GROUP BY multiple columns in SQL - Step-by-Step Execution

Concept Flow - GROUP BY multiple columns

Start with table data

↓

Select columns to group by

↓

Group rows by unique combinations of these columns

↓

Aggregate data within each group

↓

Return grouped result set

The query groups rows by unique combinations of multiple columns, then aggregates data within each group.

Execution Sample

SQL

SELECT department, role, COUNT(*) AS count
FROM employees
GROUP BY department, role;

Groups employees by department and role, counting how many employees are in each group.

Execution Table

Step	Action	Current Grouping	Rows in Group	Aggregate Result
1	Start with all rows	None	All 9 rows	None
2	Group by department='Sales', role='Manager'	('Sales', 'Manager')	2 rows	Count=2
3	Group by department='Sales', role='Associate'	('Sales', 'Associate')	2 rows	Count=2
4	Group by department='HR', role='Manager'	('HR', 'Manager')	1 row	Count=1
5	Group by department='HR', role='Associate'	('HR', 'Associate')	1 row	Count=1
6	Group by department='IT', role='Developer'	('IT', 'Developer')	2 rows	Count=2
7	Group by department='IT', role='Manager'	('IT', 'Manager')	1 row	Count=1
8	Return all grouped results	All groups	Each group's rows	Counts per group

💡 All unique combinations of department and role processed, aggregation complete.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	After Step 5	After Step 6	After Step 7	Final
Current Group	None	('Sales', 'Manager')	('Sales', 'Associate')	('HR', 'Manager')	('HR', 'Associate')	('IT', 'Developer')	('IT', 'Manager')	All groups processed
Count	N/A	2	2	1	1	2	1	Final counts per group

Key Moments - 2 Insights

Why do we group by both department and role instead of just one?

Grouping by both columns creates groups for each unique combination, as shown in execution_table rows 2-7. Grouping by only one column would mix different roles together.

What does COUNT(*) count in each group?

COUNT(*) counts the number of rows in each group, as seen in the 'Aggregate Result' column in execution_table rows 2-7.

Visual Quiz - 3 Questions

Look at the execution_table, what is the count for the group ('IT', 'Developer')?

A 1

B 3

C 2

D 0

💡 Hint

Check the 'Aggregate Result' column at Step 6 in the execution_table.

At which step does the grouping for ('HR', 'Associate') happen?

A Step 3

B Step 5

C Step 4

D Step 6

💡 Hint

Look at the 'Current Grouping' column in the execution_table.

If we remove 'role' from GROUP BY, what happens to the groups?

A Groups combine by department only

B Groups combine by role only

C Groups stay the same

D No groups are formed

💡 Hint

Refer to key_moments about grouping by multiple columns.

Concept Snapshot

GROUP BY multiple columns syntax:
SELECT col1, col2, aggregate_function()
FROM table
GROUP BY col1, col2;

Groups rows by unique combinations of col1 and col2,
then applies aggregation per group.
Useful to analyze data by multiple categories.

Full Transcript

This visual execution shows how SQL groups rows by multiple columns. Starting with all rows, the query groups them by unique combinations of department and role. Each group contains rows sharing the same values in both columns. Then, an aggregate function like COUNT(*) counts rows in each group. The execution table traces each group formed and the count result. Variable tracking shows how the current group and count change step-by-step. Key moments clarify why grouping by multiple columns creates more specific groups and what COUNT(*) counts. The quiz tests understanding of group counts, step order, and effects of changing GROUP BY columns. The snapshot summarizes syntax and behavior for quick reference.

Practice

1. What does the SQL clause GROUP BY column1, column2 do?

easy

A. Sorts the table by column1 and then column2

B. Groups rows by unique combinations of values in column1 and column2

C. Filters rows where column1 equals column2

D. Joins two tables on column1 and column2

Solution

1. Step 1: Understand GROUP BY purpose

   The GROUP BY clause groups rows that have the same values in specified columns.

2. Step 2: Apply to multiple columns

   When multiple columns are listed, grouping happens on unique combinations of those columns' values.

3. Final Answer:

   Groups rows by unique combinations of values in column1 and column2 -> Option B

4. Quick Check:

   GROUP BY multiple columns = group by combinations [OK]

Hint: GROUP BY multiple columns groups by combined unique values [OK]

Common Mistakes:

• Thinking GROUP BY sorts data
• Confusing GROUP BY with WHERE filtering
• Assuming GROUP BY joins tables

2. Which of the following is the correct syntax to group data by two columns named city and year?

easy

A. SELECT city, year FROM table GROUP city, year;

B. SELECT city, year FROM table ORDER BY city, year;

C. SELECT city, year FROM table GROUP BY city, year;

D. SELECT city, year FROM table GROUP BY city year;

Solution

1. Step 1: Recall GROUP BY syntax

   The correct syntax is GROUP BY followed by column names separated by commas.

