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https://github.com/biojppm/rapidyaml

Rapid YAML - a library to parse and emit YAML, and do it fast.
https://github.com/biojppm/rapidyaml

cplusplus cpp11 custom-allocator emitter javascript-library json parser python-library rapid serialization stl yaml

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Rapid YAML - a library to parse and emit YAML, and do it fast.

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README 

        
# Rapid YAML

[![MIT Licensed](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/biojppm/rapidyaml/blob/master/LICENSE.txt)

[![release](https://img.shields.io/github/v/release/biojppm/rapidyaml?color=g&include_prereleases&label=release%20&sort=semver)](https://github.com/biojppm/rapidyaml/releases)

[![Documentation Status](https://readthedocs.org/projects/rapidyaml/badge/?version=latest)](https://rapidyaml.readthedocs.io/latest/?badge=latest)



[![PyPI](https://img.shields.io/pypi/v/rapidyaml?color=g)](https://pypi.org/project/rapidyaml/)

[![Gitter](https://badges.gitter.im/rapidyaml/community.svg)](https://gitter.im/rapidyaml/community)



[![Codecov](https://codecov.io/gh/biojppm/rapidyaml/branch/master/graph/badge.svg?branch=master)](https://codecov.io/gh/biojppm/rapidyaml)



Or ryml, for short. ryml is a C++ library to parse and emit YAML,

and do it fast, on everything from x64 to bare-metal chips without

operating system. (If you are looking to use your programs with a YAML tree

as a configuration tree with override facilities, take a look at

[c4conf](https://github.com/biojppm/c4conf)).



ryml parses both read-only and in-situ source buffers; the resulting

data nodes hold only views to sub-ranges of the source buffer. No

string copies or duplications are done, and no virtual functions are

used. The data tree is a flat index-based structure stored in a single

array. Serialization happens only at your direct request, after

parsing / before emitting. Internally, the data tree representation

stores only string views and has no knowledge of types, but of course,

every node can have a YAML type tag. ryml makes it easy and fast to

read and modify the data tree.



ryml is available as a single header file, or it can be used as a

simple library with cmake -- both separately (ie

build->install->`find_package()`) or together with your project (ie with

`add_subdirectory()`). (See below for examples).



ryml can use custom global and per-tree memory allocators and error

handler callbacks, and is exception-agnostic. ryml provides a default

implementation for the allocator (using `std::malloc()`) and error

handlers (using using either exceptions, `longjmp()` or

`std::abort()`), but you can opt out and provide your own memory

allocation and eg, exception-throwing callbacks.



ryml does not depend on the STL, ie, it does not use any std container

as part of its data structures), but it can serialize and deserialize

these containers into the data tree, with the use of optional

headers. ryml ships with [c4core](https://github.com/biojppm/c4core), a

small C++ utilities multiplatform library.



ryml is written in C++11, and compiles cleanly with:

* Visual Studio 2015 and later

* clang++ 3.9 and later

* g++ 4.8 and later

* Intel Compiler



ryml's API documentation is [available at

ReadTheDocs](https://rapidyaml.readthedocs.io/latest/).



ryml is [extensively unit-tested in Linux, Windows and

MacOS](https://github.com/biojppm/rapidyaml/actions). The tests cover

x64, x86, wasm (emscripten), arm, aarch64, ppc64le and s390x

architectures, and include analysing ryml with:

  * valgrind

  * clang-tidy

  * gcc/clang sanitizers:

    * memory

    * address

    * undefined behavior



ryml also [runs in

bare-metal](https://github.com/biojppm/rapidyaml/issues/193), and

[RISC-V

architectures](https://github.com/biojppm/c4core/pull/69). Both of

these are pending implementation of CI actions for continuous

validation, but ryml has been proven to work there.



ryml is [available in Python](https://pypi.org/project/rapidyaml/),

and can very easily be compiled to JavaScript through emscripten (see

below).



See also [the changelog](https://github.com/biojppm/rapidyaml/tree/master/changelog)

and [the roadmap](https://github.com/biojppm/rapidyaml/tree/master/ROADMAP.md).



------



## Table of contents

* [Is it rapid?](#is-it-rapid)

  * [Comparison with yaml-cpp](#comparison-with-yaml-cpp)

  * [Performance reading JSON](#performance-reading-json)

  * [Performance emitting](#performance-emitting)

* [Quick start](#quick-start)

* [Using ryml in your project](#using-ryml-in-your-project)

  * [Package managers](#package-managers)

  * [Single header file](#single-header-file)

  * [As a library](#as-a-library)

  * [Quickstart samples](#quickstart-samples)

  * [CMake build settings for ryml](#cmake-build-settings-for-ryml)

     * [Forcing ryml to use a different c4core version](#forcing-ryml-to-use-a-different-c4core-version)

* [Other languages](#other-languages)

  * [JavaScript](#javascript)

  * [Python](#python)

* [YAML standard conformance](#yaml-standard-conformance)

  * [Test suite status](#test-suite-status)

* [Known limitations](#known-limitations)

* [Alternative libraries](#alternative-libraries)

* [License](#license)



------



## Is it rapid?



You bet! On a i7-6800K CPU @3.40GHz:

 * ryml parses YAML at about ~150MB/s on Linux and ~100MB/s on Windows (vs2017). 

