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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteBefore moving an application from SQL Server to PostgreSQL, audit every way the application depends on SQL Server behavior—not just the database schema. Inventory SQL sent by the application, stored-routine contracts, collation and string comparisons, type ranges and conversions, and anything a conversion tool cannot translate. Then test the application against PostgreSQL, reconcile data, and plan a coordinated cutover. A schema that converts successfully is not proof that the application’s queries and workflows will work.
What should the application audit cover?
Use the audit to turn hidden dependencies into specific findings that can be tested or remediated. SQL Server-to-PostgreSQL conversion tools can help identify and translate database objects, but documented conversion settings also leave some items for manual review or create stubs that fail at runtime. Track those findings alongside application-level dependencies.
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| Audit area | What to inspect | Evidence to capture |
|---|---|---|
| Application SQL | Literal and generated queries, ORM mappings, query builders, scheduled jobs, deployment scripts, and routine calls | Where each query is created or called, which SQL Server-specific syntax or built-ins it uses, and the test or fix assigned to it |
| Routine contracts | Procedures and functions invoked by application code | Names, parameter names and defaults, return values, result sets, errors, and transaction expectations |
| Strings and collation | Database, column, expression, and application assumptions about matching and sorting | Expected behavior for case, accents, ordering, joins, uniqueness, and search |
| Types and boundaries | Source and target types, values at their limits, and application serialization or binding | Range, precision, nullability, encoding, and temporal interpretation checks |
| Conversion findings | Unsupported built-ins, unresolved routines, manual-review items, and generated stubs | An owner and a test or explicit remediation for every finding |
Where can application SQL be hiding?
SQL may live outside the database objects a schema-conversion tool examines. Microsoft’s migration-tooling guidance treats finding SQL in application source as a separate discovery task. It also notes that the earlier Microsoft toolkit for this task was retired and describes approaches such as regular expressions, parsing, or custom tools for identifying application SQL.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Search the full application and its operational artifacts, not only the primary repository’s obvious query files. Include configuration, generated SQL, ORM mappings, query builders, stored-procedure calls, scheduled jobs, and deployment scripts. Flag SQL Server-only syntax and built-ins for review; a text search can help locate candidates, but it does not by itself prove that a query is safe to move.
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For each finding, record the owning feature or workflow, the location where the SQL is generated or executed, and a representative input and expected result. That gives the team a practical link between code discovery and later application tests.
How do stored routines affect the application contract?
Inventory every procedure and function the application calls, then compare its contract with the PostgreSQL implementation. A routine is more than its name and body: application code may depend on argument names, defaults, returned values or result sets, error behavior, and transaction expectations.
Named arguments deserve particular attention if callers use them. AWS’s SQL Server-to-PostgreSQL schema-conversion settings document an option to preserve original parameter names for that case. Confirm whether the chosen conversion configuration supports the way this application calls routines, and test the calls as the application actually makes them.
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Capture representative routine calls and their expected outcomes. Include success cases, invalid or boundary inputs, and any workflow that relies on a specific error or transaction result. Resolve differences in the routine or caller rather than treating successful object creation as proof of compatibility.
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Which string and collation assumptions need testing?
Record where SQL Server collation affects comparisons and ordering. Collation can be set at server, database, column, or expression scope, so a database-level default alone may not describe the behavior of every query. Application code may also make assumptions about case handling or search behavior.
Build representative tests for the values and operations the application uses. Check matching and ordering, joins, uniqueness constraints, and search results, including values that differ by case or accents where those distinctions matter to the product.
AWS documents CITEXT as an option for preserving case-insensitive comparison behavior in the relevant conversion context, but the PostgreSQL extension must be available in the target. Treat it as a possible implementation choice, not an automatic substitute for testing: verify extension availability and confirm that actual application queries behave as expected with the chosen PostgreSQL configuration.
How can type mappings change application behavior?
Map types by what they allow and how values are represented, not by matching type names. Check range limits, precision and rounding, null handling, binary and character encoding, and the meaning of dates and timestamps. Also verify how the application’s actual drivers bind and decode values; driver-specific behavior depends on the stack and is not established by a generic mapping guide.
