Monix 2.3.3 - monix.reactive.observables.ObservableLike

final def !=(arg0: Any): Boolean

Definition Classes: AnyRef → Any

final def ##(): Int

Definition Classes: AnyRef → Any

def ++[B >: A](other: Observable[B]): Self[B]

Concatenates the source with another observable.

Ordering of subscription is preserved, so the second observable starts only after the source observable is completed successfully with an onComplete. On the other hand, the second observable is never subscribed if the source completes with an error.

def +:[B >: A](elem: B): Self[B]

Creates a new Observable that emits the given element and then it also emits the events of the source (prepend operation).

def :+[B >: A](elem: B): Self[B]

Creates a new Observable that emits the events of the source and then it also emits the given element (appended to the stream).

final def ==(arg0: Any): Boolean

Definition Classes: AnyRef → Any

def ambWith[B >: A](other: Observable[B]): Self[B]

Given the source observable and another Observable, emits all of the items from the first of these Observables to emit an item and cancel the other.

final def asInstanceOf[T0]: T0

Definition Classes: Any

def asyncBoundary[B >: A](overflowStrategy: OverflowStrategy[B]): Self[B]

Forces a buffered asynchronous boundary.

Internally it wraps the observer implementation given to onSubscribe into a BufferedSubscriber.

Normally Monix's implementation guarantees that events are not emitted concurrently, and that the publisher MUST NOT emit the next event without acknowledgement from the consumer that it may proceed, however for badly behaved publishers, this wrapper provides the guarantee that the downstream Observer given in subscribe will not receive concurrent events.

WARNING: if the buffer created by this operator is unbounded, it can blow up the process if the data source is pushing events faster than what the observer can consume, as it introduces an asynchronous boundary that eliminates the back-pressure requirements of the data source. Unbounded is the default overflowStrategy, see OverflowStrategy for options.

overflowStrategy: - the overflow strategy used for buffering, which specifies what to do in case we're dealing with a slow consumer - should an unbounded buffer be used, should back-pressure be applied, should the pipeline drop newer or older events, should it drop the whole buffer? See OverflowStrategy for more details.

def bufferIntrospective(maxSize: Int): Self[List[A]]

Buffers signals while busy, after which it emits the buffered events as a single bundle.

This operator starts applying back-pressure when the underlying buffer's size is exceeded.

def bufferSliding(count: Int, skip: Int): Self[Seq[A]]

Returns an observable that emits buffers of items it collects from the source observable.

Returns an observable that emits buffers of items it collects from the source observable. The resulting observable emits buffers every skip items, each containing count items.

If the source observable completes, then the current buffer gets signaled downstream. If the source triggers an error then the current buffer is being dropped and the error gets propagated immediately.

For count and skip there are 3 possibilities:

in case skip == count, then there are no items dropped and no overlap, the call being equivalent to buffer(count)
in case skip < count, then overlap between buffers happens, with the number of elements being repeated being count - skip
in case skip > count, then skip - count elements start getting dropped between windows

count: the maximum size of each buffer before it should be emitted
skip: how many items emitted by the source observable should be skipped before starting a new buffer. Note that when skip and count are equal, this is the same operation as buffer(count)

def bufferTimed(timespan: FiniteDuration): Self[Seq[A]]

Periodically gather items emitted by an observable into bundles and emit these bundles rather than emitting the items one at a time.

This version of buffer emits a new bundle of items periodically, every timespan amount of time, containing all items emitted by the source Observable since the previous bundle emission.

If the source observable completes, then the current buffer gets signaled downstream. If the source triggers an error then the current buffer is being dropped and the error gets propagated immediately.

timespan: the interval of time at which it should emit the buffered bundle

def bufferTimedAndCounted(timespan: FiniteDuration, maxCount: Int): Self[Seq[A]]

Periodically gather items emitted by an observable into bundles and emit these bundles rather than emitting the items one at a time.

The resulting observable emits connected, non-overlapping buffers, each of a fixed duration specified by the timespan argument or a maximum size specified by the maxCount argument (whichever is reached first).

If the source observable completes, then the current buffer gets signaled downstream. If the source triggers an error then the current buffer is being dropped and the error gets propagated immediately.

timespan: the interval of time at which it should emit the buffered bundle
maxCount: is the maximum bundle size, after which the buffered bundle gets forcefully emitted

def bufferTimedWithPressure(period: FiniteDuration, maxSize: Int): Self[Seq[A]]

Periodically gather items emitted by an observable into bundles and emit these bundles rather than emitting the items one at a time.

Periodically gather items emitted by an observable into bundles and emit these bundles rather than emitting the items one at a time. Back-pressure the source when the buffer is full.

