In this paper, we introduce GraphLake, a purpose-built graph compute engine for Lakehouse. GraphLake is built on top of the commercial graph database TigerGraph. It maps Lakehouse tables to vertex and edge types in a labeled property graph and supports graph analytics over Lakehouse tables using GSQL. To minimize startup time, it loads only the graph topology. Furthermore, it introduces a series of techniques to ensure query efficiency over Lakehouse tables, including a graph-aware caching mechanism and two Lakehouse-optimized parallel primitives. Extensive experiments demonstrate that GraphLake significantly outperforms PuppyGraph, the current state-of-the-art graph compute engine for Lakehouse, by achieving both lower startup and query time.
Cite
@article{arxiv.2603.03705,
title = {GraphLake: A Purpose-Built Graph Compute Engine for Lakehouse},
author = {Shige Liu and Songting Chen and Chengjie Qin and Mingxi Wu and Jianguo Wang},
journal= {arXiv preprint arXiv:2603.03705},
year = {2026}
}