Rattler: Rust crates for fast handling of conda packages

Rattler is a library that provides common functionality used within the conda ecosystem (what is conda & conda-forge?). The goal of the library is to enable programs and other libraries to easily interact with the conda ecosystem without being dependent on Python. Its primary use case is as a library that you can use to provide conda related workflows in your own tools.

Rattler is written in Rust and tries to provide a clean API to its functionalities (see: Components). With the primary goal in mind we aim to provide bindings to different languages to make it easy to integrate Rattler in non-rust projects.

Rattler is actively used within the https://prefix.dev backend.

Showcase

This repository also contains a binary (use cargo run to try) that shows some of the capabilities of the library. This is an example of installing an environment containing cowpy and all its dependencies from scratch (including Python!):

Give it a try!

Before you begin, make sure you have the following prerequisites:

• A recent version of git
• A recent version of micromamba

Follow these steps to clone, compile, and run the rattler project:

# Clone the rattler repository along with its submodules:
git clone --recursive https://github.com/mamba-org/rattler.git
cd rattler

# Set up an environment with the required dependencies for compiling rattler and libsolv:
micromamba create -f environment.yml
micromamba activate rattler-env

# Compile and execute rattler to create a JupyterLab instance:
cargo run --release --bin rattler create jupyterlab

The above command will execute the rattler executable in release mode. It will download and install an environment into the .prefix folder that contains jupyterlab and all the dependencies required to run it (like python)

Run the following command to start jupyterlab:

# on windows
.\.prefix\Scripts\jupyter-lab.exe

# on linux or macOS
 ./.prefix/bin/jupyter-lab

Voila! You have a working installation of jupyterlab installed on your system! You can of course install any package you want this way. Try it!

Contributing 😍

We would love to have you contribute! For a good list of things you could help us with, take a look at our good first issues. If you want to go deeper though, any open issue is up for grabs. Just let us know what you start on something.

For questions, requests or a casual chat, we are very active on our discord server. You can join our discord server via this link.

Components

Rattler consists of several crates that provide different functionalities.

• rattler_conda_types: foundational types for all datastructures used withing the conda eco-system.
• rattler_package_streaming: provides functionality to download, extract and create conda package archives.
• rattler_repodata_gateway: downloads, reads and processes information about existing conda packages from an index.
• rattler_shell: code to activate an existing environment and run programs in it.
• rattler_solve: a backend agnostic library to solve the package satisfiability problem.
• rattler_virtual_packages: a crate to detect system capabilities.
• rattler: functionality to create complete environments from scratch using the crates above.
• rattler-bin: an example of a package manager using all the crates above (see: showcase)

You can find these crates in the crates folder.

What is conda & conda-forge?

The conda ecosystem provides cross-platform, binary packages that you can use with any programming language. conda is an open-source package management system and environment management system that can install and manage multiple versions of software packages and their dependencies. conda is written in Python. The aim of Rattler is to provide all functionality required to work with the conda ecosystem from Rust. Rattler is not a reimplementation of conda. conda is a package management tool. Rattler is a library to work with the conda ecosystem from different languages and applications. For example, it powers the backend of https://prefix.dev.

conda-forge is a community-driven effort to bring new and existing software into the conda ecosystem. It provides tens-of-thousands of up-to-date packages that are maintained by a community of contributors. For an overview of available packages see https://prefix.dev.