# Toward Adaptive Causal Consistency for Replicated Data Stores

## TL;DR

This paper introduces a dynamic approach to causal consistency in key-value stores, allowing applications to customize and adapt tracking and checking groups to better meet their specific needs.

## Contribution

It proposes a flexible system with configurable tracking and checking groups, enabling dynamic adjustments to optimize causal consistency for diverse applications.

## Key findings

- Supports application-specific customization of causal consistency
- Enables dynamic reconfiguration of tracking and checking groups
- Improves balance between consistency and availability

## Abstract

Causal consistency for key-value stores has two main requirements (1) do not make a version visible if some of its dependencies are invisible as it may violate causal consistency in the future and (2) make a version visible as soon as possible so that clients have the most recent information (to the extent feasible). These two requirements conflict with each other. Existing key-value stores that provide causal consistency (or detection of causal violation) utilize a static approach in the trade-off between these requirements. Depending upon the choice, it assists some applications and penalizes some applications. We propose an alternative where the system provides a set of tracking groups and checking groups. This allows the application to choose the settings that are most suitable for that application. Furthermore, these groups can be dynamically changed based on application requirements.