zstd VS LZ4

> 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

LZ4 is a bit more complicated, but seems faster: https://github.com/lz4/lz4/blob/dev/doc/lz4_Block_format.md

According to these https://github.com/lz4/lz4 values you need around ten (10) quite modern cores in parallel to accomplish around 8GB/s.

The popular NoSQL database Cassandra utilizes a compression algorithm called LZ4 to reduce the footprint of data at rest. LZ4 is characterized by very fast compression speed at the cost of a higher compression ratio. This is a design choice that allows Cassandra to maintain high write throughput while also benefiting from compression in some capacity.

Yeah, sure, when you have monster core counts. on regular systems, not so much, here's from their own github page. it achieves, eh, 5GB/s on memory to memory transfers, i.e. best case scenario. so, uh, no? i'm not even sure it's any better than the CPU decompressor one Nvidia used.

> The downside of lz4 is that it can’t be configured to run at higher & slower compression ratios.

lz4 has some level of configurability? https://github.com/lz4/lz4/blob/v1.9.4/lib/lz4frame.h#L194

There's also LZ4_HC.

Since nobody mentioned it, I'll add lz4 (https://github.com/lz4/lz4).

Get your bootloader unlocked via Download mode and then obtain your stock firmware, preferably for your current region https://samfw.com (Download mode: CARRIER_CODE). Get the boot image from AP with 7zip, unpack from LZ4 with https://github.com/lz4/lz4/releases (drag and drop), patch with Magisk https://github.com/topjohnwu/magisk/releases/latest, grab the new image, name it "boot.img" and pack it into a .tar with 7zip and flash to AP with odin https://odindownload.com

After some investigations and experiments, I found out that it was the PNG compression (well, decompression I should say) that took a while. So I've made some experiments using the LZ4 compression library, which is focused on decompression speed, and it turned out to be an excellent solution!

Root with magisk: whether you use OneUI ≤3 or 4, patch the specific image needed for it (pre 4: boot, after 4: recovery) and flash it to the device. Boot it and enjoy root. https://github.com/lz4/lz4/releases can help extracting it from the AP tarball.