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What are the differences between StateFlow and LiveData?

as I mentioned in the title, I'm curious about the general differences between the two. Can you help with this? I couldn't find the specific differences as there are complex examples on the internet.
What are the differences in terms of performance?
In which scenarios does it provide advantages?
Using StateFlow with Kotlin Flow is advantageous. But what is the risk of not switching to StateFlow in a project using LiveData?
Is Google deprecating LiveData? :)

I just switched to StateFlow, so this is a great time for me to answer your question.
What are the differences in terms of performance?
Honestly, I don't know, but since it's pushed by Kotlin and Android, just trust them :).
In which scenarios does it provide advantages?
For LiveData you are not forced to give an initial value, it may end up writing more code in init{}; But for StateFlow you are Forced to give an initial value (including null), it may save your code a bit.
For LiveData even if you give an initial value, you still need to do Null Check when you access its value (see this), it's kind of annoying. But that's not gonna happen on StateFlow - it will be what it should be.
For LiveData you cannot easily, or elegantly observe data changes JUST inside ViewModel, you are gona use observeForever() which is also mentioned in here. But for StateFlow it's easy, do it like following:
class FirstViewModel() : ViewModel() {
val uiScope = viewModelScope
val name = MutableStateFlow("Sam") //must have initial value
//val name = MutableStateFlow<String?>(null) //null is acceptable
init {
observeName()
}
private fun observeName() = uiScope.launch { //must run in coroutine scope
name.collect { name -> //for Fragment / Activity, use lifecycleScope.launch{}
//do your stuff
}
}
}
Using StateFlow with Kotlin Flow is advantageous. But what is the risk of not switching to StateFlow in a project using LiveData?
What is the risk of not switching to Kotlin in a project using Java? :)
Is Google deprecating LiveData?
I would say yes, and they would say no, no for "not yet to say it loudly" :).

Collecting from Flow in UI with repeatOnLifeCycle

I started to replace LiveData with Flow since it is more flexible. But then I find out you need to write enormous amount of boilerplate code to observe from Flow in UI.
In the StateFlow documentation, it says that
LiveData.observe() automatically unregisters the consumer when the view goes to the STOPPED state, whereas collecting from a StateFlow or any other flow does not stop collecting automatically. To achieve the same behavior,you need to collect the flow from a Lifecycle.repeatOnLifecycle block.
It's also mentioned in the article by Manuel Vivo that using collecting from lifecycleScope.launchWhenX is dangerous and should not be used in UI because the producer flow will not stop emitting.
He recommended us to use
// Listen to multiple flows
lifecycleScope.launch {
lifecycle.repeatOnLifecycle(Lifecycle.State.STARTED) {
// As collect is a suspend function, if you want to collect
// multiple flows in parallel, you need to do so in
// different coroutines
launch {
flow1.collect { /* Do something */ }
}
launch {
flow2.collect { /* Do something */ }
}
}
}
The amount of boilerplate code is too much. Is it not possible to do it in a two liner like what LiveData does?
viewModel.movieData.observe(viewLifecycleOwner) {
...
}
Why is it so complex to collect from Flow in UI? Is it advisable to convert the Flow to LiveData with asLiveData()?

You could build extensions to reduce the boilerplate
inline fun <T> Flow<T>.collectIn(
owner: LifecycleOwner,
minActiveState: Lifecycle.State = Lifecycle.State.STARTED,
coroutineContext: CoroutineContext = EmptyCoroutineContext,
crossinline action: suspend CoroutineScope.(T) -> Unit
) = owner.addRepeatingJob(minActiveState, coroutineContext) {
collect {
action(it)
}
}
This makes collecting flows similar to LiveData as
flow.collectIn(viewLifecycleOwner){ /* do stuff */ }

First answer your first question: Flow is a cold flow. And Flow is stateless. If you provide Flow, then it means that you need to construct and collect Flow frequently.
In another case, if Hot Flow is provided, such as (StateFlow), although the hot flow provides state (.value), it does not know anything about the life cycle of Android. As you said, you can use launchWhenXXX() to collect Flow.
When using launchWhenXXX(), you must pay attention to the life cycle of the hot flow. When to start collect and when to end collect, these need to be paid attention to. So it seems very troublesome. Of course, Flow is a way to get rid of using LiveData.
For details, please refer to: https://proandroiddev.com/should-we-choose-kotlins-stateflow-or-sharedflow-to-substitute-for-android-s-livedata-2d69f2bd6fa5
The second question: LiveData manages the life cycle of Android. Flow.asLiveData() is completely desirable. At this time, only a simple Observe is needed.

