MapReduce is a popular, but still insufficiently understood paradigm for large-scale, distributed, data-intensive computation. The variety of MapReduce applications and deployment environments makes it difficult to model MapReduce performance and generalize design improvements. In this paper, we present a methodology to understand performance tradeoffs for MapReduce workloads. Using production workload traces from Facebook and Yahoo, we develop an empirical workload model and use it to generate and replay synthetic workloads. We demonstrate how to use this methodology to answer "what-if" questions pertaining to system size, data intensity and hardware/software configuration.
Title
A Methodology for Understanding MapReduce Performance Under Diverse Workloads
Published
2010-11-09
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2010-135
Type
Text
Extent
16 p
Archive
The Engineering Library
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