research-article

Feeding frenzy: selectively materializing users' event feeds

Authors:

Adam Silberstein,

Brian F. Cooper,

Raghu RamakrishnanAuthors Info & Claims

SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

Pages 831 - 842

https://doi.org/10.1145/1807167.1807257

Published: 06 June 2010 Publication History

Abstract

Near real-time event streams are becoming a key feature of many popular web applications. Many web sites allow users to create a personalized feed by selecting one or more event streams they wish to follow. Examples include Twitter and Facebook, which a user to follow other users' activity, and iGoogle and My Yahoo, which allow users to follow selected RSS streams. How can we efficiently construct a web page showing the latest events from a user's feed? Constructing such a feed must be fast so the page loads quickly, yet reflects recent updates to the underlying event streams. The wide fanout of popular streams (those with many followers) and high skew (fanout and update rates vary widely) make it difficult to scale such applications.

We associate feeds with consumers and event streams with producers. We demonstrate that the best performance results from selectively materializing each consumer's feed: events from high-rate producers are retrieved at query time, while events from lower-rate producers are materialized in advance. A formal analysis of the problem shows the surprising result that we can minimize global cost by making local decisions about each producer/consumer pair, based on the ratio between a given producer's update rate (how often an event is added to the stream) and a given consumer's view rate (how often the feed is viewed). Our experimental results, using Yahoo!'s web-scale database PNUTS, shows that this hybrid strategy results in the lowest system load (and hence improves scalability) under a variety of workloads.

References

[1]

Twitter API. http://apiwiki.twitter.com/.

[2]

D. Agrawal, A. E. Abbadi, A. K. Singh, and T. Yurek. Efficient view maintenance at data warehouses. In SIGMOD, 1997.

Digital Library

[3]

P. Agrawal et al. Asynchronous view maintenance for VLSD databases. In SIGMOD, 2009.

Digital Library

[4]

S. Agrawal, S. Chaudhuri, and V. Narasayya. Automated selection of materialized views and indexes for SQL databases. In VLDB, 2000.

Digital Library

[5]

S. Babu, K. Munagala, and J. Widom. Adaptive caching for continuous queries. In ICDE, 2005.

Digital Library

[6]

M. Cafarella et al. Data management projects at Google. SIGMOD Record, 34--38(1), March 2008.

Digital Library

[7]

S. Ceri and J. Widom. Deriving production rules for incremental view maintenance. In VLDB, 1991.

Digital Library

[8]

B. Chandramouli and J. Yang. End-to-end support for joins in large-scale publish/subscribe systems. In VLDB, 2008.

Digital Library

[9]

B. F. Cooper et al. PNUTS: Yahoo!'s hosted data serving platform. In VLDB, 2008.

Digital Library

[10]

I. Eure. Looking to the future with Cassandra. http://blog.digg.com/?p=966.

[11]

C. Garrod et al. Scalable query result caching for web applications. In VLDB, 2008.

Digital Library

[12]

G. Graefe. B-tree indexes for high update rates. SIGMOD Record, 35(1):39--44, March 2006.

Digital Library

[13]

H. Gupta. Selection of views to materialize in a data warehouse. In ICDT, 1997.

Digital Library

[14]

P. Haas and J. Hellerstein. Ripple joins for online aggregation. In SIGMOD, 1999.

Digital Library

[15]

A. Halevy. Answering queries using views: A survey. VLDB Journal, 10(4):270--294, 2001.

Digital Library

[16]

G. Luo. Partial Materialized Views. In ICDE, 2007.

[17]

G. Luo, J. F. Naughton, C. J. Ellmann, and M. Watzke. A comparison of three methods for join view maintenance in parallel RDBMS. In ICDE, 2003.

[18]

S. Madden, M. Franklin, J. Hellerstein, and W. Hong. TinyDB: An acquisitional query processing system for sensor networks. TODS, 30(1):122--173, March 2005.

Digital Library

[19]

S. Martello and P. Toth. Knapsack Problems: Algorithms and Computer Implementations. Wiley-Interscience, 1990.

Digital Library

[20]

C. Mohan and I. Narang. Algorithms for creating indexes for very large tables without quiescing updates. In SIGMOD, 1992.

Digital Library

[21]

C. Olston, J. Jiang, and J. Widom. Adaptive filters for continuous queries over distributed data streams. In SIGMOD, 2003.

Digital Library

[22]

C. Re and D. Suciu. Materialized views in probabilistic databases: for information exchange and query optimization. In VLDB, 2007.

Digital Library

[23]

K. Salem, K. Beyer, B. Lindsay, and R. Cochrane. How to roll a join: Asynchronous incremental view maintenance. In SIGMOD, 2000.

Digital Library

[24]

P. Seshadri and A. Swami. Generalized partial indexes. In ICDE, 1995.

Digital Library

[25]

M. Stonebraker. The case for partial indexes. SIGMOD Record, 18(4):4--11, 1989.

Digital Library

[26]

E. Weaver. Improving running components at Twitter. http://blog.evanweaver.com/articles/2009/03/13/-qcon-presentation/.

[27]

S. Wu, J. Li, B. Ooi, and K.-L. Tan. Just-in-time query retrieval over partially indexed data on structured P2P overlays. In SIGMOD, 2008.

