PostgreSQL upgrades are easy to underestimate. A new version may look compatible at the application level, but the real question is whether your existing queries, indexes, transactions, connection pooling, and ORM-generated SQL continue to behave as expected.
This becomes even more important when testing a beta release such as PostgreSQL 19. A beta is useful for finding compatibility and performance issues before an upgrade, but it should not be treated as a production database.
For .NET teams using PostgreSQL through applications built with ASP.NET Core, Entity Framework Core, Dapper, or Npgsql, the best approach is to test PostgreSQL 19 against queries that resemble actual application workloads.
This article explains how to build that test environment, capture representative queries, run repeatable tests, and identify problems before they reach production.
Why Test PostgreSQL 19 With Production-Like Queries?
A database version upgrade can affect more than SQL syntax.
Your application depends on several layers working together:
PostgreSQL behavior
Npgsql driver compatibility
Entity Framework Core or Dapper
Connection pooling
Transactions
Indexes and execution plans
Data types
Extensions
Application-specific query patterns
A simple SELECT test does not cover these interactions.
For example, an application might execute a query containing multiple joins, filtering, pagination, ordering, and parameters:
SELECT
o.id,
o.customer_id,
o.total_amount,
o.created_at
FROM orders o
WHERE o.customer_id = @customerId
AND o.status = @status
ORDER BY o.created_at DESC
LIMIT @limit OFFSET @offset;
The important question is not whether PostgreSQL can execute this query.
The important question is whether it behaves acceptably with your application's real data volume, indexes, parameter patterns, concurrency, and transaction behavior.
What Does Production-Like Testing Mean?
Production-like does not mean copying your entire production database into a test environment.
Instead, it means reproducing the characteristics that influence database behavior.
A useful test dataset should approximate:
| Area | Production-Like Requirement |
|---|
| Row counts | Similar order of magnitude |
| Data distribution | Similar common and uncommon values |
| Indexes | Same important indexes |
| Schema | Same tables, constraints, and types |
| Queries | Representative application queries |
| Parameters | Common and edge-case values |
| Concurrency | Realistic concurrent requests |
| Transactions | Typical application transaction patterns |
| Connection pooling | Similar pool configuration |
For example, testing a query against 10,000 rows when production contains tens or hundreds of millions of rows can produce misleading results.
Set Up PostgreSQL 19 for Testing
The first rule is simple: keep the beta database isolated from production.
A containerized environment is convenient for repeatable testing.
For example:
services:
postgres19:
image: postgres:19
container_name: postgres19-test
environment:
POSTGRES_DB: appdb
POSTGRES_USER: appuser
POSTGRES_PASSWORD: testpassword
ports:
- "5433:5432"
volumes:
- postgres19_data:/var/lib/postgresql/data
volumes:
postgres19_data:
The exact PostgreSQL 19 image/tag should be selected according to the beta build you are evaluating. Beta releases can change, so record the exact version used in every test run.
You can verify the server version with:
SELECT version();
Also capture the server settings that could influence your results.
SHOW shared_buffers;
SHOW work_mem;
SHOW max_connections;
SHOW effective_cache_size;
The goal is not necessarily to duplicate every production setting. It is to document the differences so test results can be interpreted correctly.
Use the Same Schema as Production
A useful compatibility test should begin with the same database structure used by the application.
That includes:
Tables
Primary keys
Foreign keys
Unique constraints
Indexes
Views
Functions
Extensions
Data types
For example:
CREATE TABLE customers
(
id BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
name TEXT NOT NULL,
email TEXT NOT NULL UNIQUE,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
CREATE TABLE orders
(
id BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
customer_id BIGINT NOT NULL REFERENCES customers(id),
status TEXT NOT NULL,
total_amount NUMERIC(12, 2) NOT NULL,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
CREATE INDEX ix_orders_customer_created
ON orders(customer_id, created_at DESC);
Do not simplify the schema too aggressively for testing. Removing an index or constraint can completely change query behavior.
Test the .NET Data Access Layer
The database is only one part of the compatibility chain.
