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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.
Java serialization, while convenient for persisting objects, suffers from versioning issues, hidden fields, mutable data snapshots, performance overhead, security vulnerabilities, singleton breaches, final field tampering, external dependencies, maintenance burdens, and format limitations, and the article recommends explicit serialVersionUID, custom methods, and JSON/Protobuf alternatives.
This article explains Google Protocol Buffers' binary serialization, covering its advantages and drawbacks, the encoding mechanisms of tags, varints, ZigZag, embedded and repeated fields, and provides practical best‑practice guidelines for designing robust .proto schemas.
This article introduces an open‑source Java JSON framework that uses recursive parsing and reflection to serialize and deserialize objects without third‑party libraries, supports generic and array types, and provides extensible features such as custom annotations, JsonPath, and HTML‑safe output.
This article explains how the json module's default parameter can be used to serialize unsupported Python objects, such as datetime, by providing a custom function or by subclassing JSONEncoder, and includes clear code examples demonstrating both approaches.
This article introduces Python's built‑in json module, explaining how to serialize Python objects to JSON strings and files, deserialize JSON back to Python, and demonstrates common tasks such as pretty‑printing, handling Unicode, working with lists and nested structures, custom encoders, and network responses.
Explore how Python’s built-in __dict__ attribute stores object attributes, enabling introspection, dynamic attribute manipulation, memoization, JSON serialization, custom descriptors, and debugging, while also learning its limitations with slots and built-in types for performance.
This article explains the fundamentals of Java object serialization, compares legacy and modern formats, outlines how to choose the right method, and details common issues such as static fields, transient modifiers, serialVersionUID mismatches, inheritance quirks, and custom serialization techniques with clear code examples.
Serialization in C++ bridges in‑memory objects and persistent or network formats, addressing challenges like byte order, data type compatibility, and complex structures, while the article reviews core principles, binary and text formats, major libraries such as Protobuf, Cereal, Cista++, ThorsSerializer, and offers guidance on selecting and using them effectively.
The article explains how Protocol Buffers (proto3) encode basic and composite types using varint, zigzag, fixed-size and IEEE‑754 formats, describes tag and length field structures, and presents optimization strategies such as selecting size‑efficient types, flattening nested messages, and delta‑encoding to significantly reduce serialized byte‑stream size.
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.
Python's pickle module provides a simple way to serialize and deserialize complex objects such as lists, dictionaries, and class instances, allowing them to be saved to or loaded from files or byte streams, with examples of pickling, unpickling, using dumps/loads, protocol versions, and security considerations.
This article explains the fundamentals of Java serialization and deserialization, compares various serialization formats, demonstrates code examples, highlights common issues such as static, transient fields and serialVersionUID mismatches, and shows how to implement custom serialization with the Externalizable interface.
Java serialization, while convenient for object persistence, suffers from security vulnerabilities, versioning issues, performance constraints, and difficulties handling complex object graphs, prompting developers to evaluate alternatives such as JSON, Protocol Buffers, and Apache Avro, each offering distinct trade‑offs in readability, efficiency, and safety.
The article explains serialization and deserialization fundamentals, highlights pitfalls such as ambiguous binary streams and platform‑dependent field sizes, then compares Java’s native ObjectOutputStream, JSON libraries, Hessian binary RPC, and Google’s Protobuf, guiding readers to select the appropriate format based on performance, payload size, and type‑safety needs.
This article explains how to expose a lightweight, dependency‑free client API for a Rust‑based database by defining a simple TCP‑based JSON serialization protocol, detailing non‑functional and functional requirements, the Message and Command structures, and providing a concrete Ping command implementation in Rust.
The article surveys the 22 most downloaded Python packages on PyPI, explaining each library's main purpose, typical use cases, download counts, and providing concise code examples to illustrate how they simplify tasks such as HTTP requests, compatibility, AWS interaction, data parsing, and encryption.
This article provides practical Python code examples for various messaging and serialization tools—including Celery, RQ, Huey, ZeroMQ, kafka‑python, Pika, stomp.py, nats‑py, gRPC, Thrift, Protobuf, Avro, msgpack, and Flatbuffers—demonstrating how to set up producers, consumers, and services for asynchronous processing and data exchange.