Spotify SQL Interview Questions

Spotify is a popular music streaming platform that uses data analysis and management to improve user experience and provide personalized content. Spotify heavily relies on SQL (Structured Query Language) to manage its vast database and derive valuable insights.

Whether you're preparing for a job interview at Spotify or aiming to sharpen your SQL skills, practicing with targeted questions is crucial. In this guide, we'll explore 15 essential SQL interview questions tailored for Spotify, designed to help you understand the kinds of challenges you might face and how to tackle them effectively.

Top 15 Spotify SQL Interview Questions

Here are some of the most important SQL questions that might encounter in a Spotify interview

Question 1: Top 5 Artists with Most Songs in Top 10 Global Chart Positions.

Assuming there are three Spotify tables: 'music_artists', 'music_tracks', and 'global_chart_rank', containing information about the artists, songs, and music charts, respectively.

To find the top 5 artists with the highest number of songs appearing in the Top 10 of the 'global_chart_rank' table. The query should display the artist names in ascending order along with their song appearance counts.

music_artists:

artist_id	artist_name
1	Artist A
2	Artist B
3	Artist C
4	Artist D
5	Artist E

music_tracks:

song_id	song_title	artist_id
1	Song 1	1
2	Song 2	2
3	Song 3	1
4	Song 4	3
5	Song 5	4
6	Song 6	2
7	Song 7	5
8	Song 8	1
9	Song 9	3
10	Song 10	4

global_chart_rank:

chart_id	song_id	rank
1	1	5
2	2	1
3	3	9
4	4	7
5	5	3
6	6	2
7	7	8
8	8	4
9	9	10
10	10	6

Query:

WITH top_10_songs AS (
    SELECT song_id
    FROM global_chart_rank
    WHERE rank <= 10
),
artist_song_counts AS (
    SELECT t.artist_id, COUNT(*) AS song_count
    FROM top_10_songs ts
    JOIN music_tracks t ON ts.song_id = t.song_id
    GROUP BY t.artist_id
),
ranked_artists AS (
    SELECT
        m.artist_name,
        ascnt.song_count,
        DENSE_RANK() OVER (ORDER BY ascnt.song_count DESC) AS rank
    FROM artist_song_counts ascnt
    JOIN music_artists m ON ascnt.artist_id = m.artist_id
)
SELECT artist_name, song_count
FROM ranked_artists
WHERE rank <= 5
ORDER BY rank, artist_name;

Output:

Explanations:

The query identifies the top 5 artists with the most songs in the top 10 global chart positions. It does so by counting song appearances in the top 10, ranking the artists by song count, and then selecting and sorting the top 5 artists alphabetically. This provides a clear view of the most successful artists based on chart performance.

Question 2: What are the Differences Between Inner and Full Outer Join?

An inner join and a full outer join are both types of ways to combine information from two or more tables in a database. The main difference between them is how they handle rows that don't have matching values in both tables.

Inner Join: An inner join returns only the rows that have matching values in both tables.

Example:

SELECT A.column1, B.column2
FROM TableA A
INNER JOIN TableB B ON A.common_column = B.common_column;

Full Outer Join: A full outer join returns all the rows from both tables. Where there are no matches, NULL values are used to fill in the gaps.

Example:

SELECT A.column1, B.column2
FROM TableA A
FULL OUTER JOIN TableB B ON A.common_column = B.common_column;

Question 3: Identify Spotify's Most Frequent Listeners

Assuming there are two tables: 'members' and 'member_listen_history', which contain information about the members and their listening history, respectively. Write a query to identify the top 5 members who have listened to the most unique tracks in the last 30 days.

Display the top 5 member names in ascending order of their member_id, along with the count of unique tracks they have listened to. Assume today's date is '2023-03-22'.

members Table:

member_id	member_name	registration_date	email
101	alice	2021-10-02	[email protected]
102	bob	2022-05-22	[email protected]
103	charlie	2022-01-01	[email protected]
104	dave	2021-07-15	[email protected]
105	eve	2021-12-24	[email protected]

member_listen_history Table:

listen_id	member_id	listen_date	track_id
1	101	2023-03-02	100
2	101	2023-03-02	101
3	101	2023-03-03	100
4	102	2023-03-03	103
5	102	2023-03-03	104
6	103	2023-03-03	100
7	104	2023-03-03	104
8	105	2023-03-03	100

