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Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases

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This document describes the design of Amazon Aurora, a cloud-native relational database service built to handle high-throughput, online transaction processing (OLTP) workloads. The paper highlights the challenges of traditional database architectures in cloud environments, specifically the I/O bottleneck created by network traffic. Aurora addresses these issues by leveraging a novel service-oriented architecture where the storage service is decoupled from the database engine. This approach significantly reduces network I/O and enables efficient crash recovery, failover, and self-healing capabilities. The paper further explains how Aurora achieves consensus on durable state across storage nodes through an asynchronous scheme, avoiding expensive and chatty recovery protocols. The authors conclude by sharing insights gained from their customers on the evolving needs of modern cloud applications for database services.

https://assets.amazon.science/dc/2b/4ef2b89649f9a393d37d3e042f4e/amazon-aurora-design-considerations-for-high-throughput-cloud-native-relational-databases.pdf

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43 episodes

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Manage episode 487366641 series 3670304
Content provided by The Binary Breakdown. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Binary Breakdown or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

This document describes the design of Amazon Aurora, a cloud-native relational database service built to handle high-throughput, online transaction processing (OLTP) workloads. The paper highlights the challenges of traditional database architectures in cloud environments, specifically the I/O bottleneck created by network traffic. Aurora addresses these issues by leveraging a novel service-oriented architecture where the storage service is decoupled from the database engine. This approach significantly reduces network I/O and enables efficient crash recovery, failover, and self-healing capabilities. The paper further explains how Aurora achieves consensus on durable state across storage nodes through an asynchronous scheme, avoiding expensive and chatty recovery protocols. The authors conclude by sharing insights gained from their customers on the evolving needs of modern cloud applications for database services.

https://assets.amazon.science/dc/2b/4ef2b89649f9a393d37d3e042f4e/amazon-aurora-design-considerations-for-high-throughput-cloud-native-relational-databases.pdf

  continue reading

43 episodes

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