Introduction
PostgreSQL is widely used for applications where database logic goes beyond simple tables and CRUD operations. Modern applications often work with JSON documents, foreign tables, reporting databases, and data that is spread across multiple PostgreSQL servers.
That flexibility also means that a PostgreSQL upgrade should not be tested only with basic SELECT, INSERT, UPDATE, and DELETE statements.
PostgreSQL 19 Beta 3 is a good example of why targeted regression testing matters. The beta release includes fixes for a postgres_fdw issue involving array comparisons with implicit type coercion and a separate fix for JSON deparsing involving JSON_ARRAY(query).
These changes are small in description but important from a testing perspective. A query can execute successfully while still returning an unexpected result, or generated SQL can be structurally different from what the application expects.
This article looks at how developers can build practical regression tests around JSON and postgres_fdw when evaluating PostgreSQL 19 Beta 3.
What Is postgres_fdw?
postgres_fdw is PostgreSQL's foreign-data wrapper for accessing data stored on another PostgreSQL server. It allows a local PostgreSQL database to work with tables that physically reside in a remote PostgreSQL database.
A simplified architecture looks like this:
Application
|
v
Local PostgreSQL
|
| postgres_fdw
v
Remote PostgreSQL
|
v
Remote Table
From the application's perspective, a foreign table can look similar to an ordinary table.
For example:
SELECT *
FROM remote_customers;
The actual data, however, may be stored on another PostgreSQL server.
Why Foreign Data Wrappers Need Regression Testing
A local query can involve several layers:
Local SQL
|
v
Query Planner
|
v
FDW Pushdown
|
v
Remote SQL
|
v
Remote PostgreSQL
|
v
Result
A problem in any part of this process can produce unexpected behavior.
This is particularly important when the query contains:
Arrays
Type conversions
Functions
Joins
Filters
Aggregations
JSON expressions
PostgreSQL's documentation explains that postgres_fdw attempts to push suitable query conditions to the remote server to reduce the amount of data transferred.
That optimization is useful, but it also makes query-planning and type-handling behavior worth testing.
PostgreSQL 19 Beta 3 Changes
The PostgreSQL 19 Beta 3 release notes identify several changes relevant to this article.
Among them are:
postgres_fdw:
Fix wrong query results when pushing down
an array comparison such as field = ANY($1)
involving implicit type coercion.
JSON:
Fix missing FORMAT clause when deparsing
JSON_ARRAY(query).
These fixes are explicitly listed in the PostgreSQL 19 Beta 3 announcement.
This gives developers two concrete regression-testing areas:
1. postgres_fdw array comparisons
2. JSON_ARRAY(query) deparsing
The goal should not be to reproduce an issue artificially just because it appears in release notes.
Instead, test whether the application's real workload exercises the affected areas.
Important Note About Beta Releases
PostgreSQL Beta releases are intended for testing, not production use. The PostgreSQL project specifically encourages developers to run their normal workloads against beta releases to identify compatibility problems before the final release.
That makes PostgreSQL 19 Beta 3 appropriate for a controlled regression environment.
A simple test environment could be:
Developer Machine
|
+---- PostgreSQL 19 Beta 3
|
+---- Test Application
|
+---- Remote PostgreSQL
Do not use a beta database as a production database merely because a particular test passes.
Setting Up a JSON Test Table
Start with a simple table containing JSON data.
CREATE TABLE orders
(
id BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
customer_id BIGINT NOT NULL,
details JSONB NOT NULL,
created_at TIMESTAMPTZ NOT NULL DEFAULT now()
);
Insert some representative data:
INSERT INTO orders
(
customer_id,
details
)
VALUES
(
101,
'{
"status": "completed",
"items": 3,
"total": 250.50
}'
),
(
102,
'{
"status": "pending",
"items": 1,
"total": 75.00
}'
);
Now the test database contains both relational and JSON data.
