Hadoop is an open-source framework written by java and used for big
data processing. It consists of two main components: Hadoop
Distributed File System (HDFS) and MapReduce. HDFS is used to
store data while MapReduce is used to distribute and process an
application tasks in a distributed processing form. Recently, several
researchers employ Hadoop for processing big data. The results
indicate that Hadoop performs well with Large Files (files larger than
Data Node block size). Nevertheless, Hadoop performance decreases
with small files that are less than its block size. This is because, small
files consume the memory of both the DataNode and the NameNode,
and increases the execution time of the applications (i.e. decreases
MapReduce performance). In this paper, the problem of the small files
in Hadoop is defined and the existing approaches to solve this problem
are classified and discussed. In addition, some open points that must
be considered when thinking of a better approach to improve the
Hadoop performance when processing the small files.