In fact, as many as 50% of Gartner client inquiries around the topic of AI involve a discussion around the use of graph technology. Although graph technologies are not new to data and analytics, there has been a shift in the thinking around them as organizations identify an increasing number of use cases. Graph forms the foundation of modern data and analytics with capabilities to enhance and improve user collaboration, machine learning models and explainable AI.
While offering a NetworkX-like API, the cuGraph library is a collection of GPU accelerated graph algorithms based on GPU DataFrames.Īs Gartner’s analysis on the “Top 10 Data and Analytics Trends” pointed out for many consecutive years, the graph field is of high relevance. With the latest release of the adapter it is also possible to export ArangoDB graphs into the RAPIDS cuGraph graph format. Initially, the ArangoDB-Networkx-cuGraph Adapter was designed to export Graphs from ArangoDB into NetworkX, a Python package for graph analysis. All supported data models & access patterns can be combined in queries allowing for maximal flexibility. To name an example, cuGraph’s Pagerank algorithm on a large real world dataset is 491x faster than NetworkX.īrief Introduction to the ArangoDB-NetworkX-cuGraph Adapter ArangoDB is a scalable open-source multi-model database natively supporting graph, document and search. This results in huge performance speedups for graph analytics compared to NetworkX. Any graph stored in ArangoDB can easily be exported, converted into the cuGraph format and is ready to perform any graph analytics algorithm of cuGraph.
Outline In this blog, we demonstrate how RAPIDS GPU-based cuGraph library can be applied on graphs stored in the database ArangoDB.
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