So there is a new builder function for LiveData which is:
val someLiveData = liveData {
// do something
}
Can anyone explain exactly what this new builder function solve? How does it solve issues on rotation? How does it relate to webservice calls?
Any inputs would be appreciated. Thanks in advance.
Can anyone explain exactly what this new builder function solve?
The current documentation on liveData { } it is pretty good and gives many examples. Here are some benefits you get for free by using it:
Automatic support for timeout and canceling through the optional timeoutInMs (which defaults to 5 seconds).
No need to explicitly launch a coroutine from an init { } block to initialize a MutableLiveData<T> (this hypothetical coroutine is referred to as C below).
No need to worry about what scope to launch C in
No need to maintain code to wait with launching C until it is actually needed (i.e. the LiveData has any registered and active observers).
No need to write code for relaunching C when the LiveData is re-activated.
How does it solve issues on rotation?
LiveData in itself does not solve any issue with preserving state across e.g. screen rotation. That's what ViewModel is for. Typically you have LiveData properties in your ViewModel. But there is not direct relationship between screen rotation problems and liveData { }
How does it relate to webservice calls?
Since the block you pass to liveData { } is a suspend function, you can use coroutine support in your webservice. For example, Retrofit 2.6.0 and later supports suspend modifiers in its HTTP request function definitions, which makes it very convenient to use in the liveData { } code block.
Related
How can I get the latest value of a Flow? I don't have a StateFlow where I need that latest value. This is the condensed scenario:
There is a repository exposing a StateFlow
val repositoryExposedStateFlow: StateFlow<SomeType> = MutableStateFlow(...)
Additionally there are mappers transforming that StateFlow like
val mappedFlow: Flow<SomeOtherType> = repositoryExposedStateFlow.flatMapLatest { ... }
mappedFlow is no StateFlow anymore, but just a Flow. Thus, I cannot get the latest/current value as I can when there's StateFlow.
Anyhow, I need the latest value in that Flow at some point. Since this point is not in a ViewModel, but some Use Case implementation, I cannot simply perform a stateIn and hold the latest value in the ViewModel all the time the ViewModel is alive -- otherwise I had to pass on the value to all Use Cases. Actually, within a Use Case I trigger a network refresh which leads to emitting of new values on the StateFlow and thus on the mappedFlow, too.
In the Use Cases I have CoroutineScopes though. So I came up with
suspend fun <T> Flow<T>.getState(): T {
return coroutineScope {
val result = stateIn(
scope = this
).value
coroutineContext.cancelChildren()
result
}
}
Without using coroutineContext.cancelChildren() the method will never return, because coroutineScope blocks the caller until all child coroutines have finished. As stateIn never finishes, I manually cancel all children.
Apparently this is a bad thing to do.
But how can I solve this problem in a better way? In my perception the problem arises from StateFlow mapping resulting in regular Flow instances.
Yes, all you need is to call first() on the flow. Since it is backed by a StateFlow upstream, the first() call will get the current value of that backing StateFlow, run it through whatever transformations happen from the downstream operators, and return that value.
This effectively gets you the same result as your attempt above.
The downside is that all the downstream operators must be run, so it is potentially expensive.
This is only possible if there is an upstream StateFlow. Otherwise, there is no concept of a latest value for you to be able to retrieve.
I would challenge your need to get the latest value, though. Typically, you collect flows, so you're already working with a current value. Flows are intended for reactive programming.
The company I just started working at uses a so called Navigator, which I for now interpreted as a stateless ViewModel. My Navigator receives some usecases, with each contains 1 suspend function. The result of any of those usecases could end up in a single LiveData. The Navigator has no coroutine scope, so I pass the responsibility of scoping suspending to the Fragment using fetchValue().
Most current code in project has LiveData in the data layer, which I tried not to. Because of that, their livedata is linked from view to dao.
My simplified classes:
class MyFeatureNavigator(
getUrl1: getUrl1UseCase,
getUrl1: getUrl1UseCase
) {
val url = MediatorLiveData<String>()
fun goToUrl1() {
url.fetchValue { getUrl1() }
}
fun goToUrl2() {
url.fetchValue { getUrl2() }
}
fun <T> MediatorLiveData<T>.fetchValue(provideValue: suspend () -> T) {
val liveData = liveData { emit(provideValue()) }
addSource(liveData) {
removeSource(liveData)
value = it
}
}
}
class MyFeatureFragment : Fragment {
val viewModel: MyFeatureViewModel by viewModel()
val navigator: MyFeatureNavigator by inject()
fun onViewCreated() {
button.setOnClickListener { navigator.goToUrl1() }
navigator.url.observe(viewLifecycleOwner, Observer { url ->
openUrl(url)
})
}
}
My two questions:
Is fetchValue() a good way to link a suspend function to LiveData? Could it leak? Any other concerns?
