SQL conversion should begin with structure, not a list of words to replace.
Consider a query that joins customers and orders, filters a date range, calculates revenue, groups results by region, and returns the ten highest totals.
Changing one function name is not enough. The converted query must preserve how all of those parts work together.
Start with the source and target dialects
The same SQL construct may need a different translation depending on where it starts and where it will run.
For example, limiting rows can use TOP, LIMIT, FETCH, or another form. Date arithmetic, string concatenation, identifier quoting, and data types also vary.
A structured conversion process makes the source and target dialects explicit before applying changes.
Understand the statement structure
A SQL statement contains relationships:
• Selected expressions
• Source tables
• Joins
• Filters
• Groups
• Sorting
• Limits
• Nested queries
Treating the statement as a structure helps keep connected changes consistent. A conversion can update a function while preserving its position, arguments, aliases, and surrounding expression.
Apply dialect rules consistently
Repeatability matters when a migration contains many scripts.
The same source construct should follow the same conversion rule throughout the project. Consistent rules make review easier and reduce the chance that similar queries receive unrelated rewrites.
Keep behavior-sensitive areas visible
Some differences cannot be solved safely through syntax alone.
Examples include:
• NULL handling
• Implicit type conversion
• Numeric precision
• Time-zone behavior
• Collation and case sensitivity
• Procedural SQL
• Dynamic SQL
These areas should be surfaced for review. A warning is more useful than silently pretending two database behaviors are identical.
Preserving intent requires testing
Structured conversion creates a stronger first pass, but only testing can confirm that the target query preserves the required behavior.
Use representative data and compare:
• Row counts
• Totals and averages
• Boundary dates
• NULL values
• Duplicate records
• Sort order
• Error cases
The goal of conversion is not merely to produce SQL that runs. It is to help developers reach target SQL that behaves as intended.
Sqlinfy supports that process with explicit dialect selection, structured conversion, readable output, and diagnostics. Human review and testing remain the final authority.