LadybugDB¶

Overview¶

LadybugDB is an emerging embedded graph database focusing on simplicity and performance, utilizing a native C++ engine with a Cypher-like query language (powered by Kuzu under the hood).

Feature	Details
Architecture	Embedded Native Library
Language	C++ (with Node.js bindings)
Query Language	Cypher-compatible
License	MIT
Data Model	Labeled Property Graph

Performance Profile¶

LadybugDB shows competitive performance in our benchmarks, particularly excelling in multi-hop traversals where its native graph representation outpaces relational and pure JavaScript engines.

Strengths¶

Memory Efficiency: Low memory footprint relative to Neo4j.
Traversal Speed: Fast k-hop traversals.
Cypher Support: Easy to adopt if coming from Neo4j.

Limitations¶

Syntax Nuances: As an early-stage engine, its Cypher parser has some limitations (e.g., specific requirements for variable-length paths and MATCH syntax).
Batch Ingestion: Can be sensitive to batch sizes and specific CREATE patterns over MERGE.

Implementation Details¶

Our benchmark implements the LadybugDB engine using @ladybugdb/core:

Storage: In-memory for benchmarking (:memory: database)
Ingestion: Uses batched CREATE statements with prepared statements to maximize throughput.
Traversals: Uses manually chained MATCH patterns due to current limitations with variable-length path syntax in its Cypher parser.
Algorithms: Capable of executing native Cypher-based PageRank iterations.

Code Example¶

import lbug from '@ladybugdb/core';

// Initialize
const db = new lbug.Database(':memory:');
await db.init();
const conn = new lbug.Connection(db);
await conn.init();

// Create schema
await conn.query('CREATE NODE TABLE IF NOT EXISTS User(id STRING, PRIMARY KEY(id))');
await conn.query('CREATE REL TABLE IF NOT EXISTS FOLLOWS(FROM User TO User)');

// Insert data
await conn.query(`CREATE (:User {id: 'u1'})`);

// Query
const result = await conn.query(`MATCH (a:User)-[:FOLLOWS]->(b:User) RETURN b.id`);
const rows = await result.getAll();