Ringo: Interactive Graph Analytics on Big-Memory Machines

TL;DR

Ringo is an interactive graph analytics system optimized for big-memory machines, enabling efficient analysis, manipulation, and exploration of large graphs with over 200 functions, facilitating rapid data mining workflows.

Contribution

Ringo introduces a high-performance, easy-to-use graph analytics system leveraging large-memory machines, integrating data manipulation and extensive analytics functions for improved graph analysis.

Findings

Single big-memory machine offers excellent performance for large graph analytics.

Ringo simplifies graph construction from relational data tables.

Supports rapid, iterative data exploration workflows.

Abstract

We present Ringo, a system for analysis of large graphs. Graphs provide a way to represent and analyze systems of interacting objects (people, proteins, webpages) with edges between the objects denoting interactions (friendships, physical interactions, links). Mining graphs provides valuable insights about individual objects as well as the relationships among them. In building Ringo, we take advantage of the fact that machines with large memory and many cores are widely available and also relatively affordable. This allows us to build an easy-to-use interactive high-performance graph analytics system. Graphs also need to be built from input data, which often resides in the form of relational tables. Thus, Ringo provides rich functionality for manipulating raw input data tables into various kinds of graphs. Furthermore, Ringo also provides over 200 graph analytics functions that can then be applied to constructed graphs. We show that a single big-memory machine provides a very attractive platform for performing analytics on all but the largest graphs as it offers excellent performance and ease of use as compared to alternative approaches. With Ringo, we also demonstrate how to integrate graph analytics with an iterative process of trial-and-error data exploration and rapid experimentation, common in data mining workloads.