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2010

The Performance of MapReduce: An In-depth Study

9 years 2 months ago
The Performance of MapReduce: An In-depth Study
MapReduce has been widely used for large-scale data analysis in the Cloud. The system is well recognized for its elastic scalability and fine-grained fault tolerance although its performance has been noted to be suboptimal in the database context. According to a recent study [19], Hadoop, an open source implementation of MapReduce, is slower than two state-of-the-art parallel database systems in performing a variety of analytical tasks by a factor of 3.1 to 6.5. MapReduce can achieve better performance with the allocation of more compute nodes from the cloud to speed up computation; however, this approach of “renting more nodes” is not cost effective in a pay-as-you-go environment. Users desire an economical elastically scalable data processing system, and therefore, are interested in whether MapReduce can offer both elastic scalability and efficiency. In this paper, we conduct a performance study of MapReduce (Hadoop) on a 100-node cluster of Amazon EC2 with various levels of p...
Dawei Jiang, Beng Chin Ooi, Lei Shi, Sai Wu
Added 30 Jan 2011
Updated 30 Jan 2011
Type Journal
Year 2010
Where PVLDB
Authors Dawei Jiang, Beng Chin Ooi, Lei Shi, Sai Wu
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