Regional Sales Analytics with Uni (Rust)¶

Combining graph traversal with columnar aggregation across multiple regions and product categories.

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("Category")
            .property("name", DataType::String)
        .label("Order")
            .property("amount", DataType::Float64)
        .edge_type("SHIPPED_TO",  &["Order"], &["Region"])
        .edge_type("IN_CATEGORY", &["Order"], &["Category"])
        .apply()
        .await
}).unwrap();

println!("Sales analytics schema created");

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

2. Ingest Data¶

4 regions, 3 categories, 27 orders distributed non-uniformly.

In [ ]:

// Regions
let region_props = vec![
    HashMap::from([("name".to_string(), json!("North"))]),
    HashMap::from([("name".to_string(), json!("South"))]),
    HashMap::from([("name".to_string(), json!("East"))]),
    HashMap::from([("name".to_string(), json!("West"))]),
];
let region_vids = run!(db.bulk_insert_vertices("Region", region_props)).unwrap();
let (north, south, east, west) = (region_vids[0], region_vids[1], region_vids[2], region_vids[3]);

// Categories
let cat_props = vec![
    HashMap::from([("name".to_string(), json!("Electronics"))]),
    HashMap::from([("name".to_string(), json!("Apparel"))]),
    HashMap::from([("name".to_string(), json!("Home & Garden"))]),
];
let cat_vids = run!(db.bulk_insert_vertices("Category", cat_props)).unwrap();
let (electronics, apparel, home_garden) = (cat_vids[0], cat_vids[1], cat_vids[2]);

// Orders: (amount, region_vid, category_vid)
let orders_raw = vec![
    // East: high-value electronics
    (1200.0, east, electronics), (980.0, east, electronics),
    (450.0,  east, apparel),     (120.0, east, apparel),
    (85.0,   east, apparel),     (60.0,  east, home_garden),
    // West: strong Home & Garden
    (890.0, west, home_garden), (620.0, west, home_garden),
    (450.0, west, home_garden), (340.0, west, home_garden),
    (500.0, west, electronics), (210.0, west, apparel),
    (180.0, west, apparel),
    // South: most electronics
    (750.0, south, electronics), (680.0, south, electronics),
    (590.0, south, electronics), (520.0, south, electronics),
    (480.0, south, electronics), (300.0, south, apparel),
    (250.0, south, home_garden),
    // North: balanced mix
    (400.0, north, electronics), (350.0, north, electronics),
    (280.0, north, apparel),     (260.0, north, apparel),
    (240.0, north, apparel),     (320.0, north, home_garden),
    (290.0, north, home_garden),
];

let order_props: Vec<_> = orders_raw.iter()
    .map(|(amt, _, _)| HashMap::from([("amount".to_string(), json!(*amt))]))
    .collect();

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

let shipped_edges: Vec<_> = order_vids.iter().zip(orders_raw.iter())
    .map(|(&ov, &(_, rv, _))| (ov, rv, HashMap::new()))
    .collect();
run!(db.bulk_insert_edges("SHIPPED_TO", shipped_edges)).unwrap();

let category_edges: Vec<_> = order_vids.iter().zip(orders_raw.iter())
    .map(|(&ov, &(_, _, cv))| (ov, cv, HashMap::new()))
    .collect();
run!(db.bulk_insert_edges("IN_CATEGORY", category_edges)).unwrap();

run!(db.flush()).unwrap();
println!("Inserted {} orders", order_vids.len());

// Regions let region_props = vec![ HashMap::from([("name".to_string(), json!("North"))]), HashMap::from([("name".to_string(), json!("South"))]), HashMap::from([("name".to_string(), json!("East"))]), HashMap::from([("name".to_string(), json!("West"))]), ]; let region_vids = run!(db.bulk_insert_vertices("Region", region_props)).unwrap(); let (north, south, east, west) = (region_vids[0], region_vids[1], region_vids[2], region_vids[3]); // Categories let cat_props = vec![ HashMap::from([("name".to_string(), json!("Electronics"))]), HashMap::from([("name".to_string(), json!("Apparel"))]), HashMap::from([("name".to_string(), json!("Home & Garden"))]), ]; let cat_vids = run!(db.bulk_insert_vertices("Category", cat_props)).unwrap(); let (electronics, apparel, home_garden) = (cat_vids[0], cat_vids[1], cat_vids[2]); // Orders: (amount, region_vid, category_vid) let orders_raw = vec![ // East: high-value electronics (1200.0, east, electronics), (980.0, east, electronics), (450.0, east, apparel), (120.0, east, apparel), (85.0, east, apparel), (60.0, east, home_garden), // West: strong Home & Garden (890.0, west, home_garden), (620.0, west, home_garden), (450.0, west, home_garden), (340.0, west, home_garden), (500.0, west, electronics), (210.0, west, apparel), (180.0, west, apparel), // South: most electronics (750.0, south, electronics), (680.0, south, electronics), (590.0, south, electronics), (520.0, south, electronics), (480.0, south, electronics), (300.0, south, apparel), (250.0, south, home_garden), // North: balanced mix (400.0, north, electronics), (350.0, north, electronics), (280.0, north, apparel), (260.0, north, apparel), (240.0, north, apparel), (320.0, north, home_garden), (290.0, north, home_garden), ]; let order_props: Vec<_> = orders_raw.iter() .map(|(amt, _, _)| HashMap::from([("amount".to_string(), json!(*amt))])) .collect(); let order_vids = run!(db.bulk_insert_vertices("Order", order_props)).unwrap(); let shipped_edges: Vec<_> = order_vids.iter().zip(orders_raw.iter()) .map(|(&ov, &(_, rv, _))| (ov, rv, HashMap::new())) .collect(); run!(db.bulk_insert_edges("SHIPPED_TO", shipped_edges)).unwrap(); let category_edges: Vec<_> = order_vids.iter().zip(orders_raw.iter()) .map(|(&ov, &(_, _, cv))| (ov, cv, HashMap::new())) .collect(); run!(db.bulk_insert_edges("IN_CATEGORY", category_edges)).unwrap(); run!(db.flush()).unwrap(); println!("Inserted {} orders", order_vids.len());

3. Revenue by Region¶

Total revenue and order count per region, ordered by revenue.

