Advanced topics on federated entities

This article describes complex behaviors of federated entities beyond those covered in entity basics.

Using advanced @keys

Depending on your entities' fields and usage, you may need to use more advanced @keys. For example, you may need to define a compound @key if multiple fields are required to uniquely identify an entity. If different subgraphs interact with different fields an entity, you may need to define multiple—and sometimes differing—@keys for the entity.

Compound @keys

A single @key can consist of multiple fields, the combination of which uniquely identifies an entity. This is called a compound or composite key. In the following example, the combination of both username and domain fields is required to uniquely identify the User entity:

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type User @key(fields: "username domain") {
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  username: String!
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  domain: String!
4
}

Nested fields in compound @keys

Compound keys can also include nested fields. In the following example, the User entity's @key consists of both a user's id and the id of that user's associated Organization:

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type User @key(fields: "id organization { id }") {
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  id: ID!
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  organization: Organization!
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}
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type Organization {
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  id: ID!
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}

Multiple @keys

When different subgraphs interact with different fields of an entity, you may need to define multiple @keys for the entity. For example, a Reviews subgraph might refer to products by their ID, whereas an Inventory subgraph might use SKUs.

In the following example, the Product entity can be uniquely identified by either its id or its sku:

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type Product @key(fields: "id") @key(fields: "sku") {
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  id: ID!
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  sku: String!
4
  name: String!
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  price: Int
6
}

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type Product @key(fields: "id") @key(fields: "id sku") {
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  # ...
3
}

Referencing entities with multiple keys

A subgraph that references an entity without contributing any fields can use any @key fields in its stub definition. For example, if the Products subgraph defines the Product entity like this:

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type Product @key(fields: "id") @key(fields: "sku") {
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  id: ID!
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  sku: String!
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  name: String!
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  price: Int
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}

Then, a Reviews subgraph can use either id or sku in the stub definition:

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# Either:
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type Product @key(fields: "id", resolvable: false) {
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  id: ID!
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}
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# Or:
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type Product @key(fields: "sku", resolvable: false) {
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  sku: String!
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}

When resolving a reference for an entity with multiple keys, you can determine how to resolve it based on which key is present. For example, if you're using @apollo/subgraph, it could look like this:

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// Products subgraph
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const resolvers = {
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  Product: {
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    __resolveReference(productRepresentation) {
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      if(productRepresentation.sku){
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        return fetchProductBySku(productRepresentation.sku);
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      } else {
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        return fetchProductByID(productRepresentation.id);
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      }
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    }
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  },
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  // ...other resolvers...
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}

Differing @keys across subgraphs

Although an entity commonly uses the exact same @key field(s) across subgraphs, you can alternatively use different @keys with different fields. For example, you can define a Product entity shared between subgraphs, one with sku and upc as its @keys, and the other with only upc as the @key field:

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type Product @key(fields: "sku") @key(fields: "upc") {
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  sku: ID!
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  upc: String!
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  name: String!
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  price: Int
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}

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type Product @key(fields: "upc") {
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  upc: String!
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  inStock: Boolean!
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}

To merge entities between subgraphs, the entity must have at least one shared field between subgraphs. For example, operations can't merge the Product entity defined in the following subgraphs because they don't share any fields specified in the @key selection set:

❌

type Product @key(fields: "sku") {
  sku: ID!
  name: String!
  price: Int
}

type Product @key(fields: "upc") {
  upc: String!
  inStock: Boolean!
}

Operations with differing @keys

Differing keys across subgraphs affect which of the entity's fields can be resolved from each subgraph. Requests can resolve fields if there is a traversable path from the root query to the fields.

Take these subgraph schemas as an example:

type Product @key(fields: "sku") {
  sku: ID!
  upc: String!
  name: String!
  price: Int
}

type Query {
  product(sku: ID!): Product
  products: [Product!]!
}

type Product @key(fields: "upc") {
  upc: String!
  inStock: Boolean!
}

The queries defined in the Products subgraph can always resolve all product fields because the product entity can be joined via the upc field present in both schemas.

