Cray Graph Engine and Graph Analytics for Big Data

Cray Graph Engine: Uncover Valuable Insights

We live in a connected world, and a graph database is about data connections at its core. Viewing related data with a graph database, in addition to relational database tables, uncovers valuable insights that were previously hidden.

That’s why graph analytics is gaining traction in the world of big data. Cray customers are using graph analytics to solve real-world problems such as detecting cyberattacks, creating value from internet of things sensor data and precisely identifying drug interactions faster than ever before. Graph analytics and graph databases have become a powerhouse in detecting never-before-seen connections and emergent patterns.

Cray’s unique approach to graph analytics leverages high-performance hardware and decades of experience with big data and pattern recognition, combined with industry-standard software engineered for sophisticated graph analytics. The result is the Cray Graph Engine (CGE). CGE lets you analyze data using pattern matching and filtering, sophisticated graph algorithms and analysis, in a powerful, interactive system that scales to graphs with billions of edges.

Key benefits of the Cray Graph Engine

Scalable
Integrates with classical graph algorithms
Blazing-fast results
Industry-standard database structure and query language

Breakthrough Insight. Breakout Opportunities.

The structure and language of a database shape the type of queries you can run. The Cray Graph Engine is a semantic database using Resource Description Framework (RDF) triples to represent the data, SPARQL as the query language and extensions to support mathematical algorithms.

RDF — Stores each data item as a triple containing a subject-predicate-object. For example, “John is the parent of Susan”:

SPARQL — Query language built to access RDF data, syntactically similar to SQL
Extensions — New operators to equip our graph database system with classical graph algorithms

CGE’s approach of combining graph database structure and mathematical algorithms provide very comprehensive capabilities for ad-hoc, iterative and interactive analytics.

Speed. At Scale.

The Cray Graph Engine, running on our Urika-GX agile analytics platform and Cray XC series supercomputers with Urika-XC analytics, is often 1-2 orders of magnitude faster than the competition, running on a similarly-sized system.

Designed to leverage the Cray Aries™ interconnect for better I/O and a high degree of parallelism, the Cray Graph Engine delivers a powerful, interactive graph solution that scales to graphs with tens of billions of edges. And perhaps most important, CGE provides results in minutes — not hours or days.

Featured Resources

The New Wave of Graph Analytics

A Bloor Group white paper

How the Cray Graph Engine Manages Graph Databases

A look at how the Cray graph engine represents graphs and what makes it a robust solution for graph queries.

Infographic: Getting Started with Graph Analytics

Ready to consider graph analytics? Start with these three steps.

Cray Graph Analytics

What is graph, how is it used and what do you need? Cray graph expert Dr. James Maltby explains in three brief videos.

See All Resources

Graph Analysis Finds Relationships in Data

Yes, the data deluge is here — but even more significant is the explosion of highly related data. This information is generated in areas such as the internet of things (IoT), social ecosystems, and of course natural or biological systems.

To unleash the value in this related data, you need a different way of looking at it. Graph is this necessary different approach. A graph database does not replace relational databases, which are the foundation of analytics today. It’s an add-on. Because graph is designed to work with unstructured data, it doesn’t require joins or schemas. Adding it to a Hadoop® and Spark™ environment lets you run iterative and pattern queries that you couldn’t run previously.

Graph databases store data in nodes (vertices) and edges rather than tables, as in relational databases. They are the most efficient way of looking for relationships between data items, patterns of relationships or interactions between multiple data items.

Graph Database Nodes & Edges

This graph structure is very different from the table structure of a standard relational database. While traditional relational database shine at queries looking for information about some item, or sums or averages of many items of the same type of information, they struggle with queries that care more about how the data is connected than the data points themselves

There are several methods for graph databases. On the CGE overview page, we go into detail on the how and the why of our approach.

Hypergraphs: Can connect any number of nodes. Work well for domains with mostly many-to-many relationships.
Triples (semantic graph databases): Use a subject-predicate-object data structure based on W3C standards to represent every known fact, such as “George likes steak.” Works well for combining many varieties of data sources for analysis.
Property graphs: Any pair of nodes can be connected by an edge. In addition, any node or edge can have a table of additional data attached to it which works well when you have some data that doesn’t need to be linked to anything else. These graphs represent a compromise between semantic and relational databases to connect small amounts of table data to one point on a graph.

So when should you consider adding this cutting-edge approach to big data analytics? When your analytics needs meet at least two of the below circumstances, a graph database is the solution.

Problems and queries have become too complex for the current environment to process
You care as much about the data relationships and patterns as the data items
You’re encountering “unrunnable” queries: The queries will not complete or they take too much time
Current data models and schemas will not support your queries
You have lots of new and disparate data sources (IoT, web, natural) that are inherently unstructured

Graph Delivers for Cybersecurity, Precision Medicine and Much More

What kinds of applications can make good use of a graph database? Applications where it’s useful to find patterns of relationships between data items. This is not every database query — it’s no accident that relational databases are so popular — but there are many significant applications that can only be analyzed accurately and efficiently via a graph database:

In many intelligence and law enforcement applications, it’s important to look for a pattern of events. Phone calls, courier messages, travel, money transfers — any one of these events may look innocuous, but the view of all of them together and how they are directly or indirectly related to each other may be ominous.
Similarly, investment banks guarding against insider trading have to look for a suspicious pattern of actions not necessarily any single action. An investment banker gets insider information about a stock. She emails an information technologist at the same bank. The IT worker phones the banker. At close of business, the two badge out within seconds of each other. That night, the IT employee buys the stock.
The bioinformatics research community has largely adopted graph databases and the SPARQL query language. They are a natural fit to the huge network of relationships between all the chemicals present in the human body. One of Cray’s bioinformatics customers traced a chain of such relationships from a drug designed to combat AIDS out to a discovery that the same drug might be effective against breast cancer.
Many vendors of consumer products have become interested in social network analysis, which involves constructing a graph of relationships between people. For example, among Facebook users, graph searches might reveal which person is probably influential over his or her friends, which groups of friends share a common interest and so on — which could lead to some sophisticated targeted marketing strategies.