Abstract
The Hadoop platform for Map-Reduce is extensively for Big Data batch analytics as well as interactive applications in e-commerce, telecom, media, retail, social networking, and other areas. However, to date no industry standard benchmarks exist to evaluate the true performance of a Hadoop cluster.
Current Hadoop benchmarks such as HiBench, Terasort, etc. in the open source domain fail to capture the real usages and performance of a Hadoop cluster in a datacenter. Given that typical Hadoop deployments process jobs under strict Service Level Agreement requirements, benchmarks are needed to evaluate the effects of concurrently running such diverse analytics jobs for performance comparison and cluster configuration.
In this paper, we present the methodology and the development of a customer usage representative Hadoop benchmark which includes a mix of job types, variety of data sizes, with inter-job arrival times as in a typical datacenter. We present the details of this benchmark and discuss application level, micro-architectural and cluster level performance characterization on an Intel Sandy Bridge Xeon Processor Hadoop cluster.
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Acknowledgements
Karthik Krishnan, now with Amazon Web Services at Amazon.com Inc. contributed to the significant development of this benchmark when he was at Intel. We would also like to thank our manager, Intel Fellow and Chief Server Architect of the Data Center Group, Dr. Faye Briggs for encouraging us to develop this benchmark for platform performance architectures projections.
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Saletore, V.A., Krishnan, K., Viswanathan, V., Tolentino, M.E. (2014). HcBench: Methodology, Development, and Full-System Characterization of a Customer Usage Representative Big Data/Hadoop Benchmark. In: Rabl, T., Raghunath, N., Poess, M., Bhandarkar, M., Jacobsen, HA., Baru, C. (eds) Advancing Big Data Benchmarks. WBDB WBDB 2013 2013. Lecture Notes in Computer Science(), vol 8585. Springer, Cham. https://doi.org/10.1007/978-3-319-10596-3_7
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DOI: https://doi.org/10.1007/978-3-319-10596-3_7
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