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| 数据库管理系统中数据异常体系化定义与分类 |
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李海翔1, 李晓燕2, 刘畅1, 杜小勇3,4, 卢卫3,4, 潘安群1
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1.腾讯科技(北京)有限公司, 北京 100080;2.北京大学 数学科学学院 信息与计算科学系, 北京 100871;3.数据工程与知识工程教育部重点实验室(中国人民大学), 北京 100872;4.中国人民大学 信息学院, 北京 100872
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| 摘要: |
| 数据异常尚没有统一的定义，其含义是指可能破坏数据库一致性状态的特定数据操作模式.已知的数据异常有脏写、脏读、不可重复读、幻读、丢失更新、读偏序和写偏序等.为了提高并发控制算法的效率，数据异常也被用于定义隔离级别，采用较弱的隔离级别以提高事务处理系统的效率.体系化地研究了数据异常以及对应的隔离级别，发现了22种未被其他文献报告过的新的数据异常，并对全部数据异常进行分类.基于数据异常的分类，提出了新的且不同粒度的隔离级别体系，揭示基于数据异常定义隔离级别的规律，使得对于数据异常和隔离级别等相关概念的认知可以更加简明. |
| 关键词: 事务处理 数据异常 隔离级别 并发访问控制算法 数据库 |
| DOI：10.13328/j.cnki.jos.006442 |
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| 基金项目:国家重点研发计划（2017YFB1001803）；国家自然科学基金（61872008） |
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| Systematic Definition and Classification of Data Anomalies in Data Base Management Systems |
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LI Hai-Xiang1, LI Xiao-Yan2, LIU Chang1, DU Xiao-Yong3,4, LU Wei3,4, PAN An-Qun1
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1.Tencent Technology (Beijing) Co., Ltd, Beijing 100080, China;2.Department of Information and Computational Sciences, School of Mathematical Sciences, Peking University, Beijing 100871, China;3.Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), Ministry of Education, Beijing 100872, China;4.School of Information, Renmin University of China, Beijing 100872, China
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| Abstract: |
| There is no unified definition of data anomalies, which refers to the specific data operation mode that may destroy the consistency of the database. Known data anomalies include Dirty Write, Dirty Read, Non-repeatable Read, Phantom, Read Skew, Write Skew, etc. In order to improve the efficiency of concurrency control algorithms, data anomalies are also used to define the isolation levels, because the weak isolation level can improve the efficiency of transaction processing systems. This work systematically studies the data anomalies and the corresponding isolation levels. Twenty-two new data anomalies are reported that have not been reported by other researches, and all data anomalies are classified miraculously. Based on the classification of data anomalies, two new isolation levels with different granularity are proposed, which reveals the rule of defining isolation levels based on data anomalies and makes the cognition of data anomalies and isolation levels more concise. |
| Key words: transaction processing data anomalies isolation level concurrency control algorithm database |
