A Brief Study of Open Source Graph Databases

TL;DR

This paper compares various open source graph database platforms by implementing real-world algorithms on large synthetic graphs, highlighting their capabilities, interfaces, and performance differences.

Contribution

It provides a comparative analysis of open source graph databases, focusing on their ability to handle large-scale data and support complex graph algorithms.

Findings

STINGER shows high performance on large graphs

Different platforms vary significantly in ease of use and scalability

Some platforms excel in real-time updates and complex analysis

Abstract

With the proliferation of large irregular sparse relational datasets, new storage and analysis platforms have arisen to fill gaps in performance and capability left by conventional approaches built on traditional database technologies and query languages. Many of these platforms apply graph structures and analysis techniques to enable users to ingest, update, query and compute on the topological structure of these relationships represented as set(s) of edges between set(s) of vertices. To store and process Facebook-scale datasets, they must be able to support data sources with billions of edges, update rates of millions of updates per second, and complex analysis kernels. These platforms must provide intuitive interfaces that enable graph experts and novice programmers to write implementations of common graph algorithms. In this paper, we explore a variety of graph analysis and storage platforms. We compare their capabil- ities, interfaces, and performance by implementing and computing a set of real-world graph algorithms on synthetic graphs with up to 256 million edges. In the spirit of full disclosure, several authors are affiliated with the development of STINGER.