Mobile E-Book Reader App Development

Developing a Mobile E-Book Reader App

Making a simple reader that opens EPUB is a week's task. Making a reader that is pleasant to use is a different story. Smooth scrolling of long chapters, correct rendering of CSS from EPUB3, synchronization of reading position across devices, night mode without artifacts — each of these details requires individual solutions. For example, custom fonts from EPUB3 need CSS path normalization, and sync requires conflict resolution by timestamp.

Choosing the right rendering and sync stack saves up to 60% of debugging time. We use Readium Toolkit — a proven library that powers major readers (LitRes, Storytel). This choice guarantees stable support for EPUB 2/3, PDF, audiobooks, and built-in LCP DRM. With experience from over 10 reader projects, we are ready to offer optimal architecture.

Formats and Rendering

Primary formats: EPUB 2/3, PDF, FB2. MOBI and AZW3 on request.

Why Readium is the Best Choice for EPUB Rendering?

EPUB is a ZIP archive containing HTML/CSS/XML. Native rendering requires an intermediate layer. Readium is the de facto standard for mobile readers with open source. It supports EPUB 2/3, PDF, audiobooks, and LCP DRM. Readium Swift Toolkit (iOS) and Readium Kotlin Toolkit (Android) are used by LitRes Library, Storytel, and many others. Readium renders EPUB via an embedded WKWebView/WebView with its own JavaScript engine injection. This gives full support for book CSS, including custom fonts and complex typography.

// iOS — opening a book via Readium
let publication = try await streamer.open(asset: bookAsset, allowUserInteraction: false)
let navigator = try EPUBNavigatorViewController(
    publication: publication,
    config: .init(
        editingActions: EditingAction.defaultActions,
        fontOverrides: userSettings.fontOverrides
    )
)

Speed comparison: Readium renders EPUB 2x faster than a custom WebView parser with CSS injection without optimizations.

How to Handle PDF and FB2?

On iOS — PDFKit (native, fast, supports annotations). On Android — PdfRenderer (built-in, basic) or PdfiumAndroid for complex documents with vector graphics. For FB2, we use a custom XML parser with rendering via TextView (Android) / AttributedString (iOS). Or conversion to EPUB on server or device via fb2c or Calibre-like logic.

Reading Settings

Font, size, line spacing, margins, theme (light/dark/sepia) — stored locally in UserDefaults / SharedPreferences and applied via CSS injection into Readium:

val preferences = EPUBPreferences(
    fontFamily = FontFamily.GEORGIA,
    fontSize = Percentage(1.2),
    lineHeight = Double(1.5),
    theme = Theme.SEPIA
)
navigator.submitPreferences(preferences)

Important: app theme and reader theme are separate. If a user reads in dark mode but switches the system to light, the reader should not automatically change theme. Reader settings are independent of system settings.

Progress and Bookmarks

Current reading position — Locator in Readium terminology. It is an object with href (chapter path), progression (0.0–1.0 within chapter), and a text fragment for precise restoration after book updates.

Bookmarks, annotations, highlights — stored in Room / Core Data as Annotation(bookId, locator, text, color, note).

How to Sync Progress Across Devices?

If sync is needed, position and annotations are sent to the server on each change. WorkManager / BGTaskScheduler sends accumulated changes when network is available. Conflicts are resolved by timestamp: the later position wins. Readium supports OPDS Annotations — if the server supports it, sync is almost free.

We guarantee sync won't cause data loss: we test on 10+ devices simultaneously.

Library and Downloads

Books are stored in Files (iOS) / filesDir (Android) — not cacheDir, otherwise the OS may delete them when space is low. On iOS 16+ you can use FileManager with ubiquityContainerIdentifier for iCloud file sync.

Download via URLSession.downloadTask (iOS) / DownloadManager (Android) with resume support (resumeData). Progress displayed in library. Full-text search inside EPUB — via indexing all chapter text into an FTS4/FTS5 Room table.

Typical Problems and Solutions

Custom fonts from EPUB3 (@font-face) fail to load if the CSS path uses ../Fonts/ — path normalization needed. WKWebView on iOS truncates long pages if isScrollEnabled = false — proper pagination configuration required. PDF with embedded CJK fonts on PdfRenderer Android gives rendering artifacts, so we use Pdfium. For each case we have internal utilities: CSS path normalizer, automatic WKWebView pagination config, and CJK font detector to switch to Pdfium.

What's Included in Development

• App architecture and rendering stack selection.
• UI/UX adaptation for iOS and Android.
• Integration of Readium Toolkit for EPUB/PDF and FB2 parser.
• Setup of progress and annotation sync via server.
• Implementation of reading settings (font, theme, margins).
• Testing on physical devices (at least 10 models).
• API and build documentation, source code and access transfer.
• 6 months free support after release.

Timelines

Cost is determined after defining format set and sync requirements. Get a consultation — we'll find the optimal solution. Order reader development with quality guarantee: we provide 6 months free support after release.

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.