GraphQL Persisted Queries: Speed and Security for Your API

GraphQL Persisted Queries: Speed Up Your API by Orders of Magnitude

We frequently encounter a situation: a client application sends enormous GraphQL queries (e.g., query { user { posts { comments { author { ... } } } } }) weighing 10–15 KB. Over mobile networks, this results in a TTFB of 3–5 seconds. Persisted Queries solve this radically: instead of the full query body, the client sends only a SHA256 hash (44 bytes). The server (or CDN) returns a cached response. This reduces traffic by 60% and enables HTTP-level caching. According to Apollo, APQ cut request size by 20x for repeated calls.

Our experience: over the last few years we have implemented this technique in 30+ projects. In one case (an e-commerce site with a million products), APQ reduced request size from 8 KB to 400 bytes, and LCP dropped from 3.2s to 1.1s. We guarantee a clean implementation without security trade-offs.

How Automatic Persisted Queries (APQ) Work

APQ is a two-phase protocol. The client first sends a POST request with only the hash. If the server cache does not contain the query, it returns 404. Then the client retries with the full query body. The server saves the hash→query pair and returns data. All subsequent calls are just the hash, and you can switch to GET requests that are cached on the CDN.

Client          Server          CDN/Cache
  │                │                │
  │ POST {hash}    │                │
  │───────────────>│                │
  │ 404 Not Found  │                │
  │<───────────────│                │
  │                │                │
  │ POST {hash + query body}        │
  │───────────────>│                │
  │ {data}  [store hash→query]      │
  │<───────────────│                │
  │                │                │
  │ GET ?hash=...  │                │
  │──────────────────────────────>  │
  │               {data} from cache │
  │<──────────────────────────────  │

Comparison: APQ vs Registered PQ vs No PQ

Optimizing GraphQL queries with Persisted Queries boosts API performance and improves Core Web Vitals.

Order Persisted Queries implementation — we'll show metrics before and after on a test bench.

Why Use Registered Persisted Queries?

Registered Persisted Queries (RPQ) are APQ + a whitelist. In production, only queries whose hashes appear in a manifest are allowed. This eliminates attacks via __schema or arbitrary mutations. The manifest is generated from client code at build time.

# Generate manifest from client operations
npx generate-persisted-query-manifest \
  --documents "src/**/*.graphql" \
  --output persisted-query-manifest.json

// persisted-query-manifest.json (fragment)
{
  "format": "apollo-persisted-query-manifest",
  "version": 1,
  "operations": [
    {
      "id": "dc67510fb4289672bea757e862d6b00e...",
      "name": "GetPosts",
      "type": "query",
      "body": "query GetPosts($limit: Int) { posts(first: $limit) { ... } }"
    }
  ]
}

On the server, you only need middleware that substitutes the query body from the manifest and rejects unknown hashes.

Typical Problems and Their Solutions

How We Implement Persisted Queries: Step by Step

1. Audit current GraphQL API: analyze request sizes, duplicate frequency, existing caching.
2. Design: choose approach (APQ or RPQ), define TTL, configure Redis for distributed cache.
3. Client implementation: integrate createPersistedQueryLink in Apollo Client or equivalent for Relay/Urql.
4. Server implementation: enable APQ (built into Apollo Server), configure cache, add middleware for RPQ if needed.
5. Testing: verify caching correctness, monitor hit rates.
6. Deploy and CDN: configure Nginx for GET caching, enable Cloudflare or Vercel Edge.

What Is Included

• Documentation of schema and manifest.
• Setup of cache monitoring (Prometheus/Grafana).
• Team training on maintenance and manifest updates.
• Post-release support for 2 weeks.
• Handover of repository and documentation.

We use proven configurations. Example Nginx setup:

proxy_cache_path /var/cache/nginx/graphql
  levels=1:2 keys_zone=graphql:10m max_size=100m
  inactive=1h use_temp_path=off;

location /graphql {
  if ($request_method = GET) {
    proxy_cache graphql;
    proxy_cache_key "$uri$is_args$args";
    proxy_cache_valid 200 5m;
    proxy_cache_use_stale error timeout updating;
    add_header X-Cache-Status $upstream_cache_status;
  }
  proxy_pass http://api_backend;
}

Timeline and Cost

Basic APQ setup with Redis and CDN — 1 to 2 business days. Full cycle with Registered Persisted Queries, manifest, and monitoring — 3 to 5 days. Cost is calculated individually based on schema complexity and number of clients.

Accelerate your GraphQL API — contact us for a project evaluation. We'll show metrics before and after on a test bench.

API Development with REST, GraphQL, WebSocket, and tRPC

A client comes to us with a Postman collection of 200 endpoints and says: 'Everything works, but the frontend is slow.' We open the Network tab — 47 sequential requests to load one dashboard page. Each one waits for the previous. This is not a server speed issue — it's an API architecture problem. With 10 years on the market, we've redesigned dozens of such integrations, and we guarantee: the right protocol and contract solve the problem at its root.

