Federated API
A federated API joins many backend services into one single schema for client apps. You set it up by linking subgraphs to a central router that plans queries. Engineering teams use this design so frontend apps can fetch data in one call. Still, teams often give the router broad permissions to read and write backend data. When one subgraph has loose rules, any user can pull sensitive records across services. How a federated API routes requests and checks roles In a standard setup, each backend
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A federated API joins many backend services into one single schema for client apps. You set it up by linking subgraphs to a central router that plans queries. Engineering teams use this design so frontend apps can fetch data in one call. Still, teams often give the router broad permissions to read and write backend data. When one subgraph has loose rules, any user can pull sensitive records across services.
How a federated API routes requests and checks roles
In a standard setup, each backend team owns one small service and its schema. These small schemas register with a central router that builds one combined graph. When a client app sends a query, the router reads the whole request first. It breaks that single query into smaller steps for each backend service to run.
Identity checks usually happen when the client first reaches the main router edge. The router checks the user token and passes identity headers to the backend services. Some teams enforce role checks at the router, while others check inside each service. Splitting these checks across many teams makes it hard to track who sees what. One team might check user roles on every field, while another team forgets.
What native federation solves and what it leaves open
This design solves a big problem for frontend developers who build fast apps. Client apps no longer need to call five different endpoints to load one page. Backend teams can also ship code on their own schedule without blocking other teams. Native identity checks confirm who logs in before the query plan starts to run.
Yet this setup leaves a wide gap while requests run in flight. Role checks at login only decide if a user can reach the service. They do not inspect the actual data fields or mutations inside an active call. A valid user might run a deep query that joins billing data with user profiles. If the backend database returns emails, phone numbers, or card numbers, they flow back. Native routing does not rewrite matched fields in results before the client reads them.
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How to tighten control over distributed queries
To reduce risk, teams must look past simple login checks at the edge. Start by mapping which subgraphs hold sensitive records like national IDs or card numbers. Limit the service roles so each backend only reads its own database tables. Do not share one admin database role across all the services in your graph.
Next, set clear rules for how user context travels between your internal services. Every backend call should carry the original user identity, not just a system key. Teams also need a clear log of every query step and database call. When you log queries at the field level, you can spot bad rules fast. Data masking rules can also rewrite sensitive fields in results during live queries.
Govern statements and responses across databases and APIs
Good config and scoped roles are needed setup. They do not check single queries in flight or mask sensitive fields in returned rows. Across Postgres, MySQL, SQL Server, MongoDB, ClickHouse, Kubernetes APIs, SSH, and internal HTTP endpoints, hoop.dev sits inline in the protocol beside each supported service as an open-source proxy, a sidecar for your database or API. For every query or command an AI agent or engineer runs against production, hoop.dev decides to pass, mask, block, or hold it for human approval.
First, hoop.dev guardrails block banned queries before they run or hold risky ones for review, and hoop.dev data masking rewrites matched sensitive values in responses in memory. Furthermore, hoop.dev holds SOC 2 Type II and is open source on GitHub.
Frequently asked questions
How does a federated API differ from a monolith graph?
A monolith keeps all schema types and database calls inside one single code base. A federated setup splits the schema across separate services that join into one graph.
Where should teams check user permissions in a federated graph?
Check the user identity token at the edge router first to block bad traffic. Then enforce fine role and data rules close to the backend data sources.
Can a federated query expose sensitive data by mistake?
Yes, a user can join fields across services to pull data they should not mix. Field rules and data masking help rewrite sensitive fields like emails or card numbers.