Year: 2024
Type: article
Abstract: This study investigates workload management challenges in SAP HANA’s in-memory columnar storage, specifically focusing on the complexities of handling mixed OLTP and OLAP workloads. Traditional database management approaches often fail to address SAP HANA’s unique architecture, which combines high-speed transaction processing with real-time analytics. Drawing on research into mixed workload optimization (May et al., 2015) and SAP HANA’s architectural evolution (Färber et al., 2017), this paper explores workload throttling, prioritization, and real-time monitoring strategies to optimize resource allocation. Our findings offer practical guidance for database administrators managing critical business applications with high data demands, such as finance and supply chain. Additionally, we examine tools and techniques for performance tracing in third party applications like Tableau (Tableau Software, 2023), providing an integrated perspective on maintaining optimal SAP HANA performance under peak load conditions.
Authors: Puneet Aggarwal, Amit Aggarwal
Institution: Deloitte (United States)
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4407001531