Kotlin Coroutines Flow with Room and state handling

I'm trying out the new coroutine's flow, my goal is to make a simple repository that can fetch data from a web api and save it to db, also return a flow from the db.
I'm using room and firebase as the web api, now everything seems pretty straight forward until i try to pass errors coming from the api to the ui.
Since i get a flow from the database which only contains the data and no state, what is the correct approach to give it a state (like loading, content, error) by combining it with the web api result?
Some of the code i wrote:
The DAO:
#Query("SELECT * FROM users")
fun getUsers(): Flow<List<UserPojo>>
The Repository:
val users: Flow<List<UserPojo>> = userDao.getUsers()
The Api call:
override fun downloadUsers(filters: UserListFilters, onResult: (result: FailableWrapper<MutableList<UserApiPojo>>) -> Unit) {
val data = Gson().toJson(filters)
functions.getHttpsCallable("users").call(data).addOnSuccessListener {
try {
val type = object : TypeToken<List<UserApiPojo>>() {}.type
val users = Gson().fromJson<List<UserApiPojo>>(it.data.toString(), type)
onResult.invoke(FailableWrapper(users.toMutableList(), null))
} catch (e: java.lang.Exception) {
onResult.invoke(FailableWrapper(null, "Error parsing data"))
}
}.addOnFailureListener {
onResult(FailableWrapper(null, it.localizedMessage))
}
}
I hope the question is clear enough
Thanks for the help
Edit: Since the question wasn't clear i'll try to clarify. My issue is that with the default flow emitted by room you only have the data, so if i were to subscribe to the flow i would only receive the data (eg. In this case i would only receive a list of users). What i need to achieve is some way to notify the state of the app, like loading or error. At the moment the only way i can think of is a "response" object that contains the state, but i can't seem to find a way to implement it.
Something like:
fun getUsers(): Flow<Lce<List<UserPojo>>>{
emit(Loading())
downloadFromApi()
if(downloadSuccessful)
return flowFromDatabase
else
emit(Error(throwable))
}
But the obvious issue i'm running into is that the flow from the database is of type Flow<List<UserPojo>>, i don't know how to "enrich it" with the state editing the flow, without losing the subscription from the database and without running a new network call every time the db is updated (by doing it in a map transformation).
Hope it's clearer

I believe this is more of an architecture question, but let me try to answer some of your questions first.
My issue is that with the default flow emitted by room you only have
the data, so if i were to subscribe to the flow i would only receive
the data
If there is an error with the Flow returned by Room, you can handle it via catch()
What i need to achieve is some way to notify the state of the app,
like loading or error.
I agree with you that having a State object is a good approach. In my mind, it is the ViewModel's responsibility to present the State object to the View. This State object should have a way to expose errors.
At the moment the only way i can think of is a "response" object that
contains the state, but i can't seem to find a way to implement it.
I have found that it is easier to have the State object that the ViewModel controls be responsible for errors instead of an object that bubbles up from the Service layer.
Now with these questions out of the way, let me try to propose one particular "solution" to your issue.
As you mention, it is common practice to have a Repository that handles retrieving data from multiple data sources. In this case, the Repository would take the DAO and an object that represents getting data from the network, let's call it Api. I am assuming that you are using FirebaseFirestore, so the class and method signature would look something like this:
class Api(private val firestore: FirebaseFirestore) {
fun getUsers() : Flow<List<UserApiPojo>
}
Now the question becomes how to turn a callback based API into a Flow. Luckily, we can use callbackFlow() for this. Then Api becomes:
class Api(private val firestore: FirebaseFirestore) {
fun getUsers() : Flow<List<UserApiPojo> = callbackFlow {
val data = Gson().toJson(filters)
functions.getHttpsCallable("users").call(data).addOnSuccessListener {
try {
val type = object : TypeToken<List<UserApiPojo>>() {}.type
val users = Gson().fromJson<List<UserApiPojo>>(it.data.toString(), type)
offer(users.toMutableList())
} catch (e: java.lang.Exception) {
cancel(CancellationException("API Error", e))
}
}.addOnFailureListener {
cancel(CancellationException("Failure", e))
}
}
}
As you can see, callbackFlow allows us to cancel the flow when something goes wrong and have someone donwnstream handle the error.
Moving to the Repository we would now like to do something like:
val users: Flow<List<User>> = Flow.concat(userDao.getUsers().toUsers(), api.getUsers().toUsers()).first()
There are a few caveats here. first() and concat() are operators you will have to come up with it seems. I did not see a version of first() that returns a Flow; it is a terminal operator (Rx used to have a version of first() that returned an Observable, Dan Lew uses it in this post). Flow.concat() does not seem to exist either. The goal of users is to return a Flow that emits the first value emitted by any of the source Flows. Also, note that I am mapping DAO users and Api users to a common User object.
We can now talk about the ViewModel. As I said before, the ViewModel should have something that holds State. This State should represent data, errors and loading states. One way that can be accomplished is with a data class.
data class State(val users: List<User>, val loading: Boolean, val serverError: Boolean)
Since we have access to the Repository the ViewModel can look like:
val state = repo.users.map {users -> State(users, false, false)}.catch {emit(State(emptyList(), false, true)}
Please keep in mind that this is a rough explanation to point you in a direction, there are many ways to accomplish state management and this is by no means a complete implementation. It may not even make sense to turn the API call into a Flow, for example.

