1. MapReduce and Parallel DMBSs: Friends or Foes? Michael Stonebraker, Daniel Abadi, David J. Dewitt, Sam Madden, Erik Paulson, Andrew Pavlo, Alexander Rasin Communications of the ACM, vol. 53, iss. 1, pp. 64-71, 2010. Presentation and slides by Elisa Tvete, Jim Avery

2. P ARALLEL DBMS ARCHITECTURE Multiple nodes running database software “Shared-nothing nodes” - separate CPU, memory, disks Data horizontally partitioned across all nodes Each node runs query on own data Results returned to central processing node Central node calculates final result

3. M AP R EDUCE ARCHITECTURE Several computing nodes used Data not pre-loaded Query has “Map” and “Reduce” components Key/value data is distributed to nodes Nodes perform “Map” step Results are returned to central processing node

4. P ERFORMANCE T RADE - OFFS D EMONSTRATION Three systems: – Hadoop MR Framework – Vertica, a column-store relational database – DBMS-X, a row-based database Three tasks: – Original MR Grep task SELECT * FROM Data WHERE field LIKE `%XYZ%'; – Web log task SELECT sourceIP, SUM(adRevenue) FROM UserVisits GROUP BY sourceIP; – Join task

5. D EMONSTRATION R ESULTS

6. MR C OMPLEMENTS P ARALLEL DBMS MR good at extract-transform-load queries Extract raw data, process it, load into DBMS Can perform complex analytics more easily Queries not suitable for single SQL query Can use data without strictly defined schema MR functions can enhance parallel DBMS!

7. C ONCLUSION Architectural Differences – Repetitive record parsing – Compression – Pipelining – Scheduling Discussion Coexistence

8. R ESOURCES M. Stonebraker, D. Abadi, D. J. DeWitt, S. Madden, E. Paulson, A. Pavlo, and A. Rasin, "MapReduce and Parallel DBMSs: Friends or Foes?," Communications of the ACM, vol. 53, iss. 1, pp. 64-71, 2010. A. Pavlo, E. Paulson, A. Rasin, D. J. Abadi, D. J. DeWitt, S. Madden, and M. Stonebraker. A Comparison of Approaches to Large-Scale Data Analysis. Brown University Data Management Research Group, 26 Feb. 2013. Web. 24 Aug 2011.