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 2
receive state 3
receive state 4
receive state 5
receive state 6
receive state 7
receive state 8
receive state 9
receive 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-
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。- 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。
-
- 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 9
received again shared flow 10
Emit 1 0ms
Emit 2 105ms
received again shared flow 1
Emit 3 210ms
received again shared flow 2
Emit 4 314ms
received again shared flow 3
Emit 5 415ms
received again shared flow 4
Emit 6 519ms
received again shared flow 5
Emit 7 620ms
received again shared flow 6
Emit 8 721ms
received again shared flow 7
Emit 9 826ms
received again shared flow 8
Emit 10 927ms
received again shared flow 9
received 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 1
Emit 3 209ms
received again shared flow 2
Emit 4 315ms
received again shared flow 3
Emit 5 418ms
received again shared flow 4
Emit 6 523ms
received again shared flow 5
Emit 7 627ms
received again shared flow 6
Emit 8 732ms
received again shared flow 7
Emit 9 833ms
received again shared flow 8
Emit 10 937ms
received again shared flow 9
received 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 leak our multiple sending of START
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