System G Distributed Graph Database

TL;DR

System G is a novel distributed graph database optimized for fast data insertions and large-volume concurrent queries, designed to efficiently store and process interconnected data on modern distributed architectures.

Contribution

The paper introduces System G, a distributed graph database with a novel architecture supporting efficient storage, fast insertions, and high concurrency on modern computing platforms.

Findings

Efficient data storage and query processing demonstrated on state-of-the-art platforms.

Optimized for fast insertions and large-volume concurrent queries.

Scalable architecture combining single node instances into a distributed system.

Abstract

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.