Typed GraphQL API with Apollo Client, Subscriptions, and Cache

When developing a mobile application, you often face a dilemma: REST API either returns too much data (overfetching) or requires multiple requests (underfetching). GraphQL solves this but adds complexity. We develop a typed GraphQL API that perfectly fits the mobile client data model. Our experience — over 5 years and 50+ implemented projects — guarantees stable operation even under high load. Comparison with REST: on complex screens, GraphQL reduces traffic by 2–3 times, and with Persisted Queries — by an additional 10–20%. This provides real savings — up to 5,000 rubles per month for an app with 10,000 active users.

"GraphQL is a query language for APIs that allows clients to request only the data they need." — Wikipedia

Scenarios for Justified Use of GraphQL

GraphQL adds complexity: a server implementation (resolvers, schema, DataLoader), a client library, and team training. Justified scenarios:

• Different clients (iOS, Android, Web) need to be served from one API, and their data requirements differ significantly.
• Rapidly changing UI — you can add fields to a query without changing the server.
• Nested data with variable depth (social graph, catalog with categories).

For CRUD with predictable data structure, REST is simpler. GraphQL is not a silver bullet. We help choose the right tool and design the schema to avoid typical mistakes.

Type Safety with Apollo Client

Apollo Client generates type-safe query classes from .graphql files. This reduces the number of bugs by 30–40% compared to manual JSON handling.

Android (Apollo Kotlin)

# app/src/main/graphql/GetProduct.graphql
query GetProduct($id: ID!) {
  product(id: $id) {
    id
    name
    price
    thumbnail {
      url
      width
      height
    }
  }
}

// auto-generated type GetProductQuery.Data
val response = apolloClient.query(GetProductQuery(id = productId)).execute()
val product = response.data?.product

apolloClient is configured once with HttpEngine, authorization headers, and cache:

val apolloClient = ApolloClient.Builder()
    .serverUrl("https://api.example.com/graphql")
    .addHttpHeader("Authorization", "Bearer $token")
    .normalizedCache(MemoryCacheFactory(maxSizeBytes = 10 * 1024 * 1024))
    .build()

normalizedCache — normalized cache by id field. Querying a product from the feed and from the detail page returns the same object in memory — an update in one place automatically reflects everywhere.

iOS (Apollo iOS)

let client = ApolloClient(
    networkTransport: RequestChainNetworkTransport(
        interceptorProvider: DefaultInterceptorProvider(store: store),
        endpointURL: URL(string: "https://api.example.com/graphql")!
    ),
    store: store
)

client.fetch(query: GetProductQuery(id: productId)) { result in
    switch result {
    case .success(let response):
        let product = response.data?.product
    case .failure(let error):
        print(error)
    }
}

Subscriptions for Real-Time

GraphQL subscriptions — WebSocket channel for real-time updates: chats, live prices, order statuses. Example schema:

subscription OnOrderStatusChanged($orderId: ID!) {
  orderStatusChanged(orderId: $orderId) {
    status
    updatedAt
  }
}

On Android, subscriptions are connected via WebSocketNetworkTransport as Flow/Coroutine.

How Apollo Client Accelerates Development?

Code generation from .graphql files eliminates manual DTO writing and mapping. Changing the schema immediately updates all clients — mismatches are caught at compile time. This speeds up iterations: changing a field on the server doesn't require synchronization with the mobile team. In a project with 20+ screens, time savings on coordination reach 30%, resulting in budget savings of up to 200,000 rubles.

Why DataLoader is Mandatory for GraphQL?

Without DataLoader, a query for 100 products would issue 100 separate SQL queries for categories. DataLoader batches them into a single SELECT ... WHERE id IN (...). This is a mandatory pattern when designing the server side. We implement it from the start, avoiding performance degradation under load.

Optimization: Persisted Queries

Automatic Persisted Queries (APQ): the client sends the SHA256 hash of the query. The server returns data if it knows the hash; otherwise, it requests the full text. Apollo Client supports APQ out of the box. This saves traffic and speeds up requests.

Error Handling

GraphQL returns HTTP 200 even on errors. Errors are in the response body: {"data": { "product": null }, "errors": [{ "message": "Product not found" }]}. The client must check the errors array regardless of the HTTP status. Apollo Client provides the list of errors in response.errors.

Comparison: GraphQL vs REST for Mobile

GraphQL API Development Stages

How to Set Up Apollo Client on Android: Step-by-Step

1. Install the Apollo Kotlin library via Gradle.
2. Create an ApolloClient instance with the server URL and cache.
3. Define .graphql queries in the graphql folder.
4. Execute the query via apolloClient.query() and handle the result.

Order GraphQL API development — get an estimate in 1 business day.

let store = ApolloStore()
let client = ApolloClient(
  networkTransport: RequestChainNetworkTransport(
    interceptorProvider: DefaultInterceptorProvider(store: store),
    endpointURL: URL(string: "https://api.example.com/graphql")!
  ),
  store: store
)

Schema Design for Mobile Screens

We analyze each app screen: what data is needed, with what frequency, what relations between entities exist. For example, for a product card in an e-commerce app — name, price, image, characteristics. The GraphQL query will be exactly that, without extra fields. This reduces load on server and client, and speeds up rendering.

What is Included in GraphQL API Development for Mobile Apps

• Schema design based on client requirements (mobile screens, query frequency).
• Resolver implementation with DataLoader and batching.
• Apollo Client setup on both platforms: cache, subscriptions, authentication.
• Persisted Queries integration to reduce traffic.
• Schema documentation in GraphQL Playground / GraphiQL.

Timeline: 2–4 weeks depending on schema size. We guarantee stable API operation under load. Contact us — we'll evaluate your project in one business day. Get a consultation on implementing GraphQL in your mobile app.