Testing JSON Extraction
Start with a simple query:
SELECT
id,
details->>'status' AS status
FROM orders;
Then test JSON values in filtering:
SELECT *
FROM orders
WHERE details->>'status' = 'completed';
These tests establish a baseline before moving into more complex JSON expressions.
Testing JSON Aggregation
Applications often need to construct JSON results from relational data.
For example:
SELECT JSON_AGG(
JSON_BUILD_OBJECT(
'id', id,
'customerId', customer_id,
'status', details->>'status'
)
)
FROM orders;
The important thing is to validate both:
JSON Structure
+
JSON Values
A query that returns valid JSON is not necessarily returning the correct JSON.
Testing JSON_ARRAY(query)
PostgreSQL 19 includes a fix related to deparsing JSON_ARRAY(query).
This makes it useful to include query-based JSON construction in a regression suite.
For example:
SELECT JSON_ARRAY(
SELECT details
FROM orders
);
The exact SQL shape should be adapted to the syntax supported by the PostgreSQL version and the application's requirements.
The test should verify:
Valid SQL
Valid JSON
Correct number of elements
Correct element values
Expected formatting semantics
What Does Deparsing Mean?
Deparsing is essentially the process of reconstructing SQL from an internal representation.
This matters in PostgreSQL because several database components work with parsed or planned representations of queries.
A simplified flow is:
SQL
|
v
Parser
|
v
Internal Representation
|
v
Planner / Other Component
|
v
Deparsed SQL
If the deparser produces incorrect SQL, the resulting query can behave differently from the original intent.
That is why a seemingly small deparser fix can deserve a regression test.
Building a JSON Regression Test
A useful test should define expected output explicitly.
For example:
Input:
2 orders
Expected:
JSON array containing exactly 2 elements
Then execute the query and validate:
Element count = 2
First ID = expected ID
Second ID = expected ID
Avoid relying only on:
Query executed successfully
The test should verify the actual result.
Testing JSON NULL Behavior
JSON and SQL NULL values can behave differently.
Consider:
SELECT
details->'discount'
FROM orders;
If the property does not exist, the result may not behave like an ordinary SQL column containing NULL.
Therefore, include test data such as:
{
"status": "completed"
}
and:
{
"status": "completed",
"discount": null
}
Then test both cases.
This can reveal application assumptions that would otherwise remain hidden.
Testing Nested JSON
Real applications rarely use only flat JSON objects.
For example:
{
"customer": {
"name": "Alice"
},
"payment": {
"method": "card",
"status": "paid"
}
}
Test nested access:
SELECT
details #>> '{customer,name}' AS customer_name,
details #>> '{payment,status}' AS payment_status
FROM orders;
Then verify the returned values.
Testing JSON With .NET
A .NET application may map JSON data through EF Core or access it through raw SQL.
For example:
var orders = await db.Orders
.Where(x => x.Details.RootElement
.GetProperty("status")
.GetString() == "completed")
.ToListAsync();
The exact expression depends on the PostgreSQL provider and application model.
When evaluating PostgreSQL 19 Beta 3, test the complete path:
.NET Application
|
v
EF Core / Provider
|
v
Generated SQL
|
v
PostgreSQL 19
|
v
JSON Result
This is more valuable than testing the database in isolation.
What Is an FDW Array Comparison?
One of the PostgreSQL 19 Beta 3 fixes concerns postgres_fdw when pushing down an array comparison such as:
field = ANY($1)
when implicit type coercion is involved. PostgreSQL's release announcement identifies this as a case that could produce wrong query results and was fixed in Beta 3.
This is an excellent example of a bug that may be difficult to detect through ordinary syntax validation.
Understanding ANY
Consider a query like:
SELECT *
FROM remote_customers
WHERE customer_id = ANY($1);
Conceptually, this asks PostgreSQL to find rows where:
customer_id
matches one of the values supplied in the array parameter.
For example:
[101, 205, 310]
means:
customer_id = 101
OR
customer_id = 205
OR
customer_id = 310
The important detail is the data type of both sides.
