Kuzu V0 136 🌟

The v0.3.6 release focuses on refining the user experience while hardening the underlying infrastructure. Key areas of focus include: Enhanced Query Performance

Once installed, a simple database can be initialized with a few lines of code:

Kuzu’s ability to handle structured properties alongside complex topological relationships makes it ideal for hybrid search scenarios. Developers can filter by attributes (e.g., date, category) while simultaneously traversing graph edges. Technical Specifications Storage Engine kuzu v0 136

Kuzu v0.3.6 represents a significant milestone in the evolution of embeddable graph database management systems. Designed specifically for query speed and ease of use, this version introduces critical updates to the storage engine, query processor, and integration ecosystem. Introduction to Kuzu

Are you planning to use for a GraphRAG project or for general data analytics ? The v0

The Python client received updates to better handle large result sets using Arrow-based data transfers.

import kuzu db = kuzu.Database('./my_graph_db') conn = kuzu.Connection(db) # Create a schema conn.execute("CREATE NODE TABLE User(name STRING, age INT64, PRIMARY KEY (name))") conn.execute("CREATE REL TABLE Follows(FROM User TO User)") # Ingest data conn.execute("CREATE (:User {name: 'Alice', age: 30})") conn.execute("CREATE (:User {name: 'Bob', age: 25})") conn.execute("MATCH (a:User), (b:User) WHERE a.name = 'Alice' AND b.name = 'Bob' CREATE (a)-[:Follows]->(b)") Use code with caution. Conclusion Technical Specifications Storage Engine Kuzu v0

Support for concurrent reads and writes without locking issues. Query Language

Kuzu implements a significant subset of , the most widely adopted graph query language. This allows developers familiar with Neo4j to transition to Kuzu with a near-zero learning curve. Getting Started with v0.3.6 Installing the latest version is straightforward via pip: pip install kuzu==0.3.6