 * **ryml parses JSON at about ~450MB/s on Linux**, faster than sajson (didn't

   try yet on Windows).

 * compared against the other existing YAML libraries for C/C++:

   * ryml is in general between 2 and 3 times faster than [libyaml](https://github.com/yaml/libyaml)

   * ryml is in general between 10 and 70 times faster than

     [yaml-cpp](https://github.com/jbeder/yaml-cpp), and in some cases as

     much as 100x and [even

     200x](https://github.com/biojppm/c4core/pull/16#issuecomment-700972614) faster.



[Here's the benchmark](./bm/bm_parse.cpp). Using different

approaches within ryml (in-situ/read-only vs. with/without reuse), a YAML /

JSON buffer is repeatedly parsed, and compared against other libraries.



### Comparison with yaml-cpp



The first result set is for Windows, and is using a [appveyor.yml config

file](./bm/cases/appveyor.yml). A comparison of these results is

summarized on the table below:



| Read rates (MB/s)            | ryml   | yamlcpp | compared     |

|------------------------------|--------|---------|--------------|

| appveyor / vs2017 / Release  | 101.5  | 5.3     |  20x / 5.2%  |

| appveyor / vs2017 / Debug    |   6.4  | 0.0844  |  76x / 1.3%  |



The next set of results is taken in Linux, comparing g++ 8.2 and clang++ 7.0.1 in

parsing a YAML buffer from a [travis.yml config

file](./bm/cases/travis.yml) or a JSON buffer from a [compile_commands.json

file](./bm/cases/compile_commands.json). You

can [see the full results here](./bm/results/parse.linux.i7_6800K.md).

Summarizing:



| Read rates (MB/s)           | ryml   | yamlcpp | compared   |

|-----------------------------|--------|---------|------------|

| json   / clang++ / Release  | 453.5  | 15.1    |  30x / 3%  |

| json   /     g++ / Release  | 430.5  | 16.3    |  26x / 4%  |

| json   / clang++ / Debug    |  61.9  | 1.63    |  38x / 3%  |

| json   /     g++ / Debug    |  72.6  | 1.53    |  47x / 2%  |

| travis / clang++ / Release  | 131.6  | 8.08    |  16x / 6%  |

| travis /     g++ / Release  | 176.4  | 8.23    |  21x / 5%  |

| travis / clang++ / Debug    |  10.2  | 1.08    |   9x / 1%  |

| travis /     g++ / Debug    |  12.5  | 1.01    |  12x / 8%  |



The 450MB/s read rate for JSON puts ryml squarely in the same ballpark

as [RapidJSON](https://github.com/Tencent/rapidjson) and other fast json

readers

([data from here](https://lemire.me/blog/2018/05/03/how-fast-can-you-parse-json/)).

Even parsing full YAML is at ~150MB/s, which is still in that performance

ballpark, albeit at its lower end. This is something to be proud of, as the

YAML specification is much more complex than JSON: [23449 vs 1969 words](https://www.arp242.net/yaml-config.html#its-pretty-complex).



### Performance reading JSON



So how does ryml compare against other JSON readers? Well, it's one of the

fastest! 



The benchmark is the [same as above](./bm/parse.cpp), and it is reading

the [compile_commands.json](./bm/cases/compile_commands.json), The `_arena`

suffix notes parsing a read-only buffer (so buffer copies are performed),

while the `_inplace` suffix means that the source buffer can be parsed in

place. The `_reuse` means the data tree and/or parser are reused on each

benchmark repeat.



Here's what we get with g++ 8.2:



| Benchmark             | Release,MB/s | Debug,MB/s  |

|:----------------------|-------------:|------------:|

| rapidjson_arena       |       509.9  |       43.4  |

| rapidjson_inplace     |      1329.4  |       68.2  |

| sajson_inplace        |       434.2  |      176.5  |

| sajson_arena          |       430.7  |      175.6  |

| jsoncpp_arena         |       183.6  |    ? 187.9  |

| nlohmann_json_arena   |       115.8  |       21.5  |

| yamlcpp_arena         |        16.6  |        1.6  |

| libyaml_arena         |       113.9  |       35.7  |

| libyaml_arena_reuse   |       114.6  |       35.9  |

| ryml_arena            |       388.6  |       36.9  |

| ryml_inplace          |       393.7  |       36.9  |

| ryml_arena_reuse      |       446.2  |       74.6  |

| ryml_inplace_reuse    |       457.1  |       74.9  |



You can verify that (at least for this test) ryml beats most json

parsers at their own game, with the only exception of

[rapidjson](https://github.com/Tencent/rapidjson). And actually, in

Debug, [rapidjson](https://github.com/Tencent/rapidjson) is slower

than ryml, and [sajson](https://github.com/chadaustin/sajson)

manages to be faster (but not sure about jsoncpp; need to scrutinize there

the suspicious fact that the Debug result is faster than the Release result).



