The Problem with Traditional Schema Management

Oct 13, 2025 by orovajewels.com 13 views

Introduction

In the world of GraphQL schema management, traditional approaches often rely on a centralized schema definition that is used to validate and generate code. However, this approach can be limiting, especially when dealing with complex schema mutations. In this article, we'll explore a new approach to GraphQL schema management using Pico, a pure function-based framework that eliminates the need for a centralized schema.

The Problem with Traditional Schema Management

Traditional schema management approaches often rely on a centralized schema definition that is used to validate and generate code. However, this approach can be limiting, especially when dealing with complex schema mutations. One of the main issues with traditional schema management is that it requires functions to be impure, meaning they can modify external state. This is fundamentally incompatible with Pico, which requires that functions are pure.

Mutating Parameters: A False Solution

At first glance, it may seem like we can get around this issue by mutating parameters instead of the schema itself. However, this approach is still fundamentally incompatible with Pico. Mutating parameters is not a pure function, and it can lead to unexpected behavior and bugs.

A New Approach: Storing Fields in the DB

So, what's the solution? Instead of relying on a centralized schema definition, we can store all the fields that we're currently using in the schema in the database. This approach eliminates the need for a centralized schema definition and allows us to use pure functions to manage the schema.

Generating a Reader AST

To generate a reader AST from a GraphQL schema, we can use a recursive function that traverses the schema and builds a call tree. Here's an example of what the call tree might look like:

get_parsed_iso_literal(someIdentifier)
get_type("Query") -> query_object_id
- parse_graphql_schema
  - schema file input
get_field(query_object_id, "Foo") -> ensure there are no collisions? Idk tbh
- parse_graphql_schema
  - schema file input
get_field(query_object_id, "bar") -> bar_object_id, ensure it's an object etc
- parse_graphql_schema
  - schema file input
get_field(bar_object_id, "baz") -> ensure it's a scalar

Key Takeaways

Here are the key takeaways from our new approach to GraphQL schema management:

At no point do we validate everything, ideally including in the "parse_graphql_schema" step, e.g. we can proceed, even if there's another iso literal in a bad state
There is no schema, just free fns

Benefits of the New Approach

The new approach to GraphQL schema management using Pico has several benefits, including:

Improved scalability: By storing fields in the database, we can easily scale our schema management system to handle large and complex schemas.
Increased flexibility: With a pure function-based approach, we can easily modify and extend our schema management system without worrying about breaking existing code.
Better maintainability: By eliminating the need for a centralized schema definition, we can make our schema management system easier to maintain and update.

Conclusion

In conclusion, replacing schema with Pico is a new approach to GraphQL schema management that eliminates the need for a centralized schema definition. By storing fields in the database and using pure functions to manage the schema, we can improve scalability, increase flexibility, and make our schema management system easier to maintain and update. Whether you're building a new GraphQL schema management system or updating an existing one, this approach is definitely worth considering.

Future Work

There are several areas where we can improve our new approach to GraphQL schema management using Pico, including:

Adding support for schema validation: While we don't validate everything in the "parse_graphql_schema" step, we can still add support for schema validation to ensure that our schema is correct and consistent.
Improving performance: By optimizing our recursive function and using caching, we can improve the performance of our schema management system and make it more efficient.
Adding support for multiple schema formats: While we currently support only one schema format, we can add support for multiple schema formats to make our schema management system more flexible and adaptable.

References

Appendix

Here is some additional information that may be helpful:

Schema management system architecture: Our schema management system architecture is based on a microservices architecture, with each service responsible for a specific aspect of schema management.
Schema format: We currently support only one schema format, but we can add support for multiple schema formats in the future.
Schema validation: While we don't validate everything in the "parse_graphql_schema" step, we can still add support for schema validation to ensure that our schema is correct and consistent.
[Epic] Replace Schema with Pico: A Q&A Article =====================================================

Introduction

In our previous article, we explored a new approach to GraphQL schema management using Pico, a pure function-based framework that eliminates the need for a centralized schema definition. In this article, we'll answer some of the most frequently asked questions about this approach.

Q: What is Pico, and how does it relate to GraphQL schema management?

A: Pico is a pure function-based framework that allows you to write scalable and maintainable code. In the context of GraphQL schema management, Pico provides a way to manage schema fields without relying on a centralized schema definition.

Q: Why do we need to replace schema with Pico?

A: Traditional schema management approaches often rely on a centralized schema definition that is used to validate and generate code. However, this approach can be limiting, especially when dealing with complex schema mutations. By replacing schema with Pico, we can improve scalability, increase flexibility, and make our schema management system easier to maintain and update.

Q: How does Pico handle schema validation?

A: While we don't validate everything in the "parse_graphql_schema" step, we can still add support for schema validation to ensure that our schema is correct and consistent. This can be done by adding additional validation steps or by using a separate validation service.

Q: Can I use Pico with multiple schema formats?

A: Yes, we can add support for multiple schema formats to make our schema management system more flexible and adaptable. This can be done by adding additional schema format parsers or by using a schema format-agnostic approach.

Q: How does Pico handle schema mutations?

A: Pico provides a way to manage schema fields without relying on a centralized schema definition. This means that we can easily add or remove schema fields without affecting the rest of the schema.

Q: Can I use Pico with existing GraphQL schema management tools?

A: Yes, we can use Pico with existing GraphQL schema management tools. This can be done by integrating Pico with the existing tools or by using Pico as a replacement for the existing tools.

Q: What are the benefits of using Pico for GraphQL schema management?

A: The benefits of using Pico for GraphQL schema management include:

Improved scalability: By storing fields in the database, we can easily scale our schema management system to handle large and complex schemas.
Increased flexibility: With a pure function-based approach, we can easily modify and extend our schema management system without worrying about breaking existing code.
Better maintainability: By eliminating the need for a centralized schema definition, we can make our schema management system easier to maintain and update.

Q: What are the challenges of using Pico for GraphQL schema management?

A: Some of the challenges of using Pico for GraphQL schema management include:

Learning curve: Pico is a new approach to GraphQL schema management, and it may require some time to learn and adapt to.
Integration with existing tools: Integrating Pico with existing GraphQL schema management tools may require some additional effort.
Schema validation: While we don't validate everything in the "parse_graphql_schema" step, we may need to add additional validation steps or use a separate validation service.