Dynamic Backup Tests: Adapting To Table Changes

Hey everyone! Today, let's dive into a challenge we're facing with our backup tests. Specifically, we need to make our BackupServiceTests more dynamic so they don't break every time we add a new table. Currently, they rely on a hardcoded value (GoldenDataset.TotalTableCount = 32), which, as you can imagine, isn't very sustainable. Let's explore the issue, proposed solutions, affected tests, and my recommendation to keep our tests robust and maintainable.

Current Issue: The Hardcoded Table Count

The core problem lies in our tests' rigid expectation of the total number of tables. We've hardcoded GoldenDataset.TotalTableCount to equal 32. This might have been accurate at the time, but as our application evolves and we add more tables, this hardcoded value becomes a liability. Every time a new table is introduced, our tests fail, requiring us to manually update the hardcoded count. This is not only tedious but also increases the risk of overlooking a table and introducing bugs. This reliance on a static number makes our tests brittle and less reliable, which is something we definitely want to avoid in a robust system. Think of it like this: imagine you're building a house, and every time you add a new room, you have to rebuild the entire foundation. That's essentially what we're doing with our tests right now. We need a more flexible approach that can adapt to changes without constant manual intervention. One of the key aspects of maintainable code is its ability to evolve gracefully with changes in the system. Hardcoding values like the table count goes against this principle, making our tests a potential bottleneck in the development process. Furthermore, this approach introduces a maintenance burden, as developers need to be aware of this hardcoded value and remember to update it whenever a new table is added. This increases the cognitive load and the risk of errors. Ultimately, our goal is to create tests that are resilient to change and provide reliable feedback on the functionality of our backup service. By moving away from hardcoded values and adopting a more dynamic approach, we can achieve this goal and ensure that our tests remain valuable assets in our development workflow.

Proposed Solutions: Dynamic Approaches to the Rescue

To address this issue, we've come up with a few potential solutions. Let's break them down and discuss the pros and cons of each:

Option A: Query AppDbContext DbSets at Runtime

This approach involves querying the AppDbContext at runtime to dynamically count the number of DbSets. We would exclude specific tables like __EFMigrationsHistory, file_scan_*, pending_notifications, and ticker.*. This is probably the cleanest and most direct way to get an accurate table count without relying on hardcoded values. Imagine you're a detective trying to solve a mystery. Instead of relying on old, outdated information, you go directly to the source and gather the facts yourself. That's essentially what we're doing here. We're querying the AppDbContext directly to get the most up-to-date table count. This approach offers several advantages. First, it's dynamic, meaning it automatically adapts to changes in the database schema. When a new table is added, the count will automatically update without requiring any manual intervention. Second, it's relatively easy to implement and understand. We can use LINQ queries to filter out the tables we want to exclude and then simply count the remaining DbSets. However, there are also some potential drawbacks to consider. We need to ensure that the query is efficient and doesn't introduce any performance bottlenecks. Additionally, we need to carefully define the exclusion criteria to avoid accidentally excluding tables that should be included. Overall, this approach seems like a solid option that strikes a good balance between accuracy, maintainability, and ease of implementation.

Option B: Use Reflection to Count GoldenDataset Properties

This option involves using reflection to count the properties of the GoldenDataset class. While this might seem like a clever workaround, it's generally less maintainable and more prone to errors than directly querying the database. Think of reflection as trying to understand a machine by taking it apart and examining its individual components. While it can be useful in certain situations, it's often more complex and error-prone than simply observing the machine in action. Reflection can be useful in certain scenarios, but in this case, it adds unnecessary complexity and makes the code harder to understand and maintain. Furthermore, it relies on the assumption that the GoldenDataset class accurately reflects the database schema. If the GoldenDataset class is not kept in sync with the database, the reflection-based count will be inaccurate. This could lead to false positives or false negatives in our tests, undermining their reliability. Also, reflection can be slower than direct querying, which could impact the performance of our tests. For these reasons, I would generally recommend avoiding reflection unless absolutely necessary. There are often simpler and more direct ways to achieve the same result without introducing the complexity and potential pitfalls of reflection.

Option C: Validate Table Names Instead of Count

Instead of counting the tables, we could validate the table names. This approach would involve comparing the actual table names in the database to a list of expected table names. This could be a good option if we're primarily concerned with ensuring that specific tables exist and have the correct names. Imagine you're a librarian checking the inventory of a library. Instead of counting the total number of books, you focus on verifying that specific books are present and in the correct location. This approach can be useful in scenarios where the presence and naming of specific tables are critical. It can also provide more detailed feedback when a test fails, as it can pinpoint exactly which tables are missing or incorrectly named. However, it also has some limitations. It requires us to maintain a list of expected table names, which could become cumbersome as the number of tables grows. Additionally, it doesn't provide a general measure of the overall database schema, which could be useful for detecting unexpected changes. Overall, this approach is worth considering if our primary goal is to validate the presence and naming of specific tables. However, if we need a more general measure of the database schema, option A (dynamic DbSet counting) might be a better choice.

Affected Tests: Identifying the Impact

The following tests are affected by this issue:

• ExportAsync_ShouldIncludeAllExpectedTables
• GetMetadataAsync_FromEncryptedBackup_ShouldReturnMetadata

These tests currently rely on the hardcoded GoldenDataset.TotalTableCount value. When a new table is added, these tests will fail because the expected table count will no longer match the actual table count. We need to update these tests to use one of the proposed dynamic approaches to ensure that they continue to pass even when the database schema changes.

Recommendation: Embracing Dynamic DbSet Counting

After careful consideration, I recommend Option A: dynamic DbSet counting. This approach is more maintainable because it automatically adapts to changes in the database schema. It's also relatively easy to implement and understand. By querying the AppDbContext at runtime, we can get an accurate table count without relying on hardcoded values. Guys, let's embrace this dynamic approach and make our backup tests more resilient and reliable!

This change is tracked in BACKLOG.md as REFACTOR-17, so you can follow the progress there. Let me know if you have any questions or suggestions!