README.md

reflect-cpp

📖 Documentation: https://rfl.getml.com

reflect-cpp is a C++-20 library for fast serialization, deserialization and validation using reflection, similar to pydantic in Python, serde in Rust, encoding in Go or aeson in Haskell.

As the aforementioned libraries are among the most widely used in the respective languages, reflect-cpp fills an important gap in C++ development. It reduces boilerplate code and increases code safety.

Design principles for reflect-cpp include:

• Close integration with containers from the C++ standard library
• Close adherence to C++ idioms
• Out-of-the-box support for JSON
• Simple installation
• Simple extendability to other serialization formats
• Simple extendability to custom classes
• Being one of the fastest serialization libraries in existence, as demonstrated by our benchmarks

Table of Contents

On this page

• Serialization formats
• Feature Overview
  • Simple Example
  • More Comprehensive Example
  • Error messages
  • JSON schema
  • Enums
  • Algebraic data types
  • Extra fields
  • Reflective programming
  • Standard Library Integration
• The team behind reflect-cpp
• License

More in our documentation:

• Installation ↗
• Benchmarks ↗
• How to contribute ↗
• Compiling and running the tests ↗

Serialization formats

reflect-cpp provides a unified reflection-based interface across different serialization formats. It is deliberately designed in a very modular way, using concepts, to make it as easy as possible to interface various C or C++ libraries related to serialization. Refer to the documentation for details.

The following table lists the serialization formats currently supported by reflect-cpp and the underlying libraries used:

Format	Library	Version	License	Remarks
JSON	yyjson	>= 0.8.0	MIT	out-of-the-box support, included in this repository
Avro	avro-c	>= 1.11.3	Apache 2.0	Schemaful binary format
BSON	libbson	>= 1.25.1	Apache 2.0	JSON-like binary format
CBOR	tinycbor	>= 0.6.0	MIT	JSON-like binary format
flexbuffers	flatbuffers	>= 23.5.26	Apache 2.0	Schema-less version of flatbuffers, binary format
msgpack	msgpack-c	>= 6.0.0	BSL 1.0	JSON-like binary format
TOML	toml++	>= 3.4.0	MIT	Textual format with an emphasis on readability
UBJSON	jsoncons	>= 0.176.0	BSL 1.0	JSON-like binary format
XML	pugixml	>= 1.14	MIT	Textual format used in many legacy projects
YAML	yaml-cpp	>= 0.8.0	MIT	Textual format with an emphasis on readability

Support for more serialization formats is in development. Refer to the issues for details.

Please also refer to the conanfile.py or vcpkg.json in this repository.

Feature Overview

Simple Example

#include <rfl/json.hpp>
#include <rfl.hpp>

struct Person {
  std::string first_name;
  std::string last_name;
  int age;
};

const auto homer =
    Person{.first_name = "Homer",
           .last_name = "Simpson",
           .age = 45};

// We can now write into and read from a JSON string.
const std::string json_string = rfl::json::write(homer);
auto homer2 = rfl::json::read<Person>(json_string).value();

The resulting JSON string looks like this:

{"first_name":"Homer","last_name":"Simpson","age":45}

You can transform the field names from snake_case to camelCase like this:

const std::string json_string = 
  rfl::json::write<rfl::SnakeCaseToCamelCase>(homer);
auto homer2 = 
  rfl::json::read<Person, rfl::SnakeCaseToCamelCase>(json_string).value();

The resulting JSON string looks like this:

{"firstName":"Homer","lastName":"Simpson","age":45}

Or you can use another format, such as YAML.

#include <rfl/yaml.hpp>

// ... (same as above)

const std::string yaml_string = rfl::yaml::write(homer);
auto homer2 = rfl::yaml::read<Person>(yaml_string).value();

The resulting YAML string looks like this:

first_name: Homer
last_name: Simpson
age: 45

This will work for just about any example in the entire documentation and any supported format, except where explicitly noted otherwise:

rfl::avro::write(homer);
rfl::bson::write(homer);
rfl::cbor::write(homer);
rfl::flexbuf::write(homer);
rfl::msgpack::write(homer);
rfl::toml::write(homer);
rfl::ubjson::write(homer);
rfl::xml::write(homer);

rfl::avro::read<Person>(avro_bytes);
rfl::bson::read<Person>(bson_bytes);
rfl::cbor::read<Person>(cbor_bytes);
rfl::flexbuf::read<Person>(flexbuf_bytes);
rfl::msgpack::read<Person>(msgpack_bytes);
rfl::toml::read<Person>(toml_string);
rfl::ubjson::read<Person>(ubjson_bytes);
rfl::xml::read<Person>(xml_string);

