OLAP Aggregation Based on Dimension-oriented Storage

OLAP (online analytical processing) applications are based on a variety of aggregate queries on large-scale data. As aggregation is always performed on columns, traditional row-oriented storage, in which all the columns of a data row are stored together, has seriously restricted its performance. This paper proposes a dimension-oriented storage model based on HBase, and a new parallel aggregation technique, which accomplishes aggregation operations with parallel MapReduce jobs. Finally, compared with Hive on standard TPC-H data set, our technique is demonstrated to improve performance of core aggregate operations significantly.