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.
Title
Shark: Fast Data Analysis Using Coarse-grained Distributed Memory
Published
2013-05-01
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2013-35
Type
Text
Extent
31 p
Archive
The Engineering Library
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