An Efficient Filter Strategy for Theta-Join Query in Distributed Environment - Wenjie Liu; Zhanhuai Li; Yuntao Zhou (Proceedings - International Workshops on Parallel Processing, 2017)
Theta-join query is a very popular application in traditional databases, but due to tremendous computation cost and communication cost in distributed environment, it is not efficiently processed for big data. Current researches focus on processing theta-join by using MapReduce framework, which mainly consider the overheads of load balance in the network, when the data sets become larger, massive intermediate results lead to high communication cost. In this work, we propose a filter method for theta-join to reduce the computation and communication cost in distributed environment, which can effectively improve the theta-join query. We consider both the load balance in the cluster and the memory cost in the parallel framework. We have implemented our method in a popular general-purpose data processing framework, Spark. The experimental results demonstrate that our method can significantly improve the performance of theta-joins comparing the state-of-art solutions.