Graph processing is data driven, and general purpose graph computation requires a high degree of random data access. Despite great progress in disk technology, it still cannot provide the level of random access that is required by graph computation. On the other hand, memory-based approaches usually do not scale well due to the capacity limit of a single machine. In this paper, we introduce Trinit