More Comprehensive Example

#include <iostream>
#include <rfl/json.hpp>
#include <rfl.hpp>

// Age must be a plausible number, between 0 and 130. This will
// be validated automatically.
using Age = rfl::Validator<int, rfl::Minimum<0>, rfl::Maximum<130>>;

struct Person {
  rfl::Rename<"firstName", std::string> first_name;
  rfl::Rename<"lastName", std::string> last_name = "Simpson";
  std::string town = "Springfield";
  rfl::Timestamp<"%Y-%m-%d"> birthday;
  Age age;
  rfl::Email email;
  std::vector<Person> children;
};

const auto bart = Person{.first_name = "Bart",
                         .birthday = "1987-04-19",
                         .age = 10,
                         .email = "[email protected]"};

const auto lisa = Person{.first_name = "Lisa",
                         .birthday = "1987-04-19",
                         .age = 8,
                         .email = "[email protected]"};

const auto maggie = Person{.first_name = "Maggie",
                           .birthday = "1987-04-19",
                           .age = 0,
                           .email = "[email protected]"};

const auto homer =
    Person{.first_name = "Homer",
           .birthday = "1987-04-19",
           .age = 45,
           .email = "[email protected]",
           .children = std::vector<Person>({bart, lisa, maggie})};

// We can now transform this into a JSON string.
const std::string json_string = rfl::json::write(homer);
std::cout << json_string << std::endl;

// We can also directly write into std::cout (or any other std::ostream).
rfl::json::write(homer, std::cout) << std::endl;

This results in the following JSON string:

{"firstName":"Homer","lastName":"Simpson","town":"Springfield","birthday":"1987-04-19","age":45,"email":"[email protected]","children":[{"firstName":"Bart","lastName":"Simpson","town":"Springfield","birthday":"1987-04-19","age":10,"email":"[email protected]","children":[]},{"firstName":"Lisa","lastName":"Simpson","town":"Springfield","birthday":"1987-04-19","age":8,"email":"[email protected]","children":[]},{"firstName":"Maggie","lastName":"Simpson","town":"Springfield","birthday":"1987-04-19","age":0,"email":"[email protected]","children":[]}]}

We can also create structs from the string:

auto homer2 = rfl::json::read<Person>(json_string).value();

// Fields can be accessed like this:
std::cout << "Hello, my name is " << homer.first_name() << " "
          << homer.last_name() << "." << std::endl;

// Since homer2 is mutable, we can also change the values like this:
homer2.first_name = "Marge";

std::cout << "Hello, my name is " << homer2.first_name() << " "
          << homer2.last_name() << "." << std::endl;

Error messages

reflect-cpp returns clear and comprehensive error messages:

const std::string faulty_json_string =
    R"({"firstName":"Homer","lastName":12345,"town":"Springfield","birthday":"04/19/1987","age":145,"email":"homer(at)simpson.com"})";
const auto result = rfl::json::read<Person>(faulty_json_string);

Yields the following error message:

Found 5 errors:
1) Failed to parse field 'lastName': Could not cast to string.
2) Failed to parse field 'birthday': String '04/19/1987' did not match format '%Y-%m-%d'.
3) Failed to parse field 'age': Value expected to be less than or equal to 130, but got 145.
4) Failed to parse field 'email': String 'homer(at)simpson.com' did not match format 'Email': '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'.
5) Field named 'children' not found.

