Skip to main content
Back to Dev generators

Dev

Mock Database Schema Generator

Used by developers, writers, and creators worldwide.

A mock database schema generator saves hours of boilerplate when you need a solid CREATE TABLE statement fast. Paste the output straight into psql, MySQL Workbench, or a Flyway migration file and start iterating immediately. Choose from five real-world presets — Users, Products, Orders, Blog Posts, Audit Log — across PostgreSQL, MySQL, and SQLite, and the generator handles dialect-specific syntax for you. Prototyping often stalls at the database layer while developers debate column names and types. This tool short-circuits that. Every schema includes primary keys, NOT NULL constraints, defaults, foreign keys where relevant, and indexes on high-traffic columns like email and status. Override the table name and every reference updates consistently.

Loading usage…

Free forever — no account required

How to use

  1. Choose your options above
  2. Click Generate
  3. Copy your result

Detailed instructions

  1. Type your desired table name into the Table Name field, or leave it as the default.
  2. Select your target SQL dialect — PostgreSQL, MySQL, or SQLite — from the dialect dropdown.
  3. Choose the table preset that best matches your use case from the Table Preset dropdown.
  4. Click Generate and review the CREATE TABLE statement, including columns, constraints, and indexes.
  5. Copy the output and paste it into your database client, migration file, or documentation.

Use Cases

  • •Seeding a PostgreSQL staging database with a realistic users or orders schema before writing any application code
  • •Pasting a dialect-correct CREATE TABLE block into a Flyway or Liquibase migration as a versioned starting point
  • •Providing working SQL examples in a technical blog post or YouTube tutorial without hand-writing DDL
  • •Comparing how the same orders table is defined across PostgreSQL, MySQL, and SQLite in a database course
  • •Dropping a ready-made audit log schema into an open-source starter kit so contributors have a real reference

Tips

  • →Generate the same preset in all three dialects side by side to spot syntax differences when porting a schema.
  • →Use the Audit Logs preset as a starting point for any table that needs created_at, updated_at, and actor tracking.
  • →Rename the table to match your domain before copying — constraint and index names will update automatically, saving a tedious find-and-replace.
  • →Combine multiple generated schemas in one migration file: generate Users first, then Orders, so foreign key references resolve in the correct order.
  • →For tutorial content, the SQLite preset runs without any server setup, making it ideal for code examples that readers can execute immediately.
  • →Strip the DROP TABLE IF EXISTS guard when using the output in a production migration to avoid accidental data loss in CI pipelines.

FAQ

can I run the generated SQL directly in psql or MySQL Workbench

Yes. The output is valid DDL you can paste straight into psql, MySQL Workbench, DBeaver, or TablePlus and execute without edits. It includes a DROP TABLE IF EXISTS guard so re-running in a dev environment is safe. For migration tools like Flyway, save it as a versioned .sql file in your migrations folder. For Prisma or TypeORM, use it as a structural reference when hand-writing the equivalent model definition.

what's the difference between PostgreSQL, MySQL, and SQLite output

Each dialect gets syntax-appropriate DDL. PostgreSQL uses SERIAL or UUID with gen_random_uuid() and standard BOOLEAN. MySQL uses AUTO_INCREMENT and TINYINT(1) for booleans. SQLite uses INTEGER PRIMARY KEY AUTOINCREMENT and TEXT for most string columns since SQLite's type system is loosely typed. Picking the right dialect means you can copy the output directly without manual find-and-replace on types or keywords.

is the generated schema safe to use in production as-is

Treat it as a strong starting template rather than a final schema. The output follows real-world conventions — sensible VARCHAR lengths, foreign key constraints, indexes on columns likely to appear in WHERE clauses — but you should review it against your specific load profile, add application constraints, and think through your indexing strategy before deploying. It is designed to spark the right architecture conversation, not end it.