### Performance emitting



[Emitting benchmarks](bm/bm_emit.cpp) also show similar speedups from

the existing libraries, also anecdotally reported by some users [(eg,

here's a user reporting 25x speedup from

yaml-cpp)](https://github.com/biojppm/rapidyaml/issues/28#issue-553855608). Also, in

some cases (eg, block folded multiline scalars), the speedup is as

high as 200x (eg, 7.3MB/s -> 1.416MG/s).



### CI results and request for files



While a more effective way of showing the benchmark results is not

available yet, you can browse through the [runs of the benchmark

workflow in the

CI](https://github.com/biojppm/rapidyaml/actions/workflows/benchmarks.yml)

to scroll through the results for yourself.



Also, if you have a case where ryml behaves very nicely or not as

nicely as claimed above, we would definitely like to see it! Please

open an issue, or submit a pull request adding the file to

[bm/cases](bm/cases), or just send us the files.



------



## Quick start



If you're wondering whether ryml's speed comes at a usage cost, you

need not: with ryml, you can have your cake and eat it too. Being

rapid is definitely NOT the same as being unpractical, so ryml was

written with easy AND efficient usage in mind, and comes with a two

level API for accessing and traversing the data tree.



The following snippet is a very quick overview taken from quickstart

sample ([see on

doxygen](https://rapidyaml.readthedocs.io/latest/group__doc__quickstart.html)/[see