Why Type Coercion Matters
Suppose:
customer_id
BIGINT
but the supplied array is:
INTEGER[]
PostgreSQL may need to perform an implicit conversion.
The query now involves:
BIGINT
|
v
Comparison
^
|
INTEGER[]
The PostgreSQL 19 Beta 3 fix specifically addresses a postgres_fdw case where array comparison pushdown combined with implicit type coercion could produce wrong results.
This is exactly the kind of regression that should be tested using real parameter types.
Building an FDW Test Environment
Create a remote table:
CREATE TABLE remote_customers
(
id BIGINT PRIMARY KEY,
name TEXT NOT NULL
);
Add test data:
INSERT INTO remote_customers (id, name)
VALUES
(101, 'Alice'),
(205, 'Bob'),
(310, 'Charlie'),
(450, 'David');
Then expose the table through postgres_fdw.
The local database can define a foreign table corresponding to the remote table.
The exact connection configuration depends on your test environment.
Test Array-Based Filtering
Once the foreign table is configured, test:
SELECT *
FROM remote_customers
WHERE id = ANY(ARRAY[101, 205]);
The expected result is:
101 | Alice
205 | Bob
Now introduce different integer types.
For example:
SELECT *
FROM remote_customers
WHERE id = ANY(ARRAY[101, 205]::INTEGER[]);
Then compare the result with an explicitly compatible type:
SELECT *
FROM remote_customers
WHERE id = ANY(ARRAY[101, 205]::BIGINT[]);
The purpose is to verify that implicit and explicit conversions produce the expected logical result.
Compare Local and FDW Queries
One of the strongest regression techniques is to compare a local query with an equivalent foreign query.
For example:
Local PostgreSQL
|
v
Local Table
|
v
Expected Result
and:
Local PostgreSQL
|
v
postgres_fdw
|
v
Remote Table
|
v
Actual Result
The result sets should match.
This creates a practical oracle:
Local Query Result
=
FDW Query Result
assuming the underlying datasets are equivalent.
Testing With Different Array Sizes
Do not test only two IDs.
Use:
Empty array
One value
Two values
Many values
Duplicate values
NULL values where applicable
For example:
ARRAY[101]
then:
ARRAY[101, 205, 310]
and finally a larger set.
This can expose issues that only occur with specific parameter shapes.
Test Empty Arrays
An empty array is a useful edge case:
SELECT *
FROM remote_customers
WHERE id = ANY(ARRAY[]::BIGINT[]);
The expected result should be clearly defined by the application.
Usually, no rows should match.
The important part is that the application should know what it expects rather than relying on assumptions.
Test Duplicate Values
Consider:
ARRAY[101, 101, 205]
The result should not contain duplicate database rows merely because the input array contains duplicate values.
Expected:
101 | Alice
205 | Bob
not:
101 | Alice
101 | Alice
205 | Bob
This is a useful semantic test.
Test the Query Plan
Because postgres_fdw can push conditions to the remote server, inspect the execution plan when performance matters.
For example:
EXPLAIN
SELECT *
FROM remote_customers
WHERE id = ANY(ARRAY[101, 205]::BIGINT[]);
The exact plan depends on the environment.
The purpose is to determine whether the intended condition is being pushed down appropriately.
Do not assume that every query must be fully pushed down.
The goal is to understand what PostgreSQL is actually doing.
Testing Implicit vs Explicit Types
Create a test matrix:
| Local Column | Array Type | Expected |
|---|
| BIGINT | BIGINT[] | Correct |
| BIGINT | INTEGER[] | Correct |
| INTEGER | INTEGER[] | Correct |
| TEXT | TEXT[] | Correct |
| Different compatible types | Implicit conversion | Validate carefully |
This helps identify cases where type coercion changes query behavior.
Testing Application Parameters
The real-world case is often not a hard-coded array.
A .NET application may provide parameters dynamically:
var customerIds = new long[]
{
101,
205,
310
};
The application then passes those values to a database query.
