JsonifyTools
    Schema Toolkit · 100% Local · Browser Only

    Schema to SQL

    Convert JSON Schema into starter SQL table definitions for relational database prototyping.

    Browser only Valid Schema / Data
    JSON Schema
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    SQL DDL Statement
    9 lines
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    Schema Metrics

    Fields
    12
    Objects
    2
    Arrays
    1
    Constraints
    4

    Field Hierarchy Tree

    ❖Root(object)
    └─id*(integer)
    └─name*(string)
    └─email*(string)
    email
    └─active(boolean)
    └─createdAt(string)
    date-time
    └─profile(object)
    └─phone(string)
    └─country(string)
    └─company(string)
    └─roles(array)
    └─items[](string)

    Schema Constraints

    $.name(1 rule)
    minLength:1
    $.email(1 rule)
    format:email
    $.createdAt(1 rule)
    format:date-time
    $.roles[](1 rule)
    enum:["admin","developer","member"]
    JSON Schema to SQL DDL Converter
    PostgreSQL • MySQL • SQLite

    Convert JSON Schema to SQL CREATE TABLE — Online DDL Generator

    Transform your JSON Schema definitions into production-ready SQL CREATE TABLE statements for PostgreSQL, MySQL, and SQLite. Our schema to SQL converter automatically maps JSON types to SQL column types, required arrays to NOT NULL constraints,enum values to CHECK or ENUM constraints, format: uuid to native UUID columns, and format: date-time to TIMESTAMPTZ. Ideal for rapid database prototyping, Flyway migration scripts, and API-first relational modeling.

    3 Supported
    SQL Dialects
    12+ Rules
    Type Mappings
    NOT NULL / CHECK
    Constraint Inference
    Browser Only
    Zero Server Uploads
    Live Conversion Examples

    Interactive Schema → SQL Conversion Scenarios

    Click each scenario to see how JSON Schema properties are converted into SQL columns, constraints, and data types:

    1. User Accounts Table (PostgreSQL)

    CREATE TABLE users

    Converts a JSON Schema user object into a PostgreSQL CREATE TABLE statement with UUID primary key, VARCHAR constraints from minLength/maxLength, and TIMESTAMPTZ columns from date-time format.

    JSON Schema Input
    {
  "title": "users",
  "type": "object",
  "required": ["id", "email", "username", "created_at"],
  "properties": {
    "id": { "type": "string", "format": "uuid" },
    "email": { "type": "string", "format": "email" },
    "username": { "type": "string", "minLength": 3, "maxLength": 30 },
    "is_active": { "type": "boolean" },
    "created_at": { "type": "string", "format": "date-time" }
  }
}
    Generated SQL DDL Output
    CREATE TABLE users (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  email VARCHAR(255) NOT NULL,
  username VARCHAR(30) NOT NULL,
  is_active BOOLEAN DEFAULT TRUE,
  created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
    Type Mapping Reference

    JSON Schema → SQL Type Mapping Matrix

    Complete reference showing how every JSON Schema type, format, and constraint maps to PostgreSQL, MySQL, and SQLite column types:

    JSON SchemaPostgreSQLMySQLSQLiteNotes
    "type": "string"VARCHAR(255) / TEXTVARCHAR(255) / TEXTTEXTTEXT used when no maxLength specified
    "type": "integer"INTEGER / BIGINTINT / BIGINTINTEGERBIGINT for large ranges (> 2 billion)
    "type": "number"NUMERIC(p,s) / DOUBLEDECIMAL(p,s) / DOUBLEREALPrecision from minimum/maximum hints
    "type": "boolean"BOOLEANBOOLEAN (TINYINT)INTEGER (0/1)SQLite lacks native boolean
    "format": "uuid"UUIDCHAR(36)TEXTPostgreSQL has native UUID type
    "format": "date-time"TIMESTAMPTZDATETIMETEXT (ISO)TIMESTAMPTZ includes timezone
    "format": "date"DATEDATETEXT (ISO)ISO 8601 date string
    "format": "email"VARCHAR(255)VARCHAR(255)TEXTApplication-level email validation
    "type": "object"JSONBJSONTEXT (JSON)Nested objects → JSON columns
    "type": "array"JSONB / Separate TableJSON / Separate TableTEXT (JSON)Normalized via junction tables
    "enum": [...]CHECK / CREATE TYPEENUM(...)CHECKPostgreSQL uses CHECK or custom TYPE
    "required": [...]NOT NULLNOT NULLNOT NULLRequired properties → NOT NULL constraint
    Under the Hood

