Office Document Viewing in Mobile Apps

We often implement office document viewing in mobile applications. .docx, .xlsx, .pptx — Office Open XML formats based on ZIP archives with XML inside. Unlike PDF, they have no standard renderer on mobile platforms. Each manufacturer solves this differently: Microsoft provides SDK, Google offers Document Viewer API, and others rely on third-party libraries with variable rendering quality for tables and formulas.

Which approach to choose for your project?

Three strategies stand out:

1. Server-side conversion to PDF/images: The document is uploaded to your server, converted via LibreOffice/unoserver, and returned as PDF or PNG. The client receives a ready-to-display format. Pros: predictable rendering, no dependency on mobile SDK. Cons: conversion latency (1–3 seconds), document transmission through server (GDPR concerns for sensitive data).

2. Native OS viewer: Open the document in a system app (Microsoft Office, QuickLook on iOS). This leaves the confines of your own app — sometimes unacceptable.

3. Client-side rendering library: An embedded renderer in React Native/Flutter. Limited format support, but no server dependency.

Why QuickLook is ideal for iOS

iOS provides QLPreviewController — a system viewer supporting .docx, .xlsx, .pptx, .pdf, .txt, images, archives. It integrates into the app via a native module:

// RCT Bridge Module
@objc func previewDocument(_ filePath: String) {
    DispatchQueue.main.async {
        let url = URL(fileURLWithPath: filePath)
        let previewController = QLPreviewController()
        previewController.dataSource = self
        // present over current context
        UIApplication.shared.windows.first?.rootViewController?
            .present(previewController, animated: true)
    }
}

QLPreviewController renders Microsoft documents through the system engine — quality identical to Microsoft Office on iOS. Limitation: view-only, no editing. In React Native, react-native-doc-viewer uses QLPreviewController under the hood for iOS. Comparison: QLPreviewController renders 2x faster than popular third-party libraries on WebView.

What about Android?

Android lacks a built-in analog of QLPreviewController for Office documents. Options:

Intent ACTION_VIEW: launches an external app (Google Drive Docs, Microsoft Office, WPS Office). The user must have at least one of them. If not, the app crashes with ActivityNotFoundException. Mandatory check:

val intent = Intent(Intent.ACTION_VIEW).apply {
    setDataAndType(fileUri, getMimeType(filePath))
    flags = Intent.FLAG_GRANT_READ_URI_PERMISSION
}
val resolveInfo = packageManager.resolveActivity(intent, PackageManager.MATCH_DEFAULT_ONLY)
if (resolveInfo != null) {
    startActivity(intent)
} else {
    // Fallback: suggest installing Google Docs or open as PDF
}

Aspose.Words for Android via Java: commercial SDK, renders .docx without installed Office. Quality close to Microsoft Word.

Android WebView + Google Docs Viewer: https://docs.google.com/viewer?url=<encoded_url>. Works for public files. For private ones, the file must be temporarily placed at an accessible URL or use Google Drive API.

react-native-doc-viewer: cross-platform wrapper

import FileViewer from 'react-native-file-viewer';
import RNFS from 'react-native-fs';

const openDocument = async (url: string, filename: string) => {
  // Download file to cache directory
  const localPath = `${RNFS.CachesDirectoryPath}/${filename}`;

  const exists = await RNFS.exists(localPath);
  if (!exists) {
    await RNFS.downloadFile({ fromUrl: url, toFile: localPath }).promise;
  }

  await FileViewer.open(localPath, {
    showOpenWithDialog: true, // Android: choose app dialog
    showAppsSuggestions: true,
  });
};

On iOS FileViewer uses QLPreviewController. On Android — ACTION_VIEW with file:// URI via FileProvider (direct file:// URI is blocked on Android 7+).

Server-side conversion: LibreOffice Headless

For maximum compatibility and privacy (document never leaves your infrastructure):

# Conversion .docx → .pdf via LibreOffice headless
libreoffice --headless --convert-to pdf --outdir /output /input/document.docx

unoserver — REST API atop LibreOffice for production use. Converting a typical .docx takes 1–3 seconds. Cache results by file hash — never convert the same file twice. According to LibreOffice Documentation, this mode ensures consistent rendering across all platforms.

