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LinkBench: a database benchmark based on the Facebook social graph

Authors:

Timothy G. Armstrong,

Vamsi Ponnekanti,

Dhruba Borthakur,

Mark CallaghanAuthors Info & Claims

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

Pages 1185 - 1196

https://doi.org/10.1145/2463676.2465296

Published: 22 June 2013 Publication History

Abstract

Database benchmarks are an important tool for database researchers and practitioners that ease the process of making informed comparisons between different database hardware, software and configurations. Large scale web services such as social networks are a major and growing database application area, but currently there are few benchmarks that accurately model web service workloads.

In this paper we present a new synthetic benchmark called LinkBench. LinkBench is based on traces from production databases that store "social graph" data at Facebook, a major social network. We characterize the data and query workload in many dimensions, and use the insights gained to construct a realistic synthetic benchmark. LinkBench provides a realistic and challenging test for persistent storage of social and web service data, filling a gap in the available tools for researchers, developers and administrators.

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Cited By

Dai YLiu JArpaci-Dusseau AArpaci-Dusseau RMa XWon Y(2024)SymbiosisProceedings of the 22nd USENIX Conference on File and Storage Technologies10.5555/3650697.3650701(51-70)Online publication date: 27-Feb-2024
https://dl.acm.org/doi/10.5555/3650697.3650701
Tong BZhou YZhang CTang JTang JYang LLi QLin MBao ZLi JChen L(2024)Galaxybase: A High Performance Native Distributed Graph Database for HTAPProceedings of the VLDB Endowment10.14778/3685800.368581417:12(3893-3905)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.14778/3685800.3685814
Chen ZZhang FChen YFang XFeng GZhu XChen WDu X(2024)Enabling Window-Based Monotonic Graph Analytics with Reusable Transitional Results for Pattern-Consistent QueriesProceedings of the VLDB Endowment10.14778/3681954.368197917:11(3003-3016)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3681979
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Index Terms

LinkBench: a database benchmark based on the Facebook social graph
1. Information systems
  1. Data management systems

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Information

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

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

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]

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New York, NY, United States

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Published: 22 June 2013

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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

Dai YLiu JArpaci-Dusseau AArpaci-Dusseau RMa XWon Y(2024)SymbiosisProceedings of the 22nd USENIX Conference on File and Storage Technologies10.5555/3650697.3650701(51-70)Online publication date: 27-Feb-2024
https://dl.acm.org/doi/10.5555/3650697.3650701
Tong BZhou YZhang CTang JTang JYang LLi QLin MBao ZLi JChen L(2024)Galaxybase: A High Performance Native Distributed Graph Database for HTAPProceedings of the VLDB Endowment10.14778/3685800.368581417:12(3893-3905)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.14778/3685800.3685814
Chen ZZhang FChen YFang XFeng GZhu XChen WDu X(2024)Enabling Window-Based Monotonic Graph Analytics with Reusable Transitional Results for Pattern-Consistent QueriesProceedings of the VLDB Endowment10.14778/3681954.368197917:11(3003-3016)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3681979
Eslami NDayan N(2024)Memento Filter: A Fast, Dynamic, and Robust Range FilterProceedings of the ACM on Management of Data10.1145/36988202:6(1-27)Online publication date: 20-Dec-2024
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Yu WLuo SYu ZCong G(2024)CAMAL: Optimizing LSM-trees via Active LearningProceedings of the ACM on Management of Data10.1145/36771382:4(1-26)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3677138
Lin LChen JSun RZhang JWang V(2024)A Unified Graph Framework for Storage-Compute Coupled Cluster and High-Density Computing ClusterProceedings of the International Workshop on Big Data in Emergent Distributed Environments10.1145/3663741.3664790(1-6)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3663741.3664790
Zhu ZSaha AAthanassoulis MSarkar S(2024)KVBench: A Key-Value Benchmarking SuiteProceedings of the Tenth International Workshop on Testing Database Systems10.1145/3662165.3662765(9-15)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3662165.3662765
Liu JWang FMo DLuo S(2024)Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in a Colossal Configuration SpaceProceedings of the ACM on Management of Data10.1145/36549782:3(1-26)Online publication date: 30-May-2024
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Chatterjee SPekala MKruglyak LIdreos S(2024)Limousine: Blending Learned and Classical Indexes to Self-Design Larger-than-Memory Cloud Storage EnginesProceedings of the ACM on Management of Data10.1145/36393022:1(1-28)Online publication date: 26-Mar-2024
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Shi JWang BXu Y(2024)Spruce: a Fast yet Space-saving Structure for Dynamic Graph StorageProceedings of the ACM on Management of Data10.1145/36392822:1(1-26)Online publication date: 26-Mar-2024
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