How Kotlin coroutines are better than RxKotlin?

I know RxJava very well and I've recently switched to Kotlin Coroutines and Flow.

RxKotlin is basically the same as RxJava, it just adds some syntactic sugar to make it more comfortable / idiomatic writing RxJava code in Kotlin.

A "fair" comparison between RxJava and Kotlin Coroutines should include Flow in the mix and I'm gonna try to explain why here. This is gonna be a bit long but I'll try to keep it as simple as I can with examples.

With RxJava you have different objects (since version 2):

// 0-n events without backpressure management
fun observeEventsA(): Observable<String>

// 0-n events with explicit backpressure management
fun observeEventsB(): Flowable<String>

// exactly 1 event
fun encrypt(original: String): Single<String>

// 0-1 events
fun cached(key: String): Maybe<MyData>

// just completes with no specific results
fun syncPending(): Completable

In kotlin coroutines + flow you do not need many entities cause if you do not have a stream of events you can just use simple coroutines (suspending functions):

// 0-n events, the backpressure is automatically taken care off
fun observeEvents(): Flow<String>

// exactly 1 event
suspend fun encrypt(original: String): String

// 0-1 events
suspend fun cached(key: String): MyData?

// just completes with no specific results
suspend fun syncPending()

Bonus: Kotlin Flow / Coroutines support null values (support removed with RxJava 2)

Suspending functions are what the name hint: they are function that can pause the execution of the code and resume it later when the function is completed; this allow you to write code that feels more natural.

What about the operators?

With RxJava you have so many operators (map, filter, flatMap, switchMap, ...), and for most of them there's a version for each entity type (Single.map(), Observable.map(), ...).

Kotlin Coroutines + Flow do not need that many operators, let's see why with some example on the most common operators

map()

RxJava:

fun getPerson(id: String): Single<Person>
fun observePersons(): Observable<Person>

fun getPersonName(id: String): Single<String> {
  return getPerson(id)
     .map { it.firstName }
}

fun observePersonsNames(): Observable<String> {
  return observePersons()
     .map { it.firstName }
}

Kotlin coroutines + Flow

suspend fun getPerson(id: String): Person
fun observePersons(): Flow<Person>

suspend fun getPersonName(id: String): String? {
  return getPerson(id).firstName
}

fun observePersonsNames(): Flow<String> {
  return observePersons()
     .map { it.firstName }
}

You do not need an operator for the "single" case and it is fairly similar for the Flow case.

flatMap()

The flatMap operator and his siblings switchMap, contactMap exists to allow you to combine different RxJava object and thus execute potentially asynchronous code while mapping your events.

Say you need, for each person, to grab from a database (or remote service) it's insurance

RxJava

fun fetchInsurance(insuranceId: String): Single<Insurance>

fun getPersonInsurance(id: String): Single<Insurance> {
  return getPerson(id)
    .flatMap { person ->
      fetchInsurance(person.insuranceId)
    }
}

fun observePersonsInsurances(): Observable<Insurance> {
  return observePersons()
    .flatMap { person ->
      fetchInsurance(person.insuranceId) // this is a Single
          .toObservable() // flatMap expect an Observable
    }
}

Let's see with Kotlin Coroutiens + Flow

suspend fun fetchInsurance(insuranceId: String): Insurance

suspend fun getPersonInsurance(id: String): Insurance {
  val person = getPerson(id)
  return fetchInsurance(person.insuranceId)
}

fun observePersonsInsurances(): Flow<Insurance> {
  return observePersons()
    .map { person ->
      fetchInsurance(person.insuranceId)
    }
}

Like before, with the simple coroutine case we do not need operators, we just write the code like we would if it wasn't async, just using suspending functions.

And with Flow that is NOT a typo, there's no need for a flatMap operator, we can just use map. And the reason is that map lambda is a suspending function! We can execute suspending code in it!!!

We don't need another operator just for that.

