Experiences and challenges in building a data intensive system for data migration

  • Marco Scavuzzo
  • Elisabetta Di Nitto
  • Danilo Ardagna
Article

Abstract

Data Intensive (DI) applications are becoming more and more important in several fields of science, economy, and even in our normal life. Unfortunately, even if some technological frameworks are available for their development, we still lack solid software engineering approaches to support their development and, in particular, to ensure that they offer the required properties in terms of availability, throughput, data loss, etc.. In this paper we report our action research experience in developing-testing-reengineering a specific DI application, Hegira4Cloud, that migrates data between widely used NoSQL databases. We highlight the issues we have faced during our experience and we show how cumbersome, expensive and time-consuming the developing-testing-reengineering approach can be in this specific case. Also, we analyse the state of the art in the light of our experience and identify weaknesses and open challenges that could generate new research in the areas of software design and verification.

Keywords

Data intensive applications Experiment-driven action research Big data Data migration 

References

  1. Abdelzaher T, Diao Y, Hellerstein J, Lu C, Zhu X (2008) Introduction to control theory and its application to computing systems. In: Liu Z, Xia C (eds) Performance Modeling and Engineering. Springer, USA, pp 185–215Google Scholar
  2. Abadi D (2012) Consistency tradeoffs in modern distributed database system design: Cap is only part of the story. IEEE Computer, 45(2)Google Scholar
  3. Ardagna D, Casale G, Ciavotta M, Pérez JF, Wang W (2014) Quality-of-service in cloud computing, Modeling techniques and their applications, Journal of Internet Services and ApplicationsGoogle Scholar
  4. Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2010) A view of cloud computing. Commun ACM 53:50–58CrossRefGoogle Scholar
  5. Atzeni P, Bellomarini L, Bugiotti F, Celli F, Gianforme G (2012) A runtime approach to model-generic translation of schema and data, Inf. SystGoogle Scholar
  6. Baskerville R, Myers M (2004) Special issue on action research in information systems: Making is research relevant to practice—foreword. MIS Q 28(3):329–335Google Scholar
  7. Becker S, Koziolek H, Reussner R (2009) The palladio component model for model-driven performance prediction. J Syst Softw 82(1):3–22CrossRefGoogle Scholar
  8. Bernardi S, Dranca L, Merseguer J (2016) A model-driven approach to survivability requirement assessment for critical systems. In: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and ReliabilityGoogle Scholar
  9. Brewer E (2012) CAP twelve years later: How the rules have changed. Computer 45:23–29CrossRefGoogle Scholar
  10. Casale G, Ardagna D, Artac M, Barbier F, Nitto ED, Henry A, Iuhasz G, Joubert C, Merseguer J, Munteanu VI, Pérez JF, Petcu D, Rossi M, Sheridan C, Spais I, Vladui̇c D (2015) Dice: Quality-driven development of data-intensive cloud applications. In: Proceedings of the 7th International Workshop on Modelling in Software Engineering (MiSE)Google Scholar
  11. Ceri S, Widom J (1993) Managing semantic heterogeneity with production rules and persistent queues. In: Proceedings of the Nineteenth International Conference on Very Large Data Bases, pp 108–119Google Scholar
  12. Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T, Fikes A, Gruber RE (2006) 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, pp 15–15 USENIX AssociationGoogle Scholar
  13. Chauhan A (2012) How the size of an entity is caclulated in Windows Azure table storage?. http://goo.gl/ch9YXu
  14. Chen CP, Zhang C-Y (2014) Data-intensive applications, challenges, techniques and technologies: A survey on big data. Inf Sci 275(0):314–347CrossRefGoogle Scholar
  15. Cluet S, Connor RCH, Hull R, Maier D, Matthes F, Suciu D (1998) Panel session: Metadata for database interoperation. In: Proceedings of the 6th International Workshop on Database Programming Languages, DBLP-6, (London, UK, UK). Springer, pp 35–37Google Scholar
