I tried to do something with Flux streaming objects and after handling all elements do some last work and finish a Mono but it doesn't work:
// data and id comming from a webrequest
// myRepository is a org.springframework.data.r2dbc.repository.R2dbcRepository
myRepository.findById(id)
.flatMap(dbObject -> doSomethingWithDbObjectAndSave(dbObject , data))
.then (doOnFinish(data))
.subscribe();
Mono<DbObject> doSomethingWithDbObjectAndSave (DbObject dbo, DataObject data){
...
}
Mono<Void> doOnFinish(DataObject data){
...
}
The problem:
Even I try this, the function "doOnFinish" is called before the first element pass doSomethingWithDbObjectAndSave" but I change something on the data object and would like to do this before!
The I tried to change the code:
myRepository.findById(id)
.flatMap(dbObject -> doSomethingWithDbObjectAndSave(dbObject , data))
.last()
.flatMap(dbObject -> doOnFinish(data))
.subscribe();
I hoped, that I could use the last element to trigegr the onFinish function but I got
"flux#last() didn't observe any onnext signal" and do not undetstand this!
Anyone any idea?
then(methodCall(data)) will eagerly evaluate the parameter expression and thus call methodCall even before then is entered. You need a method which lazily evaluates its parameters.
I think you are looking for doOnComplete:
public final Flux<T> doOnComplete(Runnable onComplete)
Add behavior (side-effect) triggered when the Flux completes successfully.
myRepository.findById(id)
.flatMap(dbObject -> doSomethingWithDbObjectAndSave(dbObject , data))
.doOnComplete(() -> doOnFinish(data))
.subscribe();
Wow ... hours later I found a solution.
doOnComplete doesn't return the stream with the async call from doOnFinish (Mono).
Now I found this:
myRepository.findById(id)
.flatMap(dbObject -> doSomethingWithDbObjectAndSave(dbObject , data))
.takeLast(1)
.flatMap(dbObject -> doOnFinish(data))
.subscribe();
That works for me ....
I think flatMap is redundant here. Instead, it is possible to do:
myRepository.findById(id)
.flatMap(dbObject -> doSomethingWithDbObjectAndSave(dbObject , data))
.then (Mono.deffer(()->doOnFinish(data)))
.subscribe();
Related
i currently have this Reactor code where im not sure im doing this the idiomatic way.
My requirements are that for a list of accountIds, I make 2 requests which are done one after the other. One to delete the account data, the other is to trigger an event afterwards. The second request is only made if the first one succeeds.
At the end, i would like to know if all of the sets of requests were successful. I have achieved this with the code below.
Flux.fromIterable(List.of("accountId", "someOtherAccountId"))
.flatMap(accountId -> someWebclient.deleteAccountData(accountId)
.doOnSuccess(response -> log.info("Delete account data success"))
.onErrorResume(e -> {
log.info("Delete account data failure");
return Mono.empty();
})
.flatMap(deleteAccountDataResponse -> {
return eventServiceClient.triggerEvent("deleteAccountEvent")
.doOnSuccess(response -> log.info("Delete account event success"))
.onErrorResume(e -> {
log.info("Delete account event failure");
return Mono.empty();
});
}))
.count()
.subscribe(items -> {
if (items.intValue() == accountIdsToForget.size()) {
log.info("All accountIds deleted and events triggered successfully");
} else {
log.info("Not all accoundIds deleted and events triggered successfully");
}
});
Is there a better way to achieve this?
As the webclients can return errors for 4xx and 5xx, i am having to swallow that up with onErrorResume in order to prevent the error from bubbling up. Similarly, the only way i have been able to capture if all of the accountIds have been processed is by checking the size of the Flux against the size of the List which it was started with
Disclaimer: it is a little subjective how to provide a better solution. In this answer, I will provide my personal choice of error handling, that, in my opinion, provides best extensibility and readability.
I would model a result/report object (kind like Either in functional paradigm), so that each success or error is sent as a "next signal" downstream.
It requires a little more code/boilerplate, but the benefit is that we end up with a flow of successes and failures produced on the fly. It allows to detect errors early, and ease both error recovery and pipeline extensibility (for example, it is then very easy to switch between fail-fast and error silencing strategies, or to build complex reports from upstream results, etc.).