My main reason to only use coroutines (and flow) in the data layer, is 'because Google said so'. What's a better reason for this? And: what's the best trade off in being consistent with the project and current good coding practices?
Is fetchValue() a good way to link a suspend function to LiveData?
Could it leak? Any other concerns?
Generally it should work. You probably should remove the previous source of the MediatorLiveData before adding new one, otherwise if you get two calls to fetchValue in a row, the first url can be slower to fetch, so it will come later and win.
I don't see any other correctness concerns, but this code is pretty complicated, creates a couple of intermediate objects and generally difficult to read.
My main reason to only use coroutines (and flow) in the data layer,
is 'because Google said so'. What's a better reason for this?
Google has provided a lot of useful extensions to use coroutines in the UI layer, e.g. take a look at this page. So obviously they encourage people to use it.
Probably you mean the recommendation to use LiveData instead of the Flow in the UI layer. That's not a strict rule and it has one reason: LiveData is a value holder, it keeps its value and provides it immediately to new subscribers without doing any work. That's particularly useful in the UI/ViewModel layer - when a configuration change happens and activity/fragment is recreated, the newly created activity/fragment uses the same view model, subscribes to the same LiveData and receives the value at no cost.
At the same time Flow is 'cold' and if you expose a flow from your view model, each reconfiguration will trigger a new flow collection and the flow will be to execute from scratch.
So e.g. if you fetch data from db or network, LiveData will just provide the last value to new subscriber and Flow will execute the costly db/network operation again.
So as I said there is no strict rule, it depends on the particular use-case. Also I find it very useful to use Flow in view models - it provides a lot of operators and makes the code clean and concise. But than I convert it to a LiveData with help of extensions like asLiveData() and expose this LiveData to the UI. This way I get best from both words - LiveData catches value between reconfigurations and Flow makes the code of view models nice and clean.
Also you can use latest StateFlow and SharedFlow often they also can help to overcome the mentioned Flow issue in the UI layer.
Back to your code, I would implement it like this:
class MyFeatureNavigator(
getUrl1: getUrl1UseCase,
getUrl1: getUrl1UseCase
) {
private val currentUseCase = MutableStateFlow<UseCase?>(null)
val url = currentUseCase.filterNotNull().mapLatest { source -> source.getData()}.asLiveData()
fun goToUrl1() {
currentUseCase.value = getUrl1
}
fun goToUrl2() {
currentUseCase.value = getUrl2
}
}
This way there are no race conditions to care about and code is clean.
And: what's the best trade off in being consistent with the project
and current good coding practices?
That's an arguable question and it should be primarily team decision. In most projects I participated we adopted this rule: when fixing bugs, doing maintenance of existing code, one should follow the same style. When doing big refactoring/implementing new features one should use latest practices adopted by the team.
I tried using Kotlin Flow to be some kind of message container which should pass this message to all observers (collectors). I do not want to use LiveData on purpose because it need to be aware of lifecycle.
Unfortunately I have noticed that if one collector collects message from flow no one else can receive it.
What could I use to achieve "one input - many output".
You can use StateFlow or SharedFlow, they are Flow APIs that enable flows to optimally emit state updates and emit values to multiple consumers.
From the documentation, available here:
StateFlow: is a state-holder observable flow that emits the current and new state updates to its collectors. The current state value can also be read through its value property.
SharedFlow: a hot flow that emits values to all consumers that collect from it. A SharedFlow is a highly-configurable generalization of StateFlow.
A simple example using state flow with view model:
class myViewModel() : ViewModel() {
val messageStateFlow = MutableStateFlow("My inicial awesome message")
}
You can emit a new value using some scope:
yourScope.launch {
messageStateFlow.emit("My new awesome message")
}
You can collect a value using some scope:
yourScope.launch {
messageStateFlow.collect {
// do something with your message
}
}
Attention: Never collect a flow from the UI directly from launch or the launchIn extension function to update UI. These functions process events even when the view is not visible. You can use repeatOnLifecycle as the documentation sugests.
You can try BehaviorSubject from rxJava. Is more comfortable to use than poor kotlin.flow. Seems like this link is for you: BehaviorSubject vs PublishSubject
val behaviorSubject = BehaviorSubject.create<MyObject> {
// for example you can emit new item with it.onNext(),
// finish with error like it.onError() or just finish with it.onComplete()
somethingToEmit()
}
behaviorSubject.subscribe {
somethingToHandle()
}
How can I get the value of a Flow outside a coroutine similarly to LiveData?
