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SQL NULL Values

Introduction​

In SQL databases, a NULL value represents missing or unknown data. Unlike other values, NULL is not zero, an empty string, or a space - it's the absence of any value. Understanding how to work with NULL values is crucial for effective database management and query writing.

Think of NULL as a placeholder that says, "This information is not available." For example, if a customer hasn't provided their phone number, that field would contain a NULL value rather than being empty or containing a default value.

Understanding NULL Values​

What NULL Represents​

  • NULL indicates missing, unknown, or inapplicable data
  • NULL is not equivalent to zero or an empty string
  • NULL is not equal to another NULL (two unknowns aren't necessarily the same)

How NULL Behaves​

The most important thing to understand about NULL values is that they behave differently in comparisons:

  • NULL is not equal to anything, including another NULL
  • NULL is not greater than or less than any value
  • Any arithmetic operation involving NULL results in NULL
  • Any comparison with NULL using standard operators (=, <, >) yields UNKNOWN (not TRUE or FALSE)

Testing for NULL Values​

Since NULL cannot be compared using the equality operator (=), SQL provides special operators to test for NULL values.

Using IS NULL and IS NOT NULL​

To check if a column contains a NULL value:

SELECT column_name, another_column
FROM table_name
WHERE column_name IS NULL;

To find rows where a column is not NULL:

SELECT column_name, another_column
FROM table_name
WHERE column_name IS NOT NULL;

Example: Finding Missing Contact Information​

Let's say we have a customers table with contact information:

-- Creating a sample customers table
CREATE TABLE customers (
    customer_id INT PRIMARY KEY,
    first_name VARCHAR(50),
    last_name VARCHAR(50),
    email VARCHAR(100),
    phone VARCHAR(20)
);

-- Inserting sample data
INSERT INTO customers VALUES
(1, 'John', 'Smith', '[email protected]', '555-1234'),
(2, 'Sarah', 'Johnson', '[email protected]', NULL),
(3, 'Michael', 'Brown', NULL, '555-5678'),
(4, 'Emily', 'Davis', '[email protected]', '555-9012'),
(5, 'David', 'Wilson', NULL, NULL);

Now, to find customers with missing phone numbers:

SELECT customer_id, first_name, last_name
FROM customers
WHERE phone IS NULL;

Output:

customer_id | first_name | last_name
------------|------------|----------
2           | Sarah      | Johnson
5           | David      | Wilson

To find customers with both email and phone information:

SELECT customer_id, first_name, last_name
FROM customers
WHERE email IS NOT NULL AND phone IS NOT NULL;

Output:

customer_id | first_name | last_name
------------|------------|----------
1           | John       | Smith
4           | Emily      | Davis

NULL Values in Calculations​

NULL in Arithmetic Operations​

When a NULL value is used in calculations, the result is typically NULL:

-- Creating a sample orders table
CREATE TABLE orders (
    order_id INT PRIMARY KEY,
    product_name VARCHAR(100),
    quantity INT,
    unit_price DECIMAL(10,2),
    discount DECIMAL(10,2)
);

-- Inserting sample data
INSERT INTO orders VALUES
(101, 'Laptop', 1, 999.99, 100.00),
(102, 'Mouse', 2, 24.99, NULL),
(103, 'Keyboard', 1, 49.99, 5.00),
(104, 'Monitor', NULL, 299.99, 30.00);

If we try to calculate the total price with discount:

SELECT 
    order_id, 
    product_name,
    quantity,
    unit_price,
    discount,
    (quantity * unit_price) - discount AS total_price
FROM orders;

Output:

order_id | product_name | quantity | unit_price | discount | total_price
---------|--------------|----------|------------|----------|------------
101      | Laptop       | 1        | 999.99     | 100.00   | 899.99
102      | Mouse        | 2        | 24.99      | NULL     | NULL
103      | Keyboard     | 1        | 49.99      | 5.00     | 44.99
104      | Monitor      | NULL     | 299.99     | 30.00    | NULL

Notice that when either quantity is NULL or discount is NULL, the calculated total_price is also NULL.

