In my app i use the snapshot listener connected to a collection in the firestore database.
private fun getMessages() {
Firebase.firestore.collection(Constants.MESSAGES)
.orderBy(Constants.SENT_ON)
.addSnapshotListener { value, e ->
if (e != null) {
Log.w(Constants.TAG, "Listen failed.", e)
return#addSnapshotListener
}
val list = emptyList<Map<String, Any>>().toMutableList()
if (value != null) {
for (doc in value) {
val data = doc.data
list.add(data)
}
}
updateMessages(list)
}
}
In the collection there are 1000 items.
Is my code snippet the most efficient one ? What i want is that a new app user the first time he connects to the database uses 1000 reads. After that due to the snappshot listener only the new documents in the collection.
The code looks fine at first glance to me.
The only thing I'd do is add a limit to the query for what you think is a reasonable maximum that any user may want to see.
What you can consider is implementing pagination or endless scrolling with query cursors, and only load the even smaller subset that the user can actually see on their device screen.
Related
At 'urichecking2' log, I can see there is value. But in 'uriChecking' the uriList is null.
why the uriList.add not work??
private fun getPhotoList() {
val fileName = intent.getStringExtra("fileName")
Log.d("fileNameChecking", "$fileName")
val listRef = FirebaseStorage.getInstance().reference.child("image").child(fileName!!)
var tmpUrl:Uri = Uri.parse(fileName)
Log.d("firstTmpUri","$tmpUrl")
listRef.listAll()
.addOnSuccessListener { listResult ->
for (item in listResult.items) {
item.downloadUrl.addOnCompleteListener { task ->
if (task.isSuccessful) {
tmpUrl = task.result
Log.d("secondTmpUri","$tmpUrl")
Log.d("urichecking2","$task.result")
uriList.add(task.result)
} else {
}
}.addOnFailureListener {
// Uh-oh, an error occurred!
}
}
}
Log.d("thirdTmpUri","$tmpUrl")
Log.d("urichecking", "$uriList")
}
If I do this, the log is output in the order of first, third, and second, and the desired value is in second, but when third comes out, it returns to the value of first.
The listAll method (like most cloud APIs these days, including downloadUrl which you also use) is asynchronous, since it needs to make a call to the server - which may take time. This means the code executes in a different order than you may expect, which is easiest to see if you add some logging:
Log.d("Firebase","Before starting listAll")
listRef.listAll()
.addOnSuccessListener { listResult ->
Log.d("Firebase","Got listResult")
}
Log.d("Firebase","After starting listAll")
When you run this code it outputs:
Before starting listAll
After starting listAll
Got listResult
This is probably not the order you expected, but it perfectly explains why you can't see the list result. By the time your Log.d("urichecking", "$uriList") runs, none of the uriList.add(task.result) has been called yet.
The solution for this is always the same: any code that needs the list result, has to be inside the addOnCompleteListener callback, be called from there, or be otherwise synchronized.
So in its simplest way:
listRef.listAll()
.addOnSuccessListener { listResult ->
for (item in listResult.items) {
item.downloadUrl.addOnCompleteListener { task ->
if (task.isSuccessful) {
uriList.add(task.result)
Log.d("urichecking", "$uriList")
}
}
}
}
This is an incredibly common mistake to make if you're new to programming with asynchronous APIs, so I recommend checking out
Asynchronous programming techniques in the Kotlin language guide
How to get URL from Firebase Storage getDownloadURL
Can someone help me with logic of the firebase on success listener
Why does my function that calls an API or launches a coroutine return an empty or null value?
I am not getting an efficient way to check below mentioned condition.
What I want to achieve is that-
There are some processes that need to be done if their corresponding boolean is true suggesting to start the process. So I want to check if a particular condition is done only if it should be started.
