macos-cross-compiler VS zstd

Make is excellent if you use it properly to model your dependencies. This works really well for languages like C/C++, but I think Make really struggles with languages like Go, JavaScript, and Python or when your using a large combination of technologies.

I've found Earthly [0] to be the _perfect_ tool to replace Make. It's a familiar syntax (combination of Dockerfiles + Makefiles). Every target is run in an isolated Docker container, and each target can copy files from other targets. This allows Earthly to perform caching and parallelization for free, and in addition you get lots of safety with containerization. I've been using Earthly for a couple of years now and I love it.

Some things I've built with it:

* At work [1], we use it to build Docker images for E2E testing. This includes building a Go project, our mkdocs documentation, our Vue UI, and a ton of little scripts all over the place for generating documentation, release notes, dependency information (like the licenses of our deps), etc.

* I used it to create my macOS cross compiler project [2].

* A project for playing a collaborative game of Pokemon on Discord [3]

IMO Makefiles are great if you have a few small targets. If you're looking at more than >50 lines, if your project uses many languages, or you need to run targets in a Docker container, then Earthly is a great choice.

[0]: https://earthly.dev/

[1]: https://p3m.dev/

[2]: https://github.com/shepherdjerred/macos-cross-compiler

[3]: https://github.com/shepherdjerred/discord-plays-pokemon

It will depend heavily on the docker image you're trying to ship. For example with macos-cross-compiler[0] the resulting binary is over 2GB. With python:alpine[1] it's only 25MB.

Because image isn't copied whether the image is 2GB or 25MB the startup time will be nearly instantaneous for both.

The runtime adds 6-7MB of overhead although I expect that this can be reduced to less than 3MB with some work.

[0]: https://github.com/shepherdjerred/macos-cross-compiler

> In such cases, the serialized binary are mostly in 200~1000 bytes. Not big enough for zstd to work

You're not referring to the same dictionary that I am. Look at --train in [1].

If you have a training corpus of representative data, you can generate a dictionary that you preshare on both sides which will perform much better for very small binaries (including 200-1k bytes).

If you want maximum flexibility (i.e. you don't know the universe of representative messages ahead of time or you want maximum compression performance), you can gather this corpus transparently as messages are generated & then generate a dictionary & attach it as sideband metadata to a message. You'll probably need to defer the decoding if it references a dictionary not yet received (i.e. send delivers messages out-of-order from generation). There are other techniques you can apply, but the general rule is that your custom encoding scheme is unlikely to outperform zstd + a representative training corpus. If it does, you'd need to actually show this rather than try to argue from first principles.

[1] https://github.com/facebook/zstd/blob/dev/programs/zstd.1.md

> Doesn't take large amount of GPU resources

This is an understatement, zstd dictionary compression and decompression are blazingly fast: https://github.com/facebook/zstd/blob/dev/README.md#the-case...

My real-world use case for this was JSON files in a particular schema, and the results were fantastic.

This VFS will read a sqlite file after it has been compressed using [zstd seekable format](https://github.com/facebook/zstd/blob/dev/contrib/seekable_f...). Built to support read-only databases for full-text search. Benchmarks are provided in README.

Of course, you may get different results with another dataset.

gzip (zlib -6) [ratio=32%] [compr=35Mo/s] [dec=407Mo/s]

zstd (zstd -2) [ratio=32%] [compr=356Mo/s] [dec=1067Mo/s]

NB1: The default for zstd is -3, but the table only had -2. The difference is probably small. The range is 1-22 for zstd and 1-9 for gzip.

NB2: The default program for gzip (at least with Debian) is the executable from zlib. With my workflows, libdeflate-gzip iscompatible and noticably faster.

NB3: This benchmark is 2 years old. The latest releases of zstd are much better, see https://github.com/facebook/zstd/releases

For a high compression, according to this benchmark xz can do slightly better, if you're willing to pay a 10× penalty on decompression.

xz -9 [ratio=23%] [compr=2.6Mo/s] [dec=88Mo/s]

zstd -9 [ratio=23%] [compr=2.6Mo/s] [dec=88Mo/s]

Compression, synchronization and backup systems often use rolling hash to implement "content-defined chunking", an effective form of deduplication.

In optimized implementations, Rabin-Karp is likely to be the bottleneck. See for instance https://github.com/facebook/zstd/pull/2483 which replaces a Rabin-Karp variant by a >2x faster Gear-Hashing.

Get the data https://publicdistst.blob.core.windows.net/data/root.tar.zst magnet:?xt=urn:btih:84931cd80409ba6331f2fcfbe64ba64d4381aec5&dn=root.tar.zst How to extract https://github.com/facebook/zstd Linux (debian): `sudo apt install zstd` ``` tar -I 'zstd -d -T0' -xvf root.tar.zst ```

I've done that experiment with zstd before.

https://github.com/facebook/zstd/blob/dev/programs/zstd.1.md...

Not sure about brotli though.

If you want this functionality in zstd itself, check this out: https://github.com/facebook/zstd/pull/2349