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DSM vs. NSM: CPU performance tradeoffs in block-oriented query processing

Authors:

Marcin Zukowski,

Peter BonczAuthors Info & Claims

DaMoN '08: Proceedings of the 4th international workshop on Data management on new hardware

Pages 47 - 54

https://doi.org/10.1145/1457150.1457160

Published: 13 June 2008 Publication History

Abstract

Comparisons between the merits of row-wise storage (NSM) and columnar storage (DSM) are typically made with respect to the persistent storage layer of database systems. In this paper, however, we focus on the CPU efficiency tradeoffs of tuple representations inside the query execution engine, while tuples flow through a processing pipeline. We analyze the performance in the context of query engines using so-called "block-oriented" processing -- a recently popularized technique that can strongly improve the CPU efficiency. With this high efficiency, the performance trade-offs between NSM and DSM can have a decisive impact on the query execution performance, as we demonstrate using both microbenchmarks and TPC-H query 1. This means that NSM-based database systems can sometimes benefit from converting tuples into DSM on-the-fly, and vice versa.

References

[1]

D. Abadi, S. Madden, and M. Ferreira. Integrating Compression and Execution in Column-Oriented Database Systems. In SIGMOD, 2006.

Digital Library

[2]

D. J. Abadi, D. S. Myers, D. J. DeWitt, and S. R. Madden. Materialization Strategies in a Column-Oriented DBMS. In ICDE, 2007.

[3]

S. Agarwal and H. Daeubler. Reducing Database Size by Using Vardecimal Storage Format. Microsoft, 2007.

[4]

A. Ailamaki, D. J. DeWitt, M. D. Hill, and M. Skounakis. Weaving Relations for Cache Performance. In VLDB, Rome, Italy, 2001.

Digital Library

[5]

P. Boncz, M. Zukowski, and N. Nes. MonetDB/X100: Hyper-Pipelining Query Execution. In CIDR, 2005.

[6]

P. A. Boncz. Monet: A Next-Generation DBMS Kernel For Query-Intensive Applications. Ph.d. thesis, Universiteit van Amsterdam, May 2002.

[7]

P. A. Boncz, S. Manegold, and M. L. Kersten. Database Architecture Optimized for the New Bottleneck: Memory Access. In VLDB, pages 54--65, Edinburgh, 1999.

Digital Library

[8]

S. Chen, A. Ailamaki, P. B. Gibbons, and T. C. Mowry. Improving hash join performance through prefetching. ACM Trans. Database Syst., 32(3):17, 2007.

Digital Library

[9]

A. Copeland and S. Khoshafian. A Decomposition Storage Model. In SIGMOD, Austin, TX, USA, 1985.

Digital Library

[10]

N. H. Daniel J. Abadi, Samuel R. Madden. Column-Stores vs. Row-Stores: How Different Are They Really? In SIGMOD, Vancouver, Canada, 2008.

Digital Library

[11]

A. Halverson, J. L. Beckmann, J. F. Naughton, and D. J. DeWitt. A Comparison of C-Store and Row-Store in a Common Framework. Technical Report TR1570, University of Wisconsin-Madison, 2006.

[12]

R. A. Hankins and J. M. Patel. Data Morphing: An Adaptive, Cache-Conscious Storage Technique. In VLDB, pages 417--428, Berlin, Germany, 2003.

Digital Library

[13]

S. Harizopoulos, V. Liang, D. Abadi, and S. Madden. Performance Tradeoffs in Read-Optimized Databases. In VLDB, 2006.

Digital Library

[14]

S. Heman, N. Nes, M. Zukowski, and P. Boncz. Vectorized Data Processing on the Cell Broadband Engine. In DAMON, 2007.

Digital Library

[15]

S. Padmanabhan, T. Malkemus, R. Agarwal, and A. Jhingran. Block Oriented Processing of Relational Database Operations in Modern Computer Architectures. In ICDE, Heidelberg, Germany, 2001.

Digital Library

[16]

M. Stonebraker, D. J. Abadi, A. Batkin, X. Chen, M. Cherniack, M. Ferreira, E. Lau, A. Lin, S. Madden, E. J. O'Neil, P. E. O'Neil, A. Rasin, N. Tran, and S. B. Zdonik. C-Store: A Column-oriented DBMS. In VLDB, Trondheim, Norway, 2005.

Digital Library

[17]

T. Westmann, D. Kossmann, S. Helmer, and G. Moerkotte. The implementation and performance of compressed databases. SIGMOD Record, 29(3):55--67, September 2000.

