Checking Robustness Between Weak Transactional Consistency Models

TL;DR

This paper investigates how to verify if a program's behavior remains consistent when replacing strong database transaction models with weaker ones, ensuring correctness while improving performance.

Contribution

It introduces a polynomial-time reduction approach to check robustness between weaker consistency models and serializability, along with a practical proof technique.

Findings

Robustness checking reduces to state reachability under serializability.

The approach is applicable to models like causal, prefix, and snapshot isolation.

Validated on challenging distributed systems applications.

Abstract

Concurrent accesses to databases are typically encapsulated in transactions in order to enable isolation from other concurrent computations and resilience to failures. Modern databases provide transactions with various semantics corresponding to different trade-offs between consistency and availability. Since a weaker consistency model provides better performance, an important issue is investigating the weakest level of consistency needed by a given program (to satisfy its specification). As a way of dealing with this issue, we investigate the problem of checking whether a given program has the same set of behaviors when replacing a consistency model with a weaker one. This property known as robustness generally implies that any specification of the program is preserved when weakening the consistency. We focus on the robustness problem for consistency models which are weaker than standard serializability, namely, causal consistency, prefix consistency, and snapshot isolation. We show that checking robustness between these models is polynomial time reducible to a state reachability problem under serializability. We use this reduction to also derive a pragmatic proof technique based on Lipton's reduction theory that allows to prove programs robust. We have applied our techniques to several challenging applications drawn from the literature of distributed systems and databases.