等值连接作为数据分析中最常用、代价最高的操作之一，在Spark上的实现和优化与传统并行数据库有很大的差别，传统并行数据仓库中基于数据预划分的连接算法在Spark上难以实现，而目前被广泛采用的Broadcast Join和Repartition Join性能较差，如何提高连接性能成为基于Spark的海量数据分析的关键.本研究将SimiJoin与Partition Join的优势相结合，并基于Spark上的特性提出了一种优化的等值连接算法.代价分析和实验表明本算法比现有基于Spark的数据分析系统中的连接算法性能提升1~2倍.

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.