// Suspend function 'first' should be called only from a coroutine or another suspend function
flowOf(1).first()
// value is null
flowOf(1).asLiveData().value
// works
MutableLiveData(1).value
Context
I'm avoiding LiveData in the repository layer in favor of Flow. Yet, I need to set, observe and collect the value for immediate consumption. The later is useful for authentication purpose in a OkHttp3 Interceptor.
You can do this
val flowValue: SomeType
runBlocking(Dispatchers.IO) {
flowValue = myFlow.first()
}
Yes its not exactly what Flow was made for.
But its not always possible to make everything asynchronous and for that matter it may not even always be possible to 'just make a synchronous method'. For instance the current Datastore releases (that are supposed to replace shared preferences on Android) do only expose Flow and nothing else. Which means that you will very easiely get into such a situation, given that none of the Lifecycle methods of Activities or Fragments are coroutines.
If you can help it you should always call coroutines from suspend functions and avoid making runBlocking calls. A lot of the time it works like this. But it´s not a surefire way that works all the time. You can introduce deadlocks with runBlocking.
Well... what you're looking for isn't really what Flow is for. Flow is just a stream. It is not a value holder, so there is nothing for you retrieve.
So, there are two major avenues to go down, depending on what your interceptor needs.
Perhaps your interceptor can live without the data from the repository. IOW, you'll use the data if it exists, but otherwise the interceptor can continue along. In that case, you can have your repository emit a stream but also maintain a "current value" cache that your interceptor can use. That could be via:
BroadcastChannel
LiveData
a simple property in the repository that you update internally and expose as a val
If your interceptor needs the data, though, then none of those will work directly, because they will all result in the interceptor getting null if the data is not yet ready. What you would need is a call that can block, but perhaps evaluates quickly if the data is ready via some form of cache. The details of that will vary a lot based on the implementation of the repository and what is supplying the Flow in the first place.
You could use something like this:
fun <T> SharedFlow<T>.getValueBlockedOrNull(): T? {
var value: T?
runBlocking(Dispatchers.Default) {
value = when (this#getValueBlockedOrNull.replayCache.isEmpty()) {
true -> null
else -> this#getValueBlockedOrNull.firstOrNull()
}
}
return value
}
You can use MutableStateFlow and MutableSharedFlow for emitting the data from coroutine and receiving the data inside Activity/Fragment. MutableStateFlow can be used for state management. It requires default value when initialised. Whereas MutableSharedFlow does not need any default value.
But, if you don't want to receive stream of data, (i.e) your API call sends data only once, you can use suspend function inside coroutine scope and the function will perform the task and return the result like synchronous function call.
To get the value of a Flow outside of a coroutine, the best option is to create the flow as a StateFlow and then call the value property on the StateFlow.
class MyClass {
private val mutableProperty = MutableStateFlow(1)
val property = mutableProperty.asStateFlow()
...
mutableProperty.value = 2
}
...
val readProperty = MyClass().property.value
val propertyAsFlow = MyClass().property as Flow<Int>
I'm trying to learn the Arrow library and improve my functional programming by transitioning some of my Android Kotlin code from more imperative style to functional style. I've been doing a type of MVI programming in the application to make testing simpler.
"Traditional" Method
ViewModel
My view model has a LiveData of the view's state plus a public method to pass user interactions from the view to the viewmodel so the view model can update state in whatever way is appropriate.
class MyViewModel: ViewModel() {
val state = MutableLiveData(MyViewState()) // MyViewState is a data class with relevant data
fun instruct(intent: MyIntent) { // MyIntent is a sealed class of data classes representing user interactions
return when(intent) {
is FirstIntent -> return viewModelScope.launch(Dispatchers.IO) {
val result = myRoomRepository.suspendFunctionManipulatingDatabase(intent.myVal)
updateStateWithResult(result)
}.run { Unit }
is SecondIntent -> return updateStateWithResult(intent.myVal)
}
}
}
Activity
The Activity subscribes to the LiveData and, on changes to state, it runs a render function using the state. The activity also passes user interactions to the view model as intents (not to be confused with Android's Intent class).
class MyActivity: AppCompatActivity() {
private val viewModel = MyViewModel()
override fun onCreateView() {
viewModel.state.observe(this, Observer { render(it) })
myWidget.onClickObserver = {
viewModel.instruct(someIntent)
}
}
private fun render(state: MyViewState) { /* update view with state */ }
}
Arrow.IO Functional Programming
I'm having trouble finding examples that aren't way over my head using Arrow's IO monad to make impure functions with side effects obvious and unit-testable.