The test should verify:
C# Type
|
v
Provider Parameter Type
|
v
PostgreSQL Parameter Type
|
v
FDW Comparison
|
v
Result
This is important because application-level types and PostgreSQL types do not always map in exactly the same way.
Testing JSON and FDW Together
Complex applications may use both features.
For example:
Local Database
|
+---- JSON filtering
|
+---- postgres_fdw
|
v
Remote PostgreSQL
A realistic query might:
Filter remote records.
Push a condition to the remote server.
Return JSON data.
Construct a JSON result locally.
This is a more advanced integration test.
However, do not start with this scenario.
First verify:
JSON alone
FDW alone
Then combine them.
This makes troubleshooting much easier.
Regression Test Design
A good regression test should contain four parts:
Input
|
v
Query
|
v
Expected Result
|
v
Assertion
For example:
Input:
Customer IDs = [101, 205]
Query:
FDW query using ANY()
Expected:
Alice, Bob
Assertion:
Actual result == Expected result
For JSON:
Input:
Two order rows
Query:
JSON_ARRAY(query)
Expected:
Two JSON elements
Assertion:
JSON structure and values match
Testing Against Previous PostgreSQL Versions
If you are evaluating an upgrade, run the same tests against:
Current PostgreSQL
|
v
PostgreSQL 19 Beta 3
Compare:
Result
Execution Plan
Execution Time
Errors
Warnings
The goal is not necessarily to find differences and call them bugs.
Some behavior changes may be intentional.
The benchmark should identify unexpected differences that require investigation.
Automating the Tests
For .NET applications, database regression tests can be incorporated into an integration-test project.
A simplified test could look like:
[Fact]
public async Task RemoteCustomerQuery_Should_ReturnExpectedCustomers()
{
var ids = new long[] { 101, 205 };
var results = await repository
.GetCustomersAsync(ids);
Assert.Equal(2, results.Count);
Assert.Contains(results, x => x.Id == 101);
Assert.Contains(results, x => x.Id == 205);
}
For JSON:
[Fact]
public async Task OrderJson_ShouldContainExpectedStatus()
{
var order = await repository.GetOrderAsync(101);
Assert.Equal(
"completed",
order.Status);
}
The actual implementation will depend on the data-access architecture.
Testing Production-Like Data
Synthetic data is useful, but production-like data is better for compatibility testing.
Include:
Small JSON documents
Large JSON documents
Missing properties
NULL values
Nested objects
Arrays
Large ID lists
Different numeric types
For FDW testing, include:
Small remote tables
Large remote tables
Indexed columns
Non-indexed columns
Different data types
Different parameter sizes
This makes the regression suite more representative.
Common Mistakes
Mistake 1: Testing Only Successful Execution
A query can execute and still return incorrect data.
Mistake 2: Ignoring Data Types
Implicit type coercion is specifically relevant to the PostgreSQL 19 Beta 3 postgres_fdw fix.
Mistake 3: Testing Only Local Tables
FDW behavior must be tested through the foreign-data wrapper.
Mistake 4: Ignoring JSON Structure
Valid JSON is not necessarily correct JSON.
Mistake 5: Using Only Small Test Data
Small datasets may hide query-planning and FDW performance problems.
Mistake 6: Combining Too Many Variables
Test JSON and FDW independently before testing complex combinations.
Mistake 7: Ignoring the Application Layer
A database query that works manually may behave differently when executed through the application's database provider.
Mistake 8: Running Beta Software in Production
PostgreSQL explicitly advises against using beta releases in production environments.
Troubleshooting
| Problem | What to Check |
|---|
| FDW returns unexpected rows | Compare local and remote query results |
| Array comparison behaves differently | Check parameter and column data types |
| Remote filter is not pushed down | Inspect the execution plan |
| JSON result has wrong structure | Validate each JSON element independently |
| JSON property is missing | Test missing vs explicit null |
| .NET result differs from psql | Inspect generated SQL and parameter types |
| Query works locally but not through FDW | Check remote execution and type coercion |
| Tests are intermittent | Check remote database state and replication/network conditions |
| PostgreSQL upgrade changes results | Compare execution plans and exact query behavior |
Best Practices
Test the Exact PostgreSQL Version
Do not assume that behavior from another release is identical.
