My SQL holds a strong second position with a score of 1111.49 in the most popular relational database management system (RDBMS) worldwide. It was originally founded by MySQL AB as an open-source solution and was later acquired by Oracle Corporation. Despite being under the same umbrella, MySQL retains its open-source nature and separate product line from Oracle’s commercial database offerings.
Given the widespread use of MySQL by organizations like GitHub, Airbnb, and Uber for their data-related solutions, you’ll likely encounter questions about MySQL during data and tech interview rounds.
As a candidate in the data science and analytics field, you’ve come to the right place to find SQL and MySQL-based questions from beginner to advanced. We’ve compiled a list of 45 MySQL interview questions and answers we believe you’ll find intriguing.
Here are a few basic MySQL interview questions to warm you up:
“A DBMS (database management system) is software that manages databases. An RDBMS (relational database management system) is a type of DBMS that uses a relational model to organize data into tables with rows and columns and supports SQL for database access.”
“ACID stands for atomicity, consistency, isolation, and durability. Atomicity ensures all operations within a transaction are completed; if not, the transaction is aborted. Consistency ensures the database remains in a valid state before and after the transaction. Isolation ensures transactions are securely and independently processed without interference. Durability ensures that once a transaction is committed, it remains so, even in the event of a system failure.”
“A primary key uniquely identifies each record in a table and cannot be NULL, ensuring entity integrity. A unique constraint also ensures all values in a column or a set of columns are distinct across rows, but unlike the primary key, it allows one NULL value.”
*“I would start by checking the MySQL server status to ensure it’s running. Then, I would examine the MySQL error logs for any clues. Next, I would verify the network connectivity and firewall settings between the client and server. I would then review the MySQL configuration parameters such as max_allowed_packet
and wait_timeout
to ensure they are set appropriately.”*
“INNER JOIN returns rows when there is a match in both tables. LEFT JOIN returns all rows from the left table and matched rows from the right table; if no match, NULLs are returned for columns from the right table. RIGHT JOIN is the opposite of LEFT JOIN, returning all rows from the right table and matched rows from the left table. FULL OUTER JOIN returns rows when there is a match in one of the tables, returning NULLs for non-matching rows from either table.”
“To optimize a slow MySQL query, I’d first analyze the query itself and its execution plan using EXPLAIN. This helps identify bottlenecks like missing indexes or inefficient WHERE clauses. Then, I can optimize with targeted actions like creating indexes or refining the query to leverage existing ones. Finally, I’d test and monitor to ensure the optimization holds under the real workload.”
“Indexes significantly enhance query performance by reducing the amount of data the database engine needs to scan to find the results. They allow faster data retrieval using binary search or other efficient lookup algorithms. Proper indexing can lead to reduced I/O operations, lower CPU usage, and faster query execution times.”
“Securing a MySQL database involves several measures:
mysql_secure_installation
script.”**“Using SELECT *
retrieves all columns from a table, which can lead to inefficiencies if only a subset of columns is needed. Specifying column names in a SELECT statement retrieves only the required data, improving performance and reducing network load.”*
“A subquery, also known as an inner query or nested query, is a query within another SQL query. It is used to perform operations in multiple steps, often providing a result set that the outer query can use for filtering, comparison, or aggregation.”
“Stored procedures in MySQL are precompiled SQL codes stored in the database, which can be executed with a single call. Advantages include improved performance through precompilation, reduced network traffic by executing multiple statements in one call, enhanced security by encapsulating business logic, and maintainability through modularization of repetitive tasks.”
“To prevent SQL injection attacks, employ parameterized queries or prepared statements to separate SQL code from user input. Validate and sanitize input data to ensure it conforms to expected formats. Use the least privilege principles by granting minimal database permissions to users. Regularly update and patch database software to mitigate known vulnerabilities. Implement web application firewalls (WAFs) and input validation techniques to filter out malicious inputs.”
“The WHERE clause filters rows based on specified criteria before the query retrieves data from the database. It restricts the result set to only those rows that meet the conditions specified. The HAVING clause, on the other hand, filters rows based on aggregated values, such as those returned by GROUP BY clauses. It applies conditions to groups created by the GROUP BY clause and filters the groups based on the specified criteria.”
