Cleaning Antipatterns in an SQL Query Log
Cleaning Antipatterns in an SQL Query Log
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DOI：
10.1109/tkde.2017.2772252
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发表时间：
2018-03
影响因子：
8.9
通讯作者：
N. Arzamasova;Martin Schäler;Klemens Böhm
中科院分区：
文献类型：
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作者：
N. Arzamasova;Martin Schäler;Klemens Böhm
如今，许多科学数据集向公众开放。对于他们的运营商来说，重要的是要了解用户感兴趣的内容。在本文中，我们研究了从数据库的查询日志中提取和分析模式的问题。我们专注于设计错误（Antipatterns），这通常会导致不必要的SQL语句。这样的反原生不仅对绩效产生负面影响。他们还对SQL日志的任何后续分析引入了偏见。我们提出了一个框架，旨在在任意SQL查询日志中发现模式和对抗图案并清洁对抗图案。为了研究我们的方法的有用性，并揭示了有关对反生物在现实世界系统中存在的见解，我们检查了Skyserver项目的SQL日志，其中包含超过4000万个查询。在排名前15位的模式中，我们发现了六个对抗图案。该结果以及其他结果可以得出结论，即反生物可能会伪造重构和任何其他下游分析。
Today, many scientific data sets are open to the public. For their operators, it is important to know what the users are interested in. In this paper, we study the problem of extracting and analyzing patterns from the query log of a database. We focus on design errors (antipatterns), which typically lead to unnecessary SQL statements. Such antipatterns do not only have a negative effect on performance. They also introduce bias on any subsequent analysis of the SQL log. We propose a framework designed to discover patterns and antipatterns in arbitrary SQL query logs and to clean antipatterns. To study the usefulness of our approach and to reveal insights regarding the existence of antipatterns in real-world systems, we examine the SQL log of the SkyServer project, containing more than 40 million queries. Among the top 15 patterns, we have found six antipatterns. This result as well as other ones gives way to the conclusion that antipatterns might falsify refactoring and any other downstream analyses.