Open Source Mac JSON Serialization Libraries
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JSON Serialization Libraries for Mac
View 15352 business solutionsBrowse free open source JSON Serialization Libraries and projects for Mac below. Use the toggles on the left to filter open source JSON Serialization Libraries by OS, license, language, programming language, and project status.
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gSOAP Toolkit
Development toolkit for Web Services and XML data bindings for C & C++
The gSOAP toolkit is an extensive suite of portable C and C++ software to develop XML Web services with powerful type-safe XML data bindings. Easy-to-use code-generator tools allow you to directly integrate XML data in C and C++. Serializes native application data in XML. Includes WSDL/XSD schema binding and auto-coding tools, stub/skeleton compiler, Web server integration with Apache module and IIS extension, high-performance XML processing with schema validation, fast MIME/MTOM streaming, SOAP and REST Web API development, WS-* protocols (WS-Security, WS-Policy, WS-ReliableMessaging, etc), XML-RPC and JSON. Licensed under GPLv2. Visit the developer center with guides, tutorials, and examples at www.genivia.com/dev.html The documentation of the toolkit, libraries, plugins and more is available at www.genivia.com/docs.html Commercial-use licenses and expert technical support services are available at competitive pricing levels, please visit www.genivia.com/products.html -
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TOML
Tom Preston-Werner's obvious, minimal language
Tom's Obvious, Minimal Language. By Tom Preston-Werner, Pradyun Gedam, et al. TOML aims to be a minimal configuration file format that's easy to read due to obvious semantics. TOML is designed to map unambiguously to a hash table. TOML should be easy to parse into data structures in a wide variety of languages. TOML shares traits with other file formats used for application configuration and data serialization, such as YAML and JSON. TOML and JSON both are simple and use ubiquitous data types, making them easy to code for or parse with machines. TOML and YAML both emphasize human readability features, like comments that make it easier to understand the purpose of a given line. TOML differs in combining these, allowing comments (unlike JSON) but preserving simplicity (unlike YAML). Because TOML is explicitly intended as a configuration file format, parsing it is easy, but it is not intended for serializing arbitrary data structures. -
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JsonCpp
A C++ library for interacting with JSON
JSON is a lightweight data-interchange format. It can represent numbers, strings, ordered sequences of values, and collections of name/value pairs. JsonCpp is a C++ library that allows manipulating JSON values, including serialization and deserialization to and from strings. It can also preserve existing comment in unserialization/serialization steps, making it a convenient format to store user input files. The branch 00.11.zis a new branch, its major version number 00 is to show that it is different from 0.y.z and 1.y.z, the main purpose of this branch is to make a balance between the other two branches. Thus, users can use some new features in this new branch that introduced in 1.y.z, but can hardly applied into 0.y.z. You can download and install JsonCpp using the vcpkg dependency manager. The JsonCpp port in vcpkg is kept up to date by Microsoft team members and community contributors. -
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ArduinoJson
JSON library for Arduino and embedded C++. Simple and efficient
ArduinoJson is a C++ JSON library for Arduino and IoT (Internet Of Things). ArduinoJson has a simple and intuitive syntax to handle objects and arrays. ArduinoJson supports both JSON serialization and deserialization. ArduinoJson uses a fixed memory allocation, allowing to work on devices with very little RAM. ArduinoJson can filter large inputs to keep only fields that are relevant to your application, thereby saving a lot of memory. ArduinoJson can parse directly from an input Stream or std::istream. ArduinoJson can produce compact or prettified documents. ArduinoJson works directly with strings stored in program memory. ArduinoJson supports two coding styles, with implicit or explicit casts. ArduinoJson deduplicates strings in the JSON document. When you have several identical keys or values, the JsonDocument only stores one of each. In practice, this feature reduces memory consumption by 20-30%. -
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PeerJS
Simple peer-to-peer with WebRTC
PeerJS wraps the browser's WebRTC implementation to provide a complete, configurable, and easy-to-use peer-to-peer connection API. Equipped with nothing but an ID, a peer can create a P2P data or media stream connection to a remote peer. To broker connections, PeerJS connects to a PeerServer. Note that no peer-to-peer data goes through the server. The server acts only as a connection broker. If you don't want to run your own PeerServer, we offer a free cloud-hosted version of PeerServer. PeerServer is open source and is written in node.js. You can easily run your own. PeerJS simplifies peer-to-peer data, video, and audio calls. Add the PeerJS client library to your webpage. The Peer object is where we create and receive connections. PeerJS uses PeerServer for session metadata and candidate signaling. Every Peer object is assigned a random, unique ID when it's created. -
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Framework Benchmarks
