Welcome to JLD2.jl

JLD2.jl is a high-performance, pure Julia library for saving and loading arbitrary Julia data structures. It's designed as a fast binary serialization format that produces files according to the well-known HDF5 format but doesn't rely on external libraries like the HDF5 C-library — making it an ideal choice for native Julia workflows.

📦 What is JLD2.jl?

JLD2 stands for Julia Data Format 2, a file format and serialization library tailored to work seamlessly with native Julia types. Whether you're working with arrays, dictionaries, structs, or custom types, JLD2 provides a reliable way to store your data to disk and retrieve it later — all in pure Julia.

🚀 Why Use JLD2?

✅ Save and load any Julia type, including custom structs
✅ Fast and efficient, thanks to native Julia code
✅ Custom IO support — can read and write to either Vector{UInt8} or custom IO implementations
✅ Portable within Julia versions — great for caching, prototyping, and long-term storage
✅ Customizable type loading — offers fine-grained control to update outdated stored structures upon load

✨ Example: Saving and Loading Data

using JLD2

# Some sample data
model = (name = "Transformer", layers = 12, params = 300_000_000)
scores = [0.91, 0.87, 0.93]

# Save to a file
@save "model_state.jld2" model scores

# Load back later
@load "model_state.jld2" model scores

println(model.name)  # Output: Transformer

You can also save individual variables or load just what you need:

@save "data.jld2" a=1 b=[1,2,3] c="hi"
@load "data.jld2" b c

Below is a comparison of JLD2 and other common Julia libraries for data storage and serialization. Choose the tool that best fits your needs:

Library	Best For	Notes
JLD2.jl	Native Julia data (structs, arrays, Dicts, etc.)	Fast, flexible, pure Julia, HDF5-compatible, no external dependencies
Serialization.jl	Any Julia object	Built-in, very fast, but not cross-version safe or portable outside Julia
MAT.jl	Interop with MATLAB `.mat` files	Read/write MATLAB files, good for sharing with MATLAB users
HDF5.jl	Full-featured HDF5 access	Direct interface to HDF5 C library, cross-language, supports advanced HDF5 features
Arrow.jl	Tabular data, dataframes, analytics	Excellent for columnar data, cross-language (Python, R, etc.), DataFrame support
CSV.jl	Lightweight table exports/imports	Great for human-readable, row-based data, simple and widely supported
JSON.jl	Web services, config files, nested dicts	Portable, text-based, less efficient for complex Julia types

If your goal is to store structured tables (like DataFrames) for analysis or sharing between tools, Arrow.jl or CSV.jl may be a better fit. But if you're dealing with rich, nested, Julia-native data, or just want a fast way to persist your models, simulations, or internal state, JLD2 is the right tool.

💬 Contributing

JLD2 is community-driven! We welcome contributions, bug reports, and suggestions at the GitHub repository.

If you're interested in improving documentation, fixing issues, or proposing new features, we’d love your help.

🧭 Next Steps