How do I convert JSON to SQL INSERT statements?

Paste your JSON array into the editor, select your SQL dialect (MySQL, PostgreSQL, SQLite, or SQL Server), enter a table name, and click Convert to SQL. The tool generates ready-to-run INSERT statements instantly, all processed locally in your browser.

Is my JSON data secure when using this tool?

Yes, 100%. All JSON to SQL conversion happens entirely in your browser using JavaScript. No data is ever sent to our servers or any third party. Your JSON data — including sensitive schemas and production values — never leaves your device.

Which SQL dialects does this converter support?

The converter supports MySQL, PostgreSQL, SQLite, and SQL Server (T-SQL). Each dialect generates correctly formatted INSERT statements with appropriate type handling — for example, booleans as TINYINT(1) for MySQL and TRUE/FALSE for PostgreSQL.

How do I import the generated SQL into MySQL or PostgreSQL?

For MySQL: use phpMyAdmin (SQL tab), MySQL Workbench, or the CLI command: mysql -u username -p database_name < file.sql. For PostgreSQL: use pgAdmin (Query Tool) or the CLI command: psql -U username -d database_name -f file.sql.

Can this tool handle large JSON files?

Yes. The tool can handle JSON files with thousands of records. Performance depends on your device's memory and CPU. For files over 10,000 records, conversion may take a few seconds. For very large datasets (100,000+ records), we recommend splitting into batches of 10,000 for more manageable SQL output files.

How do I handle nested JSON objects when converting to SQL?

You have two options: (1) Flatten the nested JSON before converting — transform address.city into a flat column address_city. (2) Store nested objects in a JSON column (MySQL), JSONB column (PostgreSQL), or NVARCHAR(MAX) column (SQL Server) and query with path expressions later.

How do I store and query JSON in SQL Server?

Store JSON in an NVARCHAR(MAX) column. Use JSON_VALUE() to extract scalar values, JSON_QUERY() to extract objects or arrays, OPENJSON() to parse JSON into relational rows, and JSON_MODIFY() to update specific properties without replacing the entire JSON string. JSON support was introduced in SQL Server 2016.

Does the tool generate CREATE TABLE statements?

Currently the tool generates INSERT INTO statements. For the CREATE TABLE DDL, refer to the JSON to SQL data type mapping table on this page to select the correct column types for your JSON keys, then create the table manually before running the INSERT statements.

Can I convert a REST API JSON response to SQL?

Yes. Copy the JSON response from your API (from Postman, browser DevTools, or curl), paste it into the editor, and convert. If the API response wraps data in a property like {"data": [...]}, extract the inner array first before pasting.

JSON to SQL Converter — Free INSERT Statement Generator (2026)

Q: Can this tool handle large JSON files?

Yes. The tool can handle JSON files with thousands of records. Performance depends on your device's memory and CPU. For files over 10,000 records, conversion may take a few seconds. For very large datasets (100,000+ records), we recommend splitting into batches of 10,000 for more manageable SQL output files.

Q: How do I handle nested JSON objects when converting to SQL?

You have two options: (1) Flatten the nested JSON before converting — transform address.city into a flat column address_city. (2) Store nested objects in a JSON column (MySQL), JSONB column (PostgreSQL), or NVARCHAR(MAX) column (SQL Server) and query with path expressions later.

Q: What is the difference between JSON and JSONB in PostgreSQL?

JSON stores the raw JSON text, preserving whitespace and key order. JSONB stores JSON in a decomposed binary format — slower to insert but significantly faster to query and supports GIN indexing. For production use cases that require querying JSON fields, JSONB is recommended.

Q: How do I store and query JSON in SQL Server?

Store JSON in an NVARCHAR(MAX) column. Use JSON_VALUE() to extract scalar values, JSON_QUERY() to extract objects or arrays, OPENJSON() to parse JSON into relational rows, and JSON_MODIFY() to update specific properties without replacing the entire JSON string. JSON support was introduced in SQL Server 2016.

Q: Does the tool generate CREATE TABLE statements?

Currently the tool generates INSERT INTO statements. For the CREATE TABLE DDL, refer to the JSON to SQL data type mapping table on this page to select the correct column types for your JSON keys, then create the table manually before running the INSERT statements.

Q: Can I convert a REST API JSON response to SQL?