Using COALESCE to Handle NULL in Calculations​

The COALESCE function returns the first non-NULL value in a list, making it perfect for providing default values:

SELECT 
    order_id, 
    product_name,
    COALESCE(quantity, 0) AS actual_quantity,
    unit_price,
    COALESCE(discount, 0) AS actual_discount,
    (COALESCE(quantity, 0) * unit_price) - COALESCE(discount, 0) AS total_price
FROM orders;

Output:

order_id | product_name | actual_quantity | unit_price | actual_discount | total_price
---------|--------------|-----------------|------------|-----------------|------------
101      | Laptop       | 1               | 999.99     | 100.00          | 899.99
102      | Mouse        | 2               | 24.99      | 0.00            | 49.98
103      | Keyboard     | 1               | 49.99      | 5.00            | 44.99
104      | Monitor      | 0               | 299.99     | 30.00           | -30.00

NULL in Aggregate Functions​

Most aggregate functions in SQL ignore NULL values, which is important to understand when analyzing data.

Example: Average Calculation​

Let's calculate the average discount:

-- Without handling NULL values
SELECT AVG(discount) AS average_discount
FROM orders;

Output:

average_discount
-----------------
45.00

This average only includes the non-NULL discount values (100.00, 5.00, and 30.00).

COUNT Behavior with NULL​

The COUNT(*) function counts all rows, while COUNT(column_name) counts only non-NULL values in that column:

-- Count all rows
SELECT COUNT(*) AS total_orders FROM orders;

-- Count orders with a known quantity
SELECT COUNT(quantity) AS orders_with_quantity FROM orders;

Output:

total_orders
------------
4

orders_with_quantity
--------------------
3

Functions for Handling NULL Values​

SQL provides several functions to handle NULL values in your queries:

COALESCE Function​

Returns the first non-NULL value in a list:

SELECT 
    product_name,
    COALESCE(discount, 0) AS discount_to_apply
FROM orders;

Output:

product_name | discount_to_apply
-------------|------------------
Laptop       | 100.00
Mouse        | 0.00
Keyboard     | 5.00
Monitor      | 30.00

NULLIF Function​

Returns NULL if two expressions are equal, otherwise returns the first expression:

-- Creating a sample division example
CREATE TABLE math_operations (
    value1 INT,
    value2 INT
);

INSERT INTO math_operations VALUES
(10, 2),
(15, 3),
(8, 0),  -- Division by zero problem
(20, 5);

-- Safe division using NULLIF to avoid division by zero
SELECT 
    value1, 
    value2, 
    value1 / NULLIF(value2, 0) AS safe_division
FROM math_operations;

Output:

value1 | value2 | safe_division
-------|--------|---------------
10     | 2      | 5
15     | 3      | 5
8      | 0      | NULL
20     | 5      | 4

IFNULL/NVL Function​

Some database systems provide IFNULL (MySQL) or NVL (Oracle) functions that work like COALESCE but with only two parameters:

-- MySQL syntax
SELECT 
    product_name,
    IFNULL(discount, 0) AS discount_to_apply
FROM orders;

-- Oracle syntax would be:
-- SELECT product_name, NVL(discount, 0) AS discount_to_apply FROM orders;

NULL in WHERE Clauses and Joins​

Impact on Filtering Data​

Remember that NULL values require special treatment in WHERE clauses:

-- This will NOT find rows where discount is NULL
SELECT * FROM orders WHERE discount = NULL;  -- Incorrect approach

-- This is the correct way to find NULL values
SELECT * FROM orders WHERE discount IS NULL;

NULL Values in Joins​

NULL values can complicate joins between tables. For example, if you join tables on columns that contain NULL values, those rows might not appear in the result set with an inner join:

-- Creating a related products table
CREATE TABLE product_details (
    product_name VARCHAR(100) PRIMARY KEY,
    category VARCHAR(50),
    in_stock BOOLEAN
);

INSERT INTO product_details VALUES
('Laptop', 'Electronics', TRUE),
('Keyboard', 'Accessories', TRUE),
('Headphones', 'Audio', FALSE);
-- Note: Mouse and Monitor are missing

-- Joining with the orders table
SELECT 
    o.order_id,
    o.product_name,
    p.category
FROM orders o
INNER JOIN product_details p ON o.product_name = p.product_name;

Output:

order_id | product_name | category
---------|--------------|----------
101      | Laptop       | Electronics
103      | Keyboard     | Accessories

Notice that Mouse and Monitor orders don't appear in the results because they don't have matching entries in the product_details table.

