rcosteira79/android-skills

datastore

Use when persisting key-value preferences or small typed settings on Android or KMP with Jetpack DataStore — Preferences vs Typed (Proto/JSON) vs Room selection, the IOException and corruption-recovery error traps, serializers with corruption handlers, and…

Quelltext ansehen

Verwendung

  1. Installationsbefehl kopieren und das Quellpaket vor der Installation in der eigenen Agentenumgebung prüfen.
  2. Benötigte Werkzeuge, Zugangsdaten und Hosts im Original prüfen.
  3. Eingabe und gewünschtes Ergebnis beschreiben und die Ausgabe mit den Quellen abgleichen.
npx skills add https://github.com/rcosteira79/android-skills --skill datastore

Vor dem Start

  • Die Installation umfasst weder Modellabo noch API-Kontingent oder Dateizugriffsrechte.
  • Sterne und Installationen zeigen Aktivität, keine unabhängige Qualitäts- oder Sicherheitsprüfung.
Originales Skill-Dokument

Aus dem Quell-Repository gerendert; Überschriften, Beispiele, Code, Tabellen, Links und Bilder bleiben erhalten.

Jetpack DataStore for Android and KMP

Reactive coroutine-based key-value / typed storage; the same androidx.datastore:datastore-preferences-core runs on Android, iOS, and JVM — only the file-path producer is platform-specific. (Web support is 1.3.0-alpha only, and it's sessionStorage/OPFS-backed there, not file-path-based.) This reference covers the storage-choice call plus the two error/path traps, not the basics of Preferences keys, edit { }, or Serializer<T>. Adapted from Meet-Miyani/compose-skill; MIT. Related: android-skills:android-data-layer, android-skills:kmp-boundaries, android-skills:kotlin-flows.

Preferences vs Typed vs Room

NeedStorage
Key-value flags (theme, locale, onboarding done)Preferences DataStore
One typed object, many related fields, schema evolutionTyped DataStore + Serializer<T>
Relational data, indexes, WHERE / JOIN, >100 entries, pagingRoom
Payloads above ~50KB per writeRoom / filesystem — DataStore rewrites the whole file on every edit

Rule of thumb: if a WHERE clause would be useful, use Room.

The two traps

`.catch` must match `IOException` specifically — never a broad `catch`. A bare catch { emit(emptyPreferences()) } swallows CancellationException (breaking structured concurrency) and hides serializer/corruption errors behind a silent empty state.

kotlin
val settings: Flow<UserSettings> = dataStore.data
    .catch { e -> if (e is IOException) emit(emptyPreferences()) else throw e }
    .map { p -> UserSettings(p[Keys.DARK_MODE] ?: false, p[Keys.LOCALE] ?: "en") }

Typed-DataStore corruption recovery triggers on `CorruptionException`, NOT `IOException`. The serializer's readFrom must wrap parse failures in CorruptionException, and the store needs a ReplaceFileCorruptionHandler — without it, one corrupt file makes every read fail permanently.

kotlin
object AppSettingsSerializer : Serializer<AppSettings> {
    override val defaultValue = AppSettings()
    override suspend fun readFrom(input: InputStream): AppSettings =
        try { Json.decodeFromString(input.readBytes().decodeToString()) }
        catch (e: SerializationException) { throw CorruptionException("Cannot read AppSettings", e) }
    override suspend fun writeTo(t: AppSettings, output: OutputStream) =
        output.write(Json.encodeToString(t).encodeToByteArray())
}
val store = DataStoreFactory.create(
    serializer = AppSettingsSerializer,
    corruptionHandler = ReplaceFileCorruptionHandler { AppSettings() },
    produceFile = { File(context.filesDir, "app_settings.json") },
)

KMP factory — per-platform path (not the temp dir)

kotlin
// commonMain
fun createPreferencesDataStore(producePath: () -> String): DataStore<Preferences> =
    PreferenceDataStoreFactory.createWithPath(produceFile = { producePath().toPath() })
// android: context.filesDir.resolve(PREFS_FILE).absolutePath
// ios:     NSDocumentDirectory via NSFileManager
// jvm:     File(System.getProperty("user.home"), ".myapp") — NOT java.io.tmpdir (the OS may wipe it on reboot)

(In a composable, never runBlocking on DataStore — it parks the main thread on disk I/O, risking an ANR, and re-runs every recomposition. Expose a StateFlow and collect with collectAsStateWithLifecycle.)

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