Query:

SELECT m.member_id, m.member_name, COUNT(DISTINCT mlh.track_id) as total_unique_tracks_listened
FROM members m
INNER JOIN member_listen_history mlh ON m.member_id = mlh.member_id
WHERE mlh.listen_date BETWEEN '2023-02-22' AND '2023-03-22'
GROUP BY m.member_id, m.member_name
ORDER BY total_unique_tracks_listened DESC
LIMIT 5;

Output:

Explantions:

This query identifies the top 5 members who have listened to the most unique tracks in the last 30 days. It joins the 'members' and 'member_listen_history' tables, counts the distinct tracks each member listened to, and then lists the top 5 members in descending order of their unique track count.

Question 4: Analyze Artist Popularity Over Time

Let's assume you are a Data Analyst at Spotify. You are given a data table named 'musician_listens' containing daily listening counts for different musicians. The table has three columns: 'musician_id', 'listen_date', and 'daily_listens'.

You are required to write a SQL query to calculate the 7-day rolling average of daily listens for each musician. The rolling average should be calculated for each day for each musician based on the previous 7 days (including the current day).

musician_listens Example Input:

musician_id	listen_date	daily_listens
1	2022-06-01	15000
1	2022-06-02	21000
1	2022-06-03	17000
2	2022-06-01	25000
2	2022-06-02	27000
2	2022-06-03	29000

Query:

SELECT 
    musician_id, 
    listen_date, 
    AVG(daily_listens) OVER (
        PARTITION BY musician_id 
        ORDER BY listen_date 
        RANGE BETWEEN INTERVAL '6 days' PRECEDING AND CURRENT ROW
    ) AS rolling_avg_listens
FROM musician_listens
ORDER BY musician_id, listen_date;

Output:

Explantion:

This query calculates the 7-day rolling average of daily listens for each musician. By using the AVG function with a window frame defined as the past 7 days (including the current day), the query provides insights into the trend of each musician's daily listens over time.

Question 5: What is Denormalization?

Denormalization is a technique used to speed up database performance by intentionally adding duplicate data. Unlike normalization, which aims to minimize redundancy, denormalization sacrifices some data integrity in favor of faster data retrieval. This can be especially helpful when you need to combine information from different tables.

Question 6: Total users signed up

Write a SQL query to count the total number of users in the users table.

Table: users

user_id	username	sign_up_date	email
1001	user1	2021-02-10	[email protected]
2002	user2	2022-05-22	[email protected]
3003	user3	2022-01-01	[email protected]
4004	user4	2021-07-15	[email protected]
5005	user5	2021-12-24	[email protected]

Table: user_listen_history

listen_id	user_id	listen_date	track_id
1	1001	2023-03-02	100
2	1001	2023-03-02	101
3	1001	2023-03-03	100
4	2002	2023-03-03	103
5	2002	2023-03-03	104
6	3003	2023-03-03	100
7	4004	2023-03-03	104
8	5005	2023-03-03	100

Query:

SELECT COUNT(*) AS total_users
FROM users;

Output:

Explantion:

This query counts the total number of users in the 'users' table. By using the COUNT(*) function, it calculates the total number of rows in the table, representing the total number of registered users on the platform. The result is displayed in a column named total_users.

Question 7: Find the Most Recent Listen Date for Each User

Write a SQL query to retrieve the usernames of users who signed up before January 1, 2022.

Query:

SELECT u.user_id, u.username, MAX(ulh.listen_date) AS "Most Recent Listen Date"
FROM users u
JOIN user_listen_history ulh ON u.user_id = ulh.user_id
GROUP BY u.user_id, u.username;

Output:

Explantion:

This query retrieves the usernames of users who signed up before January 1, 2022. By joining the 'users' and 'user_listen_history' tables and grouping by user_id and username, it calculates the maximum listen date for each user. The result shows the usernames and their most recent listen dates.

Question 8: Identify Users Who Listened to a Specific Song

Retrieve the usernames of users who listened to the song with track_id 100 on the listen_date '2023-03-03'.

