Benchmarks

All benchmarks were computed on macOS using Python 3.12.4 (for non-uv tools), and come with a few important caveats:

Benchmark performance may vary dramatically across different operating systems and filesystems. In particular, uv uses different installation strategies based on the underlying filesystem's capabilities. (For example, uv uses reflinking on macOS, and hardlinking on Linux.)
Benchmark performance may vary dramatically depending on the set of packages being installed. For example, a resolution that requires building a single intensive source distribution may appear very similar across tools, since the bottleneck is tool-agnostic.

This document benchmarks against Trio's docs-requirements.in, as a representative example of a real-world project.

In each case, a smaller bar (i.e., lower) is better.

Warm Installation

Benchmarking package installation (e.g., uv sync) with a warm cache. This is equivalent to removing and recreating a virtual environment, and then populating it with dependencies that you've installed previously on the same machine.

Cold Installation

Benchmarking package installation (e.g., uv sync) with a cold cache. This is equivalent to running uv sync on a new machine or in CI (assuming that the package manager cache is not shared across runs).

Warm Resolution

Benchmarking dependency resolution (e.g., uv lock) with a warm cache, but no existing lockfile. This is equivalent to blowing away an existing requirements.txt file to regenerate it from a requirements.in file.

Cold Resolution

Benchmarking dependency resolution (e.g., uv lock) with a cold cache. This is equivalent to running uv lock on a new machine or in CI (assuming that the package manager cache is not shared across runs).

Reproduction

All benchmarks were generated using the scripts/benchmark package, which wraps hyperfine to facilitate benchmarking uv against a variety of other tools.

The benchmark script itself has a several requirements:

A local uv release build (cargo build --release).
An installation of the production uv binary in your path.
The hyperfine command-line tool installed on your system.

To benchmark resolution against pip-compile, Poetry, and PDM:

uv run resolver \
    --uv-project \
    --poetry \
    --pdm \
    --pip-compile \
    --benchmark resolve-warm --benchmark resolve-cold \
    --json \
    ../requirements/trio.in

To benchmark installation against pip-sync, Poetry, and PDM:

uv run resolver \
    --uv-project \
    --poetry \
    --pdm \
    --pip-sync \
    --benchmark install-warm --benchmark install-cold \
    --json \
    ../requirements/compiled/trio.txt

Both commands should be run from the scripts/benchmark directory.

After running the benchmark script, you can generate the corresponding graph via:

cargo run -p uv-dev --all-features render-benchmarks resolve-warm.json --title "Warm Resolution"
cargo run -p uv-dev --all-features render-benchmarks resolve-cold.json --title "Cold Resolution"
cargo run -p uv-dev --all-features render-benchmarks install-warm.json --title "Warm Installation"
cargo run -p uv-dev --all-features render-benchmarks install-cold.json --title "Cold Installation"

You need to install the Roboto Font if the labels are missing in the generated graph.

Acknowledgements

The inclusion of this BENCHMARKS.md file was inspired by the excellent benchmarking documentation in Orogene.

Troubleshooting

Flaky benchmarks

If you're seeing high variance when running the cold benchmarks, then it's likely that you're running into throttling or DDoS prevention from your ISP. In that case, ISPs forcefully terminate TCP connections with a TCP reset. We believe this is due to the benchmarks making the exact same requests in a very short time (especially true for uv). A possible workaround is to connect to VPN to bypass your ISPs filtering mechanism.

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Uh oh!