The resulting observable emits connected, non-overlapping buffers, each of a fixed duration specified by the period argument.

The bundles are emitted at a fixed rate. If the source is silent, then the resulting observable will start emitting empty sequences.

If the source observable completes, then the current buffer gets signaled downstream. If the source triggers an error then the current buffer is being dropped and the error gets propagated immediately.

A maxSize argument is specified as the capacity of the bundle. In case the source is too fast and maxSize is reached, then the source will be back-pressured.

The difference with bufferTimedAndCounted is that bufferTimedWithPressure applies back-pressure from the time when the buffer is full until the buffer is emitted, whereas bufferTimedAndCounted will forcefully emit the buffer when it's full.

period: the interval of time at which it should emit the buffered bundle
maxSize: is the maximum buffer size, after which the source starts being back-pressured

def bufferTumbling(count: Int): Self[Seq[A]]

Periodically gather items emitted by an observable into bundles and emit these bundles rather than emitting the items one at a time.

Periodically gather items emitted by an observable into bundles and emit these bundles rather than emitting the items one at a time. This version of buffer is emitting items once the internal buffer has reached the given count.

If the source observable completes, then the current buffer gets signaled downstream. If the source triggers an error then the current buffer is being dropped and the error gets propagated immediately.

count: the maximum size of each buffer before it should be emitted

def bufferWithSelector[S](selector: Observable[S], maxSize: Int): Self[Seq[A]]

Periodically gather items emitted by an observable into bundles and emit these bundles rather than emitting the items one at a time, whenever the selector observable signals an event.

The resulting observable collects the elements of the source in a buffer and emits that buffer whenever the given selector observable emits an onNext event, when the buffer is emitted as a sequence downstream and then reset. Thus the resulting observable emits connected, non-overlapping bundles triggered by the given selector.

If selector terminates with an onComplete, then the resulting observable also terminates normally. If selector terminates with an onError, then the resulting observable also terminates with an error.

If the source observable completes, then the current buffer gets signaled downstream. If the source triggers an error then the current buffer is being dropped and the error gets propagated immediately.

A maxSize argument is specified as the capacity of the bundle. In case the source is too fast and maxSize is reached, then the source will be back-pressured.

selector: is the observable that triggers the signaling of the current buffer
maxSize: is the maximum bundle size, after which the source starts being back-pressured

def bufferWithSelector[S](selector: Observable[S]): Self[Seq[A]]

Periodically gather items emitted by an observable into bundles and emit these bundles rather than emitting the items one at a time, whenever the selector observable signals an event.

The resulting observable collects the elements of the source in a buffer and emits that buffer whenever the given selector observable emits an onNext event, when the buffer is emitted as a sequence downstream and then reset. Thus the resulting observable emits connected, non-overlapping bundles triggered by the given selector.

If selector terminates with an onComplete, then the resulting observable also terminates normally. If selector terminates with an onError, then the resulting observable also terminates with an error.

If the source observable completes, then the current buffer gets signaled downstream. If the source triggers an error then the current buffer is being dropped and the error gets propagated immediately.

selector: is the observable that triggers the signaling of the current buffer

def clone(): AnyRef

Attributes: protected[java.lang]
Definition Classes: AnyRef
Annotations: @native() @throws( ... )

def collect[B](pf: PartialFunction[A, B]): Self[B]

Applies the given partial function to the source for each element for which the given partial function is defined.

pf: the function that filters and maps the source
returns: an observable that emits the transformed items by the given partial function

def combineLatest[B](other: Observable[B]): Self[(A, B)]

Creates a new observable from the source and another given observable, by emitting elements combined in pairs.

Creates a new observable from the source and another given observable, by emitting elements combined in pairs. If one of the observables emits fewer events than the other, then the rest of the unpaired events are ignored.

See zip for an alternative that pairs the items in strict sequence.

other: is an observable that gets paired with the source

def combineLatestMap[B, R](other: Observable[B])(f: (A, B) ⇒ R): Self[R]

Creates a new observable from the source and another given observable, by emitting elements combined in pairs.

Creates a new observable from the source and another given observable, by emitting elements combined in pairs. If one of the observables emits fewer events than the other, then the rest of the unpaired events are ignored.

See zipMap for an alternative that pairs the items in strict sequence.

other: is an observable that gets paired with the source
f: is a mapping function over the generated pairs

def completed: Self[Nothing]

Ignores all items emitted by the source Observable and only calls onCompleted or onError.

returns: an empty Observable that only calls onCompleted or onError, based on which one is called by the source Observable

def concat[B](implicit ev: <:<[A, Observable[B]]): Self[B]

Concatenates the sequence of observables emitted by the source into one observable, without any transformation.