Yes. Copy the JSON response from your API (from Postman, browser DevTools, or curl), paste it into the editor, and convert. If the API response wraps data in a property like {"data": [...]}, extract the inner array first before pasting.

Convert JSON to SQL INSERT statements for MySQL, PostgreSQL, SQLite, and SQL Server. Instantly generate database-ready queries from API responses, migration scripts, or test data. No uploads. No server. Runs entirely in your browser.

MySQLPostgreSQLSQLiteSQL Server

Select SQL Dialect

Table Name

100% Secure – All processing happens locally in your browser

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What is JSON to SQL Conversion?

JSON to SQL conversion is the process of transforming structured JSON data — objects, arrays, or nested documents — into SQL INSERT statements that can be executed directly against a relational database. This is one of the most common data engineering tasks developers face when migrating API responses, NoSQL exports, or configuration data into a relational system like MySQL, PostgreSQL, SQLite, or SQL Server.

Unlike CSV imports, JSON preserves data types — strings, integers, booleans, and nulls — which makes it ideal as an intermediate format for database seeding, ETL pipelines, and test data generation. The challenge is mapping JSON's flexible schema to SQL's strict, typed column structure without data loss or type mismatch errors.

Database seedingAPI response → SQLNoSQL to relational migrationETL pipeline inputTest data generationData export from REST APIs

How to Use This Tool

Paste your JSON array or object into the editor on the left
Select your target SQL dialect — MySQL, PostgreSQL, SQLite, or SQL Server
Enter a table name (e.g. users)
Click "Convert to SQL" to generate the INSERT statements
Review, then copy to clipboard or download the .sql file

All processing happens 100% in your browser — no data is ever sent to a server.

Features

Generates standard SQL INSERT INTO statements
Supports MySQL, PostgreSQL, SQLite, and SQL Server dialects
Automatic SQL string escaping (handles quotes, special characters)
Handles flat JSON objects and JSON arrays
Null value preservation — maps JSON null to SQL NULL
Boolean normalization — true/false to 1/0 for MySQL, TRUE/FALSE for PostgreSQL
Download output as a .sql file
Privacy-first — fully offline, zero server transmission

JSON to SQL Data Type Mapping Reference

One of the most common sources of import errors is a mismatch between JSON value types and SQL column types. The table below shows exactly how each JSON data type maps to the correct SQL column type across all four major dialects.

JSON Type	Example Value	MySQL	PostgreSQL	SQLite	SQL Server
String	`"hello"`	VARCHAR(255)	TEXT	TEXT	NVARCHAR(255)
Integer	`42`	INT	INTEGER	INTEGER	INT
Float / Decimal	`3.14`	DECIMAL(10,4)	NUMERIC(10,4)	REAL	DECIMAL(10,4)
Boolean	`true / false`	TINYINT(1)	BOOLEAN	INTEGER (0/1)	BIT
Null	`null`	NULL	NULL	NULL	NULL
ISO Date string	`"2024-01-15"`	DATE	DATE	TEXT	DATE
ISO DateTime string	`"2024-01-15T10:30:00Z"`	DATETIME	TIMESTAMP WITH TIME ZONE	TEXT	DATETIME2
Nested object	`{"id":1}`	JSON	JSONB	TEXT (JSON)	NVARCHAR(MAX)
Array	`[1, 2, 3]`	JSON	JSONB / ARRAY	TEXT (JSON)	NVARCHAR(MAX)

* Nested JSON objects and arrays are stored as JSON/JSONB columns. To normalize them into relational rows, flatten the nested structure before conversion or use dialect-specific JSON functions post-import.

How to Handle Nested JSON in SQL

Nested JSON — where an object contains other objects or arrays as values — is the most common challenge in JSON-to-SQL conversion. Relational databases are designed for flat, tabular data, so you have two options when your JSON has nested structure:

Option 1Flatten Before Converting

Transform address.city into a flat column address_city before pasting into this tool. Best for simple one-level nesting. Tools like jq or a quick JavaScriptObject.keys() loop can flatten your JSON automatically.

Option 2Store as JSON Column

Store the nested object in a JSON (MySQL) or JSONB (PostgreSQL) column and query it with path expressions later. Best for flexible, semi-structured data you query infrequently. SQL Server uses NVARCHAR(MAX) with JSON_VALUE().

