I have a scenario where 1 observable listens for events, which should then fire another asynchrounous event, and wait before it runs the next item in the source Observable.
The first observable can be triggered much faster than the the async event, and it must wait for the async event to complete before it takes another item from the 1st observable.
So.. essentially I need to set up a 'queue' from the first observable (as I cant lose the data from source 1)
Source 2 should take 1 item at a time from the queue, run it, remove the item from the queue, and go onto the next item in the queue .
src1- --ev1---ev2---ev3----ev4---ev5--ev6---
src2- --ev1------------ev2-------------ev3--------ev4-------ev5------ev6
--------------async-----------async---------async------async------asyc
I was looking at the RX docs and it seems that pausibleBuffered could be a solution but I noticed it has been removed in RX5, which is what I am using. Can someone give advice as the right way to accomplish this ?
Thanks!
You can use mergeScan to run async operations one by one because it needs the previous async operation’s result to run an async operation.
const src2 = src1.mergeScan((_, value) => doSomething(value));
http://reactivex.io/rxjs/class/es6/Observable.js~Observable.html#instance-method-mergeScan
Related
I want to make sure that all events, which were submitted before complete was invoked on the observable, are logged.
I'm aware that operators exist that stop emission of events (takeUntil, etc.) completely.
The question that I have is whether other operators exist which would lead to emissions not being sent if the complete on the subject is sent too 'early'. Are there cases where it would be beneficial to wait with the completion of the observable until the event was handled by the observer?
For example, are there situations (imagine any other RxJS operator instead of the delay) where the following code ...
const subj = new Subject<string>();
subj.pipe(delay(500))
.subscribe((val) => {
console.log(val);
subj.complete();
});
subj.next('1');
... makes more sense than that ...
const subj = new Subject<string>();
subj.pipe(delay(500))
.subscribe((val) => {
console.log(val);
});
subj.next('1');
subj.complete();
... when the subject should only emit one value?
Or is completing the subject immediately after next always safe in such situations?
If there are other factors I'm not aware of (e.g. synchronous vs. asynchronous execution of code) please mention them as well.
In general,
subj.next('1');
subj.complete();
is safe. As far as I know, none of the existing RxJS operators use a completion to cancel/unsubscribe observables early.
That being said, I can easily create such an operator myself. switchMap will cancel currently running inner observables when it receives it's next emission. You could, for example, create a custom operator that unsubscribes and exists early when it receives a complete event.
If your worried about that, however, you're out of luck. No matter what workaround you imagine, I can probably write an operator that will ruin your code. For example filter(_ => false) will stop the 1 from being emitted before the complete in either of the two cases you've described.
In the end, you and your coworkers must write good code (and test!) and RxJS doesn't change that.
I've written this piece of code,
timer(0, 100)
.pipe(
windowTime(1000),
take(3),
flatMap( value => value.pipe(toArray())))
I don't understand why in my subscribe I only get 2 values. Whatever n take I always get n-1 values in my subscribe.
Could you explain to me why ?
Edit: I suspect a bug therefore I opened a bug.
Interesting problem!
So, windowTime(1000) will emit a new window every 1000ms. But what exactly is a window? A window is Subject instance.
windowTime can manage multiple windows, but when you're only providing its first argument(called windowTimeSpan), there will be only one active window. What this means is that after windowTimeSpan ms pass, the current window will be closed and a new one will be created and pushed into the stream.
When a window is closed, it means that it will send a complete notification. This is a very important aspect.
When you subscribe, a window is going to be created immediately and pushed into the stream.
By doing flatMap(value => value.pipe(toArray()))), you can register observers for the current window(subject). It's semantically the same as subject.pipe(toArray()).subscribe(subscriber)
Why does it behave this way?
timer(0, 100)
.pipe(
// Emit a value(window) every 1000ms and close(complete) the prev one
windowTime(1000),
take(3),
flatMap( value => value.pipe(toArray()))
)
Firstly, let's have a look at flatMap. flatMap is the same as mergeMap. What mergeMap does is to manage a number(concurrent, defaults to INFINITY) of inner observables. An inner observable is being tracked until it completes.