Query:

SELECT u.username
FROM users u
JOIN user_listen_history ulh ON u.user_id = ulh.user_id
WHERE ulh.track_id = 100
AND ulh.listen_date = '2023-03-03';

Output:

Explantion:

This query identifies users who listened to the song with track_id 100 on March 3, 2023. By joining the 'users' and 'user_listen_history' tables and filtering for the specific track_id and listen_date, it retrieves the usernames of users who listened to that song on the specified date.

Question 9: Find Users with Most Listened Tracks

Identify the top 3 users who have listened to the most unique tracks.

SELECT u.username, COUNT(DISTINCT ulh.track_id) AS unique_tracks_listened
FROM users u
JOIN user_listen_history ulh ON u.user_id = ulh.user_id
GROUP BY u.username
ORDER BY unique_tracks_listened DESC
LIMIT 3;

Output:

Explantion:

This query identifies the top 3 users who have listened to the most unique tracks. By joining the 'users' and 'user_listen_history' tables, counting the distinct track_ids for each user, and sorting them in descending order, it retrieves the usernames of the top 3 users with the highest unique track counts.

Question 10: Average Listening Duration for Each Music Genre on Spotify

Spotify aims to gain insights into the average listening duration for each genre of music on their platform. As a data scientist, your task is to craft a SQL query to compute the average listening duration per genre.

Table: songs

song_id	song_name	genre_id	duration_seconds
1	Song 1	1	180
2	Song 2	2	240
3	Song 3	1	200
4	Song 4	3	300
5	Song 5	4	220

Table: genres

genre_id	genre_name
1	Pop
2	Rock
3	Hip Hop
4	Electronic

Table: user_listen_history

listen_id	user_id	song_id	listen_duration	listen_date
1	1001	1	120	2023-03-01
2	1002	2	180	2023-03-01
3	1001	3	150	2023-03-02
4	1003	4	250	2023-03-02
5	1002	5	200	2023-03-03

Query:

SELECT g.genre_name, AVG(ulh.listen_duration) AS avg_listen_duration
FROM user_listen_history ulh
JOIN songs s ON ulh.song_id = s.song_id
JOIN genres g ON s.genre_id = g.genre_id
GROUP BY g.genre_name;

Output:

Explantion:

This query computes the average listening duration for each music genre on Spotify. By joining the 'user_listen_history', 'songs', and 'genres' tables, it calculates the average listen duration per genre and presents the results showing each genre's average listening duration.

Question 11: Total Listening Duration per Genre for Each User

Suppose Spotify wants to determine the total listening duration per genre for each user. Write a SQL query to calculate the total listening duration in seconds for each combination of user and genre, based on the user_listen_history, songs, and genres tables provided.

Query:

SELECT ulh.user_id, g.genre_id, SUM(ulh.listen_duration) AS total_listen_duration
FROM user_listen_history ulh
JOIN songs s ON ulh.song_id = s.song_id
JOIN genres g ON s.genre_id = g.genre_id
GROUP BY ulh.user_id, g.genre_id;

Output:

Explantion:

This query calculates the total listening duration per genre for each user on Spotify. By joining the 'user_listen_history', 'songs', and 'genres' tables and grouping by user and genre, it sums up the listen durations and presents the total listening duration for each combination of user and genre.

Question 12: Define a new Column using SUM() OVER (PARTITION BY ) Clauses

Query:

SELECT 
    ulh.*,
    SUM(ulh.listen_duration) OVER (PARTITION BY ulh.user_id, s.genre_id) AS total_listen_duration_per_user_genre
FROM 
    user_listen_history ulh
JOIN 
    songs s ON ulh.song_id = s.song_id;

Output:

Explanaton:

This query introduces a new column, 'total_listen_duration_per_user_genre', which calculates the total listening duration per user and genre combination. By using the SUM() OVER (PARTITION BY) clause, it sums the listen durations for each user's interactions with songs of different genres, providing insights into user preferences.

Question 13: Explain the difference between the HAVING and WHERE clauses in SQL queries.

The HAVING and WHERE clauses are both used to filter rows in SQL queries, but they operate at different stages of the query execution.

• WHERE clauses: WHERE keyword is used for fetching filtered data in a result set. It is used to fetch data according to particular criteria. WHERE keyword can also be used to filter data by matching patterns.
  
• HAVING clauses: In simpler terms MSSQL, the HAVING clause is used to apply a filter on the result of GROUP BY based on the specified condition. The conditions are Boolean type i.e. use of logical operators  (AND, OR). This clause was included in SQL as the WHERE keyword failed when we use it with aggregate expressions.

