldMapping),
		topic,
		properties,
		deserializationSchema,
		startupMode,
		specificStartupOffsets,
		startupTimestampMillis);
}
 

@Override
protected KafkaTableSourceBase getExpectedKafkaTableSource(
		TableSchema schema,
		Optional<String> proctimeAttribute,
		List<RowtimeAttributeDescriptor> rowtimeAttributeDescriptors,
		Map<String, String> fieldMapping,
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema,
		StartupMode startupMode,
		Map<KafkaTopicPartition, Long> specificStartupOffsets) {

	return new Kafka08TableSource(
		schema,
		proctimeAttribute,
		rowtimeAttributeDescriptors,
		Optional.of(fieldMapping),
		topic,
		properties,
		deserializationSchema,
		startupMode,
		specificStartupOffsets
	);
}
 

@Override
protected KafkaTableSourceBase createKafkaTableSource(
	TableSchema schema,
	Optional<String> proctimeAttribute,
	List<RowtimeAttributeDescriptor> rowtimeAttributeDescriptors,
	Map<String, String> fieldMapping,
	String topic,
	Properties properties,
	DeserializationSchema<Row> deserializationSchema,
	StartupMode startupMode,
	Map<KafkaTopicPartition, Long> specificStartupOffsets,
	long startupTimestampMillis) {

	return new KafkaTableSource(
		schema,
		proctimeAttribute,
		rowtimeAttributeDescriptors,
		Optional.of(fieldMapping),
		topic,
		properties,
		deserializationSchema,
		startupMode,
		specificStartupOffsets,
		startupTimestampMillis);
}
 

/**
 * Creates a generic Kafka {@link StreamTableSource}.
 *
 * @param schema                Schema of the produced table.
 * @param topic                 Kafka topic to consume.
 * @param properties            Properties for the Kafka consumer.
 * @param deserializationSchema Deserialization schema for decoding records from Kafka.
 */
protected KafkaTableSourceBase(
		TableSchema schema,
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema) {
	this(
		schema,
		Optional.empty(),
		Collections.emptyList(),
		Optional.empty(),
		topic, properties,
		deserializationSchema,
		StartupMode.GROUP_OFFSETS,
		Collections.emptyMap(),
		DEFAULT_STARTUP_TIMESTAMP_MILLIS);
}
 

@Override
protected KafkaDynamicSourceBase getExpectedScanSource(
		DataType producedDataType,
		String topic,
		Properties properties,
		DecodingFormat<DeserializationSchema<RowData>> decodingFormat,
		StartupMode startupMode,
		Map<KafkaTopicPartition, Long> specificStartupOffsets,
		long startupTimestamp) {
	return new Kafka011DynamicSource(
			producedDataType,
			topic,
			properties,
			decodingFormat,
			startupMode,
			specificStartupOffsets,
			startupTimestamp);
}
 

@Override
protected KafkaTableSourceBase getExpectedKafkaTableSource(
		TableSchema schema,
		Optional<String> proctimeAttribute,
		List<RowtimeAttributeDescriptor> rowtimeAttributeDescriptors,
		Map<String, String> fieldMapping,
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema,
		StartupMode startupMode,
		Map<KafkaTopicPartition, Long> specificStartupOffsets) {

	return new Kafka011TableSource(
		schema,
		proctimeAttribute,
		rowtimeAttributeDescriptors,
		Optional.of(fieldMapping),
		topic,
		properties,
		deserializationSchema,
		startupMode,
		specificStartupOffsets
	);
}
 

