<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sentry on Mi&amp;Bee Blog</title><link>https://blog.mickeyzzc.tech/en/tags/sentry/</link><description>Recent content in Sentry on Mi&amp;Bee Blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>蓝宝石的傻话</managingEditor><lastBuildDate>Mon, 22 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.mickeyzzc.tech/en/tags/sentry/rss.xml" rel="self" type="application/rss+xml"/><item><title>RUM Time-Series Storage: Sentry, Datadog RUM and Grafana Faro</title><link>https://blog.mickeyzzc.tech/en/posts/telemetry/obs-tech-05-rum-storage/</link><pubDate>Mon, 22 Jun 2026 00:00:00 +0000</pubDate><guid>https://blog.mickeyzzc.tech/en/posts/telemetry/obs-tech-05-rum-storage/</guid><description>&lt;p&gt;Real User Monitoring (RUM) differs fundamentally from backend monitoring in its data model: RUM collects &lt;strong&gt;event streams with massive high-cardinality attributes&lt;/strong&gt; — every user visit generates session_id, user_id, page_url, and hundreds of dimensions. Traditional TSDB label-set models degrade severely under this pattern.&lt;/p&gt;
&lt;p&gt;This article analyzes the time-series storage design of three mainstream RUM solutions: Sentry (open-source all-in-one), Datadog RUM (SaaS benchmark), and Grafana Faro (Grafana LGTM ecosystem extension). The common trend across all three is &lt;strong&gt;leveraging columnar OLAP instead of TSDB&lt;/strong&gt;.&lt;/p&gt;</description></item></channel></rss>