Development and Research of Batch Implementation of SQL-queries Based on the Rules of Their Ordering in Cloud Environments

Abstract

The study proposes strategies for grouping (batching) queries in relational databases based on random grouping, as well as on prioritizing the values of individual performance metrics – execution time, DTU usage, and CPU load – and analyses their impact on the performance of the Azure SQL Database cloud platform service. The research methodology included creating a database in Azure SQL Database at different Azure service tiers – from S0 to S12 – to model various configurations of computing resources. To simulate realistic scenarios of working with the service, a database of a trading company with large sets of test data and several test database queries of varying complexity was used. Query batching strategies were developed: random grouping, grouping by ascending/descending query execution time, resource intensity (DTU consumption), and CPU load. Each strategy was tested across all resource configurations through multiple test trials, ensuring the relevance of the obtained results for an objective analysis. The results obtained demonstrated the necessity of using a differentiated approach to selecting query batching strategies depending on database size, query complexity, and the choice of query prioritization models in batch mode.