Kuzu V0 136 Full – Trusted & Premium

conn.execute("CREATE (:Person name: 'Alice', age: 30)") conn.execute("CREATE (:Person name: 'Bob', age: 25)") conn.execute("MATCH (a:Person name: 'Alice'), (b:Person name: 'Bob') CREATE (a)-[:Knows since: 2020]->(b)")

| Feature | Description | Benefit | |---------|-------------|---------| | Full‑text index (FTI) for node/relationship properties | Integrated BM25‑based inverted index that can be queried with CONTAINS and MATCH_TEXT. | Enables fast keyword search on textual attributes (e.g., product descriptions, logs). | | Hybrid storage engine | Combines a row‑store for hot‑spot vertices with a column‑store for bulk edges. | Improves cache locality and reduces memory consumption for dense graphs. | | Multi‑threaded query execution (up to 64 cores) | Parallelizes both pattern‑matching and aggregation phases automatically. | 2‑3× speed‑up on modern 24‑core CPUs for typical traversals. | | Python‑native API (kuzu-py) 2.0 | Auto‑generated type hints, context‑manager support, and native pandas.DataFrame conversion. | Seamless integration with data‑science stacks; no manual serialization. | | Rust bindings 1.5 | Safe, zero‑copy FFI layer with async support. | Lets Rust applications embed Kuzu without an external C‑wrapper. | | Explain plan visualizer | CLI command kuzu explain <query> outputs a DOT graph that can be rendered with GraphViz. | Makes performance debugging approachable for non‑DBA developers. | | Bulk‑loader CLI (kuzu import) | Supports CSV, Parquet, and NDJSON with schema inference and optional compression. | Load >100 M edges in under 5 minutes on a 32‑core VM. | | Improved durability | Optional write‑ahead log (WAL) with snapshotting. The default “in‑memory only” mode remains unchanged. | Gives developers a simple path to persistence without sacrificing speed. | | Security hardening | TLS‑enabled client‑side sockets (when run in server mode), and sandboxed UDF execution. | Makes Kuzu viable for multi‑tenant environments. |

TL;DR: v0.13.6 turns Kuzu from a pure “in‑process graph engine” into a full‑featured, production‑ready graph database while preserving its hallmark low‑latency performance. kuzu v0 136 full

The landscape of graph databases has long been dominated by server-client architectures, requiring significant operational overhead for deployment and maintenance. Kuzu introduces a paradigm shift by offering a graph database that is embeddable (similar to SQLite) but optimized for heavy analytical processing (OLAP) and transactional integrity (OLTP) hybrid workloads.

Version 0.1.36 focuses on stability, extended data type support, and query execution efficiency. It targets developers and data scientists who require the expressiveness of the Cypher query language without the infrastructure burden of traditional server-based graph databases like Neo4j. TL;DR: v0

# Add a textual column and create a full‑text index
conn.execute("ALTER NODE Person ADD COLUMN bio STRING;")
conn.execute("""
INSERT INTO Person (id, name, age, city, bio) VALUES
    (4, 'Dave', 38, 'Sydney', 'Loves open‑source graph databases and AI.'),
    (5, 'Eve',  29, 'Boston', 'Works on natural‑language processing.');
""")
conn.execute("CREATE FULLTEXT INDEX person_bio_idx ON Person(bio);")
# 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)

Output

[('Dave', 'Sydney')]

To justify upgrading to the kuzu v0 136 full, consider these community-sourced benchmarks (tested on AWS c5.4xlarge, 100GB synthetic social graph): The landscape of graph databases has long been

The "full" package’s improved query planner makes recursive analytical queries actually usable in production.

Once installed, verify the "full" capabilities by creating a database and running a recursive query.

import kuzu