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Hyrise-R: Scale-out and Hot-Standby through Lazy Master Replication for Enterprise Applications

Authors:

Matthias Uflacker,

Hasso PlattnerAuthors Info & Claims

IMDM '15: Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics

Article No.: 7, Pages 1 - 7

https://doi.org/10.1145/2803140.2803147

Published: 31 August 2015 Publication History

Abstract

In-memory database systems are well-suited for enterprise workloads, consisting of transactional and analytical queries. A growing number of users and an increasing demand for enterprise applications can saturate or even overload single-node database systems at peak times. Better performance can be achieved by improving a single machine's hardware but it is often cheaper and more practicable to follow a scale-out approach and replicate data by using additional machines.

In this paper we present Hyrise-R, a lazy master replication system for the in-memory database Hyrise. By setting up a snapshot-based Hyrise cluster, we increase both performance by distributing queries over multiple instances and availability by utilizing the redundancy of the cluster structure. This paper describes the architecture of Hyrise-R and details of the implemented replication mechanisms. We set up Hyrise-R on instances of Amazon's Elastic Compute Cloud and present a detailed performance evaluation of our system, including a linear query throughput increase for enterprise workloads.

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Cited By

Hiller JKimmerlin MPlauth MHeikkila SKlauck SLindfors VEberhardt FBursztynowski DSantos JHohlfeld OWehrle K(2018)Giving Customers Control Over Their Data: Integrating a Policy Language into the Cloud2018 IEEE International Conference on Cloud Engineering (IC2E)10.1109/IC2E.2018.00050(241-249)Online publication date: Apr-2018
https://doi.org/10.1109/IC2E.2018.00050
Hayakawa YEggert LHonda MSantry D(2017)PrismProceedings of the 2017 Symposium on Cloud Computing10.1145/3127479.3127480(181-188)Online publication date: 24-Sep-2017
https://dl.acm.org/doi/10.1145/3127479.3127480
Kimmerlin MPlauth MHeikkila SNiemi T(2017)A practical evaluation of a network expansion mechanism in an openstack cloud federation2017 IEEE 6th International Conference on Cloud Networking (CloudNet)10.1109/CloudNet.2017.8071540(1-6)Online publication date: Sep-2017
https://doi.org/10.1109/CloudNet.2017.8071540
Show More Cited By

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cover image ACM Other conferences

IMDM '15: Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics

August 2015

63 pages

ISBN:9781450337137

DOI:10.1145/2803140

Copyright © 2015 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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SAMSUNG: SAMSUNG
VLDB Endowment: Very Large Database Endowment
Microsoft: Microsoft

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 August 2015

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IMDM '15: VLDB Workshop on In-Memory Data Mangement and Analytics

August 31, 2015

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Cited By

Hiller JKimmerlin MPlauth MHeikkila SKlauck SLindfors VEberhardt FBursztynowski DSantos JHohlfeld OWehrle K(2018)Giving Customers Control Over Their Data: Integrating a Policy Language into the Cloud2018 IEEE International Conference on Cloud Engineering (IC2E)10.1109/IC2E.2018.00050(241-249)Online publication date: Apr-2018
https://doi.org/10.1109/IC2E.2018.00050
Hayakawa YEggert LHonda MSantry D(2017)PrismProceedings of the 2017 Symposium on Cloud Computing10.1145/3127479.3127480(181-188)Online publication date: 24-Sep-2017
https://dl.acm.org/doi/10.1145/3127479.3127480
Kimmerlin MPlauth MHeikkila SNiemi T(2017)A practical evaluation of a network expansion mechanism in an openstack cloud federation2017 IEEE 6th International Conference on Cloud Networking (CloudNet)10.1109/CloudNet.2017.8071540(1-6)Online publication date: Sep-2017
https://doi.org/10.1109/CloudNet.2017.8071540
Wang JDuan HMin GYing GZheng S(2016)Goldfish: In-Memory Massive Parallel Processing SQL Engine Based on Columnar Store2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)10.1109/iThings-GreenCom-CPSCom-SmartData.2016.49(142-149)Online publication date: Dec-2016
https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2016.49

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