Mastering the Art of Handling #[non_exhaustive] Structs in Rust Unit Tests with MongoDB and Axum

As a seasoned Rust developer, you’re no stranger to the occasional gotcha when it comes to testing your code. One particularly pesky issue is dealing with #[non_exhaustive] structs in unit tests, especially when working with MongoDB and Axum. Fear not, dear reader, for this article is here to guide you through the treacherous waters of testing non-exhaustive structs and emerge victorious on the other side.

What are #[non_exhaustive] Structs?

Before we dive into the meat of the article, let’s take a quick detour to understand what #[non_exhaustive] structs are. In Rust, when you define an enum or a struct with the #[non_exhaustive] attribute, you’re telling the compiler that this type might have additional variants or fields in the future. This is particularly useful when working with external libraries or APIs that might add new fields or variants without your knowledge.

#[non_exhaustive]
enum Color {
    Red,
    Green,
    Blue,
    // Add more colors in the future?
}

By marking a struct or enum as non-exhaustive, you’re ensuring that your code will continue to compile even if new variants or fields are added in the future. However, this convenience comes at a cost – it makes testing a lot more complicated.

The Problem with Testing #[non_exhaustive] Structs

When you define a non-exhaustive struct, Rust can’t guarantee that your test cases cover all possible variants or fields. This means that your tests might pass today, but fail tomorrow when a new variant or field is added. This is where things get messy.

Let’s say you have a struct like this:

#[non_exhaustive]
struct User {
    id: i32,
    name: String,
    // Add more fields in the future?
}

In your test, you might try to create a User instance like this:

let user = User { id: 1, name: "John".to_string() };

However, what if the User struct gets a new field, say, email, in the future? Your test will suddenly fail, even though it was working perfectly fine before.

Enter MongoDB and Axum

Now that we’ve set the stage, let’s bring MongoDB and Axum into the mix. When working with these technologies, you’ll often need to serialize and deserialize data to and from your database. This is where things get particularly tricky with non-exhaustive structs.

Imagine you have a MongoDB collection with a schema like this:

Field	Type
id	i32
name	string
email	string

You use Axum to create an API endpoint that retrieves a user from the database and returns it as a JSON response. In your test, you might want to create a sample user document and verify that the API endpoint returns the correct data.

let user_doc = doc! {
    "_id": 1,
    "name": "John",
    "email": "[email protected]",
};

let app = axum_app(); // Your Axum app instance

let res = app.get("/users/1").await?;

assert_eq!(res.status(), 200);

let user: User = res.json().await?;

assert_eq!(user.id, 1);
assert_eq!(user.name, "John".to_string());
assert_eq!(user.email, "[email protected]".to_string());

But wait, what if the User struct gets a new field in the future, say, phone_number? Your test will fail, even though the API endpoint is still returning the correct data.

Solving the Problem with Custom Serialization

One way to solve this problem is to use custom serialization with MongoDB and Axum. By defining a custom serialization function, you can ensure that your test data is always up-to-date with the latest fields and variants.

Let’s define a custom serialization function for the User struct:

use serde::{Serialize, Deserialize};

#[derive(Serialize, Deserialize)]
struct User {
    id: i32,
    name: String,
    // Add more fields in the future?
}

impl User {
    fn from_doc(doc: &mut Bson) -> Result {
        let mut user = User {
            id: doc.get_i32("id")?,
            name: doc.get_str("name")?.to_string(),
            // Add more fields in the future?
        };

        // Add custom logic to handle unknown fields
        for (key, value) in doc.iter() {
            if key != "id" && key != "name" {
                // Handle unknown fields here
                println!("Unknown field: {} = {:?}", key, value);
            }
        }

        Ok(user)
    }
}

In this example, we’ve defined a custom from_doc function that takes a MongoDB document and returns a User instance. This function is flexible enough to handle any additional fields that might be added in the future.

Testing with Custom Serialization

Now that we have our custom serialization function, let’s update our test to use it:

let user_doc = doc! {
    "_id": 1,
    "name": "John",
    "email": "[email protected]",
};

let app = axum_app(); // Your Axum app instance

let res = app.get("/users/1").await?;

assert_eq!(res.status(), 200);

let user_doc = res.json().await?;

let user = User::from_doc(&mut user_doc)?;

assert_eq!(user.id, 1);
assert_eq!(user.name, "John".to_string());
assert_eq!(user.email, "[email protected]".to_string());

In this updated test, we’re using the custom from_doc function to deserialize the MongoDB document into a User instance. This ensures that our test will continue to pass even if new fields are added to the User struct in the future.

Best Practices for Handling #[non_exhaustive] Structs

Now that we’ve seen how to handle #[non_exhaustive] structs in unit tests with MongoDB and Axum, let’s summarize some best practices to keep in mind:

• Use custom serialization functions to handle unknown fields and variants.
• Keep your test data up-to-date with the latest fields and variants.
• Use a flexible testing approach that can adapt to changes in the underlying data structure.
• Avoid hardcoding specific field names or values in your tests.
• Use a testing framework that allows for easy integration with custom serialization functions.

By following these best practices, you’ll be well-equipped to handle the challenges of testing #[non_exhaustive] structs with MongoDB and Axum.

Conclusion

In conclusion, handling #[non_exhaustive] structs in unit tests with MongoDB and Axum requires a thoughtful approach to custom serialization and flexible testing. By using the techniques outlined in this article, you’ll be able to write robust and adaptable tests that can keep up with the ever-changing landscape of your data structure.

Remember, testing is all about anticipating the unexpected. By embracing the uncertainty of #[non_exhaustive] structs, you’ll be better equipped to handle the complexities of real-world testing scenarios. Happy testing!

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