SQL JOINs Interview Questions and Answers for 2025

One real-world example of using a join to solve a problem is combining customer and order data from separate tables in a retail company's database.

Imagine that a retail company has two separate tables in its database: one for customer information and one for order information. The customer table includes columns for customer ID, name, and address, while the order table includes columns for order ID, customer ID, and purchase date.

To create a report that displays the customer's name, address, and all of their past orders, a join would be necessary to combine the customer and order data. The join would be performed using the customer ID column, which exists in both tables and can be used to link each customer to their respective orders.

The resulting table would include columns for customer ID, name, address, order ID, and purchase date. This would allow the retail company to easily view and analyze customer and order data in one place rather than having to reference multiple tables.

It's no surprise that this can pop up as one of the SQL scenario-based interview questions on JOINs.  

To return every possible combination of rows from two tables using a cartesian join, we would first select both tables and join them using a cross join or cartesian join. This would allow us to return a result set that includes every possible combination of rows from the two tables.

For example:

SELECT * FROM table1
 CROSS JOIN table2

This would return a result set that includes every combination of rows from both tables, with each row from table1 being paired with every row from table2.

There are a few situations where a subquery might be faster than a join: 

1. The subquery is returning a smaller result set: If the subquery is returning a significantly smaller result set than the join, it may be faster to execute the subquery first and then use the results to join to the larger table. 
2. The join is on a large, complex table: If the join is on a large, complex table with many indexes and a high number of rows, it may be faster to execute the subquery first and then use the results to join to the smaller table. 
3. The subquery is using an index: If the subquery is using an index to filter the results, it may be faster to execute the subquery first and then use the results to join to the other table, rather than using a join which may not be able to take advantage of the index. 
4. The join is using a full outer join: Full outer joins can be slow to execute due to the need to combine both the left and right tables, so it may be faster to execute a subquery first and then use the results to perform a left or right outer join. 

A common yet one of the most popular SQL JOINS interview questions, don't miss this one.

The fastest join to process is typically the inner join. This is because an inner join only returns rows that have a match in both tables being joined. Therefore, the database engine only has to search for matches in the specified columns, rather than searching for matches and also eliminating rows that do not have a match (as is the case with outer joins).

Another factor that can impact the speed of a join is the use of indexes. If the columns being joined have appropriate indexes, the join can be faster as the database engine can use these indexes to more efficiently locate the matching rows.

In summary, the inner join is generally the fastest join to process because it only returns matching rows and the use of indexes can further improve its performance.

1. First, make sure to properly index the tables you are joining. This will improve the performance of the join and reduce the amount of memory required. 
2. Consider using a SELECT * FROM (SELECT * FROM table1 WHERE condition1 UNION SELECT * FROM table2 WHERE condition2) as subquery instead of a full outer join if possible. This will allow you to filter out unnecessary rows before the join, which can help reduce the amount of data being joined and therefore decrease memory usage. 
3. Use LIMIT and OFFSET clauses to break up the full outer join into smaller chunks and process them one at a time. This can help prevent memory issues by limiting the amount of data being processed at any given time. 
4. If you are using a database management system (DBMS) that supports partitioning, consider partitioning your tables by a common key to improve the performance of the full outer join. 
5. If you are using a programming language such as Python or R, consider using a library that allows you to perform distributed full outer joins, which can help reduce memory usage by distributing the data across multiple machines. 

Here is an example of a Union All statement and a Full Outer Join:

Imagine we have two tables, Table A and Table B, with the following data:

Table A:

Table B:

We want to join these two tables together to get a result set that includes all the data from both tables, so we could use a Full Outer Join like this:

SELECT *
FROM TableA
FULL OUTER JOIN TableB ON TableA.ID = TableB.ID

This would give us the following result set:

However, here is an example of a Union All statement:

Table A:

Table B:

SELECT ID, Name 
FROM TableA 
UNION ALL 
SELECT ID,Name 
FROM TableB  

Output:

For example, let's say we have two tables: 

Table 1: Customers 

Table 2: Orders

We can use an aggregation on a join to calculate the total amount of money each customer has spent. The resulting table would look like this:

To achieve this, we can use the following SQL query:

SELECT Customers.Customer ID, Customers.Customer Name, SUM(Orders.Total Amount) AS Total Spent FROM Customers INNER JOIN Orders ON Customers.Customer ID = Orders.Customer ID GROUP BY Customers.Customer ID, Customers.Customer Name

One example where a self-join may be better than a subquery is when querying a table to find the names of employees and their managers.

