1 / 8

MapReduce and Parallel DMBSs: Friends or Foes?

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.

hien
Télécharger la présentation

MapReduce and Parallel DMBSs: Friends or Foes?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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. 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

  3. 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

  4. 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 • SELECT * FROM Data WHERE field LIKE `%XYZ%'; • Web log task • SELECT sourceIP, SUM(adRevenue) FROM UserVisits GROUP BY sourceIP; • Join task

  5. Demonstration Results

  6. MR Complements Parallel 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. Conclusion • Architectural Differences • Repetitive record parsing • Compression • Pipelining • Scheduling • Discussion • Coexistence

  8. 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/>

More Related