MapReduce and Parallel DBMSs: Together at Last - MIT Database ...
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MapReduce and Parallel DBMSs: Together at Last - MIT Database ...
Read data from several different sources. Parse and clean. Perform complex transformations. Decide what attribute data to store. Load the information into a DBMS.
Allows for quick-and-dirty data analysis.
January 28, 2010
Semi-Structured Data MapReduce systems can easily stored semistructured data since no schema is needed: Typically key/value records with a varying number of attributes.
Awkward to stored in relational DBMS: Wide-tables with many nullable attributes. Column store fairs better.
January 28, 2010
Limited Budget Operations MapReduce frameworks: Community supported and driven. Attractive for projects with modest budgets and requirements.
Parallel DBMSs are expensive: No open-source option.
January 28, 2010
Together At Last? What can MapReduce learn from Databases? Fast query times. Schemas. Supporting tools.
What can Databases learn from MapReduce? Ease of use, “out of box” experience. Attractive fault tolerance properties. Fast load times.
January 28, 2010
MR+DBMS Integration Vertica now integrates directly with Hadoop: Hadoop jobs can use Vertica as input source. Push Map/Reduce tasks down directly into DBMS nodes.
Other notable commercial MR integrations: Greenplum AsterData Sybase IQ
January 28, 2010
MR+DBMS Integration HadoopDB (Yale+Brown): Replace Hadoop’s distributed file system with multiple database instances. Rewrite Hive query plans into localized SQL for each execution node.
Position available for HadoopDB @ Yale
January 28, 2010
Other Work MRi (Wisconsin): Improving Hadoop by adding DBMS technologies that are transparent to users. Ported GiST Search Trees to Hadoop.
SQL Server 2008 R2 (Microsoft): Including “MapReduce-like” functionality into parallel data warehouse version of MSSQL (Project Madison)
January 28, 2010
Conclusion Complete benchmark information and source code is available at our website: http://database.cs.brown.edu/sigmod09/