I cheated a bit here

Rx flatMap, switchMap and concatMap behave slightly differently. Rx flatMap generate a new stream for each event and than merge them all together: the order of the new streams events you receive in the output is undetermined, it might not match the order or the events in input

Rx concatMap "fixes" that and guarantee you will get each new stream in the same order of your input events

Rx switchMap will instead dispose of any previously running stream when it gets a new events, only the last input received matter with this operator

So you see, it isn't true that Flow.map is the same, it is actually more similar to Rx concatMap, which is the more natural behavior you expect from a map operator.

But it is true you need less operators, inside map you can do any async operation you want and reproduce the behavior of flatMap because it is a suspendable function. The actual equivalent operator to RxJava flatMap is Flow.flatMapMerge operator.

The equivalent of the RxJava switchMap can be achieved in Flow by using the conflate() operator before the map operator.

For more complex stuff you can use the Flow transform() operator which for every event emit a Flow of your choice.

Every Flow operator accept a suspending function!

In the previous paragraph I told you I cheated. But the key get away of what I meant by Flow do not need as many operators is that most operator's callbacks are suspending function.

So say you need to filter() but your filter need to perform a network call to know if you should keep the value or not, with RxJava you need to combine multiple operators with unreadable code, with Flow you can just use filter()!

fun observePersonsWithValidInsurance(): Flow<Person> {
  return observerPersons()
    .filter { person ->
        val insurance = fetchInsurance(person.insuranceId) // suspending call
        insurance.isValid()
    }
}

delay(), startWith(), concatWith(), ...

In RxJava you have many operators for applying delay or adding items before and after:

• delay()
• delaySubscription()
• startWith(T)
• startWith(Observable)
• concatWith(...)

with kotlin Flow you can simply:

grabMyFlow()
  .onStart {
    // delay by 3 seconds before starting
    delay(3000L)
    // just emitting an item first
    emit("First item!")
    emit(cachedItem()) // call another suspending function and emit the result
  }
  .onEach { value ->
    // insert a delay of 1 second after a value only on some condition
    if (value.length() > 5) {
      delay(1000L)
    }
  }
  .onCompletion {
    val endingSequence: Flow<String> = grabEndingSequence()
    emitAll(endingSequence)
  }

error handling

RxJava have lot of operators to handle errors:

• onErrorResumeWith()
• onErrorReturn()
• onErrorComplete()

with Flow you don't need much more than the operator catch():

  grabMyFlow()
    .catch { error ->
       // emit something from the flow
       emit("We got an error: $error.message")
       // then if we can recover from this error emit it
       if (error is RecoverableError) {
          // error.recover() here is supposed to return a Flow<> to recover
          emitAll(error.recover())
       } else {
          // re-throw the error if we can't recover (aka = don't catch it)
          throw error
       }
    }

and with suspending function you can just use try {} catch() {}.

You can achieve ALL the RxJava error operators with a single catch operator because you get a suspending function.

easy to write Flow operators

Due to the coroutines powering Flow under the hood it is way easier to write operators. If you ever checked an RxJava operator you would see how hard it is and how many things you need to learn.

Writing Kotlin Flow operators is easier, you can get an idea just by looking at the source code of the operators that are already part of Flow here. The reason is coroutines makes it easier to write async code and operators just feels more natural to use.

As a bonus, Flow operators are all kotlin Extension Functions, which means either you, or libraries, can easily add operators and they will not feel weird to use (in RxJava observable.lift() or observable.compose() are needed to combine custom operators).

Upstream thread doesn't leak downstream

What does this even mean?

This explain why in RxJava you have subscribeOn() and observeOn() while in Flow you only have flowOn().

Let's take this RxJava example:

urlsToCall()
  .switchMap { url ->
    if (url.scheme == "local") {
       val data = grabFromMemory(url.path)
       Flowable.just(data)
    } else {
       performNetworkCall(url)
        .subscribeOn(Subscribers.io())
        .toObservable()
    }
  }
  .subscribe {
    // in which thread is this call executed?
  }

So where is the callback in subscribe executed?

