abstract

BUZZARD: a NUMA-aware in-memory indexing system

Authors:

Lukas M. Maas,

Thomas Kissinger,

Dirk Habich,

Wolfgang LehnerAuthors Info & Claims

SIGMOD '13: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

Pages 1285 - 1286

https://doi.org/10.1145/2463676.2465342

Published: 22 June 2013 Publication History

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Abstract

With the availability of large main memory capacities, in-memory index structures have become an important component of modern data management platforms. Current research even suggests index-based query processing as an alternative or supplement for traditional tuple-at-a-time processing models. However, while simple sequential scan operations can fully exploit the high bandwidth provided by main memory, indexes are mainly latency bound and spend most of their time waiting for memory accesses.

Considering current hardware trends, the problem of high memory latency is further exacerbated as modern shared-memory multiprocessors with non-uniform memory access (NUMA) become increasingly common. On those NUMA platforms, the execution time of index operations is dominated by memory access latency that increases dramatically when accessing memory on remote sockets. Therefore, good index performance can only be achieved through careful optimization of the index structure to the given topology.

BUZZARD is a NUMA-aware in-memory indexing system. Using adaptive data partitioning techniques, BUZZARD distributes a prefix-tree-based index across the NUMA system and hands off incoming requests to worker threads located on each partition's respective NUMA node. This approach reduces the number of remote memory accesses to a minimum and improves cache utilization. In addition, all indexes inside BUZZARD are only accessed by their respective owner, eliminating the need for synchronization primitives like compare-and-swap.

References

[1]

M. Bohm, B. Schlegel, P. B. Volk, U. Fischer, D. Habich, and W. Lehner. Efficient In-Memory Indexing with Generalized Prefix Trees. In BTW, 2011.

Google Scholar

[2]

T. Kissinger, B. Schlegel, D. Habich, and W. Lehner. QPPT: Query Processing on Prefix Trees. CIDR, 2013.

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Cited By

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Usman SMehmood RKatib IAlbeshri A(2022)Data Locality in High Performance Computing, Big Data, and Converged Systems: An Analysis of the Cutting Edge and a Future System ArchitectureElectronics10.3390/electronics1201005312:1(53)Online publication date: 23-Dec-2022
https://doi.org/10.3390/electronics12010053
Liu GChen LChen S(2022)Zen+: a robust NUMA-aware OLTP engine optimized for non-volatile main memoryThe VLDB Journal10.1007/s00778-022-00737-132:1(123-148)Online publication date: 6-Apr-2022
https://doi.org/10.1007/s00778-022-00737-1
Magalhaes AMonteiro JBrayner A(2021)Main Memory Database RecoveryACM Computing Surveys10.1145/344219754:2(1-36)Online publication date: 5-Mar-2021
https://dl.acm.org/doi/10.1145/3442197
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Index Terms

BUZZARD: a NUMA-aware in-memory indexing system
1. Information systems
  1. Data management systems
    1. Data structures
      1. Data access methods

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Information

Published In

SIGMOD '13: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

June 2013

1322 pages

ISBN:9781450320375

DOI:10.1145/2463676

General Chairs:
Kenneth Ross
Columbia University
,
Divesh Srivastava
AT&T Research
,
Program Chair:
Dimitris Papadias
HKUST

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2013

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Conference

SIGMOD/PODS'13

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SIGMOD

SIGMOD/PODS'13: International Conference on Management of Data

June 22 - 27, 2013

New York, New York, USA

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SIGMOD '13 Paper Acceptance Rate 76 of 372 submissions, 20%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

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Usman SMehmood RKatib IAlbeshri A(2022)Data Locality in High Performance Computing, Big Data, and Converged Systems: An Analysis of the Cutting Edge and a Future System ArchitectureElectronics10.3390/electronics1201005312:1(53)Online publication date: 23-Dec-2022
https://doi.org/10.3390/electronics12010053
Liu GChen LChen S(2022)Zen+: a robust NUMA-aware OLTP engine optimized for non-volatile main memoryThe VLDB Journal10.1007/s00778-022-00737-132:1(123-148)Online publication date: 6-Apr-2022
https://doi.org/10.1007/s00778-022-00737-1
Magalhaes AMonteiro JBrayner A(2021)Main Memory Database RecoveryACM Computing Surveys10.1145/344219754:2(1-36)Online publication date: 5-Mar-2021
https://dl.acm.org/doi/10.1145/3442197
Jokar MZhang LChong FJacob B(2018)Cooperative NV-NUMAProceedings of the International Symposium on Memory Systems10.1145/3240302.3240308(67-78)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1145/3240302.3240308
Yao SZhao YShao HZhang AZhang CLi SAbdelzaher T(2018)RDeepSenseProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31611811:4(1-26)Online publication date: 8-Jan-2018
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Wangwiwattana CDing XLarson E(2018)PupilNet, Measuring Task Evoked Pupillary Response using Commodity RGB Tablet CamerasProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31611641:4(1-26)Online publication date: 8-Jan-2018
https://dl.acm.org/doi/10.1145/3161164
Martens WNeven FNiewerth MSchwentick T(2017)BonXaiACM Transactions on Database Systems10.1145/310596042:3(1-42)Online publication date: 24-Aug-2017
https://dl.acm.org/doi/10.1145/3105960
Kaminski MKostylev EGrau B(2017)Query Nesting, Assignment, and Aggregation in SPARQL 1.1ACM Transactions on Database Systems10.1145/308389842:3(1-46)Online publication date: 12-Aug-2017
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Bernstein GShah CLemire CDevito ZFisher MLevis PHanrahan P(2016)EbbACM Transactions on Graphics10.1145/289263235:2(1-12)Online publication date: 2-May-2016
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Nguyen DLe VChung MPham TThoai N(2016)Accelerating Range Query Execution of In-Memory Stores: A Performance Study2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)10.1109/HPCC-SmartCity-DSS.2016.0043(237-244)Online publication date: Dec-2016
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