JSON to SQL

Click Convert to generate SQL INSERT statements from JSON

An industrial-grade, zero-login online JSON to SQL DDL/DML converter built for backend engineers and DBAs. Fully compatible with MySQL, PostgreSQL (including binary JSONB), SQLite, and SQL Server dialects. Powered by a global vertical type inference engine, it instantly converts large JSON object arrays into high-throughput multi-value bulk INSERT scripts and auto-generates optimized CREATE TABLE schemas. Featuring advanced recursive flattening and stringification options, it is the ultimate utility for cleaning webhook logs, migrating API payloads, and seeding QA stress-testing databases locally and securely.

JSON to CSV

JSONPath Query

SQL Builder

CSV to JSON

YAML Formatter

XML Formatter

Use Cases

Parsing and cleaning unstructured JSON payloads harvested from webhooks, API responses, or ELK gateways to bulk-import them into relational production databases.
Reverse-engineering database DDL schemas based on mock contract JSON files provided by frontend teams during agile development cycles.
Generating high-volume bulk SQL INSERT seeding scripts containing thousands of rows for database stress testing and QA workflow validations.
De-nesting deeply layered object structures using automated recursive flattening to fit into clean, easy-to-query relational database tables.

Features

Extended Bulk Insert Optimization: Merges thousands of records into high-throughput, multi-value INSERT statements to minimize database transaction overhead.
Global Vertical Type Inference: Scans the entire JSON payload to accurately deduce optimal database types like TINYINT, BIGINT, VARCHAR(N), TEXT, and TIMESTAMP.
Automated CREATE TABLE DDL: Automatically generates structural DDL scripts equipped with primary key setups and precise NULL/NOT NULL constraint detection.
Advanced Hierarchy Flattening: Offers recursive unnesting into flat relational columns or explicit stringification matching binary JSONB schemas.
Serverless Client-Side Sandbox: Runs entirely in your local browser memory, ensuring corporate data privacy compliance without data ever leaving your machine.

How to Use

1.Paste your raw JSON text or drag and drop a local `.json` file into the left editor to load the dataset into local memory.
2.Select your target relational database dialect (MySQL, PostgreSQL, SQLite, or SQL Server) and configure your nested flattening preferences.
3.Instantly preview the auto-compiled CREATE TABLE schema and high-efficiency multi-value INSERT statements in the right panel.
4.Click 'Copy SQL' or hit 'Download .sql' to export the script for immediate execution inside DBeaver, Navicat, DataGrip, or CLI terminals.

FAQ

How does this online tool handle strict SQL dialect and escaping differences among MySQL, PostgreSQL, SQLite, and SQL Server?

This tool features an embedded SQL Dialect Compiler that precisely addresses the lower-level nuances of major RDBMS: it injects backticks (`) and maps booleans to 1/0 for MySQL, applies square brackets ([]) for SQL Server, and handles single-quote escaping alongside time-zone aware timestamps or `JSONB` data types for PostgreSQL. It strictly escapes special characters in strings (e.g., single quotes `'` to `''`), preventing syntax errors during batch execution.

How does the type inference engine scan the JSON data to determine the optimal DDL schema for CREATE TABLE?

Unlike basic converters that only check the first record, GeekFormat implements a global vertical scanning algorithm. It traverses every element in the JSON array to evaluate value boundaries: integers are mapped to `INT` or `BIGINT`, precision decimals trigger `DECIMAL`, and ISO 8601 strings map to `TIMESTAMP`. For strings, it dynamically calculates the maximum length across all records to specify a tailored `VARCHAR(N)` or scales up to `TEXT`, producing clean DDL schemas with accurate nullability.

How are highly nested JSON objects or complex multi-dimensional arrays flattened into relational database columns?

To handle complex nested structures, we provide two granular strategies: 1. **Recursive Flattening**: ungesting hierarchies into flat 2D column names using configurable separators (e.g., {\"meta\": {\"user_id\": 1}} becomes `meta_user_id`); 2. **JSON Serialization**: compressing child objects or arrays into standard JSON strings to fit relational `JSON` or optimized binary `JSONB` columns, striking a perfect balance between normalization and performance.

Are there any performance bottlenecks or security risks when converting massive JSON text files online?

None at all. For high-volume workloads, the tool utilizes extended multi-value bulk insert syntax: `INSERT INTO table (cols...) VALUES (...), (...);`. This reduces transaction overhead and accelerates database writing throughput by up to 20x. For security, GeekFormat operates under a strict Zero-Trust Architecture, processing 100% of your data locally inside your browser's sandbox memory (Client-side evaluation). No corporate logs or sensitive payloads ever touch external servers.