/**
 * Creates a generic Kafka {@link StreamTableSource}.
 *
 * @param schema                      Schema of the produced table.
 * @param proctimeAttribute           Field name of the processing time attribute.
 * @param rowtimeAttributeDescriptors Descriptor for a rowtime attribute
 * @param fieldMapping                Mapping for the fields of the table schema to
 *                                    fields of the physical returned type.
 * @param topic                       Kafka topic to consume.
 * @param properties                  Properties for the Kafka consumer.
 * @param deserializationSchema       Deserialization schema for decoding records from Kafka.
 * @param startupMode                 Startup mode for the contained consumer.
 * @param specificStartupOffsets      Specific startup offsets; only relevant when startup
 *                                    mode is {@link StartupMode#SPECIFIC_OFFSETS}.
 * @param startupTimestampMillis	  Startup timestamp for offsets; only relevant when startup
 *                                    mode is {@link StartupMode#TIMESTAMP}.
 */
protected KafkaTableSourceBase(
		TableSchema schema,
		Optional<String> proctimeAttribute,
		List<RowtimeAttributeDescriptor> rowtimeAttributeDescriptors,
		Optional<Map<String, String>> fieldMapping,
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema,
		StartupMode startupMode,
		Map<KafkaTopicPartition, Long> specificStartupOffsets,
		long startupTimestampMillis) {
	this.schema = TableSchemaUtils.checkNoGeneratedColumns(schema);
	this.proctimeAttribute = validateProctimeAttribute(proctimeAttribute);
	this.rowtimeAttributeDescriptors = validateRowtimeAttributeDescriptors(rowtimeAttributeDescriptors);
	this.fieldMapping = fieldMapping;
	this.topic = Preconditions.checkNotNull(topic, "Topic must not be null.");
	this.properties = Preconditions.checkNotNull(properties, "Properties must not be null.");
	this.deserializationSchema = Preconditions.checkNotNull(
		deserializationSchema, "Deserialization schema must not be null.");
	this.startupMode = Preconditions.checkNotNull(startupMode, "Startup mode must not be null.");
	this.specificStartupOffsets = Preconditions.checkNotNull(
		specificStartupOffsets, "Specific offsets must not be null.");
	this.startupTimestampMillis = startupTimestampMillis;
}
 

@Override
public DeserializationSchema<Row> createDeserializationSchema(Map<String, String> properties) {
	final DescriptorProperties descriptorProperties = getValidatedProperties(properties);

	// create and configure
	final JsonRowDeserializationSchema.Builder schema =
		new JsonRowDeserializationSchema.Builder(createTypeInformation(descriptorProperties));

	descriptorProperties.getOptionalBoolean(JsonValidator.FORMAT_FAIL_ON_MISSING_FIELD)
		.ifPresent(flag -> {
			if (flag) {
				schema.failOnMissingField();
			}
		});
	descriptorProperties.getOptionalBoolean(JsonValidator.FORMAT_IGNORE_PARSE_ERRORS)
		.ifPresent(flag -> {
			if (flag) {
				schema.ignoreParseErrors();
			}
		});
	return schema.build();
}
 

/**
 * Creates a Kafka 0.11 {@link StreamTableSource}.
 *
 * @param schema                      Schema of the produced table.
 * @param proctimeAttribute           Field name of the processing time attribute.
 * @param rowtimeAttributeDescriptors Descriptor for a rowtime attribute
 * @param fieldMapping                Mapping for the fields of the table schema to
 *                                    fields of the physical returned type.
 * @param topic                       Kafka topic to consume.
 * @param properties                  Properties for the Kafka consumer.
 * @param deserializationSchema       Deserialization schema for decoding records from Kafka.
 * @param startupMode                 Startup mode for the contained consumer.
 * @param specificStartupOffsets      Specific startup offsets; only relevant when startup
 *                                    mode is {@link StartupMode#SPECIFIC_OFFSETS}.
 */
public Kafka011TableSource(
		TableSchema schema,
		Optional<String> proctimeAttribute,
		List<RowtimeAttributeDescriptor> rowtimeAttributeDescriptors,
		Optional<Map<String, String>> fieldMapping,
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema,
		StartupMode startupMode,
		Map<KafkaTopicPartition, Long> specificStartupOffsets) {

	super(
		schema,
		proctimeAttribute,
		rowtimeAttributeDescriptors,
		fieldMapping,
		topic,
		properties,
		deserializationSchema,
		startupMode,
		specificStartupOffsets);
}
 

@Override
protected KafkaTableSourceBase getExpectedKafkaTableSource(
		TableSchema schema,
		Optional<String> proctimeAttribute,
		List<RowtimeAttributeDescriptor> rowtimeAttributeDescriptors,
		Map<String, String> fieldMapping,
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema,
		StartupMode startupMode,
		Map<KafkaTopicPartition, Long> specificStartupOffsets) {

	return new Kafka010TableSource(
		schema,
		proctimeAttribute,
		rowtimeAttributeDescriptors,
		Optional.of(fieldMapping),
		topic,
		properties,
		deserializationSchema,
		startupMode,
		specificStartupOffsets
	);
}
 