On the other hand, queries added to the Inventory subgraph can't resolve fields from the Products subgraph:

type Product @key(fields: "sku") {
  sku: ID!
  upc: String!
  name: String!
  price: Int
}

type Product @key(fields: "upc") {
  upc: String!
  inStock: Boolean!
}

type Query {
  productsInStock: [Product!]!
}

The productsInStock query can't resolve fields from the Products subgraph since the Products subgraph's Product type definition doesn't include upc as a key field, and sku isn't present in the Inventory subgraph.

If the Products subgraph includes @key(fields: "upc"), all queries from the Inventory subgraph can resolve all product fields:

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type Product @key(fields: "sku") @key(fields: "upc") {
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  sku: ID!
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  upc: String!
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  name: String!
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  price: Int
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}

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type Product @key(fields: "upc") {
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  upc: String!
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  inStock: Boolean!
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}
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type Query {
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  productsInStock: [Product!]!
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}

Migrating entities and fields

As your supergraph grows, you might want to move parts of an entity to a different subgraph. This section describes how to perform these migrations safely.

Incremental migration with @override

Let's say our Payments subgraph defines a Bill entity:

type Bill @key(fields: "id") {
  id: ID!
  amount: Int!
  payment: Payment
}

type Payment {
  # ...
}

Then, we add a dedicated Billing subgraph to our supergraph. It now makes sense to move billing functionality there. When we're done migrating, we want our deployed subgraph schemas to look like this:

type Bill @key(fields: "id") {
  id: ID!
  payment: Payment
}

type Payment {
  # ...
}

type Bill @key(fields: "id") {
  id: ID!
  amount: Int!
}

The @override directive enables us to perform this migration incrementally with no downtime.

First, we deploy a new version of the Billing subgraph that defines and resolves the Bill fields we want to move:

type Bill @key(fields: "id") {
  id: ID!
  amount: Int!
  payment: Payment
}

type Payment {
  # ...
}

type Bill @key(fields: "id") {
  id: ID!
  amount: Int! @override(from: "Payments")
}

The @override directive says, "Resolve this field in this subgraph instead of in the Payments subgraph."

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extend schema
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  @link(url: "https://specs.apollo.dev/federation/v2.3",
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        import: ["@key", "@shareable", "@override"])

Next, we update our router's supergraph schema to include the updated Billing subgraph. If you're using managed federation, you do this by publishing the Billing subgraph's schema to GraphOS with rover subgraph publish.

When the router receives its updated supergraph schema, it immediately starts resolving the Bill.amount field from the Billing subgraph while continuing to resolve Bill.payment from the Payments subgraph.

Now that Bill.amount is resolved in the Billing subgraph, we can safely remove that field (and its resolver) from the Payments subgraph:

type Bill @key(fields: "id") {
  id: ID!
  payment: Payment
}

type Payment {
  # ...
}

type Bill @key(fields: "id") {
  id: ID!
  amount: Int! @override(from: "Payments")
}

After making this change, we deploy our updated Payments subgraph and again update our router's supergraph schema.

Finally, we can remove the @override directive from the Billing subgraph, because it no longer has any effect:

type Bill @key(fields: "id") {
  id: ID!
  payment: Payment
}

type Payment {
  # ...
}

type Bill @key(fields: "id") {
  id: ID!
  amount: Int!
}

After we deploy the Billing subgraph and publish this final schema change, we're done! We've migrated Bill.amount to the Billing subgraph with zero downtime.

Optimizing for fewer deploys with manual composition

Using @override to migrate entity fields enables us to migrate fields incrementally with zero downtime. However, doing so requires three separate schema publishes. If you're using manual composition, each schema change requires redeploying your router. With careful coordination, we can perform the same migration with only a single router redeploy.

1. In the Billing subgraph, define the Bill entity, along with its corresponding resolvers. These new resolvers should behave identically to the Payment subgraph resolvers they're replacing.

   GraphQL

   Payments subgraph

   type Bill @key(fields: "id") {
     id: ID!
     amount: Int!
     payment: Payment
   }
   
   type Payment {
     # ...
   }

   Copy

   GraphQL

   Billing subgraph

   type Bill @key(fields: "id") {
     id: ID!
     amount: Int!
   }

   Copy

2. Deploy the updated Billing subgraph to your environment, but do not publish the updated schema yet.

   • At this point, the Billing subgraph can successfully resolve Bill objects, but the router doesn't know this yet because its supergraph schema hasn't been updated. Publishing the schema would cause a composition error.

