We have a button in the UI, which, when pressed, will make some remote network call in its own coroutine. However, if the user spams the button for whatever reason, it is possible that the remote data might somehow get corrupted. We would like to prevent this by discarding all requests until the current one is completed.
There are many ways to do this. I have create a simple extension function on CoroutineScope to only launch if the CoroutineScope is not active. This is what I have created:
Extension Function
fun CoroutineScope.safeLaunch(dispatcher: CoroutineDispatcher, block: () -> Unit): Job {
return if (!isActive) {
launch(dispatcher) {
block()
}
} else {
launch {}
}
}
Example Use
fun loadNotifications() {
viewModelScope.safeLaunch(IO) {
getNotifications.invoke() // Suspend function invoke should only be from a coroutine or another suspend function
}
}
The problem is, the above won't compile as I get an error saying
Suspend function invoke should only be from a coroutine or another
suspend function
Does anyone know what I'm doing wrong or how to make it work?
There are multiple problems with this code:
Fixing the error you mentioned is very easy and requires to only specify block as suspendable: block: suspend () -> Unit.
isActive doesn't mean the job/scope is actively running something, but that it hasn't finished. isActive in your example always returns true, even before launching any coroutine on it.
If your server can't handle concurrent actions, then you should really fix this on server side. Limiting the client isn't a proper fix as it can be still exploited by users. Also, you need to remember that multiple clients can perform the same action at the same time.
As you mentioned, there are several ways how this situation can be handled on the client side:
In the case of UI and the button, it is probably the best for the user experience to disable the button or overlay the screen/button with a loading indicator. It gives the user the feedback that the operation is running in the background and at the same time it fixes the problem with multiple calls to the server.
In general case, if we just need to limit concurrency and reject any additional tasks while the last one is still running, probably the easiest is to use Mutex:
private val scope = CoroutineScope(EmptyCoroutineContext)
private val mutex = Mutex()
fun safeLaunch(block: suspend () -> Unit) {
if (!mutex.tryLock()) {
return
}
scope.launch {
try {
block()
} finally {
mutex.unlock()
}
}
}
Note we need a separate mutex per scope or per the type of the task. I don't think it is possible to create such utility as a generic extension function, working with any coroutine scope. Actually, we can implement it in a very similar way to your original code, but by looking at the current job's children. Still, I consider such solution hacking and I discourage it.
Related
I execute UI related operation in the onSubscribe action of do operator. I obtain an
Main Thread Checker: UI API called on a background thread
console error in Xcode. I tried to use observe(on: MainScheduler.instance) but without success.
This my snippet:
checkCurrentNetworkAndConnect().observe(on: MainScheduler.instance).do(onSubscribe: {
// UI related stuff
})
I also tried with subscribe(on: MainScheduler.instance) but doesn't work.
How can I achieve this?
This will happen if you are calling subscribe on a background thread. If you call subscribe on a background thread, then the Observable will be subscribed to on that background thread. To solve this, you will need to use subscribe(on:) after the do operator...
Something like this:
func example() {
checkCurrentNetworkAndConnect()
.do(onSubscribe: {
print("UI related stuff")
})
.subscribe(on: MainScheduler.instance)
.subscribe()
}
With the above, even if you call example() on a background thread, the onSubscribe: closure will be executed on the main thread.
If for some reason you also want the checkCurrentNetworkAndConnect() on a background thread, then you can do that with another subscribe(on:) above the do.
All this said, you should not be performing significant side effects inside a do(onSubscribe:) in the first place. It's one thing to put debug code in there but not anything more than that. I've been using RxSwift for 6 years and I've never needed to use a do(onSubscribe:) at all, much less for significant code. There is likely a better way to accomplish the ultimate goal you are trying to accomplish.
In response to your comment. How you go about doing what you want depends very much on specifics not provided. Below I have made some simplifying assumptions. If your problem is different, I suggest you post a new question with the details...
func example(operations: [Observable<Void>], messages: [String], label: UILabel, disposeBag: DisposeBag) {
Observable.concat(operations.enumerated().map { index, op in
op.map { messages[index + 1] }
})
.startWith(messages[0])
.observe(on: MainScheduler.instance)
.bind(to: label.rx.text)
.disposed(by: disposeBag)
}
The code above assumes:
that you have an array of operations expressed as observables
that none of them need data provided by a previous operation
that none of them emit data you care to use or keep
that every operation has a message that needs to be displayed before the operation starts
that there is an additional message to be displayed when the last operation completes
After reading the official docs on coroutine cancelation, If for the example I have the following code:
val job = scope.launch {
val userId = networkOperationOne()
//check if coroutine is still active before calling operation two?
val userDetails = networkOperationTwo(userId)
}
Should I check isActive before calling network call two?
Let's assume that job.cancel() was called while networkOperationOne() is still in progress and that I'm not calling any suspending function that automatically does the cancelation for me.
