Clifford Engle

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2013-35
May 1, 2013

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-35.pdf

Shark is a research data analysis system built on a novel coarse-grained distributed shared-memory abstraction. Shark marries query processing with deep data analysis, providing a unified system for easy data manipulation using SQL and pushing sophisticated analysis closer to data. It scales to thousands of nodes in a fault-tolerant manner. Shark can answer queries 40X faster than Apache Hive and run machine learning programs 25X faster than MapReduce programs in Apache Hadoop on large datasets. This is a complete overview of the development of Shark, including design decisions, performance details, and comparison with existing data warehousing solutions. It demonstrates some of Shark's distinguishing features including its in-memory columnar caching and its unified machine learning interface.

Advisor: Michael Franklin

BibTeX citation:

@mastersthesis{Engle:EECS-2013-35,
    Author = {Engle, Clifford},
    Title = {Shark: Fast Data Analysis Using Coarse-grained Distributed Memory},
    School = {EECS Department, University of California, Berkeley},
    Year = {2013},
    Month = {May},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-35.html},
    Number = {UCB/EECS-2013-35},
    Abstract = {Shark is a research data analysis system built on a novel coarse-grained distributed shared-memory abstraction. Shark marries query processing with deep data analysis, providing a unified system for easy data manipulation using SQL and pushing sophisticated analysis closer to data. It scales to thousands of nodes in a fault-tolerant manner. Shark can answer queries 40X faster than Apache Hive and run machine learning programs 25X faster than MapReduce programs in Apache Hadoop on large datasets. This is a complete overview of the development of Shark, including design decisions, performance details, and comparison with existing data warehousing solutions. It demonstrates some of Shark's distinguishing features including its in-memory columnar caching and its unified machine learning interface.}
}

EndNote citation:

%0 Thesis
%A Engle, Clifford
%T Shark: Fast Data Analysis Using Coarse-grained Distributed Memory
%I EECS Department, University of California, Berkeley
%D 2013
%8 May 1
%@ UCB/EECS-2013-35
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-35.html
%F Engle:EECS-2013-35