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ii Vector Spatial data types such as lines, polygons or regions etc usually comprises of hundreds of latitude-longitude pairs to accurately represent the geometry of spatial features such as towns, rivers or villages. This leads to spatial data operations being computationally and memory inten-sive. Also, there exist certain real world scenarios which generates an extremely large amount of spatial data. For example, NASA’s Earth Observing System (EOS), for instance, generates 1 terabyte of data every day. A solution to deal with this is to distribute the spatial operations amongst multiple computational nodes. Parallel spatial databases attempt to do this but at very small scales (of the order of few 10s of nodes at most). Another approach would be to use dis-tributed approaches such as Map-Reduce since spatial data is cleanly distributable by exploiting their spatial locality. It affords us the advantage of being able to harness commodity hardware operating in a shared nothing mode while at the same time lending robustness to the computation since parts of the computation can be restarted on failure. But at the same time, MapReduce is a completely batch processing paradigm and score extremely poor on performance. This is due t