Architectural Deep Dive and Performance Evaluation: Scaling Relational Workloads with Amazon Aurora
Architectural Deep Dive and Performance Evaluation: Scaling Relational Workloads with Amazon Aurora
rahul goel
TLDR
This paper explores Aurora’s unique architecture, focusing on its distributed storage system, adaptive scaling mechanisms, and high availability features, and provides insights into best practices for optimizing Aurora-based systems to achieve cost-efficient and resilient database solutions.
Abstract
Amazon Aurora, a cloud-native relational database engine, has emerged as a leading solution for scalable and high-performance database management. This paper explores Aurora’s unique architecture, focusing on its distributed storage system, adaptive scaling mechanisms, and high availability features. Through real-world case studies, we analyze how organizations have leveraged Aurora to enhance performance and scalability. Additionally, insights from prior benchmarking studies [21] provide an in-depth understanding of Aurora’s efficiency in handling dynamic workloads. This study provides insights into best practices for optimizing Aurora-based systems to achieve cost-efficient and resilient database solutions.
