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This tutorial shows how to use Jackson's polymorphic deserialization in Spring Boot 3 to handle variable JSON request bodies by defining a common Order interface, concrete implementations, and a REST controller that routes calculations based on the order type, eliminating redundant endpoints.
This article explores Jackson's ObjectMapper in Spring Boot 3, covering basic serialization/deserialization, converting between objects, JSON strings, JsonNode, and collections, custom serializers/deserializers, handling unknown fields, and performance tuning with the Afterburner module and JMH benchmarks.
This article demonstrates how to use Spring Boot 3's @JsonFormat annotation for precise date, time, and enum serialization, covering configuration via properties, annotation attributes, locale settings, shape options, lenient parsing, and case‑insensitive deserialization with practical code examples.
This article explains how the Jackson @JsonView annotation can be used in Spring back‑end projects to selectively serialize object fields, reduce bandwidth, improve security, and handle nested associations by defining view interfaces and applying them on entity fields and controller methods.
This article explains how to use Jackson's @JsonView annotation in Spring Boot 3 to flexibly control JSON output for different REST scenarios, covering basic usage, programmatic view selection, view‑based MVC responses, deserialization restrictions, and default‑inclusion configuration with practical code examples.
This article explains how different Java boolean naming conventions affect JSON serialization across fastjson, Gson, and Jackson, demonstrates the resulting inconsistencies with code examples, and recommends using a plain "success" field with an isSuccess getter to ensure reliable cross‑library behavior.
This article explains why Spring Boot 3 uses Jackson for JSON handling, describes four effective methods—JsonNode, Map, @JsonAnySetter, and custom deserializer—to process dynamic JSON properties, and promotes a continuously updated collection of over 90 Spring Boot case studies with source code.
This article compares six popular Java JSON parsing solutions—Jackson, Gson, FastJSON, JsonPath, org.json, and manual parsing—detailing their features, code examples, advanced options, and pros and cons to help developers choose the right tool for various scenarios.
Java developers can parse JSON using six popular approaches—Jackson for high‑performance, annotation‑driven serialization; Gson for a lightweight, easy‑to‑use API; FastJSON for speed; JsonPath for XPath‑style nested extraction; org.json for simple utility; or manual parsing for full control—each suited to different performance and complexity needs.
This tutorial demonstrates how to programmatically customize JSON serialization and deserialization in Spring Boot by implementing JsonSerializer, JsonDeserializer, JsonObjectSerializer, and JsonObjectDeserializer, complete with code examples, test endpoints, and important usage notes.
This article walks through the design and implementation of symmetric AES encryption for both GET and POST API endpoints in a Spring Boot application, covering requirement analysis, data model definition, custom RequestBodyAdvice and ResponseBodyAdvice, serialization challenges with FastJSON vs Jackson, and final configuration to ensure consistent JSON output across encrypted and non‑encrypted responses.
This article explains why consistent date formatting is crucial in Spring Boot APIs, outlines common scenarios such as front‑end data exchange, database storage and logging, and presents multiple server‑side and client‑side solutions—including SimpleDateFormat, DateTimeFormatter, global Jackson settings, annotations, custom converters, and timestamp output—complete with code examples.
This article demonstrates how to handle Java Long precision loss in Spring Boot 3 front‑end displays, configures Jackson to serialize Long as String, uses @JsonValue for single‑property serialization, and creates custom annotations with a SensitiveSerializer for data masking, complete with code examples and screenshots.
This article explains various Spring Boot approaches for formatting dates in API responses, covering frontend JavaScript utilities, Java SimpleDateFormat and DateTimeFormatter, global Jackson settings, annotation‑based formatting, custom converters, ControllerAdvice initBinder, and timestamp output methods.
This article walks through the design and implementation of symmetric request/response encryption for a Spring Boot API, covering requirement analysis, data models, custom ControllerAdvice for decryption and encryption, serialization challenges with FastJson and Jackson, and final configuration to keep encrypted payloads consistent across Android, iOS, and H5 clients.
This article explains how to implement unified data desensitization in Java Spring Boot applications by creating custom annotations, enums, serializers, and utility methods that automatically mask personal information during JSON serialization, complete with code examples and test results.
This tutorial explores Jackson's rich annotation set in Spring Boot 3.2.5, demonstrating how to serialize and deserialize JSON with built‑in annotations like @JsonAnyGetter, @JsonGetter, @JsonPropertyOrder, and @JsonIgnore, how to create custom annotations, and how to disable annotation processing for fine‑grained control.
In Spring Boot you can customize JSON request and response handling by annotating fields with @JsonSerialize/@JsonDeserialize, registering a global ObjectMapper module, adding a PropertyEditor via @ControllerAdvice, creating a custom HttpMessageConverter, or using AOP interception, selecting the approach that matches your project’s complexity.
The article demonstrates how to mask sensitive Java backend data by using Hutool's DesensitizedUtil for common types and a custom @Desensitization annotation with a DesensitizationTypeEnum‑driven Jackson serializer (or Fastjson ValueFilter) to apply flexible masking rules during JSON serialization.
The investigation revealed that G1GC pauses were caused by uncontrolled growth of the JVM StringTable, triggered by Jackson’s InternCache interning countless random JSON field names, and was mitigated by enlarging the young generation, disabling field‑name interning, tuning G1 parameters, and monitoring StringTable size.