You can combine the items emitted by multiple observables so that they act like a single sequence by using this operator.

The difference between the concat operation and mergeis that concat cares about the ordering of sequences (e.g. all items emitted by the first observable in the sequence will come before the elements emitted by the second observable), whereas merge doesn't care about that (elements get emitted as they come). Because of back-pressure applied to observables, concat is safe to use in all contexts, whereas merge requires buffering.

returns: an observable that emits items that are the result of flattening the items emitted by the observables emitted by the source

def concatDelayErrors[B](implicit ev: <:<[A, Observable[B]]): Self[B]

Concatenates the sequence of observables emitted by the source into one observable, without any transformation.

You can combine the items emitted by multiple observables so that they act like a single sequence by using this operator.

The difference between the concat operation and mergeis that concat cares about the ordering of sequences (e.g. all items emitted by the first observable in the sequence will come before the elements emitted by the second observable), whereas merge doesn't care about that (elements get emitted as they come). Because of back-pressure applied to observables, concat is safe to use in all contexts, whereas merge requires buffering.

This version is reserving onError notifications until all of the observables complete and only then passing the issued errors(s) downstream. Note that the streamed error is a CompositeException, since multiple errors from multiple streams can happen.

returns: an observable that emits items that are the result of flattening the items emitted by the observables emitted by the source

def concatMap[B](f: (A) ⇒ Observable[B]): Self[B]

Applies a function that you supply to each item emitted by the source observable, where that function returns observables, and then concatenating those resulting sequences and emitting the results of this concatenation.

The difference between the concat operation and mergeis that concat cares about the ordering of sequences (e.g. all items emitted by the first observable in the sequence will come before the elements emitted by the second observable), whereas merge doesn't care about that (elements get emitted as they come). Because of back-pressure applied to observables, concat is safe to use in all contexts, whereas merge requires buffering.

def concatMapDelayErrors[B](f: (A) ⇒ Observable[B]): Self[B]

Applies a function that you supply to each item emitted by the source observable, where that function returns sequences and then concatenating those resulting sequences and emitting the results of this concatenation.

This version is reserving onError notifications until all of the observables complete and only then passing the issued errors(s) downstream. Note that the streamed error is a CompositeException, since multiple errors from multiple streams can happen.

f: a function that, when applied to an item emitted by the source, returns an observable
returns: an observable that emits items that are the result of flattening the items emitted by the observables emitted by the source

def countF: Self[Long]

Creates a new Observable that emits the total number of onNext events that were emitted by the source.

Note that this Observable emits only one item after the source is complete. And in case the source emits an error, then only that error will be emitted.

def debounce(timeout: FiniteDuration): Self[A]

Only emit an item from an observable if a particular timespan has passed without it emitting another item.

Note: If the source observable keeps emitting items more frequently than the length of the time window, then no items will be emitted by the resulting observable.

timeout: the length of the window of time that must pass after the emission of an item from the source observable in which that observable emits no items in order for the item to be emitted by the resulting observable

See also: echoOnce for a similar operator that also mirrors the source observable

def debounceRepeated(period: FiniteDuration): Self[A]

Emits the last item from the source Observable if a particular timespan has passed without it emitting another item, and keeps emitting that item at regular intervals until the source breaks the silence.

So compared to regular debounceTo this version keeps emitting the last item of the source.

Note: If the source Observable keeps emitting items more frequently than the length of the time window then no items will be emitted by the resulting Observable.

period: the length of the window of time that must pass after the emission of an item from the source Observable in which that Observable emits no items in order for the item to be emitted by the resulting Observable at regular intervals, also determined by period

See also: echoRepeated for a similar operator that also mirrors the source observable

def debounceTo[B](timeout: FiniteDuration, f: (A) ⇒ Observable[B]): Self[B]

Doesn't emit anything until a timeout period passes without the source emitting anything.

Doesn't emit anything until a timeout period passes without the source emitting anything. When that timeout happens, we subscribe to the observable generated by the given function, an observable that will keep emitting until the source will break the silence by emitting another event.

Note: If the source observable keeps emitting items more frequently than the length of the time window, then no items will be emitted by the resulting Observable.

timeout: the length of the window of time that must pass after the emission of an item from the source Observable in which that Observable emits no items in order for the item to be emitted by the resulting Observable
f: is a function that receives the last element generated by the source, generating an observable to be subscribed when the source is timing out

def defaultIfEmpty[B >: A](default: ⇒ B): Self[B]

Emit items from the source, or emit a default item if the source completes after emitting no items.

def delayOnComplete(delay: FiniteDuration): Self[A]

Delays emitting the final onComplete event by the specified amount.

def delayOnNext(duration: FiniteDuration): Self[A]

Returns an Observable that emits the items emitted by the source Observable shifted forward in time by a specified delay.

