Kùzu: A simple, extremely fast, and embeddable graph database
Nov 2, 2023
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Guest Semih Salihoglu, co-creator of Kuzu, discusses the concept of a property graph, differences between property graphs and RDF in graph databases, the need for switching databases, the design and storage techniques of Kuzu, integration with other programming languages, advantages of DuckDB, and compatibility and streaming in real time.
Kuzu is a graph database management system designed for query speed and scalability, challenging the need for distributed solutions.
Kuzu's ability to handle complex recursive queries and find patterns in large-scale graphs makes it applicable in fraud detection, recommendations, and risk analysis.
Kuzu prioritizes query speed, scalability, and ease of use, catering to analytics and ad hoc querying while aiming to simplify graph data integration.
Deep dives
Kuzu: A Graph Database for Query Speed and Scalability
Kuzu is a graph database management system built for query speed and scalability. It is designed to handle property graphs, which are a data model based on Neo4j's data model. Property graphs allow for object-oriented modeling with sets of records as nodes and relationships. Kuzu aims to make large-scale graph management accessible on a single machine, challenging the assumption that graph databases require distributed solutions. With its focus on performance and scalability, Kuzu implements advanced techniques such as factorization and hash join algorithms. While Kuzu currently integrates with Python and offers APIs for Java, Rust, C, and Node.js, it is actively working on expanding its connectors and integrations. The system is open source and encourages contributions from the community.
Use Cases of Kuzu: Analyzing Fund Data, AWS Permission Analysis, Social Media Analysis
Kuzu has found use in various domains, including analyzing fund data in Canadian banks, performing AWS permission analysis, and conducting social media analysis. The system's ability to handle complex recursive queries and find patterns in large-scale graphs makes it applicable in fraud detection, recommendations, and risk analysis. While the community is still growing, Kuzu aims to provide users with the features needed to streamline graph-based analytics and empower data scientists and analysts to explore and derive insights from graph data.
Distinguishing Kuzu from Other Graph Databases
Kuzu sets itself apart from other graph databases by prioritizing query speed, scalability, and ease of use. It draws inspiration from DuckDB, an embeddable analytical database, to provide a lightweight and efficient graph database solution. Unlike some graph databases that focus on real-time streaming or complex machine learning applications, Kuzu caters to the needs of analytics and ad hoc querying. While Kuzu currently targets users who already possess graph data, it aims to simplify the graph data integration process and expand its connectors to accommodate various data sources.
Challenges and Opportunities in the Graph Database Field
The graph database field still faces challenges in bridging the gap between academic advancements and industry adoption. Graph machine learning, for example, has gained popularity in academia, but its application in industry is limited to a few sophisticated tech companies. The industry needs more accessible tools and frameworks for creating knowledge graphs from diverse data sources. Despite these challenges, Kuzu is poised to make a contribution by demonstrating the efficiency and scalability of graph-based analytics and by streamlining the management of large-scale graphs on a single machine.
Growing the Kuzu Community and Future Directions
The Kuzu community is primarily composed of contributors and core developers from the University of Waterloo. However, Kuzu welcomes contributions from the wider community and is working on expanding its user base and developer relations. Future plans include integrating with popular graph visualization libraries such as NetworkX, CyDusCape, and Pyvis, as well as exploring use cases in the field of retrieval-augmented generation (RAG) using knowledge graphs. Kuzu aims to demonstrate the value and ease of working with graphs, making them accessible to a wider range of users and domains.
Semih Salihoglu is an Associate Professor at University of Waterloo, and co-creator of Kuzu an open source embeddable property graph database management system.
