Kùzu: Graph Learning Applications Need a Modern Graph DBMS

ziyi Chen, Xiyang Feng, Guodong Jin, Chang Liu, Semih Salihoglu

Keywords: graph database, graph database management system, systems for graph learning

TL;DR: We present Kùzu, a new graph database management system that is geared towards graph learning applications.

Abstract: Building a graph learning application requires performing a series of data processing steps, such as extracting data from tabular sources into a graph, cleaning the graph, extracting node/edge features, moving the data into a graph learning library to generate embeddings, and possibly saving these embeddings in a software for further processing. Many of these steps can be performed in an efficient way by database management systems (DBMSs), which come with high-level data models and query languages, and functionalities to export datasets into other formats. However, no current DBMS is tailored for graph learning pipelines. We present Kùzu, an open-sourced graph DBMS that aims to fill this gap. Kùzu is an embeddable system that runs as part of users' applications, implements the property graph data model and the openCypher query language, a graph-optimized storage structures, and join algorithms. Kùzu can ingest data from several tabular raw file formats and export data to popular graph learning libraries. We present Kùzu's design goals, architecture, our ongoing work, and demonstrate how it can be used to train large GNN models that do not fit into main memory. Kùzu is available under a permissive license.

Submission Type: Extended abstract (max 4 main pages).

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Submission Number: 67