Source for the TechEmpower Framework Benchmarks project
If you're new to the project, welcome! Please feel free to ask questions here. We encourage new frameworks and contributors to ask questions. We're here to help! This project provides representative performance measures across a wide field of web application frameworks. With much help from the community, coverage is quite broad and we are happy to broaden it further with contributions. The project presently includes frameworks on many languages including Go, Python, Java, Ruby, PHP, C#, F#,Clojure, Groovy, Dart, JavaScript, Erlang, Haskell, Scala, Perl, Lua, C, and others. The current tests exercise plaintext responses, JSON serialization, database reads and writes via the object-relational mapper (ORM), collections, sorting, server-side templates, and XSS counter-measures. Future tests will exercise other components and greater computation. -
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ObjectMapper
Simple JSON Object mapping written in Swift
ObjectMapper is a framework written in Swift that makes it easy for you to convert your model objects (classes and structs) to and from JSON. Once your class implements Mappable, ObjectMapper allows you to easily convert to and from JSON. Use functions to validate JSON prior to object serialization provide an existing cached object to be used for mapping, return an object of another type (which also conforms to BaseMappable) to be used for mapping. For instance, you may inspect the JSON to infer the type of object that should be used for mapping. ObjectMapper supports dot notation within keys for easy mapping of nested objects. ObjectMapper also supports custom transforms that convert values during the mapping process. To use a transform, simply create a tuple with map["field_name"] and the transform of your choice on the right side of the <- operator. -
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Spring Data Redis
Provides support to increase developer productivity in Java
Provides support to increase developer productivity in Java when using Redis, a key-value store. Uses familiar Spring concepts such as a template class for core API usage and lightweight repository-style data access. The primary goal of the Spring Data project is to make it easier to build Spring-powered applications that use new data access technologies such as non-relational databases, map-reduce frameworks, and cloud-based data services. Connection package as low-level abstraction across multiple Redis drivers (Lettuce and Jedis). Exception translation to Spring’s portable Data Access exception hierarchy for Redis driver exceptions. RedisTemplate that provides a high level abstraction for performing various Redis operations, exception translation and serialization support. Pubsub support (such as a MessageListenerContainer for message-driven POJOs). Redis Sentinel and Redis Cluster support. Reactive API using the Lettuce driver. JDK, String, JSON and Spring Object/X. -
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jwx
Implementation of various JWx (Javascript Object Signing
Go module implementing various JWx (JWA/JWE/JWK/JWS/JWT, otherwise known as JOSE) technologies. If you are using this module in your product or your company, please add your product and/or company name in the Wiki! It really helps keeping up our motivation. Complete coverage of JWA/JWE/JWK/JWS/JWT, not just JWT+minimum tool set. Supports JWS messages with multiple signatures, both compact and JSON serialization. Supports JWS with detached payload. Supports JWS with unencoded payload (RFC7797). Supports JWE messages with multiple recipients, both compact and JSON serialization. Most operations work with either JWK or raw keys e.g. *rsa.PrivateKey, *ecdsa.PrivateKey, etc). Opinionated, but very uniform API. Everything is symmetric, and follows a standard convention. Arguments are organized as explicit required paramters and optional WithXXXX() style options. -
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vcr
Record your test suite's HTTP interactions and replay them
Record your test suite's HTTP interactions and replay them during future test runs for fast, deterministic, accurate tests. We're looking for more maintainers. If you'd like to help maintain a well-used gem please spend some time reviewing pull requests, issues, or participating in discussions. VCR will replay the response from iana.org when the HTTP request is made. This test is now fast (no real HTTP requests are made anymore), deterministic (the test will continue to pass, even if you are offline, or iana.org goes down for maintenance) and accurate (the response will contain the same headers and body you get from a real request). You can use a different cassette library directory (e.g., "test/vcr_cassettes"). The recorded requests and responses are stored on disk in a serialization format of your choice (currently YAML and JSON are built in, and you can easily implement your own custom serializer) and can easily be inspected and edited. -
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DocArray
The data structure for multimodal data
