Meta SQL Interview Questions on Grouped Sales, Invited Customers, and Author Metrics

This set of SQL interview questions uses a bookstore database with the following tables: books, authors, transactions, and customers.

books

• book_id INT (KEY)
• title VARCHAR
• author_id INT
• publication_date DATE
• category VARCHAR
• price DOUBLE

authors

• author_id INT (KEY)
• first_name VARCHAR
• last_name VARCHAR
• birthday DATE
• website_url VARCHAR

transactions

• transaction_id INT (KEY)
• book_id INT
• customer_id INT
• payment_amount DOUBLE
• book_count INT
• tax_rate DOUBLE
• discount_rate DOUBLE
• transaction_date DATE
• payment_type VARCHAR

customers

• customer_id INT (KEY)
• first_name VARCHAR
• last_name VARCHAR
• registration_date DATE
• interested_in_categories VARCHAR
• is_rewards_member BOOLEAN
• invited_by_customer_id INT

Questions

• What was the total value of sales and the number of unique paying customers, grouped and sorted in descending order by payment type?
• Existing customers can invite other people to sign up to the bookstore. Find the IDs of the top 5 customers, ordered by the average payment per book made by the people they invited.
• Find the total number of authors. What percentage of them have a website URL that contains .com, and what percentage never made a sale?

This Meta SQL problem focuses on grouped aggregation, relationship traversal, and percentage-based analysis. The first query groups transactions by payment type and computes total sales and the number of distinct paying customers. The second query follows the invitation chain through customers.invited_by_customer_id, then ranks inviters by the average payment per book made by the customers they invited. The third query is an author-level analysis that counts all authors, measures the share whose website URL contains ".com", and finds the share of authors who never made a sale. Overall, the key techniques are GROUP BY, COUNT(DISTINCT …), joins, and conditional aggregation.