<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Performance on Osman Goni Nahid</title><link>https://osmangoninahid.github.io/tags/performance/</link><description>Recent content in Performance on Osman Goni Nahid</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 19 Sep 2026 00:00:00 +0400</lastBuildDate><atom:link href="https://osmangoninahid.github.io/tags/performance/index.xml" rel="self" type="application/rss+xml"/><item><title>We were hashing every byte twice: making 500 GiB volume imports fast</title><link>https://osmangoninahid.github.io/hashing-every-byte-twice/</link><pubDate>Sat, 19 Sep 2026 00:00:00 +0400</pubDate><guid>https://osmangoninahid.github.io/hashing-every-byte-twice/</guid><description>&lt;p&gt;On the platform I work on, a &lt;em&gt;workspace volume&lt;/em&gt; is a Kubernetes PVC that users fill from Hugging Face or an S3 bucket and then mount into training or inference workloads. The import runs as a one-off pod: download, then record metadata (a file tree with per-file hashes, and one digest for the whole volume) back to the control plane.&lt;/p&gt;&#10;&lt;p&gt;Customers importing 500 GiB+ models started reporting that the download finished in a reasonable time and then the pod sat for &lt;strong&gt;hours&lt;/strong&gt; in the metadata step. This is the story of why, and what fixed it.&lt;/p&gt;</description></item></channel></rss>