Much like how SQLite revolutionized relational data by living inside the application process, Kùzu does the same for graph data. It is built for:
built from the ground up for query speed, scalability, and seamless integration into modern data science workflows. Named after the Sumerian word for "wisdom", Kùzu serves as a vital component in the modern AI and analytics stack, filling a unique niche by bringing high-performance graph processing directly inside your application process.
milestone, representing a major step in its maturity as a disk-based, transactional GDBMS. Key Features of Kùzu v0.12.0 kuzu v0 120
When working with Kùzu v0.1.20, consider the following best practices to ensure a smooth experience:
The v0.12.0 release focuses on expanding the database's versatility and performance, particularly for AI and vector-based search. Much like how SQLite revolutionized relational data by
import kuzu # 1. Initialize an on-disk database and connection db = kuzu.Database('./my_graph_db') conn = kuzu.Connection(db) # 2. Define the schema conn.execute("CREATE NODE TABLE User(name STRING, age INT64, PRIMARY KEY (name))") conn.execute("CREATE REL TABLE Follows(FROM User TO User)") # 3. Insert some data conn.execute("CREATE (:User name: 'Alice', age: 30)") conn.execute("CREATE (:User name: 'Bob', age: 25)") conn.execute("CREATE (:User name: 'Charlie', age: 35)") conn.execute("MATCH (a:User name: 'Alice'), (b:User name: 'Bob') CREATE (a)-[:Follows]->(b)") conn.execute("MATCH (b:User name: 'Bob'), (c:User name: 'Charlie') CREATE (b)-[:Follows]->(c)") # 4. Execute a multi-hop query result = conn.execute("MATCH (a:User)-[:Follows]->()-[:Follows]->(c:User) RETURN a.name, c.name") while result.has_next(): print(result.get_next()) Use code with caution. 💡 Use Cases for Kùzu v0.12.0
Kuzu continues to align closer with standard OpenCypher while adding unique extensions for performance. milestone, representing a major step in its maturity
Kùzu v0.1.2.0: Elevating the Standards of Graph Database Performance