Hi I have a BehaviorSubject with a simple type int, I add the value 5 to it and then add another value 5. The stream listener sent me two events.
How to force check the values and not send an event if the value is equal to the last value.
Sample code:
class TestBloc {
TestBloc(){
testBehavior.stream.listen((event) {
print('Event value = $event');
});
addValueToStream();
addValueToStream();
}
final testBehavior = BehaviorSubject<int>();
void addValueToStream() {
testBehavior.add(5);
}
}
What you're looking for is distinct() method of BehaviorSubject().
Take a look at this from the documentation:
Skips data events if they are equal to the previous data event.
The returned stream provides the same events as this stream, except
that it never provides two consecutive data events that are equal.
That is, errors are passed through to the returned stream, and data
events are passed through if they are distinct from the most recently
emitted data event.
and here is how you implement it:
class TestBloc {
TestBloc() {
testBehavior.distinct((a, b) => a == b).listen((event) {
print('Event value = $event');
});
addValueToStream();
addValueToStream();
}
final testBehavior = BehaviorSubject<int>();
void addValueToStream() {
testBehavior.add(5);
}
}
I would like to set up an Rx subscription that can respond to an event right away, and then ignore subsequent events that happen within a specified "cooldown" period.
The out of the box Throttle/Buffer methods respond only once the timeout has elapsed, which is not quite what I need.
Here is some code that sets up the scenario, and uses a Throttle (which isn't the solution I want):
class Program
{
static Stopwatch sw = new Stopwatch();
static void Main(string[] args)
{
var subject = new Subject<int>();
var timeout = TimeSpan.FromMilliseconds(500);
subject
.Throttle(timeout)
.Subscribe(DoStuff);
var factory = new TaskFactory();
sw.Start();
factory.StartNew(() =>
{
Console.WriteLine("Batch 1 (no delay)");
subject.OnNext(1);
});
factory.StartNewDelayed(1000, () =>
{
Console.WriteLine("Batch 2 (1s delay)");
subject.OnNext(2);
});
factory.StartNewDelayed(1300, () =>
{
Console.WriteLine("Batch 3 (1.3s delay)");
subject.OnNext(3);
});
factory.StartNewDelayed(1600, () =>
{
Console.WriteLine("Batch 4 (1.6s delay)");
subject.OnNext(4);
});
Console.ReadKey();
sw.Stop();
}
private static void DoStuff(int i)
{
Console.WriteLine("Handling {0} at {1}ms", i, sw.ElapsedMilliseconds);
}
}
The output of running this right now is:
Batch 1 (no delay)
Handling 1 at 508ms
Batch 2 (1s delay)
Batch 3 (1.3s delay)
Batch 4 (1.6s delay)
Handling 4 at 2114ms
Note that batch 2 isn't handled (which is fine!) because we wait for 500ms to elapse between requests due to the nature of throttle. Batch 3 is also not handled, (which is less alright because it happened more than 500ms from batch 2) due to its proximity to Batch 4.
What I'm looking for is something more like this:
Batch 1 (no delay)
Handling 1 at ~0ms
Batch 2 (1s delay)
Handling 2 at ~1000s
Batch 3 (1.3s delay)
Batch 4 (1.6s delay)
Handling 4 at ~1600s
Note that batch 3 wouldn't be handled in this scenario (which is fine!) because it occurs within 500ms of Batch 2.
EDIT:
Here is the implementation for the "StartNewDelayed" extension method that I use:
/// <summary>Creates a Task that will complete after the specified delay.</summary>
/// <param name="factory">The TaskFactory.</param>
/// <param name="millisecondsDelay">The delay after which the Task should transition to RanToCompletion.</param>
/// <returns>A Task that will be completed after the specified duration.</returns>
public static Task StartNewDelayed(
this TaskFactory factory, int millisecondsDelay)
{
return StartNewDelayed(factory, millisecondsDelay, CancellationToken.None);
}
/// <summary>Creates a Task that will complete after the specified delay.</summary>
/// <param name="factory">The TaskFactory.</param>
/// <param name="millisecondsDelay">The delay after which the Task should transition to RanToCompletion.</param>
/// <param name="cancellationToken">The cancellation token that can be used to cancel the timed task.</param>
/// <returns>A Task that will be completed after the specified duration and that's cancelable with the specified token.</returns>
public static Task StartNewDelayed(this TaskFactory factory, int millisecondsDelay, CancellationToken cancellationToken)
{
// Validate arguments
if (factory == null) throw new ArgumentNullException("factory");
if (millisecondsDelay < 0) throw new ArgumentOutOfRangeException("millisecondsDelay");
// Create the timed task
var tcs = new TaskCompletionSource<object>(factory.CreationOptions);
var ctr = default(CancellationTokenRegistration);
// Create the timer but don't start it yet. If we start it now,
// it might fire before ctr has been set to the right registration.
