Optimizing Hadoop for Small File Management


  • M. Elmahouti Information Systems Engineering Research Group, Dept. Telecommunications. National School of Applied Science of Tetouan, Abdelmalek Essaadi University, Tetuan, Morocco
  • O. Achandair Information System Engineering Resarch Group Abdelmalek Essaadi University, National School of Applied Sciences, Tetouan, Morocco.
  • S. Khoulji Information System Engineering Resarch Group Abdelmalek Essaadi University, National School of Applied Sciences, Tetouan, Morocco.
  • M.L. Kerkeb Information System Engineering Resarch Group Abdelmalek Essaadi University, National School of Applied Sciences, Tetouan, Morocco.




Cloud Hadoop, HDFS, Small Files, SequenceFile, MapFile


 HDFS is one of the most used distributed file systems, that offer a high availability and scalability on low-cost hardware. HDFS is delivered as the storage component of Hadoop framework. Coupled with map reduce, which is the processing component, HDFS and MapReduce become the de facto platform for managing big data nowadays. However, HDFS was designed to handle specifically a huge number of large files, while when it comes to a large number of small files, Hadoop deployments may be not efficient. In this paper, we proposed a new strategy to manage small files. Our approach consists of two principal phases. The first phase is about consolidating more than only one client’s small files input, and store the inputs continuously in the first allocated block, in a SequenceFile format, and so on into the next blocks. That way we avoid multiple block allocations for different streams, to reduce calls for available blocks and to reduce the metadata memory on the NameNode. This is because groups of small files packaged in a SequenceFile on the same block will require one entry instead of one for each small file. The second phase consists of analyzing attributes of stored small files to distribute them in such a way that the most called files will be referenced by an additional index as a MapFile format to reduce the read throughput during random access.



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How to Cite

Elmahouti, M., Achandair, O., Khoulji, S., & Kerkeb, M. (2017). Optimizing Hadoop for Small File Management. Transactions on Engineering and Computing Sciences, 5(4). https://doi.org/10.14738/tmlai.54.3214



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

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