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MapReduce Data Skewness Handling: A Systematic Literature Review

  • Mohammad Amin Irandoost
  • Amir Masoud RahmaniEmail author
  • Saeed Setayeshi
Article
  • 17 Downloads

Abstract

One of the most successful techniques in large-scale data-intensive computations is MapReduce programming. MapReduce is based on a divide and conquer approach that uses commodity computers, also known as nodes, for parallel processing. The scalability and performance of this technique are more related to the type of data distribution in map and reduce tasks. Because of many reasons such as node failure, network failure, data skewness, etc. completion time of one task could be longer than other tasks, job completion time is determined by the slowest task. One of the most important reasons for requiring more time to finish one task compared to other tasks is the skewness of data. Despite the widespread use of MapReduce because of its high flexibility and tolerability of the error, with the presence of data skewness, it cannot fully utilize the nodes for parallel processing. The objectives of this study were to review related articles and classify them based on the type of problem addressed and to determine the advantages and disadvantages of them. Open issues were also defined to present guidelines for future research on this subject. In order to achieve the aforementioned objectives, some research questions were defined and answered. In this review, it was concluded that there are important parameters have not been considered in MapReduce data skewness handling approaches.

Keywords

Data skewness MapReduce Load balancing Big data Systematic literature review Survey 

Notes

References

  1. 1.
    Li, J., Liu, Y., Pan, J., Zhang, P., Chen, W., Wang, L.: Map-Balance-Reduce: an improved parallel programming model for load balancing of MapReduce. Future Gener. Comput. Syst. (2017).  https://doi.org/10.1016/j.future.2017.03.013 Google Scholar
  2. 2.
    Zhang, F., Malluhi, Q.M., Elsayed, T., Khan, S.U., Li, K., Zomaya, A.Y.: CloudFlow: a data-aware programming model for cloud workflow applications on modern HPC systems. Future Gener. Comput. Syst. 51, 98–110 (2015)CrossRefGoogle Scholar
  3. 3.
    Hwang, K., Xu, Z.: Scalable Parallel Computing: Technology, Architecture, Programming. McGraw-Hill Inc, New York (1998)zbMATHGoogle Scholar
  4. 4.
    Jin, H.: High Performance Mass Storage and Parallel I/O: Technologies and Applications. Wiley, Hoboken (2001)Google Scholar
  5. 5.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  6. 6.
    Hadoop, A.: Retrieved from http://hadoop.apache.org (2011). Accessed Jan 2018
  7. 7.
    Zhang, X., Wu, Y., Zhao, C.: MrHeter: improving MapReduce performance in heterogeneous environments. Clust. Comput. 19(4), 1691–1701 (2016)CrossRefGoogle Scholar
  8. 8.
    Chen, Q., Yao, J., Xiao, Z.: LIBRA: lightweight data skew mitigation in MapReduce. IEEE Trans. Parallel Distrib. Syst. 26(9), 2520–2533 (2015)CrossRefGoogle Scholar
  9. 9.
    Dhawalia, P., Kailasam, S., Janakiram, D.: Chisel++: handling partitioning skew in MapReduce framework using efficient range partitioning technique. In: Proceedings of the Sixth International Workshop on Data Intensive Distributed Computing, Vancouver, BC, Canada 2014, pp. 21–28. ACM, 2608021Google Scholar
  10. 10.
    Ibrahim, S., Jin, H., Lu, L., Wu, S., He, B., Qi, L.: LEEN: locality/fairness-aware key partitioning for MapReduce in the cloud. In: Cloud Computing Technology and Science (CloudCom), 2010 IEEE Second International Conference on, Nov. 30 2010–Dec. 3 2010, pp. 17–24 (2010)Google Scholar
  11. 11.
    Jiadong, Y., Chen, H., Fei, H.: SASM: improving spark performance with adaptive skew mitigation. In: 2015 IEEE International Conference on Progress in Informatics and Computing (PIC), 18–20 Dec. 2015, pp. 102–107 (2015)Google Scholar
