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Summary: The Case for Being Lazy:
How to Leverage Lazy Evaluation in MapReduce
Kristi Morton, Magdalena Balazinska,
and Dan Grossman
Department of Computer Science &
Engineering, University of Washington
kmorton,magda,djg@cs.washington.edu
Christopher Olston
Yahoo! Research
olston@yahoo-inc.com
ABSTRACT
In this paper, we study the benefits and overheads of lazy
MapReduce processing, where the input data is partitioned
and only the smallest subset of these partitions are processed
to meet a user's need at any time. We also develop guidelines
for successfully applying the lazy MapReduce computation
technique to reduce processing times of analysis tasks.
Categories and Subject Descriptors
H.2.4 [Database Management]: Systems--Parallel databases,
Query processing
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