It seems that I can use Code A to run a task periodically.
You know soundDb() can be fired every 100ms, it's just like to run periodically.
Is it a good way to use kotlin-coroutines run a task periodically?
Code A
fun calCurrentAsyn() {
viewModelScope.launch {
var b = 0.0
for (i in 1..5) {
b = b + soundDb()
delay(100)
}
b = b / 5.0
myInfo.value = b.toString() + " OK Asyn " + a++.toString()
}
}
suspend fun soundDb(): Double {
var k=0.0
for (i in 1..500000000){
k=k+i
}
return k
}
Added Content:
To Joffrey: Thanks!
1: I know that Code B is the better style, will the effect of execution be same between Code A and Code B ?
Code B
viewModelScope.launch {
val b = computeCurrent()
myInfo.value = "$b OK Asyn ${a++}"
}
suspend fun computeCurrent(): Double {
var b = 0.0
repeat(5) {
b += soundDb()
delay(100)
}
return b / 5.0
}
suspend fun soundDb(): Double {
var k=0.0
for (i in 1..500000000){
k=k+i
}
return k
}
2: I hope to get information regularly from a long running coroutine with a periodic task, how can I cancel the flow soundDbFlow().runningAverage() ?
Code C
viewModelScope.launch {
soundDbFlow().runningAverage().collect {
println("Average = $it") // do something with it
}
}
3: You know I can use Timer().scheduleAtFixedRate to get information regularly in background thread just like Code D, which is the between Timer().scheduleAtFixedRate and Flow ?
Code D
private fun startTimer() {
timer = Timer()
timer.scheduleAtFixedRate(timerTask {
recordingTime = recordingTime + 1
val temp = fromCountToTimeByInterval(recordingTime, 10)
_timeElapse.postValue(temp)
}, 0, 10)
}
private fun stopTimer() {
timer.cancel()
recordingTime = 0
_timeElapse.postValue("00:00.00")
}
The approach (launch + loop) to repeat a task is not bad in itself, but the question is rather about how you want this coroutine to affect the rest of the application.
It's hard to tell whether this is an example for the sake of the question, or your actual code. If it is your actual code, your use case is not a classic "periodic task run":
it has a fixed number of iterations
it only has side effects at the end of the execution
This is an indication that it may make more sense to write this code as a suspend function instead:
suspend fun computeCurrent(): Double {
var b = 0.0
repeat(5) {
b += soundDb()
delay(100)
}
return b / 5.0
}
And then use it like this, to make it clearer where the results are used:
viewModelScope.launch {
val b = computeCurrent()
myInfo.value = "$b OK Asyn ${a++}"
}
Maybe you won't actually need to launch that coroutine in an async way (it probably depends on how you make other similar calls).
If you needed to get information regularly (not just at the end) from a long running coroutine with a periodic task, you might want to consider building a Flow instead and collecting it to apply the side-effects:
import kotlinx.coroutines.flow.*
import kotlin.time.Duration
import kotlin.time.Duration.Companion.milliseconds
fun soundDbFlow(period: Duration = 100.milliseconds) = flow {
while (true) {
emit(soundDb())
delay(period)
}
}
fun Flow<Double>.runningAverage(): Flow<Double> = flow {
var valuesCount = 0
var sum = 0.0
collect { value ->
sum += value
valuesCount++
emit(sum / valuesCount)
}
}
And then the usage could be something like:
viewModelScope.launch {
soundDbFlow().take(5).runningAverage().collect {
println("Average = $it") // do something with it
}
}
About the amended question:
I know that Code B is the better style, will the effect of execution be same between Code A and Code B ?
Code A and Code B behave the same. My point was indeed half about the style, because making it a simple suspend function makes it clear (to you and readers) that it only returns a single value. This seems to be a mistake that you make also in your newly added soundDb() function and I'm not sure it's clear to you that loops are not streams, and that you're only returning one value from those functions (not updating anything several times).