Each time the source Observable emits an item, delay starts a timer, and when that timer reaches the given duration, the Observable returned from delay emits the same item.

NOTE: this delay refers strictly to the time between the onNext event coming from our source and the time it takes the downstream observer to get this event. On the other hand the operator is also applying back-pressure, so on slow observers the actual time passing between two successive events may be higher than the specified duration.

duration: - the delay to shift the source by
returns: the source Observable shifted in time by the specified delay

def delayOnNextBySelector[B](selector: (A) ⇒ Observable[B]): Self[A]

Returns an Observable that emits the items emitted by the source Observable shifted forward in time.

This variant of delay sets its delay duration on a per-item basis by passing each item from the source Observable into a function that returns an Observable and then monitoring those Observables. When any such Observable emits an item or completes, the Observable returned by delay emits the associated item.

selector: is a function that returns an Observable for each item emitted by the source Observable, which is then used to delay the emission of that item by the resulting Observable until the Observable returned from selector emits an item
returns: the source Observable shifted in time by the specified delay

def delaySubscription(timespan: FiniteDuration): Self[A]

Hold an Observer's subscription request for a specified amount of time before passing it on to the source Observable.

timespan: is the time to wait before the subscription is being initiated.

def delaySubscriptionWith(trigger: Observable[Any]): Self[A]

Hold an Observer's subscription request until the given trigger observable either emits an item or completes, before passing it on to the source Observable.

If the given trigger completes in error, then the subscription is terminated with onError.

trigger: the observable that must either emit an item or complete in order for the source to be subscribed.

def dematerialize[B](implicit ev: <:<[A, Notification[B]]): Self[B]

Converts the source Observable that emits Notification[A] (the result of materialize) back to an Observable that emits A.

def distinct: Self[A]

Suppress the duplicate elements emitted by the source Observable.

WARNING: this requires unbounded buffering.

def distinctByKey[K](key: (A) ⇒ K): Self[A]

Given a function that returns a key for each element emitted by the source Observable, suppress duplicates items.

WARNING: this requires unbounded buffering.

def distinctUntilChanged: Self[A]

Suppress duplicate consecutive items emitted by the source Observable

def distinctUntilChangedByKey[K](key: (A) ⇒ K): Self[A]

Suppress duplicate consecutive items emitted by the source Observable

def doAfterSubscribe(cb: () ⇒ Unit): Self[A]

Executes the given callback just after the subscription happens.

See also: doOnSubscribe for executing a callback just before a subscription happens.

def doAfterTerminate(cb: (Option[Throwable]) ⇒ Unit): Self[A]

Executes the given callback after the stream has ended either with an onComplete or onError event, or when the streaming stops by a downstream Stop being signaled.

This differs from doOnTerminate in that this happens *after* the onComplete or onError notification.

See also: doAfterTerminateEval for a version that allows for asynchronous evaluation by means of Task.

def doAfterTerminateEval(cb: (Option[Throwable]) ⇒ Task[Unit]): Self[A]

Evaluates the task generated by the given callback after the stream has ended either with an onComplete or onError event, or when the streaming stops by a downstream Stop being signaled.

This operation subsumes doOnEarlyStopEval and the callback-generated Task will back-pressure the source when applied for Stop events returned by onNext and thus the upstream source will receive the Stop result only after the task has finished executing.

This differs from doOnTerminateEval in that this happens *after* the onComplete or onError notification.

See also: doAfterTerminate for a simpler version that doesn't allow asynchronous execution.

def doOnComplete(cb: () ⇒ Unit): Self[A]

Executes the given callback when the stream has ended with an onComplete event, but before the complete event is emitted.

Unless you know what you're doing, you probably want to use doOnTerminate and doOnSubscriptionCancel for proper disposal of resources on completion.

cb: the callback to execute when the onComplete event gets emitted

See also: doOnCompleteEval for a version that allows for asynchronous evaluation by means of Task.

def doOnCompleteEval(task: Task[Unit]): Self[A]

Evaluates the given task when the stream has ended with an onComplete event, but before the complete event is emitted.

The task gets evaluated and is finished *before* the onComplete signal gets sent downstream.