/**
 * Creates a generic Kafka {@link StreamTableSource}.
 *
 * @param schema                      Schema of the produced table.
 * @param proctimeAttribute           Field name of the processing time attribute.
 * @param rowtimeAttributeDescriptors Descriptor for a rowtime attribute
 * @param fieldMapping                Mapping for the fields of the table schema to
 *                                    fields of the physical returned type.
 * @param topic                       Kafka topic to consume.
 * @param properties                  Properties for the Kafka consumer.
 * @param deserializationSchema       Deserialization schema for decoding records from Kafka.
 * @param startupMode                 Startup mode for the contained consumer.
 * @param specificStartupOffsets      Specific startup offsets; only relevant when startup
 *                                    mode is {@link StartupMode#SPECIFIC_OFFSETS}.
 */
protected KafkaTableSourceBase(
		TableSchema schema,
		Optional<String> proctimeAttribute,
		List<RowtimeAttributeDescriptor> rowtimeAttributeDescriptors,
		Optional<Map<String, String>> fieldMapping,
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema,
		StartupMode startupMode,
		Map<KafkaTopicPartition, Long> specificStartupOffsets) {
	this.schema = Preconditions.checkNotNull(schema, "Schema must not be null.");
	this.proctimeAttribute = validateProctimeAttribute(proctimeAttribute);
	this.rowtimeAttributeDescriptors = validateRowtimeAttributeDescriptors(rowtimeAttributeDescriptors);
	this.fieldMapping = fieldMapping;
	this.topic = Preconditions.checkNotNull(topic, "Topic must not be null.");
	this.properties = Preconditions.checkNotNull(properties, "Properties must not be null.");
	this.deserializationSchema = Preconditions.checkNotNull(
		deserializationSchema, "Deserialization schema must not be null.");
	this.startupMode = Preconditions.checkNotNull(startupMode, "Startup mode must not be null.");
	this.specificStartupOffsets = Preconditions.checkNotNull(
		specificStartupOffsets, "Specific offsets must not be null.");
}
 

@Test
public void testSchemaDerivation() {
	final Map<String, String> properties = new HashMap<>();
	properties.putAll(new Schema().schema(TableSchema.fromTypeInfo(SCHEMA)).toProperties());
	properties.putAll(new Csv().deriveSchema().toProperties());

	final CsvRowSerializationSchema expectedSer = new CsvRowSerializationSchema.Builder(SCHEMA).build();
	final CsvRowDeserializationSchema expectedDeser = new CsvRowDeserializationSchema.Builder(SCHEMA).build();

	final SerializationSchema<?> actualSer = TableFactoryService
		.find(SerializationSchemaFactory.class, properties)
		.createSerializationSchema(properties);

	assertEquals(expectedSer, actualSer);

	final DeserializationSchema<?> actualDeser = TableFactoryService
		.find(DeserializationSchemaFactory.class, properties)
		.createDeserializationSchema(properties);

	assertEquals(expectedDeser, actualDeser);
}
 

@Override
protected KafkaDynamicSourceBase createKafkaTableSource(
		DataType producedDataType,
		String topic,
		Properties properties,
		DecodingFormat<DeserializationSchema<RowData>> decodingFormat,
		StartupMode startupMode,
		Map<KafkaTopicPartition, Long> specificStartupOffsets,
		long startupTimestampMillis) {

	return new Kafka010DynamicSource(
		producedDataType,
		topic,
		properties,
		decodingFormat,
		startupMode,
		specificStartupOffsets,
		startupTimestampMillis);
}
 

@Override
protected KafkaTableSourceBase createKafkaTableSource(
		TableSchema schema,
		Optional<String> proctimeAttribute,
		List<RowtimeAttributeDescriptor> rowtimeAttributeDescriptors,
		Map<String, String> fieldMapping,
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema,
		StartupMode startupMode,
		Map<KafkaTopicPartition, Long> specificStartupOffsets) {

	return new Kafka011TableSource(
		schema,
		proctimeAttribute,
		rowtimeAttributeDescriptors,
		Optional.of(fieldMapping),
		topic,
		properties,
		deserializationSchema,
		startupMode,
		specificStartupOffsets);
}
 

/**
 * Creates a Kafka 0.8 {@link StreamTableSource}.
 *
 * @param schema                Schema of the produced table.
 * @param topic                 Kafka topic to consume.
 * @param properties            Properties for the Kafka consumer.
 * @param deserializationSchema Deserialization schema for decoding records from Kafka.
 */
public Kafka08TableSource(
		TableSchema schema,
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema) {

	super(schema, topic, properties, deserializationSchema);
}
 

/**
 * Creates a Kafka 0.9 {@link StreamTableSource}.
 *
 * @param schema                Schema of the produced table.
 * @param topic                 Kafka topic to consume.
 * @param properties            Properties for the Kafka consumer.
 * @param deserializationSchema Deserialization schema for decoding records from Kafka.
 */
public Kafka09TableSource(
		TableSchema schema,
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema) {

	super(schema, topic, properties, deserializationSchema);
}
 

private void testJsonSchemaDeserializationSchema(Map<String, String> properties) {
	final DeserializationSchema<?> actual2 = TableFactoryService
		.find(DeserializationSchemaFactory.class, properties)
		.createDeserializationSchema(properties);
	final JsonRowDeserializationSchema expected2 = new JsonRowDeserializationSchema.Builder(JSON_SCHEMA)
		.failOnMissingField()
		.build();
	assertEquals(expected2, actual2);
}
 

