Kamran Ahmed
2 months ago
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--- |
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title: Book Performance |
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description: Practice using aggregate functions with temporal data and joins |
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order: 120 |
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type: challenge |
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setup: | |
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```sql |
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CREATE TABLE book ( |
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id INT PRIMARY KEY, |
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title VARCHAR(255), |
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category VARCHAR(100), |
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release_date DATE |
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); |
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|
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CREATE TABLE sale ( |
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id INT PRIMARY KEY, |
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book_id INT, |
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sale_timestamp TIMESTAMP, |
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quantity INT, |
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unit_price DECIMAL(10,2) |
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); |
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|
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INSERT INTO book (id, title, category, release_date) VALUES |
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(1, 'The Great Gatsby', 'Fiction', '2024-01-01'), |
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(2, 'SQL Mastery', 'Technical', '2024-01-15'), |
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(3, 'Data Science 101', 'Technical', '2024-02-01'), |
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(4, 'Pride and Prejudice', 'Fiction', '2024-02-15'); |
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|
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INSERT INTO sale (id, book_id, sale_timestamp, quantity, unit_price) VALUES |
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(1, 1, '2024-02-01 10:30:00', 2, 19.99), |
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(2, 1, '2024-02-15 14:20:00', 1, 19.99), |
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(3, 2, '2024-02-01 11:15:00', 3, 29.99), |
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(4, 2, '2024-02-20 16:45:00', 2, 29.99), |
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(5, 3, '2024-02-05 09:30:00', 1, 24.99), |
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(6, 3, '2024-02-25 13:20:00', 2, 24.99), |
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(7, 4, '2024-02-15 15:45:00', 1, 14.99), |
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(8, 4, '2024-02-28 10:10:00', 3, 14.99); |
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``` |
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--- |
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|
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The bookstore manager wants to analyze book performance for February 2024. They need a report showing how each book performed during that month, including details about when the book was released. |
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|
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Given the following data in table `book`: |
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|
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| id | title | category | release_date | |
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| --- | ------------------- | --------- | ------------ | |
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| 1 | The Great Gatsby | Fiction | 2024-01-01 | |
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| 2 | SQL Mastery | Technical | 2024-01-15 | |
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| 3 | Data Science 101 | Technical | 2024-02-01 | |
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| 4 | Pride and Prejudice | Fiction | 2024-02-15 | |
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|
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And the following data in table `sale`: |
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|
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| id | book_id | sale_timestamp | quantity | unit_price | |
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| --- | ------- | ------------------- | -------- | ---------- | |
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| 1 | 1 | 2024-02-01 10:30:00 | 2 | 19.99 | |
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| 2 | 1 | 2024-02-15 14:20:00 | 1 | 19.99 | |
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| 3 | 2 | 2024-02-01 11:15:00 | 3 | 29.99 | |
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| 4 | 2 | 2024-02-20 16:45:00 | 2 | 29.99 | |
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| 5 | 3 | 2024-02-05 09:30:00 | 1 | 24.99 | |
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| 6 | 3 | 2024-02-25 13:20:00 | 2 | 24.99 | |
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| 7 | 4 | 2024-02-15 15:45:00 | 1 | 14.99 | |
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| 8 | 4 | 2024-02-28 10:10:00 | 3 | 14.99 | |
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|
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Write a query that shows: |
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|
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- Book title |
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- Category |
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- Whether the book was released in February (show as 'New Release' or 'Existing') |
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- Total quantity sold |
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- Total revenue |
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- Number of sales transactions |
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|
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Only include books that had sales in February 2024, and order the results by total revenue in descending order. |
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## Expected Output |
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| title | category | release_status | total_quantity | total_revenue | sale_count | |
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| ------------------- | --------- | -------------- | -------------- | ------------- | ---------- | |
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| SQL Mastery | Technical | Existing | 5 | 149.95 | 2 | |
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| Data Science 101 | Technical | New Release | 3 | 74.97 | 2 | |
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| The Great Gatsby | Fiction | Existing | 3 | 59.97 | 2 | |
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| Pride and Prejudice | Fiction | New Release | 4 | 59.96 | 2 | |
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|
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## Solution |
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|
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```sql |
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SELECT |
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b.title, |
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b.category, |
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CASE |
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WHEN EXTRACT(MONTH FROM b.release_date) = 2 THEN 'New Release' |
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ELSE 'Existing' |
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END as release_status, |
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SUM(s.quantity) as total_quantity, |
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SUM(s.quantity * s.unit_price) as total_revenue, |
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COUNT(*) as sale_count |
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FROM book b |
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INNER JOIN sale s ON b.id = s.book_id |
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WHERE |
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EXTRACT(MONTH FROM s.sale_timestamp) = 2 |
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AND EXTRACT(YEAR FROM s.sale_timestamp) = 2024 |
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GROUP BY |
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b.title, |
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b.category, |
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CASE |
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WHEN EXTRACT(MONTH FROM b.release_date) = 2 THEN 'New Release' |
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ELSE 'Existing' |
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END |
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ORDER BY total_revenue DESC; |
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``` |
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|
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### Explanation |
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Let's break down how this query works: |
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First, we join the tables to get book information with sales: |
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|
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```sql |
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FROM book b |
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INNER JOIN sale s ON b.id = s.book_id |
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``` |
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We filter for February 2024 sales: |
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```sql |
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WHERE |
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EXTRACT(MONTH FROM s.sale_timestamp) = 2 |
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AND EXTRACT(YEAR FROM s.sale_timestamp) = 2024 |
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``` |
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|
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We use a CASE statement to identify new releases: |
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|
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```sql |
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CASE |
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WHEN EXTRACT(MONTH FROM b.release_date) = 2 THEN 'New Release' |
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ELSE 'Existing' |
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END as release_status |
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``` |
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|
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We calculate aggregates for each book: |
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```sql |
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SUM(s.quantity) as total_quantity, |
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SUM(s.quantity * s.unit_price) as total_revenue, |
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COUNT(*) as sale_count |
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``` |
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Finally, we group by the necessary columns and order by revenue: |
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|
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```sql |
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GROUP BY |
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b.title, |
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b.category, |
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CASE |
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WHEN EXTRACT(MONTH FROM b.release_date) = 2 THEN 'New Release' |
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ELSE 'Existing' |
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END |
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ORDER BY total_revenue DESC |
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``` |
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