Coo: Rethink Data Anomalies In Databases

TL;DR

This paper introduces Coo, a comprehensive framework for systematically defining and analyzing data anomalies in databases, addressing ambiguities and formalizing the concept to improve understanding and control.

Contribution

Coo provides a complete, formalized approach to define data anomalies, classifies infinite anomalies, and introduces new isolation levels with quantitative analysis of concurrency control algorithms.

Findings

Existing data anomalies are only a subset of all possible anomalies.

Coo is theoretically complete in formalizing data anomalies.

Quantitative analysis of concurrency and rollback rates in mainstream algorithms.

Abstract

Transaction processing technology has three important contents: data anomalies, isolation levels, and concurrent control algorithms. Concurrent control algorithms are used to eliminate some or all data anomalies at different isolation levels to ensure data consistency. Isolation levels in the current ANSI standard are defined by disallowing certain kinds of data anomalies. Yet, the definitions of data anomalies in the ANSI standard are controversial. On one hand, the definitions lack a mathematical formalization and cause ambiguous interpretations. On the other hand, the definitions are made in a case-by-case manner and lead to a situation that even a senior DBA could not have infallible knowledge of data anomalies, due to a lack of a full understanding of its nature. While revised definitions in existing literature propose various mathematical formalizations to correct the former argument, how to address the latter argument still remains an open problem. In this paper, we present a general framework called Coo with the capability to systematically define data anomalies. Under this framework, we show that existing reported data anomalies are only a small portion. While we theoretically prove that Coo is complete to mathematically formalize data anomalies, we employ a novel method to classify infinite data anomalies. In addition, we use this framework to define new isolation levels and quantitatively describe the concurrency and rollback rate of mainstream concurrency control algorithms. These works show that the C and I of ACID can be quantitatively analyzed based on all data anomalies.