Clustering is a fundamental task in Knowledge Discovery and Data mining. It aims to discover the unknown nature of data by grouping together data objects that are more similar. While hundreds of clustering algorithms have been proposed, many are complex and do not scale well as more data become available, making then inadequate to analyze very large datasets. In addition, many clustering algorithms are sequential, thus inherently difficult to parallelize. We propose PatchWork, a novel clustering algorithm to address those issues. PatchWork is a distributed density clustering algorithm with linear computational complexity and linear horizontal scalability. It presents several desirable characteristics in knowledge discovery, in particular, it does not require a priori the number of clusters to identify, and offers a natural protection against outliers and noise. In addition, PatchWork makes it possible to discover spatially large clusters instead of dense clusters only. PatchWork relies on the map/reduce paradigm to parallelize computations and was implemented using Apache Spark, the distributed computation framework. As a result, PatchWork can cluster a billion points in a few minutes only, a 40x improvement over the distributed implementation of k-means in Spark MLLib.