Andre Bogus
Dec 7, 2020 ⋅ 8 min read

Rust compression libraries

Andre Bogus Andre "llogiq" Bogus is a Rust contributor and Clippy maintainer. A musician-turned-programmer, he has worked in many fields, from voice acting and teaching, to programming and managing software projects. He enjoys learning new things and telling others about them.

Recent posts:

Using Rust And Leptos To Build Beautiful Declarative User Interfaces

Using Rust and Leptos to build beautiful, declarative UIs

Leptos is an amazing Rust web frontend framework that makes it easier to build scalable, performant apps with beautiful, declarative UIs.

Eze Sunday
Nov 30, 2023 ⋅ 10 min read
5 Best JavaScript Multi-Dimensional Array Libraries

5 best JavaScript multidimensional array libraries

Learn more about the 5 best JavaScript libraries for dealing with multidimensional arrays, such as ndarray, math.js, and NumJs.

Pascal Akunne
Nov 30, 2023 ⋅ 4 min read
Dom Scandinaro Leader Spotlight

Leader Spotlight: Leading by experience with Dom Scandinaro

We spoke with Dom about his approach to balancing innovation with handling tech debt and to learn how he stays current with technology.

Jessica Srinivas
Nov 30, 2023 ⋅ 6 min read
Vite Adoption Guide Overview Examples And Alternatives

Vite adoption guide: Overview, examples, and alternatives

Vite is a versatile, fast, lightweight build tool with an exceptional DX. Let’s explore when and why you should adopt Vite in your projects.

David Omotayo
Nov 29, 2023 ⋅ 16 min read
View all posts

4 Replies to "Rust compression libraries"

  1. Zip compressing 100mb random data in 60kb? That’s impossible. In fact, it’s very similar for all test inputs, another huge red flag.

    What is happening (from a quick look): After compression, you take the position of the Cursor instead of the length of the compressed data – the whole point of a Cursor is that it’s seekable.

    Also, take a look at the lz4_flex and lzzzz, decompressing in 5.3/7.6 ns – impossibly fast – regardless of input (with one exception that has realistic time). It’s not actually decompressing the data. I don’t know why though, maybe criterion::black_box is failing?

  2. Hi, this is a nice comparison and you put quite some effort into it.

    However, as like many other statistics, it would require additional details on how the data was measured to make the data usable.
    Such as, are those numbers from a single run? Is it a mean value? If it is a mean value, how often was it executed (cold cache, hot cache, …)? If it is a mean value, how big are the outliers, variance or standard deviation.

    Disclaimer: did not look at the Github project, since I prefer to have this information directly available with the data tables.

Leave a Reply