Tool Overview

This tool turns SQL INSERT statements into JSON. You paste INSERT INTO text and get a JSON array of objects back.

Databases use SQL. Many APIs and apps use JSON. Turning INSERT statements into JSON by hand is slow and easy to get wrong. This tool parses the INSERT and builds JSON so you can use the data in code or APIs without writing a parser.

It is for developers and anyone who has INSERT statements (e.g. from a dump or script) and needs JSON. You can use it with basic knowledge of SQL and JSON.

Background and Concepts

SQL INSERT statements list a table name, column names, and one or more rows of values. JSON can represent the same rows as an array of objects with keys and values. A related operation involves converting JSON to CSV as part of a similar workflow.

INSERT statements appear in dumps, migration scripts, and export tools. JSON is common in APIs and configs. When you have INSERT text and need JSON (e.g. for a mock API or import), you must parse the statement and build objects. Doing that by hand is tedious and error-prone.

This tool only supports INSERT INTO statements. It does not run SQL or accept query results. You paste the INSERT text; the tool extracts the table name, column names, and value rows, then builds a JSON array where each row is one object. You can choose whether to convert values to numbers and booleans or keep them as strings.

Key Features

• INSERT INTO only. The tool parses only INSERT INTO statements. You paste one or more rows in the VALUES part (e.g. INSERT INTO users (id, name) VALUES (1, 'John'), (2, 'Jane');). Other SQL (SELECT, UPDATE, etc.) is not supported and will show an error.
• Table and columns. The tool reads the table name and uses it in the result (e.g. for the download file name). If you list columns in parentheses after the table name, those become the keys of each JSON object. If you omit columns (INSERT INTO t VALUES (...)), keys are column1, column2, and so on.
• Multiple rows. One INSERT can have many rows in VALUES. Each row becomes one object in the JSON array. Rows are split by commas outside quotes and parentheses so quoted commas do not break the split.
• Smart types. When enabled, the tool tries to convert values to numbers, booleans, or JSON null. For example 123 becomes a number, true becomes a boolean, NULL becomes null. When disabled, values stay strings (except quotes are stripped). This helps when you need real types in JSON or when you want to keep everything as strings.
• Dialect option. You can pick a dialect (MySQL, Postgres, SQLite). The parser handles common INSERT syntax; the dialect is for your reference and may affect future behavior.
• Quotes and nulls. Values in single, double, or backticks are parsed; the quotes are removed in the JSON. The word NULL (case-insensitive) is turned into JSON null when smart types are on. Escaped quotes inside strings are handled so commas inside quoted values do not split rows.
• View modes. You can show only the SQL input, only the JSON output, or both side by side. This helps when you have long text and want to focus on one panel.
• Copy and download. You can copy the JSON to the clipboard or download it as a file. The file name uses the table name from the INSERT (or "data" if none). Use clear to remove input and output.
• Optional AI optimize. You can send the SQL to an optimization step and get a suggested rewritten INSERT and an explanation. You can apply it or cancel. This is optional and not required for conversion.

Common Use Cases

Mock data. You have INSERT statements from a dump or script and need JSON for a mock API or front-end. Paste the INSERT, get the array of objects, and use it in your app. For adjacent tasks, converting JSON to YAML addresses a complementary step.

Migration or import. You have INSERT text from one system and need to feed the same data into a tool that expects JSON. Convert to JSON and then use that in your import pipeline.

Testing. Creating JSON payloads from sample INSERT data for tests or documentation.

Quick inspection. You have a long INSERT and want to see the data as JSON to inspect or edit. When working with related formats, converting YAML to JSON can be a useful part of the process.

Learning. Seeing how INSERT rows map to JSON objects helps understand both formats.

How to Use This Tool (Step-by-Step)

1. Open the SQL to JSON converter page.
2. Paste your INSERT INTO statement into the SQL input area. It must start with INSERT INTO, then a table name, then optionally a parenthesized list of columns, then VALUES, then one or more rows in parentheses. Multiple rows can be separated by commas.
3. Open the options menu if you want to change settings. Choose dialect (MySQL, Postgres, or SQLite) and turn Smart Types on or off. Smart Types convert numbers, booleans, and NULL to JSON types; when off, values stay strings.
4. Wait a moment. The tool converts automatically after you type or change options. The JSON appears in the output area. If you see a parsing error, check that the input is a single INSERT INTO statement and that quotes and commas are correct.
5. Use the view buttons to show only SQL, only JSON, or both. Check the record count and table name next to the output.
6. Use copy to put the JSON in the clipboard or download to save it as a file. Use clear to start over.
7. Optionally, use Optimize to get an AI-suggested rewrite of your SQL. You can apply it or cancel. Conversion works the same with or without it.

Calculations and Logic

The tool does not do numeric calculations. It parses text and builds structure.

The input is matched as an INSERT INTO statement: table name, optional column list, then VALUES and the value list. The value list is split into rows by commas that are outside quotes and outside parentheses, so (1,'a,b',2) stays one row. Each row string is then parsed: outer parentheses are removed, and the inner part is split by commas that are outside quotes to get individual values. Each value is cleaned: trimmed, NULL (case-insensitive) is kept as the string "null" for later typing, and quoted strings have their quotes stripped. If Smart Types is on, each cleaned value is passed to a type step: "null" or empty becomes JSON null, "true"/"false" become boolean, numeric-looking strings become numbers, and the rest stay strings. If Smart Types is off, values stay as cleaned strings. If columns were given, they are used as keys and values are assigned by position; extra values get keys like columnN. If no columns were given, keys are column1, column2, etc. The result is an array of objects. Input size, row count, and column count are limited; if limits are exceeded, an error is returned instead of data. In some workflows, converting JSON to XML is a relevant follow-up operation.

Tips, Limitations and Best Practices

Only INSERT INTO. The tool does not accept SELECT results or other SQL. Paste only INSERT INTO statements. If you have query results from a database, export them as CSV or JSON from your database tool, or use a CSV to JSON converter.

One statement. Paste a single INSERT statement. Multiple statements or mixed SQL may not parse correctly.

Quotes. Use quotes around values that contain commas or parentheses so the parser does not split them wrongly. The tool handles single, double, and backtick quotes. For related processing needs, converting XML to JSON handles a complementary task.

Smart types. Use Smart Types when you need numbers and booleans in JSON. Turn it off when you need to preserve leading zeros or exact string forms.

Size limits. Input size, row count, and column count have maximums. Very large INSERT text may be rejected. Split into smaller chunks if needed.

Dialect. The parser aims at common INSERT syntax. Dialect choice (MySQL, Postgres, SQLite) may affect future parsing; if your INSERT uses non-standard syntax, conversion may fail.

Privacy. Do not paste sensitive or secret data if you use the Optimize step; that data may be sent for processing.