skip to main content
10.1145/2463676.2463710acmconferencesArticle/Chapter ViewAbstractPublication PagesmodConference Proceedingsconference-collections
research-article

Hekaton: SQL server's memory-optimized OLTP engine

Published:22 June 2013Publication History

ABSTRACT

Hekaton is a new database engine optimized for memory resident data and OLTP workloads. Hekaton is fully integrated into SQL Server; it is not a separate system. To take advantage of Hekaton, a user simply declares a table memory optimized. Hekaton tables are fully transactional and durable and accessed using T-SQL in the same way as regular SQL Server tables. A query can reference both Hekaton tables and regular tables and a transaction can update data in both types of tables. T-SQL stored procedures that reference only Hekaton tables can be compiled into machine code for further performance improvements. The engine is designed for high con-currency. To achieve this it uses only latch-free data structures and a new optimistic, multiversion concurrency control technique. This paper gives an overview of the design of the Hekaton engine and reports some experimental results.

References

  1. Florian Funke, Alfons Kemper, Thomas Neumann: HyPer-sonic Combined Transaction AND Query Processing. PVLDB 4(12): 1367--1370 (2011)Google ScholarGoogle Scholar
  2. Martin Grund, Jens Krüger, Hasso Plattner, Alexander Zeier, Philippe Cudré-Mauroux, Samuel Madden: HYRISE - A Main Memory Hybrid Storage Engine. PVLDB 4(2): 105--116 (2010) Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Martin Grund, Philippe Cudré-Mauroux, Jens Krüger, Samuel Madden, Hasso Plattner: An overview of HYRISE - a Main Memory Hybrid Storage Engine. IEEE Data Eng. Bull. 35(1): 52--57 (2012)Google ScholarGoogle Scholar
  4. Stavros Harizopoulos, Daniel J. Abadi, Samuel Madden, Mi-chael Stonebraker: OLTP through the looking glass, and what we found there. SIGMOD 2008: 981--992 Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. IBM SolidDB, http://www.ibm.com/software/data/soliddbGoogle ScholarGoogle Scholar
  6. Ryan Johnson, Ippokratis Pandis, Nikos Hardavellas, Anasta-sia Ailamaki, Babak Falsafi: Shore-MT: a scalable storage manager for the multicore era. EDBT 2009: 24--35 Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Robert Kallman, Hideaki Kimura, Jonathan Natkins, Andrew Pavlo, Alex Rasin, Stanley B. Zdonik, Evan P. C. Jones, Samuel Madden, Michael Stonebraker, Yang Zhang, John Hugg, Daniel J. Abadi: H-store: a high-performance, distrib-uted main memory transaction processing system. PVLDB 1(2): 1496--1499 (2008) Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Alfons Kemper, Thomas Neumann: HyPer: A hybrid OLTP&OLAP main memory database system based on virtual memory snapshots. ICDE 2011: 195--206 Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Per-Åke Larson, Spyros Blanas, Cristian Diaconu, Craig Freedman, Jignesh M. Patel, Mike Zwilling: High-Performance Concurrency Control Mechanisms for Main-Memory Databases. PVLDB 5(4): 298--309 (2011) Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Justin J. Levandoski, David B. Lomet, Sudipta Sengupta, The Bw-Tree: A B-tree for New Hardware Platforms, ICDE 2013 (to appear).Google ScholarGoogle Scholar
  11. The LLVM Compiler Infrastructure, http://llvm.org/Google ScholarGoogle Scholar
  12. Maged M. Michael. 2004. Hazard Pointers: Safe Memory Reclamation for Lock-Free Objects. IEEE Trans. Parallel Dis-trib. Syst. 15, 6 (June 2004), 491--504. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Maged M. Michael. 2002. High performance dynamic lock-free hash tables and list-based sets. In Proceedings of the four-teenth annual ACM symposium on Parallel algorithms and ar-chitectures (SPAA '02): 73--82. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Thomas Neumann: Efficiently Compiling Efficient Query Plans for Modern Hardware. PVLDB 4(9): 539--550 (2011) Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Oracle TimesTen, http://www.oracle.com/technetwork/products/timesten/overview/index.htmlGoogle ScholarGoogle Scholar
  16. Ippokratis Pandis, Ryan Johnson, Nikos Hardavellas, Anasta-sia Ailamaki: Data-Oriented Transaction Execution. PVLDB 3(1): 928--939 (2010) Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Phoenix compiler framework, http://en.wikipedia.org/wiki/Phoenix_(compiler_framework)Google ScholarGoogle Scholar
  18. SAP In-Memory Computing, http://www.sap.com/solutions/technology/in-memory-computing-platform/hana/overview/index.epxGoogle ScholarGoogle Scholar
  19. Sybase In-Memory Databases, http://www.sybase.com/manage/in-memory-databasesGoogle ScholarGoogle Scholar
  20. Håkan Sundell, Philippas Tsiga, Lock-free deques and doubly linked lists, Journal of Parallel and Distributed Computing - JPDC , 68(7): 1008--1020, (2008) Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. VoltDB, http://voltdb.comGoogle ScholarGoogle Scholar

Index Terms

  1. Hekaton: SQL server's memory-optimized OLTP engine

                Recommendations

                Comments

                Login options

                Check if you have access through your login credentials or your institution to get full access on this article.

                Sign in
                • Published in

                  cover image ACM Conferences
                  SIGMOD '13: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
                  June 2013
                  1322 pages
                  ISBN:9781450320375
                  DOI:10.1145/2463676

                  Copyright © 2013 ACM

                  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 ACM 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]

                  Publisher

                  Association for Computing Machinery

                  New York, NY, United States

                  Publication History

                  • Published: 22 June 2013

                  Permissions

                  Request permissions about this article.

                  Request Permissions

                  Check for updates

                  Qualifiers

                  • research-article

                  Acceptance Rates

                  SIGMOD '13 Paper Acceptance Rate76of372submissions,20%Overall Acceptance Rate785of4,003submissions,20%

                PDF Format

                View or Download as a PDF file.

                PDF

                eReader

                View online with eReader.

                eReader