There are some boolean variables
var shouldStartProcessA
var shouldStartProcessB
var shouldStartProcessC
var isADone
var isBDone
var isCDone
if (shouldStartProcessA && shouldStartProcessB && shouldStartC) {
if (isADone && isBDone && isCDone) {
// Every process completed
}
}
if (shouldStartProcessA && shouldStartProcessB) {
if (isADone && isBDone) {
// Every process completed
}
}
if (shouldStartProcessA && shouldStartC) {
if (isADone && isCDone) {
// Every process completed
}
}
if (shouldStartProcessB && shouldStartC) {
if (isBDone && isCDone) {
// Every process completed
}
}
if (shouldStartProcessA) {
if (isADone) {
// Every process completed
}
}
if (shouldStartProcessB) {
if (isBDone) {
// Every process completed
}
}
if (shouldStartProcessC) {
if (isCDone) {
// Every process completed
}
}
This type of validating condition grows exponentially by introducing every other boolean. I am struggling to find a straightforward implementation to check these conditions.
Instead of doing things this way, I'd recommend a data structure that allows you to add tasks and check their state. There are a lot of ways to do that, but the basic idea is you can iterate over all the items and use functions like all to confirm they're all in the appropriate state. That way you don't have to hand-wire everything together
You could use a Map and add tasks to it, initially mapping them to false and setting that to true when they're completed. Or create a Set and add your tasks to that (I'm assuming you want one of each at most), and remove them / move them to a "done" list when they complete. That kind of idea. You could create an enum class to represent your tasks if you want, so each one is its own instance (e.g. Process.A, like having a dedicated, fixed variable) and you can easily use those in your logic
If you really want variables for each process, instead of a data structure, I'd still recommend rolling each pair into a single state, something like this:
enum class State {
UNUSED, PENDING, DONE
}
var processA = State.UNUSED
var processB = State.PENDING
// etc
// you can easily check them like this:
// create a list of all the variables you want to check - we're using references
// to the properties themselves (with the ::), not the current value!
val allProcesses = listOf(::processA, ::processB)
// now you have that collection, you can easily iterate over them all
// and work out what's what - we need to use get() to get the current values
val allFinished = allProcesses
.filterNot { it.get() == State.UNUSED } // ignore unused processes
.all { it.get() == State.DONE } // check ALL the required ones are DONE
You could write that check there as a single all condition, but the point is to show you you can be flexible with it, and build up your logic by filtering out the stuff you're not interested in, if you create a useful set of states
If you really do want to (or have to) stick with the current "pairs of variables" setup, you can do something similar:
// wiring them up again, creating a list of Pairs so we can iterate over them easily
val allProcesses = listOf(
::shouldStartProcessA to ::isADone,
::shouldStartProcessB to ::isBDone,
::shouldStartProcessC to ::isCDone
)
// gotta check 'em all - only returns true if that ALL meet the condition
val allComplete = allProcesses.all { (shouldStart, isDone) ->
// the get() syntax is awkward, but basically for everything we're checking
// if it either doesn't need to start, or it does but it's also done
!shouldStart.get() || (shouldStart.get() && isDone.get())
}
so adding new processes is just a case of adding their variables to the list of pairs, and they get included in the checking
You don't need the property reference stuff (::/.get()) if you create the lists right before you check them, but if you want to define them once in advance (and the property values can change after that) then that's how you'd do it. Otherwise you can just do the normal shouldStartProcessA to isADone etc, which is probably fine for most cases - I'm showing the reference stuff as a more general example for handling this kind of thing
I suppose, you should create two lists of Boolean and add variables consequently.
val list1 = listOf(shouldStartProcessA, shouldStartProcessB, shouldStartC)
val list2 = listOf(isADone, isBDone, isCDone)
Then iterate over both lists and check that items in corresponding positions have the same values.
