#serialization

Swift Protobuf offers efficient and type-safe serialization using Google's Protocol Buffer, integrating smoothly with Swift to generate code from .proto files via the 'protoc' compiler. It includes a runtime library and supports binary and JSON serialization. The project emphasizes safety, correctness, and Swift language idioms, while also enabling interoperability with languages like Java and Python. Designed for Swift 5.8 and above, it facilitates easy data interchange and is suitable for developers looking to enhance data handling capabilities in Swift applications.

Quick-protobuf is a Rust library for efficient serialization and deserialization of protobuf files with minimal allocations. It includes 'pb-rs' for generating Rust code from .proto files, eliminating the need for 'protoc'. Ideal for developers needing speed in handling specific field types. Despite its lesser-known status, quick-protobuf offers faster processing and simpler code structure compared to rust-protobuf. Explore its examples and framework to understand code generation and Rust module usage.

MLeap facilitates the deployment of machine learning pipelines from Spark and Scikit-learn to a portable format with a high-performance execution engine. It supports lightweight execution without Spark dependencies and integrates seamlessly with JVM using JSON and Protobuf serialization. Compatible with PySpark and Scikit-learn, MLeap provides extensive documentation to customize types, optimize transformers, and implement pipelines.

The vtprotobuf repository delivers the `protoc-gen-go-vtproto` plug-in, streamlining Protocol Buffers in Vitess with enhanced serialization and deserialization. Utilizing `gogo/protobuf` bases, it supports ProtoBuf APIv2, minimizing reflection and memory use. Featuring tailored functions for size, equality, and serialization, it integrates effortlessly with GRPC, Twirp, and Connect. Code generation with `buf` automates optimal performance for Protocol Buffers in applications.

DocArray offers a versatile Python library for handling multimodal data, including representation, storage, and retrieval, tailored for AI applications. It integrates smoothly with frameworks like PyTorch, TensorFlow, and FastAPI, supporting vector databases such as Weaviate and Redis. It also facilitates data transmission using JSON or Protobuf, fitting well within distributed systems and microservice frameworks. By utilizing Pydantic for defining machine-learning data models, it aids in efficient data management, making it a valuable tool for AI developers.

Catalogue provides a minimalistic, zero-dependency library that facilitates the creation of function and object registries, improving serialization and customization processes. Instead of direct function implementation, string identifiers are used to simplify object serialization, offering extensive flexibility. Suitable for Python 3.6+, Catalogue supports seamless function decoration and registration, making it well-suited for developers interested in building extensible Python packages.