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An Introduction to Real-Time Data Management

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Real-Time & Stream Data Management

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

In recent years, users have come to expect reactivity from their applications, i.e. they assume that changes made by other users are immediately reflected in the interfaces they are using. Examples are shared worksheets and websites targeting social interaction. These applications require the underlying data storage to publish new and updated information as soon as it is created: Data access is push-based. In contrast, traditional database management has been tailored towards pull-based data access where information is only made available as a direct response to a client request.

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Notes

  1. 1.

    In this book, we use the terms “real-time database” and “real-time database system” synonymously.

  2. 2.

    While we focus on relational database systems in this book, a preference for pull-based over push-based access is also evident in graph databases [Jun+17], object databases [Wie15, Ch. 9], and other databases with non-relational data models [OM10, Ch. 5, Sec. I].

References

  1. Alexander Alexandrov, Rico Bergmann, Stephan Ewen, et al. “The Stratosphere Platform for Big Data Analytics”. In: The VLDB Journal (2014). ISSN: 1066-8888. doi: 10.1007/s00778-014-0357-y URL: http://dx.doi.org/10.1007/s00778-014-0357-y.

    Article  Google Scholar 

  2. Baqend. Baqend Accessed: 2018-05-10. 2018. URL: https://www.baqend.com/.

  3. Sirish Chandrasekaran et al. “TelegraphCQ: Continuous Dataflow Processing”. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data. SIGMOD ’03. San Diego, California: ACM, 2003, pp. 668–668. ISBN: 1-58113-634-X. doi: 10.1145/872757.872857. URL: http://doi.acm.org/10.1145/872757.872857.

  4. Fay Chang et al. “Bigtable: A Distributed Storage System for Structured Data”. In: Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7. OSDI ’06. Seattle, WA: USENIX Association, 2006, pp. 15–15. URL: http://dl.acm.org/citation.cfm?id=1267308.1267323.

  5. Donald D. Chamberlin et al. “A History and Evaluation of System R”. In: Commun. ACM 24.10 (Oct. 1981), pp. 632–646. ISSN: 0001-0782. doi: 10.1145/358769.358784. URL: http://doi.acm.org/10.1145/358769.358784.

    Article  Google Scholar 

  6. Peter Pin-Shan Chen. “The Entity-relationship Model: Toward a Unified View of Data”. In: SIGIR Forum 10.3 (Dec. 1975), pp. 9–9. ISSN: 0163-5840. doi: 10.1145/1095277.1095279. URL: http://doi.acm.org/10.1145/1095277.1095279.

    Article  Google Scholar 

  7. E. F. Codd. “A Relational Model of Data for Large Shared Data Banks”. In: Commun. ACM 13.6 (June 1970), pp. 377–387. ISSN: 0001-0782. doi: 10.1145/362384.362685. URL: http://doi.acm.org/10.1145/362384.362685.

    Article  Google Scholar 

  8. U. Dayal et al. “The HiPAC Project: Combining Active Databases and Timing Constraints”. In: SIGMOD Rec. 17.1 (Mar. 1988), pp. 51–70. ISSN: 0163-5808. doi: 10.1145/44203.44208. URL: http://doi.acm.org/10.1145/44203.44208.

    Article  Google Scholar 

  9. G. DeCandia et al. “Dynamo: Amazon’s highly available key-value store”. In: ACM SOSP. Vol. 14. 17. 2007, pp. 205–220. (Visited on 09/12/2012).

    Google Scholar 

  10. Jeffrey Dean and Sanjay Ghemawat. “MapReduce: Simplified Data Processing on Large Clusters”. In: Proceedings of the 6th Conference on Symposium on Opearting Systems Design & Implementation - Volume 6. OSDI’04. San Francisco, CA: USENIX Association, 2004, pp. 10–10. URL: http://dl.acm.org/citation.cfm?id=1251254.1251264.

