Coding/Learn/sql/sql not null unique
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 NOT NULL & UNIQUE: Data Invariants
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Lesson 48 of 61
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SQL NOT NULL & UNIQUE: Data Invariants

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In Plain English

`NOT NULL` guarantees that a field can never contain a missing `NULL` value. `UNIQUE` guarantees that no two rows can share the exact same value in the specified column (or combination of columns). Behind the scenes, the database automatically builds a unique index to enforce `UNIQUE` constraints in $O(\log N)$ time.

The Mental Model

"`NOT NULL` is like a required field with a red asterisk on a passport application; `UNIQUE` is the passport number itself, which no two citizens can share."

Deep Dive: How It Works

NOT NULL Enforcement: Insert or update attempts with NULL are rejected before touching disk pages.

UNIQUE Index Creation: SQL engines create a backing unique B-tree index automatically to enforce uniqueness during inserts.

Composite UNIQUE: `UNIQUE(tenant_id, slug)` allows identical slugs across different organizations while keeping them unique within a tenant.

Syntax Blueprint

CREATE TABLE users (
  id INTEGER PRIMARY KEY,
  email TEXT NOT NULL UNIQUE,
  username TEXT NOT NULL,
  CONSTRAINT unq_user UNIQUE(username)
);

Enforces column nullability and distinctness at the schema level.

PointCore Rules to Remember

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Auto-Index Generation: Applying `UNIQUE` automatically creates a fast lookup index.
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Composite Uniqueness: Multi-column unique constraints enforce multi-tenant isolation.
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NULL in UNIQUE: Standard SQL allows multiple NULLs in UNIQUE columns (unless NOT NULL is also applied).

Common Beginner Traps & How to Fix Them

Mistake:Assuming `UNIQUE` prevents `NULL` entries automatically.

Why it happens: In standard SQL, NULL is unknown, so `NULL != NULL` allows multiple NULL rows unless `NOT NULL` is explicitly combined.

How to fix: Always combine `NOT NULL UNIQUE` when values must exist and be unique.

Pro Tip: Use composite `UNIQUE(org_id, slug)` for multi-tenant SaaS routing where slugs only need to be unique per organization.

Live Interactive Example

Hit Run Code to see it live
Enforcing Nullability and Distinctness with NOT NULL and UNIQUE
Python 3.12
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Output Console
Click "Run Code" to view the rendered output.
How it works: Both user records were successfully stored with enforced uniqueness on email and username.

Your Turn: Micro Challenge

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

Micro Exercise

Create API Keys Table

  • PointCreate a table `api_keys (id INTEGER PRIMARY KEY, key_hash TEXT NOT NULL UNIQUE, client_name TEXT NOT NULL)`.
  • PointInsert a key ("hash_991", "MobileApp") and query the table.
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Sandbox Output
Click "Run & Check" to test your solution.

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