    How JSON Schema to SQL Conversion Works Step-by-Step

    Our schema to SQL engine uses a multi-pass pipeline to produce accurate, production-quality DDL statements:

    Step 1

    Parse Schema & Extract Metadata

    The engine parses the JSON Schema definition, reads the "title" field as the table name, and enumerates all property definitions along with the "required" array.

    Step 2

    Map Types to SQL Columns

    Each property's type and format keywords are mapped to dialect-specific SQL column types (e.g. format:uuid → UUID in PostgreSQL, CHAR(36) in MySQL) using the type mapping matrix.

    Step 3

    Apply Constraints & Indexes

    Required fields become NOT NULL. Enum arrays become CHECK or ENUM constraints. Fields named "id" or with format:uuid are inferred as PRIMARY KEY. Minimum/maximum generate CHECK (col >= N) constraints.

    Step 4

    Emit DDL & Export

    The final CREATE TABLE statement is assembled with proper indentation, constraint ordering, and dialect-specific syntax. The output is copy-paste ready for psql, mysql CLI, migration tools, or ORMs.

    Professional Guide

    Best Practices for JSON Schema to SQL Conversion

    Follow these best practices to generate clean, production-quality SQL DDL from your JSON Schema definitions:

    1. Use title as Table Name Convention

    Always set "title" in your JSON Schema to define the SQL table name. Use snake_case (e.g. "user_accounts") to match SQL naming conventions and avoid quoting issues across PostgreSQL, MySQL, and SQLite.

    2. Mark Primary Keys with format: uuid or Naming Conventions

    Use "format": "uuid" for UUID primary keys or name the field "id" / "table_id" so the converter can infer PRIMARY KEY constraints automatically. Avoid ambiguous field names for identifiers.

    3. Define required Array for NOT NULL Constraints

    Every field listed in the "required" array becomes a NOT NULL column in SQL. Carefully design your required fields — forgetting to include critical columns leads to nullable data integrity issues in production.

    4. Use enum for CHECK Constraints Instead of Free Text

    Map restricted values like status, role, and category to JSON Schema "enum" arrays. These translate to SQL ENUM types (MySQL), CHECK constraints (PostgreSQL), or application-level validation (SQLite).

    Use maxLength for VARCHAR

    Set "maxLength" on string properties to generate VARCHAR(N) instead of unlimited TEXT columns — this improves index performance and storage.

    Add minimum/maximum for CHECKs

    Numeric constraints automatically translate to SQL CHECK clauses (CHECK price >= 0.01), enforcing data integrity at the database level.

    Separate Arrays into Tables

    For one-to-many relationships, model array properties as separate junction tables with foreign keys rather than JSONB columns for better query performance.

    Version Migration Scripts

    Use Flyway (V1__create_table.sql) or Liquibase naming conventions for generated DDL files so they integrate seamlessly into your database migration pipeline.

    Full Schema Studio Suite

    Schema Studio Features — 7 Tools in One Platform

    Beyond SQL conversion, our Schema Studio platform includes a complete suite of JSON Schema developer tools. Explore each feature with live examples:

    1. Schema to SQL DDL Generator

    Convert JSON Schema definitions into CREATE TABLE DDL statements for PostgreSQL, MySQL, and SQLite with automatic type mapping, NOT NULL constraints, and primary key inference.

