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Free online tool to quickly convert JSON data into SQL INSERT statements. Supports both array and object formats.
Overview
Understand what the tool solves, how it works, and the boundaries of its data.
This converter maps a JSON object to one database row, or an array of objects to several rows. Object keys become column names; values become SQL literals. Choose MySQL, PostgreSQL, or SQLite, name the target table, and decide whether the output should include a CREATE TABLE statement and use one multi-row INSERT or a separate insert for each record.
JSON distinguishes objects, arrays, strings, numbers, booleans, and null. Here, an array is expected to contain objects rather than arbitrary JSON values. The converter gathers the union of keys across the records, keeping the order in which each key first appears. A missing key in one row and an explicit null both produce SQL NULL for that cell. See RFC 8259 for the JSON data model.
The default table name is my_table, MySQL is selected initially, and both table creation and multi-row insertion start enabled. MySQL table and column names are enclosed in backticks; PostgreSQL and SQLite use double quotes. String values are enclosed in single quotes, with an embedded single quote doubled. Booleans appear as TRUE/FALSE in MySQL and PostgreSQL, and 1/0 in SQLite. null is emitted as the unquoted SQL keyword NULL.
When table creation is enabled, the tool estimates a starter type from the values in each column: integer-like numbers become INT, decimal numbers become DOUBLE, booleans become BOOLEAN, and strings become VARCHAR(255) or TEXT when their observed length exceeds 255 characters. Nested objects and arrays are serialized as JSON text and treated as strings for this estimate. These rules describe this converter's output; they are not a database-independent schema design method.
Checked September 29, 2026. Consult the documentation for the database version you use.
Guide
Follow the workflow and verify inputs and outputs with practical examples.
Paste one object for a single record, or an array such as [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Riley"}]. Each array item must be an object with at least one key. Check commas, quotation marks, and braces if parsing fails.
Enter the intended table name and select MySQL, PostgreSQL, or SQLite. Leave table creation on for a starter schema, or turn it off when you already have a table definition. Choose batched insertion for one INSERT containing the listed rows, or turn it off for one statement per row.
Compare each column and value with your intended schema. Confirm inferred types, identifiers, null behavior, and dialect-specific syntax. Use the copy control to copy the generated SQL only after checking it against the destination database.
For input [{"id": 1, "name": "Ava"}, {"id": 2, "name": "O'Brien"}] and table people, the column list is id, name. The missing id or name in any individual object would appear as NULL. The name O'Brien is quoted as 'O''Brien' so the apostrophe is represented within a SQL string literal.
Use cases
See how the tool fits into real work and everyday tasks.
A developer has a short array of predictable test records and wants a SQL script to review or adapt. Generate the starter table and batched insert, then compare the estimated types and constraints with the project's actual schema before using it.
An analyst or teammate receives a small JSON export and wants to see how its top-level fields map to rows and columns. The output helps reveal field differences between records, while missing values and nested data still need a deliberate database plan.
A learner can keep the same sample and switch between the three available dialects to compare identifier quotes and boolean literals. Treat the result as an illustration of generated syntax and verify details in the selected database's documentation.
Q&A
Find concise answers to common questions and confusing cases.
Yes. A single object is treated as one row. An array can contain multiple row objects; a top-level scalar, a nested array used as a row, or a non-object array item produces an input error.
They are serialized into JSON text for that cell; their inner properties are not expanded into separate columns or related tables.
The output column set includes keys found in any row. A row without one of those keys, or with a value of null, gets SQL NULL in that position. Check whether the destination column permits nulls.
Not necessarily. Types are simple estimates based on sample values, and the generated table has no primary key, index, default, or business constraint. Edit and validate the definition for your schema and database version.
Notes
Review scope, result limitations, and important precautions before use.
The output is SQL text, not a live database import, a schema-aware migration, or a parameterized query. A quoted value does not provide the same protection as binding parameters in application code. For code that sends untrusted values to a database, follow the database driver's parameterization guidance; see the OWASP SQL Injection Prevention Cheat Sheet. Check names, inferred types, nullability, numeric ranges, and dialect syntax against the target database. Review a small sample in a safe development environment before using generated statements with important data.
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