Kuzu Link File

In independent tests (using the LDBC Social Network Benchmark scaling factor 1), Kuzu Link consistently outperforms other embedded graph stores like SQLite with graph extensions and DuckDB with recursive CTEs.

| Query Type (Depth) | Kuzu Link (ms) | SQLite + JOINs (ms) | DuckDB (Recursive CTE) | |-------------------|----------------|----------------------|-------------------------| | 2-hop neighbors | 8 | 142 | 55 | | 4-hop neighbors | 47 | 8,210 (timeout) | 892 | | Path existence check (6 hops) | 210 | >30,000 | 4,100 | kuzu link

Why? Kuzu Link stores adjacency pointers directly. There is no hash table lookup for each hop—just pointer chasing, which is friendly to CPU caches. For deep traversals (4+ hops), the performance gap widens exponentially. In independent tests (using the LDBC Social Network

db = kuzu.Database('./test.db') conn = kuzu.Connection(db) Step 3: Define a Schema (Cypher DDL) #

Step 3: Define a Schema (Cypher DDL)

# Create a Node table called 'User'
conn.execute("CREATE NODE TABLE User (name STRING, age INT64, PRIMARY KEY (name))")
In the rapidly evolving landscape of data management and knowledge representation, the tools we use to connect and query information determine the speed of our insights. Enter Kuzu Link—a term that is beginning to generate significant buzz among database architects, graph enthusiasts, and backend developers. But what exactly is Kuzu Link, and why is it becoming a critical component of modern data stacks?
This comprehensive guide will explore everything you need to know about Kuzu Link, from its core technical architecture to practical implementation strategies, performance benchmarks, and future roadmap.