How to Start Integrating API into a Mobile App?

The request goes out, the response doesn't come, timeout — 30 seconds. The user stares at the spinner. No network — mobile card in the subway. Or the network is there, but the server returns 200 with an HTML error page instead of JSON — and the app crashes on JSONDecoder.decode(). We see such cases on every second project. So integrating API into a mobile app is not just calling an endpoint, but designing a reliable network layer: error handling, caching, offline mode, certificate pinning. Order an audit of your current network layer — we will evaluate the project in 1 day. Our team guarantees a thorough analysis and provides a detailed roadmap.

Standard libraries like URLSession and OkHttp provide basic HTTP clients, but for production you need retries with exponential backoff, status code validation, typed deserialization, and network state monitoring. Without this, the app loses data and users. We have been doing mobile development for 5 years and implemented more than 30 projects with API integration on iOS, Android, and Flutter — from startups to enterprise solutions.

How to Choose a Protocol for API Integration?

REST remains the standard for most projects. But when a profile screen needs 5 fields out of 40, GraphQL eliminates over-fetching and reduces traffic by 30–60%. gRPC is justified for thousands of requests per minute (trading, IoT) — binary serialization is 3–5 times faster than JSON. WebSocket is the only choice for real-time without polling (messages, notifications).

Practical example: For a fintech app, we replaced REST (40 fields) with GraphQL — response size dropped from 12 KB to 2.5 KB, screen render time decreased by 70%. Traffic savings were significant. Our certified iOS and Android developers have deep experience with all these protocols — you can rely on proven solutions.

How to Ensure Reliable Connection and Offline-First?

Users lose network in the subway, elevator, tunnel. A mobile app must work without internet — at least in read-only mode. We implement the offline-first pattern:

1. On screen open, first show data from the local cache (Core Data / Room).
2. Simultaneously perform a network request, update UI after response.
3. If network is unavailable — show cached data and a 'no connection' label.
4. When network is restored, automatically synchronize changes.

For HTTP response caching we use URLCache (iOS) and OkHttp Cache (Android) with Cache-Control support. For structured data — SwiftData / Room. NWPathMonitor / ConnectivityManager.NetworkCallback monitor network state and trigger updates.

REST and Client Library Selection

Alamofire (iOS) — de facto standard for Swift projects. On top of URLSession it adds request chaining, response validation, automatic retry, certificate pinning via ServerTrustManager. AF.request() with .validate() returns an error for any status code outside 200–299. Without .validate(), Alamofire considers 404 and 500 as successful responses. With Swift Concurrency — async version via serializingDecodable.

Retrofit (Android) — annotation-based HTTP client on top of OkHttp. An interface with annotations compiles into implementation. @GET, @POST, @Path, @Query, @Body — declarative API description. OkHttp under the hood: connection pooling, transparent gzip, HTTP/2 multiplex. HttpLoggingInterceptor — logging in debug builds. Authenticator — automatic token refresh on 401.

Ktor (KMM/Flutter) — multiplatform HTTP client. On iOS it works via Darwin engine (URLSession), on Android — via OkHttp. Single code for both platforms with KMM architecture.

GraphQL: When REST Falls Short

REST returns a fixed structure. A profile screen needs name, avatar, email — the server sends 40 fields. Over-fetching. GraphQL solves this: the client requests exactly the needed fields. This is critical for mobile where traffic and parsing time are real constraints. Apollo iOS and Apollo Kotlin generate typed classes from schema: schema.graphql + query files → strict types at compile time. Subscriptions via WebSocket — real-time without polling. Limitation: GraphQL is harder to cache at the HTTP level. Apollo uses a normalized in-memory cache InMemoryNormalizedCache — requests with overlapping data update the cache without duplication.

WebSocket: Real-Time Without Extra Traffic

Polling (setInterval every 5 seconds) — battery and traffic waste. WebSocket is a persistent bidirectional connection. iOS: URLSessionWebSocketTask (native, iOS 13+). Android: OkHttp WebSocket. Mandatory reconnect handling: on onFailure — exponential backoff (1s → 2s → 4s → 8s → max 60s). Socket.IO is an overlay with automatic reconnect, but for new projects native WebSocket is preferable (fewer dependencies).

gRPC: For High-Load Services

gRPC with protobuf — binary serialization: smaller size, faster parsing. grpc-swift for iOS, grpc-kotlin for Android. The protobuf schema compiles to typed classes. Streaming (server-side, client-side, bidirectional) is a native feature. Application threshold: high request frequency (trading, IoT) or critical latency. For regular CRUD, REST is simpler to debug and monitor.

Certificate Pinning and Security

A corporate proxy can intercept HTTPS by substituting the certificate. Certificate pinning prevents this: the app accepts only a specific certificate or public key. Alamofire: ServerTrustManager with PinnedCertificatesTrustEvaluator. OkHttp: CertificatePinner with SHA-256 hash. Apple's App Transport Security documentation recommends pinning certificates for sensitive data. Operational complexity: on certificate rotation, older app versions stop working. Solution — pinning to the CA public key or support multiple pins with a grace period.

What Is Included in the Work

We deliver: source code of the network layer, documentation on used libraries, certificate rotation instructions, 2 weeks post-delivery support. Our experience guarantees that the solution will be stable and maintainable.

Timeline and Cost

Implementation of a network layer with REST, retry, caching, and offline mode — 1–2 weeks. Adding GraphQL or WebSocket — another 1–2 weeks. gRPC — 2–3 weeks, including code generation. The cost is calculated individually after analyzing the API and offline behavior requirements. We will evaluate the project in 1 day — contact us for a consultation. Get a reliable API integration with guaranteed quality.