blocks|key|487588|text|如果可以将数据解析为单独的字段，然后按查询求和，并按执行次数和平均值排序，则可以使用电子表格。你知道..。普通的数据分析工具。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|487589|如果需要，可以编写一个基于正则表达式的应用程序，将日志拆分成CSV。在此期间，您甚至可以为日志名称编写一个一致性引擎，检查平均值，并制作您想要的工具。|487590|当然，可能会有人进来说"Duh，使用‘analysisMySQLProDemon40005K%2B’！“在我发完这篇文章之后。|487591|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

You could use a spreadsheet if you can parse the data into individual fields, then sum by query and sort by count of executions and average. Y'know... Normal data analysis stuff.

If you need to, write a regex based application to split the log up into CSV. While you're at it, you could even write a concordance engine for the log names, check averages, and make the tool you're after.

Of course, someone is likely to come in and say "Duh, use 'analysisMySQLProDemon40005K+!'" after I post this.

blocks|key|487603|text|查看Maatkit|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|487604|mk-query-digest+-解析日志和更多内容。分析、转换、过滤、审查和报告查询。|487605|entityMap|0|LINK|mutability|MUTABLE|url|http://www.maatkit.org/^0|2|7|0|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@$A|O|B|P|1|Q]]|C|$]]|$1|D|3|E|5|6|7|R|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]]]

Check out <a href="http://www.maatkit.org/" rel="nofollow noreferrer">Maatkit</a>:

mk-query-digest - Parses logs and more. Analyze, transform, filter, review and report on queries.

blocks|key|510436|text|这个脚本给出了比maakit+mk-query-digest更清晰的响应：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|510437|https://github.com/LeeKemp/mysql-slow-query-log-parser/|offset|length|510438|entityMap|0|LINK|mutability|MUTABLE|url^0|0|0|1J|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|N|8|@]|9|@$D|O|E|P|1|Q]]|A|$]]|$1|F|3|-4|5|6|7|R|8|@]|9|@]|A|$]]]|G|$H|$5|I|J|K|A|$L|C]]]]

This script gives a more clear response than the maakit mk-query-digest:

<a href="https://github.com/LeeKemp/mysql-slow-query-log-parser/" rel="nofollow">https://github.com/LeeKemp/mysql-slow-query-log-parser/</a>

blocks|key|510188|text|这个在线分析器对我来说工作得很好，而且它是免费的！只需拖放您的慢查询文件。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|510189|https://www.slowquerylog.com/analyzer|offset|length|510190|entityMap|0|LINK|mutability|MUTABLE|url^0|0|0|11|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|N|8|@]|9|@$D|O|E|P|1|Q]]|A|$]]|$1|F|3|-4|5|6|7|R|8|@]|9|@]|A|$]]]|G|$H|$5|I|J|K|A|$L|C]]]]

This online analyzer worked really well for me, and it's free! Just drag and drop your slow query file.

<a href="https://www.slowquerylog.com/analyzer" rel="nofollow noreferrer">https://www.slowquerylog.com/analyzer</a>

blocks|key|487697|text|为了更好地阅读慢日志，我使用：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|487698|mysqldumpslow+-s+c+-t+10+/var/log/mysql_slow_queries.log|code-block|syntax|javascript|487699|编辑慢速日志的路径。|487700|示例输出：|487701|Reading+mysql+slow+query+log+from+/var/log/mysql_slow_queries.log
Count:+1++Time=21.52s+(21s)++Lock=0.00s+(0s)++Rows=3000.0+(3000),+foo@localhost
++SELECT+*+FROM+students+WHERE
++(student_grade+>+N+AND+student_date+<+N)
++ORDER+BY+RAND()+ASC+LIMIT+N|487702|此外，只调用mysqldumpslow而不调用任何选项，将打印一些有用的设置，如当前query_cache_size、慢速日志的路径等。|offset|length|style|CODE|487703|entityMap^0|0|0|0|0|0|6|D|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|W|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|X|8|@]|9|@]|A|$]]|$1|K|3|L|5|D|7|Y|8|@]|9|@]|A|$E|F]]|$1|M|3|N|5|6|7|Z|8|@$O|10|P|11|Q|R]]|9|@]|A|$]]|$1|S|3|-4|5|6|7|12|8|@]|9|@]|A|$]]]|T|$]]

For a better reading of the slow log I use:
<pre><code>mysqldumpslow -s c -t 10 /var/log/mysql_slow_queries.log
</code></pre>
Edit the path to the slow log.
Sample output:
<pre><code>Reading mysql slow query log from /var/log/mysql_slow_queries.log
Count: 1 Time=21.52s (21s) Lock=0.00s (0s) Rows=3000.0 (3000), foo@localhost
 SELECT * FROM students WHERE
 (student_grade &gt; N AND student_date &lt; N)
 ORDER BY RAND() ASC LIMIT N
</code></pre>
Also, calling just the <code>mysqldumpslow</code> without any options, would print some useful settings like current query_cache_size, path to slow log, etc.

I have an extensive slow query log which was running for a few weeks. I would like to parse it, which would put the highest occurring queries at the top (with number of executions and average times of execution), and it goes from there in descending order. 

What tool/command can I use to accomplish that?

How to parse a mysql slow query log into something useful?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我有一个大范围的慢查询日志，它已经运行了几个星期。我想解析它，这将把出现频率最高的查询放在顶部(具有执行次数和平均执行时间)，并从那里开始按降序排列。我可以使用什么工具/命令来完成此任务？

问如何将mysql慢查询日志解析成有用的东西？
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问如何将mysql慢查询日志解析成有用的东西？EN