SQL queries differ across database systems primarily due to variations in syntax, supported features, and proprietary extensions. While SQL standards exist (like SQL-92 or SQL:2016), vendors often implement their own extensions or omit certain features. For example, PostgreSQL supports the ILIKE operator for case-insensitive pattern matching, while MySQL requires LIKE with a case-insensitive collation. Similarly, SQL Server uses TOP to limit query results, whereas MySQL and PostgreSQL use LIMIT (though PostgreSQL also supports FETCH FIRST for standards compliance). These differences mean developers must adjust queries when switching between systems like MySQL, PostgreSQL, Oracle, or SQL Server.
Another key difference lies in handling dates, strings, and other data types. For instance, extracting parts of a date varies: PostgreSQL uses EXTRACT(YEAR FROM date_column), SQL Server uses YEAR(date_column), and MySQL supports both. String concatenation also diverges: SQL Server and Oracle use + or ||, while MySQL requires the CONCAT() function. Additionally, database-specific functions like PostgreSQL’s JSONB operators for querying JSON data or MySQL’s GROUP_CONCAT() for aggregating strings have no direct equivalents in other systems. These nuances force developers to learn system-specific functions or rely on ORMs that abstract some differences.
Schema and transaction management also vary. For example, auto-incrementing IDs are implemented as AUTO_INCREMENT in MySQL, SERIAL in PostgreSQL, and IDENTITY in SQL Server. Transaction isolation levels or locking mechanisms may behave differently: PostgreSQL’s MVCC (Multi-Version Concurrency Control) contrasts with SQL Server’s lock-based approach. Even basic operations like altering tables can differ—adding a column with a default value in SQL Server requires WITH VALUES to populate existing rows, while PostgreSQL handles it automatically. These discrepancies highlight the importance of understanding a database’s specific behavior when writing or optimizing queries.