  16. Das et al (2012) All aboard the databus!: Linkedin’s scalable consistent change data capture platform. In: Proceedings of the Third ACM Symposium on Cloud Computing, SoCC ’12, (New York, NY, USA), pp 18:1–18:14, ACMGoogle Scholar
  17. Das S, Narasayya V, Li F, Syamala M (2013) Cpu sharing techniques for performance isolation in multi-tenant relational database-as-a-service. In: Proceedings of the VLDB Endowment. Very Large Data Bases Endowment Inc., vol 7, p 12Google Scholar
  18. Didona D, Romano P (2015) Hybrid machine learning/analytical models for performance prediction: A tutorial. In: Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering, Austin, TX, USA, January 31 - February 4, 2015, pp 341–344Google Scholar
  19. Duggan J, Papaemmanouil O, Cetintemel U, Upfal E (2014) Contender: A resource modeling approach for concurrent query performance prediction. In: EDBT, pp 109–120Google Scholar
  20. Ferry N, Solberg A, Jamshidi P, Osman R, Wang W, Seycek S, Gligor V, Sucasa R, Abhervé A (2015) MODAClouds evaluation report–Final versivon. Deliverable D8.5.2, Available from http://www.modaclouds.eu/wp-content/uploads/2012/09/MODAClouds_D3.7.2_MODACloudsEvaluationReportFinalVersion1.pdf [accessed 5 January 2017]
  21. Godfrey R et al (2014) Information technology – advanced message queuing protocol (AMQP) v1.0 specification. http://www.iso.org/iso/home/store/catalogue_tc/catalogue_detail.htm?csnumber=64955
  22. Gorton I, Klein J (2015) Distribution, data, deployment: Software architecture convergence in big data systems. IEEE Softw 32(3):78–85CrossRefGoogle Scholar
  23. Hacigumus H, Chi Y, Wu W, Zhu S, Tatemura J, Naughton JF (2013) Predicting query execution time: Are optimizer cost models really unusable?. In: Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013), ICDE ’13, (Washington, DC, USA), pp 1081–1092 IEEE Computer SocietyGoogle Scholar
  24. Harizopoulos S, Abadi DJ, Madden S, Stonebraker M (2008) OLTP Through the Looking Glass, and What We Found There. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD ’08, (New York, NY, USA), pp 981–992 ACMGoogle Scholar
  25. Herodotou H, Dong F, Babu S (2011) No one (cluster) size fits all: automatic cluster sizing for data-intensive analytics. In: SoCC, p 18Google Scholar
  26. Hill Z, Li J, Mao M, Ruiz-alvarez A, Humphrey M (2010) Early observations on the performance of windows azure. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, pp 367–376 ACMGoogle Scholar
  27. Hunt P, Konar M, Junqueira FP, Reed B (2010a) ZooKeeper: Wait-free Coordination for Internet-scale Systems. In: Proceedings of the 2010 USENIX Conference on USENIX Annual Technical Conference, USENIXATC’10, (Berkeley, CA, USA), pp 11–11 USENIX AssociationGoogle Scholar
  28. Kambatla K, Kollias G, Kumar V, Grama A (2014) Trends in big data analytics. J Parallel Distrib Comput 74(7):2561–2573. Special Issue on Perspectives on Parallel and Distributed ProcessingCrossRefGoogle Scholar
  29. Kent W (1983) A simple guide to five normal forms in relational database theory. Commun ACM 26:120–125CrossRefGoogle Scholar
  30. Kroß J, Brunnert A, Krcmar H (2015) Modeling big data systems by extending the palladio component model. Softwaretechnik-Trends 3:35Google Scholar
  31. Li M, Zeng L, Meng S, Tan J, Zhang L, Butt AR, Fuller N (2014) Mronline: Mapreduce online performance tuning. In: Proceedings of the 23rd International Symposium on High-performance Parallel and Distributed Computing, HPDC ’14, pp 165–176Google Scholar
  32. Lightstone S, Surendra M, Diao Y, Parekh SS, Hellerstein JL, Rose K, Storm AJ, Garcia-Arellano C (2007) Control theory: a foundational technique for self managing databases. In: ICDE Workshops, pp 395– 403Google Scholar
  33. Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers AH (2012) Big data: The next frontier for innovation, competition, and productivity. McKinsey Global InstituteGoogle Scholar
  34. Marr B (2015) Big Data: 20 Mind-Boggling Facts Everyone Must Read. http://www.forbes.com/sites/bernardmarr/2015/09/30/big-data-20-mind-boggling-facts-everyone-must-read. [Forbes Online; accessed January 2017]
  35. Menascé DA, Gomaa H (2000) A method for design and performance modeling of client/server systems. IEEE Trans Softw Eng 26(11):1066–1085CrossRefGoogle Scholar