Let's try to apply this to your example. For simplicity, I will mock deletion and notification service with two methods that return an empty result on success:
static Mono<Void> delete(String account) {
if (account.isBlank()) return Mono.error(new IllegalArgumentException("EMPTY ACCOUNT !"));
else return Mono.empty();
}
static Mono<Void> notify(String event) {
if (event.isBlank()) return Mono.error(new IllegalArgumentException("UNKNOWN EVENT !"));
return Mono.empty();
}
I would make this steps:
Create result model:
sealed interface Result { String accountId(); }
sealed interface Error extends Result { Throwable cause(); }
record DeletionError(String accountId, Throwable cause) implements Error {}
record NotifyError(String accountId, Throwable cause) implements Error {}
record Success(String accountId) implements Result {}
Then, we can prepare our pipeline that will wrap our delete and notify operations to make them produce result objects:
static Flux<Result> deleteAndNotify(Flux<String> accounts) {
Function<String, Mono<Result>> safeDelete = account
-> delete(account)
.<Result>thenReturn(new Success(account))
.onErrorResume(err -> Mono.just(new DeletionError(account, err)));
Function<Result, Mono<Result>> safeNotify = deletionResult -> deletionResult instanceof Success
? notify("deleteAccountEvent")
.thenReturn(deletionResult)
.onErrorResume(err -> Mono.just(new NotifyError(deletionResult.accountId(), err)))
: Mono.just(deletionResult);
return accounts.flatMap(safeDelete)
.flatMap(safeNotify);
}
With the code above, you can already receive errors as they arrive. A simple program:
var results = deleteAndNotify(Flux.just("a1", "a2", " ", "a3"));
results.subscribe(System.out::println);
prints:
Success[accountId=a1]
Success[accountId=a2]
DeletionError[accountId= , cause=java.lang.IllegalArgumentException: EMPTY ACCOUNT !]
Success[accountId=a3]
Now, it becomes very simple to adapt your flow of control:
if we want to keep track of errors only, we just have to chain a simple filter: results.filter(it -> it instanceof Error)
To fail-fast, just map error result to a real error: results.flatMap(result -> result instanceof Error err ? Mono.error(err.cause()) : Mono.just(result))
You want to get an idea of the flow throughput ? Just time it: results.timed()
etc.
And if you want to count, you can now directly count errors and successes on the fly. It provides a few advantages:
You are not forced to know the number of accounts to delete in advance to verify if any error happened
You can have a live monitoring of the failed/succeeded operations
We can program counting like that:
record Count(long success, long deleteFailed, long notifyFailed) {
Count() { this(0, 0, 0); }
Count newSuccess() { return new Count(success + 1, deleteFailed, notifyFailed); }
Count newDeletionFailure() { return new Count(success, deleteFailed + 1, notifyFailed); }
Count newNotifyFailure() { return new Count(success, deleteFailed, notifyFailed + 1); }
}
var counting = results.scanWith(Count::new, (count, result) -> switch (result) {
case Success s -> count.newSuccess();
case DeletionError de -> count.newDeletionFailure();
case NotifyError ne -> count.newNotifyFailure();
});
Subscribing to this counting flow using the same input accounts as above would produce that kind of input:
Count[success=0, deleteFailed=0, notifyFailed=0]
Count[success=1, deleteFailed=0, notifyFailed=0]
Count[success=2, deleteFailed=0, notifyFailed=0]
Count[success=2, deleteFailed=1, notifyFailed=0]
Count[success=3, deleteFailed=1, notifyFailed=0]
If you want only a total count, then either use counting.last() or replace scanWith by reduceWith operator.
I hope this answer is of any help to you to better model pipelines/DAG/flows of operations.
I have a Springboot API that makes a maximum of 6 Stored Procedure calls using the callable statement. I want to make this call asynchronous. Is it possible to achieve this using CompleteableFuture(java8)???
Database connections are typically not thread-safe. Are you planning to use one connection per call?
If yes, following code will execute the callable statements in parallel. Please note I have used vavr library to simplify the exception handling.
public List<Boolean> concurrentCalls(Supplier<Connection> connectionSupplier, List<String> statements) {
Function<String, Either<Throwable, Boolean>> executeStatement = statement ->
Try.of(() -> connectionSupplier.get()
.prepareCall(statement)
.execute())
.toEither();
List<CompletableFuture<Boolean>> completableFutures = statements.stream()
.map(statement ->
CompletableFuture.supplyAsync(() -> executeStatement.apply(statement))
.thenApply( Either::get) // Handle exceptions as required
)
.collect(Collectors.toList());
return CompletableFuture.allOf( completableFutures.toArray( new CompletableFuture[0]))
.thenApply( any ->
completableFutures
.stream()
.map(CompletableFuture::join)
.collect(Collectors.toList())
)
.join();
}
I'm presently experiencing a really strange and frustrating issue at the moment.
I have some code that is being tested that runs through a reactive call chain containing a series of filtering operations.
As the test runs through the code and a 'false' value is returned, the code still passes through to the next call in the chain instead of just returning.
Since I'm still a 'reactive newbie' I'm figuring I'm probably not doing something incorrectly here in the reactive code chain.