// Input: nested JSON

{
  "id": 1,
  "name": "Alice",
  "address": { "city": "Austin", "zip": "78701" }
}

// Option 1 — flattened output

INSERT INTO users (id, name, address_city, address_zip)
VALUES (1, 'Alice', 'Austin', '78701');

// Option 2 — JSON column output (PostgreSQL)

INSERT INTO users (id, name, address)
VALUES (1, 'Alice', '{"city":"Austin","zip":"78701"}');

Database Migration FAQs

How to import JSON to MySQL or PostgreSQL?

Follow these steps to import your JSON data into MySQL or PostgreSQL:

Convert your JSON using this tool and select your target database dialect
Copy the generated SQL INSERT statements or download the .sql file
MySQL: Use phpMyAdmin (SQL tab), MySQL Workbench, or CLI: mysql -u username -p database_name < file.sql
PostgreSQL: Use pgAdmin (Query Tool) or CLI: psql -U username -d database_name -f file.sql
Verify data import by running SELECT queries on your table

Is my data secure during conversion?

Yes — 100%. All JSON to SQL conversion runs entirely in your browser using JavaScript. No data is ever transmitted to our servers or any third party. This makes the tool safe for use with production data, sensitive schemas, and proprietary API responses. Your JSON stays on your device.

Can I convert large JSON files?

Yes. The tool handles large JSON files with thousands of records efficiently. Because conversion runs locally, performance depends on your device's memory and CPU. For files exceeding 10,000 records, conversion may take a few seconds. For very large datasets, test with a 100-record sample first to verify the column mapping looks correct before processing the full file.

Common JSON to SQL Conversion Errors (And How to Fix Them)

These are the most frequent issues developers encounter when converting JSON to SQL INSERT statements:

❌ Invalid JSON — Unexpected token

Cause: Trailing commas, unquoted keys, or single quotes in the JSON input.

Fix: Validate your JSON first using our JSON Formatter tool. JSON requires double-quoted keys and no trailing commas. Replace single-quoted strings with double quotes.

❌ Column count mismatch across rows

Cause: Different JSON objects in the array have inconsistent keys.

Fix: Ensure every object in your JSON array has the same set of keys. Add null values for missing keys in incomplete objects before converting.

❌ SQL syntax error near apostrophe (') in string value

Cause: A string value contains a single quote that is not escaped for SQL.

Fix: The tool auto-escapes quotes, but if you're running converted SQL manually, ensure strings with apostrophes are escaped (e.g., O\'Brien).

❌ Incorrect integer value: '' for column (MySQL)

Cause: An empty string is being inserted into an INT/BIGINT column.

Fix: Replace empty string values in your JSON with null (not "") for numeric columns before conversion.

❌ JSON input is an object, not an array

Cause: The tool expects a JSON array of objects. A single JSON object is not a valid input for multi-row INSERT generation.

Fix: Wrap your single object in an array: [{...}] instead of {...}.

SQL Dialect Differences: MySQL vs PostgreSQL vs SQLite vs SQL Server

Each SQL database engine handles INSERT syntax slightly differently. The tool auto-adjusts output based on your selected dialect, but understanding the key differences helps you debug issues faster:

MySQL

›Booleans are stored as TINYINT(1) — true → 1, false → 0
›Backtick quoting for reserved column names: `order`, `name`
›Use utf8mb4 charset for full Unicode (emojis, CJK characters)
›AUTO_INCREMENT for primary keys
›JSON type column supported since MySQL 5.7.8

PostgreSQL

›Native BOOLEAN type — true/false keywords directly
›Double-quote identifiers for reserved words: "order", "name"
›JSONB (binary JSON) preferred over JSON for queryable storage
›SERIAL or GENERATED ALWAYS AS IDENTITY for auto-increment
›Dollar-quoted strings ($$) avoid escaping issues with apostrophes

SQLite

›Dynamic typing — stores any value in any column type
›No native BOOLEAN — use INTEGER 0/1
›No native DATETIME type — use TEXT in ISO 8601 format
›AUTOINCREMENT keyword for integer primary keys
›Ideal for local development, mobile apps, and embedded databases

SQL Server

›Use NVARCHAR instead of VARCHAR for Unicode support
›Square bracket quoting for reserved words: [order], [name]
›Store JSON in NVARCHAR(MAX) columns; query with JSON_VALUE()
›BIT column type for booleans — 1/0
›IDENTITY(1,1) for auto-increment primary keys

Trust, Transparency & Expert Verification

Methodology: ANSI SQL Standards

This JSON to SQL Converter is independently developed and maintained by Raviraj Bhosale (Founder, jsonformatters.com) to help developers generate clean, production-ready database scripts with full data privacy.

