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    <title>Tooling on Jamie Craane personal blog</title>
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      <title>Parsing, Visualizing and Analyzing Log Data with Claude Code</title>
      <link>https://jamiecraane.dev/2026/11/06/analyzing-log-data/</link>
      <pubDate>Thu, 11 Jun 2026 00:00:00 +0000</pubDate>
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      <description>&lt;h3 id=&#34;introduction&#34;&gt;Introduction&lt;/h3&gt;&#xA;&lt;p&gt;A production system feels sluggish, you suspect the database, and you have a log file full of slow query entries staring back at you. The data you need is right there. The hard part is turning a few thousand lines of text into answers.&lt;/p&gt;&#xA;&lt;p&gt;Recently I had exactly this situation: a log file from production containing slow database queries, and four questions:&lt;/p&gt;&#xA;&lt;ul&gt;&#xA;&lt;li&gt;&lt;strong&gt;Which queries are slow?&lt;/strong&gt;&lt;/li&gt;&#xA;&lt;li&gt;&lt;strong&gt;What is the distribution of the execution time?&lt;/strong&gt;&lt;/li&gt;&#xA;&lt;li&gt;&lt;strong&gt;When are the queries slow?&lt;/strong&gt;&lt;/li&gt;&#xA;&lt;li&gt;&lt;strong&gt;What are the most frequently executed queries?&lt;/strong&gt;&lt;/li&gt;&#xA;&lt;/ul&gt;&#xA;&lt;p&gt;These are the bread-and-butter questions of database performance work. And yet, answering them well is surprisingly awkward with the tools we usually reach for.&lt;/p&gt;</description>
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