Validate SQL syntax with schema and database engine aware validator

A SQL validator should do more than point at the first syntax error. SQLAI reviews the whole query, explains what is wrong in plain language and returns a corrected version you can compare with the original. It works with the query alone or with your schema and database rules attached.

Check SQL before it runs

Paste a query, choose the database engine and version, and run the validator. SQLAI checks the syntax for the selected dialect and reviews the query for semantic problems that a parser alone will not catch: unknown columns, ambiguous joins, type mismatches and constructs that do not exist in that engine.

Use it for a query you are about to deploy, a report that broke after a schema change, a legacy query with no documentation, or SQL generated by another tool. The validator is conversational, so you can ask why a specific line is invalid or request a different correction.

Try the SQL validator on a query of your own.

See every issue, not just the first one

Many validators stop at the first parse error. SQLAI reports the issues it finds across the query and explains each one, so you can fix the query in one pass instead of iterating one error at a time. The feedback distinguishes syntax problems from semantic ones and points to the part of the query that needs attention.

When a query has several problems, the explanation helps you decide which fix comes first. A missing JOIN condition may be the real cause of a later ambiguity; a cast may be valid syntax but the wrong type for the comparison. Understanding the chain is usually faster than applying isolated corrections.

Understand why the query fails

An error message such as "syntax error near..." is not a diagnosis. SQLAI explains the rule behind the failure and what the engine expects instead. That makes the validator useful for learning as well as fixing: you can see why a window function needs an ORDER BY, why a GROUP BY column is missing, why a subquery returns more than one row, or why a date literal is invalid in your dialect.

If the query is valid but suspicious, the validator can also flag patterns worth reviewing before execution, such as a DELETE without a WHERE clause, a join that may multiply rows or a filter that makes an index unusable.

Get a corrected query and compare the diff

After the review, SQLAI can return a corrected query. The original and corrected versions can be compared side by side, so you can verify each change before running anything. The correction is a proposal, not a guarantee: review the diff, confirm that the intended logic is preserved and test the query on a copy or with a read-only role.

SQLAI does not execute the query against your database. Result rows are not added to the conversation automatically. See the privacy policy for how prompts and schema information are handled.

Validate against your schema and dialect

A query can be syntactically valid and still be wrong for your database. Attach a schema or connect a supported database so the validator can check table and column names, relationships and types against the real structure. Optional database rules capture conventions that are not visible in the schema, such as tenant filters, naming standards or disallowed constructs.

The dialect matters as much as the schema. TOP is not LIMIT, IIF is not CASE, and Oracle's ROWNUM, PostgreSQL's ILIKE and BigQuery's QUALIFY do not transfer unchanged. SQLAI validates for the engine and version you select. See all supported databases and versions for the current list.

Supported databases

The SQL validator supports the engines SQLAI supports, including PostgreSQL, MySQL, MariaDB, SQL Server, Oracle PL/SQL, SQLite, IBM DB2, Snowflake, BigQuery, Redshift, ClickHouse, Trino, Presto, Vertica, SAP HANA, Teradata, Firebird, InterBase, Sybase and Microsoft Access, plus NoSQL and query languages such as MongoDB, Splunk SPL, Salesforce SOQL/SOSL and PartiQL. The exact public API names and version coverage are on the supported databases page.

If the query is valid but slow, move to the SQL optimizer. If it is valid and you need to understand it, use the SQL explainer.

Who it is for

• Developers checking a query before it reaches code review or production.
• Analysts fixing a dashboard query after a schema or engine change.
• DBAs reviewing SQL from an application or a migration.
• Data teams handling legacy SQL with no documentation.
• Learners who want to understand the error, not just replace the query.

Frequently asked questions

What is a SQL validator?

A SQL validator checks a query for syntax and semantic problems and explains what needs to be fixed. SQLAI also returns a corrected query and lets you compare it with the original, using the schema and dialect you select.

Does it only find syntax errors?

No. SQLAI also reviews semantic issues such as unknown columns, ambiguous joins, type mismatches and constructs that are not valid in the selected engine. Attaching a schema makes those checks more specific to your tables and relationships.

Can it fix the query for me?

It can suggest a corrected query, but you should review the change and test it. A corrected query is a proposal, not a guarantee that the result matches your intent. Use the diff to confirm every change before running it.

Can I validate different SQL dialects?

Yes. Select the database engine and version before validating. SQLAI checks the query for that dialect, including engine-specific functions, pagination syntax, date handling and identifier rules. See supported databases for the list.

Do I need a database connection or schema?

No. You can validate a query with no schema. Attaching a schema or connecting a database improves identifier and type checks, and database rules help with conventions that are not visible in the schema.

Does the validator run my query?

No. SQLAI does not execute the query against your database. You review the corrected SQL and run it yourself, ideally on a copy or with a read-only role.

Can I compare the original and corrected SQL?

Yes. The validator can return a corrected version alongside the review, and the changes can be compared before you use the query. Review the diff for logic changes, not just syntax.

Review before production

AI-generated corrections can contain errors. Review the diff, confirm that filters, joins and result columns still match the intended logic, and test the query against a copy or with a read-only role before production. Validation does not replace testing with real data.