This tool parses a SQL CREATE TABLE statement and extracts all column names, outputting them as a clean CSV (Comma-Separated Values) header row. It intelligently identifies column definitions while skipping constraints like PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK, and INDEX declarations. You can customize the delimiter, quote style, and output format to suit your needs—perfect for generating CSV templates, data import headers, or documentation.

The parser is dialect-agnostic and works with MySQL, PostgreSQL, SQL Server, SQLite, Oracle, and MariaDB. It handles quoted identifiers (backticks `, double quotes ", and square brackets []), nested type parameters like DECIMAL(10,2), and various constraint formats. Whether your SQL uses IF NOT EXISTS, TEMPORARY TABLE, or schema-qualified names, the tool adapts seamlessly.

Constraints such as PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK, INDEX, KEY, FULLTEXT, and SPATIAL are automatically detected and excluded from the column list. The parser recognizes these keywords (case-insensitive) at the start of a definition line and filters them out. Table-level CONSTRAINT declarations are also ignored, ensuring only actual data columns appear in your CSV header.

Absolutely. All parsing and conversion happens entirely in your browser using client-side JavaScript. No data is ever sent to any server, uploaded, stored, or logged. You can safely paste sensitive schema definitions without any privacy concerns. This also means the tool works offline once the page is loaded—no internet connection required for conversion.

You can choose from four delimiters: comma (,), semicolon (;), tab (\t), and pipe (|). For quoting, select none (raw column names), double quotes (CSV-standard), or backticks (MySQL-style). The output format can be inline CSV (all columns on one line) or one column per line (useful for vertical lists or generating data dictionaries). You can also download the result as a .csv file with one click.

CREATE TABLE ... AS SELECT statements do not explicitly list column definitions, so the parser cannot extract column names directly from them. The tool will display a helpful message suggesting you provide the underlying SELECT query's column list or use a DESCRIBE statement on the resulting table. For standard CREATE TABLE (col1 type1, col2 type2, ...) syntax, parsing works flawlessly.

Manually extracting column names from complex SQL is tedious and error-prone—especially with multi-line definitions, nested types, constraints, and quoted identifiers. This tool automates the process in milliseconds, eliminates human error, handles edge cases consistently, and provides flexible output formatting. It's an essential productivity booster for database developers, data analysts, and anyone working with SQL schema definitions regularly.