  There is no shortage of outlier detection (OD) algorithms in the literature,
yet a vast body of them are designed for a single machine. With the increasing
reality of already cloud-resident datasets comes the need for distributed OD
techniques. This area, however, is not only understudied but also short of
public-domain implementations for practical use. This paper aims to fill this
gap: We design Sparx, a data-parallel OD algorithm suitable for shared-nothing
infrastructures, which we specifically implement in Apache Spark. Through
extensive experiments on three real-world datasets, with several billions of
points and millions of features, we show that existing open-source solutions
fail to scale up; either by large number of points or high dimensionality,
whereas Sparx yields scalable and effective performance. To facilitate
practical use of OD on modern-scale datasets, we open-source Sparx under the
Apache license at https://tinyurl.com/sparx2022.