“The DELETE statement removes rows from a table based on specified conditions, allowing for selective deletion. It also generates transaction logs and triggers, providing the possibility of rolling back changes. The TRUNCATE statement removes all rows from a table, resetting auto-incremented columns and deallocating storage space. Unlike DELETE, TRUNCATE is not transactional and cannot be rolled back. Additionally, it resets identity columns to their seed value.”
“MySQL replication is a process where data from one MySQL server (master) is copied to one or more MySQL servers (slaves). It supports disaster recovery by providing real-time data redundancy, allowing failover to a replica if the master fails, and enabling load balancing for read operations across multiple servers.”
Let’s now move on to the intermediate-level MySQL questions designed exclusively for data science domain candidates.
rides
table contains information about the trips of Uber users across America.Write a query to get the average commute time (in minutes) for each commuter in New York (NY) and the average commute time (in minutes) across all commuters in New York.
Example:
Input:
**rides**
table
Column | Type |
---|---|
id | INTEGER |
commuter_id | INTEGER |
start_dt | DATETIME |
end_dt | DATETIME |
city | VARCHAR |
Output:
Column | Type |
---|---|
commuter_id | INTEGER |
avg_commuter_time | FLOAT |
avg_time | FLOAT |
Display results like:
commuter_id | avg_commuter_time | avg_time |
---|---|---|
11 | 27 | 45 |
22 | 97 | 45 |
33 | 11 | 45 |
Answer:
SELECT
a.commuter_id,
a.avg_commuter_time,
b.avg_time
FROM (
SELECT
commuter_id,
city,
FLOOR(AVG(TIMESTAMPDIFF(MINUTE, start_dt, end_dt))) AS avg_commuter_time
FROM rides
WHERE city = 'NY'
GROUP BY commuter_id
) a
LEFT JOIN (
SELECT
FLOOR(AVG(TIMESTAMPDIFF(MINUTE, start_dt, end_dt))) AS avg_time,
city
FROM rides
WHERE city = 'NY'
) b
ON a.city = b.city
IT
, HR
, and Marketing
, and also have a total for Other
departments, grouped by fiscal quarters.Write a query to display this result.
Note: Display only quarters where at least one transaction occurred. Quarter names should be Q1, Q2, Q3 and Q4. Q1 is from January to March.
Example:
Input:
**transactions**
table
Column | Type |
---|---|
transaction_id | INTEGER |
department | VARCHAR |
amount | FLOAT |
transaction_date | DATE |
Output:
Column | Type |
---|---|
quarter | VARCHAR |
it_spending | FLOAT |
hr_spending | FLOAT |
marketing_spending | FLOAT |
other_spending | FLOAT |
Answer:
SELECT
CASE
WHEN EXTRACT(MONTH FROM transaction_date) BETWEEN 1 AND 3 THEN 'Q1'
WHEN EXTRACT(MONTH FROM transaction_date) BETWEEN 4 AND 6 THEN 'Q2'
WHEN EXTRACT(MONTH FROM transaction_date) BETWEEN 7 AND 9 THEN 'Q3'
WHEN EXTRACT(MONTH FROM transaction_date) BETWEEN 10 AND 12 THEN 'Q4'
END AS quarter,
SUM(CASE WHEN department = 'IT' THEN amount ELSE 0 END) AS it_spending,
SUM(CASE WHEN department = 'HR' THEN amount ELSE 0 END) AS hr_spending,
SUM(CASE WHEN department = 'Marketing' THEN amount ELSE 0 END) AS marketing_spending,
SUM(CASE WHEN department NOT IN ('IT', 'HR', 'Marketing') THEN amount ELSE 0 END) AS other_spending
FROM
transactions
GROUP BY
Quarter;
Example:
Input:
**employees**
table
Column | Type |
---|---|
employee_id | INTEGER |
job_title | VARCHAR |
salary | FLOAT |
overtime_hours | INTEGER |
overtime_rate | FLOAT |
Output:
Column | Type |
---|---|
job_title | VARCHAR |
total_salaries | FLOAT |
total_overtime_payments | FLOAT |
total_compensation | FLOAT |
Answer:
SELECT
job_title,
SUM(salary) AS total_salaries,
SUM(overtime_hours * overtime_rate) AS total_overtime_payments,
SUM(salary + (overtime_hours * overtime_rate)) AS total_compensation
FROM
employees
GROUP BY
job_title;
user_experiences
