Efficient Star Join for Column-oriented Data Store in the MapReduce Environment

Abstract

Map Reduce is a parallel computing paradigm that has gained a lot of attention from both industry and academia recent years. Unlike parallel DBMSs, with Map Reduce, it is easier for non-expert to develop scalable parallel programs for analytical applications over huge data sets across clusters of commodity machines. As the nature of scan-oriented processing, the performance of Map Reduce for relation operators can be enhanced dramatically since it is inevitably accessing lots of unnecessary data tuples, especially for table join operators. In this paper, we propose an efficient star join strategy called HdBmp join for column-oriented data store by using a three-level content aware index (i.e., HdBmp Index). Armed with this index, most of the unnecessary tuples in the join processing can be filtered out, and consequently result in immense reduction in both communication cost and execution time. Our extensive experimental studies confirm the efficiency, scalability and effectiveness of our new proposed join methods.