Digital Library

[18]

J. Zhou and K. A. Ross. Implementing database operations using SIMD instructions. In SIGMOD, Madison, USA, 2002.

Digital Library

[19]

J. Zhou and K. A. Ross. Buffering Accesses to Memory-Resident Index Structures. In VLDB, Berlin, Germany, 2003.

Digital Library

[20]

M. Zukowski, S. Heman, and P. Boncz. Architecture-Conscious Hashing. In DAMON, Chicago, IL, USA, 2006.

Digital Library

[21]

M. Zukowski, S. Heman, N. Nes, and P. Boncz. Super-Scalar RAM-CPU Cache Compression. In ICDE, Atlanta, GA, USA, 2006.

Digital Library

Cited By

Klyuchikov EPolyntsov MChizhov AMikhailova EChernishev G(2024)Hybrid Materialization in a Disk-Based Column-StoreProceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632422(164-172)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1145/3632410.3632422
Kuiper LBoncz PMühleisen H(2024)Robust External Hash Aggregation in the Solid State Age2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00288(3753-3766)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00288
Kuiper LMühleisen H(2023)These Rows Are Made for Sorting and That’s Just What We’ll Do2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00159(2050-2062)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00159
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Index Terms

DSM vs. NSM: CPU performance tradeoffs in block-oriented query processing

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Information

Published In

cover image ACM Conferences

DaMoN '08: Proceedings of the 4th international workshop on Data management on new hardware

June 2008

57 pages

ISBN:9781605581842

DOI:10.1145/1457150

Editors:
Qiong Luo
Hong Kong University of Science and Technology
,
Kenneth A. Ross
Columbia University

Copyright © 2008 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]

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SIGMOD: ACM Special Interest Group on Management of Data
Microsoft: Microsoft
Intel: Intel
IBM: IBM

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 June 2008

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DaMoN '08

Sponsor:

SIGMOD
Microsoft
Intel
IBM

DaMoN '08: Data Management on New Hardware

June 13, 2008

Vancouver, Canada

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Overall Acceptance Rate 94 of 127 submissions, 74%

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Cited By

Klyuchikov EPolyntsov MChizhov AMikhailova EChernishev G(2024)Hybrid Materialization in a Disk-Based Column-StoreProceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632422(164-172)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1145/3632410.3632422
Kuiper LBoncz PMühleisen H(2024)Robust External Hash Aggregation in the Solid State Age2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00288(3753-3766)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00288
Kuiper LMühleisen H(2023)These Rows Are Made for Sorting and That’s Just What We’ll Do2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00159(2050-2062)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00159
Habich DPietrzyk JKrause AHildebrandt JLehner W(2022)To use or not to use the SIMD gather instruction?Proceedings of the 18th International Workshop on Data Management on New Hardware10.1145/3533737.3535089(1-5)Online publication date: 12-Jun-2022
https://dl.acm.org/doi/10.1145/3533737.3535089
Hildebrandt JPietrzyk JKrause AHabich DLehner W(2022)Partition-based SIMD Processing and its Application to Columnar Database SystemsDatenbank-Spektrum10.1007/s13222-022-00431-023:1(53-63)Online publication date: 7-Dec-2022
https://doi.org/10.1007/s13222-022-00431-0
Gubner TLeis VBoncz P(2021)Optimistically Compressed Hash Tables & Strings in theUSSRACM SIGMOD Record10.1145/3471485.347150050:1(60-67)Online publication date: 17-Jun-2021
https://dl.acm.org/doi/10.1145/3471485.3471500
Liu YTseng H(2021)NDS: N-Dimensional StorageMICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3466752.3480122(28-45)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3466752.3480122
Gubner TLeis VBoncz P(2020)Efficient Query Processing with Optimistically Compressed Hash Tables & Strings in the USSR2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00033(301-312)Online publication date: Apr-2020
https://doi.org/10.1109/ICDE48307.2020.00033
Fang YZou CChien A(2019)Accelerating raw data analysis with the ACCORDA software and hardware architectureProceedings of the VLDB Endowment10.14778/3342263.334263412:11(1568-1582)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.14778/3342263.3342634
Sanuki M(2019)Improvement of EZ-GCD algorithm based on extended hensel constructionACM Communications in Computer Algebra10.1145/3338637.333864952:4(148-150)Online publication date: 30-May-2019
https://dl.acm.org/doi/10.1145/3338637.3338649
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