This project is a framework for the efficient serialization and deserialization of data structures. It provides a unified, macro-based interface that automates the conversion of complex internal objects into standardized formats and reconstructs them from raw input streams or buffers. By leveraging compile-time code generation, the library minimizes manual implementation overhead while ensuring consistent logic across diverse data types.
The framework distinguishes itself through a format-agnostic data model and a visitor-based parsing architecture that decouples data structures from specific output representations. It supports high-performance processing through incremental streaming, which allows for transcoding large datasets without loading them entirely into memory, and zero-copy data borrowing, which maps input data directly to memory references to avoid unnecessary allocations. These capabilities enable the library to operate effectively in both standard and memory-constrained environments.
Beyond core conversion, the project offers extensive configuration for schema management and structural mapping. Users can define custom serialization rules, override default trait bounds, and implement granular control over field filtering, default value population, and variant representation. The system also supports the integration of third-party types and provides robust error reporting to facilitate reliable data exchange across various systems and services.
