Current graph systems can easily process billions of data, however when increased to exceed hundred billions, the performance decreases dramatically, time series data always be very huge, consequently computation on time series graphs still remains challenging nowadays. In current piece of work, we introduces SharkGraph, a (distributed file system) DFS-based time series graph system, used a novel storage structure (Time Series Graph Data File) TGF, By reading file stream to iterate graph computation, SharkGraph is able to execute batch graph query, simulation, data mining, or clustering algorithm on exceed hundred billions edge size industry graph. Through well defined experiments that shows SharkGraph performs well on large-scale graph processing, also can support time traversal for graphs, and recover state at any position in the timeline. By repeating experiments reported for existing distributed systems like GraphX, we demonstrate that SharkGraph can easily handle hundreds billions of data, rather than GraphX which met many problems such as memory issues and skewed distribution on graph traversal. Compared with other graph systems SharkGraph uses less memory and more efficiently to process the same graph.