How to solve common problems encountered when using Jackson DataFormat: Avro framework

When using Jackson DataFormat: AVRO framework, some common problems may be encountered.This article will introduce these issues and provide some solutions and Java code examples. 1. Question: AVRO mode file failed loading When using the Jackson DataFormat: Avro framework, the problem that cannot be loaded with the AVRO mode file may be encountered. solution: Make sure that the AVRO mode file exists and set its path correctly. ObjectMapper mapper = new ObjectMapper(new AvroFactory()); // Load the AVRO mode file mapper.writer(new AvroSchema(AvroUtils.parseSchema(new File("path/to/avro_schema.avsc")))); 2. Question: The generated avro data is not serialized as expected When using Jackson DataFormat: AVRO framework for serialization, the generated AVRO data may not match the expected. solution: Make sure that the Java object to be serialized is matched with the AVRO mode and correctly map the field. // Define the Java object public class Person { private String name; private int age; // You must provide a non -ginseng constructor function public Person() {} // getters 和 setters ... } // Create avro data Person person = new Person(); person.setName("John"); person.setAge(30); // Sequences to AVRO data byte[] avroData = mapper.writer().withType(Person.class).writeValueAsBytes(person); 3. Question: The AVRO data can be transformed into Java objects When using the Jackson DataFormat: AVRO framework for counter -sequence, it is possible to encounter the problem that the AVRO data can be transformed into a Java object. solution: Make sure that the AVRO data that needs to be carried out is matched with the Java object type, and the AVRO mode file is set correctly. // Revitalize AVRO data Person deserializedPerson = mapper.reader().forType(Person.class).withType(Person.class).readValue(avroData); 4. Question: Low serialization/derivativeization performance is low During the large -scale data processing process, the performance of using Jackson DataFormat: AVRO framework may be lower. solution: Consider using buffer and batch processing to improve performance.You can adjust it by covering the default configuration. // Use buffer and batch processing ObjectMapper mapper = new ObjectMapper(new AvroFactory() .configure(AvroParser.Feature.READ_BUFFER_SIZE, 16384) .configure(AvroGenerator.Feature.WRITE_BUFFER_SIZE, 16384) .configure(SerializationFeature.WRITE_SINGLE_ELEM_ARRAYS_UNWRAPPED, true)); 5. Question: Use the custom serializer/back -sequencer In some cases, the serializer and dependent serializer of AVRO data may be required. solution: Implementing `com.fasterxml.jackson.dataBind.Ser.StdSerializer` and` com.fasterxml.jackson.dataBind.std.stddeserializer` interfaces to create custom serializers and risks, and use `@` `JSONSERIALIZE` and `@jsondeserialize` annotations are applied to the Java object. // Custom serializer public class CustomSerializer extends StdSerializer<Person> { // Constructor public CustomSerializer() { super(Person.class); } // serialization method public void serialize(Person person, JsonGenerator gen, SerializerProvider provider) throws IOException { // Customized serialization logic ... } } / Third public class CustomDeserializer extends StdDeserializer<Person> { // Constructor public CustomDeserializer() { super(Person.class); } // Revitalization method public Person deserialize(JsonParser parser, DeserializationContext context) throws IOException { // Customize the logic ... } } // Apply a custom serializer and a rotor serializer @JsonSerialize(using = CustomSerializer.class) @JsonDeserialize(using = CustomDeserializer.class) public class Person { // ... } These are some common problems and solutions that may encounter when using Jackson DataFormat: Avro framework.By checking the code and adjusting the configuration, you should be able to successfully use AVRO for serialization and derivativeization.