Traditional whole-program static analysis (e.g., a points-to analysis that models the heap) encounters scalability problems for realistic applications. We propose a "featherweight'' analysis that combines a dynamic snapshot of the heap with otherwise full static analysis of program behavior. The analysis is extremely scalable, offering speedups of well over 3x, with complexity empirically evaluated to grow linearly relative to the number of reachable methods. The analysis is also an excellent tradeoff of precision and recall (relative to different dynamic executions): while it can never fully capture all program behaviors (i.e., it cannot match the near-perfect recall of a full static analysis) it often approaches it closely while achieving much higher (3.5x) precision.