It depends on how networkOperationOne and networkOperationTwo are suspending.
They may internally be cooperative anyway, which means you do not have to check isActive.
When in doubt, throw in ensureActive() to perform the check and act accordingly.
In this case, the conditional check is negligible compared to the network request so add one in.
I got following functions for making server calls
suspend fun <T: BaseResponse> processPost(post:Post):T? {
val gson=Gson()
val data=gson.toJson(post.reqData)
val res= sendPost(data,post.script)
Log.d("server","res:"+res.first)
//process response here
return null
}
private fun sendPost(data:String,url:String):Pair<String,Int> {
//send data to server
}
In some cases processPost may enter into infinite loop(for instance to wait for access token refresh).Of course this code should never be run on the main thread.But when I mark this function as suspend IDE is highliting it as redundant.Its not big deal but I'm curious how then can I restrict function execution on the main thread?
It seems that you have quite some learning on coroutines to do. It’s impossible to cover all you need to know in one single answer. That’s what tutorials are for. Anyway I will try to answer just the points you asked. It may not make sense before you learn the concepts, I’m sorry if my answer does not help.
Just like many other things, coroutines are not magic. If you don’t understand what something does, you cannot hope it has the properties you want. It may sound harsh but I want to stress that such mentality is a major cause of bugs.
Making a function suspending allows you to call other suspending functions in the function body. It does not make blocking calls non-blocking, nor does it automatically jump threads for you.
You can use withContext to have the execution jump to another thread.
suspend fun xyz() = withContext(Dispatchers.IO) {
...
}
When you call xyz in the main thread, it’ll hand the task to the IO dispatcher. Without being blocked, it can then handle other stuff in the app.
EDIT regarding the comment.
Sorry for being so patronizing and making a wrong guess about your misconception.
If you just want the compiler/the IDE to shut up about the warning, you can simply add #Suppress("RedundantSuspendModifier") to the function. But you shouldn't, because the compiler knows better than you, at least for now.
The great thing about coroutines is that you can write in direct style without blocking the main thread.
launch(Dispatchers.Main) {
val result = makeAnHttpCall() // this can take a long time
messWithUi(result) // changes to the UI has to be in the main thread
}
I hope it is obvious by now that the suspend modifier is not going to stop the main thread from calling the function.
#Suppress("RedundantSuspendModifier")
suspend fun someHeavyComputation(): Result {
return ...
}
launch(Dispatchers.Main) {
val result = someHeavyComputation() // this will run in the main thread
messWithUi(result)
}
Now if you want the computation not to be done in the main thread:
suspend fun someHeavyComputation() = withContext(Dispatchers.Default) {
... // this will be in a thread pool
}
Further reading: Blocking threads, suspending coroutines.
I often have to execute code on a separate thread that is long running, blocking, instable and\or has a potential to hang forever. Since the existence of TPL the internet is full of examples that nicely cancel a task with the cancellation token but I never found an example that kills a task that hangs. Code that hangs forever is likely to be expected as soon as you communicate with hardware or call some third party code. A task that hangs cannot check the cancellation token and is doomed to stay alive forever. In critical applications I equip those tasks with alive signals that are sent on regular time intervals. As soon as a hanging task is detected, it is killed and a new instance is started.
The code below shows an example task that calls a long running placeholder method SomeThirdPartyLongOperation() which has the potential to hang forever. The StopTask() first checks if the task is still running an tries to cancel it with the cancellation token. If that doesn’t work, the task hangs and the underlying thread is interrupted\aborted old school style.
private Task _task;
private Thread _thread;
private CancellationTokenSource _cancellationTokenSource;
public void StartTask()
{
_cancellationTokenSource = new CancellationTokenSource();
_task = Task.Factory.StartNew(() => DoWork(_cancellationTokenSource.Token), _cancellationTokenSource.Token, TaskCreationOptions.LongRunning, TaskScheduler.Default);
}
public void StopTask()
{
if (_task.Status == TaskStatus.RanToCompletion)
return;
_cancellationTokenSource.Cancel();
try
{
_task.Wait(2000); // Wait for task to end and prevent hanging by timeout.
}
catch (AggregateException aggEx)
{
List<Exception> exceptions = aggEx.InnerExceptions.Where(e => !(e is TaskCanceledException)).ToList(); // Ignore TaskCanceledException
foreach (Exception ex in exceptions)
{
// Process exception thrown by task
}
}
if (!_task.IsCompleted) // Task hangs and didn't respond to cancellation token => old school thread abort
{
_thread.Interrupt();
if (!_thread.Join(2000))
{
_thread.Abort();
}
}
_cancellationTokenSource.Dispose();
if (_task.IsCompleted)
{
_task.Dispose();
}
}
private void DoWork(CancellationToken cancellationToken)
{
if (string.IsNullOrEmpty(Thread.CurrentThread.Name)) // Set thread name for debugging
Thread.CurrentThread.Name = "DemoThread";
_thread = Thread.CurrentThread; // Save for interrupting/aborting if thread hangs
for (int i = 0; i < 10; i++)
{
cancellationToken.ThrowIfCancellationRequested();
SomeThirdPartyLongOperation(i);
}
}
Although I’ve been using this construct for some years now, I want to know if there are some potential mistakes in it. I’ve never seen an example of a task that saves the underlying thread or gives it a name to simplify debugging, so I’m a bit unsure if this is the right way to go. Comment on any detail is welcome!
