HadoopDB
An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads.

DR@Y
Database Research at Yale University


HadoopDB is:

  1. A hybrid of DBMS and MapReduce technologies that targets analytical workloads
  2. Designed to run on a shared-nothing cluster of commodity machines, or in the cloud
  3. An attempt to fill the gap in the market for a free and open source parallel DBMS
  4. Much more scalable than currently available parallel database systems and DBMS/MapReduce hybrid systems.
  5. As scalable as Hadoop, while achieving superior performance on structured data analysis workloads
For more detail, check out the DBMS Musings blog post, or the paper below.

The Paper:

HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads. Azza Abouzeid, Kamil Bajda-Pawlikowski, Daniel J. Abadi, Avi Silberschatz, Alex Rasin. In Proceedings of VLDB, 2009. [PDF]

News:

Press:

Links:

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Project Members

Contact

E-mail Kamil Bajda-Pawlikowski, Azza Abouzeid, Daniel Abadi, or Avi Silberschatz ({kbajda, azza, dna, avi}@cs.yale.edu) for questions or comments.



HadoopDB Team - Yale University 2009 Last update: 09-08-03