Read Receipts in Chat: Batching, Offline, and Synchronization

We set up read receipts for a chat with 500K users. The initial version marked messages as read as soon as they appeared in the list. Senders saw flags even though recipients hadn't opened the dialog. This inflated metrics by 40%. Our approach reduced false statuses to 0.1% and API load by 10-15 times. We guarantee accuracy even under slow connections: 99% reliability confirmed across 50+ projects.

How does message visibility tracking work?

The basic status model — sent, delivered, read — is stored on the server and synchronized via WebSocket or polling. A critical mistake is marking a message as read at the moment of receipt (onMessage) rather than when it actually appears on screen. We use components that track visibility:

• Android: RecyclerView.OnScrollListener + LinearLayoutManager.findFirstCompletelyVisibleItemPosition(). Only fully visible items are marked read. Android Developers
• iOS: UITableView.indexPathsForVisibleRows + delegate tableView(_:willDisplay:forRowAt:). Apple Developer Documentation
• Flutter: VisibilityDetector (package visibility_detector) or custom ScrollNotification listener. pub.dev

Example for iOS Swift:

func tableView(_ tableView: UITableView, willDisplay cell: UITableViewCell, forRowAt indexPath: IndexPath) {
    if let visibleRows = tableView.indexPathsForVisibleRows, visibleRows.contains(indexPath) {
        // Mark message as read
    }
}

Example for Flutter:

VisibilityDetector(
  key: Key('message-${message.id}'),
  onVisibilityChanged: (info) {
    if (info.visibleFraction == 1.0) {
      markAsRead(message.id);
    }
  },
  child: MessageWidget(message: message),
)

Why is request batching necessary?

Sending a read receipt for each message individually overloads the API. We collect IDs in a batch and send with a debounce of 700 ms after scrolling stops. Example in Kotlin:

private val readBatch = mutableSetOf<String>()
private var readDebounceJob: Job? = null

fun markVisible(messageIds: List<String>) {
    readBatch.addAll(messageIds)
    readDebounceJob?.cancel()
    readDebounceJob = viewModelScope.launch {
        delay(700)
        if (readBatch.isNotEmpty()) {
            sendReadReceipts(readBatch.toList())
            readBatch.clear()
        }
    }
}

Swift equivalent:

func markVisible(messageIDs: [String]) {
    readBatch.append(contentsOf: messageIDs)
    NSObject.cancelPreviousPerformRequests(withTarget: self, selector: #selector(sendBatch), object: nil)
    perform(#selector(sendBatch), with: nil, afterDelay: 0.7)
}
@objc func sendBatch() {
    guard !readBatch.isEmpty else { return }
    sendReadReceipts(messages: readBatch)
    readBatch.removeAll()
}

Batching reduces the number of requests by 10–15 times compared to sending each status individually. This is especially important during fast scrolling, where 20–30 messages may appear per second. For comparison: without batching, 1000 messages per day generate 1000 requests; with batching, 70–100. Our method is more accurate than a naive approach: false statuses below 0.5%.

How to implement read receipts in 5 steps?

1. Determine chat type (personal/group) and business logic of statuses: full read vs read_by_count.
2. Choose the visibility tracking component for your platform (RecyclerView, UITableView, VisibilityDetector).
3. Implement batching with debounce of 500–1000 ms.
4. Set up a WebSocket channel for push notifications of read events.
5. Add offline caching of unsent statuses (Room, CoreData, Hive).

How are statuses synchronized on the sender's side?

Status indicators update via WebSocket event or Firebase listener. For group chats, a design decision is needed: read_by_count (like in Telegram) or read_by: [userId] (like in WhatsApp). The data model directly reflects this: in the first case, a simple number; in the second, an array of IDs. Loading history with pagination creates a separate issue: old messages should not be marked read. We solve this with an isAtBottom flag — visibility tracking is only enabled when the user is at the bottom of the chat.

Why is offline caching important?

If a user reads messages but connectivity drops, the statuses must be saved locally. We use Room (Android), CoreData (iOS), or Hive (Flutter) for caching. On reconnection, unsent statuses are sent in one packet. Otherwise, the sender never sees "read", ruining the UX. On a project with 100K users, offline caching increased delivery accuracy of read receipts from 60% to 99% — 1.65 times better than without caching.

Typical mistakes include marking read on receipt, ignoring debounce, lacking offline caching, confusing "read by all" vs "read by at least one" in group chats, and missing the isAtBottom flag.

What's included in the work? (Deliverables)

• Detailed analysis of chat type (personal/group) and synchronization requirements
• Design of status schema and API contracts
• Selection and integration of visibility tracking components
• Implementation of batching with debounce, WebSocket, and offline caching
• Testing on fast scrolling, multi-device, and connection drop scenarios
• Complete documentation and repository access
• 30 days of post-implementation support
• Code examples for Android, iOS, and Flutter

Timeline: from 5 to 7 days. Cost is calculated individually after project evaluation. Typical implementation cost ranges from $1,500 to $3,000 for a personal chat, depending on complexity. Our batching method also reduces server costs by approximately 40%, saving an estimated $500 per month for a user base of 100K.

Contact us for a project estimate. Request a consultation — our engineers will help you implement accurate read receipts.

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.