: The easiest way to get started is by installing the Kuzu Python client, which will be familiar to most data scientists and developers. Simply run:
Unlike traditional graph databases that rely on client-server architectures, Kùzu is purpose-built to execute fast queries on single-node, multi-core machines. It targets data engineering pipelines, GenAI systems, and local analytical applications.
High-efficiency data retrieval tailored for analytical queries. kuzu v0 136 full
# Search for a keyword search_res = conn.execute(""" MATCH (p:Person) WHERE p.bio MATCH_TEXT 'graph' RETURN p.name, p.city; """).fetchall() print(search_res)
: The term "full" could imply completeness, suggesting that this version is considered comprehensive or complete in its current form. However, in software development, "full" might not always mean the software is feature-complete but rather that it's a significant milestone. : The easiest way to get started is
If you'd like, I can also show you how to perform against other graph databases to see the performance difference.
Let’s break down the technical specifications of this release: If you'd like, I can also show you
To justify upgrading to the , consider these community-sourced benchmarks (tested on AWS c5.4xlarge, 100GB synthetic social graph):
Use Kuzu as a feature store to feed graph neural networks (GNNs) in PyTorch Geometric or DGL.
You can get started with the latest version through its official Python API.