Kotlin Flow 三 StateFlow 和 SharedFlow
StateFlow
StateFlow 和 LiveData 差不多，都是可观察的数据容器。在 StateFlow 中任何数据的发送，它的每一个接收器都能接收到。在 StateFlow 和 SharedFlow 中收集器也可以被称为订阅者，不过这个订阅者会挂起当前协程，而且永远不会结束。
private val state = MutableStateFlow(1)suspend fun simpleStateFlow() {
coroutineScope {
launch {
delay(1000)
state.collect {
println("before state value $it")
}
}
launch {
for (i in 1..100) {
state.emit(i)
delay(100)
}
}
launch {
state.collect {
println("state value $it")
}
}
}}需要注意的是 collect 是一个挂起函数，所以一旦调用 collect 协程就会被挂起，所以上述的例子中在一个协程中发送数据，在两个协程中接收数据。
和 LiveData 不同的在于， LiveData 不需要初始值，但 StateFlow 需要。
LiveData 会与 Activity 绑定，当 View 进入 STOPED 状态时， LiveData.observer() 会自动取消注册，而从 StateFlow 或任意其他数据流收集数据的操作并不会停止。如需实现相同的行为，需要从 Lifecycle.repeatOnLifecycle 块收集数据流。
StateFlow 是热流，并不是冷流。并且 StateFlow 的 collect 收不到调用之前发射的数据。
val state = MutableStateFlow(1)coroutineScope {
launch {
for (i in 0..10) {
state.emit(i)
delay(1000)
}
}
launch {
delay(2000)
state.collect {
println("receive state $it")
}
}}可以看到最终的结果是：
receive state 2receive state 3receive state 4receive state 5receive state 6receive state 7receive state 8receive state 9receive state 10
因为在接受之前 delay 了 2s，所以最后是从 2 开始接收的。
把普通的 Flow 转化成 StateFlow 。
val flow = flow {
for (i in 0..4) {
emit(i)
delay(100)
}}coroutineScope {
val stateFlow = flow.stateIn(this)
launch {
stateFlow.collect {
println("receive flow.stateIn value $it")
}
}}我们同样可以像 LiveData 一样直接获取它的值。
stateFlow.value
StateFlow 分为 StateFlow 和 MutableStateFlow 。就像 LiveData 和 MutableLiveData 一样。 StateFlow 只能接收数据，不能发送数据，而 MutableStateFlow 即可以发送也可以接收。
private suspend fun simpleStateFlowAndMutableStateFlow() {
val mutableStateFlow = MutableStateFlow(1)
coroutineScope {
launch {
collectData(mutableStateFlow.asStateFlow())
}
launch {
(1..10).forEach {
delay(100)
mutableStateFlow.emit(it)
}
}
}}如上代码所述，可以将 MutableStateFlow 通过 asStateFlow 转换成 StateFlow 。
StateFlow 中给我们提供了一个协程安全的并发修改 StateFlow 中的值的方法 compareAndSet 。该方法能够保证原子的修改 StateFlow 的值。该方法是通过 CAS 来修改值。
public fun compareAndSet(expect: T, update: T): Boolean
将当前的值和期待的值进行比较，如果相等则更新当前的值，并返回 true，如果不相等则返回 false。这里的比较并修改是原子性的。
SharedFlow
SharedFlow 和 StateFlow 相比，他有缓冲区区，并可以定义缓冲区的溢出规则，已经可以定义给一个新的接收器发送多少数据的缓存值。
SharedFlow 同样有与之对应的 MutableSharedFlow 。 MutableSharedFlow 的参数如下：
replay给一个新的订阅者发送的缓冲区的数量。extraBufferCapacity除了 replay 的数量之外的缓冲区的大小。onBufferOverflow缓冲区溢出规则SUSPEND挂起DROP_OLDEST移除旧的值DROP_LATEST移除新的值
SharedFlow 的缓冲区大于是 replay + extraBufferCapacity 。
注意相比于 MutableStateFlow ， MutableSharedFlow 不需要初始值。
suspend fun simpleSharedFlow() {
val sharedFlow = MutableSharedFlow<Int>(
replay = 5,
extraBufferCapacity = 3,
)
coroutineScope {
launch {
sharedFlow.collect {
println("collect1 received shared flow $it")
}
}
launch {
(1..10).forEach {
sharedFlow.emit(it)
delay(100)
}
}
// wait a minute
delay(1000)
launch {
sharedFlow.collect {
println("collect2 received shared flow $it")
}
}
}}同样的，我们可以把普通的 Flow 转换成 SharedFlow。
suspend fun simpleConvertToSharedFlow(started: SharingStarted) {
