The Question

Top Product per Category with Tie-breaking

Given a 'products' table (product_id, product_name, category_name) and a transactional 'product_sales' table (product_id, sales_quantity, rating), identify the best-selling product within each category. The 'best-selling' product is defined as having the highest aggregate sales quantity. In the event of a tie in sales volume, the product with the higher average rating should be selected. If a tie still exists after considering both metrics, return all such tied products. The final output must include the category name and product name, sorted alphabetically by category name.

PostgreSQL

Window Function

CTE

INNER JOIN

Aggregate Function

Questions & Insights

Clarifying Questions

Aggregated vs. Transactional Data: Is the product_sales table already aggregated (one row per product), or is it a transaction log (multiple rows per product)?

Assumption: It is transactional. I will calculate the total sales_quantity and the average rating per product before ranking.

Tie-breaking Scenarios: If two products have the same total sales and the same average rating, should both be returned?

Assumption: I will use RANK(). If there is a perfect tie in both metrics, both products will be returned for that category, as is standard in competitive ranking unless a final tie-breaker (like product_id) is specified.

Missing Data: How should products with no sales or no ratings be handled?

Assumption: Products must exist in the product_sales table to be considered "best-selling." We will use an INNER JOIN.

Schema Assumptions:

products: product_id is the Primary Key (PK). category_name is a dimension attribute.

product_sales: product_id is a Foreign Key (FK) referencing the products table.

Relationship: products to product_sales is 1:N.

Thinking Process

Metric Calculation: First, I need to compute the "Performance Metrics" for each product. This requires grouping by product_id and calculating SUM(sales_quantity) and AVG(rating).

Contextual Awareness: To rank within categories, I must join the aggregated sales data back to the products table to retrieve category_name and product_name.

Ranking Logic: I will use the RANK() window function. The PARTITION BY clause will be the category_name, and the ORDER BY clause will handle the primary metric (sales) and the secondary metric (rating) in descending order.

Filtering: Using a Common Table Expression (CTE) or a subquery, I will filter the results to only include rows where the rank is 1.

Final Formatting: The prompt requires the final output to be ordered by category_name alphabetically.

Implementation Breakdown

Problem Set

Goal: Identify the top-performing product per category.

Primary Metric: Highest total sales_quantity.

Secondary Metric (Tie-breaker): Highest average rating.

Constraints: Return only category name and product name.

Ordering: Result set must be sorted alphabetically by category name.

Approach

CTEs: Used for modularity—separating the aggregation logic from the ranking logic.

Window Functions:RANK() will be used to handle the requirements of "best" within a group.

Aggregates:SUM and AVG to consolidate transactional data.

Joins:INNER JOIN between products and product_sales.

Computational Cost: This approach is

O(N \log N)

due to the sorting required by the Window Function. In a distributed environment (like Greenplum/Postgres-XL), the PARTITION BY would trigger a data shuffle on the partition key.

Implementation

Wrap Up

Advanced Topics

Indexing: To optimize this query, a composite index on product_sales(product_id, sales_quantity, rating) would allow for an Index-Only Scan in some PostgreSQL versions, reducing I/O.

Scalability: If the product_sales table is massive (billions of rows), the aggregation should be performed as a separate materialized view or an incremental summary table to avoid re-scanning the entire history for every query.

Window Function Optimization: PostgreSQL's optimizer handles RANK() efficiently, but if we only needed exactly one product per category regardless of ties, DISTINCT ON (category_name) is a PostgreSQL-specific syntax that is often faster than a Window Function:

    SELECT DISTINCT ON (category_name) 
        category_name, 
        product_name
    FROM ProductPerformance
    ORDER BY category_name, total_sales DESC, avg_rating DESC;

Statistics: Ensure ANALYZE has been run on both tables so the optimizer can choose between a Hash Join or a Nested Loop depending on the size of the product catalog vs. the sales volume.