One documented example shows why boundaries matter: the AWS playbook for SQL Server 2019 to Aurora PostgreSQL describes SQL Server TINYINT as an unsigned 8-bit value and lists PostgreSQL SMALLINT as the corresponding target type. Since the target type does not have the same range, check stored and incoming values against the source limits and review any application validation, casts, or assumptions about the type’s bounds.
For each type used by the application, identify representative values at the minimum and maximum, values near precision or rounding boundaries, nulls where permitted, and temporal values whose interpretation matters. Test serialization and reads through the real application path as well as the database query itself.
How should conversion-tool findings be handled?
Maintain a work queue for every unsupported built-in, routine, and other unresolved conversion item. AWS documentation says unsupported T-SQL built-ins may be reported for manual review. It also describes an alternate setting that creates stub functions: those stubs can compile but raise runtime errors when called.
Do not mark a finding resolved merely because the target schema builds. Assign each item an owner and a disposition: implement equivalent behavior, revise the application to avoid the dependency, or document why the path is not used and verify that it cannot be reached. Link the disposition to a test or other concrete check. Treat generated runtime-error stubs as unresolved until they are replaced or the corresponding application path is removed.
How do you prove the application works on PostgreSQL?
Use the audit findings to build tests around the queries and workflows they affect. Run those tests against both the existing SQL Server behavior and the PostgreSQL target where practical, and compare results rather than relying only on successful connections or schema creation.
- Compare returned values and row counts for representative reads.
- Check ordering, matching, joins, and search results against the intended string behavior.
- Exercise routine calls using the application’s actual parameter conventions and verify outputs and errors.
- Test writes, boundary values, nulls, and the workflows that depend on transaction outcomes.
- Confirm that no unresolved conversion finding is reached by a tested application path.
This test plan follows from the documented semantic differences and unresolved conversion cases; it is a recommended validation approach, not a report of tests already performed. Include application-level tests because a database-object converter cannot establish that every code path sends compatible SQL or interprets returned values correctly.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What must be settled before cutover?
Reconcile source and target data before switching application traffic, and coordinate the switch with the teams responsible for the application and its business workflows. Microsoft’s guidance for SQL Server-to-Azure SQL migrations supports those general principles, but it is not a PostgreSQL migration procedure. Choose PostgreSQL-compatible data movement, synchronization, validation, and rollback methods for the actual target and workload.
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Before approving a cutover plan, make sure the team has decided:
- How acceptable downtime and any ongoing synchronization will be handled.
- How source and target data will be reconciled and which discrepancies block the switch.
- Which application and business owners approve the timing and workflow impact.
- How the team will detect a failed cutover and restore service, including the rollback conditions.
The right operational design depends on the application’s workload, data volume, PostgreSQL deployment, and availability requirements. Azure SQL-specific methods should not be assumed to apply to PostgreSQL.
How should you choose a conversion approach?
Compare conversion approaches against the application and target you actually have. The AWS conversion settings document behaviors such as handling case-insensitive comparisons, preserving routine parameter names, and reporting or stubbing unsupported SQL. Microsoft’s migration-tooling discussion addresses discovery of application SQL, which is a separate capability from converting database objects.
- Does the approach scan application source code, database objects, or both?
- Does it report unsupported SQL for review, rewrite it, or create stubs that can fail at runtime?
- Can it address the application’s string-comparison needs and routine-parameter conventions?
- Does it support the selected PostgreSQL target and version?
Separately evaluate data movement and cutover against downtime tolerance, synchronization needs, operational setup, data volume, validation, and rollback. The cited Microsoft material compares approaches for Azure SQL, not PostgreSQL; use it only as context for the decision factors, not as instructions for a PostgreSQL migration.
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What depends on your specific stack?
The exact application language, database driver, SQL Server version, PostgreSQL version, workload, authentication design, and availability needs determine details that a general audit cannot settle. Verify those details against the chosen PostgreSQL deployment before committing to behavior or cutover specifics, particularly for driver behavior, transaction isolation, identity generation, security, performance, replication, and rollback.
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