on github](samples/quickstart.cpp). After cloning ryml

(don't forget the `--recursive` flag for git), you can very easily

build and run this executable using any of the build samples, eg the

[`add_subdirectory()` sample](samples/add_subdirectory/) (see [the relevant section](#quickstart-samples)).



```cpp

// Parse YAML code in place, potentially mutating the buffer:

char yml_buf[] = "{foo: 1, bar: [2, 3], john: doe}";

ryml::Tree tree = ryml::parse_in_place(yml_buf);



// ryml has a two-level API:

//

// The lower level index API is based on the indices of nodes,

// where the node's id is the node's position in the tree's data

// array. This API is very efficient, but somewhat difficult to use:

size_t root_id = tree.root_id();

size_t bar_id = tree.find_child(root_id, "bar"); // need to get the index right

CHECK(tree.is_map(root_id)); // all of the index methods are in the tree

CHECK(tree.is_seq(bar_id));  // ... and receive the subject index



// The node API is a lightweight abstraction sitting on top of the

// index API, but offering a much more convenient interaction:

ryml::ConstNodeRef root = tree.rootref();  // a const node reference

ryml::ConstNodeRef bar = tree["bar"];

CHECK(root.is_map());

CHECK(bar.is_seq());



// The resulting tree stores only string views to the YAML source buffer.

CHECK(root["foo"] == "1");

CHECK(root["foo"].key().str == yml_buf + 1);

CHECK(bar[0] == "2");

CHECK(root["john"] == "doe");



//------------------------------------------------------------------

// To get actual values, you need to deserialize the nodes.

// Deserializing: use operator>>

{

    int foo = 0, bar0 = 0, bar1 = 0;

    std::string john_str;

    std::string bar_str;

    root["foo"] >> foo;

    root["bar"][0] >> bar0;

    root["bar"][1] >> bar1;

    root["john"] >> john_str; // requires from_chars(std::string). see API doc.

    root["bar"] >> ryml::key(bar_str); // to deserialize the key, use the tag function ryml::key()

    CHECK(foo == 1);

    CHECK(bar0 == 2);

    CHECK(bar1 == 3);

    CHECK(john_str == "doe");

    CHECK(bar_str == "bar");

}



//------------------------------------------------------------------

// To modify existing nodes, use operator= or operator<<.



// operator= assigns an existing string to the receiving node.

// The contents are NOT copied, and this pointer will be in effect

// until the tree goes out of scope! So BEWARE to only assign from

// strings outliving the tree.

wroot["foo"] = "says you";

wroot["bar"][0] = "-2";

wroot["bar"][1] = "-3";

wroot["john"] = "ron";

// Now the tree is _pointing_ at the memory of the strings above.

// In this case it is OK because those are static strings and will

// outlive the tree.

CHECK(root["foo"].val() == "says you");

CHECK(root["bar"][0].val() == "-2");

CHECK(root["bar"][1].val() == "-3");

CHECK(root["john"].val() == "ron");

// But WATCHOUT: do not assign from temporary objects:

// {

//     std::string crash("will dangle");

//     root["john"] = ryml::to_csubstr(crash);

// }

// CHECK(root["john"] == "dangling"); // CRASH! the string was deallocated



// operator<< first serializes the input to the tree's arena, then

// assigns the serialized string to the receiving node. This avoids

// constraints with the lifetime, since the arena lives with the tree.

CHECK(tree.arena().empty());

wroot["foo"] << "says who";  // requires to_chars(). see serialization samples below.

wroot["bar"][0] << 20;

wroot["bar"][1] << 30;

wroot["john"] << "deere";

CHECK(root["foo"].val() == "says who");

CHECK(root["bar"][0].val() == "20");

CHECK(root["bar"][1].val() == "30");

CHECK(root["john"].val() == "deere");

CHECK(tree.arena() == "says who2030deere"); // the result of serializations to the tree arena



//------------------------------------------------------------------

// Adding new nodes:



// adding a keyval node to a map:

CHECK(root.num_children() == 5);

wroot["newkeyval"] = "shiny and new"; // using these strings

wroot.append_child() << ryml::key("newkeyval (serialized)") << "shiny and new (serialized)"; // serializes and assigns the serialization

CHECK(root.num_children() == 7);

CHECK(root["newkeyval"].key() == "newkeyval");

CHECK(root["newkeyval"].val() == "shiny and new");

CHECK(root["newkeyval (serialized)"].key() == "newkeyval (serialized)");

CHECK(root["newkeyval (serialized)"].val() == "shiny and new (serialized)");



//------------------------------------------------------------------

// Emitting:



ryml::csubstr expected_result = R"(foo: says who

bar:

- 20

- 30

- oh so nice

- oh so nice (serialized)

john: in_scope

float: 2.4

digits: 2.400000

newkeyval: shiny and new

newkeyval (serialized): shiny and new (serialized)

newseq: []

newseq (serialized): []

newmap: {}

newmap (serialized): {}

I am something: indeed

)";



// emit to a FILE*

ryml::emit_yaml(tree, stdout);

// emit to a stream

std::stringstream ss;

ss << tree;

std::string stream_result = ss.str();

// emit to a buffer:

std::string str_result = ryml::emitrs_yaml(tree);

// can emit to any given buffer:

char buf[1024];

ryml::csubstr buf_result = ryml::emit_yaml(tree, buf);

// now check

CHECK(buf_result == expected_result);

CHECK(str_result == expected_result);

CHECK(stream_result == expected_result);



//------------------------------------------------------------------

// UTF8

ryml::Tree langs = ryml::parse_in_arena(R"(

en: Planet (Gas)

fr: Planète (Gazeuse)

ru: Планета (Газ)

ja: 惑星（ガス）

zh: 行星（气体）

# UTF8 decoding only happens in double-quoted strings,

# as per the YAML standard

decode this: "\u263A \xE2\x98\xBA"

and this as well: "\u2705 \U0001D11E"

not decoded: '\u263A \xE2\x98\xBA'

neither this: '\u2705 \U0001D11E'

)");

// in-place UTF8 just works:

CHECK(langs["en"].val() == "Planet (Gas)");

CHECK(langs["fr"].val() == "Planète (Gazeuse)");

CHECK(langs["ru"].val() == "Планета (Газ)");

CHECK(langs["ja"].val() == "惑星（ガス）");

CHECK(langs["zh"].val() == "行星（气体）");

// and \x \u \U codepoints are decoded, but only when they appear

// inside double-quoted strings, as dictated by the YAML

// standard:

CHECK(langs["decode this"].val() == "☺ ☺");

CHECK(langs["and this as well"].val() == "✅ 𝄞");

CHECK(langs["not decoded"].val() == "\\u263A \\xE2\\x98\\xBA");

CHECK(langs["neither this"].val() == "\\u2705 \\U0001D11E");



//------------------------------------------------------------------

// Getting the location of nodes in the source:

//

// Location tracking is opt-in:

ryml::Parser parser(ryml::ParserOptions().locations(true));

// Now the parser will start by building the accelerator structure:

ryml::Tree tree2 = parser.parse_in_arena("expected.yml", expected_result);

// ... and use it when querying

ryml::Location loc = parser.location(tree2["bar"][1]);

CHECK(parser.location_contents(loc).begins_with("30"));

CHECK(loc.line == 3u);

CHECK(loc.col == 4u);

```



------



## Using ryml in your project



### Single header file

ryml is provided chiefly as a cmake library project, but it can also

be used as a single header file, and there is a [tool to

amalgamate](./tools/amalgamate.py) the code into a single header

file. The amalgamated header file is provided with each release, but

you can also generate a customized file suiting your particular needs

(or commit):



```console

[user@host rapidyaml]$ python3 tools/amalgamate.py -h

usage: amalgamate.py [-h] [--c4core | --no-c4core] [--fastfloat | --no-fastfloat] [--stl | --no-stl] [output]



positional arguments:

  output          output file. defaults to stdout



optional arguments:

  -h, --help      show this help message and exit

  --c4core        amalgamate c4core together with ryml. this is the default.

  --no-c4core     amalgamate c4core together with ryml. the default is --c4core.

  --fastfloat     enable fastfloat library. this is the default.

  --no-fastfloat  enable fastfloat library. the default is --fastfloat.

  --stl           enable stl interop. this is the default.

  --no-stl        enable stl interop. the default is --stl.