What toArray does it to accumulate values until its source completes. In this case, until the current subject(window) completes. This happens when a window is closed, more specifically, when 1000ms pass.
So, as delineated before, a window will be created immediately on subscription.
After 0ms a value(0) comes in, after 100ms another value(1) and so forth until value 9 comes in. In the meanwhile all these values were collected by toArray. So 9's arrival also marks 1000ms, that is also when the current window will be closed(will emit a complete notification). When this happens, toArray will receive the notification and will send the collected values to the data consumer.
After this a new window is created(second value for take(3)). Then, value 10 comes in, then value 11 and so forth until 19, which marks another 1000ms, which will cause the current window to complete and a new one to be created. But this new window would represent the 3rd value for take(3). This means that take will unsubscribe from its source and will emit a complete notification.
As a result, the source will not be able to receive any other values, so this should explain why you're only getting 2 arrays.
The scenario I am working on is composed by 3 Observables.
StartObs: this Observable emits when I need to start a sequence of processings - the data emitted is a processID of the process I need to fulfill
DoStuffObs: this Observable emits commands upon which I have to do something - I want to start listening to such Observable just after StartObs has emitted and I need the processID of the process to perform my duties with the function doTheWork(command, processId)
EndObs: this Observable emits when I have to end the processing of a certain processID and have to go back to listen to the next emission of StartObs
So basically is: Start, DoStuff until End and then go back to listening to the next Start.
It is also guaranteed that after one Start comes sooner or later one End and that it is not possible to have 2 Start without an End in between or 2 End without a Start in between.
The first 2 steps can be achieved via switchMap, like
StartObs
.switchMap(processId => DoStuff.map(command => ({command, processId})))
.tap(data => doTheWork(data.command, data.processId))
What is not clear to me is how to deal with EndObs.
The option of using takeUntil does not work, since I do not want to complete the chain started with StartObs since I have to go back to listening for the next process to start.
Actually, I think takeUntil() is the best choice here in combination with repeat().
StartObs
.switchMap(processId => DoStuff.map(command => ({command, processId})))
.tap(data => doTheWork(data.command, data.processId))
.takeUntil(EndObs)
.repeat();
When the chain completes with takeUntil() it will immediately resubscribe thanks to repeat() and the whole process will start all over again.
Below is code from the Ngrx example: https://github.com/ngrx/example-app/blob/master/src/effects/book.ts My question is why in the first #Effect, it uses switchMap while the others use mergeMap. Is that because the first #Effect is dealing with network, and with the switchMap you can cancel the previous network request if it's running?
#Effect() search$ = this.updates$
.whenAction(BookActions.SEARCH)
.map<string>(toPayload)
.filter(query => query !== '')
.switchMap(query => this.googleBooks.searchBooks(query)
.map(books => this.bookActions.searchComplete(books))
.catch(() => Observable.of(this.bookActions.searchComplete([])))
);
#Effect() clearSearch$ = this.updates$
.whenAction(BookActions.SEARCH)
.map<string>(toPayload)
.filter(query => query === '')
.mapTo(this.bookActions.searchComplete([]));
#Effect() addBookToCollection$ = this.updates$
.whenAction(BookActions.ADD_TO_COLLECTION)
.map<Book>(toPayload)
.mergeMap(book => this.db.insert('books', [ book ])
.mapTo(this.bookActions.addToCollectionSuccess(book))
.catch(() => Observable.of(
this.bookActions.addToCollectionFail(book)
))
);
#Effect() removeBookFromCollection$ = this.updates$
.whenAction(BookActions.REMOVE_FROM_COLLECTION)
.map<Book>(toPayload)
.mergeMap(book => this.db.executeWrite('books', 'delete', [ book.id ])
.mapTo(this.bookActions.removeFromCollectionSuccess(book))
.catch(() => Observable.of(
this.bookActions.removeFromCollectionFail(book)
))
);
}
You are correct; switchMap will unsubscribe from the Observable returned by its project argument as soon as it has invoked the project function again to produce a new Observable.