Question 14: Determine Each User's Favourite Artist Based on Listening Habits

As a Data Analyst at Spotify, suppose your team is interested in understanding the listening habits of users. You are provided with the following tables:

• user_info table contains information about users.
• track_info table contains information about songs.
• artist_info table contains information about song artists.
• user_streams table logs every song listened to by each user.

The following relationships hold:

• Each song has a single artist, but an artist is not limited to one song.
• Multiple people can listen to the same song at the same time, and each user can listen to different songs.

Table: user_info

user_id	username	sign_up_date	email
1001	user1	2021-02-10	[email protected]
2002	user2	2022-05-22	[email protected]
3003	user3	2022-01-01	[email protected]
4004	user4	2021-07-15	[email protected]
5005	user5	2021-12-24	[email protected]

Table: track_info

track_id	track_name	artist_id	duration_seconds
1	Song 1	1001	180
2	Song 2	1002	240
3	Song 3	1001	200
4	Song 4	1003	300
5	Song 5	1004	220

Table: artist_info

artist_id	artist_name
1001	Artist 1
1002	Artist 2
1003	Artist 3
1004	Artist 4

Table: user_streams

stream_id	user_id	track_id	stream_date
1	1001	1	2023-03-01
2	1002	2	2023-03-01
3	1001	3	2023-03-02
4	1003	4	2023-03-02
5	1002	5	2023-03-03

Query:

SELECT 
    u.username, 
    a.artist_name
FROM (
    SELECT 
        us.user_id, 
        ti.artist_id, 
        COUNT(*) AS num_songs,
        RANK() OVER (PARTITION BY us.user_id ORDER BY COUNT(*) DESC) as rank
    FROM 
        user_streams us
    JOIN 
        track_info ti ON us.track_id = ti.track_id
    GROUP BY 
        us.user_id, 
        ti.artist_id
) AS sub_query
JOIN 
    user_info u ON u.user_id = sub_query.user_id
JOIN 
    artist_info a ON a.artist_id = sub_query.artist_id
WHERE 
    sub_query.rank = 1;

Output:

Explantion:

This query determines each user's favorite artist based on their listening habits. By ranking the number of songs each user has streamed for each artist and selecting the top-ranking artist for each user, it reveals the most listened-to artist for each user.

Question 15: Find the User who has Streamed the most Songs by the Same Artist.

Query:

SELECT u.user_id, u.username, a.artist_name, COUNT(*) AS stream_count
FROM user_streams us
JOIN user_info u ON us.user_id = u.user_id
JOIN track_info ti ON us.track_id = ti.track_id
JOIN artist_info a ON ti.artist_id = a.artist_id
GROUP BY u.user_id, u.username, a.artist_name
ORDER BY stream_count DESC
LIMIT 1;

Output:

Explantion:

This query identifies the user who has streamed the most songs by the same artist. By joining user information, song streams, track details, and artist information, it calculates the number of streams for each user-artist combination and retrieves the user with the highest stream count for a single artist.

Tips & Tricks to Clear SQL Interview Questions

• Understand the Basics: Ensure you have a solid understanding of fundamental SQL concepts like SELECT statements, WHERE clauses, joins, and aggregate functions.
  
• Practice Regularly: Regular practice with a variety of SQL problems is key. Use online platforms or SQL databases to practice writing and optimizing queries.
  
• Learn Advanced Concepts: Beyond the basics, familiarize yourself with advanced SQL topics like window functions, CTEs (Common Table Expressions), and subqueries.
  
• Optimize Your Queries: Learn how to write efficient queries and understand the importance of indexing and query optimization techniques.
  
• Real-World Scenarios: Try to work on real-world datasets and problems. This will help you understand the practical applications of SQL and prepare you for scenario-based questions.
  
• Review and Refactor: Regularly review your queries and seek feedback. Refactor your queries for better performance and readability.

Conclusion

Preparing for a SQL interview at Spotify involves mastering a range of SQL concepts and understanding how to apply them to real-world scenarios. By practicing these top 15 questions, you’ll be well-equipped to tackle SQL challenges and demonstrate your ability to manage and analyze data effectively. Remember, the key to success is consistent practice and a thorough understanding of both basic and advanced SQL topics.