Hellishly Slow Level 13 Deflate Compression

https://kirill.korins.ky/articles/hellishly-slow-level-13-deflate-compression/

by zX41ZdbW • 1 month ago

94 26 1 month ago

kirill.korins.ky

jbosh• 26 days ago

I love it. So much in computers is trade offs and this was a fun read exploring it.

It would be interesting to see some economics of what 8,000% increase in encoding time takes to make that money back in terms of storage or bandwidth. I also wonder how brotli/lzma would compare here. Are there some obscene modes on those that had similar results?

a_t482• 26 days ago

zstd has higher level modes. Default is -3. I saw a good tradeoff between compression speed and ratio up to -9 or so. From -20 to -22 it will use much more memory and IIRC can have downstream effects on decompression speed. I'm using -9 for my container registry and plan to recompress at a higher level for commonly accessed base layers, as well as give customers a button that lets them pay a bit more to do it themselves.

loeg• 26 days ago

To be a little pedantic, the usual zstd levels are positive integers (1-22 default 3). The negative integers denote "fast" modes with worse compression (there are only a few of these).

chickenbig• 26 days ago

Even compression level 1 or 2 is pretty good.

I once used https://github.com/google/riegeli and a low zstd compression level to store large quantities of protobuf data in an efficient manner (in terms of CPU, RAM and streaming to disk). Shame Riegeli is not well known, not well documented and does not have many tests.

userbinator2• 26 days ago

I also wonder how brotli/lzma would compare here.

Far better, just like anything else based on arithmetic coding. The main distinction here is that the output can still be decompressed with a standard Inflate implementation.

atiedebee• 25 days ago

Except that brotli uses Huffman coding. It's main claim to fame is using higher order statistics to select a Huffman table and its built-in dictionary.

This class of compression programs sees larger differences due to the way the data is modelled instead of the specific entropy coder used.

Zenst• 26 days ago

Process-intensive, but higher compression has clear strategic value. Distant satellites such as Voyager, where bandwidth is severely limited, could transmit more data using such capabilities. Equally, for long-term archival storage, improved compression would allow far greater volumes of data to be preserved on durable, life-long media formats.

XorNot2• 26 days ago

Distant space probes are power constrained though.

It's entirely possible the degradation of their RTG power sources would be more expensive doing the compression then just sending the data as is.

tobijdc• 26 days ago

There is also zopfli and it's decadent ECT that allow for more extreme tradeoffs.

Someone• 26 days ago

So, what’s the effect on memory usage?

And for decompression, the effect on memory usage and timings?

lifthrasiir• 26 days ago

For decompression, nothing changes because DEFLATE is asymmetric; compressor can spend however much time to optimize the compressed stream independently from decompressor.

masklinn• 26 days ago

Deflate also has a fixed 32K window so even with indexes and parallelism there’s only so much you can blow up memory use.

userbinator2• 26 days ago

It's interesting to see just how far Deflate can be taken, and to know that even after decades there is still some (admittedly tiny) room for improvement. Optimal LZ is well-known, and so is static Huffman, but their combination creates some additional inefficiencies(opportunities).

...and of course it's written by someone with a Russian name, and has that characteristic style common to many other articles about data compression.

blobbers• 26 days ago

As someone currently exploring grid searches of encodings + compressor combos, and currently looking at neural compressors that reduce size almost half that of a traditional compressor yet take order from ms -> minutes to operate in either direction, I appreciate a good compression post!

jedbrooke• 26 days ago

reminds me of the x264 “placebo” encoder setting

https://trac.ffmpeg.org/wiki/Encode/H.264#FAQ

pella4• 26 days ago

OpenZL is the future: https://openzl.org/

  "OpenZL delivers high compression ratios while preserving high speed, a level of performance that is out of reach for generic compressors. OpenZL takes a description of your data and builds from it a specialized compressor optimized for your specific format."

Retro_Dev• 26 days ago

OpenZL is nice, but it's often less useful than you think - it requires that you know the structure of your data, and don't care about inspecting that data outside of your program. I've extracted one too many png files from a word document (by renaming .docx to .zip) to desire OpenZL everywhere... It might be better as a short-term "data in transit" compression than for long term storage.

pella4• 26 days ago

Please check the OpenZL v0.2 + Silesia corpus benchmark.

  "OpenZL to offer 10% faster compression speed and 70% faster decompression speed compared to Zstandard level 1 on the Silesia corpus in our benchmarks."
  "OpenZL now ships its own LZ codec, exposed as ZL_GRAPH_LZ, and the serial profile in zli. It is still being actively developed to expand its feature set and improve performance on small inputs."

https://github.com/facebook/openzl/releases/tag/v0.2.0

pella4• 26 days ago

The future may be ~ AI-assisted format detection + OpenZL

(~ OpenZL-AI-LLM recognises the data structure, then guides OpenZL toward the best lossless compression path )

mike_hock• 25 days ago

1. Upload data using conventional compression method (or uncompressed)

2. Spend orders of magnitude (literally) more on compute to run the LLM on the data than any compression algorithm would ever take.

pella4• 25 days ago

  "The unreasonable effectiveness of our first foray into training leads us to believe that the graph model is uniquely  positioned to facilitate ML-guided generation of compressors. We are tempted to view this as “the next big thing” in production-scale compression. Whereas compression research has up to now eluded those without domain expertise, we believe the future of application-specific compressors will be unlocked via investment in automated learning methods."

https://arxiv.org/abs/2605.09928 [11 May 2026] OpenZL: Using Graphs to Compress Smaller and Faster

Hellishly Slow Level 13 Deflate Compression

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