Here is the code:
private Mono<GetCardNumberServiceResponseData> updateCardNumberIfLastFourValidAndShaIsNull(Card card, GetCardNumberServiceResponseData responseData) {
return Mono.just(responseData)
.filter(response -> isValidLastFour(card, response))
.defaultIfEmpty(responseData)
.filter(response -> shaIsNull(card))
.defaultIfEmpty(responseData)
.flatMap(response -> updateCardNumber(card, response));
}
This is the portion that's not evaluating correctly:
.filter(response -> isValidLastFour(card, response))
This is what 'isValidLastFour' currently looks like:
private boolean isValidLastFour(Card card, GetCardNumberServiceResponseData responseData) {
// String cardNumberFromResponse = responseData.getCardNumber();
// String lastFourFromResponse =
// cardNumberFromResponse.substring(cardNumberFromResponse.length() - 4);
// return card.getLastFour().equals(lastFourFromResponse);
return false;
}
So presently I just have it hard-coded to return 'false', but as I step through the test with the debugger, the execution just passes right through as if 'true' is being returned, so I'm really just at a loss at what might be causing this behavior.
As always, any and all help is always greatly appreciated!
If you want responseData to be the default value, in case there is an empty Mono, you have to put defaultIfEmpty at the end of the chain:
return Mono.just(responseData)
.filter(response -> isValidLastFour(card, response))
.filter(response -> shaIsNull(card))
.flatMap(response -> updateCardNumber(card, response))
.defaultIfEmpty(responseData);
Even better, you can merge those filters:
return Mono.just(responseData)
.filter(response -> isValidLastFour(card, response) && shaIsNull(card))
.flatMap(response -> updateCardNumber(card, response))
.defaultIfEmpty(responseData);
I'm trying to implement the Reactor Subscriber Context (http://projectreactor.io/docs/core/release/reference/#context) so I can pass values from my SLF4J MDC into a Flux where I then can use the values for logging.
I use the subscriberContext() method to set the value like:
someFlux().subscriberContext(Context.of(MDC_ATTRIBUTE, MDC.get(MDC_ATTRIBUTE)));
I also can access the context in the chain. For example with a flatMap:
.flatMap(r -> Mono.subscriberContext().map(ctx -> {
String name = ctx.getOrDefault(MDC_ATTRIBUTE_NAME, "NO CTX");
return r;
}))
Also doOnEach() works:
.doOnEach(signal -> {
Context ctx = signal.getContext();
if (signal.isOnNext()) {
try (MDC.MDCCloseable closeable = MDC.putCloseable(MDC_ATTRIBUTE_NAME, ctx.getOrDefault(MDC_ATTRIBUTE_NAME, "MAAAAN"))) {
log.debug("FINISHED: {}", requestName);
}
}
})
There is just one problem with it. I want to log something in doOnSubscribe, doOnError and in doOnSuccess. While I could use doOnEach to check for signal.isOnNext() or signal.isOnComplete(), I found out that signal.isOnSubscribe() is never called.
So the question is: How can I get the context in doOnSubscribe() or is this simply not possible?
It is possible, not in 100% use cases and with a bit of a trick:
Flux.just("foo")
.doOnSubscribe(sub -> {
Scannable actual = Scannable.from(sub).scan(Scannable.Attr.ACTUAL);
if (actual instanceof CoreSubscriber) {
Context context = ((CoreSubscriber) actual).currentContext();
System.out.println(context);
}
})
.map(v -> "value: " + v) //below or above doOnSubscribe is fine
.subscriberContext(Context.of("foo", "bar")) //MUST be below doOnSubscribe
.blockLast();
In RxJS I want to give every new subscriber the last item that was emitted. But how do I do that in an Observable chain?
this.http.get().map().replaySubject().refCount()
This answer refers to RxJS 5:
One way would be to use publishReplay:
this.http
.get()
.map()
.publishReplay(1)
.refCount();
If your source is a source, that completes (which would be typical for a rest-call, since it completes after a response is received), you could also use publishLast:
this.http
.get()
.map()
.publishLast()
.refCount();
And a third way (which gives you the most flexibility) would be to use an external BehaviorSubject or a ReplaySubject:
public myData$: BehaviorSubject<any> = new BehaviorSubject(null); // initial value is "null"
public requestData(): BehaviorSubject<any> {
this.http
.get()
.map()
.do(data => this.myData$.next(data))
.subscribe();
return this.myData$.skip(1); // the returned subject skips 1, because this would be the current value - of course the skip is optional and depends on the implementation of the requesting component
}
In your component(s) you can the get the data via myData$.subscribe(...) for getting the currently "cached" data or via requestData().subscribe(...) for the latest data.