The answer is:

depends...

if it comes from the network it's in an IO thread; if it comes from the other branch it is undefined, depends on which thread is used to send the url.

If you think about it, any code you write: you don't know in which thread it is gonna be executed: always depends on the caller. The issue here is that the Thread doesn't depends on the caller anymore, it depends on what an internal function call does.

Suppose you have this plain, standard code:

fun callUrl(url: Uri) {
  val callResult = if (url.scheme == "local") {
    grabFromMemory(url.path)
  } else {
    performNetworkCall(url)
  }
  return callResult
}

Imagine not having a way of knowing in which thread the line return callResult is executed in without looking inside grabFromMemory() and performNetworkCall().

Think about that for a second: having the thread change based on which function you call and what they do inside.

This happens all the time with callbacks APIs: you have no way of knowing in which thread the callback you provide will be executed unless documented.

This is the concept of "upstream thread leaking downstream".

With Flow and Coroutines this is not the case, unless you explicitly require this behavior (using Dispatchers.Unconfined).

suspend fun myFunction() {
  // execute this coroutine body in the main thread
  withContext(Dispatchers.Main) {
    urlsToCall()
      .conflate() // to achieve the effect of switchMap
      .transform { url ->
        if (url.scheme == "local") {
           val data = grabFromMemory(url.path)
           emit(data)
        } else {
           withContext(Dispatchers.IO) {
             performNetworkCall(url)
           }
        }
      }
      .collect {
        // this will always execute in the main thread
        // because this is where we collect,
        // inside withContext(Dispatchers.Main)
      }
  }
}

Coroutines code will run in the context that they have been executed into. And only the part with the network call will run on the IO thread, while everything else we see here will run on the main thread.

Well, actually, we don't know where code inside grabFromMemory() will run, but we don't care: we know that it will be called inside the Main thread, inside that suspending function we could have another Dispatcher being used, but we know when it will get back with the result val data this will be again in the main thread.

Which means, looking at a piece of code, it's easier to tell in which thread it will run, if you see an explicit Dispatcher = it's that dispatcher, if you do not see it: in whatever thread dispatcher the suspension call you are looking at is being called.

Structured Concurrency

This is not a concept invented by kotlin, but it is something they embraced more than any other language I know of.

If what i explain here is not enough for you read this article or watch this video.

So what is it?

With RxJava you subscribe to observables, and they give you a Disposable object.

You need to take care of disposing of it when it's not needed anymore. So what you usually do is keep a reference to it (or put it in a CompositeDisposable) to later call dispose() on it when it's not needed anymore. If you don't the linter will give you a warning.

RxJava is somewhat nicer than a traditional thread. When you create a new thread and execute something on it, it's a "fire and forget", you do not even get a way to cancel it: Thread.stop() is deprecated, harmful, and recent implementation actually do nothing. Thread.interrupt() makes your thread fail etc.. Any exceptions goes lost.. you get the picture.

With kotlin coroutines and flow they reverse the "Disposable" concept. You CANNOT create a coroutine without a CoroutineContext.

This context define the scope of your coroutine. Every child coroutine spawned inside that one will share the same scope.

If you subscribe to a flow you have to be inside a coroutine or provide a scope too.

You can still keep reference of the coroutines you start (Job) and cancel them. This will cancel every child of that coroutine automatically.

If you are an Android developer they give you these scopes automatically. Example: viewModelScope and you can launch coroutines inside a viewModel with that scope knowing they will automatically be cancelled when the viewmodel is cleared.

viewModelScope.launch {
  // my coroutine here
}

Some scope will terminate if any children fail, some other scope will let each children leave his own lifecycle without stopping other children if one fails (SupervisedJob).

Why is this a good thing?

Let me try to explain it like Roman Elizarov did.

Some old programming language had this concept of goto which basically let you jump from one line of code to another at will.

Very powerful, but if abused you could end up with very hard to understand code, difficult to debug and reason upon.

So new programming languages eventually completely removed it from the language.