A .NET application might use Npgsql directly:
using Npgsql;
await using var connection = new NpgsqlConnection(connectionString);
await connection.OpenAsync();
const string sql = """
SELECT id, name, email
FROM customers
WHERE email = @email
""";
await using var command = new NpgsqlCommand(sql, connection);
command.Parameters.AddWithValue("email", "[email protected]");
await using var reader = await command.ExecuteReaderAsync();
while (await reader.ReadAsync())
{
Console.WriteLine(reader.GetString(1));
}
This test checks more than SQL compatibility. It also exercises the driver-to-server communication path.
For applications using Entity Framework Core, run the application's actual LINQ queries rather than manually rewriting them as SQL.
var orders = await dbContext.Orders
.Where(x => x.CustomerId == customerId &&
x.Status == "Completed")
.OrderByDescending(x => x.CreatedAt)
.Take(50)
.ToListAsync();
This is important because an ORM-generated query can differ from the SQL a developer expects.
Build a Representative Query Set
Do not randomly select queries.
Create a test suite based on the operations your application performs most often.
A useful query set might contain:
Read Queries
Write Queries
Inserts
Updates
Deletes
Bulk operations
Upserts
Transaction Tests
await using var transaction = await dbContext.Database
.BeginTransactionAsync();
try
{
order.Status = "Completed";
await dbContext.SaveChangesAsync();
await transaction.CommitAsync();
}
catch
{
await transaction.RollbackAsync();
throw;
}
The goal is to test the patterns your application actually depends on.
Compare Execution Plans
One of the most useful tools for database upgrade testing is EXPLAIN.
Start with:
EXPLAIN
SELECT *
FROM orders
WHERE customer_id = 1001
ORDER BY created_at DESC
LIMIT 50;
For deeper testing:
EXPLAIN (ANALYZE, BUFFERS)
SELECT *
FROM orders
WHERE customer_id = 1001
ORDER BY created_at DESC
LIMIT 50;
EXPLAIN ANALYZE actually executes the query, so use it carefully when testing against data that can be modified.
Look for meaningful differences between your current PostgreSQL version and PostgreSQL 19:
The goal is not to assume that every changed execution plan is bad. Query planners can choose a different plan while producing equal or better results.
Measure .NET Query Performance
A simple benchmark can measure application-level behavior.
For example:
var stopwatch = Stopwatch.StartNew();
var result = await dbContext.Orders
.Where(x => x.Status == "Completed")
.OrderByDescending(x => x.CreatedAt)
.Take(100)
.ToListAsync();
stopwatch.Stop();
Console.WriteLine(
$"Rows: {result.Count}, Time: {stopwatch.ElapsedMilliseconds} ms");
For serious performance testing, avoid relying on a single execution.
Run multiple iterations and separate:
Cold-start behavior
Warm-cache behavior
Application startup
Connection establishment
Query execution
Otherwise, network latency or connection setup can hide the database behavior you are trying to measure.
Test Parameter Variations
A query can behave differently depending on its parameters.
Consider:
SELECT *
FROM orders
WHERE customer_id = @customerId;
Testing only one customer is insufficient.
Try:
A customer with very few orders
A customer with many orders
A nonexistent customer
Frequently accessed values
Rare values
This helps identify plan and data-distribution issues that a basic test may miss.
Test Connection Pooling
Production applications rarely open a brand-new database connection for every request.
Npgsql uses connection pooling, so test the same general pooling model used by your application.
For example:
Application
|
v
ASP.NET Core
|
v
Npgsql Connection Pool
|
v
PostgreSQL 19
Test under realistic concurrency rather than running one query at a time.
A query that works perfectly for a single request may behave differently when dozens or hundreds of application operations are executing concurrently.