  12. 12.
    Xu, Y., Qu, W., Li, Z., Liu, Z., Ji, C., Li, Y., Li, H.: Balancing reducer workload for skewed data using sampling-based partitioning. Comput. Electr. Eng. 40(2), 675–687 (2014)CrossRefGoogle Scholar
  13. 13.
    Le, Y., Liu, J., Ergün, F., Wang, D.: Online load balancing for MapReduce with skewed data input. In: IEEE INFOCOM 2014-IEEE Conference on Computer Communications, April 27 2014–May 2 2014, pp. 2004–2012 (2014)Google Scholar
  14. 14.
    Ibrahim, S., Jin, H., Lu, L., He, B., Antoniu, G., Wu, S.: Handling partitioning skew in MapReduce using LEEN. Peer-to-Peer Netw. Appl. 6(4), 409–424 (2013)CrossRefGoogle Scholar
  15. 15.
    Kwon, Y., Balazinska, M., Howe, B., Rolia, J.: SkewTune: mitigating skew in mapreduce applications. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, Scottsdale, Arizona, USA 2012, pp. 25–36. ACM, 2213840Google Scholar
  16. 16.
    Liu, Z., Zhang, Q., Zhani, M.F., Boutaba, R., Liu, Y., Gong, Z.: DREAMS: dynamic resource allocation for MapReduce with data skew. In: 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), 11–15 May 2015, pp. 18–26 (2015)Google Scholar
  17. 17.
    Asghari, P., Rahmani, A.M., Javadi, H.H.S.: Internet of things applications: a systematic review. Comput. Netw. 148, 241–261 (2019)CrossRefGoogle Scholar
  18. 18.
    White, T.: Hadoop: The Definitive Guide. O’Reilly Media, Inc. (2015)Google Scholar
  19. 19.
    Ren, K., Kwon, Y., Balazinska, M., Howe, B.: Hadoop’s adolescence: an analysis of Hadoop usage in scientific workloads. Proc. VLDB Endow. 6(10), 853–864 (2013)CrossRefGoogle Scholar
  20. 20.
    Soualhia, M., Khomh, F., Tahar, S.: Task scheduling in big data platforms: a systematic literature review. J. Syst. Softw. 134(Supplement C), 170–189 (2017)CrossRefGoogle Scholar
  21. 21.
    Li, R., Hu, H., Li, H., Wu, Y., Yang, J.: MapReduce parallel programming model: a state-of-the-art survey. Int. J. Parallel Program. 44(4), 832–866 (2016)CrossRefGoogle Scholar
  22. 22.
    Memishi, B., Ibrahim, S., Pérez, M.S., Antoniu, G.: Fault tolerance in MapReduce: a survey. In: Pop, F., Kołodziej, J., Di Martino, B. (eds.) Resource Management for Big Data Platforms: Algorithms, Modelling, and High-Performance Computing Techniques, pp. 205–240. Springer, Cham (2016)Google Scholar
  23. 23.
    Kargar, M.J., Vakili, M.: Load balancing in MapReduce on homogeneous and heterogeneous clusters: an in-depth review. Int. J. Commun. Netw. Distrib. Syst. 15(2/3), 149–168 (2015)CrossRefGoogle Scholar
  24. 24.
    Becheikh, N., Landry, R., Amara, N.: Lessons from innovation empirical studies in the manufacturing sector: a systematic review of the literature from 1993–2003. Technovation 26(5–6), 644–664 (2006)CrossRefGoogle Scholar
  25. 25.
    Kupiainen, E., Mäntylä, M.V., Itkonen, J.: Using metrics in Agile and Lean software development: a systematic literature review of industrial studies. Inf. Softw. Technol. 62, 143–163 (2015)CrossRefGoogle Scholar
  26. 26.
    Geraldi, J., Maylor, H., Williams, T.: Now, let’s make it really complex (complicated): a systematic review of the complexities of projects. Int. J. Oper. Prod. Manag. 31(9), 966–990 (2011)CrossRefGoogle Scholar
  27. 27.
    Shojaiemehr, B., Rahmani, A.M., Qader, N.N.: Cloud computing service negotiation: a systematic review. Comput. Stand. Interfaces 55, 196–206 (2018)CrossRefGoogle Scholar
  28. 28.
    Souri, A., Navimipour, N.J., Rahmani, A.M.: Formal verification approaches and standards in the cloud computing: a comprehensive and systematic review. Comput. Stand. Interfaces 58, 1–22 (2017)CrossRefGoogle Scholar
  29. 29.
    Liroz-Gistau, M., Akbarinia, R., Agrawal, D., Valduriez, P.: FP-Hadoop: efficient processing of skewed MapReduce jobs. Inf. Syst. 60, 69–84 (2016)CrossRefGoogle Scholar