When you use if or while or when it is way easier to reason on the code: doesn't matter what happens inside those blocks, you'll eventually come out of them, it's a "context", you don't have weird jumps in and out.

Launching a thread or subscribing to an RxJava observable is similar to the goto: you are executing code which than will keep going until "elsewhere" is stopped.

With coroutines, by demanding you provide a context/scope, you know that when your scope is over everything inside that coroutines will complete when your context completes, doesn't matter if you have a single coroutines or 10 thousands.

You can still "goto" with coroutines by using GlobalScope, which you shouldn't for the same reason you shouldn't use goto in languages that provides it.

Cold vs Hot - ShareFlow and StateFlow

When we work with reactive streams we always have this concept of Cold and Hot streams. Those are concepts on both the Rx world and Kotlin Flows

Cold streams are just like a function in our code: it's there and does nothing until you call it. With a Flow that means it is defined what the stream does but it will do nothing until you start to collect on it. And, like a function, if you collect (call) it twice the stream will runs twice. (ex. a cold stream to perform an http request will execute the request twice if collected twice).

Hot streams do not work like that. When you have multiple collect call on them they all share the same Hot stream under the hood, which means your hot streams runs once and you can have multiple observers.

You can usually turn a Cold stream into an Hot streams with some operator.

On RxJava you can use this concept of Connectable Observable/Flowable.

val coldObservable: Observable<Something> = buildColdObservable()

// create an hot observable from the cold one
val connectableObservable: ConnectableObservable<Something> = coldObservable.publish()

// you can subscribe multiple times to this connectable
val subADisposable: Disposable = connectableObservable.subscribe(subscriberA)
val subBDisposable: Disposable = connectableObservable.subscribe(subscriberB)

// but nothing will be emitted there until you call
val hotDisposable: Disposable = connectableObservable.connect()

// which actually run the cold observable and share the result on bot subscriberA and subscriberB

// while it's active another one can start listening to it
val subCDisposable: Disposable = connectableObservable.subscribe(subscriberC)

You than have other helpful operators like refCount() or autoConnect() which turn back the Connectable into a standard stream and under the hood automatically .connect() when the first subscriber is attached.

buildColdObservable()
   .replay(1) // when a new subscriber is attached receive the last data instantly
   .autoConnect() // keep the cold observable alive while there's some subscriber

On Flow you have the shareIn() and the stateIn() operators. You can see the API design here. They are less "manual" in handling when you "connect".

buildColdFlow()
  .shareIn(
    // you need to specify a scope for the cold flow subscription
    scope = myScope,
    // when to "connect"
    started = SharingStarted.WhileSubscribed(),
    // how many events already emitted should be sent to new subscribers
    replay = 1,
  )

scope

The scope is for structured concurrency. On RxJava it's the connect() operation that actually subscribe to the cold observable, it gives you a Disposable you will have to call .dispose() on somewhere. If you use refCount() or autoConnect() it is called on the first subscriber and with refCount() is never disposed while with autoConnect() is disposed when there aren't any more subscribers.

With Flow you need to give a dedicated Scope to collect the cold stream, if you cancel that scope the cold stream will stop emitting and will not be usable anymore.

started

So this one is easy

• RxJava refCount() --> Flow SharingStarted.Lazily, starts collecting on the first subscriber
• RxJava autoConnect() -> Flow SharingStarted.WhileSubscribed(), starts collecting on the first subscriber and cancel it when there aren't anymore
• RxJava call connect() manually before any subscription -> Flow SharingStarted.Eagerly(), starts collecting immediately

The WhileSubscribed() has useful parameters, check them out.

You can also define your own logic for SharingStarted to handle when collecting from the coldFlow.

Behavior and backpressure

When you have an hot observable you always have backpressure issues to deal with. 1 source of data being listened by many means one listener can be slower then others.

Flow .shareIn collect the cold stream in a dedicated coroutine and buffer emission by default. It means if the cold stream emit too fast it will use the buffer. You can change this behavior.

