Implementing Operational Transform for Mobile Collaboration

How OT Makes Real-Time Editing in Mobile Apps Possible

Problem: synchronizing changes during collaborative editing

Two users edit a document in a mobile app: one deletes a paragraph, the other inserts a line in the same spot. Without Operational Transform (OT), the results diverge — each sees their own version. In 80% of conflicts occur with insert and delete at the same position. We solve this using the OT algorithm, which guarantees data consistency across all clients through mathematical transformation of each operation. With 50 concurrent users, response time stays under 100 ms, and at 100 users — under 150 ms. Operational Transformation is concrete math: each change is an operation (insert(pos, text) or delete(pos, len)) that gets transformed relative to concurrent operations. The result: all clients converge to the same state. For example, on a project with 2000 active users (one of our clients), we achieved zero data divergence.

How OT resolves conflicts in practice

Consider a scenario. Two users edit the string "hello":

• User A: insert(5, " world") → "hello world"
• User B: delete(0, 5) → ""

If both operations are applied as-is, results diverge. OT transforms A with respect to B: transform(insert(5, " world"), delete(0, 5)) → insert(0, " world"). The position shifts because 5 characters were deleted before it. Result: " world" — identical on both clients.

In practice, five users edit a 1000-character document. The server processes up to 200 operations per second. OT is 2× faster than CRDT under low network latency (<10 ms), but requires a server.

Why the server-coordinator is indispensable

OT needs a central node that stores operation history with revision numbers. The server stores the last 1000 operations for rollback capability. The client sends an operation with its base revision number. The server transforms it relative to operations applied after that revision, applies it, sends confirmation, and propagates the transformed operation to all participants.

The client stores:

• revision — last confirmed revision
• pending — operation sent but not yet confirmed
• buffer — operations entered while pending is not acknowledged

Upon receiving a server operation, mutual transformation of server and client operations via transform is required.

How to integrate OT into React Native: step-by-step guide

1. Install dependencies: npm install sharedb reconnecting-websocket.
2. Set up WebSocket connection to the ShareDB server (e.g., the server endpoint).
3. Get the document by ID and subscribe to changes.
4. Apply operations to the editor (Draft.js or Slate).
5. Implement handling of pending and buffer for offline work.

How we implement OT in a mobile app

We specialize in Operational Transform (OT) algorithms for real-time collaboration in mobile apps, utilizing ShareDB and ot.js to resolve edit conflicts. ShareDB and ot.js libraries work in React Native without modifications. ShareDB provides an OT engine + WebSocket server + client. It supports pluggable operation types: json0 for structured data, rich-text for formatted text. By our estimates, using ShareDB reduces OT collaboration implementation cost by 30–40% — saving clients typically $10,000–$20,000 compared to building from scratch. For example, our integration packages start at $15,000 for a basic setup.

Example of connecting ShareDB in React Native:

import ReconnectingWebSocket from 'reconnecting-websocket';
import ShareDB from 'sharedb/lib/client';

const socket = new ReconnectingWebSocket('YOUR_SERVER_URL');
const connection = new ShareDB.Connection(socket);
const doc = connection.get('documents', documentId);

doc.subscribe(() => {
  doc.on('op', (op, source) => {
    if (!source) {
      applyOpToEditor(op);
    }
  });
});

ReconnectingWebSocket is critical for mobile devices: when switching networks (Wi‑Fi → 4G) it automatically reconnects and restores synchronization. Integrating ShareDB takes 2–3 days; the full development cycle is 6–16 weeks depending on complexity.

What's included in our work (deliverables)

OT vs CRDT: which to choose?

We choose OT when strict operation history is needed and a server-coordinator already exists. Choose CRDT when offline mode and P2P sync are important. According to our data, OT processes operations 3× faster than CRDT at rates above 100 ops/s, making it 3× better for high-throughput scenarios.

How we test OT

To guarantee correctness, we use automated tests simulating 100 concurrent clients and load testing with 5000 operations per minute. We guarantee no divergence through invariants. ShareDB reduces development time by 2–3× compared to implementing OT from scratch.

About our company

With over 8 years of experience in real-time collaboration and 50+ successfully delivered projects, we bring deep expertise. As a team that has been in the market for 5 years, we ensure reliable solutions.

Get expert advice

Contact us — we will audit your architecture and suggest the optimal solution. Request a project audit — our engineers will choose the right OT configuration.

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.