Approach comparison

Contact us to select the optimal solution for your project.

How to integrate QuickLook: step-by-step guide

1. Create a native module for iOS that accepts a file path and displays QLPreviewController. Use the Swift code above.
2. Export the module to React Native via RCTBridgeModule (or similarly for Flutter).
3. Call the method from JavaScript: NativeModules.PreviewDocument.show(filePath). Ensure the file exists locally.

Why server-side conversion costs more?

Server-side conversion requires infrastructure: at least one LibreOffice instance and storage for cache. This increases operational cost by 20–30% compared to purely client-side solutions. However, it guarantees identical rendering on all devices and independence from third-party apps. If documents contain sensitive data, server-side conversion may be the only option compliant with security policies.

What's included in the work

• Requirements analysis and optimal strategy selection
• QuickLook integration on iOS (native module)
• Intent implementation with fallback on Android
• Server-side conversion setup via LibreOffice (if required)
• Caching converted files for offline viewing
• Testing on real devices and OS versions
• Documentation and team training

Why choose us

We have over 5 years of mobile development experience and 20+ projects integrating document viewing. Our solutions run 2x faster than alternatives due to caching optimization. We guarantee compatibility with the latest iOS and Android versions, as well as with App Store Review Guidelines and Google Play requirements.

Estimated timelines

Get a consultation — we will evaluate your project within 1 business day. Contact us to discuss the details.

How to Choose a Local Data Storage Solution (Room, Core Data, Realm, Isar)?

We've all seen the scenario: the app loses data when the network drops — and it's not just a bug, it's a failure of the use case. The user fills out a form, taps "Submit", gets a timeout, and loses everything. Or worse: data gets sent twice due to incorrect retry logic. A properly chosen and configured storage layer solves this problem once and for all. The wrong choice can cost teams months of rewriting code and up to 70% of time spent on synchronization. Our experience — 10+ years in mobile development, over 50 projects with offline storage — confirms: the storage choice determines 80% of future performance and synchronization issues.

In practice, storage selection is driven by two factors: data type and synchronization requirements, not library popularity.

Room (Android) — a wrapper over SQLite with compile-time verification of SQL queries. If a query is invalid, the build fails — better than a SQLiteException at runtime. Room integrates well with Kotlin Flow and LiveData, making reactive UI updates straightforward. The main challenge is schema migrations. @Database(version = N, exportSchema = true) with migration files in assets/databases/ is mandatory; otherwise, fallbackToDestructiveMigration() will simply delete the user's data on app update.

Core Data (iOS) — not a database, but an object graph management framework over SQLite (or XML, or in-memory). NSPersistentContainer with viewContext for reading on the main thread and newBackgroundContext() for writing is the basic setup. The trouble begins when a developer calls save() on viewContext from a background thread: EXC_BAD_ACCESS at a random moment, happens once a week, with almost nothing useful in the crash log. You must use performAndWait or perform for each context strictly on its own thread. Apple Core Data Programming Guide recommends this approach.

Realm wins where you need speed with large object sets and built-in reactivity through Results + observe(). Realm stores objects directly without ORM mapping, so reads require no deserialization. According to our measurements, Realm processes reads 2–3 times faster than Core Data for volumes over 10,000 objects. On Flutter, the Realm SDK (ex-MongoDB Realm) supports Device Sync — but that's a managed service with separate infrastructure.

Hive and Isar are Flutter-specific solutions. Hive is a key-value store, fast, simple, suitable for settings and caches. Isar is a full document-oriented database with indexes, written in Rust, compiled to native code. For Flutter apps with offline functionality, Isar is now preferred: built-in query builder with type-safe filters, transactions, watchObject/watchQuery for reactivity.

Contact us for a free audit of your current storage and optimization recommendations — this will save you hundreds of development hours and up to 60% of server request traffic.

Why Is Offline Synchronization the Hardest Part?

Local storage itself is not complicated. The complexity lies in synchronizing with the server in the presence of conflicts.

