AN ANALYSIS OF APACHE SPARK AND GPU PERFORMANCES ON DATABASE SQL QUERIES FOR DISTRIBUTED NETWORKS

The use of GPU in different fields and its successful results initiate efforts to use GPU in database systems. It is also effective in distributed systems and computer networks in that accelerates computational tasks by leveraging parallel processing capabilities across multiple nodes and for tasks that require high computational power, such as network traffic analysis and real-time data processing. Digital transformation in all areas of life has led to the emergence of needs such as increased data diversity and faster data analysis. Upgrading the hardware capacity of the system or software-based studies are possible solutions to analyze this data for meeting the needs. In this study, Apache Spark and GPU performance differences are examined in commonly used SQL queries on big data. In this context, SQL queries such as grouping, sorting, and filtering, which are commonly used in data analysis, are used. While the queries performed with the GPU showed similar results in simple queries compared to the queries performed with Apache Spark, the GPU was completed 3x faster in queries requiring calculation.