Optimization strategies for A/B testing on HADOOP

Abstract

In this work, we present a set of techniques that considerably improve the performance of executing concurrent MapReduce jobs. Our proposed solution relies on proper resource allocation for concurrent Hive jobs based on data dependency, inter-query optimization and modeling of Hadoop cluster load. To the best of our knowledge, this is the first work towards Hive/MapReduce job optimization which takes Hadoop cluster load into consideration.We perform an experimental study that demonstrates 233% reduction in execution time for concurrent vs sequential execution schema. We report up to 40% extra reduction in execution time for concurrent job execution after resource usage optimization.The results reported in this paper were obtained in a pilot project to assess the feasibility of migrating A/B testing from Teradata + SAS analytics infrastructure to Hadoop. This work was performed on eBay production Hadoop cluster.

Research on Decision‐Making of Complex Venture Capital Based on Financial Big Data Platform

“…Conclusion: Solr's search performance far exceeds that of traditional relational databases, and Solr has proven to be highly efficient. Literature [10] proposed a series of methods to optimize concurrent MapReduce. Optimize Hadoop group load modeling based on datadependent dependencies and allocate appropriate resources for concurrent tasks.…”