With a self-join, the query would look something like this:

SELECT e.name AS employee, m.name AS manager FROM employees e JOIN employees m ON e.manager_id = m.employee_id

This query will join the employees table to itself, using the manager_id column to connect the employee with their manager.

Using a subquery, the query might look like this:

SELECT e.name AS employee, (SELECT name FROM employees WHERE employee_id = e.manager_id) AS manager FROM employees e

While both queries will achieve the same result, the self join may be more efficient because it only requires accessing the employees table once, rather than accessing it twice (once for the main query and once for the subquery). This can be especially beneficial when dealing with large tables or when performance is a concern.

There are several ways to avoid many-to-many joins in SQL: 

1. Use a junction table: This is a table that connects two tables through a many-to-many relationship. Instead of joining the two tables directly, you can use the junction table to join the two tables. 
2. Normalize the data: Normalization is the process of organizing a database in a way that minimizes redundancy and dependency. By normalizing the data, you can split the data into separate tables and avoid many-to-many joins. 
3. Use subqueries: Subqueries allow you to include the results of a SELECT statement within another SELECT statement. This can be used to avoid many-to-many joins. 
4. Use views: A view is a virtual table that is created based on a SELECT statement. You can create a view to avoid many-to-many joins and simplify your queries. 
5. Use denormalized data: In some cases, it may be more efficient to denormalize the data, meaning that you duplicate data in different tables to avoid many-to-many joins. This can be useful for performance reasons, but it can also increase the risk of data inconsistencies. 

Example:

Here is an example of how to update a table called "orders" from data in a table called "customers" in SQL:

UPDATE orders SET orders.customer_name = customers.name, orders.customer_email = customers.email FROM orders JOIN customers ON orders.customer_id = customers.id;  

In this example, the "orders" table is being updated with data from the "customers" table by joining the two tables on the "customer_id" column. The SET statement is used to specify which columns in the "orders" table will be updated and with which data from the "customers" table.

Yes, I have used advanced join techniques such as nested joins and hierarchical joins in my work as a data analyst. 

An example of when I would use a nested join is when I need to combine data from multiple tables that are not directly related to each other. For example, let's say I have a table of sales data and a table of customer data, and I want to join them together to get a complete view of each customer's sales history. In this case, I would first join the sales table to the customer table on the customer ID column, and then I would nest this join within another join to the product table on the product ID column. This would allow me to get all of the sales data for each customer, along with the corresponding product and customer information.

An example of when I would use a hierarchical join is when I need to combine data from a parent-child relationship within a single table. For example, let's say I have a table of employees and their managers, with each employee having a unique ID and their manager's ID listed in a separate column. In this case, I would use a hierarchical join to create a self-referencing relationship between the employee and manager IDs, allowing me to easily query the table to get the managerial hierarchy for each employee.

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Yes, I have had to troubleshoot issues with join logic in a project. One issue I encountered was when I was trying to join two tables based on a common column, but the values in the common column were not matching between the two tables. This resulted in a lot of null values in the joined table.

To resolve this issue, I first checked the data types of the common column in both tables to make sure they were the same. I also checked for any spelling or formatting differences in the values in the common column. If I still couldn't find the issue, I used a process of elimination to narrow down which rows in the tables were causing the issue by performing a series of inner and outer joins.

Ultimately, I found that there were some special characters in the common column in one of the tables that were not present in the other table. I cleaned up the data by removing these special characters and reran the join, which resulted in a successful join without any null values.

Expect to come across this, one of the most popular SQL JOINs interview questions.

A cross join, also known as a cartesian join, is a type of join in SQL that combines every row from one table with every row from another table. This results in a cartesian product of the two tables, which means that each row in the first table is paired with every row in the second table. For example, if table A has three rows and table B has four rows, the cross join would result in a table with 12 rows (3 x 4). Cross-join is SQL 99 join and Cartesian product is Oracle Proprietary join.

