How to Work with MySQL Subqueries

A subquery is an SQL (Structured Query Language) query that is nested within another SQL query. The command that the subquery is nested in is referred to as the parent query. Subqueries are used to pre-process data that is used in the parent query. Subqueries can be applied in SELECT, INSERT, UPDATE, and DELETE operations.

When subqueries are executed, the subquery is processed first before the parent query. When building MySQL applications, using subqueries offers several advantages:

• They break the SQL statements into simple logical units, which can make them easier to understand and maintain. In other words, subqueries help isolate complex parts of queries.
• They eliminate the need for using complex UNION statements and JOIN statements.
• They are used to enforce referential integrity in a scenario where foreign keys are not implemented.
• They help developers code business logic into the MySQL queries.

In this guide you will learn:

• How to use a correlated subquery
• How to use a correlated subquery in a comparison operator
• How to use a subquery as a derived table

To follow along with this guide, make sure you have the following:

1. If you have not already done so, create a Linode account and Compute Instance. See our Getting Started with Linode and Creating a Compute Instance guides.

2. Follow our Setting Up and Securing a Compute Instance guide to update your system. You may also wish to set the timezone, configure your hostname, create a limited user account, and harden SSH access.

3. The MySQL server software (or MariaDB) installed on your Linode. Please refer to the MySQL section, which contains guides that describe how to install MySQL on several Linux distributions.

To understand how subqueries work, create a sample database first. This sample database is used to run the different example queries in this guide:

1. SSH to your server and log in to MySQL as root:

    mysql -u root -p
   

   When prompted, enter the root password of your MySQL server and hit Enter to continue. Note that your MySQL server’s root password is not the same as the root password for your Linode.

   Note

   If your password is not accepted, you may need to run the previous command with sudo:

   sudo mysql -u root -p
   

2. If your password is accepted, you should see the MySQL prompt:

   mysql >

   Note

   If you are using MariaDB, you may see a prompt like the following instead:

   MariaDB [(none)]>

3. To create a sample database named test_db, run:

   CREATE DATABASE test_db;
   

   You should see this output, which confirms that the database was created successfully:

   Query OK, 1 row affected (0.01 sec)

4. Switch to the test_db database:

   USE test_db;
   

   You should see this output:

   Database changed

5. You have created the test_db and selected it. Next, create a table named customers:

    CREATE TABLE customers
    (
    customer_id BIGINT PRIMARY KEY AUTO_INCREMENT,
    customer_name VARCHAR(50)
    ) ENGINE = InnoDB;
   

   You should see this output:

   Query OK, 0 rows affected (0.03 sec)

6. Add some records to the customers table. Run the below INSERT commands one by one:

   INSERT INTO customers(customer_name) VALUES ('JOHN PAUL');
   INSERT INTO customers(customer_name) VALUES ('PETER DOE');
   INSERT INTO customers(customer_name) VALUES ('MARY DOE');
   INSERT INTO customers(customer_name) VALUES ('CHRISTINE JAMES');
   INSERT INTO customers(customer_name) VALUES ('MARK WELL');
   INSERT INTO customers(customer_name) VALUES ('FRANK BRIAN');
   

   This output is shown after each record is inserted:

   Query OK, 1 row affected (0.00 sec)
   ...

7. Verify that the customers’ information was inserted into the database. Execute this SELECT command:

   SELECT * FROM customers;
   

   You should see this list of customers:

   +-------------+-----------------+
   | customer_id | customer_name   |
   +-------------+-----------------+
   |           1 | JOHN PAUL       |
   |           2 | PETER DOE       |
   |           3 | MARY DOE        |
   |           4 | CHRISTINE JAMES |
   |           5 | MARK WELL       |
   |           6 | FRANK BRIAN     |
   +-------------+-----------------+
   6 rows in set (0.00 sec)

