Giter Club home page Giter Club logo

cargo-chef's Introduction

cargo-chef

Cache the dependencies of your Rust project and speed up your Docker builds.


Table of Contents

  1. How to install
  2. How to use
  3. Benefits vs Limitations
  4. License

How To Install

You can install cargo-chef from crates.io with

cargo install cargo-chef

How to use

โš ๏ธ cargo-chef is not meant to be run locally
Its primary use-case is to speed up container builds by running BEFORE the actual source code is copied over. Don't run it on existing codebases to avoid having files being overwritten.

cargo-chef exposes two commands: prepare and cook:

cargo chef --help

cargo-chef

USAGE:
    cargo chef <SUBCOMMAND>

SUBCOMMANDS:
    cook       Re-hydrate the minimum project skeleton identified by `cargo chef prepare` and
               build it to cache dependencies
    prepare    Analyze the current project to determine the minimum subset of files (Cargo.lock
               and Cargo.toml manifests) required to build it and cache dependencies

prepare examines your project and builds a recipe that captures the set of information required to build your dependencies.

cargo chef prepare --recipe-path recipe.json

Nothing too mysterious going on here, you can examine the recipe.json file: it contains the skeleton of your project (e.g. all the Cargo.toml files with their relative path, the Cargo.lock file is available) plus a few additional pieces of information.
In particular it makes sure that all libraries and binaries are explicitly declared in their respective Cargo.toml files even if they can be found at the canonical default location (src/main.rs for a binary, src/lib.rs for a library).

The recipe.json is the equivalent of the Python requirements.txt file - it is the only input required for cargo chef cook, the command that will build out our dependencies:

cargo chef cook --recipe-path recipe.json

If you want to build in --release mode:

cargo chef cook --release --recipe-path recipe.json

You can leverage it in a Dockerfile:

FROM rust as planner
WORKDIR app
# We only pay the installation cost once, 
# it will be cached from the second build onwards
RUN cargo install cargo-chef 
COPY . .
RUN cargo chef prepare  --recipe-path recipe.json

FROM rust as cacher
WORKDIR app
RUN cargo install cargo-chef
COPY --from=planner /app/recipe.json recipe.json
RUN cargo chef cook --release --recipe-path recipe.json

FROM rust as builder
WORKDIR app
COPY . .
# Copy over the cached dependencies
COPY --from=cacher /app/target target
COPY --from=cacher $CARGO_HOME $CARGO_HOME
RUN cargo build --release --bin app

FROM rust as runtime
WORKDIR app
COPY --from=builder /app/target/release/app /usr/local/bin
ENTRYPOINT ["/usr/local/bin/app"]

We are using four stages: the first computes the recipe file, the second caches our dependencies, the third builds the binary and the fourth is our runtime environment.
As long as your dependencies do not change the recipe.json file will stay the same, therefore the outcome of cargo cargo chef cook --release --recipe-path recipe.json will be cached, massively speeding up your builds (up to 5x measured on some commercial projects).

Benefits vs Limitations

cargo-chef has been tested on a few OpenSource projects and some of commercial projects, but our testing has definitely not exhausted the range of possibilities when it comes to cargo build customisations and we are sure that there are a few rough edges that will have to be smoothed out - please file issues on GitHub.

Benefits of cargo-chef:

A common alternative is to load a minimal main.rs into a container with Cargo.toml and Cargo.lock to build a Docker layer that consists of only your dependencies (more info here). This is fragile compared to cargo-chef which will instead:

  • automatically pick up all crates in a workspace (and new ones as they are added)
  • keep working when files or crates are moved around, which would instead require manual edits to the Dockerfile using the "manual" approach
  • generate fewer intermediate Docker layers (for workspaces)

Limitations and caveats:

  • cargo cook and cargo build must be executed from the same working directory. If you examine the *.d files under target/debug/deps for one of your projects using cat you will notice that they contain absolute paths referring to the project target directory. If moved around, cargo will not leverage them as cached dependencies;
  • cargo build will build local dependencies (outside of the current project) from scratch, even if they are unchanged, due to the reliance of its fingerprinting logic on timestamps (see this long issue on cargo's repository);

License

Licensed under either of Apache License, Version 2.0 or MIT license at your option. Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in this crate by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.

cargo-chef's People

Contributors

adwhit avatar agersant avatar arlyon avatar bend0g avatar chris13524 avatar lukemathwalker avatar markhildreth avatar matteojoliveau avatar nathanhowell avatar radupopa2010 avatar strohel avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.