<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>Tracing - vo.rs</title><link>https://vo.rs/tags/tracing/</link><description>Latest from the Tracing desk at vo.rs.</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</copyright><lastBuildDate>Thu, 25 Jul 2024 16:02:00 +0000</lastBuildDate><atom:link href="https://vo.rs/tags/tracing/" rel="self" type="application/rss+xml"/><item><title>OpenTelemetry at Home: Traces for a Two-Service App</title><link>https://vo.rs/story/opentelemetry-at-home-traces-for-a-two-service-app/</link><description>&lt;p&gt;Metrics tell you a request was slow. Logs tell you what each service said while it was being slow. Neither tells you &lt;em&gt;where the time went&lt;/em&gt; when a request passes through more than one service, and that gap is the whole reason distributed tracing exists.&lt;/p&gt;
&lt;p&gt;You hit this the moment you split an app in two. Say you have a small front-end API that takes a request and calls a back-end service to do the real work. A user reports the thing is slow. Your metrics show the front-end&amp;rsquo;s p95 latency climbing. Your logs show the front-end received the request and, some time later, returned a response. Was the delay in the front-end&amp;rsquo;s own code? In the network hop? In the back-end? In something the back-end called? You are back to correlating timestamps across two log streams by eye, which is exactly the archaeology tracing abolishes.&lt;/p&gt;</description><pubDate>Thu, 25 Jul 2024 16:02:00 +0000</pubDate></item></channel></rss>