To include all orders, you would use a LEFT JOIN:

SELECT 
    o.order_id,
    o.product_name,
    p.category
FROM orders o
LEFT JOIN product_details p ON o.product_name = p.product_name;

Output:

order_id | product_name | category
---------|--------------|----------
101      | Laptop       | Electronics
102      | Mouse        | NULL
103      | Keyboard     | Accessories
104      | Monitor      | NULL

Best Practices for Handling NULL Values​

  1. Decide when to allow NULLs: Consider which columns truly need to allow NULL values. For columns where data should always be present, use constraints like NOT NULL.

  2. Use DEFAULT values when appropriate: Instead of NULL, sometimes a default value makes more sense:

    CREATE TABLE products (
        product_id INT PRIMARY KEY,
        product_name VARCHAR(100) NOT NULL,
        description VARCHAR(500) DEFAULT 'No description available',
        in_stock BOOLEAN DEFAULT TRUE,
        discount DECIMAL(10,2) DEFAULT 0.00
    );

  3. Be consistent in your approach: Either use NULL to represent missing data or use specific default values, but try to be consistent across your database design.

  4. Always use IS NULL/IS NOT NULL: Never use = NULL or != NULL for comparisons.

  5. Handle NULLs in calculations: Use COALESCE or similar functions to provide default values in calculations.

  6. Consider the impact of NULLs on indexes: Indexing columns with many NULL values may impact performance.

Real-World Application: Customer Analysis​

Let's wrap up with a practical example of analyzing customer demographics where some data might be missing:

-- Creating a customers demographics table
CREATE TABLE customer_demographics (
    customer_id INT PRIMARY KEY,
    age INT,
    income DECIMAL(10,2),
    education_level VARCHAR(50),
    region VARCHAR(50)
);

INSERT INTO customer_demographics VALUES
(1, 35, 75000.00, 'Bachelor', 'North'),
(2, NULL, 82000.00, 'Master', 'South'),
(3, 42, NULL, 'PhD', 'West'),
(4, 29, 62000.00, NULL, 'East'),
(5, 51, 95000.00, 'Bachelor', NULL);

-- Analyzing average income by education level, handling NULL values
SELECT 
    COALESCE(education_level, 'Unknown') AS education,
    COUNT(*) AS customer_count,
    AVG(income) AS average_income,
    MIN(age) AS youngest,
    MAX(age) AS oldest
FROM customer_demographics
GROUP BY education_level
ORDER BY average_income DESC NULLS LAST;

Output:

education | customer_count | average_income | youngest | oldest
----------|----------------|----------------|----------|--------
PhD       | 1              | NULL           | 42       | 42
Master    | 1              | 82000.00       | NULL     | NULL
Bachelor  | 2              | 85000.00       | 35       | 51
Unknown   | 1              | 62000.00       | 29       | 29

Summary​

NULL values in SQL represent missing or unknown data and have special behavior that distinguishes them from regular values. Key points to remember:

  • NULL is not equal to anything, including another NULL
  • Use IS NULL and IS NOT NULL to test for NULL values
  • Arithmetic operations with NULL result in NULL
  • Use functions like COALESCE to handle NULL values in calculations
  • Most aggregate functions ignore NULL values
  • NULL values can affect joins and filtering in unexpected ways
  • Be thoughtful about when to allow NULL values in your database design

Exercises​

  1. Write a query to find all products in the orders table where either quantity or discount is unknown.

  2. Modify the calculation for total_price to apply a 10% additional discount when the regular discount is NULL.

  3. Write a query that counts how many customers have missing information in each column of the customer_demographics table.

  4. Create a report showing the percentage of complete vs. incomplete data for each column in the customer_demographics table.

Additional Resources​

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