View Model
So far I have turned my view model into:
class MyViewModel: ViewModel() {
// ...
fun instruct(intent: MyIntent): IO<Unit> {
return when(intent) {
is FirstIntent -> IO.fx {
val (result) = effect { myRoomRepository.suspendFunctionManipulatingDatabase(intent.myVal) }
updateStateWithResult(result)
}
is SecondIntent -> IO { updateStateWithResult(intent.myVal) }
}
}
}
I do not know how I am supposed to make this IO stuff run in Dispatcher.IO like I've been doing with viewModelScope.launch. I can't find an example for how to do this with Arrow. The ones that make API calls all seem to be something other than Android apps, so there is no guidance about Android UI vs IO threads.
View model unit test
Now, because one benefit I'm seeing to this is that when I write my view model's unit tests, I can have a test. If I mock the repository in order to check whether suspendFunctionManipulatingDatabase is called with the expected parameter.
#Test
fun myTest() {
val result: IO<Unit> = viewModel.instruct(someIntent)
result.unsafeRunSync()
// verify suspendFunctionManipulatingDatabase argument was as expected
}
Activity
I do not know how to incorporate the above into my Activity.
class MyActivity: AppCompatActivity() {
private val viewModel = MyViewModel()
override fun onCreateView() {
viewModel.state.observe(this, Observer { render(it) })
myWidget.onClickObserver = {
viewModel.instruct(someIntent).unsafeRunSync() // Is this how I should do it?
}
}
// ...
}
My understanding is anything in an IO block does not run right away (i.e., it's lazy). You have to call attempt() or unsafeRunSync() to get the contents to be evaluated.
Calling viewModel.instruct from Activity means I need to create some scope and invoke in Dispatchers.IO right? Is this Bad(TM)? I was able to confine coroutines completely to the view model using the "traditional" method.
Where do I incorporate Dispatchers.IO to replicate what I did with viewModelScope.launch(Dispatchers.IO)?
Is this the way you're supposed to structure a unit test when using Arrow's IO?
That's a really good post to read indeed. I'd also recommend digging into this sample app I wrote that is using ArrowFx also.
https://github.com/JorgeCastilloPrz/ArrowAndroidSamples
Note how we build the complete program using fx and returning Kind at all levels in our architecture. That makes the code polymorphic to the type F, so you can run it using different runtime data types for F at will, depending on the environment. In this case we end up running it using IO at the edges. That's the activity in this case, but could also be the application class or a fragment. Think about this as what'd be the entry points to your apps. If we were talking about jvm programs the equivalent would be main(). This is just an example of how to write polymorphic programs, but you could use IO.fx instead and return IO everywhere, if you want to stay simpler.
Note how we use continueOn() in the data source inside the fx block to leave and come back to the main thread. Coroutine context changes are explicit in ArrowFx, so the computation jumps to the passed thread right after the continueOn until you deliberately switch again to a different one. That intentionally makes thread changes explicit.
You could inject those dispatchers to use different ones in tests. Hopefully I can provide examples of this soon in the repo, but you can probably imagine how this would look.
For the syntax on how to write tests note that your program will return Kind (if you go polymorphic) or IO, so you would unsafeRunSync it from tests (vs unsafeRunAsync or unsafeRunAsyncCancellable in production code since Android needs it to be asynchronous). That is because we want our test to be synchronous and also blocking (for the latter we need to inject the proper dispatchers).
Current caveats: The solution proposed in the repo still doesn't care of cancellation, lifecycle or surviving config changes. That's something I'd like to address soon. Using ViewModels with a hybrid style might have a chance. This is Android so I'd not fear hybrid styles if that brings better productivity. Another alternative I've got in mind would maybe be something a bit more functional. ViewModels end up retaining themselves using the retain config state existing APIs under the hood by using the ViewModelStore. That ultimately sounds like a simple cache that is definitely a side effect and could be implemented wrapped into IO. I want to give a thought to this.
I would definitely also recommend reading the complete ArrowFx docs for better understanding: https://arrow-kt.io/docs/fx/ I think it would be helpful.
For more thoughts on approaches using Functional Programming and Arrow to Android you can take a look to my blog https://jorgecastillo.dev/ my plan is to write deep content around this starting 2020, since there's a lot of people interested.
In the other hand, you can find me or any other Arrow team maintainers in the Kotlinlang JetBrains Slack, where we could have more detailed conversations or try to resolve any doubts you can have https://kotlinlang.slack.com/
As a final clarification: Functional Programming is just a paradigm that resolves generic concerns like asynchrony, threading, concurrency, dependency injection, error handling, etc. Those problems can be found on any program, regardless of the platform. Even within an Android app. That is why FP is an option as valid for mobile as any other one, but we are still into explorations to provide the best APIs to fulfill the usual Android needs in a more ergonomic way. We are in the process of exploration in this sense, and 2020 is going to be a very promising year.
Hopefully this helped! Your thoughts seem to be well aligned with how things should work in this approach overall.