JSON schema

reflect-cpp also supports generating JSON schemata:

struct Person {
  std::string first_name;
  std::string last_name;
  rfl::Description<"Must be a proper email in the form [email protected].",
                   rfl::Email>
      email;
  rfl::Description<
      "The person's children. Pass an empty array for no children.",
      std::vector<Person>>
      children;
  float salary;
};

const std::string json_schema = rfl::json::to_schema<Person>();

The resulting JSON schema looks like this:

{"$schema":"https://json-schema.org/draft/2020-12/schema","$ref":"#/definitions/Person","definitions":{"Person":{"type":"object","properties":{"children":{"type":"array","description":"The person's children. Pass an empty array for no children.","items":{"$ref":"#/definitions/Person"}},"email":{"type":"string","description":"Must be a proper email in the form [email protected].","pattern":"^[a-zA-Z0-9._%+\\-]+@[a-zA-Z0-9.\\-]+\\.[a-zA-Z]{2,}$"},"first_name":{"type":"string"},"last_name":{"type":"string"},"salary":{"type":"number"}},"required":["children","email","first_name","last_name","salary"]}}}

Note that this is currently supported for JSON only, since most other formats do not support schemata in the first place.

Enums

reflect-cpp supports scoped enumerations:

enum class Shape { circle, square, rectangle };

enum class Color { red = 256, green = 512, blue = 1024, yellow = 2048 };

struct Item {
  float pos_x;
  float pos_y;
  Shape shape;
  Color color;
};

const auto item = Item{.pos_x = 2.0,  
                       .pos_y = 3.0,
                       .shape = Shape::square,
                       .color = Color::red | Color::blue};

rfl::json::write(item);

This results in the following JSON string:

{"pos_x":2.0,"pos_y":3.0,"shape":"square","color":"red|blue"}

You can also directly convert between enumerator values and strings with rfl::enum_to_string() and rfl::string_to_enum(), or obtain list of enumerator name and value pairs with rfl::get_enumerators<EnumType>() or rfl::get_enumerator_array<EnumType>().

Algebraic data types

reflect-cpp supports Pydantic-style tagged unions, which allow you to form algebraic data types:

struct Circle {
    double radius;
};

struct Rectangle {
    double height;
    double width;
};

struct Square {
    double width;
};

using Shapes = rfl::TaggedUnion<"shape", Circle, Square, Rectangle>;

const Shapes r = Rectangle{.height = 10, .width = 5};

const auto json_string = rfl::json::write(r);

This results in the following JSON string:

{"shape":"Rectangle","height":10.0,"width":5.0}

Other forms of tagging are supported as well. Refer to the documentation for details.

Extra fields

If you don't know all of your fields at compile time, no problem. Just use rfl::ExtraFields:

struct Person {
  std::string first_name;
  std::string last_name = "Simpson";
  rfl::ExtraFields<rfl::Generic> extra_fields;
};

auto homer = Person{.first_name = "Homer"};

homer.extra_fields["age"] = 45;
homer.extra_fields["email"] = "[email protected]";
homer.extra_fields["town"] = "Springfield";

This results in the following JSON string:

{"firstName":"Homer","lastName":"Simpson","age":45,"email":"[email protected]","town":"Springfield"}

Reflective programming

Beyond serialization and deserialization, reflect-cpp also supports reflective programming in general.

For instance:

struct Person {
  std::string first_name;
  std::string last_name = "Simpson";
  std::string town = "Springfield";
  unsigned int age;
  std::vector<Person> children;
};

for (const auto& f : rfl::fields<Person>()) {
  std::cout << "name: " << f.name() << ", type: " << f.type() << std::endl;
}

You can also create a view and then access these fields using std::get or rfl::get, or iterate over the fields at compile-time:

auto lisa = Person{.first_name = "Lisa", .last_name = "Simpson", .age = 8};

const auto view = rfl::to_view(lisa);

// view.values() is a std::tuple containing
// pointers to the original fields.
// This will modify the struct `lisa`:
*std::get<0>(view.values()) = "Maggie";

// All of this is supported as well:
*view.get<1>() = "Simpson";
*view.get<"age">() = 0;
*rfl::get<0>(view) = "Maggie";
*rfl::get<"first_name">(view) = "Maggie";

view.apply([](const auto& f) {
  // f is an rfl::Field pointing to the original field.
  std::cout << f.name() << ": " << rfl::json::write(*f.value()) << std::endl;
});

It also possible to replace fields:

struct Person {
  std::string first_name;
  std::string last_name;
  std::vector<Person> children;
};

const auto lisa = Person{.first_name = "Lisa", .last_name = "Simpson"};

// Returns a deep copy of "lisa" with the first_name replaced.
const auto maggie = rfl::replace(
    lisa, rfl::make_field<"first_name">(std::string("Maggie")));

