<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Distributed Tracing on Mi&amp;Bee Blog</title><link>https://blog.mickeyzzc.tech/en/tags/distributed-tracing/</link><description>Recent content in Distributed Tracing on Mi&amp;Bee Blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>蓝宝石的傻话</managingEditor><lastBuildDate>Sat, 20 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.mickeyzzc.tech/en/tags/distributed-tracing/rss.xml" rel="self" type="application/rss+xml"/><item><title>Distributed Tracing Storage Architecture: Jaeger, Tempo, SkyWalking and Commercial Solutions</title><link>https://blog.mickeyzzc.tech/en/posts/telemetry/obs-tech-04-tracing-storage/</link><pubDate>Sat, 20 Jun 2026 00:00:00 +0000</pubDate><guid>https://blog.mickeyzzc.tech/en/posts/telemetry/obs-tech-04-tracing-storage/</guid><description>&lt;p&gt;Distributed tracing storage is one of the most technically divergent areas in the observability landscape. Unlike metrics and logging, which have relatively converged storage patterns (TSDB and ClickHouse-like systems), tracing storage has split into three distinct paths due to fundamental disagreements over &lt;strong&gt;indexing strategy&lt;/strong&gt;.&lt;/p&gt;
&lt;p&gt;This chapter analyzes the storage architectures of Jaeger, Grafana Tempo, Apache SkyWalking, Zipkin, and the commercial solutions from Datadog and Splunk, concluding with a horizontal comparison.&lt;/p&gt;</description></item></channel></rss>