Reynold Shi Xin and Joshua Rosen and Matei Zaharia and Michael Franklin and Scott Shenker and Ion Stoica

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2012-214

November 26, 2012

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-214.pdf

Shark is a new data analysis system that marries query processing with complex analytics on large clusters. It leverages a novel distributed memory abstraction to provide a unified engine that can run SQL queries and sophisticated analytics functions (e.g., iterative machine learning) at scale, and efficiently recovers from failures mid-query. This allows Shark to run SQL queries up to 100× faster than Apache Hive, and machine learning programs up to 100× faster than Hadoop. Unlike previous systems, Shark shows that it is possible to achieve these speedups while retaining a MapReduce-like execution engine, and the fine-grained fault tolerance properties that such engines provide. It extends such an engine in several ways, including column-oriented in-memory storage and dynamic mid-query replanning, to effectively execute SQL. The result is a system that matches the speedups reported for MPP analytic databases over MapReduce, while offering fault tolerance properties and complex analytics capabilities that they lack.

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BibTeX citation:

@techreport{Xin:EECS-2012-214,
    Author= {Xin, Reynold Shi and Rosen, Joshua and Zaharia, Matei and Franklin, Michael and Shenker, Scott and Stoica, Ion},
    Title= {Shark: SQL and Rich Analytics at Scale},
    Year= {2012},
    Month= {Nov},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-214.html},
    Number= {UCB/EECS-2012-214},
    Abstract= {Shark is a new data analysis system that marries query processing with complex analytics on large clusters. It leverages a novel distributed memory abstraction to provide a unified engine that can run SQL queries and sophisticated analytics functions (e.g., iterative machine learning) at scale, and efficiently recovers from failures mid-query. This allows Shark to run SQL queries up to 100× faster than Apache Hive, and machine learning programs up to 100× faster than Hadoop. Unlike previous systems, Shark shows that it is possible to achieve these speedups while retaining a MapReduce-like execution engine, and the fine-grained fault tolerance properties that such engines provide. It extends such an engine in several ways, including column-oriented in-memory storage and dynamic mid-query replanning, to effectively execute SQL. The result is a system that matches the speedups reported for MPP analytic databases over MapReduce, while offering fault tolerance properties and complex analytics capabilities that they lack.},
}

EndNote citation:

%0 Report
%A Xin, Reynold Shi 
%A Rosen, Joshua 
%A Zaharia, Matei 
%A Franklin, Michael 
%A Shenker, Scott 
%A Stoica, Ion 
%T Shark: SQL and Rich Analytics at Scale
%I EECS Department, University of California, Berkeley
%D 2012
%8 November 26
%@ UCB/EECS-2012-214
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-214.html
%F Xin:EECS-2012-214