RxJs is incredibly powerful and dense, but its high level of abstraction can sometimes make code hard to understand. Let me debunk the marble diagrams and docs given by #Andy Hole a little and bring them up to date. You may find the marble syntax reference highly valuable to better understand rxjs operators from their tests (at least I found this missing/not highlighted enough in the official docs).
mergeMap
The first line in the diagram is the source Observable which emits (1,3,5) at different times. The second line in the diagram is the prototypical Observable returned by the project function i => ... passed to the .mergeMap() operator.
When the source Observable emits the item 1, mergeMap() invokes the project function with i=1. The returned Observable will emit 10 three times, every 10 frames (see marble syntax reference). The same happens when the source Observable emits item 3 and the project function creates an Observable that emits 30 three times. Note that the result of mergeMap() contains all three elements generated by each Observable returned from project.
switchMap
This is different with switchMap(), which will unsubscribe from the Observable returned by project as soon as it has invoked it again on a new element. The marble diagram indicates this with the missing third 30 item in the output Observable.
In the example you have given, this leads to the cancellation of the pending search request. This is a very nice but hard-to-get-right property, which you get for free by combining switchMap() with cancellable Observables returned by Angular's Http service. This can save you a lot of headaches without worrying about properly handling all the race conditions that typically occur with async cancellation.
You are right.
As you can see, switchMap is used with search functionality. The searchbox in this example is programmed to basically emit a search request when the user enters text in the textbox (with a 350ms debounce or delay).
This means that when the user enters 'har', ngrx sends a search request to the service. When the user enters another letter 'r', the previous request is canceled (since we are not interested in 'har' anymore, but 'harr').
It is very nicely shown in the marble diagrams provided in another answer.
In mergeMap, the previous Observables are not canceled and therefore '30' and '50' are mixed together. Using switchMap, only the 5s are emitted, because the 3's are canceled.
mergeMap
Projects each source value to an Observable which is merged in the output Observable.
Maps each value to an Observable, then flattens all of these inner Observables using mergeAll.
switchMap
Projects each source value to an Observable which is merged in the output Observable, emitting values only from the most recently projected Observable.
Maps each value to an Observable, then flattens all of these inner Observables using switch.
Source: ES6 Observables in RxJS
You don't want an API save data request to cancel. That is why you would use mergeMap. A search query can be thrown away, no loss of data, and the user might be editing their query and are not interested in the data for the old one. Hence switchMap.
Yes, if you are no longer concerned with the response of the previous request when a new Input arrives switchMap is a suitable operator than mergeMap.
This is what I have:
CitiesObservable = Observable
.FromAsync(apiClient.GetCitiesTask)
.Retry();
apiClient.GetCitiesTask returns a task of type: Task<List<City>>
The problem is that every time I add a subscriber to the observable, apiClient.GetCitiesTask gets called again. How can I cache the result once it has completed successfully?
Thanks
Question reworded
I want apiClient.GetCitiesTask to be called as many times as needed (until it doesn't fail), but once it success, all late subscribers should use a cached result.
Conclusion
2 solutions arose, one I found and the other (the selected answer).
Solution A: (actually is almost a solution)
CitiesObservable = Observable.FromAsync(apiClient.GetCitiesTask).Publish();
CitiesObservable.Connect();
// Then you can subscribe as you want. But! you won't receive the cached value on late subscribers, only the onCompleted signal.
Solution B: (by #Bluesman)
CitiesObservable = Observable.StartAsync(
() => Observable.FromAsync(apiClient.GetPlacesTask<City>).Retry().ToTask()
);
// Then you can subscribe as you want.
What about....
Observable
.StartAsync(() => Observable
.FromAsync(reserbusAPI.GetPlacesTask<City>)
.Retry()
.ToTask());
The outer StartAsync makes sure the eventual result from the created task is buffered while the inner FromAsync with Retry makes sure that GetPlacesTask is called as many times as needed. However, the whole retrying-thing still starts even before the first subscription.