Unless you know what you're doing, you probably want to use doOnTerminateEval and doOnSubscriptionCancel for proper disposal of resources on completion.

task: the task to execute when the onComplete event gets emitted

See also: doOnComplete for a simpler version that doesn't do asynchronous execution

def doOnEarlyStop(cb: () ⇒ Unit): Self[A]

Executes the given callback when the streaming is stopped due to a downstream Stop signal returned by onNext.

See also: doOnEarlyStopEval for a version that allows for asynchronous evaluation by means of Task.

def doOnEarlyStopEval(task: Task[Unit]): Self[A]

Executes the given task when the streaming is stopped due to a downstream Stop signal returned by onNext.

The given task gets evaluated *before* the upstream receives the Stop event (is back-pressured).

See also: doOnEarlyStop for a simpler version that doesn't do asynchronous execution

def doOnError(cb: (Throwable) ⇒ Unit): Self[A]

Executes the given callback when the stream is interrupted with an error, before the onError event is emitted downstream.

NOTE: should protect the code in this callback, because if it throws an exception the onError event will prefer signaling the original exception and otherwise the behavior is undefined.

See also: doOnTerminate and doOnSubscriptionCancel for handling resource disposal, also see doOnErrorEval for a version that does asynchronous evaluation by means of Task.

def doOnErrorEval(cb: (Throwable) ⇒ Task[Unit]): Self[A]

Executes the given task when the stream is interrupted with an error, before the onError event is emitted downstream.

NOTE: should protect the code in this callback, because if it throws an exception the onError event will prefer signaling the original exception and otherwise the behavior is undefined.

See also: doOnTerminateEval and doOnSubscriptionCancel for handling resource disposal, also see doOnError for a simpler version that doesn't do asynchronous execution.

def doOnNext(cb: (A) ⇒ Unit): Self[A]

Executes the given callback for each element generated by the source Observable, useful for doing side-effects.

returns: a new Observable that executes the specified callback for each element

See also: doOnNextEval for a version that allows for asynchronous evaluation by means of Task.

def doOnNextAck(cb: (A, Ack) ⇒ Unit): Self[A]

Executes the given callback on each acknowledgement received from the downstream subscriber.

This method helps in executing logic after messages get processed, for example when messages are polled from some distributed message queue and an acknowledgement needs to be sent after each message in order to mark it as processed.

See also: doOnNextAckEval for a version that allows for asynchronous evaluation by means of Task.

def doOnNextAckEval(cb: (A, Ack) ⇒ Task[Unit]): Self[A]

Executes the given callback on each acknowledgement received from the downstream subscriber, executing a generated Task and back-pressuring until the task is done.

This method helps in executing logic after messages get processed, for example when messages are polled from some distributed message queue and an acknowledgement needs to be sent after each message in order to mark it as processed.

See also: doOnNextAck for a simpler version that doesn't allow asynchronous execution.

def doOnNextEval(cb: (A) ⇒ Task[Unit]): Self[A]

Evaluates the given callback for each element generated by the source Observable, useful for triggering async side-effects.

returns: a new Observable that executes the specified callback for each element

See also: doOnNext for a simpler version that doesn't allow asynchronous execution.

def doOnStart(cb: (A) ⇒ Unit): Self[A]

Executes the given callback only for the first element generated by the source Observable, useful for doing a piece of computation only when the stream starts.

returns: a new Observable that executes the specified callback only for the first element

def doOnSubscribe(cb: () ⇒ Unit): Self[A]

Executes the given callback just before the subscription happens.

See also: doAfterSubscribe for executing a callback just after a subscription happens.

def doOnSubscriptionCancel(cb: () ⇒ Unit): Self[A]

Executes the given callback when the connection is being cancelled.

def doOnTerminate(cb: (Option[Throwable]) ⇒ Unit): Self[A]

Executes the given callback right before the streaming is ended either with an onComplete or onError event, or when the streaming stops by a downstream Stop being signaled.

It is the equivalent of calling:

doOnComplete
doOnError
doOnEarlyStop

This differs from doAfterTerminate in that this happens *before* the onComplete or onError notification.

See also: doOnTerminateEval for a version that allows for asynchronous evaluation by means of Task.

def doOnTerminateEval(cb: (Option[Throwable]) ⇒ Task[Unit]): Self[A]

Evaluates the task generated by the given callback right before the streaming is ended either with an onComplete or onError event, or when the streaming stops by a downstream Stop being signaled.

The callback-generated Task will back-pressure the source when applied for Stop events returned by onNext and thus the upstream source will receive the Stop result only after the task has finished executing.

It is the equivalent of calling:

doOnCompleteEval
doOnErrorEval
doOnEarlyStopEval

This differs from doAfterTerminateEval in that this happens *before* the onComplete or onError notification.

