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Federated GraphQL splits one logical GraphQL API across independently owned services called subgraphs. A composition step combines their schemas into a supergraph, and a router exposes that composed schema to clients. The client sends one operation to the router; the router validates it, builds a query plan, fetches fields from the appropriate subgraphs, resolves cross-service entities, and merges the results into one response.
This architecture lets teams own and release domain services independently without forcing clients to learn the underlying service boundaries. It also introduces composition, key-design, latency, failure-handling, and observability work that a single GraphQL server does not have.
The four parts of a federated GraphQL system
Subgraphs
A subgraph is a GraphQL service responsible for a bounded part of the domain: products, accounts, inventory, reviews, or another area. Its schema declares the types and fields it owns and the federation metadata needed to combine those fields with other services. Teams can develop, deploy, and scale subgraphs independently, provided their contributions still compose into a valid overall graph.
Composition
Composition reads the subgraph schemas and produces a single supergraph schema. It checks whether type definitions, field ownership, entity keys, and federation directives are compatible. Treat composition as a build step: run it in continuous integration and reject a change that would make the graph invalid before publishing a new schema.
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Supergraph schema
The supergraph is the composed result, including metadata about which subgraph can resolve each field. It is an execution contract for the router, not normally the endpoint that application developers call directly.
Router
The router is the client-facing GraphQL endpoint. It uses the supergraph to validate operations, construct query plans, call subgraphs, and merge their payloads. For performance and security, clients should query only the router, and only the router should call constituent APIs.
How one federated request is resolved
- The client sends a normal operation. The request looks like any other GraphQL query and is sent to the router URL.
- The router validates the operation. Validation uses the API surface exposed by the supergraph, so a client cannot select a field that the composed schema does not publish.
- The router determines ownership. It maps each selected field to the subgraph that owns it and creates a hierarchical query plan.
- Root fields are fetched. Independent root work can be sent to different subgraphs in parallel. The router requests key fields when a later step will need them.
- Entities cross the service boundary. When a response contains an object extended by another subgraph, the router builds an internal representation containing
__typenameand the fields required by an applicable@key. - The downstream subgraph resolves entities. The router sends those representations to
Query._entities. The entity resolver returns objects in the same order as the representations. - The router merges the result. Data from every fetch is assembled into the shape requested by the client, while errors and partial results follow the router’s configured behavior.
For example, a query can obtain a product’s name from a Products subgraph and its reviews from a Reviews subgraph without the client issuing two requests. The router first obtains the product’s upc, then sends a representation such as {"__typename":"Product","upc":"..."} to the Reviews subgraph.
Entities, keys, and the _entities field
An entity is an object type whose fields may be contributed by more than one subgraph. A subgraph marks the identifying field set with @key(fields: "..."). In a product example, the Products subgraph can define Product with upc and name, while Reviews contributes reviews for the same entity.
The representation sent between subgraphs must include __typename and every field required by at least one applicable key. The federation specification defines the resolver entry point as Query._entities(representations: [_Any!]!): [_Entity]!. A downstream resolver uses each representation to locate the entity and returns results in input order, allowing the router to place them beside the corresponding objects.
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Designing a useful key
- Choose a stable identifier that every participating service can use reliably.
- Prefer a key that is available in the first fetch, so the router does not need an extra lookup just to continue planning.
- Keep key fields small and immutable where possible; changing an identifier creates coordination and cache problems.
- Use multiple keys only when distinct access paths are genuinely required. Each additional key increases composition and resolver complexity.
Federation directives and ownership
Federation is declarative: subgraphs describe relationships in the schema rather than writing a central integration layer for every field.
@keyidentifies the fields that locate an entity in another subgraph.@externalmarks a field supplied by another subgraph so it can be referenced locally without claiming ownership.@requiresdeclares fields that a resolver needs in addition to the field being requested.@providesdocuments fields a resolver can return along a particular path.@shareable, where supported and appropriate, allows a field to be resolved by more than one subgraph under federation’s composition rules.
Use directives to express real ownership and dependencies, not to make every type appear shared. Ambiguous ownership is difficult to review and can produce composition failures or surprising query plans.
What a query plan contains
A query plan is a hierarchical execution structure generated from the supergraph. It can include:
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- Parallel branches for independent fields that do not depend on one another.
- Dependent fetches that wait until key fields or required values are available.
- Entity fetches that call a subgraph’s
Query._entitiesfield with representations from an earlier step.
Inspect plans for representative operations, not just the happy-path query. A seemingly small selection can cause a fan-out across many entities or a chain of dependent network calls. The number of hops, payload size, and downstream service behavior determine real latency; there is no universal federation latency or cost figure that applies to every graph.
Why teams choose federation
- Independent ownership: domain teams can evolve their subgraph while keeping a single client-facing contract.
- Incremental decomposition: a monolithic GraphQL schema can be split by domain rather than replaced in one migration.
- Client simplicity: clients query one schema even when data spans several services.
- Domain-oriented scaling: a busy subgraph can be scaled or optimized without deploying unrelated domains.
These benefits depend on disciplined schema governance. Federation does not remove the need to agree on naming, identifiers, authorization, lifecycle policy, and backward compatibility.
