Generate SQL INSERT statements
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JSON to INSERT statements in three steps

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Converting JSON to SQL INSERT statements

SQL INSERT statements load data into relational databases. Converting JSON to SQL maps objects to rows: each key becomes a column name, each value becomes a properly quoted column value. This tool generates syntactically correct INSERT INTO statements from JSON objects or arrays, ready for execution.

The converter handles SQL requirements: strings are single-quoted and escaped (O'Brien → O''Brien), numbers are unquoted, booleans map to TRUE/FALSE, null becomes NULL, and nested objects become JSON string literals. For arrays of objects, it generates multi-row INSERT for bulk loading efficiency.

[
  {"name":"Alice","age":30,"city":"Portland"},
  {"name":"Bob","age":25,"city":"Seattle"}
]

INSERT INTO users
  (name, age, city)
VALUES
  ('Alice', 30, 'Portland'),
  ('Bob', 25, 'Seattle');

Get the most out of this tool

• Use multi-row INSERT (VALUES (...), (...)) for bulk loading — significantly faster than individual INSERTs.
• Always escape string values — the converter handles quoting and escaping automatically to prevent SQL injection.
• Nested JSON objects are stored as JSON string columns — use database JSON functions to query them.

JSON to SQL conversion explained

SQL (Structured Query Language) databases are the backbone of most applications. Inserting JSON data into a relational database requires translating the JSON's keys to column names and the JSON's values to SQL-compatible literals. Doing this by hand for arrays of hundreds of records is impractical. This tool generates INSERT statements automatically, letting you bulk-load JSON data into any SQL-compatible database — MySQL, PostgreSQL, SQLite, SQL Server, or Oracle.

The conversion maps JSON types to SQL types. JSON strings become VARCHAR or TEXT values, wrapped in single quotes with internal single quotes escaped as two single quotes. JSON numbers become unquoted numeric literals. JSON booleans become 1/0 (for MySQL and SQLite) or TRUE/FALSE (for PostgreSQL). JSON null becomes SQL NULL. Nested objects and arrays cannot be directly represented in relational columns — they are either serialized as JSON text if the target column supports it, or require normalization into separate tables.

The converter generates two types of output. INSERT statements: INSERT INTO tableName (col1, col2, ...) VALUES (val1, val2, ...); — one per JSON object. CREATE TABLE DDL: a table definition with column names and inferred types, based on the keys and values in the JSON data. The DDL output is a starting point — you will typically need to review and adjust data types, add primary keys, define indexes, and add foreign key constraints based on your schema design.

SQL injection is a critical concern when building INSERT statements. The converter properly escapes string values — single quotes within values are doubled (He's becomes He''s) to prevent SQL injection and syntax errors. Never build INSERT statements by concatenating user-provided JSON directly into SQL strings in application code; always use parameterized queries or prepared statements instead. This tool is for data import, not for generating dynamic SQL in production.

For large datasets, INSERT statements can be batched. Rather than one statement per row, databases perform much better with batch inserts: INSERT INTO table (cols) VALUES (row1), (row2), (row3), ... . Batch inserts reduce network round-trips and allow the database to optimize writes. The converter supports batch INSERT syntax for supported databases to make bulk loading practical.

Modern databases like PostgreSQL and MySQL 5.7+ support native JSON column types. If you are loading JSON into a JSON or JSONB column, you may not need conversion at all — just wrap the JSON value in single quotes and insert it directly. The converter is most useful when you are loading flat (non-nested) JSON data into traditional relational columns where each JSON key maps to a table column.

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