Optimization Strategies for A/B Testing on HADOOP

In this work, we present a set of techniques that considerably improve the performance of executing concurrent MapRe- duce jobs. Our proposed solution relies on proper resource allocation for concurrent Hive jobs based on data depen- dency, 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.\ud We perform an experimental study that demonstrates 233% reduction in execution time for concurrent vs sequential ex- ecution schema. We report up to 40% extra reduction in execution time for concurrent job execution after resource usage optimization.\ud The results reported in this paper were obtained in a pi- lot 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 clus- ter which uses capacity scheduler. Our analytics jobs were implemented using Apache Hive