When REST stops being enough

REST works well for simple CRUD operations. But as soon as a mobile app appears alongside the web interface, over-fetching begins: the mobile app requests /api/users/123 and gets a 4KB object, but only needs name and avatar. Multiply that by a list of 50 users — 200KB traffic instead of 8KB.

GraphQL solves this with selection sets. The client describes exactly the fields it needs, and the server returns only those. On a project with React Native + Next.js, we migrated from REST to Apollo Server: payload size on the main screen dropped from 340KB to 28KB — a 92% traffic savings. Our certified engineers confirm: the typical pain when adopting GraphQL is N+1 query. A resolver for the author field on a post calls SELECT * FROM users WHERE id = ? for each post in the list. On a page with 20 posts — 21 database queries. Solved with DataLoader — it batches queries and turns them into one SELECT * FROM users WHERE id IN (...).

What is tRPC and how is it better than REST/GraphQL?

If the entire stack is TypeScript (Next.js + Node/Bun), tRPC removes a whole layer of problems. You define a procedure on the server — the client gets full type-safety automatically, without code generation and without Swagger. Renamed a field in the Zod schema — TypeScript highlights all places on the frontend where it's used. tRPC reduces code by 2 times compared to REST + Swagger + openapi-typescript: no need to maintain a separate specification and generate types — everything is inferred from runtime validators. However, tRPC is not suitable if the API is consumed by third-party clients or mobile apps in other languages — in such cases we use GraphQL or REST with OpenAPI specification.

WebSocket and real-time: when SSE, when WS?

HTTP polling every 5 seconds is an illusion of real-time with up to 5 seconds delay and useless server load. For chats, live notifications, collaborative editing — WebSocket or Server-Sent Events. SSE is a one-way stream from server to client, works over ordinary HTTP, automatically reconnects. Suitable for notifications, data streaming, progress bars. WebSocket is bidirectional, needed for chats and collaborative features. Experience shows: 80% of 'real-time' tasks are solved with SSE, not WebSocket — fewer infrastructure complexities.

A typical mistake: opening a WebSocket connection for each page component. On one project, the dashboard opened 12 parallel WS connections. The correct approach is one connection manager at the application level, subscriptions through it. In our work results, we always transfer the connection scheme and a ready solution.

Swagger / OpenAPI as a contract

Documentation written after the fact becomes outdated the day after release. We write the OpenAPI 3.1 specification before development starts; it becomes the contract between frontend and backend. The frontend generates types via openapi-typescript, the backend validates incoming data using generated schemas. Contract deviation from implementation is caught on CI, not during review. For Laravel — l5-swagger or dedoc/scramble. For Node.js — @fastify/swagger or Zod + zod-to-openapi.

How to properly authenticate an API?

JWT with long-lived access tokens without rotation is a source of problems when compromised. The correct scheme: access token for 15 minutes, refresh token for 30 days with rotation on each use. Refresh token stored in an httpOnly cookie, access token in memory (not in localStorage). For inter-service communication — API Keys with scope limitations or mTLS. OAuth 2.0 with PKCE for public clients (SPA, mobile).

How to handle versioning and backward compatibility?

Breaking changes in an API without versioning break clients. Three approaches we use in projects:

We guarantee backward compatibility through automated checks (oasdiff) on CI.

How we develop APIs: step-by-step plan

1. Analysis — audit of current integrations, data schema compilation, protocol selection (REST/GraphQL/tRPC/WebSocket).
2. Contract design — OpenAPI or SDL (GraphQL) before the first line of code.
3. Development — implementation per contract, unit tests for each endpoint.
4. Load testing — k6: 500 virtual users, 10 minutes, p95 latency ≤ 200ms.
5. Deployment — CI/CD with backward compatibility check, automatic documentation publication.
6. Team training — handover of Postman collection or Playground, connection instructions.

What is included (deliverables)

• OpenAPI 3.1 specification (or SDL for GraphQL).
• Generated client types for TypeScript / Dart / Kotlin.
• Set of automated tests covering all endpoints (unit + integration).
• Load tests (k6) and report (p50/p95/p99 latency, RPS).
• Documentation in Swagger UI / Redoc / GraphiQL.
• Team training (2–4 hour workshop).
• Support for 30 days after delivery (per contract).

Our experience

• 10+ years in the API development market.
• 200+ completed projects (REST, GraphQL, WebSocket, tRPC).
• 50+ certified engineers (AWS, Kubernetes, API Design).
• Traffic savings averaging 85% when migrating from REST to GraphQL for mobile apps.
• 100% backward compatibility — not a single broken client in the last 3 years.

Timeline

API development for a typical SaaS project with 30–50 endpoints: from 3 to 8 weeks depending on business logic complexity and number of external integrations. Migration of an existing REST API to GraphQL: from 2 to 6 weeks. Adding a WebSocket layer to an existing backend: from 1 to 3 weeks. Cost is calculated individually after an audit. Get a consultation — contact us to discuss your project.