JSON to SQL DDL & INSERT Statement Generator

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Convert JSON objects or arrays into CREATE TABLE DDL schemas and INSERT INTO SQL statements for PostgreSQL, MySQL, SQLite, and SQL Server.

🔒 100% Client-Side: All data is processed locally in your browser and never leaves your device (Zero Server Uploads).

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Interactive Workspace

JSON Source Input

[
  {
    "id": 1,
    "username": "alex_dev",
    "email": "alex@example.com",
    "role": "admin",
    "is_active": true,
    "created_at": "2026-08-07T08:00:00Z"
  },
  {
    "id": 2,
    "username": "sarah_designer",
    "email": "sarah@example.com",
    "role": "user",
    "is_active": true,
    "created_at": "2026-08-07T08:30:00Z"
  }
]

Generated SQL (POSTGRESQL)

-- Create Table DDL (POSTGRESQL)
CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  username VARCHAR(255),
  email VARCHAR(255),
  role VARCHAR(255),
  is_active BOOLEAN,
  created_at TIMESTAMP WITH TIME ZONE
);

-- Insert Records (2 rows)
INSERT INTO users (id, username, email, role, is_active, created_at) VALUES (1, 'alex_dev', 'alex@example.com', 'admin', TRUE, '2026-08-07T08:00:00Z');
INSERT INTO users (id, username, email, role, is_active, created_at) VALUES (2, 'sarah_designer', 'sarah@example.com', 'user', TRUE, '2026-08-07T08:30:00Z');

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How do I use JSON to SQL DDL & INSERT Statement Generator?

Paste a JSON array of objects (or single JSON object) into the input editor, or click 'Sample' to load a sample dataset.

Enter your target SQL table name, select the SQL dialect (PostgreSQL, MySQL, SQLite, SQL Server), and toggle `CREATE TABLE` DDL or `INSERT INTO` generation.

Copy the generated SQL DDL and batch `INSERT` statements or download the `.sql` migration file.

What features does JSON to SQL DDL & INSERT Statement Generator offer?

Automatic column data type inferencing (INT, VARCHAR, TIMESTAMP, JSONB)

Supports PostgreSQL, MySQL 8, SQLite, and SQL Server dialects

Generates both CREATE TABLE DDL and INSERT INTO rows

100% client-side local browser processing

JSON Data Types to Relational SQL Column Type Mapping

Converting schemaless JSON documents into relational DDL requires mapping JavaScript primitives and nested structures to dialect-specific SQL column types.

JSON to SQL Column Type Mapping Across Dialects
JSON Value Type	PostgreSQL Type	MySQL 8.0 Type	SQLite 3 Affinity
Integer vs Float	BIGINT / DOUBLE PRECISION	BIGINT / DOUBLE	INTEGER / REAL
String / ISO Date	TEXT / TIMESTAMPTZ	VARCHAR(255) / DATETIME	TEXT
Boolean (true/false)	BOOLEAN	TINYINT(1)	INTEGER (0 or 1)
Nested Object / Array	JSONB	JSON	TEXT (JSON1 extension)

Frequently Asked Questions about JSON to SQL DDL & INSERT Statement Generator

How are nested JSON objects and arrays converted into SQL columns?

Nested objects and arrays are serialized as `JSONB` columns in PostgreSQL, native `JSON` columns in MySQL, and JSON-formatted `TEXT` strings in SQLite so relational `INSERT` statements succeed without data loss.

How does the generator prevent SQL syntax errors when JSON strings contain single quotes?

Every string literal inside generated `INSERT INTO ... VALUES` clauses is sanitized according to ANSI SQL rules by doubling internal single quotes (`'O''Reilly'`) before emission.

What happens if objects in my JSON array have inconsistent keys?

The schema scanner collects the superset of all keys across every object in the JSON array. Objects missing a specific key receive an explicit `NULL` value in their corresponding `VALUES (...)` tuple.

Can I convert large JSON arrays into batched multi-row SQL INSERT statements?

Yes. Multi-row `INSERT INTO table (cols) VALUES (...), (...);` batches execute up to 20x faster than single-row inserts by minimizing database transaction round-trips.

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