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Amazon Aurora: On Avoiding Distributed Consensus for I/Os, Commits, and Membership Changes

Verbitski, Alexandre ; Gupta, Anurag ; Saha, Debanjan ; Corey, James ; Gupta, Kamal ; Brahmadesam, Murali ; Mittal, Raman ; Krishnamurthy, Sailesh ; Maurice, Sandor ; Kharatishvilli, Tengiz ; Bao, Xiaofeng

Proceedings of the 2018 International Conference on Management of Data, 2018, p.789-796

New York, NY, USA: ACM

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Título:
Amazon Aurora: On Avoiding Distributed Consensus for I/Os, Commits, and Membership Changes
Autor: Verbitski, Alexandre ; Gupta, Anurag ; Saha, Debanjan ; Corey, James ; Gupta, Kamal ; Brahmadesam, Murali ; Mittal, Raman ; Krishnamurthy, Sailesh ; Maurice, Sandor ; Kharatishvilli, Tengiz ; Bao, Xiaofeng
Assuntos: Computer systems organization -- Dependable and fault-tolerant systems and networks -- Availability ; Information systems -- Data management systems -- Database management system engines -- Database transaction processing ; Information systems -- Data management systems -- Database management system engines -- Database transaction processing -- Database recovery ; Information systems -- Data management systems -- Database management system engines -- Database transaction processing -- Transaction logging ; Information systems -- Data management systems -- Database management system engines -- Distributed database transactions ; Information systems -- Data management systems -- Database management system engines -- Parallel and distributed DBMSs -- Relational parallel and distributed DBMSs ; Information systems -- Information storage systems -- Storage architectures -- Cloud based storage ; Information systems -- Information storage systems -- Storage architectures -- Distributed storage
É parte de: Proceedings of the 2018 International Conference on Management of Data, 2018, p.789-796
Descrição: Amazon Aurora is a high-throughput cloud-native relational database offered as part of Amazon Web Services (AWS). One of the more novel differences between Aurora and other relational databases is how it pushes redo processing to a multi-tenant scale-out storage service, purpose-built for Aurora. Doing so reduces networking traffic, avoids checkpoints and crash recovery, enables failovers to replicas without loss of data, and enables fault-tolerant storage that heals without database involvement. Traditional implementations that leverage distributed storage would use distributed consensus algorithms for commits, reads, replication, and membership changes and amplify cost of underlying storage. In this paper, we describe how Aurora avoids distributed consensus under most circumstances by establishing invariants and leveraging local transient state. Doing so improves performance, reduces variability, and lowers costs.
Editor: New York, NY, USA: ACM
Idioma: Inglês

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Amazon Aurora: On Avoiding Distributed Consensus for I/Os, Commits, and Membership Changes

Verbitski, Alexandre ; Gupta, Anurag ; Saha, Debanjan ; Corey, James ; Gupta, Kamal ; Brahmadesam, Murali ; Mittal, Raman ; Krishnamurthy, Sailesh ; Maurice, Sandor ; Kharatishvilli, Tengiz ; Bao, Xiaofeng

New York, NY, USA: ACM

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