LaTeX to SQL Converter

About LaTeX to SQL Converter

Convert LaTeX tables to SQL CREATE TABLE and INSERT statements with automatic data type detection. Supports MySQL, PostgreSQL, SQLite, and SQL Server dialects.

Key Features

• CREATE TABLE: Generate DDL statements with proper column definitions
• INSERT Statements: Generate DML statements for data insertion
• Type Detection: Automatic detection of INTEGER, DECIMAL, DATE, VARCHAR, TEXT
• Multi-Dialect: Support for MySQL, PostgreSQL, SQLite, SQL Server
• Proper Quoting: Dialect-specific identifier quoting (`, ", [])
• String Escaping: Proper SQL string escaping for safety
• NULL Handling: Empty cells converted to NULL values

How to Use

1. Input LaTeX Table: Paste your LaTeX table or upload a .tex file
2. Configure Table: Set table name and SQL dialect
3. Choose Options: Toggle CREATE TABLE, INSERTs, and type detection
4. Review Output: The SQL updates automatically
5. Execute SQL: Copy and run in your database

SQL Dialects

• MySQL: Uses backticks (\`) for identifiers
• PostgreSQL: Uses double quotes (") and NUMERIC type
• SQLite: Uses double quotes (") and simplified types
• SQL Server: Uses brackets ([]) and NVARCHAR types

Example Conversion

LaTeX Input:

\begin{tabular}{lll}
\toprule
Name & Age & City \\
\midrule
John Doe & 28 & New York \\
Jane Smith & 34 & London \\
\bottomrule
\end{tabular}

SQL Output (MySQL with type detection):

-- Create table
CREATE TABLE `data_table` (
  `name` VARCHAR(50),
  `age` TINYINT,
  `city` VARCHAR(50)
);

-- Insert data
INSERT INTO `data_table` (`name`, `age`, `city`) VALUES ('John Doe', 28, 'New York');
INSERT INTO `data_table` (`name`, `age`, `city`) VALUES ('Jane Smith', 34, 'London');

SQL Output (PostgreSQL):

-- Create table
CREATE TABLE "data_table" (
  "name" VARCHAR(50),
  "age" TINYINT,
  "city" VARCHAR(50)
);

-- Insert data
INSERT INTO "data_table" ("name", "age", "city") VALUES ('John Doe', 28, 'New York');
INSERT INTO "data_table" ("name", "age", "city") VALUES ('Jane Smith', 34, 'London');

Common Use Cases

• Database Migration: Import research data into databases
• Data Seeding: Create seed data for development
• Testing: Generate test data for database testing
• Documentation: Convert documented tables to actual database tables
• Data Transfer: Move data from LaTeX documents to SQL databases

Data Type Detection

When auto-detect is enabled, the converter analyzes column values:

• TINYINT/SMALLINT/INTEGER/BIGINT: For integer values (size-based)
• DECIMAL/NUMERIC: For decimal numbers
• DATE/DATETIME: For date-formatted values (YYYY-MM-DD)
• VARCHAR(n): For short text (length-based: 50, 255)
• TEXT/NVARCHAR(MAX): For long text content

Identifier Quoting

• MySQL: \`table_name\`, \`column_name\`
• PostgreSQL: "table_name", "column_name"
• SQLite: "table_name", "column_name"
• SQL Server: [table_name], [column_name]

Usage Example

Execute in MySQL:

-- Copy generated SQL and execute
mysql -u username -p database_name < data_table.sql

-- Or in MySQL client
SOURCE data_table.sql;

Execute in PostgreSQL:

-- Execute SQL file
psql -U username -d database_name -f data_table.sql

-- Or in psql client
\i data_table.sql

Best Practices

• Review generated SQL before executing in production
• Add primary keys and indexes manually if needed
• Consider adding constraints (NOT NULL, UNIQUE, etc.)
• Test with sample data first
• Use transactions for large data imports
• Backup database before running generated SQL

Tips for Best Results

• Use descriptive table and column names
• Enable type detection for optimized storage
• Choose the correct SQL dialect for your database
• Verify data types match your requirements
• Add indexes after data import for better performance

Privacy & Security

All conversions happen locally in your browser. Your LaTeX data is never uploaded to any server, ensuring complete privacy and security.