This paper details the architecture and design of Amazon Aurora, a cloud-native relational database service. Key design choices focus on mitigating network bottlenecks inherent in high-throughput cloud systems by offloading redo processing to a separate, distributed storage service. This approach enhances durability and availability through a novel quorum model and segmented storage, significantly improving performance and reducing recovery times. The authors present performance benchmarks demonstrating Aurora's superior scalability and efficiency compared to traditional MySQL configurations, along with lessons learned from real-world customer deployments highlighting the system's suitability for modern cloud applications. Finally, the paper compares Aurora's design to related work in distributed database systems.

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