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I'm stress testing some callbackFlow code I have w...
# coroutines
c
I'm stress testing some callbackFlow code I have written. Here it is currently
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fun getBooks(): Flow<List<Book>> {
  return callbackFlow {
    val listener =
      FirebaseFirestore.getInstance()
        .collection("books")
        .addSnapshotListener { value, error ->
          if (error != null || value == null) { /*dosomething*/ }
          var books: List<Book>? = null
          runBlocking(<http://Dispatchers.IO|Dispatchers.IO>) {
              books = (value!!.toObjects())
          }
          trySend(books!!)
        }
    awaitClose { listener.remove() }
  }
}
this seems to work well... but to stress test it I wrapped 
books = (value!!.toObjects())
with 
repeat (1000)
and now my UI hangs. Shouldn't the dispatchers.io take care of this?
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runBlocking(<http://Dispatchers.IO|Dispatchers.IO>) {
  repeat(1000) {
    books = (value!!.toObjects())
  }
}
u
Dispatchers.IO schedules to the background all right. But runBlocking blocks your thread waiting for it to be done. If being asynchronous is OK for you, you can use `launch`instead of 
runBlocking
If you need to wait for 
books
to be assigned, you have an issue. Outside couroutines, you can not wait without blocking your current thread.
maybe you get away with:
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scope.launch(<http://Dispatchers.IO|Dispatchers.IO>) {
  send(value!!.toObject)
}
And then get rid of the 
!!
;-)
c
Hm. I was trying to use repeat because I'm trying to stress test whether or not toObject() takes a long time. Hence me doing repeat(1000)
u
So, what is your question?
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Shouldn't the <http://dispatchers.io|dispatchers.io> take care of this?
Not if used with runBlocking. But you can use launch like above. Put in your repeat and see, that it will no longer block your ui thread
c
You answered my original question. I was just trying to respond to your suggestion of just using
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scope.launch(<http://Dispatchers.IO|Dispatchers.IO>) {
  send(value!!.toObject)
}
but thank you for teaching. that makes sense.
I have this written now
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var books: List<Book>? = null
launch(<http://Dispatchers.IO|Dispatchers.IO>) {
  delay(5000)
  repeat(10000) {
    books = (value!!.toObjects())
  }
  trySend(books!!)
}
and no lag!
r
This isn’t going to handle backpressure well and may lead to events being emitted out of order or other bad things
Good advice is to get out of callback world as fast as you can and do your transforms in flow world
i.e. 
trySend(value)
immediately and then use 
map
, 
filter
, 
flowOn
etc to do the transforms
f
you should also call this with 
flowOn(<http://Dispatchers.IO|Dispatchers.IO>)
if you try to change dispatchers within the flow you may get exceptions due to some guarantees implemented in the flow logic that would be broken
c
interesting
So this example by google here maybe isn't the best? https://developer.android.com/kotlin/flow#callback
I reallllly wish firestore as a library had first class support for coroutines/flow.
f
in that example from Google you could add 
flowOn
to the returned flow to make it run off the main thread
u
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fun getBooks(): Flow<List<Book>> {
  return callbackFlow {
    val listener =
      FirebaseFirestore.getInstance()
        .collection("books")
        .addSnapshotListener { value, error ->
          if (error != null) { /*dosomething*/ }
          trySend(value)
        }
    awaitClose { listener.remove() }
  }
  .filterNotNull()
  .map{
    value.toObjects()
  }
  .flowOn(<http://Dispatchers.IO|Dispatchers.IO>)
@Francesc, @Colton Idle I guess, that’s a about the way
r
flowOn is not needed in the Google code because the Flow is a simple Channel backed flow so there’s nothing to run off the main thread
The code that runs on the main thread is the addSnapshotListener code but threading there is managed by the library, not by Flows so flowOn will do nothing
u
it was meant for the 
value.toObjects()
r
Hence it’s important to return as soon as possible and do anything expensive on the Flow
u
so the goal is to get the 
map
off the main thread
c
So does everyones generally agree that @uli's last code snippet is what i should be doing?
it breaks out of the callback flow as soon as possible and maps the value and flows onto IO dispatcher... so I think that just about takes everyones suggestion into account
u
Perfect time to close this thread as my train arrives in one minute ;-)
f
that works, but if your collector is slow you may drop emissions
c
if my collector is slow... i may drop emissions. pardon my ignorance. but is this "backpressure"?