/**
 * Creates a Kafka 0.10 {@link StreamTableSource}.
 *
 * @param schema                Schema of the produced table.
 * @param topic                 Kafka topic to consume.
 * @param properties            Properties for the Kafka consumer.
 * @param deserializationSchema Deserialization schema for decoding records from Kafka.
 */
public Kafka010TableSource(
		TableSchema schema,
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema) {

	super(schema, topic, properties, deserializationSchema);
}
 

/**
 * NOTE: This method is for internal use only for defining a TableSource.
 *       Do not use it in Table API programs.
 */
@Override
public DataStream<Row> getDataStream(StreamExecutionEnvironment env) {

	DeserializationSchema<Row> deserializationSchema = getDeserializationSchema();
	// Version-specific Kafka consumer
	FlinkKafkaConsumerBase<Row> kafkaConsumer = getKafkaConsumer(topic, properties, deserializationSchema);
	return env.addSource(kafkaConsumer).name(explainSource());
}
 

/**
 * Returns a version-specific Kafka consumer with the start position configured.
 *
 * @param topic                 Kafka topic to consume.
 * @param properties            Properties for the Kafka consumer.
 * @param deserializationSchema Deserialization schema to use for Kafka records.
 * @return The version-specific Kafka consumer
 */
protected FlinkKafkaConsumerBase<Row> getKafkaConsumer(
		String topic,
		Properties properties,
		DeserializationSchema<Row> deserializationSchema) {
	FlinkKafkaConsumerBase<Row> kafkaConsumer =
			createKafkaConsumer(topic, properties, deserializationSchema);
	switch (startupMode) {
		case EARLIEST:
			kafkaConsumer.setStartFromEarliest();
			break;
		case LATEST:
			kafkaConsumer.setStartFromLatest();
			break;
		case GROUP_OFFSETS:
			kafkaConsumer.setStartFromGroupOffsets();
			break;
		case SPECIFIC_OFFSETS:
			kafkaConsumer.setStartFromSpecificOffsets(specificStartupOffsets);
			break;
		case TIMESTAMP:
			kafkaConsumer.setStartFromTimestamp(startupTimestampMillis);
			break;
	}
	kafkaConsumer.setCommitOffsetsOnCheckpoints(properties.getProperty("group.id") != null);
	return kafkaConsumer;
}
 

@Override
protected FlinkKafkaConsumerBase<Row> createKafkaConsumer(
	String topic,
	Properties properties,
	DeserializationSchema<Row> deserializationSchema) {

	return new FlinkKafkaConsumer<Row>(topic, deserializationSchema, properties);
}
 

@Override
public DecodingFormat<DeserializationSchema<RowData>> createDecodingFormat(
		DynamicTableFactory.Context context,
		ReadableConfig formatConfig) {
	FactoryUtil.validateFactoryOptions(this, formatConfig);
	return new DecodingFormatMock(formatConfig.get(DELIMITER), formatConfig.get(FAIL_ON_MISSING));
}
 

@Override
public DeserializationSchema<Row> createDeserializationSchema(Map<String, String> properties) {
	final DescriptorProperties descriptorProperties = getValidatedProperties(properties);

	// create and configure
	final JsonRowDeserializationSchema schema = new JsonRowDeserializationSchema(createTypeInformation(descriptorProperties));

	descriptorProperties.getOptionalBoolean(JsonValidator.FORMAT_FAIL_ON_MISSING_FIELD)
			.ifPresent(schema::setFailOnMissingField);

	return schema;
}
 

@Test
public void testSpecificRecordWithConfluentSchemaRegistry() throws Exception {
	DeserializationSchema<Address> deserializer = AvroDeserializationSchema.forSpecific(Address.class);

	byte[] encodedAddress = writeRecord(address, Address.getClassSchema());
	Address deserializedAddress = deserializer.deserialize(encodedAddress);
	assertEquals(address, deserializedAddress);
}
 

private DeserializationSchema<Row> getDeserializationSchema(Map<String, String> properties) {
	@SuppressWarnings("unchecked")
	final DeserializationSchemaFactory<Row> formatFactory = TableFactoryService.find(
		DeserializationSchemaFactory.class,
		properties,
		this.getClass().getClassLoader());
	return formatFactory.createDeserializationSchema(properties);
}