Piranha: Optimizing Short Jobs in Hadoop

Cluster computing has emerged as a key parallel processing platform for large scale data. All major internet companies use it as their major central processing platform. One of cluster computing's most popular examples is MapReduce and its open source implementation Hadoop. These systems were originally designed for batch and massive-scale computations. Interestingly, over time their production workloads have evolved into a mix of a small fraction of large and long-running jobs and a much bigger fraction of short jobs. This came about because these systems end up being used as data warehouses, which store most of the data sets and attract ad hoc, short, data-mining queries. Moreover, the availability of higher level query languages that operate on top of these cluster systems proliferated these ad hoc queries. Since existing systems were not designed for short, latency-sensistive jobs, short interactive jobs suffer from poor response times. In this paper, we present Piranha-a system for optimizing short jobs on Hadoop without affecting the larger jobs. It runs on existing unmodified Hadoop clusters facilitating its adoption. Piranha exploits characteristics of short jobs learned from production workloads at Yahoo!(1) clusters to reduce the latency of such jobs. To demonstrate Piranha's effectiveness, we evaluated its performance using three realistic short queries. Piranha was able to reduce the queries' response times by up to 71%.