Path-based timing analysis (PBA) is a pivotal step to achieve accurate timing signoff. A core primitive extracts a large set of paths subject to path-specific or less-pessimistic timing update. However, this process in nature demands a very high computational complexity and thus has been a major bottleneck in accelerating timing closure. Therefore, we introduce in this paper a fast and scalable PBA framework with MapReduce - a recent programming paradigm invented by Google for big-data processing. Inspired by the spirit of MapReduce, we formulate our problem into tasks that are associated with keys and values and perform massively-parallel map and reduce operations on a distributed system. Experimental results demonstrated that our approach can easily analyze million nodes in a single minute.