var start = 0L
// create normal flow
val flow = (1..10).asFlow()
.onStart { start = currTime() }
.onEach {
println("Emit $it ${currTime() - start}ms")
delay(100)
}
// convert to shared flow
// need coroutine scope
coroutineScope {
val sharedFlow = flow.shareIn(this, started, replay = 2)
delay(400)
launch {
println("current time ")
sharedFlow.collect {
println("received convert shared flow $it at ${currTime() - start}ms")
}
}
}}这里的转换有些复杂，可以看到我们通过 shareIn 可以将普通的 flow 转换成 SharedFlow 。可以看到 sharedIn 有三个参数：
CoroutineScope - sharing 的协程的作用域。
SharingStarted- 启动模式stopTimeoutMillis - 配置最后一个订阅者消失后 sharing flow 停止的延时。
replayExpirationMillis - 配置 sharing flow 协程的停止和重置缓冲区之间的间隔，单位是毫秒，默认值为 Long.MAX_VALUE 缓存永远都不重置，0 表示立即重置缓存。比较难懂可以看看下面的例子。
Eagerly迫切的，渴望的，在转换完成后立即开始 sharing 数据，当上游的数据超过 replay 的时候，前面的数据就会被丢弃，相当于DROP_OLDEST。Lazily当有第一个订阅者（调用 collect）的时候开始发射数据。WhileSubscribed当第一个订阅者出现的时候立即开始，当最后一个订阅者消失的时立即停止（默认情况下），replay 数量的缓存值将永远保留（默认情况下）。这是一个函数，可以通过参数来控制当最后一个订阅者消失时的行为，以及缓存的有效期。replay 当订阅的时候回复的数量。
如果上面的函数中传递的是 Eagerly ，其输出如下：
Emit 1 2ms Emit 2 109ms Emit 3 213ms Emit 4 313ms current time received convert shared flow 2 at 412ms received convert shared flow 3 at 412ms Emit 5 413ms received convert shared flow 4 at 414ms Emit 6 518ms received convert shared flow 5 at 519ms Emit 7 619ms received convert shared flow 6 at 619ms Emit 8 720ms received convert shared flow 7 at 720ms Emit 9 822ms received convert shared flow 8 at 823ms Emit 10 926ms received convert shared flow 9 at 926ms received convert shared flow 10 at 1027ms
如果传入的是 Lazily ，其输入如下：
current time Emit 1 2ms Emit 2 105ms received convert shared flow 1 at 106ms Emit 3 209ms received convert shared flow 2 at 209ms Emit 4 313ms received convert shared flow 3 at 313ms Emit 5 415ms received convert shared flow 4 at 415ms Emit 6 518ms received convert shared flow 5 at 518ms Emit 7 622ms received convert shared flow 6 at 622ms Emit 8 725ms received convert shared flow 7 at 725ms Emit 9 826ms received convert shared flow 8 at 826ms Emit 10 932ms received convert shared flow 9 at 932ms received convert shared flow 10 at 1032ms
很明显能够看出两者的区别。
下面看看 WhileSubscribed ，这种方式非常灵活。
fun currTime() = System.currentTimeMillis()suspend fun simpleConvertToSharedFlow(started: SharingStarted) {
var start = 0L
// create normal flow
val flow = (1..10).asFlow()
.onStart { start = currTime() }
.onEach {
println("Emit $it ${currTime() - start}ms")
delay(100)
}
// convert to shared flow
// need coroutine scope
coroutineScope {
val sharedFlow = flow.shareIn(this, started, replay = 2)
val job = launch {
println("current time ")
sharedFlow.collect {
println("received convert shared flow $it at ${currTime() - start}ms")
}
}
launch {
delay(1000L)
job.cancel()
delay(110L)
sharedFlow.collect {
println("received again shared flow $it")
}
println("shared flow has stop")
}
}}@OptIn(ExperimentalTime::class)suspend fun main() {// simpleSharedFlow()
