SQL Dialect Translator

What Is SQL Conversion

SQL conversion translates database queries and schema definitions between different SQL dialects. While SQL is a standardized language (ISO/IEC 9075), each database system implements its own extensions, data types, and syntax variations. A query that runs on PostgreSQL may fail on MySQL, SQL Server, or SQLite without modification.

This tool converts SQL between major database dialects, handling syntax differences, data type mappings, and function translations — essential for database migrations, multi-database applications, and cross-platform development.

SQL Dialect Differences

Feature	PostgreSQL	MySQL	SQL Server	SQLite
Auto-increment	SERIAL / GENERATED	AUTO_INCREMENT	IDENTITY	AUTOINCREMENT
String concat	||	CONCAT()	+ or CONCAT()	||
Boolean type	BOOLEAN	TINYINT(1)	BIT	INTEGER
Current timestamp	NOW() / CURRENT_TIMESTAMP	NOW()	GETDATE()	datetime('now')
Limit results	LIMIT n	LIMIT n	TOP n / OFFSET FETCH	LIMIT n
String quoting	'single'	'single' or "double"	'single'	'single'
Identifier quoting	"double"	`backticks`	[brackets]	"double" or `backticks`
JSON support	jsonb (native)	JSON (native)	NVARCHAR + JSON functions	JSON functions (3.38+)
UPSERT	ON CONFLICT	ON DUPLICATE KEY UPDATE	MERGE	ON CONFLICT

Common Use Cases

• Database migration: Convert schemas and queries when migrating from one database system to another (e.g., MySQL to PostgreSQL, SQL Server to cloud databases)
• Multi-database support: Maintain compatible queries for applications that must support multiple database backends
• Cloud migration: Translate on-premises SQL Server queries to cloud-native databases like Aurora PostgreSQL or Cloud SQL
• Learning different dialects: Understand how the same operation is expressed in different SQL dialects
• Legacy modernization: Convert queries from older database systems to modern platforms

Best Practices

1. Test converted queries thoroughly — Automated conversion handles syntax but may miss semantic differences. Always test converted queries against actual data with edge cases.
2. Handle data type differences carefully — Date handling, numeric precision, and string collation vary significantly between databases. Verify that converted data types preserve your data correctly.
3. Convert stored procedures manually — Stored procedures, triggers, and functions use highly vendor-specific syntax that automated tools rarely convert correctly. Plan for manual rewriting.
4. Update application code too — SQL conversion is only part of a migration. Update ORM configurations, connection strings, and application-level SQL generation as well.
5. Preserve indexes and constraints — Ensure that indexes, foreign keys, and check constraints are correctly translated. Performance and data integrity depend on these being preserved.