var timer = new Timer(self =>
{
// Clean up both the cancellation token and the timer, and try to transition to completed
ctr.Dispose();
((Timer)self).Dispose();
tcs.TrySetResult(null);
});
// Register with the cancellation token.
if (cancellationToken.CanBeCanceled)
{
// When cancellation occurs, cancel the timer and try to transition to cancelled.
// There could be a race, but it's benign.
ctr = cancellationToken.Register(() =>
{
timer.Dispose();
tcs.TrySetCanceled();
});
}
if (millisecondsDelay > 0)
{
// Start the timer and hand back the task...
timer.Change(millisecondsDelay, Timeout.Infinite);
}
else
{
// Just complete the task, and keep execution on the current thread.
ctr.Dispose();
tcs.TrySetResult(null);
timer.Dispose();
}
return tcs.Task;
}
Here's my approach. It's similar to others that have gone before, but it doesn't suffer the over-zealous window production problem.
The desired function works a lot like Observable.Throttle but emits qualifying events as soon as they arrive rather than delaying for the duration of the throttle or sample period. For a given duration after a qualifying event, subsequent events are suppressed.
Given as a testable extension method:
public static class ObservableExtensions
{
public static IObservable<T> SampleFirst<T>(
this IObservable<T> source,
TimeSpan sampleDuration,
IScheduler scheduler = null)
{
scheduler = scheduler ?? Scheduler.Default;
return source.Publish(ps =>
ps.Window(() => ps.Delay(sampleDuration,scheduler))
.SelectMany(x => x.Take(1)));
}
}
The idea is to use the overload of Window that creates non-overlapping windows using a windowClosingSelector that uses the source time-shifted back by the sampleDuration. Each window will therefore: (a) be closed by the first element in it and (b) remain open until a new element is permitted. We then simply select the first element from each window.
Rx 1.x Version
The Publish extension method used above is not available in Rx 1.x. Here is an alternative:
public static class ObservableExtensions
{
public static IObservable<T> SampleFirst<T>(
this IObservable<T> source,
TimeSpan sampleDuration,
IScheduler scheduler = null)
{
scheduler = scheduler ?? Scheduler.Default;
var sourcePub = source.Publish().RefCount();
return sourcePub.Window(() => sourcePub.Delay(sampleDuration,scheduler))
.SelectMany(x => x.Take(1));
}
}
The solution I found after a lot of trial and error was to replace the throttled subscription with the following:
subject
.Window(() => { return Observable.Interval(timeout); })
.SelectMany(x => x.Take(1))
.Subscribe(i => DoStuff(i));
Edited to incorporate Paul's clean-up.
Awesome solution Andrew! We can take this a step further though and clean up the inner Subscribe:
subject
.Window(() => { return Observable.Interval(timeout); })
.SelectMany(x => x.Take(1))
.Subscribe(DoStuff);
The initial answer I posted has a flaw: namely that the Window method, when used with an Observable.Interval to denote the end of the window, sets up an infinite series of 500ms windows. What I really need is a window that starts when the first result is pumped into the subject, and ends after the 500ms.
My sample data masked this problem because the data broke down nicely into the windows that were already going to be created. (i.e. 0-500ms, 501-1000ms, 1001-1500ms, etc.)
Consider instead this timing:
factory.StartNewDelayed(300,() =>
{
Console.WriteLine("Batch 1 (300ms delay)");
subject.OnNext(1);
});
factory.StartNewDelayed(700, () =>
{
Console.WriteLine("Batch 2 (700ms delay)");
subject.OnNext(2);
});
factory.StartNewDelayed(1300, () =>
{
Console.WriteLine("Batch 3 (1.3s delay)");
subject.OnNext(3);
});
factory.StartNewDelayed(1600, () =>
{
Console.WriteLine("Batch 4 (1.6s delay)");
subject.OnNext(4);
});
What I get is:
Batch 1 (300ms delay)
Handling 1 at 356ms
Batch 2 (700ms delay)
Handling 2 at 750ms
Batch 3 (1.3s delay)
Handling 3 at 1346ms
Batch 4 (1.6s delay)
Handling 4 at 1644ms
This is because the windows begin at 0ms, 500ms, 1000ms, and 1500ms and so each Subject.OnNext fits nicely into its own window.
