SQL to JSON Converter

Why Convert SQL to JSON?

SQL is the lingua franca of relational databases — every legacy system, BI tool, and operational backend speaks it. JSON is the format of modern APIs, NoSQL stores, frontend frameworks, and serverless functions. Bridging the two is a daily task: migrating from MySQL to MongoDB, building REST API mocks from a database dump, seeding a new microservice with relational data, sharing data between teams using different stacks. This converter parses raw SQL — INSERT statements, CREATE TABLE schemas, SELECT result dumps — and produces clean JSON ready to paste into fetch() mocks, fixtures, MongoDB insertMany(), or any other JSON-consuming workflow.

Supported SQL Input Formats

• INSERT statements: INSERT INTO users (id, name, email) VALUES (1, 'Alice', 'a@x.com');
• Multi-row INSERT: INSERT INTO ... VALUES (1,'A'), (2,'B'), (3,'C');
• CREATE TABLE + INSERT pairs: uses the schema to infer column types correctly
• mysqldump output: processes complete dump files with comments, locks, etc.
• psql / pg_dump output: handles PostgreSQL-specific syntax (jsonb, arrays, RETURNING)
• mysql / psql CLI result tables: the ASCII-art result tables you see when running SELECT in a terminal
• phpMyAdmin / TablePlus / DBeaver exports: handles common GUI export formats

Type-Aware Conversion

Output Formats

• JSON array (default): [{...}, {...}] — directly usable as a JavaScript / Python data structure
• JSON Lines (.jsonl): one object per line — ideal for streaming, log-style processing, and direct ingestion by BigQuery / Athena / Spark
• Pretty-printed: 2-space or 4-space indentation for human review
• Minified: single-line output for production transmission
• Schema-only: when CREATE TABLE is the input, output JSON Schema describing column types and constraints (NOT NULL, UNIQUE, etc.)

Common Use Cases

• MySQL → MongoDB migration: mysqldump → SQL-to-JSON → MongoDB Compass import
• Mock API responses: SQL query result → JSON for Mock Service Worker, MSW, JSON Server
• Test fixtures: SELECT * from production-like data → JSON fixtures for Jest, Vitest, pytest
• Cross-team handoffs: backend dev has SQL data, frontend team needs JSON to wire up the UI
• SQL → NoSQL exports: Firestore, DynamoDB, CosmosDB all import JSON documents
• API contract documentation: CREATE TABLE → JSON Schema → OpenAPI
• Data warehouse ingestion: JSON Lines output → BigQuery / Snowflake / Redshift bulk load
• Frontend prototyping: dump a real SQL table, paste here, paste JSON output into a static data.json

Handling Edge Cases

• Multi-line strings: properly handles SQL strings containing newlines (escaped as \\n in JSON)
• Single quotes in values: SQL escapes apostrophes as '' (double single-quote); the parser correctly unescapes
• Backticks vs quotes for identifiers: MySQL uses backticks, others use double quotes — both supported
• BINARY / BLOB columns: Base64-encoded in output (you usually shouldn't put binary in JSON anyway)
• Comments: SQL -- comment and /* block comment */ are skipped, not included in output
• Transactions: BEGIN; ... COMMIT; blocks are processed; the keywords ignored
• Unicode: full UTF-8 support including emoji, CJK, accented characters

When NOT to Use This Tool

This is a focused utility for ad-hoc conversion, debugging, and prototyping. It is not a replacement for full ETL pipelines. For production data migrations (millions of rows, ongoing sync), use dedicated tools: mysqldump | mysqltocouch, AWS DMS, Airbyte, Fivetran, or write a script using your language's SQL driver + JSON serializer. Also, this tool processes whatever SQL you paste — it doesn't execute queries against a live database; you provide the SQL output as text.

Privacy

All SQL parsing and JSON generation runs in your browser using JavaScript. Your SQL data — including production records, PII, business secrets — never reaches any server. No analytics, no logging, no upload. Verify in DevTools → Network: zero outgoing requests. Safe for confidential database content.