Understanding the Problem: Aggregation Level in PostgreSQL

As a technical blogger, it’s essential to understand the nuances of SQL queries and how they interact with data. In this article, we’ll delve into the world of PostgreSQL aggregation and explore why the initial query didn’t yield the expected results.

Table Structure and Data

Before diving into the solution, let’s review the table structure and data in the question:

+---------+------------+------------+
| Customer_ID | Order_ID | Sales_Date |
+---------+------------+------------+
| 1        | 101      | 2022-01-01 |
| 1        | 102      | 2022-01-02 |
| 2        | 201      | 2022-01-03 |
| 2        | 202      | 2022-01-04 |
+---------+------------+------------+

The orders table contains three columns: Customer_ID, Order_ID, and Sales_Date. We’re interested in aggregating the prices for each customer and order, specifically for product N.

Initial Query Analysis

Let’s analyze the initial query:

SELECT Customer_ID, Order_ID, Sales_Date,
       sum(Price) OVER (PARTITION BY Customer_ID, Order_ID ORDER BY Customer_ID, Order_ID)
FROM orders
GROUP BY 1,2,3, Price;

The query uses a window function with PARTITION BY to group the data by customer and order. However, this approach doesn’t meet our requirements because it:

• Groups by all columns (Customer_ID, Order_ID, Sales_Date, and Price)
• Uses an ORDER BY clause to determine the grouping order

This means that PostgreSQL will create separate groups for each unique combination of these values.

Problem with Initial Query

The issue arises when we try to aggregate only for product N. The initial query doesn’t account for this constraint, resulting in multiple rows being returned for each customer and order.

To illustrate this, let’s modify the orders table to include a new column called Product_ID:

+---------+------------+------------+------------+
| Customer_ID | Order_ID | Sales_Date | Product_ID |
+---------+------------+------------+------------+
| 1        | 101      | 2022-01-01 | N         |
| 1        | 102      | 2022-01-02 | N         |
| 2        | 201      | 2022-01-03 | M         |
| 2        | 202      | 2022-01-04 | N         |
+---------+------------+------------+------------+

Now, the initial query will produce more rows than expected:

+---------+------------+------------+------------+
| Customer_ID | Order_ID | Sales_Date | Product_ID | sum(Price) |
+---------+------------+------------+------------+------------+
| 1        | 101      | 2022-01-01 | N         | 100       |
| 1        | 102      | 2022-01-02 | N         | 200       |
| 2        | 201      | 2022-01-03 | M         | 300       |
| 2        | 202      | 2022-01-04 | N         | 400       |
+---------+------------+------------+------------+------------+

As we can see, the query still returns multiple rows for each customer and order.

Solution: Simplified Aggregation

The solution lies in simplifying the aggregation process. Instead of using window functions, we can use a single GROUP BY clause to group the data by customer and order:

SELECT Customer_ID, Order_ID, Sales_Date,
       sum(Price) AS total_price
FROM orders
WHERE Product_ID = 'N'
GROUP BY 1,2,3;

By removing the window function and adding a WHERE clause to filter for product N, we ensure that only rows with this specific product are aggregated.

Additional Considerations

There’s another aspect to consider: how to handle cases where multiple orders have different prices for the same customer. In our simplified query:

SELECT Customer_ID, Order_ID, Sales_Date,
       sum(Price) AS total_price
FROM orders
WHERE Product_ID = 'N'
GROUP BY 1,2,3;

If there are multiple orders with different prices for the same customer, PostgreSQL will still group them together and return a single row with the sum of all prices.

To address this scenario, we need to modify our query to account for the presence of multiple orders per customer. We can achieve this by adding additional filtering conditions:

SELECT Customer_ID, Order_ID, Sales_Date,
       sum(Price) AS total_price
FROM orders
WHERE Product_ID = 'N' AND (Order_ID, Sales_Date) IN (
    SELECT Order_ID, MAX(Sales_Date)
    FROM orders
    WHERE Product_ID = 'N'
    GROUP BY 1
)
GROUP BY 1,2,3;

This revised query ensures that only the most recent order for product N is aggregated.

Conclusion

In this article, we explored the initial issue with aggregation in PostgreSQL and provided a simplified solution. By using a single GROUP BY clause and adding additional filtering conditions, we can effectively aggregate data while avoiding multiple rows per customer and order. We also discussed some important considerations when working with aggregated queries, including handling cases with multiple orders per customer.

Last modified on 2025-03-31