3. In the Payments subgraph, remove the migrated fields from the Bill entity and their associated resolvers (do not deploy this change yet):

   GraphQL

   Payments subgraph

   type Bill @key(fields: "id") {
     id: ID!
     payment: Payment
   }
   
   type Payment {
     # ...
   }

   Copy

   GraphQL

   Billing subgraph

   type Bill @key(fields: "id") {
     id: ID!
     amount: Int!
   }

   Copy

4. Compose an updated supergraph schema with your usual configuration using rover supergraph compose.

   • This updated supergraph schema indicates that the Billing subgraph resolves Bill.amount, and the Payments subgraph doesn't.

5. Assuming CI completes successfully, deploy an updated version of your router with the new supergraph schema.

   • When this deployment completes, the router begins resolving Bill fields in the Billing subgraph instead of the Payments subgraph.

   ⚠️ While your new router instances are deploying, you will probably have active router instances resolving the Bill.amount field in two different ways (with older instances still resolving it from Payments). It's important that the two subgraphs resolve the field in exactly the same way, or your clients might see inconsistent data during this rollover.

6. Deploy the updated version of your Payments subgraph without the migrated field.

   • At this point it's safe to remove this definition, because your router instances are using the Billing subgraph exclusively.

We're done! The migrated fields have been moved to a new subgraph, and we only redeployed our router once.

Contributing computed entity fields

You can define fields of an entity that are computed based on the values of other entity fields that are resolved by a different subgraph.

For example, this Shipping subgraph adds a shippingEstimate field to the Product entity. This field is calculated based on the product's size and weight, which are defined in the Products subgraph:

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type Product @key(fields: "id") {
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  id: ID!
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  size: Int @external
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  weight: Int @external
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  shippingEstimate: String @requires(fields: "size weight")
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}

As shown, you use the @requires directive to indicate which fields (and subfields) from other subgraphs are required. You also need to define the required fields and apply the @external directive to them. This directive tells the router, "This subgraph knows that these fields exist, but it can't resolve them itself."

In the above example, if a query requests a product's shippingEstimate, the router does the following, in order:

1. It queries the Products subgraph for the product's size and weight.
2. It queries the Shipping subgraph for the product's shippingEstimate. The size and weight are included in the Product object passed to the resolver for shippingEstimate:

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{
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  Product: {
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    shippingEstimate(product) {
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      return computeShippingEstimate(product.id, product.size, product.weight);
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    }
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  }
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}

Using @requires with object subfields

If a computed field @requires a field that returns an object type, you also specify which subfields of that object are required. You list those subfields with the following syntax:

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type Product @key(fields: "id") {
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  id: ID!
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  dimensions: ProductDimensions @external
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  shippingEstimate: String @requires(fields: "dimensions { size weight }")
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}

In this modification of the previous example, size and weight are now subfields of a ProductDimensions object. Note that the ProductDimensions type must be defined in both the Products and Shipping subgraphs for this to be valid.

Using @requires with fields that take arguments

The @requires directive can include fields that take arguments, like so:

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type Product @key(fields: "id") {
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  id: ID!
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  weight(units: String): Int @external
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  shippingEstimate: String @requires(fields: "weight(units:\"KILOGRAMS\")")
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}

• The router provides the specified values in its query to whichever subgraph defines the required field.
• Each specified argument value is static (i.e., the router always provides the same value).
• You can omit values for nullable arguments. You must provide values for non-nullable arguments.
• If you define your subgraph schema in an SDL file (instead of programmatically), you must escape quotes for string and enum values with backslashes (as shown above).

Resolving another subgraph's field

By default, exactly one subgraph is responsible for resolving each field in your supergraph schema (with important exceptions, like entity @key fields). But sometimes, multiple subgraphs are able to resolve a particular entity field, because all of those subgraphs have access to a particular data store. For example, an Inventory subgraph and a Products subgraph might both have access to the database that stores all product-related data.

You can enable multiple subgraphs to resolve a particular entity field. This is a completely optional optimization. When the router plans a query's execution, it looks at which fields are available from each subgraph. It can then attempt to optimize performance by executing the query across the fewest subgraphs needed to access all required fields.