The answer from Emmanuel is really close to answering what i need, i need some clarifications about some of it.
It may not even make sense to turn the API call into a Flow
You are totally right, in fact i only want to actually make it a coroutine, i don't really need it to be a flow.
If there is an error with the Flow returned by Room, you can handle it via catch()
Yes i discovered this after posting the question. But my problem is more something like:
I'd like to call a method, say "getData", this method should return the flow from db, start the network call to update the db (so that i'm going to be notified when it's done via the db flow) and somewhere in here, i would need to let the ui know if db or network errored, right?. Or should i maybe do a separate "getDbFlow" and "updateData" and get the errors separately for each one?
val users: Flow> = Flow.concat(userDao.getUsers().toUsers(), api.getUsers().toUsers()).first()
This is a good idea, but i'd like to keep the db as the single source of truth, and never return to the ui any data directly from the network

Where to do Arrow.io IO.runUnsafeSync() ? ViewModel or Activity/Fragment?

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.

Kotlin Flow vs LiveData

In the last Google I/O, Jose Alcerreca and Yigit Boyar told us that we should no longer use LiveData to fetch data. Now we should use suspend functions for one-shot fetches and use Kotlin's Flow to create a data stream. I agree that coroutines are great for one-shot fetching or other CRUD operations, such as inserting, etc. But in cases where I need a data stream, I don’t understand what advantages Flow gives me. It seems to me that LiveData is doing the same.
Example with Flow:
ViewModel
val items = repository.fetchItems().asLiveData()
Repository
fun fetchItems() = itemDao.getItems()
Dao
#Query("SELECT * FROM item")
fun getItems(): Flow<List<Item>>
Example with LiveData:
ViewModel
val items = repository.fetchItems()
Repository
fun fetchItems() = itemDao.getItems()
Dao
#Query("SELECT * FROM item")
fun getItems(): LiveData<List<Item>>
I would also like to see some examples of projects using coroutines and Flow to work with the Room or Retrofit. I found only a Google's ToDo sample where coroutines are used for one-shot fetching and then manually refetch data on changing.

Flow is sort of a reactive stream ( like rxjava ). There are a bunch of different operators like .map, buffer() ( anyway less no. Of operator compared to rxJava ). So, one of the main difference between LiveData and Flow is that u can subscribe the map computation / transformation in some other thread using
flowOn(Dispatcher....).
So, for eg :-
flowOf("A","B","C").map { compute(it) }.flowOn(Dispatchers.IO).collect {...} // U can change the execution thread of the computation ( by default its in the same dispatcher as collect )
With LiveData and map , the above can't be achieved directly !
So its recommended to keep flow in the repository level , and make the livedata a bridge between the UI and the repository !
The main difference is that
Generally a regular flow is not lifecycle aware but liveData is lifecyle aware. ( we can use stateFlow in conjunction with repeatOnLifecycle to make it lifecycle aware )
flow has got a bunch of different operators which livedata doesn't have !
But again , Its up to u how do u wanna construct your project !

As the name suggests, you can think of Flow like a continuous flow of multiple asynchronously computed values. The main difference between LiveData and Flow, from my point of view, is that a Flow continuously emits results while LiveData will update when all the data is fetched and return all the values at once. In your example you are fetching single values, which is not exactly what Flow was dsigned for [update: use StateFlow for that].
I don't have a Room example but let's say you are rendering something that takes time, but you wanna display results while rendering and buffering the next results.
private fun render(stuffToPlay: List<Any>): Flow<Sample> = flow {
val sample = Sample()
// computationally intensive operation on stuffToPlay
Thread.sleep(2000)
emit(sample)
}
Then in your 'Playback' function you can for example display the results where stuffToPlay is a List of objects to render, like:
playbackJob = GlobalScope.launch(Dispatchers.Default) {
render(stuffToPlay)
.buffer(1000) // tells the Flow how many values should be calculated in advance
.onCompletion {
// gets called when all stuff got played
}
.collect{sample ->
// collect the next value in the buffered queue
// e.g. display sample
}
}
An important characteristic of Flow is that it's builder code (here render function) only gets executed, when it gets collected, hence its a cold stream.
You can also refer to the docs at Asynchronous Flow

Considering that Flow is part of Kotlin and LiveData is part of the androidx.lifecycle library, I think that Flow is used as part of the uses cases in clean architecture (without dependencies to the framework).
LiveData, on the other hand, is lifecycle aware, so is a match with ViewModel
I have all my architecture using livedata at this moment, but Flow looks like an interesting topic to study and adopt.