What I want is:
Batch 1 (300ms delay)
Handling 1 at ~300ms
Batch 2 (700ms delay)
Batch 3 (1.3s delay)
Handling 3 at ~1300ms
Batch 4 (1.6s delay)
After a lot of struggling and an hour banging on it with a co-worker, we arrived at a better solution using pure Rx and a single local variable:
bool isCoolingDown = false;
subject
.Where(_ => !isCoolingDown)
.Subscribe(
i =>
{
DoStuff(i);
isCoolingDown = true;
Observable
.Interval(cooldownInterval)
.Take(1)
.Subscribe(_ => isCoolingDown = false);
});
Our assumption is that calls to the subscription method are synchronized. If they are not, then a simple lock could be introduced.
Use .Scan() !
This is what I use for Throttling when I need the first hit (after a certain period) immediately, but delay (and group/ignore) any subsequent hits.
Basically works like Throttle, but fires immediately if the previous onNext was >= interval ago, otherwise, schedule it at exactly interval from the previous hit. And of course, if within the 'cooling down' period multiple hits come, the additional ones are ignored, just like Throttle does.
The difference with your use case is that if you get an event at 0 ms and 100 ms, they will both be handled (at 0ms and 500ms), which might be what you actually want (otherwise, the accumulator is easy to adapt to ignore ANY hit closer than interval to the previous one).
public static IObservable<T> QuickThrottle<T>(this IObservable<T> src, TimeSpan interval, IScheduler scheduler)
{
return src
.Scan(new ValueAndDueTime<T>(), (prev, id) => AccumulateForQuickThrottle(prev, id, interval, scheduler))
.Where(vd => !vd.Ignore)
.SelectMany(sc => Observable.Timer(sc.DueTime, scheduler).Select(_ => sc.Value));
}
private static ValueAndDueTime<T> AccumulateForQuickThrottle<T>(ValueAndDueTime<T> prev, T value, TimeSpan interval, IScheduler s)
{
var now = s.Now;
// Ignore this completely if there is already a future item scheduled
// but do keep the dueTime for accumulation!
if (prev.DueTime > now) return new ValueAndDueTime<T> { DueTime = prev.DueTime, Ignore = true };
// Schedule this item at at least interval from the previous
var min = prev.DueTime + interval;
var nextTime = (now < min) ? min : now;
return new ValueAndDueTime<T> { DueTime = nextTime, Value = value };
}
private class ValueAndDueTime<T>
{
public DateTimeOffset DueTime;
public T Value;
public bool Ignore;
}
I got another one for your. This one doesn't use Repeat() nor Interval() so it might be what you are after:
subject
.Window(() => Observable.Timer(TimeSpan.FromMilliseconds(500)))
.SelectMany(x => x.Take(1));
Well the most obvious thing will be to use Repeat() here. However, as far as I know Repeat() might introduce problems so that notifications disappear in between the moment when the stream stops and we subscribe again. In practice this has never been a problem for me.
subject
.Take(1)
.Concat(Observable.Empty<long>().Delay(TimeSpan.FromMilliseconds(500)))
.Repeat();
Remember to replace with the actual type of your source.
UPDATE:
Updated query to use Concat instead of Merge
I have stumbled upon this question while trying to re-implement my own solution to the same or similar problem using .Window
Take a look, it seems to be the same as this one and solved quite elegantly:
https://stackoverflow.com/a/3224723/58463
It's an old post, but no answer could really fill my needs, so I'm giving my own solution :
public static IObservable<T> ThrottleOrImmediate<T>(this IObservable<T> source, TimeSpan delay, IScheduler scheduler)
{
return Observable.Create<T>((obs, token) =>
{
// Next item cannot be send before that time
DateTime nextItemTime = default;
return Task.FromResult(source.Subscribe(async item =>
{
var currentTime = DateTime.Now;
// If we already reach the next item time
if (currentTime - nextItemTime >= TimeSpan.Zero)
{
// Following item will be send only after the set delay
nextItemTime = currentTime + delay;
// send current item with scheduler
scheduler.Schedule(() => obs.OnNext(item));
}
// There is still time before we can send an item
else
{
// we schedule the time for the following item
nextItemTime = currentTime + delay;
try
{
await Task.Delay(delay, token);
}
catch (TaskCanceledException)
{
return;
}
// If next item schedule was change by another item then we stop here
if (nextItemTime > currentTime + delay)
return;
else
{
// Set next possible time for an item and send item with scheduler
nextItemTime = currentTime + delay;
scheduler.Schedule(() => obs.OnNext(item));
}
}
}));
});
}
First item is immediately sent, then following items are throttled. Then if a following item is sent after the delayed time, it's immediately sent too.
