JSON to SQL Converter

Instantly convert JSON data to SQL statements. Generate INSERT INTO or CREATE TABLE queries from JSON objects or arrays. Supports MySQL, PostgreSQL, SQL Server, and more.

Use valid SQL identifier (letters, numbers, underscore).
Affects string escaping and type mapping.

Upload a .json file. Content will be loaded and converted automatically.

Supports JSON object or array of objects. Max size 5 MB.
Supports array of objects or single object
Privacy first: All conversions happen locally in your browser. No data is uploaded.

How JSON to SQL Conversion Works

This tool parses valid JSON (either an object or an array of objects) and translates it into structured SQL statements. You can generate INSERT statements for each row, or a CREATE TABLE statement with inferred column types based on the JSON values. The conversion respects SQL dialect rules, string escaping, and identifier quoting to ensure the output is ready for execution in your database.

JSON → (schema inference) → SQL DDL/DML

Type Inference for CREATE TABLE

When generating a CREATE TABLE statement, the tool scans the JSON data to determine appropriate SQL data types:

JSON type Inferred SQL type (MySQL) PostgreSQL Generic
integer INT(11) INTEGER INTEGER
float / double DECIMAL(20,8) or DOUBLE DOUBLE PRECISION FLOAT
boolean TINYINT(1) BOOLEAN BOOLEAN
string (short < 255) VARCHAR(255) VARCHAR(255) VARCHAR(255)
string (long ≥ 255) TEXT TEXT TEXT
null VARCHAR(255) (fallback) TEXT VARCHAR(255)
array / object TEXT (JSON stored as string) JSONB (PostgreSQL) or TEXT TEXT
PostgreSQL JSONB Performance Tip: When using PostgreSQL dialect and CREATE TABLE mode, array/object fields are mapped to JSONB. For optimal query performance, consider creating a GIN index on JSONB columns:
CREATE INDEX idx_doc_data ON your_table USING GIN (jsonb_column);
This significantly accelerates filtering and searching inside JSON documents. See PostgreSQL JSON documentation.
Real-world use cases
  • Data migration: Move data from APIs or NoSQL databases (like MongoDB) to relational tables.
  • Testing & seeding: Quickly generate SQL insert scripts from JSON fixtures.
  • ETL pipelines: Transform JSON logs into structured SQL for analysis.
  • Documentation: Create table schemas from example JSON documents.

Options Explained

  • Table name: The name of the target SQL table. Must follow identifier rules.
  • Output mode: Choose between INSERT statements (one per JSON object) or a CREATE TABLE statement that defines the table structure.
  • Identifier quoting: Backticks (`) for MySQL, double quotes for PostgreSQL, or none for simple names. Prevents conflicts with reserved keywords.
  • SQL dialect: Affects string escaping (e.g., single quotes doubled in PostgreSQL) and type mapping.

JSON Structure Requirements

Input must be a valid JSON value:

  • Array of objects: Each object represents a row. The tool merges all keys across objects to form columns.
  • Single object: Treated as a single row. Useful for one‑off inserts.

Nested objects and arrays are converted to string representations (JSON.stringify) to maintain data integrity.

Example: JSON array to INSERT statements 
[
  {"first_name": "Alice", "age": 30, "is_active": true},
  {"first_name": "Bob", "age": 25, "is_active": false}
]
→ Generates two INSERT statements with proper escaping.

Trusted conversion logic – This tool follows SQL standard practices and has been tested against MySQL 8, PostgreSQL 14, and SQLite. The type inference is based on common conventions used in ORMs and migration tools. References: MySQL Data Types, PostgreSQL Data Types.Last reviewed Mar 2026

Frequently Asked Questions

Yes, strings are properly escaped based on the selected SQL dialect (single quotes doubled, backslashes handled). However, this tool generates SQL for you to review; always validate before executing in production to avoid unintended consequences.

Currently the tool outputs basic INSERT statements. For advanced upsert logic, you can manually modify the generated SQL. Future versions may include additional options.

Nested structures are automatically converted to JSON strings (using JSON.stringify) and stored in a TEXT column. For PostgreSQL, you can later cast to JSONB if needed.

For now, types are inferred automatically. If you need specific column definitions, you can generate the CREATE TABLE statement and modify it manually, or use the INSERT-only mode.

The generated SQL should work with MySQL, MariaDB, PostgreSQL, SQLite (with minor adjustments), and most standard RDBMS. Dialect options fine‑tune escaping and type names.
Validated against JSON Schema (RFC 8259) and SQL:2016 standard conventions. The conversion engine is designed for reliability and clarity.