2. Step 2: Check each option

   SELECT city, year FROM table GROUP BY city, year; uses correct syntax with commas. SELECT city, year FROM table ORDER BY city, year; uses ORDER BY, which is for sorting. SELECT city, year FROM table GROUP city, year; misses BY keyword. SELECT city, year FROM table GROUP BY city year; misses comma between columns.

3. Final Answer:

   SELECT city, year FROM table GROUP BY city, year; -> Option C

4. Quick Check:

   GROUP BY columns separated by commas [OK]

Hint: Use GROUP BY with commas between columns [OK]

Common Mistakes:

• Using ORDER BY instead of GROUP BY
• Omitting BY keyword after GROUP
• Missing commas between column names

3. Given the table sales with columns region, product, and amount, what will this query return?

SELECT region, product, SUM(amount) FROM sales GROUP BY region, product;

medium

A. Total sales amount for each region only

B. Syntax error due to missing GROUP BY columns

C. Total sales amount for each product only

D. Total sales amount for each region and product combination

Solution

1. Step 1: Analyze SELECT and GROUP BY columns

   The query groups rows by both region and product, so each group is a unique pair of region and product.

2. Step 2: Understand aggregation function SUM(amount)

   SUM(amount) calculates total sales amount for each group of region and product.

3. Final Answer:

   Total sales amount for each region and product combination -> Option D

4. Quick Check:

   GROUP BY region, product + SUM = totals per pair [OK]

Hint: GROUP BY columns + SUM aggregates per group [OK]

Common Mistakes:

• Thinking it sums only by one column
• Assuming syntax error without reason
• Ignoring that all selected non-aggregated columns must be grouped

4. Identify the error in this query:

SELECT department, role, COUNT(*) FROM employees GROUP BY department;

medium

A. Missing role column in GROUP BY clause

B. COUNT(*) cannot be used with GROUP BY

C. department should not be in GROUP BY

D. SELECT must include only aggregated columns

Solution

1. Step 1: Check SELECT columns vs GROUP BY columns

   Columns in SELECT that are not aggregated must appear in GROUP BY. Here, role is in SELECT but missing in GROUP BY.

2. Step 2: Understand aggregation rules

   COUNT(*) is valid, but all non-aggregated columns must be grouped to avoid errors.

3. Final Answer:

   Missing role column in GROUP BY clause -> Option A

4. Quick Check:

   All non-aggregated SELECT columns must be in GROUP BY [OK]

Hint: Include all non-aggregated SELECT columns in GROUP BY [OK]

Common Mistakes:

• Ignoring missing columns in GROUP BY
• Thinking COUNT(*) is invalid with GROUP BY
• Assuming GROUP BY only needs one column

5. You have a transactions table with columns customer_id, month, and amount. You want to find the average transaction amount per customer per month, but only for months where the customer made more than 3 transactions. Which query correctly achieves this?

hard

A. SELECT customer_id, month, AVG(amount) FROM transactions GROUP BY customer_id, month HAVING COUNT(*) > 3;

B. SELECT customer_id, month, AVG(amount) FROM transactions WHERE COUNT(*) > 3 GROUP BY customer_id, month;

C. SELECT customer_id, month, AVG(amount) FROM transactions GROUP BY customer_id HAVING COUNT(*) > 3;

D. SELECT customer_id, month, AVG(amount) FROM transactions GROUP BY month HAVING COUNT(*) > 3;

Solution

1. Step 1: Understand filtering groups with HAVING

   HAVING filters groups after grouping. To filter groups with more than 3 transactions, use HAVING COUNT(*) > 3.

2. Step 2: Group by both customer_id and month

   To get average per customer per month, group by both columns.

3. Step 3: Check each option

   SELECT customer_id, month, AVG(amount) FROM transactions GROUP BY customer_id, month HAVING COUNT(*) > 3; correctly uses GROUP BY customer_id, month and HAVING COUNT(*) > 3. SELECT customer_id, month, AVG(amount) FROM transactions WHERE COUNT(*) > 3 GROUP BY customer_id, month; misuses WHERE with COUNT(). SELECT customer_id, month, AVG(amount) FROM transactions GROUP BY customer_id HAVING COUNT(*) > 3; groups only by customer_id, missing month. SELECT customer_id, month, AVG(amount) FROM transactions GROUP BY month HAVING COUNT(*) > 3; groups only by month, missing customer_id.

4. Final Answer:

   SELECT customer_id, month, AVG(amount) FROM transactions GROUP BY customer_id, month HAVING COUNT(*) > 3; -> Option A

5. Quick Check:

   Use HAVING to filter grouped counts [OK]

Hint: Use HAVING to filter groups after GROUP BY [OK]

Common Mistakes:

• Using WHERE with aggregate functions
• Grouping by only one column when two needed
• Filtering before grouping instead of after