Amazon Aurora: On Avoiding Distributed Consensus for I/Os, Commits, and Membership Changes
Summary: Aurora pushes redo processing to a scale-out storage service, avoiding distributed consensus for I/Os, commits, and membership changes. With invariants and local transient state, it minimizes consensus, delivering higher throughput, lower latency variability, and reduced storage costs. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Alexandre Verbitski (Amazon)
- 2. Anurag Gupta (Amazon)
- 3. Debanjan Saha (Amazon)
- 4. James Corey (Amazon)
- 5. Kamal Gupta (Amazon)
- 6. Murali Brahmadesam (Amazon)
- 7. Raman Mittal (Amazon)
- 8. Sailesh Krishnamurthy (Amazon)
- 9. Sandor Maurice (Amazon)
- 10. Tengiz Kharatishvilli (Amazon)
- 11. Xiaofeng Bao (Amazon)
BibTeX Citation
@inproceedings{verbitski_sigmod18,
title = {{Amazon Aurora: On Avoiding Distributed Consensus for I/Os, Commits, and Membership Changes}},
author = {Verbitski, Alexandre and Gupta, Anurag and Saha, Debanjan and Corey, James and Gupta, Kamal and Brahmadesam, Murali and Mittal, Raman and Krishnamurthy, Sailesh and Maurice, Sandor and Kharatishvilli, Tengiz and Bao, Xiaofeng},
series = {{SIGMOD} '18},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3183713.3196937},
url = {https://dl.acm.org/doi/10.1145/3183713.3196937},
year = {2018}
}Incoming Citations (Sorted by Pagerank)
Showing 32 of 32 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 54 | Calvin: Fast Distributed Transactions for Partitioned Database Systems | 2012 | SIGMOD | 0.00040489652 |
| 63 | Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases | 2017 | SIGMOD | 0.00038609177 |
| 749 | Spanner: Becoming a SQL System | 2017 | SIGMOD | 0.00014247708 |
| 3,464 | Adaptive Logging: Optimizing Logging and Recovery Costs in Distributed In-memory Databases | 2016 | SIGMOD | 7.2615048e-05 |
| 3,960 | Capturing Global Transactions from Multiple Recovery Log Files in a Partitioned Database System | 2003 | VLDB | 6.8801848e-05 |
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