  30. 30.
    Ramakrishnan, S.R., Swart, G., Urmanov, A.: Balancing reducer skew in MapReduce workloads using progressive sampling. In: Proceedings of the Third ACM Symposium on Cloud Computing, San Jose, California 2012, pp. 1–14. ACM, 2391245Google Scholar
  31. 31.
    Slagter, K., Hsu, C.-H., Chung, Y.-C.: An adaptive and memory efficient sampling mechanism for partitioning in MapReduce. Int. J. Parallel Program. 43(3), 489–507 (2015)CrossRefGoogle Scholar
  32. 32.
    Yan, W., Xue, Y., Malin, B.: Scalable and robust key group size estimation for reducer load balancing in MapReduce. In: 2013 IEEE International Conference on Big Data, 6–9 Oct. 2013, pp. 156–162 (2013)Google Scholar
  33. 33.
    Jiong, X., Shu, Y., Xiaojun, R., Zhiyang, D., Yun, T., Majors, J., Manzanares, A., Xiao, Q.: Improving MapReduce performance through data placement in heterogeneous Hadoop clusters. In: 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum (IPDPSW), 19–23 April 2010, pp. 1–9 (2010)Google Scholar
  34. 34.
    Guo, Z., Pierce, M., Fox, G., Zhou, M.: Automatic task re-organization in MapReduce. In: Cluster Computing (CLUSTER), 2011 IEEE International Conference on 2011, pp. 335–343. IEEEGoogle Scholar
  35. 35.
    Irandoost, M.A., Rahmani, A.M., Setayeshi, S.: A novel algorithm for handling reducer side data skew in MapReduce based on a learning automata game. Inf. Sci. (2018).  https://doi.org/10.1016/j.ins.2018.11.007 Google Scholar
  36. 36.
    Gao, Y., Zhang, Y., Wang, H., Li, J., Gao, H.: A distributed load balance algorithm of MapReduce for data quality detection. In: Gao, H., Kim, J., Sakurai, Y. (eds.) Database Systems for Advanced Applications: DASFAA 2016 International Workshops: BDMS, BDQM, MoI, and SeCoP, Dallas, TX, USA, April 16–19, 2016, Proceedings, pp. 294–306. Springer International Publishing, Cham (2016)Google Scholar
  37. 37.
    Kolb, L., Thor, A., Rahm, E.: Load balancing for MapReduce-based entity resolution. In: Proceedings of the 2012 IEEE 28th International Conference on Data Engineering 2012, pp. 618–629. IEEE Computer Society, 2310387Google Scholar
  38. 38.
    Xu, Y., Zou, P., Qu, W., Li, Z., Li, K., Cui, X.: Sampling-based partitioning in MapReduce for skewed data. In: 2012 Seventh ChinaGrid Annual Conference, 20–23 Sept. 2012, pp. 1–8 (2012)Google Scholar
  39. 39.
    Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M.J., Shenker, S., Stoica, I.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Paper Presented at the Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation, San Jose, CAGoogle Scholar
  40. 40.
    Spark: http://spark.apache.org/ (2015-11-30). Accessed Jan 2018
  41. 41.
    Tang, Z., Zhang, X., Li, K., Li, K.: An intermediate data placement algorithm for load balancing in Spark computing environment. Future Gener. Comput. Syst. 78, 287–301 (2016)CrossRefGoogle Scholar
  42. 42.
    Kwon, Y., Balazinska, M., Howe, B., Rolia, J.: Skew-resistant parallel processing of feature-extracting scientific user-defined functions. In: Proceedings of the 1st ACM symposium on Cloud computing, Indianapolis, Indiana, USA 2010, pp. 75–86. ACM, 1807140Google Scholar
  43. 43.
    Liu, Z., Zhang, Q., Boutaba, R., Liu, Y., Wang, B.: OPTIMA: on-line partitioning skew mitigation for MapReduce with resource adjustment. J. Netw. Syst. Manag. 24(4), 859–883 (2016)CrossRefGoogle Scholar
  44. 44.
    Arning, A., Agrawal, R., Raghavan, P.: A linear method for deviation detection in large databases. In: KDD 1996, pp. 164–169Google Scholar
  45. 45.
    Liu, Z., Zhang, Q., Boutaba, R., Liu, Y., Gong, Z.: ROUTE: run-time robust reducer workload estimation for MapReduce. Int. J. Netw. Manag. 26(3), 224–244 (2016)CrossRefGoogle Scholar
  46. 46.
    Kumaresan, V., Baskaran, R., Dhavachelvan, P.: AEGEUS++: an energy-aware online partition skew mitigation algorithm for mapreduce in cloud. Clust. Comput. (2017).  https://doi.org/10.1007/s10586-017-1044-8 Google Scholar
  47. 47.
    Kumaresan, V., Baskaran, R.: AEGEUS: an online partition skew mitigation algorithm for mapreduce. In: Proceedings of the International Conference on Informatics and Analytics, Pondicherry, India 2016, pp. 1–8. ACM, 2980461Google Scholar
  48. 48.
    Slagter, K., Hsu, C.-H., Chung, Y.-C., Zhang, D.: An improved partitioning mechanism for optimizing massive data analysis using MapReduce. J. Supercomput. 66(1), 539–555 (2013)CrossRefGoogle Scholar
  49. 49.
    Liu, G., Zhu, X., Wang, J., Guo, D., Bao, W., Guo, H.: SP-Partitioner: a novel partition method to handle intermediate data skew in spark streaming. Future Gener. Comput. Syst. 86, 1054–1063 (2017)CrossRefGoogle Scholar
  50. 50.
    Gufler, B., Augsten, N., Reiser, A., Kemper, A.: Load balancing in mapreduce based on scalable cardinality estimates. In: 2012 IEEE 28th International Conference on Data Engineering 2012, pp. 522–533. IEEEGoogle Scholar
  51. 51.
    Fan, Y., Wu, W., Xu, Y., Chen, H.: Improving MapReduce performance by balancing skewed loads. China Commun. 11(8), 85–108 (2014)CrossRefGoogle Scholar
  52. 52.
    Guo, Y., Rao, J., Cheng, D., Zhou, X.: ishuffle: Improving hadoop performance with shuffle-on-write. IEEE Trans. Parallel Distrib. Syst. 28(6), 1649–1662 (2017)CrossRefGoogle Scholar
  53. 53.
    Rao, S., Ramakrishnan, R., Silberstein, A., Ovsiannikov, M., Reeves, D.: Sailfish: a framework for large scale data processing. In: Proceedings of the Third ACM Symposium on Cloud Computing, San Jose, California 2012, pp. 1–14. ACM, 2391233Google Scholar
  54. 54.
    Nawale, V.A., Deshpande, P.: Minimizing skew in MapReduce applications using node clustering in heterogeneous environment. In: 2015 International Conference on Computational Intelligence and Communication Networks (CICN), 12–14 Dec. 2015, pp. 136–139 (2015)Google Scholar
  55. 55.
    Zheng, S., Liu, Y., He, T., Shanshan, L., Liao, X.: SkewControl: Gini out of the bottle. In: 2014 IEEE International Parallel and Distributed Processing Symposium Workshops, 19–23 May 2014, pp. 1572–1580 (2014)Google Scholar
  56. 56.
    Dhawalia, P., Kailasam, S., Janakiram, D.: Chisel: a resource savvy approach for handling skew in mapreduce applications. In: Cloud Computing (CLOUD), 2013 IEEE Sixth International Conference on 2013, pp. 652–660. IEEEGoogle Scholar
  57. 57.
    Chen, L., Lu, W., Che, X., Xing, W., Wang, L., Yang, Y.: MRSIM: mitigating reducer skew In MapReduce. In: 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA), 27–29 March 2017, pp. 379–384 (2017)Google Scholar
  58. 58.
    Elmeleegy, K., Olston, C., Reed, B.: SpongeFiles: mitigating data skew in mapreduce using distributed memory. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, Snowbird, Utah, USA 2014, pp. 551–562. ACM, 2595634Google Scholar
  59. 59.
    Huang, T.C., Chu, K.C., Huang, G.H., Shen, Y.C., Shieh, C.K.: Smart partitioning mechanism for dealing with intermediate data skew in reduce task on cloud computing. In: 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), 27–29 March 2017, pp. 819–826 (2017)Google Scholar
  60. 60.
    Ahmad, F., Chakradhar, S.T., Raghunathan, A., Vijaykumar, T.N.: Tarazu: optimizing MapReduce on heterogeneous clusters. SIGARCH Comput. Archit. News 40(1), 61–74 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Mohammad Amin Irandoost
    • 1
  • Amir Masoud Rahmani
    • 1
    Email author
  • Saeed Setayeshi
    • 2
  1. 1.Department of Computer Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Medical Radiation EngineeringAmirkabir University of TechnologyTehranIran

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