Boost Type Safety: Optional Error Fields

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Boost Type Safety: Optional Error Fields

Hey guys, let's dive into a common problem in our codebase and how we can make things a whole lot better! We're talking about improving type safety by making the error fields in our response objects properly typed as Optional[str]. This means we need to handle those pesky None checks throughout our code. This will help make our code more robust and reduce potential runtime errors. Trust me, it's a win-win!

The Lowdown: Why This Matters

Our current setup has a little issue. The type checker, the diligent worker that it is, has been flagging some errors. Specifically, it throws a fit when we try to use methods like .lower() on the error field without checking if it's actually there. Imagine trying to read a book when the pages are blank – you'd be lost, right? Well, that's what's happening here. The type checker is saying, "Hey, you might be trying to use a string method on something that's not a string at all!"

So, what's the deal? Our response objects, the containers for data coming back from our services, have an error: str | None declaration. This tells us that the error field can either be a string (hooray, an error message!) or None (nothing to see here). The problem? The type checker can't always prove that the error field is definitely a string before we try to do something like turn it into lowercase with .lower(). This can lead to unexpected issues.

The Problem: When Things Go Wrong

During testing, we've bumped into these type-checking errors. It’s like the type checker is yelling at us, and honestly, we don’t want to ignore it! The error pops up when the type checker spots us trying to call string methods on a variable that might be None. Let's look at an example:

# This code is simplified for demonstration
assert "json" in sync_result.error.lower() or "parse" in sync_result.error.lower()

Here, the type checker is saying, "Hold up! sync_result.error could be None, and None doesn't have a .lower() method." Bang! Error! This is not the type of issue you want to discover in production, right? Thus, to avoid this, we'll need to update the code to have proper validations and data type checking.

The Root Cause: A Missing Check

The real culprit here is that the type checker can't guarantee that the error field isn't None before we try to use it. When we write code like sync_result.error.lower(), the type checker has to consider all possibilities. It sees that error is str | None, so it rightly points out that there's a risk of trying to call .lower() on None. This is something we definitely want to avoid, and that's the whole point of making sure that error is a string value and not None.

Current Band-Aid: The Workaround

Right now, we're using a workaround. It's like putting a bandage on a wound, but it's not the ultimate fix. To avoid these errors, we've had to add explicit None checks in our tests. This means we add lines of code that look like this:

assert sync_result.error is not None
assert async_result.error is not None
assert "json" in sync_result.error.lower()

These checks are like bouncers at a club. They make sure that error is actually a string before we let it do anything. This keeps the type checker happy and prevents runtime errors. This is okay for now, but it is not a clean solution. These checks clutter up our code and can make it harder to read and maintain. Plus, it's easy to forget to add these checks, leaving us vulnerable to those nasty runtime errors.

Proposed Solutions: Let's Fix This!

Here are some solid options to solve this once and for all. We want to give you options, so you know this isn't a one-size-fits-all problem.

Option 1: Stricter Type Annotations

Let's get serious about how we define the errors. The concept is that we can be more explicit about how we handle errors. We can change the error fields to properly reflect their nature as strings that could be None. Here is an example:

@dataclass
class DiscoveryDocumentResponse:
    is_successful: bool
    error: Optional[str] = None

    def get_error(self) -> str:
        """Get error message, raises if no error exists."""
        if self.error is None:
            raise ValueError("No error exists on successful response")
        return self.error

This approach uses the Optional[str] type hint, which clearly signals that the error field can be either a string or None. We also introduce a get_error method. This method helps us by returning the error string only if an error exists (it's not None). If there's no error, it raises a ValueError, making it super clear that something went wrong.

Option 2: The Result Type Pattern

This is a more advanced approach, and it’s a bit like creating two distinct types for successful and failed outcomes. We define a Success and a Failure class, and then use a Union to say that the result can be either one. It's like having two separate boxes: one for success and one for failure. This way, the type checker always knows which box we're dealing with and can act accordingly. Let's see some code:

from typing import Union

@dataclass
class Success:
    issuer: str
    jwks_uri: str
    # ... other fields

@dataclass
class Failure:
    error: str  # Always present in failure case

DiscoveryResult = Union[Success, Failure]

With this method, a Failure object will always have an error field. This eliminates the chance of calling a method on None.

Option 3: Improve Type Guards

This option involves using helper methods, or “type guards,” to narrow down the possible types of our variables. These guards help the type checker understand that if a certain condition is met, a variable must be of a specific type. It's like setting up checkpoints in your code to make sure everything is on the right track.

@dataclass
class DiscoveryDocumentResponse:
    is_successful: bool
    error: Optional[str] = None

    def has_error(self) -> TypeGuard[bool]:
        """Type guard for error existence."""
        return self.error is not None

Here, the has_error method acts as a type guard. If has_error returns True, the type checker knows that self.error must be a string. This ensures that you can safely use string methods on self.error inside a conditional block that checks has_error().

Impact Analysis: What Will Change?

So, what's going to be affected by these changes? Let's take a look. We need to be aware of what needs our attention and what will need to be changed.

Files That Will Need Tweaking

  • core/models.py: This is where our response objects are defined. We'll be updating these definitions to include our new error handling. It's basically the core of the issue, and that's where we'll start.
  • Client Code: All the parts of our code that access the .error field will need to be updated. We're talking about all the places where we're checking for errors and using the error messages. That's a good amount of work!
  • Tests: Our tests will need to be adjusted to align with how we now handle errors. We'll want to ensure that they are still working as intended.
  • Type Stubs: If we add type stubs (which are like metadata that help the type checker), they'll need to be updated too.

Backward Compatibility: Will Things Break?

  • Option 1: Great news, this is backward compatible. We can add this, and it won't break anything. The get_error method is an addition, so existing code will still work as before.
  • Option 2: This is a breaking change. We'd have to change how we define our response types. This method is a big change and will need more consideration before implementation.
  • Option 3: This is also backward compatible. The type guards are additions that won't break existing code.

Action Items: What's Next?

So, what do we need to do to fix this? Let's break it down into a to-do list.

  • Decide on an approach. We will choose between Option 1 (making error handling more explicit) and Option 3 (improving type guards).
  • Update response object definitions. Modify core/models.py with the chosen approach.
  • Add helper methods for error handling. Add methods like get_error (Option 1) or type guards (Option 3).
  • Update all error access code. Make sure all code that uses the .error field uses the new, type-safe patterns.
  • Run pyrefly. Verify that we've fixed the type errors.
  • Update tests. Make sure all our tests still work and use the new error handling patterns.
  • Document error handling patterns. Write clear documentation for the API so everyone knows how to handle errors.

Success Criteria: How Do We Know We've Succeeded?

We need to make sure we've actually fixed the problem. Here's how we'll measure our success.

  • Zero pyrefly type errors related to error field access. This is our main goal!
  • All existing tests pass. We don't want to break anything that already works.
  • Clear, documented pattern for error handling. Everyone should understand how errors are handled in the API.
  • No runtime errors from None access. The whole point is to prevent these!

Priority: How Important Is This?

This is considered Low priority. It's not a showstopper, but it is an improvement to developer experience and code quality. It will also help to make the code more safe and easier to debug.

Related Issues: Where Does This Fit In?

This is part of general code quality improvements. This will complement other issues and improve our architecture.

Notes: The Backstory

This issue was identified while we were working on async support and equivalence tests. The type checker correctly found potential issues that we didn't know we had. Thanks, type checker!