Validate Results, Not Just Errors
The strongest regression test verifies actual data.
Test Type Coercion Explicitly
Especially when arrays and FDW queries are involved.
Keep Local and Remote Test Data Controlled
Equivalent datasets make comparison much easier.
Inspect Execution Plans
Use them to understand FDW pushdown and performance behavior.
Include Edge Cases
Test:
Empty
NULL
Duplicate
Large
Nested
Mixed Type
where applicable.
Test Through the Real Application
For .NET applications, include the database provider and EF Core workflow.
Keep Beta Testing Isolated
Use containers, dedicated test databases, or other disposable environments.
Advantages
Finds Subtle Compatibility Problems
Regression tests can catch incorrect results that ordinary build tests cannot.
Improves Upgrade Confidence
Teams can validate existing workloads against PostgreSQL 19 Beta 3 before a future production upgrade.
Protects Data Correctness
Comparing expected and actual results helps detect semantic problems.
Useful for Distributed Database Architectures
postgres_fdw testing is particularly relevant when data is spread across PostgreSQL servers.
Works Well With Automated CI
Once the test environment is reproducible, the same regression suite can be executed repeatedly.
Disadvantages and Limitations
FDW Testing Requires Multiple Database Environments
A realistic test needs both local and remote database components.
Performance Results Are Environment-Specific
Network latency, indexes, hardware, and data size can change results.
JSON Tests Can Become Complex
Deeply nested JSON structures require more detailed assertions.
Beta Behavior Can Change
PostgreSQL beta releases are pre-release software, and the project notes that behavior can still change before general availability.
Not Every Difference Is a Regression
A difference between releases needs investigation before it is classified as a defect.
A Practical Regression Workflow
For a .NET team evaluating PostgreSQL 19 Beta 3, the following workflow is practical:
Create Test Databases
|
v
Load Representative Data
|
+----------------+
| |
v v
JSON Tests FDW Tests
| |
+--------+-------+
|
v
Application Tests
|
v
Compare Results
|
v
Inspect Query Plans
|
v
Record Differences
|
v
Investigate Failures
Start with focused tests and gradually increase complexity.
Example Regression Checklist
Before declaring the database upgrade test successful, verify:
[ ] JSON extraction works
[ ] JSON filtering works
[ ] JSON aggregation works
[ ] JSON_ARRAY(query) produces expected results
[ ] Missing JSON properties are handled correctly
[ ] JSON null values are handled correctly
[ ] postgres_fdw connection works
[ ] Foreign tables return expected rows
[ ] Array comparisons return correct rows
[ ] Implicit type coercion is tested
[ ] Explicit type coercion is tested
[ ] Empty arrays are tested
[ ] Duplicate array values are tested
[ ] Large parameter arrays are tested
[ ] FDW query plans are reviewed
[ ] .NET integration tests pass
[ ] Results are compared with the existing PostgreSQL version
Conclusion
PostgreSQL 19 Beta 3 provides a good opportunity to test database workloads that depend on JSON processing and foreign data access before a future upgrade. The release specifically includes a fix for incorrect postgres_fdw query results involving pushed-down array comparisons with implicit type coercion, along with a fix related to deparsing JSON_ARRAY(query). These are good examples of why database regression testing should verify actual results instead of checking only whether SQL executes successfully. For .NET developers, the strongest approach is to test the complete path from application code and database-provider parameters through PostgreSQL and, where applicable, across a remote PostgreSQL server. Start with focused JSON and FDW tests, add edge cases such as NULL, empty arrays, duplicate values, and different data types, and then compare the results with the current PostgreSQL version. PostgreSQL Beta 3 is intended for testing rather than production use, so the best use of this release is to find compatibility issues now and make the eventual upgrade smoother.