Regional Sales Analytics with Uni (Rust)¶

Combining Graph Traversal with Columnar Aggregation using Uni's native Rust API.

In [ ]:

:dep uni-db = { path = "../../../crates/uni" }
:dep tokio = { version = "1", features = ["full"] }
:dep serde_json = "1"

:dep uni-db = { path = "../../../crates/uni" } :dep tokio = { version = "1", features = ["full"] } :dep serde_json = "1"

In [ ]:

use uni::{Uni, DataType, IndexType, ScalarType, VectorMetric, VectorAlgo, VectorIndexCfg};
use std::collections::HashMap;
use serde_json::json;

// Helper macro to run async code in evcxr
macro_rules! run {
    ($e:expr) => {
        tokio::runtime::Runtime::new().unwrap().block_on($e)
    };
}

use uni::{Uni, DataType, IndexType, ScalarType, VectorMetric, VectorAlgo, VectorIndexCfg}; use std::collections::HashMap; use serde_json::json; // Helper macro to run async code in evcxr macro_rules! run { ($e:expr) => { tokio::runtime::Runtime::new().unwrap().block_on($e) }; }

In [ ]:

let db_path = "./sales_db";

// Clean up any existing database
if std::path::Path::new(db_path).exists() {
    std::fs::remove_dir_all(db_path).unwrap();
}

let db = run!(Uni::open(db_path).build()).unwrap();
println!("Opened database at {}", db_path);

let db_path = "./sales_db"; // Clean up any existing database if std::path::Path::new(db_path).exists() { std::fs::remove_dir_all(db_path).unwrap(); } let db = run!(Uni::open(db_path).build()).unwrap(); println!("Opened database at {}", db_path);

1. Schema¶

In [ ]:

run!(async {
    db.schema()
        .label("Region")
            .property("name", DataType::String)
        .label("Order")
            .property("amount", DataType::Float64)
        .edge_type("SHIPPED_TO", &["Order"], &["Region"])
        .apply()
        .await
}).unwrap();

println!("Sales analytics schema created");

run!(async { db.schema() .label("Region") .property("name", DataType::String) .label("Order") .property("amount", DataType::Float64) .edge_type("SHIPPED_TO", &["Order"], &["Region"]) .apply() .await }).unwrap(); println!("Sales analytics schema created");

2. Ingest Data¶

One region, 100 orders.

In [ ]:

// Create region
let regions = vec![
    HashMap::from([("name".to_string(), json!("North"))]),
];

let region_vids = run!(db.bulk_insert_vertices("Region", regions)).unwrap();
let north = region_vids[0];

// Create 100 orders with varying amounts
let orders: Vec<HashMap<String, serde_json::Value>> = (0..100)
    .map(|i| HashMap::from([
        ("amount".to_string(), json!(10.0 * (i + 1) as f64))
    ]))
    .collect();

let order_vids = run!(db.bulk_insert_vertices("Order", orders)).unwrap();

// Ship all orders to North region
let edges: Vec<_> = order_vids.iter()
    .map(|vid| (*vid, north, HashMap::new()))
    .collect();

run!(db.bulk_insert_edges("SHIPPED_TO", edges)).unwrap();
run!(db.flush()).unwrap();

println!("Inserted {} orders shipped to North region", order_vids.len());

// Create region let regions = vec![ HashMap::from([("name".to_string(), json!("North"))]), ]; let region_vids = run!(db.bulk_insert_vertices("Region", regions)).unwrap(); let north = region_vids[0]; // Create 100 orders with varying amounts let orders: Vec> = (0..100) .map(|i| HashMap::from([ ("amount".to_string(), json!(10.0 * (i + 1) as f64)) ])) .collect(); let order_vids = run!(db.bulk_insert_vertices("Order", orders)).unwrap(); // Ship all orders to North region let edges: Vec<_> = order_vids.iter() .map(|vid| (*vid, north, HashMap::new())) .collect(); run!(db.bulk_insert_edges("SHIPPED_TO", edges)).unwrap(); run!(db.flush()).unwrap(); println!("Inserted {} orders shipped to North region", order_vids.len());

3. Analytical Query¶

Sum of amounts for orders in a region.

In [ ]:

let query = r#"
    MATCH (r:Region {name: 'North'})<-[:SHIPPED_TO]-(o:Order)
    RETURN SUM(o.amount) as total
"#;

let results = run!(db.query(query)).unwrap();
println!("Total Sales for North Region: {:?}", results.rows[0]);

// Expected: 10 * (1 + 2 + ... + 100) = 10 * 5050 = 50500

let query = r#" MATCH (r:Region {name: 'North'})<-[:SHIPPED_TO]-(o:Order) RETURN SUM(o.amount) as total "#; let results = run!(db.query(query)).unwrap(); println!("Total Sales for North Region: {:?}", results.rows[0]); // Expected: 10 * (1 + 2 + ... + 100) = 10 * 5050 = 50500