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
- 2. Anurag Gupta
- 3. Debanjan Saha
- 4. James Corey
- 5. Kamal Gupta
- 6. Murali Brahmadesam
- 7. Raman Mittal
- 8. Sailesh Krishnamurthy
- 9. Sandor Maurice
- 10. Tengiz Kharatishvilli
- 11. Xiaofeng Bao
Incoming Citations (Sorted by Pagerank)
Showing 28 of 28 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 |
|---|---|---|---|---|
| 122 | Calvin: Fast Distributed Transactions for Partitioned Database Systems | 2012 | SIGMOD | 0.0004532783 |
| 156 | Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases | 2017 | SIGMOD | 0.00040580118 |
| 1,008 | Spanner: Becoming a SQL System | 2017 | SIGMOD | 0.00014663067 |
| 3,784 | Adaptive Logging: Optimizing Logging and Recovery Costs in Distributed In-memory Databases | 2016 | SIGMOD | 6.7664522e-05 |
| 3,830 | Capturing Global Transactions from Multiple Recovery Log Files in a Partitioned Database System | 2003 | VLDB | 6.7159198e-05 |
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