Lossless Data Compression
Oct 2, 2023

Lossless Data Compression

While I've covered image compression a decent amount in these sorts of blog posts, I wanted to dive into generic lossless data compression a bit more to see what this interesting realm of compression has to offer. Inspired by the many other existing lossless data compression comparisons, I wanted to approach this with visualizations similar to the ones I provide in my image codec comparisons. Because lossless data compression doesn't discard data, these graphs plot time vs. compressed size, allowing you to compare the various compression steps of lossless data compression algorithms relative to the time taken to compress/decompress.

Methodology & Algorithms

I want to make it clear upfront that I did not thoroughly test every single available compression algorithm under the sun, but instead hand-picked a couple that I found interesting. Notably not present are gzip & bzip2; gzip has an interesting history, but its use of Deflate & LZ77 makes it pretty much identical to ZIP for all intents & purposes. Bzip2 is interesting, but due to its age & effectiveness compared to more modern compression algorithms, I found it less interesting than more modern options. It also decompresses slower than the more space-efficient XZ. Feel free to pique my interest in these two algorithms if you're interested in seeing them included.

I must provide the usual disclaimer as well that my testing is non-scientific & shouldn't inform your use of one technology over another. That being said, here are the formats I tested:

& here are the parameters I used for compression, often after archiving to a .tar:

Finally, here are my system specs:

CPU: 16-core (8-mt/8-st) 13th Gen Intel Core i7-13700K (-MST AMCP-)
speed/min/max: 888/800/5300:5400:4200 MHz Kernel: 6.5.5-zen1-1-zen x86_64

If you'd like to do a deeper dive into my methodology, I have my script compression-plotter available on GitHub. Given you have all the dependencies, you are free to run it yourself to cross-reference any results you come up with against mine.

I used a 1.0GB generic Wikipedia download for this test, which makes it text-specific. Now, let's dive into which algorithms I chose & what they do.

ZIP (Deflate)

Most modern variants of the tried & true ZIP use the Deflate compression algorithm, which is also famously used in PNG. Deflate acts as a combination of LZ77 lossless coding & Huffman coding, where it can first use LZ77 to find patterns in the data & reduce redundancy. This is followed by using Huffman coding to assign smaller bit values to patterns found more frequently by LZ77.

Considering ZIP came out in 1989, I don't expect it to be the strongest performer on this benchmark. I'm interested to see how 7zip's supposedly improved ZIP encoder fares against the other standards, though; as the 7zip website's homepage claims: "For ZIP and GZIP formats, 7-Zip provides a compression ratio that is 2-10 % better than the ratio provided by PKZip and WinZip."

ZIP is noteworthy for its nearly universal compatibility. "Traditional ZIP" (compression method 8) is limited to 4 GB, though most ZIP compressors use Deflate64(tm) (compression level 9 in the ZIP specification) to bypass this limitation.


Using the static 7zip binary for Linux, I benchmarked 7zip not initially very interested in its compression capabilities. However, as I continued to use it, I was impressed by its usability & sane defaults; 7zip compression & decompression both thread rather effectively, which makes the format feel very fast to work with even if system & user time tell a slightly different story. It is supported just fine on my Arch Linux installation with GNOME, and it works on macOS by default. To open a 7zip (.7z) archive on Windows 10, you need the well-known 7-Zip utility.

7zip is mostly based on LZMA & LZMA2, though there is a lot more going on if you look deeper. LZMA2 is an improved version of LZMA, which itself is based on LZ77.


XZ can only compress one file at a time, so making a tar archive of the files you'd like to compress (if there are multiple) is necessary when using XZ. The XZ format itself is an improvement on LZMA, allowing for preprocessing filters similar to 7zip to increase the resulting archive's compression ratio. I've been able to decompress .tar.xz archives on macOS & Linux just fine, but Windows 10 needs 7-Zip once again.


Brotli was designed by Google, & is commonly used as a compression format on the Web. It was released in late 2013, & it is commonly used on the Web for content delivery. It is a core part of the .woff2 Web Open Font Format, allowing web fonts to be smaller when sent to users as part of a website. It is not very common to pass around .tar.br files, so it is perfectly acceptable that such files aren't really compatible anywhere. Brotli is almost universally compatible across the Web, being supported by as much as 96% of the world wide web's users.

Brotli is based on LZ77 & Huffman coding, much like ZIP. It also uses context modeling to allow the use of multiple Huffman trees for the same alphabet in the same block; without getting into the weeds, this essentially means that based on the context of the data being compressed, it can be compressed more efficiently especially if it contains multiple different kinds of data.

Brotli was co-authored & partially developed by Jyrki Alakuijala, who also worked on JPEG-XL & jpegli. JPEG-XL's metadata information can be uncompressed or Brotli-compressed.


Zstandard is a compression algorithm by Facebook known for its extremely fast decompression speeds. It was released in early 2015 and is used in a variety of different contexts. It was designed to perform similarly to older Deflate-based compression algorithms like ZIP or gzip while being overall faster. In practice, it is said to compress similarly to pure LZMA while being much faster.