See also: doOnTerminate for a simpler version that doesn't allow asynchronous execution.

def drop(n: Int): Self[A]

Drops the first n elements (from the start).

n: the number of elements to drop
returns: a new Observable that drops the first n elements emitted by the source

def dropByTimespan(timespan: FiniteDuration): Self[A]

Creates a new observable that drops the events of the source, only for the specified timestamp window.

timespan: the window of time during which the new observable must drop events emitted by the source

def dropLast(n: Int): Self[A]

Drops the last n elements (from the end).

n: the number of elements to drop
returns: a new Observable that drops the first n elements emitted by the source

def dropUntil(trigger: Observable[Any]): Self[A]

Discard items emitted by the source until a second observable emits an item or completes.

If the trigger observable completes in error, then the resulting observable will also end in error when it notices it (next time an element is emitted by the source).

trigger: the observable that has to emit an item before the source begin to be mirrored by the resulting observable

def dropWhile(p: (A) ⇒ Boolean): Self[A]

Drops the longest prefix of elements that satisfy the given predicate and returns a new observable that emits the rest.

def dropWhileWithIndex(p: (A, Int) ⇒ Boolean): Self[A]

Drops the longest prefix of elements that satisfy the given function and returns a new observable that emits the rest.

Drops the longest prefix of elements that satisfy the given function and returns a new observable that emits the rest. In comparison with dropWhile, this version accepts a function that takes an additional parameter: the zero-based index of the element.

def dump(prefix: String, out: PrintStream = System.out): Self[A]

Utility that can be used for debugging purposes.

def echoOnce(timeout: FiniteDuration): Self[A]

Mirror the source observable as long as the source keeps emitting items, otherwise if timeout passes without the source emitting anything new then the observable will emit the last item.

This is the rough equivalent of:

Observable.merge(source, source.debounce(period))

Note: If the source Observable keeps emitting items more frequently than the length of the time window then the resulting observable will mirror the source exactly.

timeout: the window of silence that must pass in order for the observable to echo the last item

def echoRepeated(timeout: FiniteDuration): Self[A]

Mirror the source observable as long as the source keeps emitting items, otherwise if timeout passes without the source emitting anything new then the observable will start emitting the last item repeatedly.

Note: If the source Observable keeps emitting items more frequently than the length of the time window then the resulting observable will mirror the source exactly.

timeout: the window of silence that must pass in order for the observable to start echoing the last item

def endWith[B >: A](elems: Seq[B]): Self[B]

Creates a new Observable that emits the events of the source and then it also emits the given elements (appended to the stream).

def endWithError(error: Throwable): Self[A]

Emits the given exception instead of onComplete.

error: the exception to emit onComplete
returns: a new Observable that emits an exception onComplete

final def eq(arg0: AnyRef): Boolean

Definition Classes: AnyRef

def equals(arg0: Any): Boolean

Definition Classes: AnyRef → Any

def executeOn(scheduler: Scheduler): Self[A]

Specify an override for the Scheduler that will be used for subscribing and for observing the source.

Normally the Scheduler gets injected implicitly when doing subscribe, but this operator overrides the injected subscriber for the given source. And if the source is normally using that injected scheduler (given by subscribe), then the effect will be that all processing will now happen on the override.

To put it in other words, in Monix it's usually the consumer and not the producer that specifies the scheduler and this operator allows for a different behavior.

This operator also includes the effects of subscribeOn, meaning that the subscription logic itself will start on the provided scheduler.

IMPORTANT: This operator is a replacement for the observeOn operator from ReactiveX, but does not work in the same way. The observeOn operator forces the signaling to happen on a given Scheduler, but executeOn is more relaxed, usage is not forced, the source just gets injected with a different scheduler and it's up to the source to actually use it. This also means the effects are more far reaching, because the whole chain until the call of this operator is affected.

Alias for Observable.fork(fa, scheduler).

def executeWithFork: Self[A]

Mirrors the source observable, but upon subscription ensure that the evaluation forks into a separate (logical) thread.

The execution is managed by the injected scheduler in subscribe().

Alias for Observable.fork(fa).

def executeWithModel(em: ExecutionModel): Self[A]

Returns a new observable that will execute the source with a different ExecutionModel.