, write a query to determine the percentage of users who held the title of “data analyst” immediately before holding the title “data scientist.” Immediate is defined as the user holding no other titles between the “data analyst” and “data scientist” roles.Example:
Input:
**user_experiences**
table
Column | Type |
---|---|
id | INTEGER |
position_name | VARCHAR |
start_date | DATETIME |
end_date | DATETIME |
user_id | INTEGER |
Output:
Column | Type |
---|---|
percentage | FLOAT |
Answer:
WITH added_previous_role AS (
SELECT user_id, position_name,
LAG (position_name)
OVER (PARTITION BY user_id)
AS previous_role
FROM user_experiences
),
experienced_subset AS (
SELECT *
FROM added_previous_role
WHERE position_name = 'Data Scientist'
AND previous_role = 'Data Analyst'
)
SELECT COUNT(DISTINCT experienced_subset.user_id)/
COUNT(DISTINCT user_experiences.user_id)
AS percentage
FROM user_experiences
LEFT JOIN experienced_subset
ON user_experiences.user_id = experienced_subset.user_id
transactions
table below, write a query that finds the third purchase of every user.Note: Sort the results by the **user_id**
in ascending order. If a user purchases two products at the same time, the lower **id**
field is used to determine which is the first purchase.
Example:
Input:
**transactions**
table
Columns | Type |
---|---|
id | INTEGER |
user_id | INTEGER |
created_at | DATETIME |
product_id | INTEGER |
quantity | INTEGER |
Output:
Columns | Type |
---|---|
user_id | INTEGER |
created_at | DATETIME |
product_id | INTEGER |
quantity | INTEGER |
Answer:
SELECT user_id, created_at, product_id, quantity
FROM (
SELECT
user_id
, created_at
, product_id
, quantity
, RANK() OVER (
PARTITION BY user_id
ORDER BY created_at ASC, id ASC
) AS rank_value
FROM transactions
) AS t
WHERE rank_value = 3
ORDER BY user_id ASC
Example:
Input:
**transactions**
table
Column | Type |
---|---|
id | INTEGER |
user_id | INTEGER |
created_at | DATETIME |
product_id | INTEGER |
quantity | INTEGER |
**products**
table
Column | Type |
---|---|
id | INTEGER |
name | VARCHAR |
price | FLOAT |
**users**
table
Column | Type |
---|---|
id | INTEGER |
name | VARCHAR |
sex | VARCHAR |
Output:
Column | Type |
---|---|
month | INTEGER |
num_customers | INTEGER |
num_orders | INTEGER |
order_amt | INTEGER |
Answer:
SELECT MONTH(t.created_at) AS month,
COUNT(DISTINCT t.user_id) AS num_customers,
COUNT(t.id) AS num_orders,
SUM(t.quantity * p.price) AS order_amt
FROM transactions t
JOIN products p
ON t.product_id = p.id
WHERE YEAR(created_at) ='2020'
GROUP BY 1
employees
, employee_projects
, and projects
, find the 3 lowest-paid employees that have completed at least 2 projects.Note: incomplete projects will have an end date of **NULL**
in the projects table.
Example:
Input:
**employees**
table
Column | Type |
---|---|
id | INTEGER |
first_name | VARCHAR |
last_name | VARCHAR |
salary | INTEGER |
department_id | INTEGER |
**employee_projects**
table
Column | Type |
---|---|
employee_id | INTEGER |
project_id | INTEGER |
**projects**
table
Column | Type |
---|---|
id | INTEGER |
title | VARCHAR |
start_date | DATE |
end_date | DATE |
budget | INTEGER |
Output:
Column | Type |
---|---|
employee_id | INTEGER |
salary | INTEGER |
completed_projects | INTEGER |
Answer:
SELECT ep.employee_id
, e.salary
, COUNT(p.id) AS completed_projects
FROM employee_projects AS ep
JOIN employees AS e
ON e.id = ep.employee_id
JOIN projects AS p
ON ep.project_id = p.id
WHERE p.end_date IS NOT NULL
GROUP BY 1
HAVING completed_projects > 1
ORDER BY 2
LIMIT 3
“Access control lists (ACLs) in MySQL manage permissions for users by defining what actions they can perform on specific database objects. ACLs can grant or revoke privileges, such as SELECT, INSERT, UPDATE, DELETE, and EXECUTE, on databases, tables, columns, and stored procedures. They provide granular control, allowing different permissions for different users and roles.”