DocArray is a library for nested, unstructured, multimodal data in transit, including text, image, audio, video, 3D mesh, etc. It allows deep-learning engineers to efficiently process, embed, search, recommend, store, and transfer multimodal data with a Pythonic API. Door to multimodal world: super-expressive data structure for representing complicated/mixed/nested text, image, video, audio, 3D mesh data. The foundation data structure of Jina, CLIP-as-service, DALL·E Flow, DiscoArt etc. Data science powerhouse: greatly accelerate data scientists’ work on embedding, k-NN matching, querying, visualizing, evaluating via Torch/TensorFlow/ONNX/PaddlePaddle on CPU/GPU. Data in transit: optimized for network communication, ready-to-wire at anytime with fast and compressed serialization in Protobuf, bytes, base64, JSON, CSV, DataFrame. Perfect for streaming and out-of-memory data. One-stop k-NN: Unified and consistent API for mainstream vector databases. -
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RestSharp
Simple REST and HTTP API Client for .NET
RestSharp is probably the most popular HTTP client library for .NET. Featuring automatic serialization and deserialization, request and response type detection, variety of authentications and other useful features, it is being used by hundreds of thousands of projects. RestSharp passed over 32 million downloads on NuGet, with average daily download count of 10,000. It's being used by many popular OSS projects, including Roslyn and Swagger. The main purpose of RestSharp is to make synchronous and asynchronous calls to remote resources over HTTP. As the name suggests, the main audience of RestSharp are developers who use REST APIs. However, RestSharp can call any API over HTTP (but not HTTP/2), as long as you have the resource URI and request parameters that you want to send comply with W3C HTTP standards. RestSharp can take care of serializing the request body to JSON or XML and deserialize the response. It can also form a valid request URI based on different parameter kinds. -
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jsonfield
A reusable Django model field for storing ad-hoc JSON data
jsonfield is a reusable model field that allows you to store validated JSON, automatically handling serialization to and from the database. To use, add jsonfield.JSONField to one of your models. Note: django.contrib.postgres now supports PostgreSQL's jsonb type, which includes extended querying capabilities. If you're an end user of PostgreSQL and want full-featured JSON support, then it is recommended that you use the built-in JSONField. However, jsonfield is still useful when your app needs to be database-agnostic, or when the built-in JSONField's extended querying is not being leveraged. e.g., a configuration field. JSONField is not intended to provide extended querying capabilities. That said, you may perform the same basic lookups provided by regular text fields (e.g., exact or regex lookups). Since values are stored as serialized JSON, it is highly recommended that you test your queries to ensure the expected results are returned. -
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NeuroJSON
Scalable, searchable and verifiable neuroimaging data exchange platfor
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ActiveModelSerializers
Serializer implementation and Rails hooks
There's been a lot of churn around AMS since it began back in Rails 3.2 and a lot of new libraries are around and the JSON:API spec has reached 1.0. If there is to be a 1.0 release of AMS, it will need to address the general needs of serialization in much the way ActiveJob can be used with different workers. The next major release is in development. We're starting simple and avoiding, at least at the outset, all the complications in AMS version, especially all the implicit behavior from guessing the serializer, to the association's serializer, to the serialization type, etc. The basic idea is that models to serializers are a one to many relationship. Everything will need to be explicit. If you want to serialize a User with a UserSerializer, you'll need to call it directly. The serializer will essentially be for defining a basic JSON:API resource object: id, type, attributes, and relationships. -
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Connexion
Swagger/OpenAPI First framework for Python on top of Flask
Connexion is a framework on top of Flask that automagically handles HTTP requests defined using OpenAPI (formerly known as Swagger), supporting both v2.0 and v3.0 of the specification. Connexion allows you to write these specifications, then maps the endpoints to your Python functions. This is what makes it unique from other tools that generate the specification based on your Python code. You are free to describe your REST API with as much detail as you want and then Connexion guarantees that it will work as you specified. We built Connexion this way in order to simplify the development process. Reduce misinterpretation about what an API is going to look like. With Connexion, you write the spec first. Connexion then calls your Python code, handling the mapping from the specification to the code. This incentivizes you to write the specification so that all of your developers can understand what your API does, even before you write a single line of code. -
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GCDWebServer
The #1 HTTP server for iOS, macOS & tvOS
GCDWebServer is a modern and lightweight GCD based HTTP 1.1 server designed to be embedded in iOS, macOS & tvOS apps. Elegant and easy to use architecture with only 4 core classes: server, connection, request and response. Well designed API with fully documented headers for easy integration and customization. Entirely built with an event-driven design using Grand Central Dispatch for best performance and concurrency. No dependencies on third-party source code. Parser for web forms submitted using "application/x-www-form-urlencoded" or "multipart/form-data" encodings (including file uploads). Allow implementation of fully asynchronous handlers of incoming HTTP requests. Minimize memory usage with disk streaming of large HTTP request or response bodies. Automatically handle transitions between foreground, background and suspended modes in iOS apps. Full support for both IPv4 and IPv6. -
