SQL Validator

Example Input

SELECT u.id, u.name, u.email COUNT(o.id) AS order_count
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
WHERE u.status = 'active'
GROUP BY u.id, u.name, u.email
ORDER BY order_count DESC;

Example Output

Validation Result: ERROR

Line 1, near 'COUNT': syntax error — unexpected token COUNT, expected comma or FROM
Fix: Add a comma after u.email before COUNT(o.id) in your SELECT list.

Corrected query:
SELECT u.id, u.name, u.email, COUNT(o.id) AS order_count
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
WHERE u.status = 'active'
GROUP BY u.id, u.name, u.email
ORDER BY order_count DESC;

Does it support large SQL queries?

Yes. The validator handles large SQL scripts efficiently — single complex queries with many joins and subqueries, and multi-statement scripts with dozens of ALTER TABLE or INSERT statements. For very large migration files in the hundreds of kilobytes, validation may take a moment longer than a simple SELECT statement, but it processes the entire input correctly. There is no character or statement count limit.

Can it handle multiple SQL statements?

Yes. Paste multiple SQL statements separated by semicolons and the validator processes each one individually, reporting errors per statement with its position in the input. This is the correct way to validate migration scripts, seed files, or stored procedure bodies that contain multiple statements. Make sure each statement ends with a semicolon — without statement terminators the parser cannot correctly identify where one statement ends and the next begins.

Does it support MySQL and PostgreSQL syntax?

The validator checks against standard SQL grammar which covers the core syntax shared by MySQL, PostgreSQL, SQL Server, SQLite, and Oracle. Standard SELECT, INSERT, UPDATE, DELETE, JOIN, GROUP BY, HAVING, ORDER BY, subqueries, CTEs, and common DDL statements all validate correctly. Highly dialect-specific constructs — MySQL backtick identifiers, PostgreSQL dollar-quoted strings, SQL Server T-SQL procedural syntax — may produce false errors because they are extensions beyond the SQL standard. The validator is most accurate for standard SQL and broadly compatible queries.

What is the difference between SQL validation and SQL formatting?

A SQL formatter fixes how the query looks — indentation, keyword capitalization, line breaks. It makes SQL readable but does not check whether the SQL is correct. A SQL validator checks whether the query structure is grammatically correct according to SQL rules. A query can be perfectly readable and syntactically wrong, or completely unformatted and syntactically valid. The right workflow is: format first to make the query readable, then validate to confirm it is syntactically correct. LearnHubly has both tools — the SQL Formatter is in the Related Tools sidebar.

Is my SQL data private when using this tool?

Yes. All validation runs locally in your browser — your SQL queries are never transmitted to any server, never logged, and never stored. This matters because SQL queries often contain your database schema structure, table names, column names, business logic conditions, and sometimes literal values. Browser-based validation means none of that internal information leaves your machine.

Why does my query fail in the database even though the validator says it is valid?

SQL validation checks syntax — grammatical correctness. A syntactically valid query can still fail at runtime for several reasons: the referenced table does not exist in the database, the column name is misspelled or belongs to a different table, you do not have permission to access the table, a subquery returns more than one row when exactly one is expected, a type mismatch occurs between compared values, or a function is called with the wrong number of arguments. These are semantic and runtime errors that a syntax validator cannot detect — only executing the query against the actual database reveals them.

Can it validate SQL with CTEs (Common Table Expressions)?

Yes. Common Table Expressions using the WITH keyword are standard SQL and validate correctly. A WITH clause followed by a SELECT that references the CTE name is checked for correct structure — the CTE definition must have a name, a column list or none, and an AS keyword followed by a parenthesized SELECT statement. Recursive CTEs using WITH RECURSIVE are also supported in the standard grammar. If your CTE validation is failing, check that the WITH clause is at the very beginning of the query and that the final SELECT references the CTE name correctly.

Does it validate CREATE TABLE and ALTER TABLE statements?

Yes. Standard DDL statements including CREATE TABLE, ALTER TABLE, DROP TABLE, CREATE INDEX, and CREATE VIEW validate correctly. The validator checks that column definitions have valid data types, that constraints are correctly specified (PRIMARY KEY, NOT NULL, UNIQUE, FOREIGN KEY with REFERENCES), and that the overall statement structure follows the SQL DDL grammar. Highly database-specific data types — PostgreSQL JSONB, MySQL TINYINT, SQL Server NVARCHAR(MAX) — may trigger warnings since they are not in the standard SQL type system, but the overall statement structure is still validated.