I'm wrapping an API that emits events in Observables and currently my datasource code looks something like this, with db.getEventEmitter() returning an EventEmitter.
const Datasource = {
getSomeData() {
return Observable.fromEvent(db.getEventEmitter(), 'value');
}
};
However, to actually use this, I need to both memoize the function and have it return a ReplaySubject, otherwise each subsequent call to getSomeData() would reinitialize the entire sequence and recreate more event emitters or not have any data until the next update, which is undesirable, so my code looks a lot more like this for every function
const someDataCache = null;
const Datasource = {
getSomeData() {
if (someDataCache) { return someDataCache; }
const subject = new ReplaySubject(1);
Observable.fromEvent(db.getEventEmitter(), 'value').subscribe(subject);
someDataCache = subject;
return subject;
}
};
which ends up being quite a lot of boilerplate for just one single function, and becomes more of an issue when there are more parameters.
Is there a better/more elegant design pattern to accomplish this? Basically, I'd like that
Only one event emitter is created.
Callers who call the datasource later get the most recent result.
The event emitters are created when they're needed.
but right now I feel like this pattern is fighting the Observable pattern, resulting a bunch of boilerplate.
As a followup to this question, I ended up commonizing the logic to leverage Observables in this way. publishReplay as cartant mentioned does get me most of the way to what I needed. I've documented what I've learned in this post, with the following tl;dr code:
let first = true
Rx.Observable.create(
observer => {
const callback = data => {
first = false
observer.next(data)
}
const event = first ? 'value' : 'child_changed'
db.ref(path).on(event, callback, error => observer.error(error))
return {event, callback}
},
(handler, {event, callback}) => {
db.ref(path).off(event, callback)
},
)
.map(snapshot => snapshot.val())
.publishReplay(1)
.refCount()
i've an observable that I create with the following code.
Observable.create(new Observable.OnSubscribe<ReturnType>() {
#Override
public void call(Subscriber<? super ReturnType> subscriber) {
try {
if (!subscriber.isUnsubscribed()) {
subscriber.onNext(performRequest());
}
subscriber.onCompleted();
} catch (Exception e) {
subscriber.onError(e);
}
}
});
performRequest() will perform a long running task as you might expect.
Now, since i might be launching the same Observable twice or more in a very short amount of time, I decided to write such transformer:
protected Observable.Transformer<ReturnType, ReturnType> attachToRunningTaskIfAvailable() {
return origObservable -> {
synchronized (mapOfRunningTasks) {
// If not in maps
if ( ! mapOfRunningTasks.containsKey(getCacheKey()) ) {
Timber.d("Cache miss for %s", getCacheKey());
mapOfRunningTasks.put(
getCacheKey(),
origObservable
.doOnTerminate(() -> {
Timber.d("Removed from tasks %s", getCacheKey());
synchronized (mapOfRunningTasks) {
mapOfRunningTasks.remove(getCacheKey());
}
})
.cache()
);
} else {
Timber.d("Cache Hit for %s", getCacheKey());
}
return mapOfRunningTasks.get(getCacheKey());
}
};
}
Which basically puts the original .cache observable in a HashMap<String, Observable>.
This basically disallows multiple requests with the same getCacheKey() (Example login) to call performRequest() in parallel. Instead, if a second login request arrives while another is in progress, the second request observable gets "discarded" and the already-running will be used instead. => All the calls to onNext are going to be cached and sent to both subscribers actually hitting my backend only once.
Now, suppouse this code:
// Observable loginTask
public void doLogin(Observable<UserInfo> loginTask) {
loginTask.subscribe(
(userInfo) -> {},
(throwable) -> {
if (userWantsToRetry()) {
doLogin(loinTask);
}
}
);
}
Where loginTask was composed with the previous transformer. Well, when an error occurs (might be connectivity) and the userWantsToRetry() then i'll basically re-call the method with the same observable. Unfortunately that has been cached and I'll receive the same error without hitting performRequest() again since the sequence gets replayed.
