Big-data analytics platforms, such as Hadoop, are appealing for scientific computation because they are ubiquitous, well-supported, and well-understood. Unfortunately, load-balancing is a common challenge of implementing large-scale scientific computing applications on these platforms. In this paper we present the design and implementation of Libra, a Hadoop-based tool for comparative metagenomics (comparing samples of genetic material collected from the environment). We describe the computation that Libra performs and how that computation is implemented using Hadoop tasks, including the techniques used by Libra to ensure that the task workloads are balanced despite nonuniform sample sizes and skewed distributions of genetic material in the samples. On a 10-machine Hadoop cluster Libra can analyze the entire Tara Ocean Viromes of ~4.2 billion reads in fewer than 20 hours.