By Tilmann Rabl, Nambiar Raghunath, Meikel Poess, Milind Bhandarkar, Hans-Arno Jacobsen, Chaitanya Baru (eds.)
This publication constitutes the completely refereed joint lawsuits of the 3rd and Fourth Workshop on huge facts Benchmarking. The 3rd WBDB used to be held in Xi'an, China, in July 2013 and the Fourth WBDB was once held in San José, CA, united states, in October, 2013. The 15 papers provided during this publication have been conscientiously reviewed and chosen from 33 displays. They concentrate on significant info benchmarks; purposes and situations; instruments, structures and surveys.
Read Online or Download Advancing Big Data Benchmarks: Proceedings of the 2013 Workshop Series on Big Data Benchmarking, WBDB.cn, Xi'an, China, July16-17, 2013 and WBDB.us, San José, CA, USA, October 9-10, 2013, Revised Selected Papers PDF
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Extra resources for Advancing Big Data Benchmarks: Proceedings of the 2013 Workshop Series on Big Data Benchmarking, WBDB.cn, Xi'an, China, July16-17, 2013 and WBDB.us, San José, CA, USA, October 9-10, 2013, Revised Selected Papers
An analysis of BGs implementation of the Zipﬁan distribution. USC DBLAB technical report 2013-02 (2013). pdf A Micro-benchmark Suite for Evaluating Hadoop RPC on High-Performance Networks Xiaoyi Lu(B) , Md. Wasi-ur-Rahman, Nusrat Sharmin Islam, and Dhabaleswar K. edu Abstract. g. ) because of its simplicity, productivity, and high performance. For RPC systems, achieving low latency and high throughput is critical. However, a standardized benchmark suite that focuses on helping users evaluate the performance of standalone Hadoop RPC is lacking in current Apache Hadoop distribution.
For the second set of experiments, we use our benchmarks to compare the throughput in diﬀerent network interconnects. For these experiments, we use BytesWritable for RPC communication. In Fig. 3(a), we show throughput comparison with seven handlers in the RPC server. We run seven clients for this experiment and each client runs on a separate node. For Fig. 3(b), we increase the handler count in server to 16 for the same number of clients. Here also 38 X. Lu et al. Fig. 1. RPC latency for BytesWritable Fig.
1007/978-3-319-10596-3 3 Hadoop RPC Benchmarks 33 getting block info and creating data blocks. Further, it is used as the core communication scheme for HBase Put/Get database operations. Building up Hadoop RPC protocol is very simple; users just need to deﬁne the interfaces and remote procedures. Hadoop RPC internally takes care of the serialization/deserialization and the proper invocation of remote procedures. Interface arguments and return types can be primitives such as String, Double, or Array, etc.