8. Create a sales table. This table uses the column customer_id to reference the customers table:

   CREATE TABLE sales
   (
   order_id BIGINT PRIMARY KEY AUTO_INCREMENT,
   customer_id BIGINT,
   sales_amount DECIMAL(17,2)
   ) ENGINE = InnoDB;
   

   This output appears:

   Query OK, 0 rows affected (0.03 sec)

9. Next, populate the sales table with some records. Run the below INSERT commands one by one:

   INSERT INTO sales (customer_id, sales_amount) VALUES ('1','25.75');
   INSERT INTO sales (customer_id, sales_amount) VALUES ('2','85.25');
   INSERT INTO sales (customer_id, sales_amount) VALUES ('5','3.25');
   INSERT INTO sales (customer_id, sales_amount) VALUES ('4','200.75');
   INSERT INTO sales (customer_id, sales_amount) VALUES ('5','88.10');
   INSERT INTO sales (customer_id, sales_amount) VALUES ('1','100.00');
   INSERT INTO sales (customer_id, sales_amount) VALUES ('2','45.00');
   INSERT INTO sales (customer_id, sales_amount) VALUES ('4','15.80');
   

   This output is shown after each record is inserted:

   Query OK, 1 row affected (0.01 sec)
   ...

10. Verify the data in the sales table. Execute this SELECT command:

    SELECT * FROM sales;
    

    This list of sales data should now be shown:

    +----------+-------------+--------------+
    | order_id | customer_id | sales_amount |
    +----------+-------------+--------------+
    |        1 |           1 |        25.75 |
    |        2 |           2 |        85.25 |
    |        3 |           5 |         3.25 |
    |        4 |           4 |       200.75 |
    |        5 |           5 |        88.10 |
    |        6 |           1 |       100.00 |
    |        7 |           2 |        45.00 |
    |        8 |           4 |        15.80 |
    +----------+-------------+--------------+
    8 rows in set (0.00 sec)

After setting up the database and the related tables, you can now implement the different subqueries in MySQL.

A correlated subquery is a type of nested query that uses the values from a parent query. These kinds of queries reference the parent query with a column. The nested query is executed once for each row in the parent query.

The example below presents a query that selects all customers. Inside the query, there is a correlated subquery that fetches the total sales amount for each customer from the sales table.

1. Run the example query:

   SELECT
   customer_id,
   customer_name,
   (SELECT SUM(sales_amount)
   FROM sales WHERE customer_id = customers.customer_id) as total_sales_amount
   FROM
   customers;
   

   In this example, the subquery is SELECT SUM(sales_amount) FROM sales WHERE customer_id = customers.customer_id, which appears in parentheses.

   A list of the total sales made by customers appears:

   +-------------+-----------------+--------------------+
   | customer_id | customer_name   | total_sales_amount |
   +-------------+-----------------+--------------------+
   |           1 | JOHN PAUL       |             125.75 |
   |           2 | PETER DOE       |             130.25 |
   |           3 | MARY DOE        |               NULL |
   |           4 | CHRISTINE JAMES |             216.55 |
   |           5 | MARK WELL       |              91.35 |
   |           6 | FRANK BRIAN     |               NULL |
   +-------------+-----------------+--------------------+
   6 rows in set (0.00 sec)

   The output above from the correlated subquery is able to give you a summarized list of the customers’ orders. Please note, since customer_ids 3 and 6 do not have any associated records in the sales table, their total_sales_amount is NULL.

2. A more elegant way to present this list is to return 0 instead of NULL for the customers with zero sales. To do this, enclose the output generated by the subquery with an IFNULL(expression, 0) statement. Run this updated command:

    SELECT
    customer_id,
    customer_name,
    IFNULL((SELECT SUM(sales_amount)
    FROM sales WHERE customer_id = customers.customer_id), 0) as total_sales_amount
    FROM
    customers;
   

   The following output appears. MySQL returns 0.00 for all rows that would have otherwise returned NULL values.