Compare PostgreSQL Versions Systematically
A simple test matrix makes the results easier to understand.
| Test | Current PostgreSQL | PostgreSQL 19 Beta | Result |
|---|
| Simple lookup | Pass | Pass | Compatible |
| Join query | Pass | Pass | Compatible |
| Aggregation | Pass | Pass | Compatible |
| Transaction test | Pass | Pass | Compatible |
| ORM query | Pass | Pass | Compatible |
| High-concurrency test | Pass | Review | Investigate |
| Execution plan | Plan A | Plan B | Review |
Avoid declaring an upgrade successful based on one metric.
A version upgrade should be evaluated across correctness, compatibility, stability, and performance.
Common Mistakes
Testing Only Simple Queries
A basic SELECT 1 proves almost nothing about application compatibility.
Test the actual query patterns generated by your application.
Using a Tiny Dataset
Small datasets often hide indexing and query-planning problems.
Use representative data distributions whenever possible.
Testing Only the Database
A PostgreSQL upgrade also affects the application-to-database integration.
Test the complete path:
.NET Application
|
v
EF Core / Dapper
|
v
Npgsql
|
v
PostgreSQL 19
Treating Beta Results as Production Certification
A beta environment is useful for early compatibility testing and finding issues before a final release. It should not be treated as proof that production is ready for an upgrade.
Record the exact beta version and repeat important tests as the release changes.
Troubleshooting Unexpected Results
When a test fails, avoid immediately blaming PostgreSQL.
Check the entire stack.
Query Is Slower
Start with:
EXPLAIN (ANALYZE, BUFFERS)
SELECT ...;
Compare the execution plan with the existing PostgreSQL version.
Then check:
Index availability
Statistics
Data distribution
Query parameters
Server configuration
Concurrent workload
Application Cannot Connect
Verify:
PostgreSQL is running
Port configuration
Credentials
Database name
SSL requirements
Npgsql version
Connection string
ORM Query Behaves Differently
Capture the SQL generated by the ORM and execute that SQL directly against both database versions.
This separates database behavior from ORM behavior.
Best Practices for PostgreSQL 19 Beta Testing
Use an isolated environment. Never use a beta database as a production dependency.
Record the exact PostgreSQL build. Beta versions can change between test cycles.
Use production-like data characteristics. Row counts and distributions matter.
Keep indexes and constraints realistic. They directly influence query plans.
Test through the real .NET data-access stack. Include EF Core, Dapper, or Npgsql as applicable.
Compare execution plans. A changed plan deserves investigation, not automatic rejection.
Test concurrency. Single-user testing does not represent most production applications.
Repeat important tests. One successful execution is not enough evidence.
Separate compatibility from performance. A query can be functionally correct but require performance investigation.
Document every difference. Record server version, driver version, configuration, schema, dataset, and test results.
Frequently Asked Questions
Can PostgreSQL 19 Beta be used for production?
A beta release should be treated as a testing and evaluation target rather than a production database. Use it to identify compatibility issues and prepare your application before a stable release.
Should I test EF Core queries or raw SQL?
Test both where applicable. EF Core applications should test the actual LINQ expressions used by the application because the generated SQL is part of the production behavior.
Is a changed execution plan automatically a problem?
No. A different plan is not necessarily worse. Compare execution time, rows processed, buffer activity, resource usage, and correctness before deciding whether the change requires action.
How much production data should I copy?
There is no universal percentage. Focus on reproducing the characteristics that affect the queries being tested, including important table sizes, data distributions, indexes, and common parameter values.
Should Npgsql also be tested during the upgrade?
Yes. The database server and .NET data-access driver form part of the same integration path. Test the application using the Npgsql version supported by your application stack and PostgreSQL target.
Conclusion
Testing a PostgreSQL upgrade is much more useful when the test environment behaves like the application environment rather than a simple database sandbox. For .NET applications, that means testing real EF Core, Dapper, or Npgsql query patterns, realistic data, indexes, transactions, connection pooling, and concurrent workloads.
PostgreSQL 19 beta provides an opportunity to find these issues before a production migration. The most valuable result is not simply a statement that the new version works. It is a documented comparison showing which queries remain compatible, which execution plans changed, where performance needs investigation, and whether the application stack is ready for the next upgrade step.