SQL HAVING

Lesson Objectives

By the end of this lesson, you will:

Learn how to filter aggregated data using HAVING
Understand the difference between WHERE and HAVING
Write SQL queries to analyze Netflix user engagement using aggregated filters

🎬 Scenario: Finding Active Netflix Users

Your manager at Netflix, Alex, wants to identify power users—those who have watched at least 3 different series.

Can you find users who have watched at least 3 series?

Your task is to query the watch_history table and filter results based on aggregated values.

history_id	user_id	series_id	watch_date	watch_duration_minutes	completed
1	1	2	“2024-02-10T18:30:00”	60	false
2	2	4	“2024-01-20T21:00:00”	50	true
3	3	1	“2024-02-01T19:45:00”	75	true
4	4	3	“2024-02-15T17:10:00”	30	false
5	5	10	“2024-02-18T23:00:00”	45	true
6	1	5	“2024-02-12T14:30:00”	50	true
7	2	7	“2024-02-15T19:00:00”	40	false
8	3	9	“2024-02-20T20:15:00”	60	true
9	4	6	“2024-02-22T21:45:00”	30	false
10	5	8	“2024-02-25T18:00:00”	40	true
11	1	4	“2024-03-01T20:00:00”	25	false
12	2	2	“2024-03-03T22:30:00”	90	true

1. Understanding the Difference Between WHERE and HAVING

Both WHERE and HAVING allow us to filter data, but they work differently:

Clause	Used For	Works With
`WHERE`	Filters individual rows before aggregation	Regular column values
`HAVING`	Filters aggregated results after `GROUP BY`	Aggregate functions

Example Comparison

Filtering Before Aggregation (WHERE)
Find all watch sessions that lasted more than 30 minutes:

1SELECT user_id, series_id, watch_duration_minutes  
2FROM watch_history  
3WHERE watch_duration_minutes > 30;

user_id	series_id	watch_duration_minutes
1	2	60
2	4	50
3	1	75
5	10	45
1	5	50
2	7	40
3	9	60
5	8	40
2	2	90

Filtering After Aggregation (HAVING)
Find users who have watched at least 3 different series:

1SELECT user_id, COUNT(series_id) AS total_shows_watched  
2FROM watch_history  
3GROUP BY user_id  
4HAVING COUNT(series_id) >= 3;

user_id	total_shows_watched
1	3
2	3

2. Using HAVING to Filter Aggregated Results

Since HAVING works after aggregation, we can use it to filter users based on how many shows they watched.

Example Query: Finding Users Who Watched 3+ Series

1SELECT user_id, COUNT(series_id) AS total_shows_watched  
2FROM watch_history  
3GROUP BY user_id  
4HAVING COUNT(series_id) >= 3;

user_id	total_shows_watched
1	3
2	3

What’s Happening?

COUNT(series_id) calculates how many different shows each user has watched.
HAVING COUNT(series_id) >= 3 filters the result after aggregation, keeping only users who watched at least 3 series.

3. Combining WHERE and HAVING

You can use WHERE and HAVING together in a query.

For example, if we want to:

Exclude very short watch sessions (WHERE watch_duration_minutes > 30)
Find users who watched at least 2 series (HAVING COUNT(series_id) >= 2)

We can write:

1SELECT user_id, COUNT(series_id) AS total_shows_watched  
2FROM watch_history  
3WHERE watch_duration_minutes > 30  
4GROUP BY user_id  
5HAVING COUNT(series_id) >= 2;

user_id	total_shows_watched
1	2
2	3
3	2
5	2

What’s Happening?

WHERE watch_duration_minutes > 30 filters out watch sessions before aggregation.
GROUP BY user_id groups data by user.
HAVING COUNT(series_id) >= 2 filters users based on the aggregated count.

✍️ SQL Exercises

Exercise 1: Finding Power Users

Alex wants a report of users who have watched at least 3 different series.

Filter condition:

Users must have watched at least 3 series.
Sort by total_shows_watched in descending order.

user_id	total_shows_watched
1	3
2	3

Write an SQL query to return the requested data.

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Exercise 2: Filtering Users Who Watched 2+ Series with Longer Sessions

Alex also wants to identify users who watched at least 2 series but only counting watch sessions longer than 30 minutes.

Filter condition:

Exclude watch sessions that lasted 30 minutes or less.
Count how many series each user watched.
Only include users who watched at least 2 series.

user_id	total_shows_watched
2	3
1	2
3	2
5	2

Write an SQL query to return the requested data.

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