MatchSpec

A Conda MatchSpec implementation in pure Rust. This allows you to parse a matchspec and validate it against a package to see if it matches.

Python Library

This library exposes a few simple functions:

match_against_matchspec()

Takes a matchspec as a str and matches it against a package_name and version (both str). Returns a bool.

import rust_matchspec
rust_matchspec.match_against_matchspec('python>=3.0', 'python', '3.10.1') # returns True

filter_package_list()

Takes a list of dicts and returns all the dicts inside that match a given matchspec. The dicts must have a name key with a str value, but all other fields are optional.

import rust_matchspec
list = [{'name': 'tensorflow', 'version': '2.10.0'},
	{'name': 'pytorch', 'version': '2.0.0'},
	{'name': 'pytorch', 'version': '1.11.1'}]

rust_matchspec.filter_package_list('pytorch>1.12', list) # returns [PackageCandidate(name=pytorch)]

Possible keys:

Key	Expected Type	Required?
name	str	yes
version	str
build	str
build_number	u32
depends	[str]
license	str
md5	str
sha256	str
size	u64
subdir	str
timestamp	u64

parallel_filter_package_list()

Using all available cores will take a list of dicts and returns all the dicts inside that match a given matchspec. The dicts must have a name key with a str value, but all other fields are optional.

Note Probably won't show any noticable speed improvements until your list of packages is in the millions.

import rust_matchspec
list = [{'name': 'tensorflow', 'version': '2.10.0'},
	{'name': 'pytorch', 'version': '2.0.0'},
	{'name': 'pytorch', 'version': '1.11.1'}]

rust_matchspec.parallel_filter_package_list('pytorch>1.12', list) # returns [PackageCandidate(name=pytorch)]

parallel_filter_package_list_with_matchspec_list()

Using all available cores will take a list of dicts and a list of Matchspecs (as str) and returns all the dicts inside that match any given matchspec. The dicts must have a name key with a str value, but all other fields are optional. May contain duplicates since it runs all of the matchspecs against the package list in parallel and does not dedup the resulting matches.

In my testing this has a very small overhead, but matching 4 matchspecs is approximately the same speed as matching a single matchspec with the other functions.

import rust_matchspec
package_list = [{'name': 'tensorflow', 'version': '2.10.0'},
	{'name': 'pytorch', 'version': '2.0.0'},
	{'name': 'pytorch', 'version': '1.11.1'}]

matchspec_list = ['python>=3.9.1', 'pytorch>1.12']

rust_matchspec.parallel_filter_package_list_with_matchspec_list(matchspec_list, package_list) # returns [PackageCandidate(name=pytorch)]

Rust Library

Example

The way you instantiate a MatchSpec is by parsing a string into the type:

use rust_matchspec::{CompoundSelector, MatchSpec, Selector};

// Create the MatchSpec by parsing a String or &str
let matchspec: MatchSpec = "main/linux-64::pytorch>1.10.2".parse().unwrap();

// You then have the data accessible inside the MatchSpec struct if you want it
// Package name is the only mandatory field in a matchspec
assert_eq!(&matchspec.package, "pytorch");

// These are optional, so they will be wrapped in an Option
assert_eq!(matchspec.channel, Some("main".to_string()));
assert_eq!(
	matchspec.version,
	Some(CompoundSelector::Single {
		selector: Selector::GreaterThan,
		version: "1.10.2".to_string(),
	})
);

// You can also check to see if a package name and version match the spec.
// This is a faster function that allows us to bypass some sometimes unnecessary tests like channel or subdir
assert!(matchspec.is_package_version_match(&"pytorch", &"1.11.0"))

Benchmarking

This library contains benchmarks aimed at checking the speed of our implementation against other languages and ensure speed doesn't regress. These are contrived benchmarks to test raw speed, so take them (and all benchmarks) with a bit of skepticism. Benchmark harnesses and the data all need to be identical for a benchmark to really provide value.

Python

The Python benchmarks use pytest-benchmark.

Steps to run the benchmarks:

# Setup the conda env
conda env create -f ./environment.yml
conda activate rust_matchspec

# Build an optimized wheel
maturin build --release

# install it
pip install ./target/wheels/rust_matchspec*.whl

# Finally, run the benchmark
pytest

Rust

The Rust benchmarks use Criterion.rs to provide the benchmarking framework. Its pretty easy to run the benchmarks on stable rust:

cargo bench 

# Or if you're on mac and get errors with Invalid Symbols:
cargo bench --no-default-features

This will automatically track benchmark timings across runs. If you do this on a laptop or workstation be aware that you may have regressions show up if you have background processes or other things happening. I would recommend always running the benchmarks at a similar level of CPU load. If you want consistent testing its probably best to quit your browser or anything in the background that might be eating CPU or doing IO.