Over 800 million people around the world share their social interactions with friends on Facebook, providing a rich body of information referred to as the social graph. In this talk, I describe how we model and serve this graph. Our model uses typed nodes (fbobjects) and edges (associations) to express the relationships and actions that happen on Facebook. We access the graph via a simple API that provides queries over the set of same-typed associations leaving an object. We have found this API to be both sufficiently expressive and amenable to a scalable implementation. In the last segment of the talk I describe the design of TAO, our graph data store. TAO is a distributed implementation of the fbobject and association API that has been serving production traffic at Facebook for more than 2 years.

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Kim GLee W(2022)In-network leaderless replication for distributed data storesProceedings of the VLDB Endowment10.14778/3523210.352321315:7(1337-1349)Online publication date: 22-Jun-2022
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Index Terms

TAO: how facebook serves the social graph
1. Information systems
  1. Data management systems
    1. Database design and models
    2. Database management system engines
      1. Parallel and distributed DBMSs

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Published In

SIGMOD '12: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

May 2012

886 pages

ISBN:9781450312479

DOI:10.1145/2213836

General Chairs:
K. Selçuk Candan
Arizona State University
,
Yi Chen
Arizona State University
,
Richard Snodgrass
University of Arizona
,
Program Chair:
Luis Gravano
Columbia University
,
Publications Chair:
Ariel Fuxman
Microsoft Research

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Association for Computing Machinery

New York, NY, United States

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Published: 20 May 2012

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SIGMOD/PODS '12: International Conference on Management of Data

May 20 - 24, 2012

Arizona, Scottsdale, USA

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SIGMOD '12 Paper Acceptance Rate 48 of 289 submissions, 17%;

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

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https://doi.org/10.1109/TC.2023.3345173
Kim GLee W(2022)In-network leaderless replication for distributed data storesProceedings of the VLDB Endowment10.14778/3523210.352321315:7(1337-1349)Online publication date: 22-Jun-2022
https://dl.acm.org/doi/10.14778/3523210.3523213
Abyaneh ALiao MZahedi S(2022)Malcolm: Multi-agent Learning for Cooperative Load Management at Rack ScaleProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/35706116:3(1-25)Online publication date: 8-Dec-2022
https://dl.acm.org/doi/10.1145/3570611
Dexter PLiu YChiu K(2022)The essence of online data processingProceedings of the ACM on Programming Languages10.1145/35633206:OOPSLA2(899-928)Online publication date: 31-Oct-2022
https://dl.acm.org/doi/10.1145/3563320
Liu YZhang XZhuang DFu XSong S(2022)DynamAP: Architectural Support for Dynamic Graph Traversal on the Automata ProcessorACM Transactions on Architecture and Code Optimization10.1145/355697619:4(1-26)Online publication date: 7-Oct-2022
https://dl.acm.org/doi/10.1145/3556976
Falzon FPaterson K(2022)An Efficient Query Recovery Attack Against a Graph Encryption SchemeComputer Security – ESORICS 202210.1007/978-3-031-17140-6_16(325-345)Online publication date: 25-Sep-2022
https://doi.org/10.1007/978-3-031-17140-6_16
Remis LLacewell C(2021)Using VDMS to index and search 100M imagesProceedings of the VLDB Endowment10.14778/3476311.347638114:12(3240-3252)Online publication date: 28-Oct-2021
https://dl.acm.org/doi/10.14778/3476311.3476381
Li HHuang JYuan HCui JMa XQiao SWu X(2021)A Two-phase Method to Balance the Result of Distributed Graph RepartitioningIEEE Transactions on Big Data10.1109/TBDATA.2021.3070194(1-1)Online publication date: 2021
https://doi.org/10.1109/TBDATA.2021.3070194
Zhu HKaffes KChen ZLiu ZKozyrakis CStoica IJin XLu SHowell J(2020)RackSchedProceedings of the 14th USENIX Conference on Operating Systems Design and Implementation10.5555/3488766.3488835(1225-1240)Online publication date: 4-Nov-2020
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Matsunobu YDong SLee H(2020)MyRocksProceedings of the VLDB Endowment10.14778/3415478.341554613:12(3217-3230)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.14778/3415478.3415546
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