```



The amalgamated header file contains all the function declarations and

definitions. To use it in the project, `#include` the header at will

in any header or source file in the project, but in one source file,

and only in that one source file, `#define` the macro

`RYML_SINGLE_HDR_DEFINE_NOW` **before including the header**. This

will enable the function definitions. For example:

```c++

// foo.h

#include 



// foo.cpp

// ensure that foo.h is not included before this define!

#define RYML_SINGLE_HDR_DEFINE_NOW

#include 

```



If you wish to package the single header into a shared library, then

you will need to define the preprocessor symbol `RYML_SHARED` during

compilation.



### As a library

The single header file is a good approach to quickly try the library,

but if you wish to make good use of CMake and its tooling ecosystem,

(and get better compile times), then ryml has you covered.



As with any other cmake library, you have the option to integrate ryml into

your project's build setup, thereby building ryml together with your

project, or -- prior to configuring your project -- you can have ryml

installed either manually or through package managers.



Currently [cmake](https://cmake.org/) is required to build ryml; we

recommend a recent cmake version, at least 3.13.



Note that ryml uses submodules. Take care to use the `--recursive` flag

when cloning the repo, to ensure ryml's submodules are checked out as well:

```bash

git clone --recursive https://github.com/biojppm/rapidyaml

```

If you omit `--recursive`, after cloning you

will have to do `git submodule update --init --recursive`

to ensure ryml's submodules are checked out.



### Package managers



ryml is available in most package managers (thanks to all the

contributors!) and linux distributions. But please be aware: those

packages are maintained downstream of this repository, so if you have

issues with the package, file a report with the respective maintainer.



Here's a quick roundup (not maintained):

* Package managers:

  * [conan](https://conan.io/center/recipes/rapidyaml)

  * [vcpkg](https://vcpkg.io/en/packages.html): `vcpkg install ryml`

  * [PyPI](https://pypi.org/project/rapidyaml/)

* Linux distributions:

  * Arch Linux/Manjaro:

    * [rapidyaml (aarch64)](https://archlinuxarm.org/packages/aarch64/rapidyaml)

    * [rapidyaml-git (AUR)](https://aur.archlinux.org/packages/rapidyaml-git/)

    * [python-rapidyaml-git (AUR)](https://aur.archlinux.org/packages/python-rapidyaml-git/)

  * [Fedora Linux](https://getfedora.org/)/[EPEL](https://docs.fedoraproject.org/en-US/epel/):

    * `dnf install rapidyaml-devel`

    * `dnf install python3-rapidyaml`

  * [Gentoo](https://packages.gentoo.org/packages/dev-cpp/rapidyaml)

  * [OpenSuse](https://build.openbuildservice.org/package/show/Emulators/rapidyaml)

  * [Slackbuilds](https://slackbuilds.org/repository/15.0/libraries/rapidyaml/)

  * [AltLinux](https://packages.altlinux.org/en/sisyphus/srpms/rapidyaml/3006055151670528141)



Although package managers are very useful for quickly getting up to

speed, the advised way is still to bring ryml as a submodule of your

project, building both together. This makes it easy to track any

upstream changes in ryml. Also, ryml is small and quick to build, so

there's not much of a cost for building it with your project.



### Quickstart samples



These samples show different ways of getting ryml into your application. All the

samples use [the same quickstart executable

source](./samples/quickstart.cpp), but are built in different ways,

showing several alternatives to integrate ryml into your project. We

also encourage you to refer to the [quickstart source](./samples/quickstart.cpp) itself, which

extensively covers most of the functionality that you may want out of

ryml.



Each sample brings a `run.sh` script with the sequence of commands

required to successfully build and run the application (this is a bash

script and runs in Linux and MacOS, but it is also possible to run in

Windows via Git Bash or the WSL). Click on the links below to find out

more about each sample:



| Sample name        | ryml is part of build?   | cmake file   | commands     |

|:-------------------|--------------------------|:-------------|:-------------|

| [`singleheader`](./samples/singleheader) | **yes**
ryml brought as a single header file,
not as a library | [`CMakeLists.txt`](./samples/singleheader/CMakeLists.txt) | [`run.sh`](./samples/singleheader/run.sh) |

| [`singleheaderlib`](./samples/singleheaderlib) | **yes**
ryml brought as a library
but from the single header file | [`CMakeLists.txt`](./samples/singleheaderlib/CMakeLists.txt) | [`run_shared.sh` (shared library)](./samples/singleheaderlib/run_shared.sh)
 [`run_static.sh` (static library)](./samples/singleheaderlib/run_static.sh) |

| [`add_subdirectory`](./samples/add_subdirectory) | **yes**                      | [`CMakeLists.txt`](./samples/add_subdirectory/CMakeLists.txt) | [`run.sh`](./samples/add_subdirectory/run.sh) |

| [`fetch_content`](./samples/fetch_content)      | **yes**                      | [`CMakeLists.txt`](./samples/fetch_content/CMakeLists.txt) | [`run.sh`](./samples/fetch_content/run.sh) |

| [`find_package`](./samples/find_package)        | **no**
needs prior install or package  | [`CMakeLists.txt`](./samples/find_package/CMakeLists.txt) | [`run.sh`](./samples/find_package/run.sh) |



