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DB Design

DDL Builder

Design tables visually and generate CREATE TABLE statements (MySQL / PostgreSQL / SQLite) together with an ER diagram. You can also paste a JSON sample to infer columns automatically. Everything runs in your browser — nothing you enter is sent anywhere.

Guide: How to use & features

  • Paste an object or array as JSON into "Generate from JSON" to add a table with column types, primary key, and NOT NULL already inferred.
  • In "Table design" you can edit the table name, column names, types, PK/NOT NULL/UNIQUE/auto-increment, and default values directly.
  • In "Relations", pick a from-table/column and a to-table/column to add a foreign key constraint and its connecting line in the ER diagram.
  • Choose a dialect (MySQL / PostgreSQL / SQLite) and click "Generate DDL" to get CREATE TABLE statements formatted for that dialect.
  • Click "Open in SQL Builder" to hand off the generated DDL directly to the Visual SQL Builder and continue building queries.

FAQ: FAQ

  • How do types differ between MySQL, PostgreSQL, and SQLite?

    Tables are stored internally with shared logical types (INTEGER, DECIMAL, VARCHAR, etc.) and only converted to each database's real types when you generate DDL. For example, BOOLEAN becomes INTEGER on SQLite (which has no boolean type), and an auto-increment primary key becomes AUTO_INCREMENT on MySQL, SERIAL on PostgreSQL, and INTEGER PRIMARY KEY AUTOINCREMENT on SQLite.

  • What happens to NOT NULL, UNIQUE, and the default value when a column is marked PK?

    A primary key is NOT NULL and UNIQUE by definition, so once a column is marked PK, its NOT NULL/UNIQUE/default settings are ignored — it's emitted as just "TYPE PRIMARY KEY" (plus AUTO_INCREMENT/SERIAL/etc. if auto-increment is on).

  • How does generating from JSON handle nested data?

    A top-level property whose value is an array of objects is automatically split into its own table with a foreign key back to the parent (e.g. an order and its line items). A nested plain object becomes a JSON-typed column instead. Nesting deeper than one level isn't split automatically, so adjust those cases by hand.

  • Can I use the generated DDL directly in a production migration?

    This tool is meant as a starting point for table design or a review artifact — it doesn't generate indexes, CHECK constraints, character sets/collations, or partitioning, which real deployments usually need. Always review the output and adjust it before using it in production.

Use cases: Common use cases

  • Sketching out tables for a new API or service

    Assemble the tables and columns you need visually in the early stages of a project and export them directly as CREATE TABLE statements.

  • Reverse-engineering a DB schema from an existing JSON response

    Paste a JSON sample from an API response or log entry to auto-generate a matching table layout and DDL as a starting point.

  • Producing an ER diagram for a team design review

    See the table structure you designed rendered instantly as a Mermaid ER diagram, useful for aligning on a design during review.

  • A full design-to-query workflow with sql-to-er / SQL Builder

    Hand the generated DDL off to SQL Builder to start building queries, or paste it back into sql-to-er later to double-check the structure — the whole flow from design to querying stays in the browser.

Notes: Notes & limitations

  • Generated DDL only covers basic constraints

    PRIMARY KEY, NOT NULL, UNIQUE, DEFAULT, and FOREIGN KEY are supported, but indexes, CHECK constraints, character sets/collations, and partitioning are not generated. Always review the output before using it in a production migration.

  • Type inference from JSON depends on your sample

    Types can only be inferred from the values present in the sample you provide. If a column can realistically hold other types of values too, adjust the inferred type by hand.

  • Table and column names aren't checked against reserved words

    Naming a table or column after a reserved word (like "order" or "group") won't trigger a warning here. If the generated DDL fails to run, check for a name collision first.

Generate from JSON (optional)

Table design

Relations (foreign keys)

DDL output

ER diagram preview

Articles for this tool

Introduction
2026-08-03

How CREATE TABLE Generation From a Visual Table Design Works | DDL Builder

A look inside DDL Builder, which turns a visually designed table into CREATE TABLE statements and an ER diagram. Covers the MySQL/PostgreSQL/SQLite type-mapping rules and the column-inference algorithm used for JSON samples.

Recent Articles

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How CREATE TABLE Generation From a Visual Table Design Works | DDL Builder

A look inside DDL Builder, which turns a visually designed table into CREATE TABLE statements and an ER diagram. Covers the MySQL/PostgreSQL/SQLite type-mapping rules and the column-inference algorithm used for JSON samples.

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GitHub Actions needs Design Patterns: Speed Up CI, Avoid the Anti-Patterns

A pattern reference for GitHub Actions job dependencies (needs). Covers diamond-shaped fan-out, concurrency cancellation, fail-fast tradeoffs, matrix-plus-needs gotchas, and reusable workflows, with copy-paste YAML examples.

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SQL JOIN Types: Complete Reference to INNER, LEFT, RIGHT & FULL

A reference for SQL JOIN types (INNER/LEFT/RIGHT/FULL/CROSS/SELF) with a cheat sheet and row-count walkthroughs. Covers why one-to-many joins duplicate rows, the WHERE-vs-ON NULL trap, and how to write multi-table joins.

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Paste a prompt, set the expected output tokens, and compare per-request API costs across Claude, GPT and Gemini models. Covers input/output pricing structure, auto-updated price tables, and estimate limits.

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Every AI coding agent wants its own rules file. Compare AGENTS.md, CLAUDE.md, .cursor/rules, copilot-instructions.md and more — what to write, a copy-paste template, and how to stop them drifting apart.

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