No Server-Side Transmission

Your SQL queries are generated 100% locally in your browser. We never transmit, log, or store your JSON data, ensuring your database schema and values remain completely confidential.

Standard SQL Compliance

The generator follows ANSI SQL and SQL-92 specifications, ensuring the generated INSERT statements are compatible with MySQL, PostgreSQL, and SQL Server.

Technical Documentation & Standards:

ISO/IEC 9075: Official International SQL Standards W3Schools: SQL Implementation & Syntax Logic JSON.org: Data Interchange Standards

Last Reviewed: February 2026 · Maintained by Raviraj Bhosale.

Verify Expertise

Expertise Behind the Tool

Hello! I’m a Web Developer and the founder of jsonformatters.com. My goal is to build tools for developers that are not only fast, but also completely secure and privacy-focused.

Keeping modern 2026 web standards in mind, I optimized this tool using React and Next.js to deliver the best possible performance.

I believe in complete transparency when it comes to my coding skills and projects. You can learn more about my professional experience by connecting with me on my LinkedIn Profile.

Frequently Asked Questions (FAQ)

How do I store JSON data in SQL Server?

Store JSON in SQL Server using an NVARCHAR(MAX) column. SQL Server does not have a dedicated JSON column type, but it provides built-in functions (JSON_VALUE, OPENJSON, JSON_QUERY, JSON_MODIFY) to read and update JSON stored in NVARCHAR columns. Use ISJSON() to validate the stored value.

How can I query JSON data in SQL Server?

SQL Server provides JSON_VALUE() to extract a scalar value from a path, JSON_QUERY() to extract an object or array, and OPENJSON() to parse JSON into a rowset. Example: SELECT JSON_VALUE(data, '$.name') FROM users WHERE ISJSON(data) = 1;

When was JSON support added to SQL Server?

JSON support was introduced in SQL Server 2016 (version 13.0), including JSON_VALUE, JSON_QUERY, JSON_MODIFY, OPENJSON, and ISJSON. It was extended in SQL Server 2022 with enhanced JSON_ARRAY() and JSON_OBJECT() constructor functions.

How do I convert JSON data to SQL table rows?

Use OPENJSON() combined with CROSS APPLY to parse JSON arrays into relational rows. Example: SELECT * FROM OPENJSON(@jsonData) WITH (id INT, name NVARCHAR(100), email NVARCHAR(200)). This is the native SQL Server approach for JSON import without a separate tool.

How do I update JSON data in SQL Server?

Use JSON_MODIFY() to update a specific property: UPDATE users SET data = JSON_MODIFY(data, '$.email', 'new@email.com') WHERE id = 1. This modifies a single value without replacing the entire JSON string, making it safe for partial updates.

What is the difference between JSON and JSONB in PostgreSQL?

JSON stores the raw text of the JSON input, preserving whitespace and key order. JSONB stores JSON in a decomposed binary format — it's slower to insert but faster to query and supports indexing with GIN indexes. For most production use cases, JSONB is preferred.

How do I auto-generate a CREATE TABLE statement from JSON?

Inferring a CREATE TABLE from JSON requires analyzing all keys and value types across every object in the array to determine the widest compatible column type. Our converter generates INSERT statements; for CREATE TABLE DDL generation, review the data type mapping table above and construct your schema manually to ensure correct type selection.

Can I convert a REST API JSON response directly to SQL?

Yes. Copy the JSON response from your API (e.g., from Postman, browser DevTools, or curl output), paste it into the editor above, and convert. If the API response has a nested data property (e.g., {"data": [...]}), extract the inner array first before pasting.

What is the maximum JSON file size this tool supports?

There is no hard file size limit. Performance depends on your device's available memory. Files up to 50,000 records convert within a few seconds on modern hardware. For files above 100,000 records, consider splitting into batches of 10,000 for more manageable SQL files.

Does this tool generate CREATE TABLE statements?

Currently, the tool generates INSERT INTO statements. For the CREATE TABLE DDL, use the data type mapping reference table above to select the correct column types for your JSON keys, then create the table manually before running the INSERT statements. A DDL generator is planned for a future update.