The other half of my point was that, since it's only a single value that you updated, it may not even need to be run in a long-running coroutine. You might integrate it with other pieces of suspending code where needed.
how can I cancel the flow soundDbFlow().runningAverage() ?
The flow is automatically canceled if the collecting coroutine is cancelled (either via the job you launched or by cancelling the whole viewModelScope - which happens automatically when the component is not needed). The flow is also cancelled if you use terminal operators that end the collection early, such as first(), takeWhile(), take(n).collect { .. }, etc.
You know I can use Timer().scheduleAtFixedRate to get information regularly in background thread just like Code D, which is the between Timer().scheduleAtFixedRate and Flow ?
It's up to you honestly. If you're using coroutines already, I'd personally favor the flow approach. scheduleAtFixedRate will not be integrated with structured concurrency and will require manual management of the cancellation.
Related
well, I have an Observable, I’ve used asFlow() to convert it but doesn’t emit.
I’m trying to migrate from Rx and Channels to Flow, so I have this function
override fun processIntents(intents: Observable<Intent>) {
intents.asFlow().shareTo(intentsFlow).launchIn(this)
}
shareTo() is an extension function which does onEach { receiver.emit(it) }, processIntents exists in a base ViewModel, and intentsFlow is a MutableSharedFlow.
fun <T> Flow<T>.shareTo(receiver: MutableSharedFlow<T>): Flow<T> {
return onEach { receiver.emit(it) }
}
I want to pass emissions coming from the intents Observable to intentsFlow, but it doesn’t work at all and the unit test keeps failing.
#Test(timeout = 4000)
fun `WHEN processIntent() with Rx subject or Observable emissions THEN intentsFlow should receive them`() {
return runBlocking {
val actual = mutableListOf<TestNumbersIntent>()
val intentSubject = PublishSubject.create<TestNumbersIntent>()
val viewModel = FlowViewModel<TestNumbersIntent, TestNumbersViewState>(
dispatcher = Dispatchers.Unconfined,
initialViewState = TestNumbersViewState()
)
viewModel.processIntents(intentSubject)
intentSubject.onNext(OneIntent)
intentSubject.onNext(TwoIntent)
intentSubject.onNext(ThreeIntent)
viewModel.intentsFlow.take(3).toList(actual)
assertEquals(3, actual.size)
assertEquals(OneIntent, actual[0])
assertEquals(TwoIntent, actual[1])
assertEquals(ThreeIntent, actual[2])
}
}
test timed out after 4000 milliseconds
org.junit.runners.model.TestTimedOutException: test timed out after
4000 milliseconds
This works
val ps = PublishSubject.create<Int>()
val mf = MutableSharedFlow<Int>()
val pf = ps.asFlow()
.onEach {
mf.emit(it)
}
launch {
pf.take(3).collect()
}
launch {
mf.take(3).collect {
println("$it") // Prints 1 2 3
}
}
launch {
yield() // Without this we suspend indefinitely
ps.onNext(1)
ps.onNext(2)
ps.onNext(3)
}
We need the take(3)s to make sure our program terminates, because MutableSharedFlow and PublishSubject -> Flow collect indefinitely.
We need the yield because we're working with a single thread and we need to give the other coroutines an opportunity to start working.
Take 2
This is much better. Doesn't use take, and cleans up after itself.
After emitting the last item, calling onComplete on the PublishSubject terminates MutableSharedFlow collection. This is a convenience, so that when this code runs it terminates completely. It is not a requirement. You can arrange your Job termination however you like.
Your code never terminating is not related to the emissions never being collected by the MutableSharedFlow. These are separate concerns. The first is due to the fact that neither a flow created from a PublishSubject, nor a MutableSharedFlow, terminates on its own. The PublishSubject flow will terminate when onComplete is called. The MutableSharedFlow will terminate when the coroutine (specifically, its Job) collecting it terminates.
