In this paper we prove the efficacy of a simple greedy algorithm for a finite horizon online resource allocation/matching problem, when the corresponding static planning linear program (SPP) exhibits a non-degeneracy condition called the general position gap (GPG). The key intuition that we formalize is that the solution of the reward maximizing SPP is the same as a feasibility Linear Program restricted to the optimal basic activities, and under GPG this solution can be tracked with bounded regret by a greedy algorithm, i.e., without the commonly used technique of periodically resolving the SPP. The goal of the decision maker is to combine resources (from a finite set of resource types) into configurations (from a finite set of feasible configurations) where each configuration is specified by the number of resources consumed of each type and a reward. The resources are further subdivided into three types - offline (whose quantity is known and available at time 0), online-queueable (which arrive online and can be stored in a buffer), and online-nonqueueable (which arrive online and must be matched on arrival or lost). Under GRG we prove that, (i) our greedy algorithm gets bounded any-time regret of $\mathcal{O}(1/ε_0)$ for matching reward ($ε_0$ is a measure of the GPG) when no configuration contains both an online-queueable and an online-nonqueueable resource, and (ii) $\mathcal{O}(\log t)$ expected any-time regret otherwise (we also prove a matching lower bound). By considering the three types of resources, our matching framework encompasses several well-studied problems such as dynamic multi-sided matching, network revenue management, online stochastic packing, and multiclass queueing systems.