Improving Small File Management in Hadoop

Authors

  • O. Achandair Information System Engineering Resarch Group Abdelmalek Essaadi University, National School of Applied Sciences, Tetouan, Morocco.
  • M. Elmahouti Information System Engineering Resarch Group Abdelmalek Essaadi University, National School of Applied Sciences, Tetouan, Morocco.
  • Samira Khoulji
  • M.L. Kerkeb Information System Engineering Resarch Group Abdelmalek Essaadi University, National School of Applied Sciences, Tetouan, Morocco.

DOI:

https://doi.org/10.14738/tmlai.54.3333

Keywords:

Cloud Hadoop, HDFS, Small Files, SequenceFile, MapFile

Abstract

Hadoop, considered nowadays as the de-facto platform for managing big data, has revolutionized the way customers manage their data. As an open-source implementation of map-reduce, it was designed to offer a high scalability and availability across clusters of thousands of machines. Through it two principals’ components, which is HDFS for a distributed storage and MapReduce as the distributed processing engine, companies and research studies are taking a big benefit from its capabilities. However, Hadoop was designed to handle large size files, so when it comes to a large number of small files, the performance can be heavily degraded. The small file problem has been well defined by researchers and Hadoop community, but most of the proposed approaches only deal with the pressure caused on the NameNode memory. Certainly, grouping small files in different possible formats, that are most of time supported by the actual Hadoop distribution, reduce the metadata entries and solve the memory limitation, but that remain only a part of the equation. Actually, the real impact that organizations need to solve when dealing with lot of small files, is the cluster performance when those files are processed in Hadoop clusters. In this paper, we proposed a new strategy to use efficiently some one of the common solution that group files in a MapFile format.The core idea, is to organize small files files based on specific attributes in MapFile output files, and use prefetching and caching mechanisms during read access. This would lead to less calls of metadata from the NameNode, and better I/O performance during MapReduce jobs. The experimental results show that this approach can help to obtain better access time when the cluster contain massive number of small files.

 


References

(1) B. White, T. Yeh, J. Lin, and L. Davis, “Web-scale computer vision using mapreduce for multimedia data mining,” in Proceedings of the Tenth International Workshop on Multimedia Data Mining. ACM, 2010, p. 9.

(2) K. Wiley, A. Connolly, J. Gardner, S. Krughoff, M. Balazinska, B. Howe, Y. Kwon, and Y. Bu, “Astronomy in the cloud: using mapreduce for image co-addition,” Astronomy, vol. 123, no. 901, pp. 366–380, 2011.

(3) W. Fang, V. Sheng, X. Wen, and W. Pan, “Meteorological data analysis using mapreduce,” The Scientific World Journal, vol. 2014, 2014.

(4) F. Wang and M. Liao, “A map-reduce based fast speaker recognition,” in Information, Communications and Signal Processing (ICICS) 2013 9th International Conference on. IEEE, 2013, pp. 1–5.

(5) K. P. Ajay, K. C. Gouda, H. R. Nagesh, “A Study for Handelling of High-Performance Climate Data using Hadoop, Proceedings of the International Conference, pp: 197-202, April 2015.

(6) D. Q. Duffy, J. L. Schnase, J. H. Thompson, S. M. Freeman, and T. L. Clune, “Preliminary evaluation of mapreduce for high-performance climate data analysis,” 2012.

(7) C. Shen, W. Lu, J. Wu, and B. Wei, “A digital library architecture supporting massive small files and efficient replica maintenance,” in Proceedings of the 10th Annual Joint Conference on Digital Libraries (JCDL '10), pp. 391–392, June 2010

(8) A. Thusoo, Z. Shao, S. Anthony, D. Borthakur, N. Jain, J. Sen Sarma, R. Murthy, and H. Liu, “Data warehousing and analytics infrastructure at facebook,” in Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. ACM, 2010, pp. 1013–1020.

(9) J. K. Bonfield and R. Staden, “ZTR: A new format for DNA sequence trace data”, Bioinformatics, vol. 18, no. 1, (2002), pp. 3–10.

(10) T. Nukarapu, B. Tang, L. Wang, and S. Lu, “Data replication in data intensive scientific applications with performance guarantee,” IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 8, pp. 1299–1306, 2011.

(11) Y. Gao and S. Zheng, "A Metadata Access Strategy of Learning Resources Based on HDFS," in proceeding International Conference on Image Analysis and Signal Processing (IASP), pp. 620—622, 2011.

(12) J. Xie, S. Yin, et al. “Improving MapReduce performance through data placement in heterogeneous Hadoop clusters”, In 2010 IEEE International Symposium on Parallel & Distributed

(13) G. Mackey; S. Sehrish; J. Wang. Improving metadata management for small files in HDFS. IEEE International Conference on Cluster Computing and Workshops (CLUSTR). 2009. pp.1-4.

(14) C. Vorapongkitipun; N. Nupairoj. Improving performance of small-file accessing in Hadoop. IEEE International Conference on Computer Science and Software Engineering (JCSSE). 2014. pp.200-205.

(15) Patel A, Mehta M A. A novel approach for efficient handling of small files in HDFS, 2015 IEEE International Advance Computing Conference (IACC), pp. 1258-1262.

(16) Y. Zhang; D. Liu. Improving the Efficiency of Storing for Small Files in HDFS. International Conference on Computer Science & Service System (CSSS). 2012. pp.2239-2242

(17) D. Dev; R. Patgiri. HAR+: Archive and metadata distribution! Why not both?. IEEE International Conference on Computer Communication and Informatics (ICCCI). 2015. pp.1-6.

(18) P. Gohil; B. Panchal; J. S. Dhobi. A novel approach to improve the performance of Hadoop in handling of small files. International Conference on Electrical, Computer and Communication Technologies (ICECCT). 2015. pp.1-5.

(19) Hoare CAR. Quicksort[J]. The Computer Journal, 1962, 5(1): 10-16

(20) E.J. O’Neil, P.E. O’Neil, and G. Weikum, “An Opti- mality Proof of the LRU-K Page Replacement Algorithm,” J. ACM, vol. 46, no. 1, 1999, pp. 92-112

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Published

2017-09-01

How to Cite

Achandair, O., Elmahouti, M., Khoulji, S., & Kerkeb, M. (2017). Improving Small File Management in Hadoop. Transactions on Machine Learning and Artificial Intelligence, 5(4). https://doi.org/10.14738/tmlai.54.3333

Issue

Section

Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems

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