G-Tran: Making Distributed Graph Transactions Fast

TL;DR

G-Tran is a high-performance distributed graph database leveraging RDMA and a decentralized architecture to efficiently handle complex graph transactions with high concurrency and data locality.

Contribution

The paper introduces G-Tran, a novel RDMA-enabled distributed graph database with a graph-native data store and optimized MV-OCC for fast, scalable graph transaction processing.

Findings

G-Tran outperforms existing graph databases on benchmark workloads.

The graph-native data store improves data locality and access speed.

Optimized MV-OCC reduces abort rates for large read/write sets.

Abstract

Graph transaction processing raises many unique challenges such as random data access due to the irregularity of graph structures, low throughput and high abort rate due to the relatively large read/write sets in graph transactions. To address these challenges, we present G-Tran -- an RDMA-enabled distributed in-memory graph database with serializable and snapshot isolation support. First, we propose a graph-native data store to achieve good data locality and fast data access for transactional updates and queries. Second, G-Tran adopts a fully decentralized architecture that leverages RDMA to process distributed transactions with the MPP model, which can achieve high performance by utilizing all computing resources. In addition, we propose a new MV-OCC implementation with two optimizations to address the issue of large read/write sets in graph transactions. Extensive experiments show that G-Tran achieves competitive performance compared with other popular graph databases on benchmark workloads.