This allows fine-tuning the options injected by the scheduler locally. Example:

observable.executeWithModel(AlwaysAsyncExecution)

em: is the ExecutionModel that will be used when evaluating the source.

def existsF(p: (A) ⇒ Boolean): Self[Boolean]

Returns an Observable which emits a single value, either true, in case the given predicate holds for at least one item, or false otherwise.

p: is a function that evaluates the items emitted by the source Observable, returning true if they pass the filter
returns: an Observable that emits only true or false in case the given predicate holds or not for at least one item

def failed: Self[Throwable]

Returns an observable that emits a single Throwable, in case an error was thrown by the source, otherwise it isn't going to emit anything.

def filter(p: (A) ⇒ Boolean): Self[A]

Only emits those items for which the given predicate holds.

p: a function that evaluates the items emitted by the source returning true if they pass the filter
returns: a new observable that emits only those items in the source for which the filter evaluates as true

def finalize(): Unit

Attributes: protected[java.lang]
Definition Classes: AnyRef
Annotations: @throws( classOf[java.lang.Throwable] )

def findF(p: (A) ⇒ Boolean): Self[A]

Returns an Observable which only emits the first item for which the predicate holds.

p: is a function that evaluates the items emitted by the source Observable, returning true if they pass the filter
returns: an Observable that emits only the first item in the original Observable for which the filter evaluates as true

def firstOrElseF[B >: A](default: ⇒ B): Self[B]

Emits the first element emitted by the source, or otherwise if the source is completed without emitting anything, then the default is emitted.

Alias for headOrElse.

def flatMap[B](f: (A) ⇒ Observable[B]): Self[B]

Applies a function that you supply to each item emitted by the source observable, where that function returns sequences that can be observed, and then concatenating those resulting sequences and emitting the results of this concatenation.

Alias for concatMap.

The difference between the concat operation and mergeis that concat cares about the ordering of sequences (e.g. all items emitted by the first observable in the sequence will come before the elements emitted by the second observable), whereas merge doesn't care about that (elements get emitted as they come). Because of back-pressure applied to observables, concat is safe to use in all contexts, whereas merge requires buffering.

def flatMapDelayErrors[B](f: (A) ⇒ Observable[B]): Self[B]

Applies a function that you supply to each item emitted by the source observable, where that function returns sequences and then concatenating those resulting sequences and emitting the results of this concatenation.

It's an alias for concatMapDelayErrors.

f: a function that, when applied to an item emitted by the source Observable, returns an Observable
returns: an Observable that emits the result of applying the transformation function to each item emitted by the source Observable and concatenating the results of the Observables obtained from this transformation.

def flatMapLatest[B](f: (A) ⇒ Observable[B]): Self[B]

An alias of switchMap.

Returns a new observable that emits the items emitted by the observable most recently generated by the mapping function.

def flatScan[R](initial: ⇒ R)(op: (R, A) ⇒ Observable[R]): Self[R]

Applies a binary operator to a start value and to elements produced by the source observable, going from left to right, producing and concatenating observables along the way.

It's the combination between scan and flatMap.

def flatScanDelayErrors[R](initial: ⇒ R)(op: (R, A) ⇒ Observable[R]): Self[R]

Applies a binary operator to a start value and to elements produced by the source observable, going from left to right, producing and concatenating observables along the way.

This version of flatScan delays all errors until onComplete, when it will finally emit a CompositeException. It's the combination between scan and flatMapDelayErrors.

def flatten[B](implicit ev: <:<[A, Observable[B]]): Self[B]

Concatenates the sequence of observables emitted by the source into one observable, without any transformation.

You can combine the items emitted by multiple observables so that they act like a single sequence by using this operator.

The difference between the concat operation and mergeis that concat cares about the ordering of sequences (e.g. all items emitted by the first observable in the sequence will come before the elements emitted by the second observable), whereas merge doesn't care about that (elements get emitted as they come). Because of back-pressure applied to observables, concat is safe to use in all contexts, whereas merge requires buffering.

Alias for concat.

returns: an observable that emits items that are the result of flattening the items emitted by the observables emitted by the source

def flattenDelayErrors[B](implicit ev: <:<[A, Observable[B]]): Self[B]

Alias for concatDelayErrors.

Concatenates the sequence of observables emitted by the source into one observable, without any transformation.

You can combine the items emitted by multiple observables so that they act like a single sequence by using this operator.

The difference between the concat operation and mergeis that concat cares about the ordering of sequences (e.g. all items emitted by the first observable in the sequence will come before the elements emitted by the second observable), whereas merge doesn't care about that (elements get emitted as they come). Because of back-pressure applied to observables, concat is safe to use in all contexts, whereas merge requires buffering.

