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Efficient Large Outer Joins over MapReduce

  • Long ChengEmail author
  • Spyros Kotoulas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9833)

Abstract

Big Data analytics largely rely on being able to execute large joins efficiently. Though inner join approaches have been extensively evaluated in parallel and distributed systems, there is little published work providing analysis of outer joins, especially on the extremely popular MapReduce platform. In this paper, we studied several current algorithms/techniques used in large outer joins. We find that some of them could meet performance bottlenecks in the presence of data skew, while others could be complex and incur significant coordination overheads when applied to the MapReduce framework. In this light, we propose a new algorithm, called POPI (Partial Outer join & Partial Inner join), which targets for efficient processing large outer joins, and most important, is lightweight and adapted to the processing model of MapReduce. We implement our method in Pig and evaluate its performance on a Hadoop cluster of up to 256 cores and datasets of 1 billion tuples. Experimental results show that our method is scalable, robust and outperforms current implementations, at least in the case of high skew.

Keywords

MapReduce Framework Hadoop Cluster MapReduce Implementation POPI Algorithm Skewed Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work is supported by the German Research Foundation (DFG) within the Collaborative Research Center SFB 912 (HAEC) and in Emmy Noether grant KR 4381/1-1 (DIAMOND).

References

  1. 1.
    DeWitt, D., Gray, J.: Parallel database systems: the future of high performance database systems. Commun. ACM 35(6), 85–98 (1992)CrossRefGoogle Scholar
  2. 2.
    Li, F., Ooi, B.C., Özsu, M.T., Wu, S.: Distributed data management using MapReduce. ACM Comput. Surv. 46(3), 31 (2014)Google Scholar
  3. 3.
    Xu, Y., Kostamaa, P.: A new algorithm for small-large table outer joins in parallel DBMS. In: ICDE, pp. 1018–1024 (2010)Google Scholar
  4. 4.
    Cheng, L., Kotoulas, S., Ward, T.E., Theodoropoulos, G.: Robust and efficient large-large table outer joins on distributed infrastructures. In: Silva, F., Dutra, I., Santos Costa, V. (eds.) Euro-Par 2014. LNCS, vol. 8632, pp. 258–269. Springer, Heidelberg (2014)Google Scholar
  5. 5.
    Blanas, S., Patel, J.M., Ercegovac, V., Rao, J., et al.: A comparison of join algorithms for log processing in Map Reduce. In: SIGMOD, pp. 975–986 (2010)Google Scholar
  6. 6.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  7. 7.
    Gates, A.F., Natkovich, O., Chopra, S., Kamath, P., Narayanamurthy, S.M., Olston, C., Reed, B., Srinivasan, S., Srivastava, U.: Building a high-level dataflow system on top of Map-Reduce: the Pig experience. PVLDB 2(2), 1414–1425 (2009)Google Scholar
  8. 8.
    Kotoulas, S., Urbani, J., Boncz, P., Mika, P.: Robust runtime optimization and skew-resistant execution of analytical SPARQL queries on pig. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 247–262. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Xu, Y., Kostamaa, P., Zhou, X., Chen, L.: Handling data skew in parallel joins in shared-nothing systems. In: SIGMOD, pp. 1043–1052 (2008)Google Scholar
  10. 10.
    Cheng, L., Kotoulas, S., Ward, T.E., Theodoropoulos, G.: Robust and skew-resistant parallel joins in shared-nothing systems. In: CIKM, pp. 1399–1408 (2014)Google Scholar
  11. 11.
    Blanas, S., Li, Y., Patel, J.M.: Design and evaluation of main memory hash join algorithms for multi-core CPUs. In: SIGMOD, pp. 37–48 (2011)Google Scholar
  12. 12.
    Olston, C., Reed, B., Srivastava, U., Kumar, R., Tomkins, A.: Pig latin: a not-so-foreign language for data processing. In: SIGMOD, pp. 1099–1110 (2008)Google Scholar
  13. 13.
    Jiang, D., Tung, A., Chen, G.: Map-Join-Reduce: toward scalable and efficient data analysis on large clusters. TKDE 23(9), 1299–1311 (2011)Google Scholar
  14. 14.
    Liao, W., Wang, T., Li, H., Yang, D., Qiu, Z., Lei, K.: An adaptive skew insensitive join algorithm for large scale data analytics. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds.) APWeb 2014. LNCS, vol. 8709, pp. 494–502. Springer, Heidelberg (2014)Google Scholar
  15. 15.
    Thusoo, A., Sarma, J.S., Jain, N., Shao, Z., Chakka, P., Anthony, S., Liu, H., Wyckoff, P., Murthy, R.: Hive: a warehousing solution over a Map-Reduce framework. PVLDB 2(2), 1626–1629 (2009)Google Scholar
  16. 16.
    Alexandrov, A., Bergmann, R., Ewen, S., Freytag, J.C., Hueske, F., Heise, A., Kao, O., Leich, M., Leser, U., Markl, V., et al.: The stratosphere platform for big data analytics. VLDB J. 23(6), 939–964 (2014)CrossRefGoogle Scholar
  17. 17.
    Bruno, N., Kwon, Y., Wu, M.C.: Advanced join strategies for large-scale distributed computation. PVLDB 7(13), 1484–1495 (2014)Google Scholar
  18. 18.
    Bellamkonda, S., Li, H.G., Jagtap, U., Zhu, Y., Liang, V., Cruanes, T.: Adaptive and big data scale parallel execution in Oracle. PVLDB 6(11), 1102–1113 (2013)Google Scholar
  19. 19.
    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: NSDI, pp. 15–28 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.cfaed, TU DresdenDresdenGermany
  2. 2.IBM ResearchDublinIreland

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