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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 PARALLEL 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 MAPREDUCE 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 PERFORMANCE TRADE-OFFS DEMONSTRATION • Three systems: – – – • Hadoop MR Framework Vertica, a column-store relational database DBMS-X, a row-based database Three tasks: – Original MR Grep task • – Web log task • – SELECT * FROM Data WHERE field LIKE `%XYZ%'; SELECT sourceIP, SUM(adRevenue) FROM UserVisits GROUP BY sourceIP; Join task DEMONSTRATION RESULTS Performance Trade-Offs Demonstration Results 1400 Time (in seconds) 1200 1000 800 Hadoop 600 DBMS-X Vertica 400 200 0 Grep Web Log Task Join MR COMPLEMENTS PARALLEL DBMS MR good at extract-transform-load queries Can perform complex analytics more easily Extract raw data, process it, load into DBMS Queries not suitable for single SQL query Can use data without strictly defined schema MR functions can enhance parallel DBMS! CONCLUSION • Architectural Differences – – – – • • Repetitive record parsing Compression Pipelining Scheduling Discussion Coexistence RESOURCES • • 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. <http://database.cs.brown.edu/projects/mapreduce-vs-dbms/>