An adaptive skew insensitive join algorithm for large scale data analytics

With data explosion in recent years, timely and cost-effective analytics over large scale data has been a hotspot of data management research. Join is an important operation in database query. However, data skew happens naturally in many applications, which will severely degrade the performance of most join algorithms. To address this problem, this paper introduces an Adaptive Skew Insensitive(ASI) join algorithm to handle with serious data skew. Based on our cost analysis, ASI join algorithm can adaptively choose the best join algorithm for different inputs. Compared with several state-of-the-art join methods through adequate experiments, our method achieves significant improvement of join efficiency dealing with data skew. © 2014 Springer International Publishing Switzerland.