The Flow constructed by PublishSubject.asFlow() drops any emissions if, at the time of the emission, collection of the Flow hasn't suspended, waiting for emissions. This introduces a race condition between being ready to collect and code that calls PublishSubject.onNext().
This, I believe, is the reason why flow collection isn't picking up the onNext emissions in your code.
It's why a yield is required right after we launch the coroutine that collects from psf.
val ps = PublishSubject.create<Int>()
val msf = MutableSharedFlow<Int>()
val psf = ps.asFlow()
.onEach {
msf.emit(it)
}
val j1 = launch {
psf.collect()
}
yield() // Use this to allow psf.collect to catch up
val j2 = launch {
msf.collect {
println("$it") // Prints 1 2 3 4
}
}
launch {
ps.onNext(1)
ps.onNext(2)
ps.onNext(3)
ps.onNext(4)
ps.onComplete()
}
j1.invokeOnCompletion { j2.cancel() }
j2.join()
Below is the piece of code which I was trying out. there are two blocks of code. the first one creates a million threads/coroutines asynchronously in map and then later adds them up. the second block creates the million coroutines and adds them in the for loop and then prints in out.
According to me the first one should be faster as it creates them in parallel . the second one should be slow as you are creating million in one coroutine.
if my question makes sense at all I would like to get some understanding of this.
fun main() {
//first block
val elapsed1 = measureTimeMillis {
val deferred = (1..1000000).map { n->
GlobalScope.async{
n
}
}
runBlocking {
val sum = deferred.sumByDouble { it.await().toDouble() }
println(sum)
}
}
//second block
val elapsed2 = measureTimeMillis {
val def2 = GlobalScope.async{
var j = 0.0;
for(i in 1..1000000) {
j+=i
}
j
}
runBlocking {
val s = def2.await()
println(s)
}
}
println("elapsed1 $elapsed1")
println("elapsed2 $elapsed2")
}
Please note that, for above small block of code, it is very difficult determine which one is faster than another.
Also, the result of above execution may vary from system to system.
Hence, we cannot confirm which block of code is faster just by running above steps.
Also, I request you to please refer to the following article to get more clarity around this area:
What is microbenchmarking?
I'm trying to write a code, that will let me test situations, in which Javas #Synchronized is not enough, to synchronize Kotlin coroutines. From my understanding, the code below:
var sharedCounter: Long = 0
#Synchronized
suspend fun updateCounter() {
delay(2)
sharedCounter++
delay(2)
yield()
}
fun main() = runBlocking {
var regularCounter: Long = 0
val scope = CoroutineScope(Dispatchers.IO + Job())
val jobs = mutableListOf<Job>()
repeat(1000) {
val job = scope.launch {
for (i in 1..1_000) {
regularCounter++
updateCounter()
}
}
jobs.add(job)
}
jobs.forEach { it.join() }
println("The number of shared counter is $sharedCounter")
println("The number of regular counter is $regularCounter")
}
should result in both sharedCounter and regularCounter NOT being equal to 1000000.
This code was based on this and this articles.
For some reason, sharedCounter always equals 1000000 and I'm not sure why.
I've tried testing larger for loops, but it did not "break" the synchronization either.
The synchronization lock is released at each suspend function call, but re-acquired before continuing. In your code, delay() and yield() are the suspension points. But sharedCounter++ is all local code, so the lock is held while it performs its three steps of getting the value, incrementing it, and setting it. So, the first delay() releases the lock, the continuation resumes so it re-locks and performs sharedCounter++, and then the lock is released again at the second delay() call.
I need to launch a number of jobs which will return a result.
In the main code (which is not a coroutine), after launching the jobs I need to wait for them all to complete their task OR for a given timeout to expire, whichever comes first.
If I exit from the wait because all the jobs completed before the timeout, that's great, I will collect their results.
