Add one-skill

one-skill/smart-data-developer/references/sql/templates/spark/query-template.sql

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+-- =====================================================================
+-- @SparkSqlName:    PAIMONA-D-SQL-{表名}-QUERY
+-- @Version:         1.0
+-- @Desc:            标准 SELECT 查询模板
+-- @TargetTables:    无（查询输出）
+-- @SourceTables:    {源表列表}
+-- @TargetDatabase:  Paimon
+-- @SourceDatabase:  Paimon
+-- =====================================================================
+
+-- ============================================================================
+-- 基础查询示例
+-- ============================================================================
+
+-- 1. 单表查询
+SELECT 
+    id,
+    name,
+    amount,
+    created_at
+FROM source_table
+WHERE day_id = '${day_id}'      -- 分区过滤
+  AND status = 'active'         -- 业务过滤
+ORDER BY created_at DESC
+LIMIT 1000;
+
+-- ============================================================================
+-- JOIN 查询示例
+-- ============================================================================
+
+-- 2. 两表 JOIN
+SELECT 
+    a.id,
+    a.name,
+    b.category_name
+FROM main_table a
+JOIN dim_table b ON a.category_id = b.id
+WHERE a.day_id = '${day_id}'
+  AND b.is_active = true;
+
+-- 3. 多表 JOIN（带别名）
+SELECT 
+    o.order_id,
+    u.user_name,
+    p.product_name,
+    oi.quantity,
+    oi.unit_price
+FROM orders o
+JOIN users u ON o.user_id = u.id
+JOIN order_items oi ON o.order_id = oi.order_id
+JOIN products p ON oi.product_id = p.id
+WHERE o.day_id = '${day_id}'
+  AND o.status IN ('completed', 'shipped');
+
+-- ============================================================================
+-- 聚合查询示例
+-- ============================================================================
+
+-- 4. GROUP BY 聚合
+SELECT 
+    department,
+    COUNT(*) AS employee_count,
+    SUM(salary) AS total_salary,
+    AVG(salary) AS avg_salary,
+    MAX(salary) AS max_salary,
+    MIN(salary) AS min_salary
+FROM employees
+WHERE day_id = '${day_id}'
+GROUP BY department
+HAVING COUNT(*) >= 5
+ORDER BY total_salary DESC;
+
+-- 5. 多字段分组 + 去重计数
+SELECT 
+    date,
+    region,
+    COUNT(*) AS order_count,
+    COUNT(DISTINCT user_id) AS unique_users,
+    SUM(amount) AS total_amount
+FROM orders
+WHERE day_id = '${day_id}'
+GROUP BY date, region;
+
+-- ============================================================================
+-- 窗口函数示例
+-- ============================================================================
+
+-- 6. ROW_NUMBER（分组取Top N）
+SELECT *
+FROM (
+    SELECT 
+        department,
+        name,
+        salary,
+        ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rn
+    FROM employees
+    WHERE day_id = '${day_id}'
+) t
+WHERE rn <= 3;     -- 每个部门薪资前3名
+
+-- 7. 累计聚合
+SELECT 
+    date,
+    amount,
+    SUM(amount) OVER (ORDER BY date) AS cumulative_amount,
+    AVG(amount) OVER (
+        ORDER BY date
+        ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
+    ) AS moving_avg_7d
+FROM daily_sales
+WHERE day_id = '${day_id}';
+
+-- 8. LAG/LEAD（环比计算）
+SELECT 
+    date,
+    amount,
+    LAG(amount, 1) OVER (ORDER BY date) AS prev_amount,
+    amount - LAG(amount, 1) OVER (ORDER BY date) AS daily_change,
+    ROUND((amount - LAG(amount, 1) OVER (ORDER BY date)) 
+        / LAG(amount, 1) OVER (ORDER BY date) * 100, 2) AS growth_rate_pct
+FROM daily_sales
+WHERE day_id = '${day_id}';
+
+-- ============================================================================
+-- 子查询示例
+-- ============================================================================
+
+-- 9. IN 子查询
+SELECT *
+FROM orders
+WHERE user_id IN (
+    SELECT id FROM users WHERE vip_level >= 3
+)
+AND day_id = '${day_id}';
+
+-- 10. EXISTS 子查询
+SELECT *
+FROM products p
+WHERE EXISTS (
+    SELECT 1 FROM inventory i 
+    WHERE i.product_id = p.id 
+      AND i.quantity > 0
+)
+AND p.day_id = '${day_id}';
+
+-- ============================================================================
+-- 条件聚合示例
+-- ============================================================================
+
+-- 11. CASE WHEN + 聚合
+SELECT 
+    date,
+    COUNT(*) AS total_orders,
+    SUM(CASE WHEN status = 'completed' THEN 1 ELSE 0 END) AS completed_count,
+    SUM(CASE WHEN status = 'cancelled' THEN 1 ELSE 0 END) AS cancelled_count,
+    SUM(CASE WHEN status = 'pending' THEN 1 ELSE 0 END) AS pending_count,
+    SUM(CASE WHEN amount > 1000 THEN amount ELSE 0 END) AS high_value_amount
+FROM orders
+WHERE day_id = '${day_id}'
+GROUP BY date;