  36. MG (2009) Uml profile for marte: Modeling and analysis of real-time embedded systemsGoogle Scholar
  37. NIST Big Data Interoperability Framework (2015) Volume 6, Reference Architecture. doi:10.6028/NIST.SP.1500-6 [accessed 14 January 2017]
  38. O’Brien R (1998) An overview of the methodological approach of action researchGoogle Scholar
  39. Picioroaga F, Nechifor S (2014) Modelling Smart City Urban Safety Planner - Final prototype design. Deliverable D8.5.2, Available from http://www.modaclouds.eu/wp-content/uploads/2012/09/MODACloudsD8.5.2_SmartCityUrbanSafetyPlannerDesignFinalPrototypeDesign.pdf [accessed 5 January 2017]
  40. Popescu A (2010) Nosql at codemash – an interesting nosql categorization. http://nosql.mypopescu.com/post/396337069/presentation-nosql-codemash-an-interesting-nosql
  41. Rolia J, Casale G, Krishnamurthy D, Dawson S, Kraft S (2009) Predictive modelling of sap erp applications: Challenges and solutions. In: Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools, VALUETOOLS ’09, (ICST, Brussels, Belgium, Belgium), pp 9:1–9:9, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)Google Scholar
  42. Sadalage PJ, Fowler M (2012) NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Addison-Wesley Professional 1st ed.Google Scholar
  43. Scavuzzo M (2013) Interoperable data migration between NoSQL columnar databases, Master’s thesis Politecnico di MilanoGoogle Scholar
  44. Scavuzzo M, Di Nitto E, Ceri S (2014) Interoperable data migration between nosql columnar databases. In: 18th IEEE International Enterprise Distributed Object Computing Conference Workshops and Demonstrations, EDOC Workshops 2014, Ulm, Germany, September 1-2, 2014, pp 154–162Google Scholar
  45. Scavuzzo M, Tamburri DA, Di Nitto E (2016) Providing Big Data Applications with Fault-Tolerant Data Migration Across Heterogeneous NoSQL Databases. In: Proceedings of the Second International Workshop on BIG Data Software Engineering, (Austin, TX, USA)Google Scholar
  46. Scoffield B (2010) Nosql – death to relational databases(?). Presentation at the CodeMash conference in Sandusky (Ohio) 2010-01-14Google Scholar
  47. Shivam P, Babu S, Chase J (2006) Active and accelerated learning of cost models for optimizing scientific applications. In: Proceedings of the 32Nd International Conference on Very Large Data Bases, VLDB ’06, pp 535–546 VLDB EndowmentGoogle Scholar
  48. Shivam P, Demberel A, Gunda P, Irwin DE, Grit LE, Yumerefendi AR, Babu S, Chase JS (2007) Automated and on-demand provisioning of virtual machines for database applications. In: SIGMOD Conference, pp 1079–1081Google Scholar
  49. Stewart C, Chakrabarti A, Griffith R (2013) Zoolander: Efficiently meeting very strict, low-latency slos. In: ICAC, pp 265–277Google Scholar
  50. Stonebraker M, Cetintemel U (2005) 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), pp 2–11 IEEE Computer SocietyGoogle Scholar
  51. Stonebraker M, Madden S, Abadi DJ, Harizopoulos S, Hachem N, Helland P (2007) The End of an Architectural Era: (It’s Time for a Complete Rewrite). In: Proceedings of the 33rd International Conference on Very Large Data Bases, VLDB ’07, pp 1150–1160 VLDB EndowmentGoogle Scholar
  52. Szyperski C, Petitclerc M, Barga R (2016) Three experts on big data engineering. IEEE Softw 33:68– 72CrossRefGoogle Scholar
  53. Tanelli M, Ardagna D, Lovera M (2011) Identification of LPV state space models for autonomic web service systems. IEEE Trans Contr Sys Techn 19(1):93–103CrossRefGoogle Scholar
  54. Terry DB, Prabhakaran V, Kotla R, Balakrishnan M, Aguilera MK, Abu-Libdeh H (2013) Consistency-based service level agreements for cloud storage. In: Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, SOSP ’13, (New York, NY, USA), pp 309–324 ACMGoogle Scholar
  55. Wong et al (2009) Oracle streams: A high performance implementation for near real time asynchronous replication. In: Ioannidis YE, Lee DL, Ng RT (eds) ICDE. IEEE, pp 1363–1374Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Dipartimento di Elettronica Informazione e BioingegneriaPolitecnico di MilanoMilanoItaly

Personalised recommendations