### CMake build settings for ryml

The following cmake variables can be used to control the build behavior of

ryml:



  * `RYML_WITH_TAB_TOKENS=ON/OFF`. Enable/disable support for tabs as

    valid container tokens after `:` and `-`. Defaults to `OFF`,

    because this may cost up to 10% in processing time.

  * `RYML_DEFAULT_CALLBACKS=ON/OFF`. Enable/disable ryml's default

    implementation of error and allocation callbacks. Defaults to `ON`.

  * `RYML_DEFAULT_CALLBACK_USES_EXCEPTIONS=ON/OFF` - Enable/disable

    the same-named macro, which will make the default error handler

    provided by ryml throw a `std::runtime_error` exception.

  * `RYML_USE_ASSERT` - enable assertions in the code regardless of

    build type. This is disabled by default. Failed assertions will

    trigger a call to the error callback.

  * `RYML_STANDALONE=ON/OFF`. ryml uses

    [c4core](https://github.com/biojppm/c4core), a C++ library with low-level

    multi-platform utilities for C++. When `RYML_STANDALONE=ON`, c4core is

    incorporated into ryml as if it is the same library. Defaults to `ON`.

  * `RYML_INSTALL=ON/OFF`. enable/disable install target. Defaults to `ON`.



If you're developing ryml or just debugging problems with ryml itself, the

following cmake variables can be helpful:

  * `RYML_DEV=ON/OFF`: a bool variable which enables development targets such as

    unit tests, benchmarks, etc. Defaults to `OFF`.

  * `RYML_DBG=ON/OFF`: a bool variable which enables verbose prints from

    parsing code; can be useful to figure out parsing problems. Defaults to

    `OFF`.



#### Forcing ryml to use a different c4core version



ryml is strongly coupled to c4core, and this is reinforced by the fact

that c4core is a submodule of the current repo. However, it is still

possible to use a c4core version different from the one in the repo

(of course, only if there are no incompatibilities between the

versions). You can find out how to achieve this by looking at the

[`custom_c4core` sample](./samples/custom_c4core/CMakeLists.txt).



------



## Other languages



One of the aims of ryml is to provide an efficient YAML API for other

languages. JavaScript is fully available, and there is already a

cursory implementation for Python using only the low-level API. After

ironing out the general approach, other languages are likely to

follow (all of this is possible because we're using

[SWIG](http://www.swig.org/), which makes it easy to do so).



### JavaScript



A JavaScript+WebAssembly port is available, compiled through [emscripten](https://emscripten.org/).



### Python



(Note that this is a work in progress. Additions will be made and things will

be changed.) With that said, here's an example of the Python API:



```python

import ryml



# ryml cannot accept strings because it does not take ownership of the

# source buffer; only bytes or bytearrays are accepted.

src = b"{HELLO: a, foo: b, bar: c, baz: d, seq: [0, 1, 2, 3]}"



def check(tree):

    # for now, only the index-based low-level API is implemented

    assert tree.size() == 10

    assert tree.root_id() == 0

    assert tree.first_child(0) == 1

    assert tree.next_sibling(1) == 2

    assert tree.first_sibling(5) == 2

    assert tree.last_sibling(1) == 5

    # use bytes objects for queries

    assert tree.find_child(0, b"foo") == 1

    assert tree.key(1) == b"foo")

    assert tree.val(1) == b"b")

    assert tree.find_child(0, b"seq") == 5

    assert tree.is_seq(5)

    # to loop over children:

    for i, ch in enumerate(ryml.children(tree, 5)):

        assert tree.val(ch) == [b"0", b"1", b"2", b"3"][i]

    # to loop over siblings:

    for i, sib in enumerate(ryml.siblings(tree, 5)):

        assert tree.key(sib) == [b"HELLO", b"foo", b"bar", b"baz", b"seq"][i]

    # to walk over all elements

    visited = [False] * tree.size()

    for n, indentation_level in ryml.walk(tree):

        # just a dumb emitter

        left = "  " * indentation_level

        if tree.is_keyval(n):

           print("{}{}: {}".format(left, tree.key(n), tree.val(n))

        elif tree.is_val(n):

           print("- {}".format(left, tree.val(n))

        elif tree.is_keyseq(n):

           print("{}{}:".format(left, tree.key(n))

        visited[inode] = True

    assert False not in visited

    # NOTE about encoding!

    k = tree.get_key(5)

    print(k)  # ''

    assert k == b"seq"               # ok, as expected

    assert k != "seq"                # not ok - NOTE THIS! 

    assert str(k) != "seq"           # not ok

    assert str(k, "utf8") == "seq"   # ok again



# parse immutable buffer

tree = ryml.parse_in_arena(src)

check(tree) # OK



# parse mutable buffer.

# requires bytearrays or objects offering writeable memory

mutable = bytearray(src)

tree = ryml.parse_in_place(mutable)

check(tree) # OK

