RapidStore: An Efficient Dynamic Graph Storage System for Concurrent Queries

TL;DR

RapidStore is a novel in-memory dynamic graph storage system that efficiently handles concurrent read and write operations by decoupling data management, significantly improving performance for real-time, evolving graph data applications.

Contribution

It introduces a decoupled system design and a new concurrency control mechanism tailored for dynamic graph storage, addressing inefficiencies of existing methods.

Findings

Enables fast, scalable concurrent graph queries.

Balances performance of inserts, searches, and scans.

Significantly improves efficiency in dynamic graph storage.

Abstract

Dynamic graph storage systems are essential for real-time applications such as social networks and recommendation, where graph data continuously evolves. However, they face significant challenges in efficiently handling concurrent read and write operations. We find that existing methods suffer from write queries interfering with read efficiency, substantial time and space overhead due to per-edge versioning, and an inability to balance performance, such as slow searches under concurrent workloads. To address these issues, we propose RapidStore, a holistic approach for efficient in-memory dynamic graph storage designed for read-intensive workloads. Our key idea is to exploit the characteristics of graph queries through a decoupled system design that separates the management of read and write queries and decouples version data from graph data. Particularly, we design an efficient dynamic graph store to cooperate with the graph concurrency control mechanism. Experimental results demonstrate that RapidStore enables fast and scalable concurrent graph queries, effectively balancing the performance of inserts, searches, and scans, and significantly improving efficiency in dynamic graph storage systems.