substrata
zstd
substrata | zstd | |
---|---|---|
7 | 109 | |
26 | 22,480 | |
- | 1.7% | |
9.7 | 9.7 | |
7 days ago | 7 days ago | |
C++ | C | |
MIT License | GNU General Public License v3.0 or later |
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substrata
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Chrome Feature: ZSTD Content-Encoding
I implemented this in my server (https://substrata.info/). Works great. Results in smaller files than zlib/deflate.
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Croquet: Live, network-transparent 3D gaming
Substrata is already open source (well, 99% open source): https://github.com/glaretechnologies/substrata
As far as scaling goes, fancy scaling solutions are currently in the 'future-problems' basket. Currently Substrata just uses a naive client-server solution with O(n^2) messaging where n is the number of connected clients - e.g. each message from a client is sent to all other clients.
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Show HN: Substrata – An Open-Source Metaverse
Substrata is a metaverse that I have been working on for a few years, and have recently open-sourced.
It's written in C++ and OpenGL. You can try out pre-built binaries here: https://substrata.info/, or build it yourself from the source.
More details are available on the Github page: https://github.com/glaretechnologies/substrata and on the website: https://substrata.info/about_substrata
I'm happy to answer any questions about it!
- Substrata C++/OpenGL Metaverse open sourced
zstd
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Rethinking string encoding: a 37.5% space efficient encoding than UTF-8 in Fury
> 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
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Drink Me: (Ab)Using a LLM to Compress Text
> 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.
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SQLite VFS for ZSTD seekable format
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.
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Chrome Feature: ZSTD Content-Encoding
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]
- Zstandard v1.5.6 – Chrome Edition
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Optimizating Rabin-Karp Hashing
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.
- Show HN: macOS-cross-compiler – Compile binaries for macOS on Linux
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Cyberpunk 2077 dev release
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 ```
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Honey, I shrunk the NPM package · Jamie Magee
I've done that experiment with zstd before.
https://github.com/facebook/zstd/blob/dev/programs/zstd.1.md...
Not sure about brotli though.
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How in the world should we unpack archive.org zst files on Windows?
If you want this functionality in zstd itself, check this out: https://github.com/facebook/zstd/pull/2349
What are some alternatives?
hubs - Duck-themed multi-user virtual spaces in WebVR. Built with A-Frame.
LZ4 - Extremely Fast Compression algorithm
croquet-for-unity-guardians - Development of C4U version of Guardians
Snappy - A fast compressor/decompressor
thirdroom - Open, decentralised, immersive worlds built on Matrix
LZMA - (Unofficial) Git mirror of LZMA SDK releases
7-Zip-zstd - 7-Zip with support for Brotli, Fast-LZMA2, Lizard, LZ4, LZ5 and Zstandard
ZLib - A massively spiffy yet delicately unobtrusive compression library.
brotli - Brotli compression format
haproxy - HAProxy Load Balancer's development branch (mirror of git.haproxy.org)
LZFSE - LZFSE compression library and command line tool
zlib-ng - zlib replacement with optimizations for "next generation" systems.