IS NULL and IS NOT NULL in MySQL - Deep Dive

Overview - IS NULL and IS NOT NULL

What is it?

IS NULL and IS NOT NULL are special conditions in SQL used to check if a value in a database is missing or present. NULL means no value or unknown value in a field. IS NULL tests if a field has no value, while IS NOT NULL tests if a field has some value. These help filter data based on whether information is missing or available.

Why it matters

Without IS NULL and IS NOT NULL, it would be hard to find records with missing information or to exclude them. This would make data analysis and cleaning difficult, leading to wrong conclusions or errors in applications. These conditions let you handle incomplete data safely and accurately.

Where it fits

Before learning IS NULL and IS NOT NULL, you should understand basic SQL SELECT queries and WHERE clauses. After this, you can learn about other comparison operators and functions that handle data filtering and aggregation.

Mental Model

Core Idea

IS NULL and IS NOT NULL check whether a database field has no value or has some value, helping you find missing or present data.

Think of it like...

Imagine a mailbox: IS NULL is like checking if the mailbox is empty (no letters), and IS NOT NULL is checking if there is at least one letter inside.

┌───────────────┐
│   Database    │
│   Records     │
├───────────────┤
│ Field Value   │
│ ┌───────────┐ │
│ │ NULL      │ │ ← IS NULL matches here
│ │ 'Hello'   │ │ ← IS NOT NULL matches here
│ │ 123       │ │ ← IS NOT NULL matches here
│ └───────────┘ │
└───────────────┘

Build-Up - 7 Steps

1

Foundation

Understanding NULL in Databases

Concept: Introduce what NULL means in a database context.

NULL represents missing or unknown data in a database field. It is not zero, empty string, or any value; it means 'no data'. For example, if a person's phone number is not provided, that field can be NULL.

Result

You know that NULL means absence of data, not zero or empty.

Understanding NULL as 'no data' is crucial because it behaves differently from normal values in comparisons.

2

Foundation

Basic SQL WHERE Clause Filtering

Concept: Learn how to filter records using WHERE with normal comparisons.

In SQL, WHERE filters rows based on conditions like =, <, >. For example, WHERE age = 30 finds people aged 30. But these comparisons do not work with NULL because NULL is unknown.

Result

You can filter data by values but not by NULL using normal operators.

Knowing that NULL cannot be compared with = or <> prepares you to learn special NULL checks.

3

Intermediate

Using IS NULL to Find Missing Data

🤔Before reading on: do you think WHERE field = NULL works to find missing data? Commit to yes or no.

Concept: Learn the correct way to find rows where a field is NULL using IS NULL.

To find rows with missing data, use WHERE field IS NULL. For example, SELECT * FROM users WHERE phone IS NULL finds users without phone numbers. Using WHERE field = NULL does NOT work because NULL is not equal to anything.

Result

You can correctly find records with missing values using IS NULL.

Understanding that IS NULL is a special operator avoids common mistakes and ensures accurate queries.

4

Intermediate

Using IS NOT NULL to Find Present Data

🤔Before reading on: do you think WHERE field <> NULL finds present data? Commit to yes or no.

Concept: Learn how to find rows where a field has some value using IS NOT NULL.

To find rows where a field has data, use WHERE field IS NOT NULL. For example, SELECT * FROM orders WHERE delivery_date IS NOT NULL finds orders that have a delivery date set. Using WHERE field <> NULL does NOT work because NULL comparisons always return unknown.

Result

You can filter out missing data and get only rows with values.

Knowing IS NOT NULL helps you exclude incomplete records and focus on meaningful data.

5

Intermediate

Combining IS NULL with Other Conditions

🤔Before reading on: do you think you can combine IS NULL with AND/OR in WHERE clauses? Commit to yes or no.

Concept: Learn how to use IS NULL and IS NOT NULL together with other filters.

You can combine IS NULL with other conditions using AND or OR. For example, SELECT * FROM employees WHERE department IS NULL OR salary > 50000 finds employees with no department or high salary. This lets you build complex filters involving missing data.

Result

You can write flexible queries that handle missing and present data together.

Understanding combination expands your ability to query real-world messy data.

6

Advanced

NULL Behavior in Joins and Aggregations

🤔Before reading on: do you think NULL values affect JOIN results and aggregate functions? Commit to yes or no.

Concept: Explore how NULL impacts JOIN operations and aggregate functions like COUNT and AVG.

In JOINs, NULL values can cause rows to be excluded or included differently depending on join type. For example, LEFT JOIN keeps rows with NULL matches on the right. Aggregate functions often ignore NULLs; COUNT(field) counts only non-NULL values, while COUNT(*) counts all rows. This affects query results and must be understood.

Result

You know how NULL influences complex queries and results.

Knowing NULL's effect on joins and aggregates prevents subtle bugs and wrong data summaries.

7

Expert

Indexing and Performance with NULL Checks

🤔Before reading on: do you think IS NULL conditions always use indexes efficiently? Commit to yes or no.

Concept: Understand how database indexes interact with IS NULL and IS NOT NULL conditions for query speed.

Some databases index NULL values differently or not at all. Queries with IS NULL or IS NOT NULL may or may not use indexes depending on database design and indexing strategy. For example, in MySQL, NULL values can be indexed, but performance depends on index type and query structure. Knowing this helps optimize queries involving NULL checks.