“A character set in MySQL defines the set of characters that can be stored in a database. Collation is a set of rules for comparing characters within a character set, dictating how text is sorted and compared. Character sets impact storage by determining the byte representation of characters, while collation impacts retrieval by defining how strings are ordered and compared, affecting operations like sorting and searching.”
“MySQL employs various locking mechanisms such as table-level locks, row-level locks, and page-level locks to maintain data consistency during concurrent access. Table-level locks lock the entire table, row-level locks lock specific rows, and page-level locks lock groups of rows within a table to prevent conflicting modifications by concurrent transactions.”
“To write a MySQL query that checks for errors and returns user-friendly messages, I would utilize the MySQL error handling mechanism, specifically the TRY…CATCH block in stored procedures. Within the TRY block, I’d execute the query, and the CATCH block would handle any errors, providing customized error messages to the user.”
“Strategies for optimizing performance when querying large tables in MySQL include indexing frequently queried columns, partitioning the table based on usage patterns, optimizing queries by using appropriate joins and WHERE clauses, denormalizing data to reduce join complexity, and utilizing caching mechanisms such as query caching and memcached for frequently accessed data.”
“Self-joins in MySQL are used to establish relationships within a single table by joining it with itself. An example scenario where a self-join might be useful is in a hierarchical structure such as an organizational chart. For instance, to retrieve the names of all employees along with their managers’ names, a self-join on the employee table can be performed based on the manager’s ID.”
“Federated tables in MySQL allow access to data from remote databases as if they are local tables. However, they come with limitations such as lack of support for transactions, limited functionality compared to native storage engines, and potential performance overhead due to network latency and data transfer.”
“To optimize the performance of a slow UPDATE query in MySQL, I would begin by analyzing the query execution plan using EXPLAIN to identify potential bottlenecks. Then, I might consider optimizing indexes, rewriting the query to minimize the number of rows updated, breaking down the query into smaller transactions, using temporary tables for complex calculations, or optimizing disk I/O by tuning buffer pool sizes and disk configuration.”
This section is strictly for interviews at companies that actively use MySQL to manage their databases. Here are a few recurring MySQL questions that are asked in senior data science role interviews:
attribution
, and user_sessions
.**conversion**
is **true**
, then the user converted to buying on that session.**channel**
column represents which advertising platform the user was attributed to for that specific session.**user_sessions**
table maps many to one session visits back to one user.First touch attribution is defined as the channel with which the converted user was associated when they first discovered the website.
Calculate the first touch attribution for each **user_id**
that is converted.
Example:
Input:
**attribution**
table
Column | Type |
---|---|
session_id | INTEGER |
channel | VARCHAR |
conversion | BOOLEAN |
**user_sessions**
table
column | type |
---|---|
session_id | INTEGER |
created_at | DATETIME |
user_id | INTEGER |
Example output:
user_id | channel |
---|---|
123 | |
145 | |
153 | |
172 | organic |
173 |
Answer:
WITH conv AS (
SELECT us.user_id
FROM attribution AS a
INNER JOIN user_sessions AS us
ON a.session_id = us.session_id
WHERE conversion = 1
GROUP BY 1 -- group by to get distinct user_ids
),
-- get the first session by user_id and created_at time.
first_session AS (
SELECT
min(us.created_at) AS min_created_at
, conv.user_id
FROM user_sessions AS us
INNER JOIN conv
ON us.user_id = conv.user_id
INNER JOIN attribution AS a
ON a.session_id = us.session_id
GROUP BY conv.user_id
)
-- join user_id and created_at time back to the original table.