```

As expected, the performance results so far are encouraging. In

a [timeit benchmark](api/python/parse_bm.py) compared

against [PyYaml](https://pyyaml.org/)

and [ruamel.yaml](https://yaml.readthedocs.io/en/latest/), ryml parses

quicker by generally 100x and up to 400x:

```

+----------------------------------------+-------+----------+----------+-----------+

| style_seqs_blck_outer1000_inner100.yml | count | time(ms) | avg(ms)  | avg(MB/s) |

+----------------------------------------+-------+----------+----------+-----------+

| parse:RuamelYamlParse                  |     1 | 4564.812 | 4564.812 |     0.173 |

| parse:PyYamlParse                      |     1 | 2815.426 | 2815.426 |     0.280 |

| parse:RymlParseInArena                 |    38 |  588.024 |   15.474 |    50.988 |

| parse:RymlParseInArenaReuse            |    38 |  466.997 |   12.289 |    64.202 |

| parse:RymlParseInPlace                 |    38 |  579.770 |   15.257 |    51.714 |

| parse:RymlParseInPlaceReuse            |    38 |  462.932 |   12.182 |    64.765 |

+----------------------------------------+-------+----------+----------+-----------+

```

(Note that the parse timings above are somewhat biased towards ryml, because

it does not perform any type conversions in Python-land: return types

are merely `memoryviews` to the source buffer, possibly copied to the tree's

arena).



As for emitting, the improvement can be as high as 3000x:

```

+----------------------------------------+-------+-----------+-----------+-----------+

| style_maps_blck_outer1000_inner100.yml | count |  time(ms) |  avg(ms)  | avg(MB/s) |

+----------------------------------------+-------+-----------+-----------+-----------+

| emit_yaml:RuamelYamlEmit               |     1 | 18149.288 | 18149.288 |     0.054 |

| emit_yaml:PyYamlEmit                   |     1 |  2683.380 |  2683.380 |     0.365 |

| emit_yaml:RymlEmitToNewBuffer          |    88 |   861.726 |     9.792 |    99.976 |

| emit_yaml:RymlEmitReuse                |    88 |   437.931 |     4.976 |   196.725 |

+----------------------------------------+-------+-----------+-----------+-----------+

```



------



## YAML standard conformance



ryml is feature complete with regards to the YAML specification. All

the YAML features are well covered in the unit tests, and expected to

work, unless in the exceptions noted below.



Of course, there are many dark corners in YAML, and there certainly

can appear cases which ryml fails to parse. Your [bug reports or pull

requests](https://github.com/biojppm/rapidyaml/issues) are very

welcome.



See also [the roadmap](./ROADMAP.md) for a list of future work.



### Known limitations



ryml deliberately makes no effort to follow the YAML standard in the

following situations:



* ryml's tree does NOT accept containers as map keys: keys stored in

  the tree must always be scalars. HOWEVER, this is a limitation only

  of the final tree. The event-based parse engine DOES parse container

  keys, as it is is meant to be used by other programming languages to

  create their native data-structures, and it is fully tested and

  fully conformant (other than the general error permissiveness noted

  below).

* Tab characters after `:` and `-` are not accepted tokens, unless

  ryml is compiled with the macro `RYML_WITH_TAB_TOKENS`. This

  requirement exists because checking for tabs introduces branching

  into the parser's hot code and in some cases costs as much as 10%

  in parsing time.

* Non-unique map keys are allowed. Enforcing key uniqueness in the

  parser or in the tree would cause log-linear parsing complexity (for

  root children on a mostly flat tree), and would increase code size

  through added structural, logical and cyclomatic complexity. So

  enforcing uniqueness in the parser would hurt users who may not care

  about it (they may not care either because non-uniqueness is OK for

  their use case, or because it is impossible to occur). On the other

  hand, any user who requires uniqueness can easily enforce it by

  doing a post-parse walk through the tree. So choosing to not enforce

  key uniqueness adheres to the spirit of "don't pay for what you

  don't use".

* `%YAML` directives have no effect and are ignored.

* `%TAG` directives are limited to a default maximum of 4 instances

  per `Tree`. To increase this maximum, define the preprocessor symbol

  `RYML_MAX_TAG_DIRECTIVES` to a suitable value. This arbitrary limit

  reflects the usual practice of having at most 1 or 2 tag directives;

  also, be aware that this feature is under consideration for removal

  in YAML 1.3.

