Installation

PALSParserJ is a Julia wrapper around the C library built by PALSParserCpp. That library is compiled from that repository rather than shipped with this package, so PALSParserJ has to be told where it is. By default it looks for a PALSParserCpp checkout beside its own, which is the layout below; if you keep PALSParserCpp somewhere else, see Pointing at a PALSParserCpp elsewhere instead.

macOS, Linux, and Windows are all supported — the correct library extension for the platform (.dylib, .so, .dll) is worked out at load time.

1. Clone the repositories

git clone https://github.com/pals-project/PALSParserCpp.git
git clone https://github.com/pals-project/PALSParserJ.jl.git

The default layout looks like this — PALSParserJ locates the compiled library relative to its own source tree, at ../PALSParserCpp/build/:

some-directory/
├── PALSParserCpp/
│   └── build/
│       └── libPALSParserCpp.dylib   (or .so / .dll)
└── PALSParserJ/

2. Build the C library

From the PALSParserCpp directory, configure and build with CMake (this needs CMake and a C++17 compiler — Apple Clang on macOS, GCC or Clang on Linux, MSVC on Windows):

cmake -S . -B build
cmake --build build

CMake fetches the rapidyaml backend automatically. The result is the shared library libPALSParserCpp.dylib (macOS), .so (Linux), or .dll (Windows) under PALSParserCpp/build/. Rebuild with cmake --build build after changing any PALSParserCpp source. See the PALSParserCpp README for more detail.

3. Activate the Julia project

From the PALSParserJ directory:

using Pkg
Pkg.activate(".")
Pkg.instantiate()

import PALSParserJ as pj

Check the installation

import PALSParserJ as pj

root = pj.create_empty_tree()
root["hello"] = "world"
println(pj.to_yaml_string(root))

If that prints hello: world, the Julia package and the underlying C library are wired up correctly.

Pointing at a PALSParserCpp elsewhere

The side-by-side layout is only the default. Two environment variables override it, read the first time PALSParserJ calls into the library — so setting either one any time before that first call works, including after using PALSParserJ:

Variable	Meaning
PALS_PARSER_CPP_DIR	Path to a PALSParserCpp checkout; its build/ directory is searched.
PALS_PARSER_CPP_LIB	Full path to the shared library itself, wherever it lives.

ENV["PALS_PARSER_CPP_DIR"] = "/opt/src/PALSParserCpp"
using PALSParserJ

PALS_PARSER_CPP_LIB wins if both are set. PALSParserJ.libparser() returns the resolved path, which is worth checking first if calls behave unexpectedly — it is resolved per session rather than baked in when the package is precompiled, so a stale precompile cache is never the cause.

If the library cannot be found, the first call fails with an error listing every path that was tried, which is usually enough to spot a missing build or a typo in the variable. Note that using PALSParserJ itself always succeeds: the library is looked up lazily so that tooling which only reads the package — building these docs, for one — does not need a C++ toolchain.