LiveGraph: A Transactional Graph Storage System with Purely Sequential Adjacency List Scans

TL;DR

LiveGraph is a graph storage system that enables efficient transactional updates and real-time analytics by ensuring adjacency list scans are purely sequential, improving performance on both workload types.

Contribution

LiveGraph introduces a novel graph-aware data structure and concurrency control mechanism that optimize adjacency list scans for transactional and analytical workloads.

Findings

Outperforms state-of-the-art graph databases in transactional workloads

Achieves significant speedups in real-time graph analytics

Maintains data consistency during concurrent transactions

Abstract

The specific characteristics of graph workloads make it hard to design a one-size-fits-all graph storage system. Systems that support transactional updates use data structures with poor data locality, which limits the efficiency of analytical workloads or even simple edge scans. Other systems run graph analytics workloads efficiently, but cannot properly support transactions. This paper presents LiveGraph, a graph storage system that outperforms both the best graph transactional systems and the best systems for real-time graph analytics on fresh data. LiveGraph does that by ensuring that adjacency list scans, a key operation in graph workloads, are purely sequential: they never require random accesses even in presence of concurrent transactions. This is achieved by combining a novel graph-aware data structure, the Transactional Edge Log (TEL), together with a concurrency control mechanism that leverages TEL's data layout. Our evaluation shows that LiveGraph significantly outperforms state-of-the-art (graph) database solutions on both transactional and real-time analytical workloads.