Causal Consistency: Beyond Memory

TL;DR

This paper introduces a new, generalized approach to defining causal consistency for any data type in distributed systems, addressing limitations of previous methods that were data type-specific.

Contribution

It formalizes and compares three variations of causal consistency, broadening applicability beyond memory to diverse data types using sequential specifications.

Findings

Defines weak causal consistency, causal convergence, and causal consistency.

Highlights differences between the variations in relation to PRAM, eventual, and sequential consistency.

Provides a formal framework adaptable to various data types in distributed systems.

Abstract

In distributed systems where strong consistency is costly when not impossible, causal consistency provides a valuable abstraction to represent program executions as partial orders. In addition to the sequential program order of each computing entity, causal order also contains the semantic links between the events that affect the shared objects -- messages emission and reception in a communication channel , reads and writes on a shared register. Usual approaches based on semantic links are very difficult to adapt to other data types such as queues or counters because they require a specific analysis of causal dependencies for each data type. This paper presents a new approach to define causal consistency for any abstract data type based on sequential specifications. It explores, formalizes and studies the differences between three variations of causal consistency and highlights them in the light of PRAM, eventual consistency and sequential consistency: weak causal consistency, that captures the notion of causality preservation when focusing on convergence ; causal convergence that mixes weak causal consistency and convergence; and causal consistency, that coincides with causal memory when applied to shared memory.