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<title>Benchmarking and comparing DwarFS</title>
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<h1 id="benchmarking-and-comparing-dwarfs">Benchmarking and
comparing DwarFS</h1>
<p>DwarFS is a filesystem developed by the user mhx on GitHub
[1], which is self-described as "A fast high compression
read-only file system for Linux, Windows, and macOS." One of my
ideas for blendOS was to layer different packages, and combined
with its compression and option to be mounted as a FUSE-based
filesystem, it's an appealing option for this use case - blendOS
is immutable, so it might as well have some compression.</p>
<h2 id="methodology">Methodology</h2>
<p>The datasets being used for this test will be the
following:</p>
<ul>
<li>25 GB of null data (just <code>000000000000</code> in
binary)</li>
<li>25 GB of random data<a href="#fn1" class="footnote-ref"
id="fnref1" role="doc-noteref"><sup>1</sup></a></li>
<li>Data for a 100 million-sided regular polygon; ~29 GB<a
href="#fn2" class="footnote-ref" id="fnref2"
role="doc-noteref"><sup>2</sup></a></li>
<li>The current Linux longterm release source (<a
href="https://cdn.kernel.org/pub/linux/kernel/v6.x/linux-6.6.58.tar.xz">6.6.58</a>
[2]); ~1.5 GB</li>
<li>For some rough latency testing:
<ul>
<li>1000 4 kilobyte files filled with null data (again, just
<code>0000000</code> in binary)</li>
<li>1000 4 kilobyte files filled with random data</li>
</ul></li>
</ul>
<p>All this data should cover both latency and read speed
testing for data that compresses differently - extremely
compressible files with null data, decently compressible files,
and random data which can't be compressed well.</p>
<h2 id="sources">Sources</h2>
<ol type="1">
<li><a href="https://github.com/mhx/dwarfs"
class="uri">https://github.com/mhx/dwarfs</a></li>
<li><a href="https://www.kernel.org/"
class="uri">https://www.kernel.org/</a></li>
</ol>
<h2 id="footnotes">Footnotes</h2>
<section id="footnotes"
class="footnotes footnotes-end-of-document" role="doc-endnotes">
<hr />
<ol>
<li id="fn1">This data is from a very early version of a math
demonstration program made by a friend. The example below shows
what the data looks like for a 3-sided regular polygon.
<details open>
<summary>
3-sided regular polygon data
</summary>
<br>
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<pre><code>[Vertex { position: Pos([0.5, 0.0, 0.0]), color: Col([0.5310667, 0.7112941, 0.7138775]) }, Vertex { position: Pos([-0.25000003, 0.4330127, 0.0]), color: Col([0.7492257, 0.3142163, 0.49905664]) }, Vertex { position: Pos([0.0, 0.0, 0.0]), color: Col([0.2046682, 0.25598457, 0.72071356]) }, Vertex { position: Pos([-0.25000003, 0.4330127, 0.0]), color: Col([0.6389981, 0.5204368, 0.077735074]) }, Vertex { position: Pos([-0.24999996, -0.43301272, 0.0]), color: Col([0.8869035, 0.30709425, 0.8658899]) }, Vertex { position: Pos([0.0, 0.0, 0.0]), color: Col([0.2046682, 0.25598457, 0.72071356]) }, Vertex { position: Pos([-0.24999996, -0.43301272, 0.0]), color: Col([0.6236294, 0.03584433, 0.7590722]) }, Vertex { position: Pos([0.5, 8.742278e-8, 0.0]), color: Col([0.6105084, 0.3593351, 0.85544324]) }, Vertex { position: Pos([0.0, 0.0, 0.0]), color: Col([0.2046682, 0.25598457, 0.72071356]) }]</code></pre>
</div>
</details>
<a href="#fnref1" class="footnote-back"
role="doc-backlink">↩︎</a></li>
<li id="fn2"><p>My code can generate up to 25 GB/s. However, it
does random writes to my drive, which is <em>much</em> slower.
So on one hand, you could say my code is so amazingly fast that
current day technologies simply can't keep up. Or you could say
that I have no idea how to code for real world scenarios.<a
href="#fnref2" class="footnote-back"
role="doc-backlink">↩︎</a></p></li>
</ol>
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