Sailesh Krishnamurthy
27 Papers
238 Citations
Sailesh Krishnamurthy is an academic researcher from Google. The author has contributed to research in topics: Computer science & Scalability. The author has an hindex of 20, co-authored 25 publications. Previous affiliations of Sailesh Krishnamurthy include Cisco Systems, Inc. & Raman Research Institute.
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Papers
•Proceedings Article
TelegraphCQ: Continuous Dataflow Processing for an Uncertain World.
Sirish Chandrasekaran,Owen Cooper,Amol Deshpande,Michael J. Franklin,Joseph M. Hellerstein,Wei Hong,Sailesh Krishnamurthy,Samuel Madden,Vijayshankar Raman,Frederick Reiss,Mehul A. Shah +10 more
- 01 Jan 2003
TL;DR: The next generation Telegraph system, called TelegraphCQ, is focused on meeting the challenges that arise in handling large streams of continuous queries over high-volume, highly-variable data streams and leverages the PostgreSQL open source code base.
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TelegraphCQ: continuous dataflow processing
Sirish Chandrasekaran,Owen Cooper,Amol Deshpande,Michael J. Franklin,Joseph M. Hellerstein,Wei Hong,Sailesh Krishnamurthy,Samuel Madden,Fred Reiss,Mehul A. Shah +9 more
- 09 Jun 2003
TL;DR: The current version of TelegraphCQ is shown, which is implemented by leveraging the code base of the open source PostgreSQL database system, which found that a significant portion of the PostgreSQL code was easily reusable.
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Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases
Alexandre Olegovich Verbitski,Anurag Windlass Gupta,Debanjan Saha,Murali Brahmadesam,Kamal Gupta,Raman Mittal,Sailesh Krishnamurthy,Sandor Maurice,Tengiz Kharatishvili,Xiaofeng Bao +9 more
- 09 May 2017
TL;DR: This paper describes the architecture of Aurora and the design considerations leading to that architecture, and describes how Aurora achieves consensus on durable state across numerous storage nodes using an efficient asynchronous scheme, avoiding expensive and chatty recovery protocols.
On-the-fly sharing for streamed aggregation
Sailesh Krishnamurthy,Chung Wu,Michael J. Franklin +2 more
- 27 Jun 2006
TL;DR: A major contribution is the sharing technique that does not require any up-front multiple query optimization, a significant departure from existing techniques that rely on complex static analyses of fixed query workloads.
Continuous analytics over discontinuous streams
Sailesh Krishnamurthy,Michael J. Franklin,Jeffrey A. Davis,Daniel Robert Farina,Pasha Golovko,Alan Li,Neil Thombre +6 more
- 06 Jun 2010
TL;DR: The approach provides the first real solution to the problem of processing streaming data that arrives arbitrarily late and serves as a critical building block for solutions to a host of hard problems such as parallelism, recovery, transactional consistency, high availability, failover, and replication.
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