While .tar.zst archives aren't going to be very popular to find on the Internet & elsewhere, it is already a very popular tool for compression in the world of open-source software. It has been integrated into both the FreeBSD kernel & the Linux kernel and is available as a filesystem compression method for the btrfs, squashfs, bcachefs, & OpenZFS filesystems. All Arch Linux packages are compressed at zstd level 20, allowing Arch packages to be decompressed 14 times faster than when Arch used XZ at the cost of an average 0.8% filesize increase. It is popular in the game emulation scene as well, as many game file formats for emulating console games support zstd compression. The ZIP file format actually supports Zstandard in compression level 93 since version 6.3.8 which was published in 2020. Content encoding using zstd is supported since Chromium 118 behind an experimental flag, meaning it might compete with Brotli on the web in the future. Apple's LZFSE algorithm is purportedly similar to Zstandard compression level 6.


I know much less about ZPAQ, but from what I can glean it uses a multitude of different compression algorithms to try to achieve the best size-to-compression-time ratio possible while producing the smallest possible archives without much concern given to decompression performance. On the official ZPAQ website, it looks like it is designed for "realistic backups that have a lot of duplicate files and a lot of already compressed files."

What I find very cool about ZPAQ is that it is an incremental journaling archiver, meaning you can add files to an existing archive based on if they were changed or not which reduces the time needed to wait for a new backup to finish. If other tools here are capable of this, I have not seen it advertised, but this is particularly cool for ZPAQ since it is so focused on compression ratio & in practice, this kind of feature may reduce the burden imposed by long compression times. ZPAQ archives aren't handled by default on my Linux installation in Nautilus.



Here are my compression performance results, measured in real time. This graph uses a logarithmic horizontal scale factor:


My personal takeaways from this test:

Here are my compression performance results, measured in user + system time (the time the computer uses across its resources, added up). This graph uses a logarithmic horizontal scale factor:


My personal takeaways from this test:


Evaluating any of these compression standards for practical use means you must take decompression into account. Let's look at the first batch of results for real time, where I tested decompression performance for the lowest & highest levels of compression effort for each standard. Once again, this graph uses a logarithmic horizontal scale factor:


My personal takeaways from this test:

Finally, here are the user + sys time results for decompression. This should tell us a more complete story about why 7zip is so fast in real time. This graph uses a logarithmic horizontal scale factor:


My personal takeaways from this test:


My conclusion overall is that there really are different scenarios where each format shines. I'll explain for each format:

Firstly, ZPAQ is going to be my choice for computer backups if I ever do them (I often don't). The incremental option alongside the incredible compression ratio makes it the best option for this use case, in my opinion, given you'll be willing to allow it to take its sweet time as it decompresses. For a backup format, I don't think that's the biggest deal in the world anyway.

7zip seems like the best option for general desktop use, and if you're distributing files, providing .7z archives might not be a terrible idea if you're looking to phase out ZIP to save server space & bandwidth while maintaining compatibility. The last holdout for support is Android, as Windows 11 can now decompress 7zip archives. 7zip's widespread support as well as its good performance earn it this position. Did you know even iOS can preview the contents of 7zip archives?

XZ is a bit of a black sheep here in a world where 7zip exists. 7zip & XZ are both open source, too. Tell me if XZ has some hidden strengths I'm missing.

Brotli as a replacement for simpler algorithms on the Web for content delivery makes complete sense. It decompresses fast & compresses well, and can be parallelized using other implementations to optimize real time encode performance. In the meantime, servers are more concerned with user & system time, so those graphs are likely more salient when considering Brotli performance where it is applicable.

Zstandard is what I'd consider to be the future of all of the above categories. In certain scenarios, if it takes off as a content delivery format, I could see it replacing Brotli if the benefits of super-fast & super-light decode improve the responsiveness of web pages & are worth sacrificing a bit of compression ratio. When using the much higher effort settings, it actually beats Brotli on all accounts. I'd be happy to pass around .tar.zst archives in the future to replace 7zip & ZIP, making speedy decode a reality on systems of all different measures of compute performance. I'm glad Zstandard is making its way as a great format to use in the FOSS community for innovative new implementations of compression like filesystem compression & accessible package compression that doesn't tax your CPU when decoding, but I'd personally like to see its use cases expand in the future.

ZIP's Deflate implementation is very clearly useful for one thing: compatibility. I'm glad the Deflate patent has expired, otherwise I'd be much more disappointed in ZIP's standardization as the default compression archive format in most situations. Personally, for the time being, I'm just glad we didn't unintentionally standardize around uncompressed archives, as I'd be willing to bet many don't know ZIPs are actually compressed.

What do you think of my results? Is there anything I missed or could have done better? Should I include bzip2 or some other algorithm next time? Let me know. Thank you for reading!

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