There are several ways to handle null values in a join: 

• Use the IS NULL or IS NOT NULL operator to filter out null values in the join condition. For example:

SELECT * FROM table1 
 INNER JOIN table2 
 ON table1.column1 = table2.column1 
 WHERE table2.column2 IS NOT NULL; 

• Use the COALESCE function to replace null values with a default value in the join condition. For example: 
  

SELECT * FROM table1 
 INNER JOIN table2 
 ON table1.column1 = COALESCE(table2.column1, 'default value');  

• Use the NULLIF function to convert null values to a different value in the join condition. For example:  

SELECT * FROM table1 
 INNER JOIN table2 
 ON table1.column1 = NULLIF(table2.column1, 'null value');  

• Use the LEFT JOIN or RIGHT JOIN instead of INNER JOIN to include null values in the results. For example:  

SELECT * FROM table1 
 LEFT JOIN table2 
 ON table1.column1 = table2.column1;  

To perform data transformation tasks using SQL joins, you would use a SELECT statement to select the data from the relevant tables and then use various types of joins (e.g. INNER JOIN, LEFT JOIN, RIGHT JOIN) to combine the data from those tables in a specific way. For example, you might use an INNER JOIN to only include data from both tables that matches on a certain column, or a LEFT JOIN to include all data from the left table and any matching data from the right table. You can then use the resulting data to transform it in various ways using additional SELECT clauses (e.g. using aggregates, CASE statements, etc.) to create a new transformed dataset.

A cartesian join, also known as a cross join, is a type of join in SQL that retrieves all possible combinations of records from two tables. To perform a cartesian join, you would need to use the CROSS JOIN keyword in your SELECT statement, followed by the names of the two tables you want to join.

For example, let's say you have two tables: Table A and Table B. Table A contains a list of products and Table B contains a list of colors. To retrieve all possible combinations of products and colors, you could use the following SQL query:

SELECT * FROM TableA CROSS JOIN TableB;

This query would return a result set containing every combination of a product from Table A and a color from Table B. For example, if Table A had 3 products and Table B had 2 colors, the result set would contain 6 rows (3 products * 2 colors).

It's important to note that cartesian joins can result in very large result sets, especially if the two tables have a large number of records. As a result, it's usually best to use more specific join conditions to limit the number of records returned. 

A WHERE clause is used to filter the rows returned from a SELECT statement, whereas an ON clause is used to specify the join criteria in a JOIN operation. 

The WHERE clause is applied after the data has been retrieved from the database, whereas the ON clause is used to determine which rows are included in the join before the data is retrieved. 

For example, in the following query: 

SELECT * FROM table1 t1 JOIN table2 t2 ON t1.id = t2.id WHERE t1.name = 'John'  

The ON clause is used to specify that the rows from table1 and table2 should be joined on the id column, while the WHERE clause filters the results to only include rows where the name in table1 is 'John'.

On the other hand, the ON clause can also include a condition to filter the rows that are included in the join, as in the following example:

SELECT * FROM table1 t1 JOIN table2 t2 ON t1.id = t2.id AND t2.status = 'active'  

In this case, the ON clause is used to specify that the rows from table1 and table2 should be joined on the id column, and only rows where the status in table2 is 'active' will be included in the join. 

The JOIN USING clause allows you to join two tables by using a common column that they both have. Here is the syntax:

SELECT * FROM table1 JOIN table2 USING (column);

This will return all rows from both tables where the values in the column are equal. The resulting table will contain all columns from both table1 and table2, with any duplicate columns being renamed with a suffix to make them unique. 

