Coding/Learn/sql/sql null values
SQL & Relational Database Engineering Complete Course
01.Introduction & Relational Database Architecture
02.Syntax, Clauses & Execution Order
03.SELECT: Column Projection & Expressions
04.SELECT DISTINCT & Row Deduplication
05.WHERE Clause: Filtering Predicates & Comparison
06.ORDER BY: Sorting, Multi-Column & NULLS Ordering
07.Logical Operators: AND, OR, NOT & Operator Precedence
08.Comments: Single-Line (--), Multi-Line (/* */) & Query Hints
09.Operators: Arithmetic, Bitwise & String Concatenation
10.LIKE Operator & Wildcard Characters (%, _)
11.IN & NOT IN: Membership Operators & Subqueries
12.BETWEEN: Range Filtering for Numbers, Dates & Text
13.NULL Values: Three-Valued Logic, IS NULL & IS NOT NULL
14.Aliases: AS Keyword for Columns & Tables
15.SELECT TOP, LIMIT & OFFSET Pagination
16.INSERT INTO: Single & Multi-Row Insertions
17.UPDATE: Modifying Existing Records & Safe WHERE Rules
18.DELETE: Removing Rows vs TRUNCATE TABLE
19.SELECT INTO & Table Backups (CREATE TABLE AS SELECT)
20.INSERT INTO SELECT: Bulk Data Migration
21.Aggregate Functions: Statistical Summaries
22.MIN() & MAX(): Finding Extremes Across Types
23.COUNT: COUNT(*), COUNT(column) & COUNT(DISTINCT)
24.SUM() & AVG(): Numerical Aggregation & Averages
25.GROUP BY: Aggregation Buckets & Categorical Metrics
26.HAVING: Filtering Aggregated Groups vs WHERE
27.Joins Architecture: Relational Modeling & Venn Diagrams
28.INNER JOIN: Matching Intersections Across Tables
29.LEFT JOIN: Preserving Unmatched Left Rows & Finding Orphans
30.RIGHT JOIN: Preserving Unmatched Right Rows
31.FULL JOIN: Full Outer Join & Complete Reconciliation
32.SELF JOIN: Hierarchies, Trees & Parent-Child Relationships
33.UNION: Combining Result Sets with Automatic Deduplication
34.UNION ALL vs UNION: Performance & Duplicates
35.EXISTS & NOT EXISTS: Correlated Subqueries
36.ANY & ALL: Quantified Comparison Subqueries
37.CASE: Conditional Expressions & Data Transposition
38.Null Functions: COALESCE, IFNULL, ISNULL & NULLIF
39.Stored Procedures, Functions & Query Parameters
40.CREATE & DROP DATABASE: Environment Lifecycle
41.CREATE TABLE: Defining Schemas & Types
42.DROP & TRUNCATE TABLE: Structure vs Data Purging
43.ALTER TABLE: Modifying Columns & Schema Evolution
44.AUTO INCREMENT & Sequences: Identity Generation
45.Date & Time Formats: Timestamp Manipulation
46.Views: Virtual Tables & Security Layers
47.Constraints Overview: Relational Integrity
48.NOT NULL & UNIQUE: Data Invariants
49.PRIMARY KEY: Entity Uniqueness & Clustering
50.FOREIGN KEY: Referential Integrity & Cascades
51.CHECK & DEFAULT: Business Rules at DB Layer
52.CREATE INDEX: B-Trees & Query Optimization
53.Injection Defense: Threat Models & Prevention
54.Parameters & Prepared Statements: Safe Execution
55.Database Engines & Hosting Architecture
56.Dialect References & Data Type Standards
57.Production Best Practices & Query Tuning Checklist
58.Interactive Exercises: Multi-Table Analytics Challenges
59.Comprehensive Assessment & Knowledge Check
60.Capstone Project: Enterprise Analytics Engine
61.Mastery Certification & Graduate Verification
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LearnSQL NULL Values: Three-Valued Logic, IS NULL & IS NOT NULL
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Lesson 13 of 61
5 mins read

SQL NULL Values: Three-Valued Logic, IS NULL & IS NOT NULL

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In Plain English

In SQL, `NULL` represents missing, unknown, or inapplicable data. It is NOT zero, NOT an empty string, and NOT a space. Because NULL represents the unknown, comparing anything with NULL using `=` or `!=` always evaluates to `UNKNOWN`. You MUST use `IS NULL` and `IS NOT NULL`.

The Mental Model

"`NULL` is a sealed mystery gift box with unknown contents. If you ask: "Is the mystery box equal to $50?" the answer is not Yes or No, but "I don't know" (UNKNOWN)."

Deep Dive: How It Works

Why `= NULL` Fails: In SQL 3-valued logic (3VL), `NULL = NULL` is UNKNOWN, not TRUE. Because WHERE only retains TRUE rows, `WHERE col = NULL` always returns 0 rows.

Testing Nullability: Always write `WHERE col IS NULL` or `WHERE col IS NOT NULL`.

NULL Propagation: Most arithmetic operations with NULL evaluate to NULL (`10 + NULL -> NULL`). Aggregate functions like `AVG()` and `SUM()` ignore NULL rows.

Syntax Blueprint

SELECT column1
FROM table_name
WHERE column1 IS NULL;

SELECT column1
FROM table_name
WHERE column1 IS NOT NULL;

Always use IS NULL or IS NOT NULL to test for missing data.

PointCore Rules to Remember

Point star
Never Use = NULL: `= NULL` is syntactically allowed in some engines but logically returns 0 records.
Point star
Three-Valued Logic (3VL): Conditions evaluate to TRUE, FALSE, or UNKNOWN.
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Aggregates Ignore NULL: COUNT(col) counts only non-null values, while COUNT(*) counts all rows.

Common Beginner Traps & How to Fix Them

Mistake:Writing `WHERE phone != '555-0100'` and expecting rows with NULL phone numbers to be returned.

Why it happens: When phone is NULL, `NULL != '555-0100'` evaluates to UNKNOWN, which WHERE discards.

How to fix: Include null check: `WHERE phone != '555-0100' OR phone IS NULL`.

Pro Tip: In PostgreSQL and modern SQL engines, you can use `IS DISTINCT FROM` (e.g. `a IS DISTINCT FROM b`) for null-safe equality comparison.

Live Interactive Example

Hit Run Code to see it live
Testing and Handling NULL Values
Python 3.12
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Output Console
Click "Run Code" to view the rendered output.
How it works: IS NULL detected missing phone numbers and IS NOT NULL isolated verified emails.

Your Turn: Micro Challenge

No pressure! Edit the starter code below and test your solution with instant feedback.

Micro Exercise

Find Records with NULL Values

  • PointSelect `name` from `reports` where `approved_at IS NULL`.
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Sandbox Output
Click "Run & Check" to test your solution.

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