| name | kotlin-flow-state-event-modeling |
| description | Use when writing or reviewing Kotlin StateFlow/SharedFlow/Channel choices, sentinel default values, stateIn placement, WhileSubscribed staleness, or MutableStateFlow update patterns. Technique-layer skill — complements the codebase-specific kotlin-expert. |
Kotlin Flow: state and event modeling
Core principle
Pick the primitive that matches replay, fan-out, and synchronous-read requirements. StateFlow, SharedFlow, Channel-backed flows, and cold Flow differ in buffering, who sees each emission, and whether .value exists. Wrong choices drop events, leak sharing coroutines, or force fake domain sentinels into state.
When to use this skill
You're writing or reviewing Kotlin code involving:
MutableStateFlow<T>(SomeSentinel) — NoUser, Empty, Loading, etc. — because the real value is async
.stateIn(...) called inside a function rather than assigned to a property
SharingStarted.WhileSubscribed(...) on a flow whose .value is read synchronously and must stay fresh
MutableSharedFlow for navigation events, snackbars, or other one-shot emissions where loss would be a bug
.map { } on a StateFlow when consumers still need synchronous .value
MutableStateFlow.value = _state.value.copy(...) or update code that builds expensive objects inside update { ... }
SharedFlow for single-consumer fire-once events
SharedFlow defaults have no replay buffer. If nothing is collecting at the exact instant of emission, the event is gone. For a single UI consumer handling exactly-once events such as navigation or snackbars, a buffered Channel exposed as a Flow often matches the semantics better:
private val _navEvents = MutableSharedFlow<NavigationEvent>()
val navEvents: SharedFlow<NavigationEvent> = _navEvents.asSharedFlow()
private val _navEvents = Channel<NavigationEvent>(Channel.BUFFERED)
val navEvents: Flow<NavigationEvent> = _navEvents.receiveAsFlow()
Channel.receiveAsFlow() is fan-out, not broadcast: with multiple collectors, each event is delivered to one collector. Channel.BUFFERED is bounded, so sends can suspend and trySend can fail. If multiple observers must all see the same event, use explicit state, durable storage, or a deliberately configured SharedFlow instead.
StateFlow polluted with invalid sentinel defaults
StateFlow forces an initial value. When the real value is async, developers sometimes invent fake domain values — NoUser, EmptyUser, placeholder IDs — and every consumer is forced to treat that sentinel as real data.
class UserSession(private val db: Db) {
private val _user = MutableStateFlow<User>(NoUser)
val user: StateFlow<User> = _user.asStateFlow()
init { scope.launch { _user.value = db.load() } }
}
One fix is phasing: don't expose the StateFlow until the real value exists.
class UserSession(private val db: Db) {
private var _user: MutableStateFlow<User>? = null
val user: StateFlow<User>
get() = checkNotNull(_user) { "Call login() first" }
suspend fun login() {
_user = MutableStateFlow(db.load())
}
}
If absence, loading, or error is a real state, model it explicitly (User?, sealed interface UserUiState, Result, etc.). The bug is a fake domain value masquerading as real data, not every initial value.
Mutate MutableStateFlow with update { ... }
Prefer MutableStateFlow.update { current -> ... } over reading .value and writing it back. update applies the transform atomically against the latest state, which avoids lost updates when multiple coroutines mutate the same state.
_state.value = _state.value.copy(
selectedId = id,
details = details,
)
_state.update { current ->
current.copy(
selectedId = id,
details = details,
)
}
Keep object creation outside the update block unless it needs the current state. The update lambda can be retried, so expensive work or side effects inside it may run more than once:
val details = Details.from(response)
_state.update { current ->
current.copy(details = details)
}
_state.update { current ->
val nextItems = current.items.replaceById(updatedItem)
current.copy(items = nextItems)
}
The block should be a pure, fast state transformation: no network calls, database writes, logging side effects, random IDs, or time reads unless those values were captured before the block.
stateIn() inside a function
fun getPreferences(): StateFlow<Prefs> =
repo.prefsFlow.stateIn(scope, SharingStarted.Eagerly, Prefs.Default)
Every call to getPreferences() launches a fresh coroutine on scope that never completes. Performance dies fast under repeated reads.
val preferences: StateFlow<Prefs> =
repo.prefsFlow.stateIn(viewModelScope, SharingStarted.Eagerly, Prefs.Default)
WhileSubscribed with synchronous .value
SharingStarted.WhileSubscribed(timeout) disconnects the upstream when there are no active collectors. While disconnected, .value returns the last cached value, which may be stale or still the initial value.
Rule: if .value must be fresh or initialized without an active collector, use SharingStarted.Eagerly or explicit initialization. WhileSubscribed is fine when stale/cached values are acceptable and consumers primarily collect asynchronously.
.map on StateFlow loses .value
val name: Flow<String> = userState.map { it.name }
If you need synchronous .value, terminate the chain with .stateIn(...):
val name: StateFlow<String> = userState
.map { it.name }
.stateIn(viewModelScope, SharingStarted.Eagerly, userState.value.name)
Community "derived state flow" utilities run the transform on every .value read — only acceptable for fast, idempotent transforms. Default to .stateIn(...).
Decision: which Flow type?
| Need | Primitive |
|---|
| State that always has a value, read by both async collectors and synchronous code | StateFlow, often with SharingStarted.Eagerly when .value matters |
Hot stream, multiple subscribers, no requirement for synchronous .value | SharedFlow |
| Discrete events for one consumer, exactly-once handoff | Consider Channel(BUFFERED).receiveAsFlow() |
| Cold stream, one consumer per collection | Plain Flow |
If you're tempted to reach for SharedFlow, ask: would dropping an emission be a bug, and how many consumers must see it? If one consumer must handle it exactly once, a Channel may fit. If every observer must see it, model durable state or configure a broadcast stream deliberately.
Quick reference
| Symptom | Problem | Fix |
|---|
MutableStateFlow<X>(FakeDomainValue) | Invalid placeholder default | Model absence explicitly or use phase initialization |
MutableSharedFlow<Event> for single-consumer nav/snackbar | Lossy default event stream | Consider Channel(BUFFERED).receiveAsFlow() |
fun foo() = flow.stateIn(...) | Per-call sharing coroutine | Make it a val / shared instance |
WhileSubscribed + .value must be fresh/initialized | Stale or initial data | SharingStarted.Eagerly or explicit initialization |
stateFlow.map { ... } consumed as state | Lost .value | Terminate with .stateIn(...) |
_state.value = _state.value.copy(...) | Non-atomic read/modify/write | _state.update { it.copy(...) } |
Expensive object creation inside update { ... } that doesn't use current state | Work can repeat if update retries | Build before update; keep only current-state transforms inside |
Red flags during review
| Thought | Reality |
|---|
"We need SharedFlow because there are multiple subscribers" | Multiple subscribers change the semantics. Channel.receiveAsFlow() is not broadcast; choose the event model deliberately. |
"We'll use WhileSubscribed to save resources" | Only if stale/initial .value reads are acceptable. Verify before applying. |
| "I'll use a sentinel until real data loads" | Consumers treat it as real domain; prefer explicit UI/state modeling or phasing. |
"I'll construct the new object inside update because it's convenient" | The lambda may retry. Construct outside unless it depends on the current state. |
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