For example, consider the following two tables: 

Table1 

Table 2

We can use the JOIN USING clause to join these two tables on the id column like this:

SELECT * FROM table1 JOIN table2 USING (id);

This will return the following table:

The JOIN USING clause is equivalent to the INNER JOIN ON clause, which you can use like this:

SELECT * FROM table1 INNER JOIN table2 ON table1.id = table2.id;

There are several ways to optimize query performance when working with large data sets and multiple joins: 

1. Use appropriate indexes: Indexes help improve the performance of queries by allowing the database to quickly locate and retrieve data. When creating indexes, make sure to choose the right columns and use the correct index type (e.g. b-tree, hash). 
2. Use efficient join algorithms: Different join algorithms have different performance characteristics. Make sure to choose the right algorithm for your data and query requirements. 
3. Use appropriate data types: Using the right data types can improve query performance by reducing the amount of memory and disk space needed to store and manipulate data. 
4. Use appropriate hardware: Having the right hardware (e.g. fast CPUs, plenty of RAM) can significantly improve query performance. 
5. Use appropriate database design: Proper database design can improve query performance by reducing the number of joins needed, optimizing data layout, and minimizing data redundancy. 
6. Use appropriate database settings: Configuring the database properly (e.g. setting the right cache sizes, configuring parallel query execution) can improve query performance. 
7. Use appropriate query optimization techniques: Techniques such as using subqueries, using materialized views, and using hints can improve query performance. 
8. Use appropriate query monitoring and tuning tools: Tools such as explain plans, query profiling, and performance metrics can help identify and fix performance bottlenecks.

There are a few steps that can be taken to ensure data security when using a join clause: 

1. Use a SELECT statement to limit the number of rows that are returned: Using a SELECT statement can help to reduce the amount of data that is returned, which can help to reduce the risk of data breaches. 
2. Use a WHERE clause to filter the data that is returned: The WHERE clause can be used to filter the data that is returned, which can help to reduce the risk of data breaches. 
3. Use a LIMIT clause to limit the number of rows that are returned: The LIMIT clause can be used to limit the number of rows that are returned, which can help to reduce the risk of data breaches. 
4. Use a HAVING clause to filter the data that is returned: The HAVING clause can be used to filter the data that is returned, which can help to reduce the risk of data breaches. 
5. Use a GROUP BY clause to group the data that is returned: The GROUP BY clause can be used to group the data that is returned, which can help to reduce the risk of data breaches. 

PostgreSQL allows you to install and use extensions to add additional functionality to the database. Extensions are installed in a specific database, and once installed they can be used like any other database object.

To install an extension, you will need to use the CREATE EXTENSION statement. This statement has the following syntax:

CREATE EXTENSION [ IF NOT EXISTS ] extension_name [ WITH ] [ SCHEMA schema_name ] [ VERSION version ] [ FROM old_version ] 

The extension_name is the name of the extension that you want to install. The IF NOT EXISTS clause is optional, and it specifies that the extension should only be installed if it does not already exist in the database.

The WITH clause is optional, and it allows you to specify additional options for the extension. The SCHEMA option allows you to specify the schema in which the extension's objects should be created. The VERSION option allows you to specify the version of the extension that you want to install. The FROM option allows you to specify the version of the extension that you are upgrading from.

For example, to install the my_extension extension in the public schema, you could use the following statement:

CREATE EXTENSION IF NOT EXISTS my_extension WITH SCHEMA public; 

To uninstall an extension, you can use the DROP EXTENSION statement. This statement has the following syntax:

DROP EXTENSION [ IF EXISTS ] extension_name [ CASCADE | RESTRICT ] 

The extension_name is the name of the extension that you want to uninstall. The IF EXISTS clause is optional, and it specifies that the extension should only be uninstalled if it exists in the database.

The CASCADE and RESTRICT options allow you to specify what should happen to objects that depend on the extension. The CASCADE option will automatically drop any objects that depend on the extension, while the RESTRICT option will prevent the extension from being dropped if there are any dependent objects.

For example, to uninstall the my_extension extension, you could use the following statement:

DROP EXTENSION my_extension CASCADE; 

To list all installed extensions in a database, you can use the \dx command in the psql command-line interface. This will show you the name, schema, and version of each installed extension.

To check the status of an extension, you can query the pg_extension system catalog table. For example, the following query will show you the name, schema, and version of the my_extension extension:

SELECT extname, extnamespace, extversion FROM pg_extension WHERE extname = 'my_extension'; 

To check for available updates to an extension, you can use the CREATE EXTENSION statement with the VERSION option. For example, the following statement will check for an update to the my_extension extension:

CREATE EXTENSION my_extension WITH VERSION 'latest'; 

If an update is available, the extension will be upgraded to the latest version. If no update is available, the extension will remain at its current version.