  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.