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Costs and trade-offs
| Decision area | Federation implication |
|---|---|
| Team ownership | Strong service autonomy, but ownership boundaries must be explicit. |
| Release workflow | Subgraphs can release independently, while composition must remain a gated compatibility check. |
| Latency | Parallel fetches can help, but dependent hops and fan-out increase tail latency. |
| Failure behavior | A downstream timeout or error can produce partial data or fail a larger operation, depending on router policy. |
| Entity design | Stable keys simplify joins; weak keys create resolver lookups and coordination overhead. |
| Operations | Router and subgraph logs, traces, metrics, and query plans must be correlated. |
| Security | The router is the public boundary and must enforce authentication, authorization, limits, and safe downstream access. |
Federation versus schema stitching
Both approaches combine multiple GraphQL sources behind one API, but they place integration responsibility in different layers.
| Question | Federation | Schema stitching |
|---|---|---|
| Where is ownership described? | Inside subgraph schemas with federation directives and composition rules. | In a stitching layer that merges or transforms independently obtained schemas. |
| How are cross-service entities resolved? | The router follows keys and invokes Query._entities for entity fetches. |
The gateway’s stitching configuration and delegation logic perform the joins. |
| How are changes governed? | Composition validates the complete supergraph before publication. | Validity depends on the stitching configuration and the source schemas it combines. |
| When might it fit? | Organizations wanting domain-owned services and a standardized federated execution model. | Situations requiring stitching-specific transformations or alternatives such as subscription arrangements; compare the exact features your system needs. |
Neither architecture is universally better. Compare ownership, deployment independence, required GraphQL features, operational maturity, and the number of cross-service joins your workload creates.
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Implementation checklist
- Draw domain boundaries and assign one owning subgraph to each field.
- Choose stable, highly available entity keys and document their lifecycle.
- Mark only genuine entities and shared fields; avoid making unrelated types depend on one another.
- Define authentication and authorization at the router and the downstream services.
- Run composition in CI for every schema change and publish only a valid supergraph.
- Generate and inspect query plans for high-volume operations, especially list queries.
- Instrument router and subgraph traces with a shared request or operation identifier.
- Set explicit downstream timeouts, retry rules, concurrency limits, and partial-failure behavior.
- Document the federation version and directives supported by every subgraph.
- Load-test realistic fan-out and entity-fetch patterns rather than measuring a single isolated resolver.
Performance, reliability, and security practices
Control fan-out and N+1 behavior
List fields are the common danger zone. A list returned by one subgraph may cause entity fetches for every item in another subgraph. Design batch-capable entity resolvers, inspect the resulting plan, and avoid selecting cross-service fields inside unbounded lists without pagination and limits.
Make failures predictable
Use bounded timeouts for every downstream call. Retries should be limited to operations that are safe to repeat and should not multiply load during an outage. Decide which fields can be omitted when a subgraph fails and which errors must fail the operation.
Trace the complete path
Router timing alone cannot explain a slow response. Correlate the router span, each subgraph fetch, entity resolution, retries, and serialization. Keep query-plan inspection available for production-shaped operations while protecting sensitive arguments and headers.
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Protect the router boundary
Apply authentication, authorization, query-depth or complexity controls, rate limits, and request-size limits at the public endpoint. Subgraphs should still authenticate trusted router traffic and enforce domain-level authorization rather than assuming every internal caller is safe.
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Composition rejects a schema
Cause: conflicting field ownership, incompatible types, invalid directive use, or a missing federation requirement. Fix: read the composition error, identify the owning subgraph, correct the directive or type contract, and rerun composition in CI before deployment.
An entity field is always null
Cause: the representation lacks a required key field, the key value is not resolvable, or the entity resolver returns the wrong order. Fix: verify that __typename and every key field are selected, test Query._entities directly, and preserve representation order.
The query is unexpectedly slow
Cause: dependent hops, list fan-out, oversized representations, or a slow downstream resolver. Fix: inspect the query plan, parallelize independent work, batch entity lookups, paginate lists, and measure each subgraph span.
A subgraph cannot be reached
Cause: service discovery, network policy, TLS, authentication, or a router timeout. Fix: test router-to-subgraph connectivity, verify credentials and certificates, check timeout settings, and confirm that health and readiness signals reflect actual GraphQL availability.
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A practical way to capture federated GraphQL documentation
If you need a visual record of a supergraph explanation or schema page, the do-it-yourself route is to open the page in a browser, dismiss consent and chat overlays, wait for lazy content, then use the browser’s screenshot or print-to-PDF command. Repeat that setup for every URL and verify that bot checks, blank pages, and failed loads did not produce misleading artifacts.
Or skip the browser setup:
ScreenshotNeo is a website screenshot API and MCP server. It accepts consent banners before capture and removes more than 60 known consent platforms, newsletter popups, and chat widgets; each step can be disabled. Only clean shots are billed: bot checks or CAPTCHAs, blank pages, timeouts, failed loads, and cache hits cost nothing, and response headers report the page verdict and billing status. Its MCP server provides take_screenshot, get_page_info, and capture_pdf for Claude, Cursor, and other MCP clients.
Using the API requires one GET request. See the ScreenshotNeo API documentation for all options.
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open("shot.webp", "wb").write(r.content)Node.js
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Learning path
Start with GraphQL schema design and production operations, then study federation-specific subgraphs, gateways, entity extension, managed composition, and deployment. Learning GraphQL (O’Reilly, ISBN 9781492030706) is a general prerequisite covering types, schema design, Apollo tooling, and production preparation. A dedicated Apollo Federation guide is useful once you understand GraphQL execution and schema governance.
Bottom line
Federated GraphQL gives independently owned services one client-facing graph. Subgraphs declare ownership and entity relationships, composition builds the supergraph, and the router turns each client operation into a query plan of root fetches, parallel branches, and key-based entity fetches. It is a strong fit when service autonomy and a unified API matter, but success depends on stable keys, composition gates, careful plans, bounded fan-out, explicit failure policy, and end-to-end tracing.
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