Or you can create structs from other structs:

struct A {
  std::string f1;
  std::string f2;
};

struct B {
  std::string f3;
  std::string f4;
};

struct C {
  std::string f1;
  std::string f2;
  std::string f4;
};

const auto a = A{.f1 = "Hello", .f2 = "World"};

const auto b = B{.f3 = "Hello", .f4 = "World"};

// f1 and f2 are taken from a, f4 is taken from b, f3 is ignored.
const auto c = rfl::as<C>(a, b);

You can also replace fields in structs using fields from other structs:

const auto a = A{.f1 = "Hello", .f2 = "World"};

const auto c = C{.f1 = "C++", .f2 = "is", .f4 = "great"};

// The fields f1 and f2 are replaced with the fields f1 and f2 in a.
const auto c2 = rfl::replace(c, a);

Support for containers

C++ standard library

reflect-cpp supports the following containers from the C++ standard library:

• std::array
• std::deque
• std::filesystem::path
• std::forward_list
• std::map
• std::multimap
• std::multiset
• std::list
• std::optional
• std::pair
• std::set
• std::shared_ptr
• std::string
• std::tuple
• std::unique_ptr
• std::unordered_map
• std::unordered_multimap
• std::unordered_multiset
• std::unordered_set
• std::variant
• std::vector
• std::wstring

Additional containers

In addition, it supports the following custom containers:

• rfl::Binary: Used to express numbers in binary format.
• rfl::Box: Similar to std::unique_ptr, but (almost) guaranteed to never be null.
• rfl::Bytestring: An alias for std::basic_string<std::byte>. Supported by BSON, CBOR, flexbuffers, msgpack and UBJSON.
• rfl::Generic: A catch-all type that can represent (almost) anything.
• rfl::Hex: Used to express numbers in hex format.
• rfl::Literal: An explicitly enumerated string.
• rfl::NamedTuple: Similar to std::tuple, but with named fields that can be retrieved via their name at compile time.
• rfl::Object: A map-like type representing a object with field names that are unknown at compile time.
• rfl::Oct: Used to express numbers in octal format.
• rfl::Ref: Similar to std::shared_ptr, but (almost) guaranteed to never be null.
• rfl::Result: Allows for exception-free programming.
• rfl::TaggedUnion: Similar to std::variant, but with explicit tags that make parsing more efficient.
• rfl::Tuple: An alternative to std::tuple that compiles considerably faster.
• rfl::Validator: Allows for automatic input validation.
• rfl::Variant: An alternative to std::variant that compiles considerably faster.

Custom classes

Finally, it is very easy to extend full support to your own classes, refer to the documentation for details.

Installation

The following compilers are supported:

• GCC 11.4 or higher
• Clang 14.0 or higher
• MSVC 17.8 (19.38) or higher

Using vcpkg

https://vcpkg.io/en/package/reflectcpp

Using Conan

https://conan.io/center/recipes/reflect-cpp

Compilation using cmake

This will compile reflect-cpp with JSON support only. You can then include reflect-cpp in your project and link to the binary.

cmake -S . -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j 4  # gcc, clang
cmake --build build --config Release -j 4  # MSVC

If you need support for any other supported serialization formats, refer to the documentation for installation instructions.

You can also include the source files into your build or compile it using cmake and vcpkg. For detailed installation instructions, please refer to the install guide.

The team behind reflect-cpp

reflect-cpp has been developed by getML (Code17 GmbH), a company specializing in software engineering and machine learning for enterprise applications. reflect-cpp is currently maintained by Patrick Urbanke and Manuel Bellersen, with major contributions coming from the community.

Related projects

reflect-cpp was originally developed for getml-community, the fastest open-source tool for feature engineering on relational data and time series. If you are interested in Data Science and/or Machine Learning, please check it out.

Professional C++ Support

For comprehensive C++ support beyond the scope of GitHub discussions, we’re here to help! Reach out at [email protected] to discuss any technical challenges or project requirements. We’re excited to support your work as independent software consultants.

License

reflect-cpp is released under the MIT License. Refer to the LICENSE file for details.

reflect-cpp includes YYJSON, the fastest JSON library currently in existence. YYJSON is written by YaoYuan and also released under the MIT License.

reflect-cpp includes compile-time-regular-expressions. CTRE is written by Hana Dusíková and released under the Apache-2.0 License with LLVM exceptions.