Seaography: Enhancing CRUD With Data-Level Hooks

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Seaography: Enhancing CRUD with Data-Level Hooks

Hey folks, let's dive into a crucial topic regarding Seaography, a fantastic tool for generating GraphQL APIs on top of SeaORM. We're talking about the need for a data-level hook to enrich the CRUD (Create, Read, Update, Delete) operations. This enhancement is vital for real-world applications and will significantly improve Seaography's versatility and security. We will discuss the current limitations, why data-level hooks are essential, and how they can be implemented to make Seaography even more powerful and production-ready. Let's get started, shall we?

The Current Limitations and Problem Description

Currently, Seaography generates GraphQL APIs using CRUD operations, but it lacks a crucial feature: a data-level hook. When you perform a Create, Update, or Delete operation, Seaography directly interacts with the database. This approach bypasses any lifecycle logic at the ActiveModel level. This means you can't inspect or validate the data being inserted, updated, or deleted. Think about it; you need to manage your business rules, security, data sanitization, and other important aspects. Without this, it's like building a house without a foundation.

Let's get into some real-world examples. Imagine a user wants to update an asset's path, and you have a mutation like this: mutation { assetsUpdate(data: { path: "new.png" }, filter: { id: 10 }) }. With the current implementation, Seaography executes the update directly on the entity. There's no hook available to access the old record and the new input data. There's no place to enforce your application-level logic. This includes things like deleting the old file when a record is updated, validating user ownership, or transforming the data. Because of this, you may need to entirely replace the generated resolvers to implement those features, which isn't ideal. The absence of this data-level hook restricts the tool's use in various applications that require lifecycle logic for the data being modified. Without it, you are significantly limited in what you can achieve.

Impact of the missing feature

The absence of this data-level hook significantly impacts Seaography's ability to handle complex and security-sensitive systems. Without it, developers must resort to manually rewriting their resolvers. This defeats the purpose of an auto-generated CRUD GraphQL API and requires extra work to implement core application logic.

Why Data-Level Hooks are Crucial

Adding data-level hooks is not just a feature; it's a necessity. Modern APIs often require lifecycle logic to be executed on the data as it's being mutated. This is a must if you plan to build a secure and production-ready application. Let's break down why this is important.

1. Security Rules

First and foremost, security. Data-level hooks allow you to enforce row-level ownership. This means you can ensure that users can only access and modify data that they own. You can also prevent changes to restricted fields, protecting sensitive information. Furthermore, you can automatically inject fields, such as a user_id from a JWT (JSON Web Token), ensuring that every record is correctly associated with the user performing the action. This level of control is simply not possible without a data-level hook.

2. Data Validation

Next, validation. You need to validate data before it gets stored in the database. With a data-level hook, you can validate file uploads based on their content, not just their names. You can enforce format rules that depend on other fields, ensuring data integrity. For example, if you have a form with a date field, a data-level hook ensures that the entered date follows a valid format. You can also disallow partial updates that violate your data invariants, maintaining consistency.

3. Side Effects

Thirdly, side effects. Real-world applications often require side effects to be triggered during CRUD operations. Consider the file system. When a record is updated and the file path changes, you might need to remove the old file. A data-level hook provides the perfect place to do this. You can also trigger background tasks, such as sending notifications or processing data, ensuring that your application is responsive. Finally, this allows you to cascade cleanup operations when a record is deleted, ensuring data integrity.

4. Data Transformation

Finally, data transformation. Often, you need to transform the data before it's stored. With a data-level hook, you can hash or normalize fields. For example, you might hash a password field before saving it to the database. You can auto-generate slugs (unique identifiers based on other fields). You can also enrich data from server-side logic. This could involve signing data or adding additional information before it's saved. All of these operations are impossible without the ability to intercept and modify the data before it's written.

Proposed Solution: A Data-Level Lifecycle Hook

The solution is simple: implement a data-level hook that is triggered before Create, Update, and Delete operations. This hook should provide developers with access to the input data being passed, the existing database record (for updates and deletes), the operation type, the resolved user context, and the final ActiveModel or value map that will be written. Let's look at the basic conceptual implementation of such a hook. This could conceptually look similar to:

async fn on_mutation(
    entity: &str,
    operation: MutationType,
    input: &MutationInput,
    old_record: Option<Model>,
    ctx: &Context,
) -> anyhow::Result<()>;

This setup would allow you to do the following:

  • Enforce row-level security: Control which users can modify specific data.
  • Validate and transform fields: Ensure data meets your rules and requirements.
  • Manage file system and other side effects: Handle related tasks, like file operations.
  • Implement complex application logic: Add your own custom business rules.

Why This Belongs in Seaography

Seaography aims to be a complete solution for auto-generating CRUD GraphQL APIs. Without data-level hooks, it falls short of this goal. The lack of this feature forces developers to abandon the automatically generated resolvers and manually create their own. This extra effort undermines the core benefit of Seaography. By adding these hooks, Seaography would become far more useful in real-world applications, allowing for secure and flexible implementations.

Conclusion: The Benefits of Implementing Data-Level Hooks

In conclusion, a data-level hook system would be a game-changer for Seaography. It would drastically improve usability, allowing developers to implement secure, real-world business logic while still leveraging the benefits of automatic schema generation. This addition would make Seaography a more robust and complete solution for creating GraphQL APIs with SeaORM, empowering developers to build complex and secure applications with ease. The implementation of this feature would be a significant step forward, unlocking the potential for many more production use cases and cementing Seaography's position as a powerful tool in the Rust ecosystem. Adding data-level hooks will make Seaography a more versatile and attractive choice for developers building complex applications.

By implementing the requested feature, Seaography can meet the growing needs of developers and offer a more comprehensive and powerful solution for creating GraphQL APIs with SeaORM. Let's make it happen, and let's make Seaography even better!