JSON to SQL SchemaGenerate CREATE TABLE SQL statements from JSON data with type inference.
JSON to SQL Schema
Generate CREATE TABLE SQL statements from JSON data with type inference.
Paste JSON data
Paste a JSON array of objects representing your data.
Configure options
Set table name and choose whether to add an auto-increment ID column.
Get CREATE TABLE
Copy the generated SQL schema for your database.
What Is JSON to SQL Schema?
JSON to SQL Schema generates CREATE TABLE SQL statements from JSON data by analyzing your data and inferring appropriate SQL column types. The tool examines values across all objects in a JSON array to determine the best SQL type for each field: integers become INTEGER, decimals become DECIMAL, dates become DATE or DATETIME, and strings become VARCHAR with appropriate length. You can customize the table name and optionally include an auto-increment primary key column.
Why Use Our JSON to SQL Schema?
- Automatic SQL type inference from JSON values (INTEGER, DECIMAL, VARCHAR, DATE, etc.).
- Analyzes all objects to determine the best type for each column.
- Optional auto-increment primary key column.
- Customizable table name.
Common Use Cases
Database Design
Quickly generate a starting schema from sample JSON data for new database tables.
Data Import Prep
Create the table structure needed before importing JSON data into a SQL database.
API to Database
Generate database schemas from API response data for data warehousing.
Prototyping
Quickly scaffold database tables from JSON mock data during development.
Technical Guide
Type inference examines each value and assigns the most specific SQL type. The algorithm checks in order: null/undefined โ TEXT (fallback), boolean โ BOOLEAN, integer numbers โ INTEGER, decimal numbers โ DECIMAL(10,2), date strings (YYYY-MM-DD) โ DATE, datetime strings โ DATETIME, long strings (>255 chars) โ TEXT, other strings โ VARCHAR(n) where n is rounded up to the nearest 50. When multiple objects have different value types for the same key, the less specific type wins (e.g., if one object has an integer and another has a string for the same key, VARCHAR is used). Column names from JSON keys are wrapped in backticks for SQL compatibility. Nested objects and arrays are skipped since they don't map to flat SQL columns. The output uses MySQL-compatible syntax with backtick quoting. For PostgreSQL, replace backticks with double quotes. For SQLite, backticks work or can be removed.
Tips & Best Practices
- 1Provide representative sample data โ the more diverse the values, the better the type inference.
- 2Nested objects are skipped โ flatten your JSON first if needed.
- 3The generated schema is a starting point โ review and adjust types and constraints.
- 4VARCHAR lengths are rounded up to multiples of 50 for common practice.
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File ConversionFrequently Asked Questions
QWhich databases are supported?
QHow are types inferred?
QWhat about foreign keys?
QAre nested objects supported?
QCan I add indexes?
About JSON to SQL Schema
JSON to SQL Schema is a free online tool from FreeToolkit.ai. All processing happens directly in your browser โ your data never leaves your device. No registration required. No ads. Just fast, reliable tools.