Result

You can write NULL queries that perform well in production.

Understanding indexing with NULL avoids slow queries and improves database efficiency.

Under the Hood

Internally, NULL is a special marker indicating absence of data, not a value. SQL uses three-valued logic: TRUE, FALSE, and UNKNOWN. Comparisons with NULL return UNKNOWN, so normal operators (=, <>) do not work. IS NULL and IS NOT NULL are special operators that explicitly test for NULL presence or absence, returning TRUE or FALSE accordingly.

Why designed this way?

SQL was designed to handle incomplete data safely. Using three-valued logic and special NULL checks prevents incorrect assumptions about missing data. Alternatives like treating NULL as zero or empty string were rejected because they cause wrong results and data corruption.

┌───────────────┐
│   Query       │
│  Condition    │
├───────────────┤
│ field = NULL  │ → UNKNOWN (no match)
│ field <> NULL │ → UNKNOWN (no match)
│ field IS NULL │ → TRUE if NULL
│ field IS NOT NULL │ → TRUE if not NULL
└───────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does WHERE field = NULL find rows with missing data? Commit to yes or no.

Common Belief: Many think WHERE field = NULL works like IS NULL to find missing data.

Reality: WHERE field = NULL always returns no rows because NULL is not equal to anything, even NULL itself.

Why it matters: Using = NULL causes queries to miss all NULL rows, leading to incomplete or wrong results.

Quick: Does WHERE field <> NULL find rows with values? Commit to yes or no.

Common Belief: Some believe WHERE field <> NULL finds all rows where field has a value.

Reality: WHERE field <> NULL returns no rows because comparisons with NULL yield UNKNOWN, not TRUE.

Why it matters: This mistake causes filters to fail, excluding all rows unexpectedly.

Quick: Do aggregate functions like COUNT count NULL values? Commit to yes or no.

Common Belief: People often think COUNT(field) counts all rows including NULLs.

Reality: COUNT(field) counts only non-NULL values; COUNT(*) counts all rows regardless of NULLs.

Why it matters: Misunderstanding this leads to wrong data summaries and reports.

Quick: Does IS NULL check work the same in all SQL databases? Commit to yes or no.

Common Belief: Some assume IS NULL behaves identically across all SQL systems.

Reality: While standard, some databases have subtle differences in NULL handling and indexing that affect IS NULL performance.

Why it matters: Ignoring these differences can cause unexpected slow queries or wrong results in production.

Expert Zone

1

NULL is not a value but a marker, so it breaks normal equality logic, requiring special handling in queries and application code.

2

Indexes may or may not include NULLs depending on database and index type, affecting query optimization strategies.

3

Three-valued logic with NULL means that combining conditions with AND/OR can produce unexpected results if NULLs are not carefully considered.

When NOT to use

Avoid relying solely on IS NULL or IS NOT NULL for data validation; use NOT NULL constraints or default values when possible. For performance-critical queries, consider redesigning schema to minimize NULLs or use partial indexes. Alternatives include COALESCE to replace NULLs with default values for easier comparisons.

Production Patterns

In real systems, IS NULL is used to find incomplete records for cleaning or reporting. IS NOT NULL filters out missing data in joins and analytics. Combined with COALESCE, it helps produce clean outputs. Indexing strategies are tuned to speed up NULL checks. Complex filters handle NULLs carefully to avoid logic errors.

Connections

Three-valued Logic

IS NULL and IS NOT NULL rely on three-valued logic (TRUE, FALSE, UNKNOWN) in SQL.

Understanding three-valued logic clarifies why normal comparisons with NULL fail and why special operators are needed.

Data Cleaning

IS NULL helps identify missing data that needs cleaning or filling.

Knowing how to find NULLs is essential for preparing accurate datasets for analysis or machine learning.

Philosophy of Knowledge (Epistemology)

NULL represents unknown or missing information, similar to the concept of 'unknown' in knowledge theory.

Recognizing NULL as unknown data connects database logic to how humans handle incomplete knowledge.

Common Pitfalls

#1 Trying to find NULL values using = operator.

Wrong approach:

SELECT * FROM users WHERE phone = NULL;

Correct approach:

SELECT * FROM users WHERE phone IS NULL;

Root cause:

Misunderstanding that NULL is not equal to anything, so = NULL never matches.

#2 Trying to find non-NULL values using <> operator.

Wrong approach:

SELECT * FROM orders WHERE delivery_date <> NULL;

Correct approach:

SELECT * FROM orders WHERE delivery_date IS NOT NULL;

Root cause:

Assuming NULL behaves like a normal value in inequality comparisons.

#3 Counting rows with COUNT(field) expecting all rows counted.

Wrong approach:

SELECT COUNT(phone) FROM users;

Correct approach:

SELECT COUNT(*) FROM users;

Root cause:

Not knowing COUNT(field) excludes NULLs, leading to undercounting.

Key Takeaways

NULL means missing or unknown data and is not a normal value.

IS NULL and IS NOT NULL are special operators to check for missing or present data.

Normal comparison operators (=, <>) do not work with NULL and always return unknown.

Understanding NULL behavior is essential for accurate data filtering, joins, and aggregation.

Proper use of IS NULL improves data quality checks and query correctness in real-world databases.