JSON to SQL
Convert a JSON array to SQL INSERT statements. Choose table name and dialect.
What Is JSON to SQL Conversion?
JSON to SQL conversion transforms a JSON array of objects into SQL INSERT statements ready to run against a relational database. Each object in the array becomes one row, and each key becomes a column. This tool supports MySQL, PostgreSQL, SQLite, and MS SQL Server syntax, and can optionally generate a CREATE TABLE statement based on the keys found in your JSON data.
How to Convert JSON to SQL INSERT Statements
- Paste a JSON array of objects into the Input pane, or click Upload.
- Click Convert — SQL
INSERTstatements appear in the Output pane. - Set the Table name (default:
my_table). - Choose a Dialect: MySQL, PostgreSQL, SQLite, or MSSQL.
- Toggle CREATE TABLE to prepend an inferred table definition.
- Toggle Batch INSERT to combine all rows into a single
INSERT … VALUES (…), (…)statement. - Click Copy to copy the SQL, or Download to save as
output.sql.
Dialect Differences
- MySQL — uses backtick identifiers (
`col`),INT/VARCHAR/FLOAT/TINYINT/TEXTtypes in CREATE TABLE. - PostgreSQL — uses double-quote identifiers (
"col"),INTEGER/TEXT/NUMERIC/BOOLEAN/JSONBtypes. - SQLite — uses double-quote identifiers,
INTEGER/TEXT/REAL/NUMERICtypes; noBOOLEAN. - MSSQL — uses bracket identifiers (
[col]),INT/NVARCHAR(MAX)/FLOAT/BITtypes.
Handling Nested Objects and Arrays
Nested objects and arrays inside your JSON are serialized to their JSON string representation and stored as text/JSONB columns. If you want to flatten nested data into separate columns, pre-process your JSON with the JSON Formatter first.
Common Use Cases
- Seed data for development — convert sample API responses into
INSERTstatements to populate a dev database. - Database migrations — bulk-insert reference data (countries, currencies, categories) from JSON exports.
- API → DB ingestion — one-off ingestion of a JSON dataset without writing custom ETL.
- Bug reproduction — convert the JSON payload that triggered a bug into INSERTs to reproduce the data state in a test database.
- Migration from NoSQL — convert MongoDB/Firebase JSON exports to relational SQL when moving stacks.
Tips & Tricks
- Batch INSERT for large datasets — enable Batch INSERT to combine all rows into one statement. Dramatically faster than individual INSERTs for > 100 rows.
- Inspect inferred CREATE TABLE — the auto-generated schema is a starting point. Refine column types (e.g.
VARCHAR(255)vsTEXT) before running in production. - Mind reserved words — if your JSON has keys like
order,group, orselect, the generated SQL quotes them properly per dialect (backticks for MySQL, double-quotes for PG). - NULL handling — JSON
nullvalues become SQLNULL. Empty strings stay as empty strings — different in SQL. - Test on a copy first — never run generated DDL against production. Test on a sandbox database, verify schema, then apply.
Related Tools
- JSON Formatter — Format and validate JSON before converting.
- JSON Validator — Confirm the JSON is valid first.
- JSON to CSV — Alternative for spreadsheet import.
- SQL Formatter — Pretty-print the generated SQL before running.
- SQL Minifier — Compress the SQL for embedding in code.
Is My Data Sent to a Server?
No. All conversion runs entirely in your browser. Your JSON data — including any PII, API keys, or proprietary structures — never leaves your machine. There is no upload, log, or analytics on the data you convert. The AI Explain feature only sends text you explicitly submit for explanation; otherwise nothing transmits.