SQLiteGraph

Embedded graph database with dual backend architecture.

Positioning: Single-binary embedded database (no server). Persistent storage with atomic batch commits. Graph algorithms + HNSW vector search in one engine. SQLite: stable, mature, excellent for adjacency queries. V3: high-performance, unlimited scale, faster for bulk traversals. See benchmarks below.

Backends

Benchmarks

See BENCHMARK_REPORT.md for detailed comparisons.

Quick Summary:

• Point lookup: V3 ~3-4× faster
• Adjacency fetch: SQLite ~20× faster (optimized prepared statements)
• Bulk traversal: V3 10-20× faster (contiguous storage)

Run benchmarks yourself:

git clone https://github.com/oldnordic/sqlitegraph.git
cd sqlitegraph/sqlitegraph-core
cargo run --example test_performance_comparison --features native-v3
cargo bench --features native-v3 -- backend_comparison

See examples/ for reproducible performance tests.

Quick Start

[dependencies]
# SQLite backend (default)
sqlitegraph = "2.0"

# OR Native V3 backend (faster traversals)
sqlitegraph = { version = "2.0", features = ["native-v3"] }

use sqlitegraph::backend::{GraphBackend, NodeSpec};
use sqlitegraph::backend::sqlite::SqliteGraphBackend;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let backend = SqliteGraphBackend::in_memory()?;
    
    let node_id = backend.insert_node(NodeSpec {
        kind: "User".to_string(),
        name: "Alice".to_string(),
        file_path: None,
        data: serde_json::json!({"age": 30}),
    })?;
    
    println!("Created node: {}", node_id);
    Ok(())
}

CLI

cargo install sqlitegraph-cli

# Query (read-only by default)
sqlitegraph --db graph.db query "MATCH (n:User) RETURN n.name"

# Algorithms
sqlitegraph --db graph.db bfs --start 1 --max-depth 3
sqlitegraph --db graph.db pagerank --iterations 100

Ecosystem

Tools built on SQLiteGraph:

Documentation

• Architecture - System design
• Manual - API guide
• Algorithms - 35 graph algorithms
• Changelog - Version history

License

GPL-3.0-or-later