Imagine you have a GraphQL API used by many apps. You want to add new features without breaking old apps. Why is schema evolution important here?
Think about how to keep old apps working while adding new features.
Schema evolution lets you add new fields or types without breaking existing clients. This way, old apps continue to work while new apps can use new features.
Given this GraphQL schema snippet:
type User { id: ID! name: String! age: Int @deprecated(reason: "Use birthYear instead") birthYear: Int }What happens if a client queries the age field?
query { user(id: "1") { id name age } }Deprecated fields still work but are marked as discouraged.
Deprecated fields remain accessible to avoid breaking clients. They show warnings in documentation but do not cause errors.
Which of these GraphQL schema changes will break existing clients?
Think about what happens if a client expects a string but gets an integer.
Changing a field's type breaks clients expecting the old type. Adding optional fields or enum values and deprecating fields are backward compatible.
You want to remove a deprecated field from your GraphQL schema without breaking clients. What is the best approach?
Think about giving clients time to adapt.
Deprecate fields first to warn clients, then remove them after enough time has passed to avoid breaking existing clients.
You need to change a field's type in a GraphQL schema, but you want to avoid breaking existing clients. Which strategy achieves this?
Think about how to keep old clients working while introducing new types.
Adding a new field with the new type and deprecating the old one allows clients to migrate gradually without breaking.