This version is reserving onError notifications until all of the observables complete and only then passing the issued errors(s) downstream. Note that the streamed error is a CompositeException, since multiple errors from multiple streams can happen.

returns: an observable that emits items that are the result of flattening the items emitted by the observables emitted by the source

def flattenLatest[B](implicit ev: <:<[A, Observable[B]]): Self[B]

Alias for switch

Convert an observable that emits observables into a single observable that emits the items emitted by the most-recently-emitted of those observables.

def foldLeftF[R](initial: ⇒ R)(op: (R, A) ⇒ R): Self[R]

Applies a binary operator to a start value and all elements of this Observable, going left to right and returns a new Observable that emits only one item before onComplete.

initial: is the initial state, specified as a possibly lazy value; it gets evaluated when the subscription happens and if it triggers an error then the subscriber will get immediately terminated with an error
op: is an operator that will fold the signals of the source observable, returning the next state

def foldWhileF[R](initial: ⇒ R)(op: (R, A) ⇒ (Boolean, R)): Self[R]

Folds the source observable, from start to finish, until the source completes, or until the operator short-circuits the process by returning false.

Note that a call to foldLeftF is equivalent to this function being called with an operator always returning true as the first member of its result.

initial: is the initial state, specified as a possibly lazy value; it gets evaluated when the subscription happens and if it triggers an error then the subscriber will get immediately terminated with an error
op: is an operator that will fold the signals of the source observable, returning either a new state along with a boolean that should become false in case the folding must be interrupted.

def forAllF(p: (A) ⇒ Boolean): Self[Boolean]

Returns an Observable that emits a single boolean, either true, in case the given predicate holds for all the items emitted by the source, or false in case at least one item is not verifying the given predicate.

p: is a function that evaluates the items emitted by the source Observable, returning true if they pass the filter
returns: an Observable that emits only true or false in case the given predicate holds or not for all the items

final def getClass(): Class[_]

Definition Classes: AnyRef → Any
Annotations: @native()

def groupBy[K](keySelector: (A) ⇒ K)(implicit keysBuffer: Synchronous[Nothing] = OverflowStrategy.Unbounded): Self[GroupedObservable[K, A]]

Groups the items emitted by an Observable according to a specified criterion, and emits these grouped items as GroupedObservables, one GroupedObservable per group.

Note: A GroupedObservable will cache the items it is to emit until such time as it is subscribed to. For this reason, in order to avoid memory leaks, you should not simply ignore those GroupedObservables that do not concern you. Instead, you can signal to them that they may discard their buffers by doing something like source.take(0).

keySelector: a function that extracts the key for each item

def hashCode(): Int

Definition Classes: AnyRef → Any
Annotations: @native()

def headF: Self[A]

Only emits the first element emitted by the source observable, after which it's completed immediately.

def headOrElseF[B >: A](default: ⇒ B): Self[B]

Emits the first element emitted by the source, or otherwise if the source is completed without emitting anything, then the default is emitted.

def ignoreElements: Self[Nothing]

Alias for completed.

Alias for completed. Ignores all items emitted by the source and only calls onCompleted or onError.

returns: an empty sequence that only calls onCompleted or onError, based on which one is called by the source Observable

def interleave[B >: A](other: Observable[B]): Self[B]

Creates a new observable from this observable and another given observable by interleaving their items into a strictly alternating sequence.

So the first item emitted by the new observable will be the item emitted by self, the second item will be emitted by the other observable, and so forth; when either self or other calls onCompletes, the items will then be directly coming from the observable that has not completed; when onError is called by either self or other, the new observable will call onError and halt.

See merge for a more relaxed alternative that doesn't emit items in strict alternating sequence.

other: is an observable that interleaves with the source
returns: a new observable sequence that alternates emission of the items from both child streams

def isEmptyF: Self[Boolean]

Returns an Observable that emits true if the source Observable is empty, otherwise false.

final def isInstanceOf[T0]: Boolean

Definition Classes: Any

def lastF: Self[A]

Only emits the last element emitted by the source observable, after which it's completed immediately.

def map[B](f: (A) ⇒ B): Self[B]

Returns a new observable that applies the given function to each item emitted by the source and emits the result.

def mapAsync[B](parallelism: Int)(f: (A) ⇒ Task[B]): Self[B]

Given a mapping function that maps events to tasks, applies it in parallel on the source, but with a specified parallelism, which indicates the maximum number of tasks that can be executed in parallel.

Similar in spirit with Consumer.loadBalance, but expressed as an operator that executes Task instances in parallel.

Note that when the specified parallelism is 1, it has the same behavior as mapTask.

parallelism: is the maximum number of tasks that can be executed in parallel, over which the source starts being back-pressured
f: is the mapping function that produces tasks to execute in parallel, which will eventually produce events for the resulting observable stream

See also: mapTask for serial execution

def mapAsync[B](f: (A) ⇒ Task[B]): Self[B]

Alias for mapTask.

def mapFuture[B](f: (A) ⇒ Future[B]): