SciHadoop: Array-based Query Processing in Hadoop

TitleSciHadoop: Array-based Query Processing in Hadoop
Publication TypeConference Paper
Year of Publication2011
AuthorsBuck, J, Watkins, N, LeFevre, J, Ioannidou, K, Maltzahn, C, Polyzotis, N, Brandt, SA
Refereed DesignationRefereed
Conference NameSuper Computing 2011
Date Published11/2011
PublisherACM
Conference LocationSeattle, WA
Abstract

Hadoop has become the de facto platform for large-scale data analysis in
commercial applications, and increasingly so in scientific applications.
However, Hadoop's byte stream data model causes inefficiencies when used to
process scientific data that is commonly stored in highly-structured,
array-based binary file formats resulting in limited scalability of Hadoop
applications in science. We introduce SciHadoop, a Hadoop plugin allowing
scientists to specify logical queries over array-based data models. SciHadoop
executes queries as map/reduce programs defined over the logical data model. We
describe the implementation of a SciHadoop prototype for NetCDF data sets and
quantify the performance of five separate optimizations that address the
following goals for several representative aggregate queries: reduce total data
transfers, reduce remote reads, and reduce unnecessary reads. Two optimizations
allow holistic aggregate queries to be evaluated opportunistically during the
map phase; two additional optimizations intelligently partition input data to
increase read locality, and one optimization avoids block scans by examining the
data dependencies of an executing query to prune input partitions.
Experiments involving a holistic function show run-time improvements of up to
8x, with drastic reductions of IO, both locally and over the network.

URLhttp://www.cs.ucsc.edu/~carlosm/Papers/buck-sc11.pdf
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