analysis test and generate command

api/libs/sql_analyzer/tests/analysis_tests.rs

@@ -2230,4 +2230,50 @@ async fn test_bigquery_count_with_interval() {
 
     assert!(table.columns.contains("message_id"), "Missing 'message_id' column");
     assert!(table.columns.contains("created_at"), "Missing 'created_at' column");
-}
+}
+
+#[tokio::test]
+async fn test_postgres_cte_with_date_trunc() {
+    let sql = r#"
+    WITH recent_data AS (
+        SELECT
+          tsr.year AS sales_year,
+          tsr.month AS sales_month,
+          tsr.metric_totalsalesrevenue AS total_revenue
+        FROM postgres.ont_ont.total_sales_revenue tsr
+        WHERE cast(concat(tsr.year, '-', tsr.month, '-01') AS date)
+          >= date_trunc('month', CURRENT_DATE) - INTERVAL '5 months'
+      )
+      SELECT
+        DATE_TRUNC('month', cast(concat(sales_year, '-', sales_month, '-01') AS date)) AS month_start,
+        COALESCE(total_revenue, 0) AS total_revenue
+      FROM recent_data
+      ORDER BY month_start ASC;
+    "#;
+
+    let result = analyze_query(sql.to_string(), "postgres").await.unwrap();
+
+    // Check CTE detection
+    assert_eq!(result.ctes.len(), 1, "Should detect one CTE");
+    let cte = &result.ctes[0];
+    assert_eq!(cte.name, "recent_data", "CTE should be named 'recent_data'");
+
+    // Check base table detection
+    assert_eq!(result.tables.len(), 1, "Should detect one base table");
+    let table = &result.tables[0];
+    assert_eq!(table.database_identifier, Some("postgres".to_string()));
+    assert_eq!(table.schema_identifier, Some("ont_ont".to_string()));
+    assert_eq!(table.table_identifier, "total_sales_revenue");
+
+    // Check columns in base table
+    assert!(table.columns.contains("year"), "Missing 'year' column");
+    assert!(table.columns.contains("month"), "Missing 'month' column");
+    assert!(table.columns.contains("metric_totalsalesrevenue"), "Missing 'metric_totalsalesrevenue' column");
+
+    // Check CTE columns
+    let cte_table = &cte.summary.tables[0];
+    assert!(cte_table.columns.contains("sales_year"), "Missing 'sales_year' in CTE");
+    assert!(cte_table.columns.contains("sales_month"), "Missing 'sales_month' in CTE");
+    assert!(cte_table.columns.contains("total_revenue"), "Missing 'total_revenue' in CTE");
+}
+

cli/cli/src/commands/generate.rs

@@ -453,6 +453,56 @@ for (unique_id, node) in &dbt_catalog.nodes {
 
         match existing_yaml_model_opt {
             Some(mut existing_model) => {
+                // --- Reconciliation Logic for Existing Model ---
+                let mut model_updated = false;
+                let original_dim_count = existing_model.dimensions.len();
+                let original_measure_count = existing_model.measures.len();
+
+                // Get the set of column names from the dbt catalog for this model
+                let catalog_column_names: HashSet<String> = catalog_node.columns
+                    .keys()
+                    .cloned()
+                    .collect();
+
+                // Remove dimensions that are no longer in the catalog
+                existing_model.dimensions.retain(|dim| {
+                    let keep = catalog_column_names.contains(&dim.name);
+                    if !keep {
+                        columns_removed_count += 1;
+                        model_updated = true;
+                        println!("    - Removing dimension '{}' (not in catalog)", dim.name.yellow());
+                    }
+                    keep
+                });
+
+                // Remove measures that are no longer in the catalog
+                existing_model.measures.retain(|measure| {
+                    let keep = catalog_column_names.contains(&measure.name);
+                    if !keep {
+                        columns_removed_count += 1;
+                        model_updated = true;
+                        println!("    - Removing measure '{}' (not in catalog)", measure.name.yellow());
+                    }
+                    keep
+                });
+
+                // Note: We do NOT remove metrics, filters, or relationships automatically
+                // as they might represent derived logic or explicitly defined connections
+                // not directly tied 1:1 with current physical columns.
+
+                // TODO: Add logic here to ADD new columns from the catalog as dimensions/measures
+                // if they don't already exist in the existing_model.
+
+                if model_updated {
+                    let yaml_string = serde_yaml::to_string(&existing_model)?;
+                    fs::write(&individual_semantic_yaml_path, yaml_string)?;
+                    models_updated_count += 1;
+                    println!("   {} Updated existing semantic model: {}", "🔄".cyan(), individual_semantic_yaml_path.display().to_string().cyan());
+                } else {
+                    // If no columns were removed, maybe check if columns need *adding* later?
+                    // For now, just indicate no changes needed based on removal.
+                    // println!("   {} No column removals needed for: {}", "✅".dimmed(), individual_semantic_yaml_path.display().to_string().dimmed());
+                }
             }
             None => { // New semantic model
                 let mut dimensions = Vec::new();