Hadoop++: Making a yellow elephant run like a cheetah (without it even noticing

MapReduce is a computing paradigm that has gained a lot of at-tention in recent years from industry and research. Unlike paral-lel DBMSs, MapReduce allows non-expert users to run complex analytical tasks over very large data sets on very large clusters and clouds. However, this comes at a price: MapReduce pro-cesses tasks in a scan-oriented fashion. Hence, the performance of Hadoop — an open-source implementation of MapReduce — often does not match the one of a well-configured parallel DBMS. In this paper we propose a new type of system named Hadoop++: it boosts task performance without changing the Hadoop framework at all (Hadoop does not even ‘notice it’). To reach this goal, rather than changing a working system (Hadoop), we inject our technology at the right places through UDFs only and affect Hadoop from inside. This has three important consequences: First, Hadoop++ signifi