minification-benchmarks VS pip

Minifiers are used to reduce the size of the bundle. They can remove unused code, shorten expressions, and so on. And Now there are already several popular minifiers, and they continue to appear: more familiar ones - written in JavaScript - Terser and UglifyJS, even Babel has its own version of the minifier, there are also more modern SWC (written in Rust) and ESBuild (written in Go), and a bunch of other lesser-known minifiers. And I recommend you to look at this repository. It contains up-to-date test results of various popular minifiers.

This minify/gzip size effect is a well known quirk to developers of javascript minifiers. The minifier's symbol mangling algorithm often has a more pronounced effect than does advanced AST optimization.

This website has real life data on the matter for popular libraries:

* https://github.com/privatenumber/minification-benchmarks

Compare the trophies indicating smallest size for Minified versus Minzipped (gzip). Generally the smallest minified size yields the smallest minified+gzip size, but there are some notable anomolies outside the range of statistical noise. It is not practical for a javascript minifier to take a compression algorithm into account - it would blow up the minify timings exponentially.

It would be helpful to see how Bun's minifier compares to the others with popular libraries:

https://github.com/privatenumber/minification-benchmarks

JavaScript

There are many minifiers such as terser and uglify. But, because minifying also require to parse the JS, it is actually possible to use esbuild and SWC to minify the code. Here's a benchmark of the main minifiers.

Here's a comparison showing the major players with comparable stats at first glance. https://github.com/privatenumber/minification-benchmarks

https://esbuild.github.io/ https://github.com/privatenumber/minification-benchmarks

privatenumber/minification-benchmarks – JS minification benchmarks: babel-minify, esbuild, terser, uglify-js

Whenever you are working on a Python project that has external dependencies installed with pip, it is strongly recommended to first create a virtual environment.

Unfortunately that feature is easy to break: https://github.com/pypa/pip/issues/9644

I think I did my part https://github.com/pypa/pip/issues/6409

$ sudo pip install y$ sudo pip install yaml WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip. Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue. To avoid this problem you can invoke Python with '-m pip' instead of running pip directly. ERROR: Could not find a version that satisfies the requirement yaml (from versions: none) ERROR: No matching distribution found for yaml

What are you implying will happen?

Using the build-in tools, you can save the exact versions of dependencies (i.e. a lock file) using "pip freeze >dependencies.txt". This should give you the exact same set of packages in two years' time.

If you want to be even more sure, you can also store hashes in the lock file. This has to be generated by a separate tools at the moment [1][2] but can be consumed by the built-in tools [3], so "pip install -r requirements.txt" is still all you need in two years' time.

[1] https://github.com/pypa/pip/issues/4732

[2] https://pip-tools.readthedocs.io/en/latest/#using-hashes

[3] https://pip.pypa.io/en/stable/topics/secure-installs/#hash-c...

Here’s the issue, https://github.com/pypa/pip/issues/11664. I think the idea would be to have some file/json description of environment that could be passed to pip to allow it to fully cross compile. They are open to supporting it just needs contributor to be found to implement it and go through review/discussion.

To be fair the only alternative is fixing Python, and even then you still would have to wait a good 5 years at least for all the old Python versions to dwindle.

It doesn't look like the fixing effort is progressing very quickly: https://github.com/pypa/pip/issues/8606

To their credit, at least they didn't close it "works as intended" which I imagine a lot of projects would.

Another good benchmark to trying to resolve apache-airflow[all]==1.10.13 using the state of PyPi on 2020-12-02, I give instructions here on how to reproduce that workflow: https://github.com/pypa/pip/issues/11836. Including a benchmark how how many extra packages your resolver should visit.