But if some of the jobs are taking longer that the timeout, my main function needs to wake as soon as the timeout expires, inspect which jobs did finish in time (if any) and which ones are still running, and work from there, without cancelling the jobs that are still running.
How would you code this kind of wait?
The solution follows directly from the question. First, we'll design a suspending function for the task. Let's see our requirements:
if some of the jobs are taking longer that the timeout... without cancelling the jobs that are still running.
It means that the jobs we launch have to be standalone (not children), so we'll opt-out of structured concurrency and use GlobalScope to launch them, manually collecting all the jobs. We use async coroutine builder because we plan to collect their results of some type R later:
val jobs: List<Deferred<R>> = List(numberOfJobs) {
GlobalScope.async { /* our code that produces R */ }
}
after launching the jobs I need to wait for them all to complete their task OR for a given timeout to expire, whichever comes first.
Let's wait for all of them and do this waiting with timeout:
withTimeoutOrNull(timeoutMillis) { jobs.joinAll() }
We use joinAll (as opposed to awaitAll) to avoid exception if one of the jobs fail and withTimeoutOrNull to avoid exception on timeout.
my main function needs to wake as soon as the timeout expires, inspect which jobs did finish in time (if any) and which ones are still running
jobs.map { deferred -> /* ... inspect results */ }
In the main code (which is not a coroutine) ...
Since our main code is not a coroutine it has to wait in a blocking way, so we bridge the code we wrote using runBlocking. Putting it all together:
fun awaitResultsWithTimeoutBlocking(
timeoutMillis: Long,
numberOfJobs: Int
) = runBlocking {
val jobs: List<Deferred<R>> = List(numberOfJobs) {
GlobalScope.async { /* our code that produces R */ }
}
withTimeoutOrNull(timeoutMillis) { jobs.joinAll() }
jobs.map { deferred -> /* ... inspect results */ }
}
P.S. I would not recommend deploying this kind of solution in any kind of a serious production environment, since letting your background jobs running (leak) after timeout will invariably badly bite you later on. Do so only if you throughly understand all the deficiencies and risks of such an approach.
You can try to work with whileSelect and the onTimeout clause. But you still have to overcome the problem that your main code is not a coroutine. The next lines are an example of whileSelect statement. The function returns a Deferred with a list of results evaluated in the timeout period and another list of Deferreds of the unfinished results.
fun CoroutineScope.runWithTimeout(timeoutMs: Int): Deferred<Pair<List<Int>, List<Deferred<Int>>>> = async {
val deferredList = (1..100).mapTo(mutableListOf()) {
async {
val random = Random.nextInt(0, 100)
delay(random.toLong())
random
}
}
val finished = mutableListOf<Int>()
val endTime = System.currentTimeMillis() + timeoutMs
whileSelect {
var waitTime = endTime - System.currentTimeMillis()
onTimeout(waitTime) {
false
}
deferredList.toList().forEach { deferred ->
deferred.onAwait { random ->
deferredList.remove(deferred)
finished.add(random)
true
}
}
}
finished.toList() to deferredList.toList()
}
In your main code you can use the discouraged method runBlocking to access the Deferrred.
fun main() = runBlocking<Unit> {
val deferredResult = runWithTimeout(75)
val (finished, pending) = deferredResult.await()
println("Finished: ${finished.size} vs Pending: ${pending.size}")
}
Here is the solution I came up with. Pairing each job with a state (among other info):
private enum class State { WAIT, DONE, ... }
private data class MyJob(
val job: Deferred<...>,
var state: State = State.WAIT,
...
)
and writing an explicit loop:
// wait until either all jobs complete, or a timeout is reached
val waitJob = launch { delay(TIMEOUT_MS) }
while (waitJob.isActive && myJobs.any { it.state == State.WAIT }) {
select<Unit> {
waitJob.onJoin {}
myJobs.filter { it.state == State.WAIT }.forEach {
it.job.onJoin {}
}
}
// mark any finished jobs as DONE to exclude them from the next loop
myJobs.filter { !it.job.isActive }.forEach {
it.state = State.DONE
}
}
The initial state is called WAIT (instead of RUN) because it doesn't necessarily mean that the job is still running, only that my loop has not yet taken it into account.
