Another fork emerged from , a company specializing in graph visualization and analytics. They created a fork called bighorn and have actively sought community partners to help maintain it.
Kùzu is designed as an optimized for high-speed query execution and scalability. Its v0.12.0 core features include:
Unlike purely in-memory databases, Kuzu can handle datasets larger than RAM by efficiently spilling to disk, maintaining performance through its columnar layout. Developer Experience kuzu v0 120
The Kuzu V0 120 distinguishes itself with a . Unlike incremental encoders, this system remembers the motor shaft position even after power loss. This eliminates the need for a homing sequence on startup, saving 5-10 seconds per machine cycle.
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Explore the source code and release notes on the official Kuzu GitHub page .
Before diving into the new features, it’s worth understanding why Kuzu exists. Modeled after the architecture of heavyweights like DuckDB, Kuzu is . It runs in-process, meaning there is no network latency, no separate database process to manage, and no complex client-server configuration. You simply import it as a library (in C++, Python, or Node.js) and query your data locally using standard Cypher. Another fork emerged from , a company specializing
You need an IP67 rating for monsoon riding, you weigh over 130 kg (the motor will struggle on hills), or you have to carry it up stairs daily.
: Support for concurrent write operations within the same process. Expanded Graph Algorithms : Implementation of new built-in algorithms, including Minimum Spanning Tree (Spanning Forest) and Betweenness Centrality Full-Text Search (FTS) Enhancements : Added support for wildcard patterns Its v0
import kuzu # Initialize database and connection db = kuzu.Database("user_network_db") conn = kuzu.Connection(db) # Create Node Schemas conn.execute("CREATE NODE TABLE User(id INT64, name STRING, age INT64, PRIMARY KEY (id))") conn.execute("CREATE NODE TABLE Topic(id STRING, name STRING, PRIMARY KEY (id))") # Create Relationship Schemas conn.execute("CREATE REL TABLE Follows(FROM User TO User)") conn.execute("CREATE REL TABLE InterestedIn(FROM User TO Topic, weight FLOAT)") Use code with caution. Loading Data