In this episode, we dive deep into the architecture and design considerations behind Amazon Aurora, a high-performance, cloud-native relational database service. Drawing insights from two foundational papers, we explore how Aurora achieves remarkable scalability and reliability without relying on distributed consensus for I/O operations, commits, and membership changes.
We’ll reference the work in the paper "Amazon Aurora: On Avoiding Distributed Consensus for I/Os, Commits, and Membership Changes" (Verbitski et al., 2019), which discusses how Aurora optimizes its internal systems to avoid the pitfalls of traditional distributed consensus protocols, making it faster and more resilient. Additionally, we’ll discuss key design principles from "Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases" (Verbitski et al., 2021), which highlights Aurora's focus on high throughput, fault tolerance, and ease of use in a cloud-native environment.
Some or all of this content is AI generated and may contain some errors. Please use with caution. Tune in for an in-depth exploration of cutting-edge database engineering and how Amazon Aurora continues to push the boundaries of what’s possible in cloud-based database management.