f
yes, you have a 
trySend
so it will try, but if the buffer is full, it will drop it
r
Yeah. callback flow has a fixed buffer so this is unavoidable when you don’t control the source
But the new code handles it much better because it’ll only map things that come out of the buffer rather than everything
n
Just add 
.conflate()
after 
callbackFlow
so that it always just keeps the latest value from firestore, I assume that's the only one you care about. And it looks like you can ditch 
.flowOn(<http://Dispatchers.IO|Dispatchers.IO>)
, I don't see any blocking code
u
r
Yep, 
conflate
and/or replace 
map
with 
mapLatest
c
ooh. Map latest...
So mapLatest or conflate. any real reason to use one over the other?
and yes @Nick Allen the flowOn I believe is necessary because toObjects is essetnially json deserialization which can take some time with larger lists that i get back.
r
They do slightly different things and you can actually use both
c
both... at the same time?
r
conflate reduces buffer size to 1 and only keeps latest
mapLatest will cancel the old map as soon as it receives a new event
n
Do not use 
mapLatest
for back-pressure. Only use it if 
mapLatest
lambda is suspending code that actually suspends (not blocking on IO dispatcher). It processes every item and when it gets a new item it cancels and waits for the previous lambda (just the lambda, nothing downstream). If the code after mapLatest is slow, then you'll still have backpressure and could miss updates. They are completely separate and you should not interchange them
c
Okay. it looks like my end result is...
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fun getBooks(): Flow<List<Book>> {
  return callbackFlow {
    val listener =
      FirebaseFirestore.getInstance()
        .collection("books")
        .addSnapshotListener { value, error ->
          if (error != null) { /*dosomething*/ }
          trySend(value)
        }
    awaitClose { listener.remove() }
  }
  .conflate()
  .filterNotNull()
  .map{
    value.toObjects()
  }
  .flowOn(<http://Dispatchers.IO|Dispatchers.IO>)
thank you everyone for teaching me a bunch of new things!
r
mapLatest
does work better if the block supports cooperative cancellation but it won’t caused missed updates any more than conflate will
f
looks good. A thing to consider is that you should not hardcode dispatchers, consider using a class that provides a wrapper for the dispatchers so that you can then replace those for tests
r
Unless the producing source is constantly faster than the mapping in which case it’ll never be able to emit anything
The alternative with just conflate is that you’ll get old data before it starts the next mapping
Oh, also sounds like your mapping is actually CPU bound and not I/O bound so better to use Default Dispatcher
n
If 
mapLatest
is used with a block that doesn't support cooperative cancellation (like deserialization), then every new event is blocked waiting for the previous lambda invocation to finish, and every item is processed, which could result in backpressure causing trySend to fail and so the most recent value could be dropped. This is the opposite of what you want, which is to keep the latest item.
u
Next train … Actually I left error handling in your callback. Whatever ‘dosomething’ is should probably also go into the stream: 
trySend(value to error)
So here comes an update:
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@Inject
@DefaultDispatcher
lateinit val defaultDispatcher : Disptachers

fun getBooks(): Flow<List<Book>> {
  return callbackFlow {
    val listener =
      FirebaseFirestore.getInstance()
        .collection("books")
        .addSnapshotListener { value, error ->
          trySend(value to error)
        }
    awaitClose { listener.remove() }
  }
  .doSomethingWithNullValueAndError() // Depends. It might be better after conflate
  .conflate()
  .map { (value, _) -> 
    value.toObjects()
  }
  .flowOn(defaultDispatcher)
r
@Nick Allen You’re right, for some reason I was thinking it’d still drop the final output but I guess it’s actually identical to map if your block can’t cancel
c
oooh that last snippet @uli looks like it ticks all of the boxes
I do have to admit though. flows are super powerful and i dont see how i would've gotten this far without all of you.
u
I just update some of my own code, a shared preferences listener. It received a key and an instance of the shared preferences. And it used to load the value in the callback. I do not think that this ever was an issue, as shared preferences are usually read from memory. But I feel the pattern of minimal work in the callback is worth following just as a habit. No reason to ever question it.
c
Sorry this bring this conversation up again, but I essentially have 10 functions now that are pretty much identical to the above (since I have like 10 different queries in my app). If I want to make a generic function that could do this, I basically came up with this. Thoughts?
