Kotlin Flow State Event Modeling

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:

// ❌ BAD
private val _navEvents = MutableSharedFlow<NavigationEvent>()
val navEvents: SharedFlow<NavigationEvent> = _navEvents.asSharedFlow()

// ✅ GOOD
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.

// ❌ BAD — sentinel leaks into the type
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.

// ✅ GOOD — bootstrap suspends; observers only see real users
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.

// BAD — read/modify/write can lose concurrent updates.
_state.value = _state.value.copy(
    selectedId = id,
    details = details,
)

// GOOD — transform starts from the latest state.
_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:

// GOOD — details does not depend on current state, so build it once.
val details = Details.from(response)
_state.update { current ->
    current.copy(details = details)
}

// GOOD — derived value depends on current state, so compute it inside.
_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

// ❌ BAD — new sharing coroutine every call
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.

// ✅ GOOD — one shared instance, computed once
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

// ❌ BAD — `name.value` won't compile; it's now a plain Flow
val name: Flow<String> = userState.map { it.name }

If you need synchronous .value, terminate the chain with .stateIn(...):

// ✅ GOOD
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?

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

Red flags during review

Related

• kotlin-control-flow — choosing when, guard conditions, exhaustiveness, smart casts, and early returns when modeling state and events.
• kotlin-coroutines-structured-concurrency — scope ownership, init launches, fire-and-forget boundaries, cancellation, runBlocking
• compose-side-effects — collecting event flows and wiring side effects in Compose
• compose-state-hoisting — where state holders expose flows to plain state-driven UI