RadixGraph: A Fast, Space-Optimized Data Structure for Dynamic Graph Storage (Extended Version)

TL;DR

RadixGraph is a novel, space-efficient data structure that enables fast, concurrent updates for dynamic graphs, significantly outperforming existing solutions in speed and memory usage.

Contribution

We introduce RadixGraph, a radix-tree-based structure with a hybrid snapshot-log architecture that achieves optimal trade-offs in query efficiency, space, and update speed for dynamic graph storage.

Findings

RadixGraph outperforms baseline methods by up to 16.27 times in update ingestion.

It reduces memory usage by an average of 40.1%.

Supports millions of concurrent updates per second.

Abstract

Dynamic graphs model many real-world applications, and as their sizes grow, efficiently storing and updating them becomes critical. We present RadixGraph, a fast and memory-efficient data structure for dynamic graph storage. RadixGraph features a carefully designed radix-tree-based vertex index that strikes an optimal trade-off between query efficiency and space among all pointer-array-based radix trees. For edge storage, it employs a hybrid snapshot-log architecture that enables amortized $O(1)$ update time. RadixGraph supports millions of concurrent updates per second while maintaining competitive performance for graph analytics. Experimental results show that RadixGraph outperforms the most performant baseline by up to $16.27\times$ across various datasets in ingesting graph updates, and reduces memory usage by an average of $40.1\%$. RadixGraph is open-source at https://github.com/ForwardStar/RadixGraph.