Multiple CTEs in one query in PostgreSQL - Mini Project: Build & Apply

Using Multiple CTEs in One PostgreSQL Query

📖 Scenario: You work in a small bookstore's database team. The store wants to analyze its sales data to find the total sales per author and then identify authors with sales above a certain threshold.

🎯 Goal: Build a PostgreSQL query using multiple Common Table Expressions (CTEs) to first calculate total sales per book, then total sales per author, and finally select authors with sales above a threshold.

📋 What You'll Learn

Create a CTE named book_sales that sums sales per book.

Create a second CTE named author_sales that sums sales per author using book_sales.

Use a final SELECT statement to get authors with total sales greater than a threshold.

Use exact CTE names and column names as specified.

💡 Why This Matters

🌍 Real World

Bookstores and many businesses use multiple CTEs to organize complex queries for reporting and analysis.

💼 Career

Knowing how to write multiple CTEs is important for data analysts and database developers to write clear and efficient SQL queries.

1

Create the book_sales CTE

Write a CTE named book_sales that selects book_id and sums quantity as total_quantity from the sales table, grouping by book_id.

PostgreSQL

-- Write the book_sales CTE here
WITH book_sales AS (
    -- Your code here
)

Hint

Use WITH book_sales AS (SELECT book_id, SUM(quantity) AS total_quantity FROM sales GROUP BY book_id).

2

Add the author_sales CTE

Add a second CTE named author_sales that joins book_sales with books on book_id, then sums total_quantity as author_total grouped by author.

PostgreSQL

WITH book_sales AS (
    SELECT book_id, SUM(quantity) AS total_quantity
    FROM sales
    GROUP BY book_id
),
-- Add the author_sales CTE here

Hint

Join book_sales with books and group by b.author.

3

Add a threshold variable

Create a variable named sales_threshold and set it to 100 to filter authors with sales above this number.

PostgreSQL

WITH book_sales AS (
    SELECT book_id, SUM(quantity) AS total_quantity
    FROM sales
    GROUP BY book_id
),
author_sales AS (
    SELECT b.author, SUM(bs.total_quantity) AS author_total
    FROM book_sales bs
    JOIN books b ON bs.book_id = b.book_id
    GROUP BY b.author
)
-- Define sales_threshold variable here

Hint

Use SELECT 100 AS sales_threshold to define the threshold.

4

Select authors with sales above the threshold

Write a final SELECT statement that selects author and author_total from author_sales where author_total is greater than 100.

PostgreSQL

WITH book_sales AS (
    SELECT book_id, SUM(quantity) AS total_quantity
    FROM sales
    GROUP BY book_id
),
author_sales AS (
    SELECT b.author, SUM(bs.total_quantity) AS author_total
    FROM book_sales bs
    JOIN books b ON bs.book_id = b.book_id
    GROUP BY b.author
)
-- Write the final SELECT statement here

Hint

Filter authors with author_total > 100.

Practice

1. What is the main purpose of using multiple CTEs (Common Table Expressions) in a single PostgreSQL query?

easy

A. To speed up the database server hardware

B. To create multiple databases at once

C. To permanently store data in the database

D. To break a complex query into smaller, readable parts

Solution

1. Step 1: Understand what CTEs do

   CTEs let you write parts of a query separately with names, making complex queries easier to read.

2. Step 2: Identify the purpose of multiple CTEs

   Using multiple CTEs helps organize and simplify complex logic by splitting it into smaller named queries.

3. Final Answer:

   To break a complex query into smaller, readable parts -> Option D

4. Quick Check:

   Multiple CTEs = Query readability [OK]

Hint: Think of CTEs as named steps in a recipe [OK]

Common Mistakes:

• Confusing CTEs with permanent tables
• Thinking CTEs improve hardware speed
• Assuming CTEs create databases

2. Which of the following is the correct syntax to define two CTEs named cte1 and cte2 in one PostgreSQL query?

easy

A. WITH cte1 AS (SELECT * FROM table1), cte2 AS (SELECT * FROM table2) SELECT * FROM cte1 JOIN cte2 ON cte1.id = cte2.id;

B. WITH cte1 (SELECT * FROM table1); WITH cte2 (SELECT * FROM table2); SELECT * FROM cte1, cte2;

C. WITH cte1 = SELECT * FROM table1, cte2 = SELECT * FROM table2 SELECT * FROM cte1 JOIN cte2;

D. WITH cte1 AS SELECT * FROM table1, cte2 AS SELECT * FROM table2 SELECT * FROM cte1 JOIN cte2;

Solution

1. Step 1: Recall correct CTE syntax

   CTEs start with WITH, each named, followed by AS and a query in parentheses, separated by commas.

2. Step 2: Check each option's syntax

   WITH cte1 AS (SELECT * FROM table1), cte2 AS (SELECT * FROM table2) SELECT * FROM cte1 JOIN cte2 ON cte1.id = cte2.id; correctly uses commas, AS, and parentheses. Others miss parentheses or use wrong separators.