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fun <T> firestoreFlow(query: Query, clazz: Class<T>, dispatcher: CoroutineDispatcher): Flow<List<T>> {
  return callbackFlow {
        val listener = query.addSnapshotListener { value, _ -> trySend(value) }
        awaitClose { listener.remove() }
      }
      .conflate()
      .filterNotNull()
      .map { value -> value.toObjects(clazz) }
      .flowOn(dispatcher)
}
f
I would remove the dispatcher from your method and let the caller add the 
flowOn
. I would also pass a lambda that does the mapping for you.
so in the 
map
you call a transform method that the caller provides
c
Interesting. yeah. I can see how those things could be passed in. I guess I'm trying to find the right level of abstraction and convenience for whoever has to come into my ApiService.kt and add a new flow. I want them to have an easy way to not mess this up (as flowables are still new to our team)
f
I see, what you had is more rigid but if that's all you need, it's easier to use
you could have 2 methods a more generic one that you could use everywhere you use Firebase, and a 2nd one that builds on top of the first that is more fine-tuned for this scenario of yours
c
yeah. i think that's what im going to do. essentially one could be shared online in a gist and anyone in the world could use (so to speak) and then I can have one that's a convenience for my codebase that builds on top of that. awesome. This has me so excited. I've learned so much the past few days. I think the only thing I'm still a bit uneasy about is how to handle errors and stuff, but maybe firestoer doesn't really send down errors if your query is correct. /shruggie
f
you can propagate the error down the flow, you can wrap the error in your custom exception and throw it, then whoever is collecting your flow can add a 
.catch
block to the flow and decide how to handle the error. The collector should have a much better context to be able to decide what to do with the error
c
Pardon my ignorance here... but how would I do that? Now that you saw it. Putting the "error handling" on the caller does make a lot more sense. But I'm not sure how that changes my observable and what that looks like on the observer.
f
give me a minute to put together a gist
something like this, you can expand on the custom exception to add more details on what went wrong to give better context to the collector
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fun <T> firestoreFlow(query: Query, clazz: Class<T>, dispatcher: CoroutineDispatcher): Flow<List<T>> {
  return callbackFlow {
        val listener = query.addSnapshotListener { value, error -> 
            if (error != null) {
                throw MyException(exception = error)
            }
            if (value == null) {
                throw MyException(
                    "Null value returned from Firestore",
                    IOException()
                )
            }
        trySend(value)
        awaitClose { listener.remove() }
      }
      .conflate()
      .filterNotNull()
      .map { value -> value.toObjects(clazz) }
      .flowOn(dispatcher)
}

fun sample() {
    getBooks()
        .onEach { /* do stuff */ }
        .catch { ex ->
            // do other stuff
        }
        .launchIn(scope)
}

class MyException(
    message: String = "Failed to load data from firestore",
    exception: Exception,
): IOException(message, exception)
you can also add a 
.retry
block to the flow to re-attempt, based on what error you got and how many times you've already retried
I realize I copied your original flow implementation with 
runBlocking
instead of the latest one, but the idea is the same
I've amended it to avoid confusion
n
To send an exception to the flow, you need to call 
close
with the exception instead of throwing it.
Otherwise you are just throwing the exception up to the Firestore code that called the listener.
f
thanks for pointing that out
c
Does an exception/"close" terminate the flow? I would think that I would want the flow to continue even if there's one error that's thrown in there.
f
You can use retry for that
c
thanks. im going to try a bit to force some failures just to see what its all about.
r
One more tip: you often can avoid passing Class in kotlin by using inline and reified https://kotlinlang.org/docs/inline-functions.html#reified-type-parameters
c
Looks like firestore IS getting support for Flows! Time to see how they implemented it! https://github.com/firebase/firebase-android-sdk/pull/1252#issuecomment-1213179027
Looks like they went for 
trySendBlocking(snapshot)
instead of 
trySend(snapshot)
Intersting
@martinbonnin Why not just use trySend instead of risking blocking the thread, which is likely to be the main one?
Good question. I'm not sure TBH. I took inspiration from https://kotlin.github.io/kotlinx.coroutines/kotlinx-coroutines-core/kotlinx.coroutines.flow/callback-flow.html
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