A remote-only Android repository can provide a clean data-layer boundary without Room, DataStore, or files on the device. It coordinates access to a network data source and returns app-facing results to the rest of the app. The tradeoff is direct: without local persistence, previously fetched data is unavailable after process death and cannot serve as durable offline content.
What “remote-only” means
Here, remote-only means the Android app does not persist feature data locally. It does not mean the server stores nothing: server-side persistence is separate. You may still keep a response in memory for the current process, and a network client may use HTTP caching if configured. Decide explicitly whether either kind of cache is allowed, because “remote-only” has no single universal definition.
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A repository is an access boundary and coordinator, not a synonym for database. Android’s data-layer guidance describes repositories as having zero to many data sources and directs other layers to access data through repositories. A network data source can therefore be the repository’s only source. See Android’s data-layer guidance.
What the app gives up without local persistence
- Offline reads: if the device cannot reach the network, the app has no durable local copy to show.
- Data after process recreation: in-memory screen state disappears when the process ends; it is not a persisted cache.
- Durable queued writes: if a write cannot reach the server, the app cannot guarantee it will be sent later without storing it locally.
Android’s offline-first guidance says, “A repository with network access in an offline-first app must always have a local data source.” That requirement is for offline-first behavior; it does not make a local database mandatory for an intentionally online-only feature. See Android’s offline-first data-layer guidance.
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Shape the repository around app-facing operations
Keep transport mechanics in the network data source. The repository should translate the response into application-level models and stable outcomes, rather than exposing wire models or making the UI call the service directly. This keeps the UI and any domain layer independent of the networking implementation.
Use suspend for a one-shot request. Use Flow when the source genuinely emits ongoing updates; a Flow does not itself persist data or guarantee an offline value. For a refresh-based screen, a contract might look like this:
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interface ItemsRepository {
suspend fun fetchItems(): ItemsResult
}
class RemoteItemsRepository(
private val service: ItemsService,
) : ItemsRepository {
override suspend fun fetchItems(): ItemsResult = try {
ItemsResult.Success(service.fetchItems().map(NetworkItem::toDomain))
} catch (error: IOException) {
ItemsResult.NetworkFailure
}
}This is an illustrative sketch, not tested production code. Define outcomes that fit the app, including how it handles HTTP errors, authentication, empty responses, and mapping. Do not convert coroutine cancellation into an ordinary network failure: propagate cancellation so lifecycle-driven cancellation continues to work.
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Represent request outcomes in the screen
Let the ViewModel own in-memory screen state and expose states the UI can render, such as loading, loaded content, an empty response, and an actionable error with a retry option. Keep this state distinct from a cache: it can help the current screen reuse a result, but it does not survive process death.
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For refreshable data, define when requests happen and what the user can do. Decide whether every screen visit fetches again, whether simultaneous requests are deduplicated, and how an older response is handled if a newer request starts. If the screen keeps a result in memory, make its lifetime and freshness behavior clear.
Handle failures and retries deliberately
Return errors in a form the app can act on rather than treating every unsuccessful request alike. A transient connectivity or server problem may justify a bounded retry; an authorization failure generally needs corrected credentials or user action, not repeated requests. Android’s offline-first guidance recommends considering the error type and maximum retry count when designing retry behavior.
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Authentication rules, rate limits, backend consistency, and request idempotency depend on the particular service. They cannot be inferred from the repository pattern, so define them against the API contract before retrying writes.
Wire dependencies at the app boundary
Provide the repository with its remote source through the app’s dependency-composition setup. This makes it possible to substitute a fake source in tests without introducing persistence. The architecture guidance supports the repository/source boundary; the specific dependency-injection framework is a project choice.
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Choose a different design if requirements change
| Option | Good fit | Main cost or limitation |
|---|---|---|
| Strict remote-only repository | The feature is intentionally online-only, requests are acceptable, and device persistence is disallowed or unnecessary. | Uncached reads depend on network availability; there is no durable offline content. |
| In-memory screen state | Reusing a response during the current process or screen session is enough. | State is lost when the process ends and is not a durable cache. |
| Room-backed local source | Data is large, relational, queryable, needs partial updates, or must remain available offline. | Requires schema and migration management plus cache-freshness and synchronization decisions. |
| DataStore-backed settings | The requirement is to persist a small preference or typed settings object. | Not intended for large datasets, partial updates, or referential integrity. |
| Paging with network and database | A large paged list needs cached browsing as pages arrive. | Android’s documented network-plus-database Paging pattern relies on a local database cache. |
Android recommends choosing storage based on data shape: Room for relational or queryable data and partial updates, DataStore for small settings-like values, and files for opaque chunks such as JSON. The relevant references are the data-layer guide, the DataStore guide, the Room guide, and the network-and-database Paging guide. These alternatives are not prerequisites for a remote-only repository; they become relevant if the product’s persistence requirements change.
Decide whether the no-persistence rule includes caches and queued work
A network library’s HTTP cache is distinct from an app-managed database, but it can retain responses on the device. Verify the client’s actual configuration and decide whether such caching violates the feature’s rule; the architecture guidance does not establish that any particular client is configured to cache.
Likewise, a durable write queue requires storing pending work somewhere locally. If the product forbids all local persistence, do not promise that a failed write will automatically reach the server after connectivity returns. The user may need to retry, or the requirement may need to allow local storage.
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