Optimizing Sales Reporting with Materialized Views

Sales reporting often involves complex aggregations across large datasets, leading to slow query performance. This challenge asks you to design and implement materialized views to significantly speed up common sales reporting queries. By pre-calculating and storing frequently used aggregations, materialized views can dramatically reduce query execution time.

Problem Description

You are tasked with optimizing sales reporting for an e-commerce platform. The platform stores sales data in a sales table with the following schema:

• sale_id (INTEGER, PRIMARY KEY): Unique identifier for each sale.
• product_id (INTEGER): Identifier for the product sold.
• sale_date (DATE): Date of the sale.
• quantity (INTEGER): Quantity of the product sold in the sale.
• price (DECIMAL): Price per unit of the product at the time of the sale.

The platform needs to generate the following reports frequently:

1. Daily Sales Summary: Total sales revenue for each day.
2. Monthly Sales Summary: Total sales revenue for each month.
3. Top Selling Products (Last 7 Days): A list of the top 5 products by total revenue sold in the last 7 days.

Your goal is to create materialized views that efficiently support these reports. You should design the materialized views to minimize refresh time and maximize query performance. You are not required to implement a refresh schedule; the focus is on the view definitions themselves. Assume the database system supports incremental materialized view refreshes (though you don't need to explicitly code for it).

Examples

Example 1:

Input:  A `sales` table with the following data:

sale_id | product_id | sale_date  | quantity | price
------- | ---------- | ---------- | -------- | ------
1       | 101        | 2023-11-01 | 2        | 25.00
2       | 102        | 2023-11-01 | 1        | 50.00
3       | 101        | 2023-11-02 | 3        | 25.00
4       | 103        | 2023-11-02 | 1        | 75.00
5       | 102        | 2023-11-03 | 2        | 50.00

Output: A materialized view definition for Daily Sales Summary.

CREATE MATERIALIZED VIEW daily_sales_summary AS
SELECT
    sale_date,
    SUM(quantity * price) AS total_revenue
FROM
    sales
GROUP BY
    sale_date;

Explanation: This materialized view pre-calculates the total revenue for each day, allowing for fast retrieval of daily sales summaries.

Example 2:

Input: A `sales` table with data spanning multiple months.

Output: A materialized view definition for Monthly Sales Summary.

CREATE MATERIALIZED VIEW monthly_sales_summary AS
SELECT
    strftime('%Y-%m', sale_date) AS sale_month,
    SUM(quantity * price) AS total_revenue
FROM
    sales
GROUP BY
    sale_month;

Explanation: This materialized view aggregates sales revenue by month, enabling quick access to monthly sales figures. The strftime function is used to extract the year and month from the sale_date.

Example 3:

Input: A `sales` table with a large number of sales records over a long period.

Output: A materialized view definition for Top Selling Products (Last 7 Days).

CREATE MATERIALIZED VIEW top_selling_products_last_7_days AS
SELECT
    product_id,
    SUM(quantity * price) AS total_revenue
FROM
    sales
WHERE
    sale_date >= date('now', '-7 days')
GROUP BY
    product_id
ORDER BY
    total_revenue DESC
LIMIT 5;

Explanation: This materialized view calculates the total revenue for each product within the last 7 days and returns the top 5 products. The date('now', '-7 days') function calculates the date 7 days ago.

Constraints

• The materialized views should be designed to be as efficient as possible for the specified reports.
• Assume the database system supports standard SQL syntax for materialized views.
• The materialized views should be relatively small in size to minimize storage costs.
• The database system supports strftime and date functions (or equivalent date manipulation functions).
• You are only required to provide the CREATE MATERIALIZED VIEW statements. No refresh logic is needed.

Notes

• Consider the frequency with which each report is run when designing the materialized views. More frequently accessed reports benefit more from materialized views.
• Think about how the materialized views can be incrementally refreshed to minimize the impact on performance. While you don't need to implement the refresh, understanding this concept is helpful.
• The LIMIT clause in the Top Selling Products view is important for performance. Without it, the query could return a very large result set.
• The specific date functions available may vary depending on the database system. Adapt the syntax accordingly. The examples use SQLite syntax.