SQL Table Generator– Create Tables from Schema

Generate SQL CREATE TABLE statements from JSON schema definitions instantly.

Paste JSON Schema

CREATE TABLE SQL

Why Use Our SQL Table Generator?

Schema to SQL

Convert JSON schema definitions to CREATE TABLE statements.

Type Inference

Auto-detect column types from sample JSON data arrays.

Constraints

Supports PRIMARY KEY, NOT NULL, UNIQUE, and DEFAULT values.

Flexible Input

Accept schema objects or sample data arrays for inference.

100% Private

All generation in-browser. Schema data never leaves your device.

Cross-Database

Output works with MySQL, PostgreSQL, SQLite, and more.

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Complete SQL Table Generator Guide

The SQL Table Generator converts JSON schema definitions into production-ready CREATE TABLE SQL statements. Instead of manually writing DDL syntax, define your table structure in a simple JSON format and get correctly formatted SQL with proper data types, constraints, and an auto-increment primary key.

The tool accepts two input formats. The first is a structured schema object with a columns array where each entry specifies name, type, and optional constraints. The second is a raw JSON data array from which column names and types are inferred automatically based on JavaScript value types.

This approach accelerates database design by letting you think in terms of data shapes rather than SQL syntax. It is particularly useful for rapid prototyping, converting API response shapes to database schemas, and generating consistent DDL across team members.

All processing runs locally in your browser, ensuring sensitive schema information remains private and secure.

The structured schema format gives you full control over column definitions:

tableName (string)

Sets the table name in the CREATE TABLE statement. Use snake_case for SQL convention. Defaults to 'table_name' if omitted.

columns (array)

Array of column objects. Each must have 'name' and 'type'. Optional: 'nullable' (boolean, default true), 'primaryKey' (boolean), 'autoIncrement' (boolean), 'unique' (boolean), 'default' (string).

Column Types

Use standard SQL types: INT, BIGINT, VARCHAR(n), TEXT, BOOLEAN, DATE, DATETIME, TIMESTAMP, DECIMAL(p,s), FLOAT, DOUBLE, JSON, BLOB, ENUM. The type string is used as-is in the output.

Data Array Inference

Alternatively, paste a JSON array of objects. The first object's keys become column names, and JavaScript types map to SQL: number to INT/DECIMAL, string to VARCHAR(255), boolean to BOOLEAN.

The structured format is recommended for production schemas where you need precise control over types and constraints. Data array inference is ideal for quick prototyping.

Follow these patterns when designing your database tables for maintainability and performance:

Surrogate Keys

Use auto-incrementing integer or UUID primary keys. Natural keys (like email) can change and cause cascade update issues. Keep natural keys as UNIQUE constraints instead.

Audit Columns

Include created_at and updated_at TIMESTAMP columns with DEFAULT CURRENT_TIMESTAMP. These are invaluable for debugging, compliance, and data lineage tracking.

Normalization

Start with Third Normal Form (3NF). Denormalize selectively for read performance after identifying slow queries. Denormalization should be an intentional optimization, not a default.

Soft Deletes

Add a deleted_at TIMESTAMP column instead of hard deleting rows. This preserves data for auditing and allows recovery while keeping queries clean with WHERE deleted_at IS NULL.

Good table design pays dividends throughout the application lifecycle. Invest time in schema design early to avoid expensive migrations later.

Adjustments needed when targeting specific database systems:

MySQL

Uses AUTO_INCREMENT for identity columns. Supports ENGINE=InnoDB for transactions. BOOLEAN is an alias for TINYINT(1). VARCHAR max is 65,535 bytes divided by character encoding.

PostgreSQL

Uses SERIAL or GENERATED ALWAYS AS IDENTITY instead of AUTO_INCREMENT. Has native BOOLEAN, UUID, JSONB, and ARRAY types. TEXT has no practical length limit.

SQLite

Uses INTEGER PRIMARY KEY for auto-increment. Has dynamic typing with type affinities. Does not enforce VARCHAR length limits. No native BOOLEAN or DATE types.

SQL Server

Uses IDENTITY(1,1) for auto-increment. NVARCHAR for Unicode strings. BIT for booleans. DATETIME2 for high-precision timestamps. Uses square brackets for identifier quoting.

Our generator produces standard SQL that provides a strong starting point for any database. Adjust dialect-specific syntax before deploying to your target system.

Choose appropriate types: Use the smallest type that fits your data. INT for counts, BIGINT for IDs at scale, VARCHAR(n) with a realistic length, and DECIMAL for money. Avoid TEXT when VARCHAR suffices.
Use NOT NULL by default: Make columns NOT NULL unless you have a specific reason for allowing nulls. Nulls add complexity to queries and application logic.
Plan for growth: Consider future data volume when choosing types. Migrating from INT to BIGINT on a large table is expensive. Start with room to grow.
Add indexes after creation: Generate CREATE INDEX statements for columns used in WHERE, JOIN, and ORDER BY clauses. Indexes dramatically improve query performance.
Version your DDL: Store CREATE TABLE statements in migration files under version control. This enables reproducible database setup and schema evolution tracking.

Thoughtful schema design saves hours of refactoring. Take time to model your data correctly before building application code on top of it.

What input format does the table generator expect?

It accepts JSON schema in two formats: an object with a 'columns' array containing column definitions (name, type, nullable), or a JSON array of sample data objects from which column types are inferred automatically.

How are column types determined from sample data?

When you provide a JSON array, the generator infers types from JavaScript values: numbers become INT or DECIMAL, strings become VARCHAR(255), and booleans become BOOLEAN. An auto-incrementing INT primary key is added automatically.

Can I specify table names in the schema?

Yes. Include a 'tableName' field in your JSON object to set the table name. If omitted, the default 'table_name' is used as a placeholder for you to rename.

What constraints are supported?

The generator supports PRIMARY KEY, AUTO_INCREMENT, NOT NULL (via nullable: false), UNIQUE, DEFAULT values, and custom column types. Complex constraints like FOREIGN KEY or CHECK must be added manually.

Which database dialects are supported?

The output uses standard SQL syntax compatible with MySQL, PostgreSQL, SQLite, and SQL Server. Minor syntax adjustments may be needed for specific database features like SERIAL vs AUTO_INCREMENT.

Can I generate tables for multiple databases?

The output is standard SQL that works across databases. For PostgreSQL, replace AUTO_INCREMENT with SERIAL. For SQL Server, use IDENTITY(1,1). The core CREATE TABLE syntax is universal.

How do I add indexes to the generated table?

The generator creates the CREATE TABLE statement. Add CREATE INDEX statements separately after the table definition for any columns that need indexing based on your query patterns.

Can I generate from an existing API response?

Yes. Paste a JSON API response array directly. The generator infers column types from the data values and creates a matching table schema. Review and adjust types as needed.

What about relationships between tables?

Foreign key relationships must be added manually. Generate each table separately, then add FOREIGN KEY constraints referencing the related tables and columns.

How accurate are the inferred types?

Type inference provides sensible defaults: integers for whole numbers, DECIMAL for decimals, VARCHAR(255) for strings. Review the output to adjust string lengths, numeric precision, and add constraints.

Is this tool secure for production schemas?

Yes. All processing runs in your browser using client-side JavaScript. Your schema data never leaves your device and is not transmitted or stored anywhere.

Can I use this with Prisma, TypeORM, or Sequelize?

The generated SQL can be used as a reference or executed directly for database setup. ORMs typically use their own schema definition format, but the SQL output clarifies the exact table structure they should produce.

SQL Table Generator Tool

Why Use Our SQL Table Generator?

Other SQL Tools

Complete SQL Table Generator Guide