A Strategy to Deal with Mass Small Files in HDFS
Shuo Zhang, Li Miao, Dafang Zhang, Yuli Wang · 2014
HDFS performs badly in storing and managing a great number of small files as a result of the great memory occupation of the single Namenode and massive seeks and hopping from datanode to datanode. Traditional solutions are only efficient for specific file size or file format. In this paper, we evaluate the performance of some different solutions such as Hbase and Avro. Then in order to compensate for the lack of their inefficiency for middle size small file, we implement a merging and prefetching mechanism. Finally for the purpose of reducing the influence of different file size distributions, we present a strategy of using different schemes for small files of different sizes. Through the experiments of performance comparison, it can be demonstrated that the strategy can improve the original HDFS's writing and reading performance by about 70%.