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

Published: 27 May 2018 Publication History

Abstract

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.

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cover image ACM Conferences
SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data
May 2018
1874 pages
ISBN:9781450347037
DOI:10.1145/3183713
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 27 May 2018

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Author Tags

  1. databases
  2. distributed systems
  3. fault tolerance
  4. log processing
  5. performance
  6. quorum models
  7. quorum sets
  8. recovery
  9. replication

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SIGMOD '18 Paper Acceptance Rate 90 of 461 submissions, 20%;
Overall Acceptance Rate 785 of 4,003 submissions, 20%

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  • (2024)Hybrid Shared-Buffer for Multi-Master DatabasesJournal of Database Management10.4018/JDM.35692035:1(1-27)Online publication date: 11-Oct-2024
  • (2024)TDSQL: Tencent Distributed Database SystemProceedings of the VLDB Endowment10.14778/3685800.368581217:12(3869-3882)Online publication date: 8-Nov-2024
  • (2024)GaussDB: A Cloud-Native Multi-Primary Database with Compute-Memory-Storage DisaggregationProceedings of the VLDB Endowment10.14778/3685800.368580617:12(3786-3798)Online publication date: 8-Nov-2024
  • (2024)Cloud-Native Databases: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339750836:12(7772-7791)Online publication date: Dec-2024
  • (2024)LBSC: A Cost-Aware Caching Framework for Cloud Databases2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00373(4911-4924)Online publication date: 13-May-2024
  • (2024)FlexpushdownDB: rethinking computation pushdown for cloud OLAP DBMSsThe VLDB Journal10.1007/s00778-024-00867-833:5(1643-1670)Online publication date: 10-Jul-2024
  • (2023)Fine-Grained Re-Execution for Efficient Batched Commit of Distributed TransactionsProceedings of the VLDB Endowment10.14778/3594512.359452316:8(1930-1943)Online publication date: 22-Jun-2023
  • (2023)InfiniStore: Elastic Serverless Cloud StorageProceedings of the VLDB Endowment10.14778/3587136.358713916:7(1629-1642)Online publication date: 1-Mar-2023
  • (2023)PolarDB-IMCI: A Cloud-Native HTAP Database System at AlibabaProceedings of the ACM on Management of Data10.1145/35897851:2(1-25)Online publication date: 20-Jun-2023
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