In the modern era, databases play a crucial role in storing data and implementing data processing logic through stored procedures, functions, triggers, and views. As the migration of databases from SQL Server or Azure SQL to PostgreSQL becomes more common, there is a need to convert SQL code to ensure a smooth and secure transition. While the syntax of these database management systems (DBMS) is similar, there are significant differences that make conversion challenging and may require a dedicated SQL converter to simplify and automate the process.
To ensure an accurate conversion of database logic from MS SQL or Azure SQL to PostgreSQL, it is recommended to start by modeling the conversion of basic SQL syntax constructions. However, it’s important to note that simply converting atomic SQL patterns and their combinations may not be sufficient for complex source code. Attention must be given to ensuring equivalent functionality of the source and destination database logic units to avoid semantic errors during the SQL conversion process.
One notable difference in syntax is that SQL Server allows local variables to be declared anywhere in the source code, while PostgreSQL requires them to be declared at the beginning of stored procedures and functions. Another challenge in converting SQL code from MS SQL or Azure SQL to PostgreSQL is the dynamic determination of result sets returned by stored functions or procedures, which is allowed in the source DBMS but not in the target system.
Triggers are another aspect of database logic that requires careful consideration for accurate SQL code conversion. In MS SQL and Azure SQL, the trigger’s source code is included within the CREATE TRIGGER statement. In PostgreSQL, however, the trigger’s body needs to be organized as a separate function, with the CREATE TRIGGER command referencing it. The typical structure of a PostgreSQL trigger includes the trigger function and the CREATE TRIGGER command:
CREATE FUNCTION trigger_function_name() RETURNS trigger AS $$
BEGIN
— Trigger logic here
RETURN NEW;
END;
$$ LANGUAGE plpgsql;
CREATE TRIGGER trigger_name
[BEFORE/AFTER] [INSERT/UPDATE/DELETE]
ON table_name
FOR EACH [ROW/STATEMENT]
EXECUTE FUNCTION trigger_function_name();
Of course, “trigger_function_name” is replaced by the real name of trigger function, “trigger_name” is replaced by the real name of trigger, and “table_name” is replaced by the real table name. The [BEFORE/AFTER], [INSERT/UPDATE/DELETE], and FOR EACH [ROW/STATEMENT] sections can be adjusted according to the specific requirements of the trigger.
To streamline the conversion of stored procedures, functions, triggers, and views from MS SQL or Azure SQL to PostgreSQL, it is advisable to use a dedicated SQL converter. This tool simplifies the conversion process by automating the translation of syntax for different elements of the database logic from the source DBMS to the target system. Utilizing such a tool can save significant time and resources that would otherwise be spent on manual conversion efforts.
The SQL converter offers capabilities such as converting stored procedures, functions, triggers, and views, supporting over 80% of SQL syntax constructions, converting service functions into PostgreSQL equivalents, and intelligently mapping SQL Server types to PostgreSQL. It can also process stored procedures, functions, triggers, and views from T-SQL scripts when a direct connection to the MS SQL database is not available.
However, it is important to acknowledge that even with advanced SQL converters, there may be intricate patterns that do not have a straightforward equivalent in the target SQL syntax. Examples of such patterns include pivot tables, recursive queries, and full-text search, which require manual post-processing during the migration. Database developers and other responsible individuals must possess comprehensive programming skills in both database management systems to effectively address these semantic differences between SQL Server and PostgreSQL syntax patterns. In most other cases, the SQL converter significantly reduces the time and effort required to convert stored procedures, functions, triggers, and views between the two DBMS.
