Exploiting MapReduce-based similarity joins

Cloud enabled systems have become a crucial component to efficiently process and analyze massive amounts of data. One of the key data processing and analysis operations is the Similarity Join, which retrieves all data pairs whose distances are smaller than a pre-defined threshold ∈. Even though multiple algorithms and implementation techniques have been proposed for Similarity Joins, very little work has addressed the study of Similarity Joins for cloud systems. This paper presents MRSimJoin, a multi-round MapReduce based algorithm to efficiently solve the Similarity Join problem. MRSimJoin efficiently partitions and distributes the data until the subsets are small enough to be processed in a single node. The proposed algorithm is general enough to be used with data that lies in any metric space. We have implemented MRSimJoin in Hadoop, a highly used open-source cloud system. We show how this operation can be used in multiple real-world data analysis scenarios with multiple data types and distance functions. Particularly, we show the use of MRSimJoin to identify similar images represented as feature vectors, and similar publications in a bibliographic database. We also show how MRSimJoin scales in each scenario when important parameters, e.g., ∈, data size and number of cluster nodes, increase. We demonstrate the execution of MRSimJoin queries using an Amazon Elastic Compute Cloud (EC2) cluster.