var n = 0
for (i in list1.indices) {
if (list1[i] == list2[i]) {
n++
} else {
n = 0
break
}
}
if (n > 0) {
// Every process completed
}
I need to load some data from server page by page until all the data is loaded. The data is considered to be fully loaded if at some point I received fewer items than I've requested. This is the working solution that I have right now:
return Observable.fromCallable { 0 }
.delay(500, TimeUnit.MILLISECONDS)
.repeat()
.scan { previousPage, _ -> previousPage + 1}
.concatMap { doLongFetch(it) }
.takeUntil { it.size < 100 }
fun doLongFetch(page: Int): Observable<List<ListItem>>() {
//Here I do the loading
}
However, there's a problem with the source observable. As you can see, it emits new values every 500 milliseconds to provide some input for the scan function. The delay is required since otherwise, it would emit thousands of values in a very short period of time, which is not required at all. Ideally, I want to remove that delay completely and make sure that the source observable emits another value only after the downstream has handled the previous one (meaning that the data has been requested and processed).
Any ideas on how I can do that?
I have difficulties making sequential calls of RxJava Single observerable. What I mean is that I have a function that makes http request using retrofit that returns a Single.
fun loadFriends(): Single<List<Friend>> {
Log.d("msg" , "make http request")
return webService.getFriends()
}
and if I subscribe from several places at the same time:
loadFriends().subscribeOn(Schedulers.io()).subscribe()
loadFriends().subscribeOn(Schedulers.io()).subscribe()
I want that loadFriends() makes only one https request but in this case I have two http request
I know how to solve this problem in blocking way:
The solution is to make loadFriends() blocking.
private val lock = Object()
prival var inMemoryCache: List<Friends>? = null
fun loadFriends(): Single<List<Friend>> {
return Single.fromCallable {
if(inMemoryCache == null) {
synchronize(lock) {
if(inMemoryCache == null) {
inMemoryCache = webService.getFriends().blockingGet()
}
}
}
inMemoryCache
}
But I want to solve this problem in a reactive way
You can remedy this by creating one common source for all your consumers to subscribe to, and that source will have the cache() operator invoked against it. The effect of this operator is that the first subscriber's subscription will be delegated downstream (i.e. the network request will be invoked), and subsequent subscribers will see internally cached results produced as a result of that first subscription.
This might look something like this:
class Friends {
private val friendsSource by lazy { webService.getFriends().cache() }
fun someFunction() {
// 1st subscription - friends will be fetched from network
friendsSource
.subscribeOn(Schedulers.io())
.subscribe()
// 2nd subscription - friends will be fetched from internal cache
friendsSource
.subscribeOn(Schedulers.io())
.subscribe()
}
}
Note that the cache is indefinite, so if periodically refreshing the list of friends is important you'll need to come up with a way to do so.
Implemented Caching by the following link:
https://blog.mindorks.com/implement-caching-in-android-using-rxjava-operators
fun getSavedAddressList(): Maybe<List<SavedAddress>?>? {
return Observable.concat(
getAddressListMemory(),
getAddressListDatabase(),
getAddressListNetwork()).firstElement()
}
fun getAddressListDatabase(): Observable<List<SavedAddress>?> {
return Observable.create(ObservableOnSubscribe { emitter: ObservableEmitter<List<SavedAddress>?> ->
val list: ArrayList<SavedAddress> = addressDao.getAddressList() as ArrayList<SavedAddress>
if (list.isNotEmpty()) {
emitter.onNext(list)
}
emitter.onComplete()
if (list.isNotEmpty())
getAddressListNetwork().subscribeOn(schedulerProvider.io())?.observeOn(schedulerProvider.io())?.subscribe()
})
}
items in the database are retrieving perfectly after storing into database
problem is network calling is not happening after getting a list from database
I want to get three data source sequentially one after another and store latest data in the database
First of all, you're leaking the getAddressListNetwork Disposable in there because you are trying to do too much inside the getAddressListDatabase.
I think what you want is this:
fun getSavedAddressList(): Observable<List<SavedAddress>> {
return Observable.concat(
getAddressListMemory(),
getAddressListDatabase(),
getAddressListNetwork()).distinctUntilChanged())
}
This will always try to fetch the addresses from the 3 sources, and only emitting if the data is different than the previous emission, meaning the data is "fresher".
To be honest with you, I think you need to have a look at the concept of "stale data" and "cache invalidation".