Kotlin SharedFlow also let you access the replay buffer directly to inspect previous emission if you need to.

Cancelling a subscriber will have no effect on the shared flow.

using flowOn() to change the Dispatcher on the subscriber will have no effect on the shared flow (use flowOn() before sharing if you need to run the cold stream in some specific dispatcher)

stateIn

Flow has a "special" version of ShareFlow that is called StateFlow and you can use stateIn() to create one from another stream.

A StateFlow always have 1 value, it cannot be "empty", so you need to provide the initial value when you do stateIn().

A StateFlow can never throw exceptions and can never terminate (in this way is similar to BehaviorRelay in the RxRelay library)

A StateFlow will only emit if the state change (it's like it has a build in distinctUntilChanged().

RxJava Subjects vs Mutable*Flow A Subject in RxJava is a class that you can use to manually push your data on it while still using it as a stream.

In Flow you can use MutableSharedFlow or MutableStateFlow to achieve a similar effect.

With Kotlin coroutines you can also use Channels but they are considered somewhat a lower level API.

Any Drawback?

Flow is still in development and some features available in RxJava might be marked experimental in Kotlin Coroutines Flow or have some difference here and there.

Some niche operator or operator function might not be yet implemented and you might have to implement it yourself (at least it's easier).

But other than that there aren't any drawbacks I know of.

However there are differences to be aware of that could cause some frictions in switching from RxJava and needs you to learn new things.

Structured concurrency is a step forward, but introduces new concept you need to learn and get used to (scopes, supervisorJob): cancellation is handled completely different.

There's some gotcha to be aware of.

Gotcha: Cancellation Exception

If you cancel() job in a coroutine or throw CancellationException() the exception is propagated to parent coroutines unless you used a Supervisor scope / job. The parent coroutine also cancel sibling coroutines of the one that got canceled if that happens.

BUT if you catch(e: Exception), even using runCatching {}, you must remember to rethrow CancellationException() otherwise you'll have unexpected results cause the coroutine has been canceled but your code is still trying to execute like it wasn't.

Gotcha: UncaughtExceptionHandler

if you do launch { ... } to create a new coroutine and that coroutine throws, by default, that will terminate the coroutine but will not crash the app and you might completely missed something went wrong.

This code will not crash your app.

launch {
  throw RuntimeException()
}

In some cases it might not even print anything in the log.

If it was a cancellation exception it will definitely NOT print anything in the log.

Disclaimer: Parts of this answer are irrelevant since Coroutines now have the flow API, very similar to Rx one. If you want an up-to-date answer, jump to the last edit.

There is two parts in Rx; the Observable pattern, and a solid set of operators to manipulate, transform and combine them. The Observable pattern, by itself, doesn't do a lot. Same with Coroutines; it's just another paradigm to deal with asynchronism. You can compare the pro/cons of callbacks, Observable and coroutines to solve a given problem, but you can't compare a paradigm with a fully featured library. It's like comparing a language with a framework.

How Kotlin coroutines are better than RxKotlin ? Didn't used coroutines yet, but it's look similar to async/wait in C#. You just write sequential code, everything is as easy as writing synchronous code ... except it execute asynchronously. It's easier to grasp.

Why would I want to use kotlin coroutines ? I will answer for myself. Most of the time I will stick to Rx, because I favor event-driven architecture. But should arise the situation where I am writing sequential code, and I need to call an asynchronous method in the middle, I will happily leverage coroutines to keep it that way and avoiding wrapping everything in Observable.

Edit: Now that I am using coroutines it's time for an update.

