# 9 Spotify SQL Interview Questions (Updated 2024)

Updated on

April 2, 2024

At Spotify, SQL is used across the company for analyzing user listening behaviors for custom playlist creation and managing databases to optimize server performance during peak streaming hours. Unsurprisingly this is why Spotify asks SQL query questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.

In case you're preparing for a SQL Assessment, here’s 9 Spotify SQL interview questions to practice, which are similar to recently asked questions at Spotify – can you solve them?

## 9 Spotify SQL Interview Questions

Sure, here is a SQL interview question potentially suitable for Spotify:

### SQL Question 1: Identify Spotify's Most Frequent Listeners

Spotify wants to identify their 'whale users', these are users who listen to the most tracks every month. They are potential customers to involve in user feedback sessions. Given the database tables and , write a SQL query to identify the top 5 users who have listened to the most unique tracks in the last 30 days. Assume today's date is .

##### Example Input:
1001user110/02/2021user1@gmail.com
2002user222/05/2022user2@yahoo.com
3003user301/01/2022user3@hotmail.com
4004user415/07/2021user4@aol.com
5005user524/12/2021user5@msn.com
##### Example Input:
listen_iduser_idlisten_datetrack_id
1100102/03/2023100
2100102/03/2023101
3100103/03/2023100
4200203/03/2023103
5200203/03/2023104
5300303/03/2023100
6400403/03/2023104
7500503/03/2023100

Here, we're joining the table and table on . The WHERE clause is used to specify the date range for the last 30 days. We then group the results by and to calculate the total number of unique tracks each user has listened to within the specified time range. This total number is counted using . Results are ordered in descending order by to reveal the top 5 users who have listened to the most unique tracks in the last 30 days.

### SQL Question 2: Analyze Artist Popularity Over Time

Question Description: Let's assume you are a Data Analyst at Spotify. You are given a data table named containing daily listening counts for different artists. The table has three columns: , , and .

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

##### Example Input:
artist_idlisten_datedaily_listens
12022-06-0115000
12022-06-0221000
12022-06-0317000
22022-06-0125000
22022-06-0227000
22022-06-0329000

Notice that the column is date formatted.

##### Example Output:
artist_idlisten_daterolling_avg_listens
12022-06-0115000.00
12022-06-0218000.00
12022-06-0317666.67
22022-06-0125000.00
22022-06-0226000.00
22022-06-0327000.00

Please pay attention to the rounding in the result.

Explanation:

This SQL query computes the rolling average by using a window function AVG with a window frame defined as "the previous 6 days plus the current row".

The clause ensures the rolling average is calculated separately for each artist.

The clause is used to order the rows in each partition by the .

The clause defines the window frame for the window function. It states that for a given row, consider all rows from 6 days before to the current row.

Finally, the function calculates the average of daily_listens over the defined window frame.

The at the end just keeps the result set ordered by artist and date.

To solve another question about calculating rates, try this TikTok SQL question on DataLemur's interactive coding environment:

### SQL Question 3: What distinguishes an inner join from a full outer join?

A full outer join returns all rows from both tables, including any unmatched rows, whereas an inner join only returns rows that match the join condition between the two tables.

For an example of each one, say you had sales data exported from Spotify's Salesforce CRM stored in a datawarehouse which had two tables: and .

: retrieves rows from both tables where there is a match in the shared key or keys.

This query will return rows from the sales and tables that have matching customer id values. Only rows with matching values will be included in the results.

: retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, NULL values will be returned for the columns of the non-matching table.

Here is an example of a SQL full outer join using the sales and tables:

### SQL Question 4: Music Streaming Statistics

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

1. table contains information about users.
2. table contains information about songs.
3. table contains information about song artists.
4. table logs every song listened to by each user.

The following relationships hold:

• Every song has one and only one artist, but an artist can have multiple songs.
• Every song can be listened to by multiple users, and every user can listen to multiple songs.

Your goal is to write a SQL query that returns each user's favourite artist, based on the number of songs they've listened to by the artist.

1user101USA
2user202UK
3user303Brazil
##### Example Input:
song_idsong_nameartist_id
101song1011001
102song1021002
103song1031001
##### Example Input:
artist_idartist_name
1001artist1001
1002artist1002
##### Example Input:
user_idsong_idstream_time
11015:00
11025:30
11036:00
21018:00
21039:00
310210:00

The above query will find the count of songs each user has listened to by each artist, grouping by both user_id and artist_id. This result is sorted in descending order of the count, and the top record for each user represents their favorite artist. The outer query then joins this result back to the artists and users tables to get the respective names.

To practice a related SQL problem on DataLemur's free online SQL coding environment, attempt this Facebook SQL Interview question:

### SQL Question 5: What is denormalization?

Denormalization is a technique used to improve the read performance of a database, typically at the expense of some write performance.