* ryml tends to be on the permissive side in several cases where the

  YAML standard dictates that there should be an error; in many of these

  cases, ryml will tolerate the input. This may be good or bad, but in

  any case is being improved on, meaning ryml will grow progressively

  less tolerant of YAML errors in the coming releases. So we strongly

  suggest to stay away from those dark corners of YAML which are

  generally a source of problems; this is good practice anyway.



If you do run into trouble and would like to investigate conformance

of your YAML code, **beware** of existing online YAML linters, many of

which are not fully conformant. Instead, try using

[https://play.yaml.io](https://play.yaml.io), an amazingly useful tool

which lets you dynamically input your YAML and continuously see the

results from all the existing parsers (kudos to @ingydotnet and the

people from the YAML test suite). And of course, if you detect

anything wrong with ryml, please [open an

issue](https://github.com/biojppm/rapidyaml/issues) so that we can

improve.



### Test suite status



As part of its CI testing, ryml uses the [YAML test

suite](https://github.com/yaml/yaml-test-suite). (See also the test

suite results at

[https://matrix.yaml.info/](https://matrix.yaml.info/), but be aware

that the results there may be using an older version of ryml.) This is

an extensive and merciless set of reference cases covering the full

YAML spec. Each of these cases has several subparts:

 * `in-yaml`: mildly, plainly or extremely difficult-to-parse YAML

 * `in-json`: equivalent JSON (where possible/meaningful)

 * `out-yaml`: equivalent standard YAML

 * `emit-yaml`: equivalent standard YAML

 * `events`: reference events according to the YAML standard



When testing, ryml parses each of the yaml/json parts, then emits the

parsed tree, then parses the emitted result and verifies that emission

is idempotent, ie that the round trip emitted result is semantically

the same as its input without any loss of information.



To ensure consistency, this happens over four successive levels of

parse->emit round trips. And to ensure correctness, each of the stages

is compared against the `events` spec from the test, which constitutes

the reference. The tests also check for equality between the reference

events in the test case and the events emitted by ryml from the data

tree parsed from the test case input. All of this is then carried out

with several variations: both unix `\n` vs windows `\r\n` line

endings, emitting to string, file or streams, which results in ~250

tests per case part.



With multiple parts per case and ~400 reference

cases in the test suite, this makes over several hundred thousand

individual tests to which ryml is subjected, which are added to the

unit tests in ryml, which also employ the same extensive combinatorial

approach.



Also, note that in [their own words](http://matrix.yaml.info/), the

tests from the YAML test suite *contain a lot of edge cases that don't

play such an important role in real world examples*. And yet, despite

the extreme focus of the test suite, currently ryml only fails a minor

fraction of the test cases, mostly related with the deliberate

limitations noted above.



Other than those limitations, by far the main issue with ryml is that

several standard-mandated parse errors fail to materialize (this will

be addressed in the coming releases). For the up-to-date list of ryml

failures in the test-suite, refer to the [list of known

exceptions](test/test_suite/test_suite_parts.cpp) from ryml's test

suite runner, which is used as part of ryml's CI setup.



------



## Alternative libraries



Why this library? Because none of the existing libraries was quite

what I wanted. When I started this project in 2018, I was aware of these two

alternative C/C++ libraries:



  * [libyaml](https://github.com/yaml/libyaml). This is a bare C

    library. It does not create a representation of the data tree, so

    I don't see it as practical. My initial idea was to wrap parsing

    and emitting around libyaml's convenient event handling, but to my

    surprise I found out it makes heavy use of allocations and string

    duplications when parsing. I briefly pondered on sending PRs to

    reduce these allocation needs, but not having a permanent tree to

    store the parsed data was too much of a downside.

  * [yaml-cpp](https://github.com/jbeder/yaml-cpp). This library may

    be full of functionality, but is heavy on the use of

    node-pointer-based structures like `std::map`, allocations, string

    copies, polymorphism and slow C++ stream serializations. This is

    generally a sure way of making your code slower, and strong

    evidence of this can be seen in the benchmark results above.



Recently [libfyaml](https://github.com/pantoniou/libfyaml)

appeared. This is a newer C library, fully conformant to the YAML

standard with an amazing 100% success in the test suite; it also offers

the tree as a data structure. As a downside, it does not work in

Windows, and it is also multiple times slower parsing and emitting.



When performance and low latency are important, using contiguous

structures for better cache behavior and to prevent the library from

trampling caches, parsing in place and using non-owning strings is of

central importance. Hence this Rapid YAML library which, with minimal

compromise, bridges the gap from efficiency to usability. This library

takes inspiration from

[RapidJSON](https://github.com/Tencent/rapidjson) and

[RapidXML](http://rapidxml.sourceforge.net/).



------

## License



ryml is permissively licensed under the [MIT license](LICENSE.txt).