A Comparative Study of Join Algorithms in MapReduce

To analyze large volumes of data, a set of techniques is present in the IT community as the MapReduce paradigm, parallel RDBMS, column storage, and combinations of those technics. MapReduce is a parallel programming model introduced by Google, which enables an easy parallelization of tasks while hiding the details and the complexity of parallel computations on very large datasets across a large number of machines. Our study will be concerned about the MapReduce Data Analytics, the most important data analysis treatments in MapReduce is the treatment of the Logs files as in the case of web applications, which can be in the form of selection operation, aggregation, or filtering, the most useful and expensive operation is that of the join, the logs files will often need to be joined with one or more table references, however the MapReduce paradigm is not designed to process multiple inputs. While processing relational data is a common need, this limitation causes difficulties and inefficiency when MapReduce is applied on relational operations like joins. The aim of this paper is to compare a number of wellknown join strategies in MapReduce, analyze the costs associated to a MapReduce program in terms of I/O and CPU used, and present some techniques of optimizations from related works.