  Motivated by the need to extract knowledge and value from interconnected
data, graph analytics on big data is a very active area of research in both
industry and academia. To support graph analytics efficiently a large number of
in memory graph libraries, graph processing systems and graph databases have
emerged. Projects in each of these categories focus on particular aspects such
as static versus dynamic graphs, off line versus on line processing, small
versus large graphs, etc. While there has been much advance in graph processing
in the past decades, there is still a need for a fast graph processing, using a
cluster of machines with distributed storage. In this paper, we discuss a novel
distributed graph database called System G designed for efficient graph data
storage and processing on modern computing architectures. In particular we
describe a single node graph database and a runtime and communication layer
that allows us to compose a distributed graph database from multiple single
node instances. From various industry requirements, we find that fast
insertions and large volume concurrent queries are critical parts of the graph
databases and we optimize our database for such features. We experimentally
show the efficiency of System G for storing data and processing graph queries
on state-of-the-art platforms.