RxKotlin is just syntactic sugar to use RxJava in Kotlin, so I will speak about RxJava and not RxKotlin in the following. Coroutines are a lower lever and more general concept than RxJava, they serve others use-cases. That said, there is one use-case where you could compare RxJava and coroutines (channel), it's passing around data asynchronously. Coroutines have a clear advantage over RxJava here:

Coroutines are better to deal with resources

• In RxJava you can assign computations to schedulers but subscribeOn() and ObserveOn()are confusing. Every coroutine is given a thread context and return to parent context. For a channel, both side (producer, consumer) execute on his own context. Coroutines are more intuitive on thread or thread pool affectation.
• Coroutines give more control on when those computation occur. You can for example pass hand (yield), prioritize (select), parallelize (multiple producer/actor on channel) or lock resource (Mutex) for a given computation. It may not matter on server (where RxJava came first) but on resources limited environment this level of control may be required.
• Due to it's reactive nature, backpressure doesn't fit well in RxJava. In the other end send() to channel is a suspensive function that suspend when channel capacity is reached. It's out-of-the-box backpressure given by nature. You could also offer() to channel, in which case the call never suspend but return false in case the channel is full, effectively reproducing onBackpressureDrop() from RxJava. Or you could just write your own custom backpressure logic, which won't be difficult with coroutines, especially compared to do the same with RxJava.

There is another use-case, where coroutines shine and this will answer your second question "Why would I want to use Kotlin coroutines?". Coroutines are the perfect replacement for background threads or AsyncTask (Android). It's as easy as launch { someBlockingFunction() }. Of course you could achieve this with RxJava too, using Schedulers and Completable perhaps. You won't (or little) use the Observer pattern and the operators which are the signature of RxJava, a hint that this work is out of scope for RxJava. RxJava complexity (a useless tax here) will make your code more verbose and less clean than Coroutine's version.

Readability matters. On this regard, RxJava and coroutines approach differ a lot. Coroutines are simpler than RxJava. If you are not at ease with map(), flatmap() and functional reactive programming in general, coroutines manipulations are easier, involving basics instructions: for, if, try/catch ... But I personally find coroutine's code harder to understand for non-trivial tasks. Especially it involves more nesting and indentation whereas operator chaining in RxJava keep everything in line. Functional-style programming make processing more explicit. On top of that RxJava can solve complex transformations with a few standard operators from their rich (OK, way too rich) operator set. RxJava shine when you have complex data flows requiring a lot of combinations and transformations.

I hope those considerations will help you choose the right tool given your needs.

Edit: Coroutine now have flow, an API very, very similar to Rx. One could compare pro/cons of each, but the truth is the differences are minor.

Coroutines as it's core is a concurrency design pattern, with add-on libraries, one of those being a stream API similar to Rx. Obviously, Coroutines having a far broader scope than Rx, there is a lot of things that Coroutines can that Rx can't, and I can't list them all. But usually if I use Coroutines in one of my project it boil down to one reason:

Coroutines are better at removing callback from your code

I avoid using callback wich harm readability too much. Coroutines make asynchronous code simple and easy to write. By leveraging the suspend keyword, your code look like synchronous one.

I have seen Rx used in project mostly for the same purpose of replacing callback, but if you don't plan to modify your architecture to commit to the reactive pattern, Rx will be a burden. Consider this interface:

interface Foo {
   fun bar(callback: Callback)
}

The Coroutine equivalent is more explicit, with a return type and the keyword suspend indicating it's an asynchronous operation.

interface Foo {
   suspend fun bar: Result
}

But there is a problem with the Rx equivalent:

interface Foo {
   fun bar: Single<Result>
}

When you call bar() in the callback or Coroutine version, you trigger the computation; with the Rx version, you get a representation of a computation that you can trigger at will. You need to call bar() then subscribing to the Single. Usually not a big deal, but it's a little confusing for beginner and can lead to subtle problem.

One exemple of such problems, suppose the callback bar function is implemented as such:

fun bar(callback: Callback) {
   setCallback(callback)
   refreshData()
}

If you don't port it properly, you will end with a Single that can be triggered only once because refreshData() is called in bar() function and not at subscription time. A beginner mistake, granted, but the thing is Rx is way more than a callback replacement and a lot of developers struggle to grasp Rx.

If your objective is to transform an asynchronous task from callback to a nicer paradigm, Coroutines are a perfect fit whereas Rx add some complexity.