Entities & Locations: Design Implications Discussion

Let's dive into a crucial discussion about how we handle entities and their locations (or spans) within our systems. This topic, brought up by Isograph Labs, raises some interesting points about efficiency and design choices, particularly when dealing with schema modifications.

The Core Issue: Location Data's Impact on Change Detection

The central problem revolves around how location data, specifically spans, can affect our ability to detect meaningful changes in our schema. Consider this scenario:

type Query { foo: Bar }
type Mutation { foo: Bar }

Now, let's modify the schema:

type Query { foo: Bar,
 baz: Qux }
type Mutation { foo: Bar }

On the surface, it seems like the Mutation type hasn't changed. Its definition, the fields it contains, remains the same. However, if we're storing location information (like the start and end positions of the Mutation type definition in the schema file), then the Mutation type has technically changed. The location data associated with it might be different because the overall file structure has shifted due to the addition of the baz field in the Query type.

This seemingly small change in location data can have significant consequences. If our system relies on short-circuiting or caching mechanisms that depend on detecting schema changes, we might inadvertently invalidate caches or trigger unnecessary re-computations. In essence, location data introduces a layer of change that might not reflect actual semantic modifications to the schema.

Why is this a problem? It boils down to efficiency. We want our systems to be smart about how they handle schema updates. If a type hasn't meaningfully changed (i.e., its fields, arguments, or overall structure are the same), we want to avoid unnecessary processing. Including location data in our change detection logic can lead to false positives, forcing us to do more work than we need to. This is especially crucial in large, complex systems where schema changes are frequent.

To address this, we need a way to differentiate between changes that matter (semantic changes) and changes that are merely superficial (location-based changes). This leads us to the proposed solutions.

Proposed Solutions: Separating Entities and Location Data

The core suggestion is to decouple the underlying entity (the actual definition of the type, field, etc.) from its location information. This can be achieved by having two sets of functions or methods:

1. Functions that return the entity without location data.
2. Functions that return the entity with location data.

This approach gives us fine-grained control over when we consider location data. When we're primarily concerned with semantic changes, we can use the functions that exclude location information. This allows us to accurately determine if the core definition of an entity has changed, regardless of its position in the schema file.

Furthermore, the proposal suggests making most structs generic over the location type. This means that the structs representing entities can be parameterized by the type of location data they hold (or don't hold, if we're using the functions that exclude location information). This adds flexibility and allows us to tailor the representation of entities based on the specific needs of a particular operation.

Benefits of Decoupling

• Improved Change Detection: By separating entity data from location data, we can create more accurate change detection mechanisms. We can easily determine if the core definition of a type or field has changed without being misled by shifts in its location within the schema.
• Enhanced Efficiency: More accurate change detection leads to improved efficiency. We can avoid unnecessary cache invalidations, re-computations, and other operations that are triggered by false positives.
• Increased Flexibility: Making structs generic over the location type provides flexibility in how we represent entities. We can choose to include or exclude location data based on the specific context.

Locations vs. Spans: A Deeper Dive

The discussion also touches on the trade-offs between storing locations and spans. A location typically refers to a single point, such as the starting position of an entity in a file. A span, on the other hand, represents a range, such as the starting and ending positions of an entity.

The proposal leans towards using locations over spans, arguing that the benefit of storing spans might not outweigh the complexity they introduce. While spans provide more information (the full extent of an entity's definition), this extra information might not be necessary for most use cases.

Think about it: for many operations, knowing the starting position of an entity is sufficient. For example, if we're displaying error messages, we usually only need to highlight the beginning of the problematic code. The end position might be less critical.

However, there are scenarios where spans can be valuable. For instance, if we're performing refactoring operations or code transformations, knowing the exact range of an entity's definition can be crucial. Similarly, advanced code analysis tools might benefit from having span information.

The Argument for Locations

The argument for primarily using locations rests on the principle of simplicity. If we can achieve our goals with less data, we should. Storing only locations reduces the storage overhead and simplifies the logic for handling location data. This can lead to performance improvements and reduced complexity.

Of course, this doesn't mean we should never use spans. It simply suggests that locations should be the default choice, and spans should be used only when there's a clear need for the extra information they provide. This is a classic engineering trade-off: weighing the benefits of additional information against the cost of storing and processing it.

Potential Implementation Strategies

So, how might we implement these ideas in practice? Here are a few potential strategies:

• Introduce a Located Wrapper Type: We could define a generic Located<T, L> type that wraps an entity T and associates it with a location L. This would allow us to easily add location information to any entity.
• Create Separate Entity Representations: We could have two distinct representations for each entity: one with location data and one without. This might involve creating separate structs or classes for each representation.
• Use Optional Location Fields: We could add optional location fields to our entity structs. This would allow us to include location data when needed and omit it when it's not required.
• Implement Trait-Based Approaches: Implement traits or interfaces to define contracts for accessing entities with and without location information. This promotes flexibility and allows for different implementations to co-exist.

Each of these strategies has its own advantages and disadvantages. The best approach will depend on the specific requirements of the system and the trade-offs we're willing to make.

Conclusion: Designing for Change and Efficiency

This discussion highlights the importance of carefully considering how we handle location data in our systems. By decoupling entities from their location information, we can improve change detection, enhance efficiency, and increase flexibility. The decision to use locations or spans depends on the specific needs of the application, but the principle of simplicity suggests that locations should be the default choice.

Ultimately, the goal is to design systems that are robust to change and performant in the face of complexity. By thoughtfully addressing issues like this, we can build systems that are both powerful and maintainable.

For further reading on GraphQL schema design and best practices, you might find the resources on the GraphQL Foundation website to be valuable.