I'm interested to know if this is idiomatic enough, or if there are better ways to code this kind of behaviour.
I have been reading kotlin docs, and if I understood correctly the two Kotlin functions work as follows :
withContext(context): switches the context of the current coroutine, when the given block executes, the coroutine switches back to previous context.
async(context): Starts a new coroutine in the given context and if we call .await() on the returned Deferred task, it will suspends the calling coroutine and resume when the block executing inside the spawned coroutine returns.
Now for the following two versions of code :
Version1:
launch(){
block1()
val returned = async(context){
block2()
}.await()
block3()
}
Version2:
launch(){
block1()
val returned = withContext(context){
block2()
}
block3()
}
In both versions block1(), block3() execute in default context(commonpool?) where as block2() executes in the given context.
The overall execution is synchronous with block1() -> block2() -> block3() order.
Only difference I see is that version1 creates another coroutine, where as version2 executes only one coroutine while switching context.
My questions are :
Isn't it always better to use withContext rather than async-await as it is functionally similar, but doesn't create another coroutine. Large numbers of coroutines, although lightweight, could still be a problem in demanding applications.
Is there a case async-await is more preferable to withContext?
Update:
Kotlin 1.2.50 now has a code inspection where it can convert async(ctx) { }.await() to withContext(ctx) { }.
Large number of coroutines, though lightweight, could still be a problem in demanding applications
I'd like to dispel this myth of "too many coroutines" being a problem by quantifying their actual cost.
First, we should disentangle the coroutine itself from the coroutine context to which it is attached. This is how you create just a coroutine with minimum overhead:
GlobalScope.launch(Dispatchers.Unconfined) {
suspendCoroutine<Unit> {
continuations.add(it)
}
}
The value of this expression is a Job holding a suspended coroutine. To retain the continuation, we added it to a list in the wider scope.
I benchmarked this code and concluded that it allocates 140 bytes and takes 100 nanoseconds to complete. So that's how lightweight a coroutine is.
For reproducibility, this is the code I used:
fun measureMemoryOfLaunch() {
val continuations = ContinuationList()
val jobs = (1..10_000).mapTo(JobList()) {
GlobalScope.launch(Dispatchers.Unconfined) {
suspendCoroutine<Unit> {
continuations.add(it)
}
}
}
(1..500).forEach {
Thread.sleep(1000)
println(it)
}
println(jobs.onEach { it.cancel() }.filter { it.isActive})
}
class JobList : ArrayList<Job>()
class ContinuationList : ArrayList<Continuation<Unit>>()
This code starts a bunch of coroutines and then sleeps so you have time to analyze the heap with a monitoring tool like VisualVM. I created the specialized classes JobList and ContinuationList because this makes it easier to analyze the heap dump.
To get a more complete story, I used the code below to also measure the cost of withContext() and async-await:
import kotlinx.coroutines.*
import java.util.concurrent.Executors
import kotlin.coroutines.suspendCoroutine
import kotlin.system.measureTimeMillis
const val JOBS_PER_BATCH = 100_000
var blackHoleCount = 0
val threadPool = Executors.newSingleThreadExecutor()!!
val ThreadPool = threadPool.asCoroutineDispatcher()
fun main(args: Array<String>) {
try {
measure("just launch", justLaunch)
measure("launch and withContext", launchAndWithContext)
measure("launch and async", launchAndAsync)
println("Black hole value: $blackHoleCount")
} finally {
threadPool.shutdown()
}
}
fun measure(name: String, block: (Int) -> Job) {
print("Measuring $name, warmup ")
(1..1_000_000).forEach { block(it).cancel() }
println("done.")
