Abstract: Equi-join is one of the most common and costly operations in data analysis. The implementations of equijoin on Spark are different from those in parallel databases. Equi-join algorithms based on data prepartitioning which are commonly used in parallel databases can hardly be implemented on Spark. Currently common used equijoin algorithms on Spark, such as broadcast join and repartition join, are not efficient. How to improve equijoin performance on Spark becomes the key of big data analysis on Spark. This work combines the advantages of SimiJoin and Partition Join and provides an optimized equijoin algorithm based on the features of Spark. It is indicated by cost analysis and experiment that this algorithm is 12 times faster than algorithms used in stateoftheart data analysis systems on Spark.

Key words: Spark, SQL, big data analysis, equi-join, in-memory computation