Code that hangs forever is likely to be expected as soon as you communicate with hardware or call some third party code.
Communication: absolutely not. There's always a way to timeout with communication APIs, so even with misbehaving hardware, there's no need to force-kill an I/O operation.
Third-party code: only if you're paranoid (or have high demands such as 24x7 automation).
Here's the bottom line:
There's no way to force-kill a task.
You can force-kill a thread, but this can easily cause serious problems with application state, possibility if introducing deadlocks in other parts of the code, and resource leaks.
You can force-kill an appdomain, which solves a large portion of app state / deadlock issues with killing threads. However, it doesn't solve them all, and there's still the problem of resource leaks.
You can force-kill a process. This is the only truly clean and reliable solution.
So, if you choose to trust the third-party code, I recommend that you just call it like any other API. If you require 100% reliability regardless of third-party libraries, you'll need to wrap the third-party dll into a separate process and use cross-process communication to call it.
Your current code force-kills a thread pool thread, which is certainly not recommended; those threads belong to the thread pool, not to you, and this is still true even if you specify LongRunning. If you go the kill-thread route (which is not fully reliable), then I recommend using an explicit thread.
The question is why is this task even hanging at all? I think there's no universal solution to this problem but you should focus on the task to be always responsible and not on forcing to interrupt it.
In this code, it looks like you're looking for a simple thread rather than a task - you shouldn't link tasks to threads - it's very likely that the task will switch to another thread after some async operations and you will end up on killing an innoccent thread that is not connected to your task anymore. If you really need to kill the whole thread then make a dedicated one just for this job.
You shouldn't also name or do anything with any thread that is used for tasks' default pool. Consider this code:
static void Main(string[] args)
{
Task.Run(sth);
Console.Read();
}
static async Task sth()
{
Thread.CurrentThread.Name = "My name";
Console.WriteLine(Thread.CurrentThread.ManagedThreadId);
await Task.Delay(1);
Console.WriteLine(Thread.CurrentThread.ManagedThreadId);
Console.WriteLine(Thread.CurrentThread.Name ?? "No name");
}
the output is:
3
4
No name
I have users connecting to a Node.js server, and when they join, I add them into a Lobby (essentially a queue). Any time there are 2 users in the lobby, I want them to pair off and be removed from the lobby. So essentially, it's just a simple queue.
I started off by trying to implement this with a Lobby.run method, which has an infinite loop (started within a process.nextTick call), and any time there are more than two entries in the queue, I remove them form the queue. However, I found that this was eating all my memory and that infinite loops like this are generally ill-advised.
I'm now assuming that emitting events via EventEmitter is the way to go. However, my concern is with synchronization. Let's assuming my Lobby is pretty simple:
Lobby = {
users: []
, join: function (user) {
this.users.push(user);
emitter.emit('lobby.join', user);
}
, leave: function (user) {
var index = this.users.indexOf(user);
this.users.splice(index, 1);
emitter.emit('lobby.leave', user);
}
};
Now essentially I assume I want to watch for users joining the lobby and pair them up, maybe something like this:
Lobby = {
...
, run: function () {
emitter.on('lobby.join', function (user) {
// TODO: determine if this.users contains other users,
// pair them off, and remove them from the array
});
}
}
As I mentioned, this does not account for synchronization. Multiple users can join the lobby at the same time, and so the event listener might pair up a single user with multiple other users instead of just one.
Can someone with more Node.js experience tell me if I am right to be concerned with this event-based approach? Any insight for improvement on this approach would be much appreciated.
You are wrong to be concerned with this. This is because Node.JS is single-threaded, there is no concurrency at all! Whenever a block of code is fired no other code (including event handlers) can be fired until the block finishes what it does. In particular if you define this empty loop in your app:
while(true) { }
then your server is crashed, no other code will ever fire, no other request will be ever handled. So be careful with blocks of code, make sure that each block will eventually end.
Back to the question... So in your case it is impossible for multiple users to be paired with the same user. And let me say one more time: this is simply because there is no concurrency in Node.JS!
On the other hand this only applies to one instance of Node.JS. If you want to scale it to many machines, then obviously you will have to implement some locking mechanism (which ensures that no other process can work with the data at the same time).