   +-------------+-----------------+--------------------+
   | customer_id | customer_name   | total_sales_amount |
   +-------------+-----------------+--------------------+
   |           1 | JOHN PAUL       |             125.75 |
   |           2 | PETER DOE       |             130.25 |
   |           3 | MARY DOE        |               0.00 |
   |           4 | CHRISTINE JAMES |             216.55 |
   |           5 | MARK WELL       |              91.35 |
   |           6 | FRANK BRIAN     |               0.00 |
   +-------------+-----------------+--------------------+
   6 rows in set (0.00 sec)

   This approach helps ensure that the output doesn’t harm any further calculations on the records.

Subqueries are useful for moving business logic into the database query level. The following business use-cases feature correlated subqueries placed inside the WHERE clause of a parent query:

• Consider a scenario where you would like to get a list of all customers registered in the database that don’t have associated sales. You can use a subquery together with the MySQL comparison operator NOT IN and retrieve these customers:

    SELECT
    customer_id,
    customer_name
    FROM
    customers
    WHERE customer_id NOT IN (SELECT customer_id FROM sales);
  

  In this example, the subquery is SELECT customer_id FROM sales, which appears in parentheses. The SQL command above outputs a list of two customers that are not found in the sales table:

  +-------------+---------------+
  | customer_id | customer_name |
  +-------------+---------------+
  |           3 | MARY DOE      |
  |           6 | FRANK BRIAN   |
  +-------------+---------------+
  2 rows in set (0.00 sec)

  In a production environment, you can use this kind of recordset to make better business decisions. For instance, you can create a script using another language like PHP or Python to email these customers and enquire if they have a problem placing an order.

• Another use-case is in data clean-up. For example, you can use a subquery to delete customers that have never placed an order:

    DELETE
    FROM
    customers
    WHERE customer_id NOT IN (SELECT customer_id FROM sales);
  

  The SQL command above deletes the two customers and outputs the following:

  Query OK, 2 rows affected (0.01 sec)

  If you execute a command to list all customers again, these customers should no longer appear in the table:

    SELECT *
    FROM
    customers;
  

  The output below confirms that the customers without associated orders were deleted:

  +-------------+-----------------+
  | customer_id | customer_name   |
  +-------------+-----------------+
  |           1 | JOHN PAUL       |
  |           2 | PETER DOE       |
  |           4 | CHRISTINE JAMES |
  |           5 | MARK WELL       |
  +-------------+-----------------+
  4 rows in set (0.00 sec)

When subqueries are used in the FROM clause of a parent query, they are referred to as derived tables. They are very important when implementing complex queries that would otherwise require a MySQL VIEW, JOIN, or UNION clause. A derived table exists in the query that created it and is not permanently saved into the database.

When subqueries are used as derived tables, they isolate the different parts of the SQL statement. In other words, the subquery provides a simplified expression of a table that can be used within the scope of the parent query.

Note

Remember, every derived table must be aliased.

Run the command below to create a derived table subquery that is aliased as order_summary:

SELECT customer_id
FROM
    (
    SELECT
    customer_id,
    count(order_id) as total_orders
    FROM sales
    group by customer_id
    ) as order_summary
WHERE order_summary.total_orders > 1;

Note

In this command, the subquery appears in parentheses as:

SELECT
customer_id,
count(order_id) as total_orders
FROM sales
group by customer_id

The above command queries the sales table to determine customers with more than 1 order. When you run the query, this output appears:

+-------------+
| customer_id |
+-------------+
|           1 |
|           2 |
|           5 |
|           4 |
+-------------+
4 rows in set (0.00 sec)

The above list shows four customer_ids that have more than one order. As an example business use-case, you can use such a query in a script that rewards customers with a bonus on their next purchase.

More Information

You may wish to consult the following resources for additional information on this topic. While these are provided in the hope that they will be useful, please note that we cannot vouch for the accuracy or timeliness of externally hosted materials.

• MySQL Subqueries