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

  • Authors:

  • Jens Dittrich;Jorge-Arnulfo Quiané-Ruiz;Alekh Jindal;Yagiz Kargin;Vinay Setty;Jörg Schad

  • Affiliations:

  • Saarland University;Saarland University;Saarland University and International Max Planck Research School for Computer Science;International Max Planck Research School for Computer Science;International Max Planck Research School for Computer Science;Saarland University

  • Venue:
  • Proceedings of the VLDB Endowment
  • Year:
  • 2010

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Abstract

MapReduce is a computing paradigm that has gained a lot of attention in recent years from industry and research. Unlike parallel 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 processes 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++ significantly outperforms Hadoop. Second, any future changes of Hadoop may directly be used with Hadoop++ without rewriting any glue code. Third, Hadoop++ does not need to change the Hadoop interface. Our experiments show the superiority of Hadoop++ over both Hadoop and HadoopDB for tasks related to indexing and join processing.