    Input Example
    {
  "title": "users",
  "required": ["id", "email"],
  "properties": {
    "id": { "type": "string", "format": "uuid" },
    "email": { "type": "string", "format": "email" },
    "age": { "type": "integer", "minimum": 18 }
  }
}
    Output Result
    CREATE TABLE users (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  email VARCHAR(255) NOT NULL,
  age INTEGER CHECK (age >= 18)
);
    Maps JSON Schema types to PostgreSQL, MySQL, and SQLite column types
    Generates NOT NULL constraints from required property arrays
    Infers PRIMARY KEY from id/uuid fields with DEFAULT gen_random_uuid()
    Converts enum arrays to CHECK constraints or MySQL ENUM types

    Why, When, & How to Convert JSON Schema to SQL

    Why Convert Schema to SQL?

    JSON Schema is the standard for API contract definitions, but relational databases need SQL DDL. Converting schema to SQL bridges the gap between API-first design and database implementation, ensuring your tables match your API contracts, reducing manual DDL writing errors, and enabling automated migration generation.

    When to Use Schema to SQL?

    Use schema to SQL conversion during initial database design sprints, when bootstrapping new microservice databases from existing API specs, when generating Flyway/Liquibase migration scripts, and when prototyping relational models from JSON payload samples for rapid iteration.

    How Does the Conversion Work?

    Paste your JSON Schema definition into the editor. The converter parses properties, maps types to SQL columns, applies NOT NULL and CHECK constraints, infers primary keys, and outputs a production-ready CREATE TABLE statement for your chosen SQL dialect (PostgreSQL, MySQL, or SQLite).

    Programmatic Schema to SQL & Migration Automation

    Automate JSON Schema to SQL conversion in your CI/CD pipeline and database migration workflow:

    // Node.js — Programmatic JSON Schema to SQL Converter
const fs = require('fs');

function schemaToSQL(schema, dialect = 'postgresql') {
  const tableName = schema.title || 'unnamed_table';
  const required = new Set(schema.required || []);
  const columns = [];

  for (const [name, prop] of Object.entries(schema.properties || {})) {
    const sqlType = mapType(prop, dialect);
    const nullable = required.has(name) ? ' NOT NULL' : '';
    columns.push(`  ${name} ${sqlType}${nullable}`);
  }

  return `CREATE TABLE ${tableName} (\n${columns.join(',\n')}\n);`;
}

function mapType(prop, dialect) {
  if (prop.format === 'uuid') return dialect === 'postgresql' ? 'UUID' : 'CHAR(36)';
  if (prop.format === 'date-time') return dialect === 'postgresql' ? 'TIMESTAMPTZ' : 'DATETIME';
  if (prop.type === 'integer') return 'INTEGER';
  if (prop.type === 'number') return 'NUMERIC(10, 2)';
  if (prop.type === 'boolean') return 'BOOLEAN';
  if (prop.type === 'object') return dialect === 'postgresql' ? 'JSONB' : 'JSON';
  return 'VARCHAR(255)';
}

const schema = JSON.parse(fs.readFileSync('schema.json', 'utf8'));
console.log(schemaToSQL(schema, 'postgresql'));
    Enterprise & DevOps

    Enterprise Database Schema Management & DevOps

    In enterprise environments, JSON Schema to SQL conversion is a critical part of database DevOps, API-first architecture, and automated migration pipelines:

    API-First Database Design

    • Define API contracts as JSON Schema before creating database tables
    • Auto-generate CREATE TABLE DDL from approved API schemas
    • Ensure 1:1 mapping between API response shapes and database columns
    • Use schema diff to detect when API changes require database migrations

    Automated Migration Pipelines

    • Generate Flyway (V1__create_table.sql) migration scripts from JSON Schemas
    • Integrate with Liquibase, Alembic, Prisma, or Knex migration tools
    • CI/CD pipeline auto-generates and validates DDL on every schema change PR
    • Rollback safety: schema diff detects destructive DDL changes before deployment

    Multi-Database Deployment

    • One JSON Schema generates DDL for PostgreSQL, MySQL, and SQLite simultaneously
    • Teams working with different database engines share a single schema source of truth
    • Cloud-native deployments (AWS RDS, GCP Cloud SQL, Azure DB) use generated DDL scripts
    • Schema-driven database provisioning for staging, preview, and production environments

    Frequently Asked Questions (FAQs)

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