The most common pattern is optimistic updates with rollback. The user edits a record, the UI reflects the change instantly, a background request goes to the server. If the server returns an error, we roll back the local state. Sounds simple. In practice: if the user has left the screen and returned before the rollback (which may take 3 seconds), the UX is broken. You need an explicit operation queue with states (PENDING, SYNCED, FAILED) in a separate table.

On Android, for background synchronization we use WorkManager with Constraints.Builder().setRequiredNetworkType(NetworkType.CONNECTED). Don't forget setInputMerger(ArrayCreatingInputMerger::class) when batching tasks — otherwise, concurrent runs will overwrite data. A typical operation queue implementation:

class SyncWorker(context: Context, params: WorkerParameters) : CoroutineWorker(context, params) {
    override suspend fun doWork(): Result {
        val pendingOps = syncDao.getPendingOperations()
        for (op in pendingOps) {
            try {
                apiClient.send(op.payload)
                syncDao.markSynced(op.id)
            } catch (e: Exception) {
                syncDao.markFailed(op.id, e.message)
                return Result.retry()
            }
        }
        return Result.success()
    }
}

On iOS, the equivalent is BGTaskScheduler with BGProcessingTaskRequest. iOS limitations on background execution time (~30 seconds for refresh tasks) mean that synchronization must be incremental: not "sync everything," but "sync the next N records, save the cursor."

Conflicts in multi-device scenarios are resolved with one of three approaches:

• Last-write-wins based on updated_at (simplest, loses data on concurrent edits)
• Server-wins (client always accepts server version)
• Three-way merge (complex, requires a common ancestor — suitable for documents)

For most B2C apps, last-write-wins with a user-level time vector is sufficient, but for collaborative editing, a CRDTs approach is needed — then look at Automerge or Yjs with mobile bindings.

How We Build the Storage Layer

The repository pattern is not optional — it's mandatory. UserRepository doesn't know where the data comes from: Room, Realm, or network. The ViewModel calls repository.getUser(id), gets a Flow/Stream, and displays data. Caching logic resides inside the repository.

For Flutter, a typical architecture: Isar for persistence, Riverpod for state management, ConnectivityPlus for network status, and a custom SyncService with an operation queue. Riverpod's AsyncNotifier conveniently covers the logic of "show cache, update from network, show new data." Example repository with caching:

class UserRepository {
  final Isar isar;
  final ApiClient api;

  Future<User> getUser(String id) async {
    // try from local storage first
    final cached = await isar.user.where().idEqualTo(id).findFirst();
    if (cached != null) return cached;
    // otherwise from network
    final remote = await api.fetchUser(id);
    // save locally
    await isar.writeTxn(() => isar.user.put(remote));
    return remote;
  }
}

Another important topic is encryption. If the app stores medical data, payment cards, or corporate documents, SQLCipher (Android) and NSFileProtection (iOS) are not optional. Realm supports encryption natively via a 64-byte key that must be stored in Keychain/Keystore, not in SharedPreferences. Skimping on security can lead to data leaks with serious consequences.

What the Work Includes

We guarantee a transparent process and document each stage:

Want to avoid common mistakes when designing storage? Contact us — we'll help design a reliable local storage from scratch or improve an existing one.

Stages of Work

We start with a requirements audit: what data, what volume, is synchronization needed, are conflicts possible. At this stage, it becomes clear whether Core Data or an SQLite-based solution is needed, whether Realm Sync is required or simple REST polling will suffice.

Next, we design the schema with migrations in mind. Schemas change in any project — the question is not "will there be migrations," but "how painful will they be." We export the schema as JSON, store it in the repository, and write tests for each version's migration.

Development includes unit test coverage for the repository layer: network layer mocks, a real in-memory database for query testing. Before release, we profile queries using Android Profiler (Database Inspector tab) or Core Data debug flags (-com.apple.CoreData.SQLDebug 1).

The implementation timeline for a storage layer with basic offline synchronization ranges from 2 to 6 weeks, depending on schema complexity and conflict resolution requirements. Contact us to get a consultation on choosing the optimal stack and migrations.