Digital Library

[28]

J. Yang and J. Widom. Temporal view self-maintenance. In EDBT, 2000.

Digital Library

[29]

K. Yi et al. Efficient maintenance of materialized top-k views. In ICDE, 2003.

[30]

J. Zhou, P.-A. Larson, and H. G. Elmongui. Lazy maintenance of materialized views. In VLDB, 2007.

Digital Library

[31]

Y. Zhou, A. Salehi, and K. Aberer. Scalable delivery of stream query results. In VLDB, 2009.

Digital Library

[32]

Y. Zhuge, H. Garcia-Molina, J. Hammer, and J. Widom. View maintenance in a warehousing environment. In SIGMOD, 1995.

Digital Library

Cited By

Flouris IGiatrakos NDeligiannakis AGarofalakis M(2022)Network-wide complex event processing over geographically distributed data sourcesInformation Systems10.1016/j.is.2019.10144288:COnline publication date: 21-Apr-2022
https://dl.acm.org/doi/10.1016/j.is.2019.101442
Lev-Ari KTian YShraer ADouglas CFu HAndreev ABeranek KDugas SGrieser AHemmo JLi GLi ZIdreos SSrivastava D(2021)QuiCKProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457567(2517-2529)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457567
Jin HLin CChen HLiu J(2020)QuickPoint: Efficiently Identifying Densest Sub-Graphs in Online Social Networks for Event Stream DisseminationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.288143532:2(332-346)Online publication date: 9-Jan-2020
https://dl.acm.org/doi/10.1109/TKDE.2018.2881435
Show More Cited By

Index Terms

Feeding frenzy: selectively materializing users' event feeds
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Parallel and distributed DBMSs

Recommendations

Identifying the influential bloggers in a community
WSDM '08: Proceedings of the 2008 International Conference on Web Search and Data Mining

Blogging becomes a popular way for a Web user to publish information on the Web. Bloggers write blog posts, share their likes and dislikes, voice their opinions, provide suggestions, report news, and form groups in Blogosphere. Bloggers form their ...
Feeding the world: a comprehensive dataset and analysis of a real world snapshot of web feeds
iiWAS '11: Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services

Web feeds allow users to retrieve new content from pages on the World Wide Web. Feeds are offered by a multitude of web pages, ranging from conventional news sites to pages with user generated content such as wikis, forums or personal blogs. They notify ...
ViewDF: Declarative incremental view maintenance for streaming data
Highlights
- This paper presents a flexible and declarative framework for incremental view maintenance over streaming data.
Abstract
We present ViewDF: a flexible and declarative framework for incremental maintenance of materialized views (i.e., results of continuous queries) over streaming data. The main component of the proposed framework is the View Delta ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

June 2010

1286 pages

ISBN:9781450300322

DOI:10.1145/1807167

General Chair:
Ahmed Elmagarmid
Purdue University, USA
,
Program Chair:
Divyakant Agrawal
University of California at Santa Barbara, USA

Copyright © 2010 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]

Sponsors

SIGMOD: ACM Special Interest Group on Management of Data

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGMOD/PODS '10

Sponsor:

SIGMOD

SIGMOD/PODS '10: International Conference on Management of Data

June 6 - 10, 2010

Indiana, Indianapolis, USA

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

55
Total Citations
View Citations
767
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Flouris IGiatrakos NDeligiannakis AGarofalakis M(2022)Network-wide complex event processing over geographically distributed data sourcesInformation Systems10.1016/j.is.2019.10144288:COnline publication date: 21-Apr-2022
https://dl.acm.org/doi/10.1016/j.is.2019.101442
Lev-Ari KTian YShraer ADouglas CFu HAndreev ABeranek KDugas SGrieser AHemmo JLi GLi ZIdreos SSrivastava D(2021)QuiCKProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457567(2517-2529)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457567
Jin HLin CChen HLiu J(2020)QuickPoint: Efficiently Identifying Densest Sub-Graphs in Online Social Networks for Event Stream DisseminationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.288143532:2(332-346)Online publication date: 9-Jan-2020
https://dl.acm.org/doi/10.1109/TKDE.2018.2881435
Chen KZhou Y(2019)Location-Centric View Selection in a Location-Based Feed-Following SystemProceedings of the 13th ACM International Conference on Distributed and Event-based Systems10.1145/3328905.3329512(67-78)Online publication date: 24-Jun-2019
https://dl.acm.org/doi/10.1145/3328905.3329512
Zhang FChen HJin H(2019)Piggyback Game: Efficient Event Stream Dissemination in Online Social Network SystemsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2018.286624230:3(692-709)Online publication date: 1-Mar-2019
https://doi.org/10.1109/TPDS.2018.2866242
Yu CXia FQian WZhou A(2019)A parallel data generator for efficiently generating "realistic" social streamsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-018-8022-z13:5(1072-1101)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s11704-018-8022-z
Xing FGui LChen HLin CJin H(2018)Efficient Event Stream Dissemination in Online Social Networks Based on Community Detection2018 IEEE International Conference on Communications (ICC)10.1109/ICC.2018.8422653(1-6)Online publication date: May-2018
https://doi.org/10.1109/ICC.2018.8422653
Han SSong JBen-Othman JGang FLiu JArai M(2017)A social-aware caching algorithm for improving performance of online social network services in a multi-cloud environmentProceedings of the 3rd International Conference on Communication and Information Processing10.1145/3162957.3163024(384-388)Online publication date: 24-Nov-2017
https://dl.acm.org/doi/10.1145/3162957.3163024
Zhong CSastry N(2017)Systems Applications of Social NetworksACM Computing Surveys10.1145/309274250:5(1-42)Online publication date: 26-Sep-2017
https://dl.acm.org/doi/10.1145/3092742
Xu WChow C(2016)A Location- and Diversity-Aware News Feed System for Mobile UsersIEEE Transactions on Services Computing10.1109/TSC.2015.24363969:6(846-861)Online publication date: 1-Nov-2016
https://doi.org/10.1109/TSC.2015.2436396
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents