Systematic definition and classification of data anomalies in DBMS (English Version)

TL;DR

This paper systematically defines and classifies data anomalies in DBMS, introduces 22 new anomalies, and proposes two new isolation levels to improve transaction processing efficiency.

Contribution

It provides a comprehensive classification of data anomalies, reports 22 new anomalies, and proposes novel isolation levels based on anomaly classification.

Findings

22 new data anomalies identified

A miraculously comprehensive classification system established

Two new isolation levels proposed based on anomalies

Abstract

There is no unified definition of Data anomalies, which refers to the specific data operation mode that may violate the consistency of the database. Known data anomalies include Dirty Write, Dirty Read, Non-repeatable Read, Phantom, Read Skew and 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 paper systematically studies the data anomalies and the corresponding isolation levels. We report twenty-two new data anomalies that other papers have not reported, and all data anomalies are classified miraculously. Based on the classification of data anomalies, two new isolation levels systems 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.