Performance Verification of IDL Architecture for Partitioned Database

Abstract

With the increasing popularity of ORM frameworks and the need to efficiently manage large volumes of data, optimization of these approaches is becoming an increasingly common subject of research. This paper focuses on analyzing the impact of partitioning of database tables on the performance of the Intelligent Data Layer (IDL), which was introduced in our previous study "Design of Data Access Architecture Using an ORM Framework". IDL enables the implementation of ORM frameworks with loaded data prediction, which overall improves and accelerates access to database data. This work extends previous experiments by optimizing IDL through database table partitioning and thoroughly analyzes the impact of this technique on system behavior and performance. We experimentally verify the applicability of this optimization technique in a practical environment and quantify the achieved performance improvements of IDL. The results show that properly designed partitioning can significantly contribute to the overall system efficiency while maintaining its flexibility and scalability.