Relational subgraph analysis, e.g. finding labeled subgraphs in a network, which are isomorphic to a template, is a key problem in many graph related applications. It is computationally challenging for large networks and complex templates. In this paper, the authors develop SAHAD, an algorithm for relational subgraph analysis using Hadoop, in which the subgraph is in the form of a tree. SAHAD is able to solve a variety of problems closely related with subgraph isomorphism, including counting labeled/unlabeled subgraphs, finding supervised motifs, and computing graphlet frequency distribution.