In [ ]:

let query_region = r#"
    MATCH (r:Region)<-[:SHIPPED_TO]-(o:Order)
    RETURN r.name AS region, COUNT(o) AS order_count, SUM(o.amount) AS total_revenue
    ORDER BY total_revenue DESC
"#;

let results = run!(db.query(query_region)).unwrap();
println!("Revenue by region:");
for row in &results.rows {
    println!("  {:?}", row);
}
assert!(results.rows.len() == 4, "Expected 4 regions, got {}", results.rows.len());

let query_region = r#" MATCH (r:Region)<-[:SHIPPED_TO]-(o:Order) RETURN r.name AS region, COUNT(o) AS order_count, SUM(o.amount) AS total_revenue ORDER BY total_revenue DESC "#; let results = run!(db.query(query_region)).unwrap(); println!("Revenue by region:"); for row in &results.rows { println!(" {:?}", row); } assert!(results.rows.len() == 4, "Expected 4 regions, got {}", results.rows.len());

4. Region × Category Breakdown¶

12-row breakdown showing revenue for every region/category pair.

In [ ]:

let query_breakdown = r#"
    MATCH (r:Region)<-[:SHIPPED_TO]-(o:Order)-[:IN_CATEGORY]->(c:Category)
    RETURN r.name AS region, c.name AS category,
           COUNT(o) AS orders, SUM(o.amount) AS revenue
    ORDER BY region, revenue DESC
"#;

let results = run!(db.query(query_breakdown)).unwrap();
println!("Region x Category breakdown ({} rows):", results.rows.len());
for row in &results.rows {
    println!("  {:?}", row);
}
assert!(results.rows.len() == 12, "Expected 12 rows (4x3), got {}", results.rows.len());

let query_breakdown = r#" MATCH (r:Region)<-[:SHIPPED_TO]-(o:Order)-[:IN_CATEGORY]->(c:Category) RETURN r.name AS region, c.name AS category, COUNT(o) AS orders, SUM(o.amount) AS revenue ORDER BY region, revenue DESC "#; let results = run!(db.query(query_breakdown)).unwrap(); println!("Region x Category breakdown ({} rows):", results.rows.len()); for row in &results.rows { println!(" {:?}", row); } assert!(results.rows.len() == 12, "Expected 12 rows (4x3), got {}", results.rows.len());

5. Top Orders per Region¶

Highest-value orders in each region.

In [ ]:

let query_top = r#"
    MATCH (r:Region)<-[:SHIPPED_TO]-(o:Order)
    RETURN r.name AS region, o.amount AS amount
    ORDER BY region, amount DESC
"#;

let results = run!(db.query(query_top)).unwrap();
println!("All orders by region (DESC):");
println!("  {} total order rows", results.rows.len());
// East should lead with $1200
println!("  First row (should be East $1200): {:?}", results.rows.first());

let query_top = r#" MATCH (r:Region)<-[:SHIPPED_TO]-(o:Order) RETURN r.name AS region, o.amount AS amount ORDER BY region, amount DESC "#; let results = run!(db.query(query_top)).unwrap(); println!("All orders by region (DESC):"); println!(" {} total order rows", results.rows.len()); // East should lead with $1200 println!(" First row (should be East $1200): {:?}", results.rows.first());

6. Best Category per Region¶

The highest-revenue category for each region.

In [ ]:

let query_best = r#"
    MATCH (r:Region)<-[:SHIPPED_TO]-(o:Order)-[:IN_CATEGORY]->(c:Category)
    RETURN r.name AS region, c.name AS category, SUM(o.amount) AS revenue
    ORDER BY region, revenue DESC
"#;

let results = run!(db.query(query_best)).unwrap();
println!("Best category per region (first result per region):");
let mut seen_regions = std::collections::HashSet::new();
for row in &results.rows {
    // rows are sorted by region, revenue DESC — take first per region
    if let Some(region_val) = row.values.first() {
        let region_str = format!("{:?}", region_val);
        if seen_regions.insert(region_str.clone()) {
            println!("  {}: {:?}", region_str, row);
        }
    }
}
// Each region has a different best category (electronics, home & garden, etc.)

let query_best = r#" MATCH (r:Region)<-[:SHIPPED_TO]-(o:Order)-[:IN_CATEGORY]->(c:Category) RETURN r.name AS region, c.name AS category, SUM(o.amount) AS revenue ORDER BY region, revenue DESC "#; let results = run!(db.query(query_best)).unwrap(); println!("Best category per region (first result per region):"); let mut seen_regions = std::collections::HashSet::new(); for row in &results.rows { // rows are sorted by region, revenue DESC — take first per region if let Some(region_val) = row.values.first() { let region_str = format!("{:?}", region_val); if seen_regions.insert(region_str.clone()) { println!(" {}: {:?}", region_str, row); } } } // Each region has a different best category (electronics, home & garden, etc.)