3. Final Answer:

   WITH cte1 AS (SELECT * FROM table1), cte2 AS (SELECT * FROM table2) SELECT * FROM cte1 JOIN cte2 ON cte1.id = cte2.id; -> Option A

4. Quick Check:

   Correct CTE syntax = WITH cte1 AS (SELECT * FROM table1), cte2 AS (SELECT * FROM table2) SELECT * FROM cte1 JOIN cte2 ON cte1.id = cte2.id; [OK]

Hint: Remember: WITH cte_name AS (query), separate multiple CTEs by commas [OK]

Common Mistakes:

• Omitting parentheses around CTE queries
• Using semicolons between CTEs
• Using '=' instead of AS

3. Given the following query, what will be the output?

WITH cte1 AS (SELECT 1 AS num), cte2 AS (SELECT num * 2 AS double_num FROM cte1) SELECT * FROM cte2;

medium

A. [{num: 2}]

B. [{num: 1, double_num: 2}]

C. [{double_num: 2}]

D. Syntax error

Solution

1. Step 1: Analyze cte1

   cte1 returns one row with column num = 1.

2. Step 2: Analyze cte2 using cte1

   cte2 selects num * 2 as double_num, so double_num = 1 * 2 = 2.

3. Step 3: Final SELECT from cte2

   Only double_num column is selected, so output is [{double_num: 2}].

4. Final Answer:

   [{double_num: 2}] -> Option C

5. Quick Check:

   cte2 output columns = double_num [OK]

Hint: Check which columns each CTE outputs carefully [OK]

Common Mistakes:

• Assuming cte2 includes all columns from cte1
• Expecting num column in final output
• Thinking query has syntax errors

4. Identify the error in this query using multiple CTEs:

WITH cte1 AS (SELECT id, name FROM users), cte2 AS (SELECT id, age FROM users) SELECT cte1.id, cte1.name, cte2.age FROM cte1 JOIN cte2 ON cte1.id = cte2.user_id;

medium

A. CTEs must be defined with semicolons

B. Column cte2.user_id does not exist

C. Missing comma between CTEs

D. Cannot join two CTEs in one query

Solution

1. Step 1: Check CTE definitions

   Both cte1 and cte2 select id and other columns from users table correctly.

2. Step 2: Check JOIN condition

   Query joins on cte1.id = cte2.user_id, but cte2 has no user_id column, only id.

3. Step 3: Identify error

   Using cte2.user_id causes a column not found error.

4. Final Answer:

   Column cte2.user_id does not exist -> Option B

5. Quick Check:

   Check column names in JOIN = Column cte2.user_id does not exist [OK]

Hint: Verify column names in JOIN conditions carefully [OK]

Common Mistakes:

• Assuming user_id exists in cte2
• Thinking semicolons separate CTEs
• Believing CTEs cannot be joined

5. You want to calculate the average order amount per customer and then find customers whose average order is above 100. Which query correctly uses multiple CTEs to achieve this?

hard

A. WITH avg_orders AS (SELECT customer_id, AVG(amount) AS avg_amount FROM orders GROUP BY customer_id), high_avg AS (SELECT customer_id FROM avg_orders WHERE avg_amount > 100) SELECT * FROM high_avg;

B. WITH avg_orders AS (SELECT customer_id, SUM(amount) AS avg_amount FROM orders GROUP BY customer_id), high_avg AS (SELECT customer_id FROM avg_orders WHERE avg_amount > 100) SELECT * FROM high_avg;

C. WITH avg_orders AS (SELECT customer_id, AVG(amount) AS avg_amount FROM orders), high_avg AS (SELECT customer_id FROM avg_orders WHERE avg_amount > 100) SELECT * FROM high_avg;

D. WITH avg_orders AS (SELECT customer_id, AVG(amount) AS avg_amount FROM orders GROUP BY customer_id) SELECT * FROM avg_orders WHERE avg_amount > 100;

Solution

1. Step 1: Calculate average order per customer

   WITH avg_orders AS (SELECT customer_id, AVG(amount) AS avg_amount FROM orders GROUP BY customer_id), high_avg AS (SELECT customer_id FROM avg_orders WHERE avg_amount > 100) SELECT * FROM high_avg; correctly uses AVG(amount) with GROUP BY customer_id in avg_orders CTE.

2. Step 2: Filter customers with avg_amount > 100

   WITH avg_orders AS (SELECT customer_id, AVG(amount) AS avg_amount FROM orders GROUP BY customer_id), high_avg AS (SELECT customer_id FROM avg_orders WHERE avg_amount > 100) SELECT * FROM high_avg; uses a second CTE high_avg to select customers meeting the condition.

3. Step 3: Final SELECT returns customers with high average orders

   WITH avg_orders AS (SELECT customer_id, AVG(amount) AS avg_amount FROM orders GROUP BY customer_id), high_avg AS (SELECT customer_id FROM avg_orders WHERE avg_amount > 100) SELECT * FROM high_avg; selects all from high_avg, giving correct result.

4. Final Answer:

   WITH avg_orders AS (SELECT customer_id, AVG(amount) AS avg_amount FROM orders GROUP BY customer_id), high_avg AS (SELECT customer_id FROM avg_orders WHERE avg_amount > 100) SELECT * FROM high_avg; -> Option A

5. Quick Check:

   Correct aggregation and filtering = WITH avg_orders AS (SELECT customer_id, AVG(amount) AS avg_amount FROM orders GROUP BY customer_id), high_avg AS (SELECT customer_id FROM avg_orders WHERE avg_amount > 100) SELECT * FROM high_avg; [OK]

Hint: Use one CTE for aggregation, another for filtering [OK]

Common Mistakes:

• Using SUM instead of AVG for average calculation
• Missing GROUP BY clause
• Not filtering in a separate CTE