Minizip-ng
zstd
| Minizip-ng | zstd | |
|---|---|---|
| - | 112 | |
| 1,216 | 23,213 | |
| 0.5% | 1.5% | |
| 8.2 | 9.6 | |
| 2 months ago | 11 days ago | |
| C | C | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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Minizip-ng
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zstd
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Current problems and mistakes of web scraping in Python and tricks to solve them!
You may have also noticed that a new supported data compression format zstd appeared some time ago. I haven't seen any backends that use it yet, but httpx will support decompression in versions above 0.28.0. I already use it to compress server response dumps in my projects; it shows incredible efficiency in asynchronous solutions with aiofiles.
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MLow: Meta's low bitrate audio codec
Zstd is a personal project? Surely it's not by accident in the Facebook GitHub organization? And that you need to sign a contract on code.facebook.com before they'll consider merging any contributions? That seems like an odd claim, unless it used to be a personal project and Facebook took it over
(https://github.com/facebook/zstd/blob/dev/CONTRIBUTING.md#co...)
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My First Arch Linux Installation
Unmount root and remount the subvolumes and the boot partition. noatime is used for better performance zstd as file compression:
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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
What are some alternatives?
ZLib - A massively spiffy yet delicately unobtrusive compression library.
LZ4 - Extremely Fast Compression algorithm
zlib-ng - zlib replacement with optimizations for "next generation" systems.
Snappy - A fast compressor/decompressor
LZMA - (Unofficial) Git mirror of LZMA SDK releases
7-Zip-zstd - 7-Zip with support for Brotli, Fast-LZMA2, Lizard, LZ4, LZ5 and Zstandard
smaz - Small strings compression library
brotli - Brotli compression format