A Java parallel streams implementation of the (K)-nearest neighbor descent algorithm is presented using a natural statistical termination criterion. Input data consist of a set (S) of (n) objects of type V, and a Function<V, Comparator>, which enables any \(x \in S\) to decide which of \(y, z \in S\setminus\\{x\\}\) is more similar to \(x\). Experiments with the Kullback-Leibler divergence Comparator support the prediction that the number of rounds of \(K\)-nearest neighbor updates need not exceed twice the diameter of the undirected version of a random regular out-degree \(K\) digraph on \(n\) vertices. Overall complexity was \(O(n K^2 log_K(n))\) in the class of examples studied. When objects are sampled uniformly from a \(d\)-dimensional simplex, accuracy of the \(K\)-nearest neighbor approximation is high up to \(d = 20\), but declines in higher dimensions, as theory would predict.