By adding redundant copies of data or grouping data together in a way that does not follow normalization rules, denormalization improves the performance and scalability of a database by eliminating costly join operations, which is important for OLAP use cases that are read-heavy and have minimal updates/inserts.

### SQL Question 6: Filter Spotify Users Based on Subscription and Activity

As a data analyst at Spotify, you are tasked with extracting a list of active Premium subscribers who have listened to at least 15 different artists in the current month. Active users are those who have logged in within the last 30 days.

Assuming you have two tables:

##### Example Input:

This query first groups the table by and calculates the count of distinct artists each user interacted with in August. It then joins this table with the table on . The WHERE clause filters for Premium users who have logged in within the last 30 days and have interacted with at least 15 different artists.

To solve a similar problem about calculating rates, solve this SQL interview question from TikTok within DataLemur's interactive SQL code editor:

### SQL Question 7: What distinguishes a left join from a right join?

"In SQL, a join generally retrieves rows from multiple tables and combines them into a single result set. For an example of the difference between a left vs. right join, suppose you had a table of Spotify orders and Spotify customers.

A retrieves all rows from the left table (in this case, the Orders table) and any matching rows from the right table (the Customers table). If there is no match in the right table, NULL values will be returned for the right table's columns.

A combines all rows from the right table (in this case, the Customers table) and any matching rows from the left table (the Orders table). If there is no match in the left table, NULL values will be displayed for the left table's columns.

### SQL Question 8: Calculate the average listening duration for each music genre on Spotify

Suppose that Spotify would like to understand better the average listening duration for each genre of music on their platform. As a data scientist, your task is to write a SQL query that calculates the average listening duration per genre for every month.

Assume you have access to a user_activity table and a songs table with the following schema:

user_activity Example Input:

activity_iduser_idsong_idtimestamplistening_duration_sec
110150012022-03-01 09:00:00210
210269852022-03-01 11:30:00120
310350012022-03-01 15:45:00300
410169852022-04-01 08:45:00180
510250012022-04-01 10:00:00240

songs Example Input:

song_idgenre
5001Rock
6985Pop

Your aim is to produce a table like:

##### Example Output:
mthgenreavg_listening_duration_sec
3Rock255
3Pop120
4Rock240
4Pop180

In order to get to the answer, we need to join the two tables on song_id and then use the GROUP BY clause. The AVG() function can be used with GROUP BY to find the average listening duration for each music genre.

This SQL query does the following:

• Joins the user_activity table (as ua) and the songs table (as s) on the song_id field.
• Uses the EXTRACT function to get the month from the timestamp in user_activity.
• Groups by month and genre.
• Uses AVG to calculate the average listening_duration_sec for each genre in each month. The result will be average listening duration per genre for every month.

To solve another question about calculating rates, solve this SQL interview question from TikTok within DataLemur's interactive coding environment:

### SQL Question 9: Find Users Who've Listened to All Albums of a Specific Artist

As an analyst at Spotify, you're tasked to identify all users who have listened to all albums of the artist "Adele". Assume you have access to a table that keeps track of user information and an table that keeps track of all instances where a user listened to an album. Here are the table structures:

user_iduser_name
1John
2Jane
3Alice

listen_iduser_idartist_namealbum_name

Using SQL, write a query to find all users who have listened to all Adele's albums (19, 21, 25).

This query works by first identifying all distinct Adele's albums and then checking whether there are any of these albums that a certain user has not listened to. If a user has listened to all Adele's albums, they will not have any of Adele's albums that they have not listened to. Such users are selected by the query.

To solve a related SQL interview question on DataLemur's free interactive SQL code editor, solve this Meta SQL interview question:

### How To Prepare for the Spotify SQL Interview

The key to acing a Spotify SQL interview is to practice, practice, and then practice some more! In addition to solving the above Spotify SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Facebook, Google and unicorn tech startups.

Each exercise has multiple hints, detailed solutions and best of all, there is an interactive coding environment so you can instantly run your SQL query and have it executed.

To prep for the Spotify SQL interview you can also be useful to practice SQL problems from other tech companies like:

But if your SQL query skills are weak, forget about going right into solving questions – improve your SQL foundations with this DataLemur SQL Tutorial.

This tutorial covers SQL topics like joining a table to itself and window functions – both of which pop up routinely in Spotify interviews.

### Spotify Data Science Interview Tips

#### What Do Spotify Data Science Interviews Cover?

For the Spotify Data Science Interview, besides SQL questions, the other types of questions to practice:

• Probability & Stats Questions
• Python or R Programming Questions
• Business Sense and Product-Sense Questions
• Machine Learning and Predictive Modeling Questions
• Behavioral & Resume-Based Questions

#### How To Prepare for Spotify Data Science Interviews?

The best way to prepare for Spotify Data Science interviews is by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from companies like Microsoft, Google & Amazon
• A Crash Course on SQL, Product-Sense & ML
• Amazing Reviews (900+ reviews, 4.5-star rating)