GraphQL Schema Best Practices

I will explain the best practice I have found by creating high maintainable GraphQL Schemas for the projects I’m working on.

General Advice

When designing your GraphQL schema, one of the common mistakes is to replicate your database tables and fields.

Think in the GraphQL Schema DX (Developer Experience); you want to simplify the use of your schema for your team.

My advice here will be to create your Schema in the exact way that you need to consume them. Avoid adding fields that you will not need to use or overcomplicate to get the information for your UI.

Start with the minimum and expand them as you need. Taking too many decisions in advance could lead to feature problems and force you to refactor the code.

Naming Conventions

Query Names: camelCase
Mutation names: camelCase
Field names: camelCase
Type names: PascalCase
Enum names: PascalCase
Enum values: ALL_CAPS
Input names: PascalCase

camelCase Many GraphQL clients are written in JavaScript, Java, Kotlin, or Swift, all of which recommend camelCase for variable names.

PascalCase This matches how classes are defined in the languages mentioned above.

ALL_CAPS Enum values should use ALL_CAPS, because they are similar to constants.

Naming Fields

Naming fields are very important because they can impact future schema changes; being very explicit early on will make things easier in the future.

Bad example:

type Product {
  id: ID!
  category: String!
  image: String
  visits: Int!
}

Good example:

type Product {
  id: ID!
  image: Image
  stats: ProductStats!
}

type ProductStats {
  visitsCount: Int!
  purchasesCount: Int!
}

type Image {
  id: ID!
  url(size: ImageSize): String
  description: String
}

enum ImageSize {
  XS
  SM
  MD
  LG
  ORIGINAL
}

Queries

Avoid writing queries named like getProduct or getAllProducts. These queries will always return something (I consider starting with the word get is redundant and makes your schema difficult to read.)
Don’t force your queries to do more than one thing, instead create different queries that are self-explanatory. e.g productById, productBySlug
The return type name is the capitalized query name with a Payload postfix to make your queries more flexible. e.g. ProductByIdPayload, ProductBySlugPayload

Bad query examples:

type Query {
  product(id: ID, slug: String): Product
  getProduct(id: ID!): Product
  getProducts: [Product]
}

Good query examples:

type Query {
  productById(id: ID!): ProductByIdPayload!
  productBySlug(id: ID!): ProductBySlugPayload!
  products: ProductPayload!
}

type ProductByIdPayload {
  product: Product
}

type ProductBySlugPayload {
  product: Product
}

type ProductPayload {
  nodes: [Product!]
}

Pagination

Returning multiple results in GraphQL could end in a very complicated schema design, but you can opt for a simple solution depending on your project.

Offset Pagination

Best for page based paginations, users can jump to a specific page. This option may be the right fit for most of the cases. If you are using an ORM it would be easy to implement.

But It has some disadvantages if your data change often; some results could be potentially skipped or returned duplicated.

type Query {
  products(page: Int, limit: Int, filters: ProductFilters): ProductConnection!
}

type ProductConnection {
  nodes: [Product!]
  pageInfo: PageInfo!
  totalCount: Int!
}

type PageInfo {
  hasNextPage: Boolean!
  hasPreviousPage: Boolean!
  currentPage: Int!
  perPage: Int!
  lastPage: Int!
}

Cursor Pagination (Relay way)

Best for infinite scroll or Load more results. (Facebook, Airbnb uses this style), In this solution, there is no concept of pages.

This one will scale well for large datasets, but it’s the most complicated to implement. With this style, you can prevent the problems of offset pagination.

type Query {
  products(first: Int, after: ID, last: Int, before: ID, filters: ProductFilters): ProductConnection!
}

type ProductConnection {
  edges: ProductEdges!
  pageInfo: PageInfo!
  totalCount: Int!
}

type ProductEdges {
  nodes: Product!
  cursor: ID!
}

type PageInfo {
  hasNextPage: Boolean!
  hasPreviousPage: Boolean!
  startCursor: ID
  endCursor: ID
}

Filters

The convention I use here is camelCase for the filters and allowing always to pass an array of IDs to make the filters more flexible (only if needed). Remember to keep only the filters that you need.

input ProductFilters {
  productIds: [ID]
  excludeProductIds: [ID]
  categoryIds: [ID]
  orderBy: ProductOrderBy
  search: String
}

enum ProductOrderBy {
  CREATED_AT_ASC
  CREATED_AT_DESC
  RANKING_ASC
  RANKING_DESC
}

Mutations

We can summarise the mutation naming conventions into 4 rules:

Mutations are named as verbs createProduct, updateProduct, deleteProduct
There must be a single argument input
The input type name is the capitalised mutation name with a Input postfix e.g CreateProductInput, UpdateProductInput
The return type name is the capitalized mutation name with a Payload postfix to make your mutations more flexible, e.g. CreateProductPayload, UpdateProductPayload

CreateProduct

type Mutation {
  createProduct(input: CreateProductInput!): CreateProductPayload!
}

input CreateProductInput {
  name: String!
  categoryId: Int!
  description: String
}

type CreateProductPayload {
  product: Product!
}

UpdateProduct

type Mutation {
  updateProduct(input: UpdateProductInput!): UpdateProductPayload!
}

input UpdateProductInput {
  productId: Int!
  name: String
  description: String
  categoryId: Int
}

type UpdateProductPayload {
  product: Product!
}

DeleteProduct

type Mutation {
  deleteProduct(input: DeleteProductInput!): DeleteProductPayload!
}

input DeleteProductInput {
  productId: Int!
}

type DeleteProductPayload {
  product: Product
}

Conclusions

These best practices are what worked for me, and I hope they are useful for you. Just remember to make your schema self-explanatory, even if it requires you to add more queries or mutations.

ref: GitHub GraphQL Schema: https://docs.github.com/en/graphql/overview/explorer