Is there a way I could have both the "same requests grouping" behavior that the transformer provides me AND the retry button?
Your question has a lot going on and it's hard to put it into direct terms. I can make a couple recommendations though. Firstly your Observable.create can be simplified by using an Observable.defer(Func0<Observable<T>>). This will run the func every time a new subscriber is subscribed and catch and channel any exceptions to the subscriber's onError.
Observable.defer(() -> {
return Observable.just(performRequest());
});
Next, you can use observable.repeatWhen(Func1<Observable<Void>, Observable<?>>) to decide when you want to retry. Repeat operators will re-subscribe to the observable after an onComplete event. This particular overload will send an event to a subject when an onComplete event is received. The function you provide will receive this subject. Your function should call something like takeWhile(predicate) and onComplete when you do not want to retry again.
Observable.just(1,2,3).flatMap((Integer num) -> {
final AtomicInteger tryCount = new AtomicInteger(0);
return Observable.just(num)
.repeatWhen((Observable<? extends Void> notifications) ->
notifications.takeWhile((x) -> num == 2 && tryCount.incrementAndGet() != 3));
})
.subscribe(System.out::println);
Output:
1
2
2
2
3
The above example shows that retries are aloud when the event is not 2 and up to a max of 22 retries. If you switch to a repeatWhen then the flatMap would contain your decision as to use a cached observable or the realWork observable. Hope this helps!
I have paged interface. Given a starting point a request will produce a list of results and a continuation indicator.
I've created an observable that is built by constructing and flat mapping an observable that reads the page. The result of this observable contains both the data for the page and a value to continue with. I pluck the data and flat map it to the subscriber. Producing a stream of values.
To handle the paging I've created a subject for the next page values. It's seeded with an initial value then each time I receive a response with a valid next page I push to the pages subject and trigger another read until such time as there is no more to read.
Is there a more idiomatic way of doing this?
function records(start = 'LATEST', limit = 1000) {
let pages = new rx.Subject();
this.connect(start)
.subscribe(page => pages.onNext(page));
let records = pages
.flatMap(page => {
return this.read(page, limit)
.doOnNext(result => {
let next = result.next;
if (next === undefined) {
pages.onCompleted();
} else {
pages.onNext(next);
}
});
})
.pluck('data')
.flatMap(data => data);
return records;
}
That's a reasonable way to do it. It has a couple of potential flaws in it (that may or may not impact you depending upon your use case):
You provide no way to observe any errors that occur in this.connect(start)
Your observable is effectively hot. If the caller does not immediately subscribe to the observable (perhaps they store it and subscribe later), then they'll miss the completion of this.connect(start) and the observable will appear to never produce anything.
You provide no way to unsubscribe from the initial connect call if the caller changes its mind and unsubscribes early. Not a real big deal, but usually when one constructs an observable, one should try to chain the disposables together so it call cleans up properly if the caller unsubscribes.
Here's a modified version:
It passes errors from this.connect to the observer.
It uses Observable.create to create a cold observable that only starts is business when the caller actually subscribes so there is no chance of missing the initial page value and stalling the stream.
It combines the this.connect subscription disposable with the overall subscription disposable
Code:
function records(start = 'LATEST', limit = 1000) {
return Rx.Observable.create(observer => {
let pages = new Rx.Subject();
let connectSub = new Rx.SingleAssignmentDisposable();
let resultsSub = new Rx.SingleAssignmentDisposable();
let sub = new Rx.CompositeDisposable(connectSub, resultsSub);
// Make sure we subscribe to pages before we issue this.connect()
// just in case this.connect() finishes synchronously (possible if it caches values or something?)
let results = pages
.flatMap(page => this.read(page, limit))
.doOnNext(r => this.next !== undefined ? pages.onNext(this.next) : pages.onCompleted())
.flatMap(r => r.data);
resultsSub.setDisposable(results.subscribe(observer));
// now query the first page
connectSub.setDisposable(this.connect(start)
.subscribe(p => pages.onNext(p), e => observer.onError(e)));
return sub;
});
}
Note: I've not used the ES6 syntax before, so hopefully I didn't mess anything up here.