ANALYSIS OF DYNAMIC DATA CONSISTENCY MODELS IN DISTRIBUTED DATABASE MANAGEMENT SYSTEMS

Abstract

The article is devoted to the analysis of modern dynamic data consistency models in distributed database management systems (DBMS). Traditional distributed DBMS used static consistency models that did not consider system state or data access patterns. However, growing data scales, transaction complexity, and performance requirements demand new approaches.

The research analyzes three main dynamic consistency models. The context-oriented model consists of consistency blocks, consistency policies, and context descriptors that dynamically determine consistency levels based on operation context and system state. The CAnDoR model uses data segmentation with continuous measurement of synchronization and response times to automatically distribute segments among nodes. The R-TBC/RTA model addresses network partitioning problems by forming hierarchical tree structures with different consistency guarantees for primary and secondary nodes. Various options for the practical implementation of mechanisms for individual models are also considered.

Comparative analysis was conducted based on architectural criteria (concept, consistency determination, data placement), adaptive criteria (load monitoring, time adaptation, fault tolerance), and integration possibilities. The comparison revealed that simpler models like context-oriented provide higher flexibility and easier integration but depend heavily on implementation, while complex models like R-TBC/RTA offer better fault tolerance guarantees but require more sophisticated implementation. The use of architecture based on more complex models together with separate mechanisms of simpler models is promising, as it may allow the creation of a more universal dynamic model of data consistency.

The results show that all models have effective consistency mechanisms for specific scenarios but differ significantly in implementation complexity. The analysis can be used for developing custom dynamic consistency models and advancing distributed systems research.