You achieve this with one of the following directives:

• @shareable
• @provides

Which directive you use depends on the following logic:

If you aren't sure whether your subgraph can always resolve a field, see Using @provides for an example of a subgraph that can't.

Ensure resolver consistency

If multiple subgraphs can resolve a field, make sure each subgraph's resolver for that field behaves identically. Otherwise, queries might return inconsistent results to clients depending on which subgraph resolves the field.

This is especially important to keep in mind when making changes to an existing resolver. If you don't make the resolver changes to each subgraph simultaneously, clients might observe inconsistent results.

Common inconsistent resolver behaviors to look out for include:

• Returning a different default value
• Throwing different errors in the same scenario

Using @shareable

The @shareable directive indicates that a particular field can be resolved by more than one subgraph:

type Product @key(fields: "id") {
  id: ID!
  name: String! @shareable
  price: Int
}

type Product @key(fields: "id") {
  id: ID!
  name: String! @shareable
  inStock: Boolean!
}

In this example, both the Products and Inventory subgraphs can resolve Product.name. This means that a query that includes Product.name might be resolvable by fetching from fewer total subgraphs.

Using @provides

The @provides directive indicates that a particular field can be resolved by a subgraph at a particular query path. Let's look at an example.

Here, our Products subgraph defines a Product.name field and marks it @shareable (this means other subgraphs are allowed to resolve it):

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type Product @key(fields: "id") {
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  id: ID!
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  name: String! @shareable
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  price: Int
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}

Meanwhile, our Inventory subgraph can also resolve a product's name, but only when that product is part of an InStockCount:

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type InStockCount {
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  product: Product! @provides(fields: "name")
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  quantity: Int!
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}
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type Product @key(fields: "id") {
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  id: ID!
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  name: String! @external
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  inStock: Boolean!
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}

Here we're using two directives in combination: @provides and @external.

• The @provides directive tells the router, "This subgraph can resolve the name of any Product object returned by InStockCount.product."
• The @external directive tells the router, "This subgraph can't resolve the name of a Product object, except wherever indicated by @provides."

Rules for using @provides

• If a subgraph @provides a field that it can't always resolve, the subgraph must mark that field as @external and must not mark it as @shareable.
  • Remember, a @shareable field can always be resolved by a particular subgraph, which removes the need for @provides.
• To include a field in a @provides directive, that field must be marked as @shareable or @external in every subgraph that defines it.

Violating any of these rules causes composition to fail.

Handling the N+1 problem

Most subgraph implementations use reference resolvers (sometimes known as entity resolvers) to handle the Query._entities field ergonomically. A reference resolver is passed a single key and returns the entity object that corresponds to that key.

Although this pattern is straightforward, it can diminish performance when a client operation requests fields from many entities. To illustrate this, let's revisit an earlier example:

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query GetReviewsWithProducts {
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  latestReviews { # Defined in Reviews
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    score
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    product {
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      id
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      price # ⚠️ NOT defined in Reviews!
7
    }
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  }
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}

As mentioned in The query plan, the router executes two queries on its subgraphs to resolve the above operation:

1. It queries the Reviews subgraph to fetch all fields except Product.price.
2. It queries the Products subgraph to fetch the price of each Product entity.

In the Products subgraph, the reference resolver for Product doesn't take a list of keys, but rather a single key. Therefore, the subgraph library calls the reference resolver once for each key:

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// Products subgraph
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const resolvers = {
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  Product: {
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    __resolveReference(productRepresentation) {
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      return fetchProductByID(productRepresentation.id);
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    }
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  },
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  // ...other resolvers...
9
}

A basic implementation of the fetchProductByID function might make a database call each time it's called. If we need to resolve Product.price for N different products, this results in N database calls. These calls are made in addition to the call made by the Reviews subgraph to fetch the initial list of reviews (and the id of each product). This is where the "N+1" problem gets its name. If not prevented, this problem can cause performance problems or even enable denial-of-service attacks.

This problem is not limited to reference resolvers! In fact, it can occur with any resolver that fetches from a data store. To handle this problem, we strongly recommend using the dataloader pattern. Nearly every GraphQL server library provides a dataloader implementation, and you should use it in every resolver. This is true even for resolvers that aren't for entities and that don't return a list. These resolvers can still cause N+1 issues via batched requests.