  11. Kapali P. Eswaran and Donald D. Chamberlin. “Functional Specifications of a Subsystem for Data Base Integrity”. In: Proceedings of the 1st International Conference on Very Large Data Bases. VLDB ’75. Framingham, Massachusetts: ACM, 1975, pp. 48–68. ISBN: 978-1-4503-3920-9. doi: 10.1145/1282480.1282484. URL: http://doi.acm.org/10.1145/1282480.1282484.

  12. Firebase. Firebase. Accessed: 2016-09-18. 2016. URL: https://firebase.google.com/.

  13. James P. Fry and Edgar H. Sibley. “Evolution of Data-Base Management Systems”. In: ACM Comput. Surv. 8.1 (Mar. 1976), pp. 7–42. ISSN: 0360-0300. doi: 10.1145/356662.356664. URL: http://doi.acm.org/10.1145/356662.356664.

    Article  Google Scholar 

  14. Felix Gessert et al. “NoSQL Database Systems: A Survey and Decision Guidance”. In: Computer Science - Research and Development (2016).

    Google Scholar 

  15. Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. “The Google File System”. In: Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles. SOSP ’03. Bolton Landing, NY, USA: ACM, 2003, pp. 29–43. ISBN: 1-58113-757-5. doi: 10.1145/945445.945450. URL: http://doi.acm.org/10.1145/945445.945450.

  16. Lukasz Golab and M. Tamer Zsu. Data Stream Management. Morgan & Claypool Publishers, 2010. ISBN: 1608452727, 9781608452729.

    Google Scholar 

  17. Joseph M. Hellerstein, Michael Stonebraker, and James Hamilton. “Architecture of a Database System”. In: Found. Trends databases 1.2 (Feb. 2007), pp. 141–259. ISSN: 1931-7883. doi: 10.1561/1900000002. URL: http://dx.doi.org/10.1561/1900000002.

    Article  Google Scholar 

  18. Martin Junghanns et al. “Handbook of Big Data Technologies”. In: ed. by Albert Y. Zomaya and Sherif Sakr. Springer International Publishing, 2017. Chap. Management and Analysis of Big Graph Data: Current Systems and Open Challenges, pp. 457–505. ISBN: 978-3-319-49340-4. doi: 10.1007/978-3-319-49340-4_14. URL: https://doi.org/10.1007/978-3-319-49340-4_14.

    Chapter  Google Scholar 

  19. Rajeev Motwani et al. “Query Processing, Approximation, and Resource Management in a Data Stream Management System”. In: CIDR 2003.

    Google Scholar 

  20. Shadi A. Noghabi et al. “Samza: Stateful Scalable Stream Processing at LinkedIn”. In: Proc. VLDB Endow. 10.12 (Aug. 2017), pp. 1634–1645. ISSN: 2150-8097. doi: 10.14778/3137765.3137770. URL: https://doi.org/10.14778/3137765.3137770.

    Article  Google Scholar 

  21. James A. O’Brien and George M. Marakas. Management Information Systems. 10th Edition. McGraw-Hill/Irwin, 2010.

    Google Scholar 

  22. Ryan Paul. “Build a realtime liveblog with RethinkDB and PubNub”. In: RethinkDB Blog (May 2015). Accessed: 2017-05-20. URL: https://rethinkdb.com/blog/rethinkdb-pubnub/.

  23. Frank van Puffelen. “Have you met the Realtime Database?” In: Fire-base Blog (July 2016). Accessed: 2017-05-20. URL: https://firebase.googleblog.com/2016/07/have-you-met-realtime-database.html.

  24. Alexandre Santoro. Case Study in Prototyping With Rapide: Shared Memory Multiprocessor System. Tech. rep. Stanford University, Mar. 1993.

    Google Scholar 

  25. Michael Stonebraker and Ugur Cetintemel. “ “One Size Fits All”: An Idea Whose Time Has Come and Gone”. In: Proceedings of the 21st International Conference on Data Engineering. ICDE ’05. Washington, DC, USA: IEEE Computer Society, 2005, pp. 2–11. ISBN: 0-7695-2285-8. doi: 10.1109/ICDE.2005.1. URL: http://dx.doi.org/10.1109/ICDE.2005.1.

