Optimized MapFile Based Storage of Small Files in Hadoop

Abstract

Hadoop is an open source software based on MapReduce framework. The Hadoop Distributed File System (HDFS) performs well while storing and managing data sets of very large size. However, the performance of HDFS suffers while handling a large number of small files since they put a lot of burden on the NameNode of HDFS both in terms of memory and access time. To overcome these defects, we merge small files into a large file and store the merged file on HDFS. Generally, when small files are merged, variation in the size distribution of files is not taken into consideration. We propose a new algorithm OMSS (Optimized MapFile based Storage of Small files) which merges the small files into a large file based on the Worst fit strategy. The strategy helps in reducing internal fragmentation in data blocks, which in turn leads to fewer data blocks consumed for the same number of small files. Less number of data blocks mean fewer memory overheads at major nodes of Hadoop cluster and hence increased efficiency of data processing. Our experimental results indicate that the time to process data on HDFS containing unprocessed small files reduces significantly to 590s when MapFile is used and it reduces further to 440s when OMSS is used. OMSS as compared to MapFile merging algorithm has a reduction of 34.7% in memory requirements.