SQL will appear here
Paste JSON and click Generate SQL
✓ Infers types automatically
✓ DDL + DML in one click
✓ 4 SQL dialects
// infer schema and generate DDL + DML from JSON
Convert JSON to SQL instantly. Infer schema and generate CREATE TABLE DDL and INSERT INTO DML statements from any JSON object or array online free.
SQL will appear here
Paste JSON and click Generate SQLEnter a JSON object or array of objects into the input box. Use the sample buttons to try an example.
Set the table name, choose your SQL dialect (MySQL, PostgreSQL, SQLite, or MSSQL), and select DDL, DML, or both.
Click Generate SQL. Copy the DDL, DML, or full SQL output with one click.
The JSON to SQL Generator analyzes your JSON structure to infer column names, data types (INTEGER, DECIMAL, BOOLEAN, TEXT, JSON), and nullability. It then produces ready-to-run SQL DDL (CREATE TABLE) and DML (INSERT INTO) statements for your chosen database engine.
MySQL, PostgreSQL, SQLite, and Microsoft SQL Server (T-SQL). Each dialect uses appropriate quoting, type names, and auto-increment syntax — for example, SERIAL in PostgreSQL, AUTOINCREMENT in SQLite, and IDENTITY in MSSQL.
The tool scans all rows in your JSON array to determine the best SQL type for each field. Integers map to INT, floats to DECIMAL, booleans to TINYINT/BOOLEAN/BIT, nested objects or arrays to JSON/JSONB/TEXT, and strings to VARCHAR or TEXT depending on length.
Nested objects and arrays are mapped to a JSON or JSONB column type. The serialized JSON string is stored as the insert value. For deep normalization you would need to manually split these into separate tables.
Yes. If you paste a single JSON object the tool wraps it automatically into a one-row array and generates DDL and a single INSERT statement.
A column is marked NULL if any row in your dataset has a null value for that key, or if the key is missing from some rows. All other columns default to NOT NULL.
Yes. Any character that is not alphanumeric or an underscore is replaced with an underscore, making column names safe for all SQL dialects. Proper quoting (backticks, double quotes, or brackets) is applied automatically.
All processing is handled server-side via a PHP API on tools.jlvextension.com. Your JSON is sent over HTTPS but is never stored or logged. For sensitive data, consider running the tool locally.
Yes. The generator always prepends an id column with the appropriate auto-increment syntax for the chosen dialect. This ensures every generated table has a primary key by default.
Working with databases often means bridging two worlds: the flexible, document-oriented world of JSON and the structured, relational world of SQL. Whether you have an API response, a configuration file, or an exported dataset in JSON format, manually writing CREATE TABLE and INSERT INTO statements is tedious and error-prone. This JSON to SQL Generator automates that process entirely — paste your JSON, choose a dialect, and get production-ready SQL in seconds.
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DDL (Data Definition Language) includes SQL statements that define the structure of a database. The most common DDL command is CREATE TABLE, which establishes column names, data types, constraints, and primary keys. DDL also covers ALTER TABLE, DROP TABLE, and similar structural commands.
DML (Data Manipulation Language) includes statements that work with the data inside tables. The most common DML command is INSERT INTO, which adds rows to a table. Other DML commands include UPDATE, DELETE, and SELECT.
This tool generates both: a DDL script to create your table and a DML script to populate it with the rows from your JSON input.
The generator scans every row in your JSON array to build the most accurate schema possible:
42) → INT / INTEGER3.14) → DECIMAL / NUMERIC / REALtrue) → TINYINT(1) in MySQL, BOOLEAN in PostgreSQL, BIT in MSSQLVARCHAR(n) with a safe length bufferTEXT / NVARCHAR(MAX)JSON in MySQL, JSONB in PostgreSQL, TEXT in SQLiteNULLIf a key appears in some rows but not others, it is automatically marked as nullable. Types are promoted to accommodate the widest value found — if a column is integer in most rows but float in one row, it becomes DECIMAL.
The tool generates valid SQL for four major database systems:
INT AUTO_INCREMENT PRIMARY KEY, ENGINE=InnoDB, utf8mb4 charsetSERIAL PRIMARY KEY, JSONB for nested data, NUMERIC for decimalsINTEGER PRIMARY KEY AUTOINCREMENT, REAL for floats, all strings as TEXTINT IDENTITY(1,1), NVARCHAR for Unicode strings, BIT for booleansJSON naturally supports nested structures — objects inside objects, arrays inside objects, and mixed depths. SQL, being relational, stores flat rows. When the generator encounters a nested object or array value, it serializes it as a JSON string and maps the column to a JSON-compatible type (JSON in MySQL, JSONB in PostgreSQL, TEXT elsewhere). This lets you store the data without loss while giving you the option to query it with JSON functions later.
For proper normalization, you would split nested objects into separate related tables with foreign keys. This tool focuses on the fast, flat-first approach that works well for prototyping, data imports, and ad hoc analysis.
Seeding a database from an API response: Fetch a JSON payload from any REST API, paste it here, and instantly get INSERT statements you can run against a development or staging database.
Prototyping database schemas: When you have sample data in JSON format, inferring the schema from real data is faster and more accurate than writing DDL by hand. Use the generated CREATE TABLE as a starting point and refine from there.
Data migration: Moving data from a document store (MongoDB, Firestore, DynamoDB) to a relational database is a common migration task. Export your documents as JSON, paste them here, and use the generated SQL as the migration script.
Test fixture generation: When writing integration tests, you often need to pre-populate a database with known data. Generate INSERT statements from your JSON test fixtures and include them in your test setup scripts.
null values for any optional fields so the nullable flag is set correctly.