research-article

Squall: Fine-Grained Live Reconfiguration for Partitioned Main Memory Databases

Authors:

Aaron J. Elmore,

Divyakant Agrawal,

Amr El AbbadiAuthors Info & Claims

SIGMOD '15: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

Pages 299 - 313

https://doi.org/10.1145/2723372.2723726

Published: 27 May 2015 Publication History

Abstract

For data-intensive applications with many concurrent users, modern distributed main memory database management systems (DBMS) provide the necessary scale-out support beyond what is possible with single-node systems. These DBMSs are optimized for the short-lived transactions that are common in on-line transaction processing (OLTP) workloads. One way that they achieve this is to partition the database into disjoint subsets and use a single-threaded transaction manager per partition that executes transactions one-at-a-time in serial order. This minimizes the overhead of concurrency control mechanisms, but requires careful partitioning to limit distributed transactions that span multiple partitions. Previous methods used off-line analysis to determine how to partition data, but the dynamic nature of these applications means that they are prone to hotspots. In these situations, the DBMS needs to reconfigure how data is partitioned in real-time to maintain performance objectives. Bringing the system off-line to reorganize the database is unacceptable for on-line applications.

To overcome this problem, we introduce the Squall technique for supporting live reconfiguration in partitioned, main memory DBMSs. Squall supports fine-grained repartitioning of databases in the presence of distributed transactions, high throughput client workloads, and replicated data. An evaluation of our approach on a distributed DBMS shows that Squall can reconfigure a database with no downtime and minimal overhead on transaction latency.

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Zheng QZhao ZLu WYao CChen YPan ADu X(2024)Lion: Minimizing Distributed Transactions Through Adaptive Replica Provision2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00161(2012-2025)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00161
Hong ZGuo SZhou EChen WHuang HZomaya A(2023)GriDB: Scaling Blockchain Database via Sharding and Off-Chain Cross-Shard MechanismProceedings of the VLDB Endowment10.14778/3587136.358714316:7(1685-1698)Online publication date: 1-Mar-2023
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Zhang QLi JZhao HXu QLu WXiao JHan FYang CDu X(2023)Efficient Distributed Transaction Processing in Heterogeneous NetworksProceedings of the VLDB Endowment10.14778/3583140.358315316:6(1372-1385)Online publication date: 20-Apr-2023
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Index Terms

Squall: Fine-Grained Live Reconfiguration for Partitioned Main Memory Databases
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Parallel and distributed DBMSs

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Published In

cover image ACM Conferences

SIGMOD '15: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

May 2015

2110 pages

ISBN:9781450327589

DOI:10.1145/2723372

General Chair:
Timos Sellis
RMIT University, Australia
,
Program Chairs:
Susan B. Davidson
University of Pennsylvania, USA
,
Zack Ives
University of Pennsylvania, USA

Copyright © 2015 ACM.

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Published: 27 May 2015

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SIGMOD

SIGMOD/PODS'15: International Conference on Management of Data

May 31 - June 4, 2015

Victoria, Melbourne, Australia

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SIGMOD '15 Paper Acceptance Rate 106 of 415 submissions, 26%;

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

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Cited By

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https://doi.org/10.1109/ICDE60146.2024.00161
Hong ZGuo SZhou EChen WHuang HZomaya A(2023)GriDB: Scaling Blockchain Database via Sharding and Off-Chain Cross-Shard MechanismProceedings of the VLDB Endowment10.14778/3587136.358714316:7(1685-1698)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.14778/3587136.3587143
Zhang QLi JZhao HXu QLu WXiao JHan FYang CDu X(2023)Efficient Distributed Transaction Processing in Heterogeneous NetworksProceedings of the VLDB Endowment10.14778/3583140.358315316:6(1372-1385)Online publication date: 20-Apr-2023
https://dl.acm.org/doi/10.14778/3583140.3583153
Li MWang WZhang J(2023)LB-Chain: Load-Balanced and Low-Latency Blockchain Sharding via Account MigrationIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.323834334:10(2797-2810)Online publication date: Oct-2023
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Huang SQin YZhang XTu YLi ZCui B(2023)Survey on performance optimization for database systemsScience China Information Sciences10.1007/s11432-021-3578-666:2Online publication date: 11-Jan-2023
https://doi.org/10.1007/s11432-021-3578-6
Zhao HLi JLu WZhang QYang WZhong JZhang MLi HDu XPan A(2023)RCBench: an RDMA-enabled transaction framework for analyzing concurrency control algorithmsThe VLDB Journal10.1007/s00778-023-00821-033:2(543-567)Online publication date: 14-Dec-2023
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Lee SPonnapalli SSinghal SAguilera MKeeton KChidambaram V(2022)DINOMOProceedings of the VLDB Endowment10.14778/3565838.356585415:13(4023-4037)Online publication date: 1-Sep-2022
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Kang JCai LLi FZhou XCao WCai SShao DIves ZBonifati AEl Abbadi A(2022)Remus: Efficient Live Migration for Distributed Databases with Snapshot IsolationProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526047(2232-2245)Online publication date: 10-Jun-2022
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