3 مستودعات
Libraries that transform JSON text or binary data into native language objects or maps.
Distinct from JSON Deserializers: None of the candidates describe general-purpose JSON-to-Object deserialization for Java without focusing on code generation or exploits.
Explore 3 awesome GitHub repositories matching data & databases · JSON Deserializers. Refine with filters or upvote what's useful.
fastjson2 is a high-performance Java library used for serializing and deserializing Java objects to and from JSON strings and byte arrays. It functions as a binary JSON serializer for encoding objects into optimized binary formats to reduce payload size and increase processing speed. The project includes a JSONPath query engine for extracting specific data and fields from JSON documents using standardized path expressions and filters. It also serves as a JSON schema validator, providing utilities to generate data contracts and validate JSON input against defined schemas to ensure structural c
Transforms JSON strings and byte arrays into Java objects or maps for application processing.
HandyJSON is a Swift JSON serialization library and data mapper. It functions as an object mapper that converts JSON data into typed Swift models and transforms Swift objects back into JSON strings for storage or transmission. The library uses reflection to automatically map JSON keys to object properties and supports custom mapping metadata to resolve naming differences between APIs and code. It provides capabilities for extracting values from deep nested paths within a JSON structure and supports the transformation of complex types such as dates and URLs. The framework handles both the des
Implements JSON deserialization with the ability to extract values from deep nested paths to populate object properties.
msgspec is a high-performance data modeling, serialization, and schema validation toolkit for Python. It serves as a type-safe serialization framework that integrates schema enforcement and data parsing into a single pass, functioning as both a data serialization library and a schema validation system based on standard Python type annotations. The project distinguishes itself through high-performance structural primitives, including compilation-based routine generation and zero-copy buffer parsing. It optimizes memory usage via garbage collection-aware layouts and reduces processing overhead
Transforms JSON bytes into typed Python structures using strict type coercion and custom hooks.