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Go support for Protocol Buffers
The Go support for Google's protocol buffers
Protocol buffers are Google's language-neutral, platform-neutral, extensible mechanism for serializing structured data, think XML, but smaller, faster, and simpler. You define how you want your data to be structured once, then you can use special generated source code to easily write and read your structured data to and from a variety of data streams and using a variety of languages. Protocol buffers currently support generated code in Java, Python, Objective-C, and C++. With our new proto3 language version, you can also work with Dart, Go, Ruby, and C#, with more languages to come. Package proto provides functions operating on protobuf messages such as cloning, merging, and checking equality, as well as binary serialization and text serialization. Package jsonpb serializes protobuf messages as JSON. Package ptypes provides helper functionality for protobuf well-known types. -
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J2J provides an intuitive way to convert Java to JSON and JSON to Java. Annotate any Java class then use JsonWriter to emit JSON from java or JsonReader to convert JSON to java objects. (source code and tutorial included) Version 2.0 in development.
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JSONAPI
jsonapi.org style payload serializer and deserializer
jsonapi provides helpers and reference code for working with the JSON:API specification, focusing on predictable serialization, deserialization, and linkage of related resources. It enforces the spec’s conventions—data, attributes, relationships, included—so clients and servers exchange data in a consistent, cacheable way. By centralizing how resource identifiers, links, and pagination metadata are emitted, it reduces subtle incompatibilities between services. The library favors explicit schemas and type-safe bindings where possible, which helps large teams avoid drift in field names and relationship structures. It also offers utilities for sparse fieldsets and compound documents, minimizing payload size while keeping related entities synchronized. With consistent error objects and status mapping, it makes API error handling uniform across clients. -
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Java Object Notation is an extension to the JSON data format suitable for the serialization of Java objects. The intuitive format and the simple yet extensible library allow both humans and computers to read and write complex object graphs.
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LEON
A simple data serialization format that emphasizes writability
LEON (Level Eight Object Notation) is a data serialization format for structured and non-structured data insired by XML and JSON and designed to be more human-readable and human-writable. -
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Twirp
A simple RPC framework with protobuf service definitions
Twirp is a simple RPC framework built on protobuf. You define a service in a .proto specification file, then Twirp will generate servers and clients for that service. It's your job to fill in the "business logic" that powers the server, and then generated clients can consume your service straight away. Twirp routing and serialization reduces the risk of introducing bugs. Both JSON and Protobuf are supported. The Protobuf protocol is designed to allow backwards compatible changes (unlike JSON, it is trivial to rename fields). Protobuf is super fast and the proto file definition also works well as documentation for your service. Twirp is similar to gRPC, but without the custom HTTP server and transport implementations: it runs on the standard library's extremely-well-tested-and-high-performance net/http Server. It can run on HTTP 1.1, not just http/2, and supports JSON serialization for easy debugging. -
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Xion (pronounced "zaion") is a text-based serialization format of object states. Xion extends the JSON format by permitting "tagged" notations. This project provides an implementation of Xion in Java, including the object model, parser and utilities.
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decimal
Arbitrary-precision fixed-point decimal numbers in go
Arbitrary-precision fixed-point decimal numbers in go. Note: Decimal library can "only" represent numbers with a maximum of 2^31 digits after the decimal point. The zero-value is 0, and is safe to use without initialization. Addition, subtraction, and multiplication with no loss of precision. Division with specified precision. Database/sql serialization/deserialization. JSON and XML serialization/deserialization. big.Int's API is built to reduce the number of memory allocations for maximal performance. This makes sense for its use-case, but the trade-off is that the API is awkward and easy to misuse. In contrast, it's difficult to make such mistakes with decimal. Decimals behave like other go numbers types: even though a = b will not deep copy b into a, it is impossible to modify a Decimal, since all Decimal methods return new Decimals and do not modify the originals. The downside is that this causes extra allocations, so Decimal is less performant.