SELECT us.user_id, channel
FROM attribution
JOIN user_sessions AS us
ON attribution.session_id = us.session_id
-- now join the first session to get a single row for each user_id
JOIN first_session
-- double join
ON first_session.min_created_at = us.created_at
AND first_session.user_id = us.user_id
swipes
that holds a row for every Tinder swipe and contains a Boolean column that determines if the swipe was a right or left swipe called is_right_swipe
. The second is a table named variants
that determine which user has which variant of an AB test.Write an SQL query to output the average number of right swipes for two different variants of a feed ranking algorithm by comparing users that have swiped 10, 50, and 100 swipes in a **feed_change**
experiment.
Note: Users have to have swiped at least 10 times to be included in the subset of users to analyze the mean number of right swipes.
Example:
Input:
**variants**
table
Columns | Type |
---|---|
id | INTEGER |
experiment | VARCHAR |
variant | VARCHAR |
user_id | INTEGER |
**swipes**
table
Columns | Type |
---|---|
id | INTEGER |
user_id | INTEGER |
swiped_user_id | INTEGER |
created_at | DATETIME |
is_right_swipe | BOOLEAN |
Output:
Columns | Type |
---|---|
variant | VARCHAR |
mean_right_swipes | FLOAT |
swipe_threshold | INTEGER |
num_users | INTEGER |
Answer:
WITH swipe_ranks AS (
SELECT
swipes.user_id
, variant
, RANK() OVER (
PARTITION BY user_id ORDER BY created_at
Answering questions about MySQL and SQL involves a blend of technical prowess, problem-solving skills, and articulation of your thought process.
More technical questions may require approaching the problem step-by-step to write efficient and accurate queries. Here are a few tips that might help you answer the MySQL interview questions.
Always listen carefully to the MySQL interview questions and repeat what you understand to grasp the question’s intent. It’s particularly effective when you’re asked to write a query against a problem. By being thorough, you’ll assure the interviewer of your ability to approach database design problems methodically.
In addition to familiarizing yourself with MySQL-specific features and syntax, make sure you have a solid understanding of basic SQL concepts. This includes SELECT, INSERT, UPDATE, DELETE statements, JOIN operations, GROUP BY, ORDER BY, and aggregate functions like COUNT, SUM, and AVG.
Nothing is better than writing queries without the support of IDEs and autocomplete features to reinforce your understanding of MySQL syntax and SQL logic. Writing various queries ranging from CRUD operations to multi-table JOINs will also make you aware of the recurring patterns in questions—aiding in quicker resolution and avoiding unnecessary computations.
JOINs are among the most used operations in MySQL. Understand different types of JOINs, namely INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN, and when to use each. Also, be comfortable writing JOIN queries involving multiple tables and handling NULL values efficiently.
Ensuring query performance, especially when using multiple JOINs in large datasets, is essential to work as a data scientist. Familiarize yourself with indexing strategies along the lines of primary keys, foreign keys, composite indexes, etc, and understand their impact on query performance.
Moreover, know how to use EXPLAIN to analyze query execution plans and identify potential bottlenecks. Also, practice writing diverse queries to be aware of common performance pitfalls, such as unnecessary nested queries, inefficient WHERE clauses, and lack of index usage.
Error handling and troubleshooting are integral parts of writing economical and efficient MySQL queries. Understand how to handle errors, either through proper validation before executing queries or using try-catch blocks in stored procedures.
Be aware of common errors, such as syntax errors, data type mismatches, and database connectivity issues. With practice, these skills will become second nature.
Learn advanced MySQL features such as stored procedures, triggers, views, and user-defined functions, and understand their use cases. Finally, stay updated on the latest features and improvements introduced in newer versions of MySQL.
We’ve compiled a list of substantive and insightful MySQL interview questions. With the solution to these questions, you’ll now be able to confidently approach your next data science interview.
Prepare for MySQL interviews by mastering SQL fundamentals, refining your query writing skills, learning advanced MySQL features, and being proficient in error handling.
As a part of the preparation strategy, go through our data science SQL questions and data analyst SQL questions to solve more notable problems.