  26. P. Schwarz et al. “Extensibility in the Starburst Database System”. In: Proceedings on the 1986 International Workshop on Object-oriented Database Systems. OODS ’86. Pacific Grove, California, USA: IEEE Computer Society Press, 1986, pp. 85–92. ISBN: 0-8186-0734-3. URL: http://dl.acm.org/citation.cfm?id=318826.318842.

  27. Michael Stonebraker Uǧur Cetintemel, and Stan Zdonik. “The 8 Requirements of Real-time Stream Processing”. In: SIGMOD Rec. 34.4 (Dec. 2005), pp. 42–47. ISSN: 0163-5808. doi: 10.1145/1107499.1107504. URL: http://doi.acm.org/10.1145/1107499.1107504.

    Article  Google Scholar 

  28. Michael Stonebraker and Lawrence A. Rowe. “The Design of POSTGRES”. In: Proceedings of the 1986 ACM SIGMOD International Conference on Management of Data. SIGMOD ’86. Washington, D.C., USA: ACM, 1986, pp. 340–355. ISBN: 0-89791-191-1. doi: 10.1145/16894.16888. URL: http://doi.acm.org/10.1145/16894.16888.

  29. Michael Stonebraker et al. “The Design and Implementation of INGRES”. In: ACM Trans. Database Syst. 1.3 (Sept. 1976), pp. 189–222. ISSN: 0362-5915. doi: 10.1145/320473.320476. URL: http://doi.acm.org/10.1145/320473.320476.

    Article  Google Scholar 

  30. Ankit Toshniwal et al. “Storm@Twitter”. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. SIGMOD ’14. Snowbird, Utah, USA: ACM, 2014, pp. 147–156. ISBN: 978-1-4503-2376-5. doi: 10.1145/2588555.2595641. URL: http://doi.acm.org/10.1145/2588555.2595641.

  31. Lena Wiese. Advanced Data Management for SQL, NoSQL, Cloud and Distributed Databases. DeGruyter 2015. ISBN: 978-3-11-044140-6. URL: http://wiese.free.fr/adm.html.

  32. Wolfram Wingerath et al. “Real-time stream processing for Big Data”. In: it - Information Technology 58.4 (2016), pp. 186–194. doi: 10.1515/itit-2016-0002. URL: http://dx.doi.org/10.1515/itit-2016-0002.

  33. Alice Yu. “What does it mean to be a real-time database? — Slava Kim at Devshop SF May 2015”. In: Meteor Blog (June 2015). Accessed: 2017-05-20.

    Google Scholar 

  34. ANSI. X3.135-1986: Information Systems – Database Language – SQL. Standard. American National Standards Institute, Oct. 1986.

    Google Scholar 

  35. Meteor Development Group. Meteor. Accessed: 2018-05-10. 2018.URL: https://www.meteor.com/.

  36. Technical Committee: ISO/IEC JTC 1 Information Technology. ISO 9075:1987: Information processing systems – Database language – SQL. Standard. International Organization for Standardization, June 1987.

    Google Scholar 

  37. Uğur Çetintemel et al. “The Aurora and Borealis Stream Processing Engines”. In: Data Stream Management: Processing High-Speed Data Streams. Ed. by Minos Garofalakis, Johannes Gehrke, and Rajeev Rastogi. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016, pp. 337–359. ISBN: 978-3-540-28608-0.

    Google Scholar 

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Authors and Affiliations

  1. Baqend GmbH, Hamburg, Hamburg, Germany

    Wolfram Wingerath & Felix Gessert

  2. University of Hamburg, Hamburg, Hamburg, Germany

    Norbert Ritter

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Wingerath, W., Ritter, N., Gessert, F. (2019). An Introduction to Real-Time Data Management. In: Real-Time & Stream Data Management. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-10555-6_1

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