Data Pagination Implementation in Mobile Apps: Cursor vs Offset

Requesting a list of 10,000 products in one call is a classic mistake our engineers see in every third project. The API returns 40 MB of JSON, the app freezes while parsing, and the user sees a white screen for 8 seconds—then leaves. We implement pagination at the contract level between client and server: this reduces load by 3–5 times and provides smooth navigation without delays. Evaluate your API's pagination—contact us for an audit.

Pagination Implementation: Offset or Cursor?

Most developers reach for offset pagination by default: ?page=2&limit=20. It works while the data is static. But add a live feed where records are inserted at the top, and the user on page 3 misses entries or sees duplicates: an INSERT at the start shifts all offsets. On tables with 500,000+ rows, offset scans up to 100,000 rows to shift the cursor—queries take 200–400 ms.

Cursor-based pagination solves this: the server returns next_cursor, the client passes it in the next request. The cursor is an opaque token (usually base64 of id + timestamp) that fixes the position in the dataset. PostgreSQL backends implement it with WHERE id < :cursor ORDER BY id DESC LIMIT 20. No duplicates, no gaps. Query speeds remain stable—50–100 ms regardless of table size.

Why Cursor Pagination Is More Efficient Than Offset?

Cursor pagination is 3 times faster than offset on tables from 100,000 rows—benchmarks show response time dropping from 300 ms to 90 ms. It also guarantees data consistency under concurrent inserts. For news feeds, chat messages, and frequently updated catalogs, it's the mandatory choice.

How to Set Up Caching with RemoteMediator?

On Android with Paging 3, cursor pagination is implemented via RemoteMediator + PagingSource:

class ItemPagingSource(
    private val api: ItemsApi,
    private val query: String
) : PagingSource<String, Item>() {

    override suspend fun load(params: LoadParams<String>): LoadResult<String, Item> {
        return try {
            val response = api.getItems(
                cursor = params.key,
                limit = params.loadSize,
                query = query
            )
            LoadResult.Page(
                data = response.items,
                prevKey = null,
                nextKey = response.nextCursor
            )
        } catch (e: HttpException) {
            LoadResult.Error(e)
        }
    }

    override fun getRefreshKey(state: PagingState<String, Item>): String? {
        return state.anchorPosition?.let { anchor ->
            state.closestPageToPosition(anchor)?.nextKey
        }
    }
}

LazyColumn in Compose connects via collectAsLazyPagingItems()—a ready-made binding that handles Loading, Error, NotLoading states without extra code.

On iOS, the equivalent is a compositional layout with UICollectionViewDiffableDataSource and NSDiffableDataSourceSnapshot. Prefetch is implemented via UICollectionViewDataSourcePrefetching: the method prefetchItemsAt is called N cells before the edge, firing a network request ahead of time. Without prefetch, at scroll speeds above 500 px/s, empty cells appear—the user waits 1–2 seconds for loading.

How to Implement Cursor Pagination on Android: Step-by-Step

1. Define the API contract: the server returns next_cursor in the JSON response.
2. Create PagingSource<String, Item> that passes the cursor in loadParams.key.
3. Connect RemoteMediator for offline cache: RemoteMediator fetches data from the network and saves it to Room.
4. In PagingSource, implement getRefreshKey to restore position after a refresh.
5. Set up LazyColumn with collectAsLazyPagingItems() and an explicit LoadStateFooter with a retry button.

Caching Strategy Comparison: RemoteMediator vs. Simple Cache

Infinite Scroll and Pull-to-Refresh

Infinite scroll without an error state is a typical problem. The network drops on page 5, request hangs, user scrolls down and nothing happens. An explicit LoadStateFooter with a retry button is needed.

In Paging 3:

adapter.addLoadStateListener { loadState ->
    binding.retryButton.isVisible = loadState.source.append is LoadState.Error
    binding.progressBar.isVisible = loadState.source.append is LoadState.Loading
}

Pull-to-refresh resets pagination to the first page via adapter.refresh()—Paging 3 invalidates PagingSource and starts fresh. On SwiftUI, it's the refreshable modifier that calls invalidateQueries() in TCA or updates the @StateObject view model.

Cache and Offline

Paging 3 + Room is the standard offline-first combo. RemoteMediator writes data to the local DB, PagingSource reads from Room rather than the network. The user sees data even without internet; fresh data loads in the background.

Key point: cache invalidation strategy. If the server updates a record, the local copy becomes stale. Solution: ETag or Last-Modified in response headers—the client sends If-None-Match, server returns 304 with no body if unchanged. Room updates only changed records via @Insert(onConflict = OnConflictStrategy.REPLACE). This reduces downloaded data by 60–80%.

What's Included in the Work

• Audit of the current API and pagination contract specification.
• Implementation of cursor/offset pagination on the client (Android/iOS/cross-platform).
• Integration of RemoteMediator and Room for offline mode.
• Configuration of prefetch and retry logic.
• Testing on real devices with poor network simulation.
• Code documentation and backend improvement recommendations.
• Post-implementation support: 2 weeks of consultation.

Timelines

Simple offset pagination without cache—1–2 days. Cursor pagination with RemoteMediator, offline cache, and retry logic—3–5 days. The cost is calculated individually after analyzing requirements and the existing API. We have 5+ years of experience in mobile development and over 30 projects with pagination—trust our specialists. Order a pagination audit of your app—we'll find the optimal solution.

Typical Pagination Implementation Mistakes

• Missing prefetch on iOS—empty cells on fast scroll.
• Using offset in feeds with frequent inserts—duplicates and gaps.
• Ignoring loading and error states: user sees no indicator.
• No caching—every scroll triggers a network request.
• Wrong page size: 5 items causes frequent loads, 100 causes long waits.

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.