Optimizing Product Sales Reporting with Materialized Views

Imagine you're working for an e-commerce company that experiences a high volume of sales transactions. Your team is responsible for generating regular reports on product sales performance, specifically focusing on the total quantity sold and revenue generated per product. Due to the sheer size of the sales data, querying the raw transaction tables for these reports is becoming increasingly slow, impacting report generation times and user experience.

This challenge requires you to design and implement a solution using materialized views to pre-aggregate and store the sales data, significantly speeding up reporting queries.

Problem Description

Your goal is to create a mechanism that efficiently calculates and stores the total quantity sold and total revenue generated for each product. You'll need to define a materialized view that aggregates sales data from a source table. This materialized view should be updated periodically or whenever the underlying data changes to ensure reports are based on relatively up-to-date information.

Key Requirements:

1. Data Aggregation: Create a materialized view that summarizes sales data.
2. Calculation: For each product, calculate:
   • total_quantity_sold: The sum of the quantities of that product sold.
   • total_revenue: The sum of (quantity * price) for all sales of that product.
3. Source Data: The materialized view should be based on a source table containing individual sales transactions.
4. Query Performance: Queries against the materialized view should be significantly faster than querying the raw transaction table directly.
5. Data Freshness: Implement a strategy for refreshing the materialized view to reflect recent sales.

Expected Behavior:

• When a query is executed to retrieve product sales summary, it should target the materialized view.
• The materialized view should contain one row per unique product.
• The aggregated values (total_quantity_sold, total_revenue) should be accurate based on the data in the source table.

Edge Cases to Consider:

• Products with no sales yet.
• Sales with zero quantity or zero price.
• Handling potential data inconsistencies (though for this challenge, assume clean input data).

Examples

Example 1:

Source Table: sales_transactions

Pseudocode for Materialized View Definition:

CREATE MATERIALIZED VIEW mv_product_sales_summary AS
SELECT
    product_id,
    SUM(quantity) AS total_quantity_sold,
    SUM(quantity * price_per_unit) AS total_revenue
FROM
    sales_transactions
GROUP BY
    product_id;

Querying the Materialized View:

SELECT product_id, total_quantity_sold, total_revenue FROM mv_product_sales_summary;

Output:

Explanation:

The materialized view aggregates sales for each product_id. For A101, quantities (2 + 3 = 5) and revenue (210.00 + 310.00 = 50.00) are summed. Similar calculations are performed for B202 and C303.

Example 2:

Source Table: sales_transactions (with new data)

Assuming the materialized view from Example 1 was created and then refreshed after new data was inserted.

Querying the Materialized View:

SELECT product_id, total_quantity_sold, total_revenue FROM mv_product_sales_summary;

Output:

Explanation:

After refreshing, the materialized view reflects the new sales. A101 now has a total quantity of 6 (5 + 1) and revenue of 60.00 (50.00 + 10.00). D404 is a new product and is correctly included with its sales data.

Constraints

• The sales_transactions table can contain millions of rows.
• product_id is a string.
• quantity is an integer, always non-negative.
• price_per_unit is a decimal/numeric type, always non-negative.
• Queries against the materialized view should return results in under 1 second.
• The system should support at least one refresh mechanism for the materialized view (e.g., manual, scheduled, or on commit if supported by the database).

Notes

• This challenge assumes you have the ability to create and manage materialized views in your SQL environment. The specific syntax for creating and refreshing materialized views might vary slightly between different SQL database systems (e.g., PostgreSQL, Oracle, SQL Server).
• Consider the trade-offs between data freshness and the performance impact of refreshing materialized views.
• Think about how you would query the raw data for scenarios where you need more granular details (e.g., individual transaction records) versus querying the materialized view for aggregated reporting.
• You'll need to define the structure of the source sales_transactions table and then implement the materialized view based on it.