simpleConvertToSharedFlow(
SharingStarted.WhileSubscribed(
stopTimeout = 100L.toDuration(DurationUnit.MILLISECONDS),
replayExpiration = 200L.toDuration(DurationUnit.MILLISECONDS)
)
)}这里配置当最后一个订阅者消失时 delay 100ms 后停止 sharing flow，在 sharing flow 停止后 200ms 后让缓存失效。这里可以通过调整 job.cancel 后的 delay 函数的时长来看看效果。当时间为 110ms 时，会重新接受到缓存 9 和 10，并重新开始 sharing flow，如果参数调整为 320ms 时，缓存会失效，会直接重新开始 sharing flow。
110ms 的结果：
current time Emit 1 1ms Emit 2 107ms received convert shared flow 1 at 108ms Emit 3 211ms received convert shared flow 2 at 211ms Emit 4 315ms received convert shared flow 3 at 315ms Emit 5 417ms received convert shared flow 4 at 417ms Emit 6 521ms received convert shared flow 5 at 521ms Emit 7 623ms received convert shared flow 6 at 624ms Emit 8 727ms received convert shared flow 7 at 727ms Emit 9 829ms received convert shared flow 8 at 829ms Emit 10 933ms received convert shared flow 9 at 933ms received again shared flow 9received again shared flow 10Emit 1 0ms Emit 2 105ms received again shared flow 1Emit 3 210ms received again shared flow 2Emit 4 314ms received again shared flow 3Emit 5 415ms received again shared flow 4Emit 6 519ms received again shared flow 5Emit 7 620ms received again shared flow 6Emit 8 721ms received again shared flow 7Emit 9 826ms received again shared flow 8Emit 10 927ms received again shared flow 9received again shared flow 10
320ms 的结果：
current time Emit 1 1ms Emit 2 106ms received convert shared flow 1 at 106ms Emit 3 210ms received convert shared flow 2 at 210ms Emit 4 314ms received convert shared flow 3 at 314ms Emit 5 414ms received convert shared flow 4 at 414ms Emit 6 517ms received convert shared flow 5 at 517ms Emit 7 623ms received convert shared flow 6 at 623ms Emit 8 726ms received convert shared flow 7 at 727ms Emit 9 827ms received convert shared flow 8 at 827ms Emit 10 931ms received convert shared flow 9 at 931ms Emit 1 0ms Emit 2 105ms received again shared flow 1Emit 3 209ms received again shared flow 2Emit 4 315ms received again shared flow 3Emit 5 418ms received again shared flow 4Emit 6 523ms received again shared flow 5Emit 7 627ms received again shared flow 6Emit 8 732ms received again shared flow 7Emit 9 833ms received again shared flow 8Emit 10 937ms received again shared flow 9received again shared flow 10
我们看下面这段源码就会很快明白：
override fun command(subscriptionCount: StateFlow<Int>): Flow<SharingCommand> = subscriptionCount .transformLatest { count ->
if (count > 0) {
emit(SharingCommand.START)
} else {
delay(stopTimeout)
if (replayExpiration > 0) {
emit(SharingCommand.STOP)
delay(replayExpiration)
}
emit(SharingCommand.STOP_AND_RESET_REPLAY_CACHE)
}
}
.dropWhile { it != SharingCommand.START } // don't emit any STOP/RESET_BUFFER to start with, only START
.distinctUntilChanged() // just in case somebody forgets it, don't lea
作者：星流星
链接：https://www.jianshu.com/p/29585473ff65
来源：简书
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