System.gc()
System.gc()
val tookOnAverage = (1..20).map { _ ->
System.gc()
System.gc()
var jobs: List<Job> = emptyList()
measureTimeMillis {
jobs = (1..JOBS_PER_BATCH).map(block)
}.also { _ ->
blackHoleCount += jobs.onEach { it.cancel() }.count()
}
}.average()
println("$name took ${tookOnAverage * 1_000_000 / JOBS_PER_BATCH} nanoseconds")
}
fun measureMemory(name:String, block: (Int) -> Job) {
println(name)
val jobs = (1..JOBS_PER_BATCH).map(block)
(1..500).forEach {
Thread.sleep(1000)
println(it)
}
println(jobs.onEach { it.cancel() }.filter { it.isActive})
}
val justLaunch: (i: Int) -> Job = {
GlobalScope.launch(Dispatchers.Unconfined) {
suspendCoroutine<Unit> {}
}
}
val launchAndWithContext: (i: Int) -> Job = {
GlobalScope.launch(Dispatchers.Unconfined) {
withContext(ThreadPool) {
suspendCoroutine<Unit> {}
}
}
}
val launchAndAsync: (i: Int) -> Job = {
GlobalScope.launch(Dispatchers.Unconfined) {
async(ThreadPool) {
suspendCoroutine<Unit> {}
}.await()
}
}
This is the typical output I get from the above code:
Just launch: 140 nanoseconds
launch and withContext : 520 nanoseconds
launch and async-await: 1100 nanoseconds
Yes, async-await takes about twice as long as withContext, but it's still just a microsecond. You'd have to launch them in a tight loop, doing almost nothing besides, for that to become "a problem" in your app.
Using measureMemory() I found the following memory cost per call:
Just launch: 88 bytes
withContext(): 512 bytes
async-await: 652 bytes
The cost of async-await is exactly 140 bytes higher than withContext, the number we got as the memory weight of one coroutine. This is just a fraction of the complete cost of setting up the CommonPool context.
If performance/memory impact was the only criterion to decide between withContext and async-await, the conclusion would have to be that there's no relevant difference between them in 99% of real use cases.
The real reason is that withContext() a simpler and more direct API, especially in terms of exception handling:
An exception that isn't handled within async { ... } causes its parent job to get cancelled. This happens regardless of how you handle exceptions from the matching await(). If you haven't prepared a coroutineScope for it, it may bring down your entire application.
An exception not handled within withContext { ... } simply gets thrown by the withContext call, you handle it just like any other.
withContext also happens to be optimized, leveraging the fact that you're suspending the parent coroutine and awaiting on the child, but that's just an added bonus.
async-await should be reserved for those cases where you actually want concurrency, so that you launch several coroutines in the background and only then await on them. In short:
async-await-async-await — don't do that, use withContext-withContext
async-async-await-await — that's the way to use it.
Isn't it always better to use withContext rather than asynch-await as it is funcationally similar, but doesn't create another coroutine. Large numebrs coroutines, though lightweight could still be a problem in demanding applications
Is there a case asynch-await is more preferable to withContext
You should use async/await when you want to execute multiple tasks concurrently, for example:
runBlocking {
val deferredResults = arrayListOf<Deferred<String>>()
deferredResults += async {
delay(1, TimeUnit.SECONDS)
"1"
}
deferredResults += async {
delay(1, TimeUnit.SECONDS)
"2"
}
deferredResults += async {
delay(1, TimeUnit.SECONDS)
"3"
}
//wait for all results (at this point tasks are running)
val results = deferredResults.map { it.await() }
//Or val results = deferredResults.awaitAll()
println(results)
}
If you don't need to run multiple tasks concurrently you can use withContext.
When in doubt, remember this like a rule of thumb:
If multiple tasks have to happen in parallel and the final result depends on completion of all of them, then use async.
For returning the result of a single task, use withContext.