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Category JSON Tools

Advanced Configuration & Schema Settings

Generate CREATE TABLE Add DROP TABLE IF EXISTS Batch Inserts (Multiple Rows) Force Quote Identifiers Lowercase Column Names Wrap in Transaction

JSON Source 0 KB

SQL Script

Rows: 0

Columns: 0

Statements: 0

Gen Time: 0ms

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About

Data migration between non-relational (NoSQL) and relational (SQL) systems is a source of critical engineering friction. The JSON to SQL Converter is an architectural utility designed to bridge the gap between unstructured JSON payloads and strict database schemas. Unlike basic stringconcatenation scripts, this tool employs a multi-pass analysis engine to ensure type safety and referential integrity.

The core problem this tool solves is the Schema Mismatch. JSON is flexible, allowing sparse arrays and mixed types; SQL is rigid. This tool scans the entire dataset to construct a Superset Schema, promoting types where necessary (e.g., if a field is an Integer in row 1 but a Float in row 100, the column is defined as DECIMAL). It handles complex edge cases such as nested objects (via flattening or JSON serialization), reserved keywords escaping, and dialect-specific syntax nuances (MySQL backticks vs. ANSI quotes).

Use this for seeding development databases, migrating legacy API data, or performing rapid ETL (Extract, Transform, Load) operations without writing boilerplate code. The engine is optimized for client-side processing, capable of handling significant datasets through batching logic.

Formulas

The core engine relies on a Type Promotion Algorithm P that determines the final SQL type T for a column based on the set of all observed values V = {v₁, v₂, ..., v_n}.

{

T ← INTEGER if ∀v∈V, is_int(v)T ← DECIMAL if ∃v, is_float(v) ∧ ¬∃v, is_text(v)T ← DATETIME if ∀v, matches_iso(v)T ← TEXT otherwise

When mapping keys to columns, the system constructs a superset vector S containing the union of all keys found in the JSON array objects.

S = n∪i=1 keys(object_i)

Reference Data

Feature	MySQL / MariaDB	PostgreSQL	Oracle PL/SQL	SQLite	SQL Server
Identifier Quote	`column`	"column"	"COLUMN"	"column"	[column]
Text Literal	"val" (Escape \')	"val" (Escape '')	"val"	"val"	N'val'
Boolean Type	TINYINT(1)	BOOLEAN	NUMBER(1)	INTEGER	BIT
Timestamp	DATETIME	TIMESTAMPTZ	TIMESTAMP	TEXT	DATETIME2
JSON Storage	JSON	JSONB	CLOB	TEXT	NVARCHAR(MAX)
Batch Insert Limit	Packet Size Limited	Unlimited	1000 Rows	Variable	1000 Rows
Auto Increment	AUTO_INCREMENT	SERIAL / GENERATED	GENERATED AS IDENTITY	AUTOINCREMENT	IDENTITY(1,1)

Frequently Asked Questions

Simple converters look at the first row and guess. This causes errors if Row 1 has an integer (10) but Row 500 has a float (10.5). Our system scans the entire dataset first. If it detects mixed number types, it promotes the column to DECIMAL or FLOAT. If it detects mixed numbers and strings, it safely falls back to VARCHAR to prevent data loss.

Yes. The tool uses asynchronous chunking logic. While converting, it yields control back to the browser every few milliseconds to keep the UI responsive. However, for files exceeding 50MB, it is recommended to use a server-side CLI tool, as browser memory limits may apply.

You have two options via the "Structure" settings. The default behavior is "Stringify", which converts the sub-object into a JSON string suitable for JSON columns in Postgres/MySQL. The alternative is "Flatten" (future update), which would create columns like "address_city".

Absolutely not. This is a client-side only tool. All parsing, analysis, and SQL generation happens strictly within your browser's JavaScript engine. Your data never leaves your device.

If your JSON objects are sparse (i.e., Object A has key "x", but Object B does not), the SQL standard requires a value for every column defined. The tool automatically detects missing keys in specific rows and injects `NULL` to maintain the structural integrity of the `INSERT` statement.

While this is a developer tool, it applies strict sanitization. It escapes single quotes based on the selected dialect (e.g., converting 'O'Reilly' to 'O''Reilly' for SQL Standard). However, always use parameterized queries in your actual application code; this tool is for seeding/migration scripts only.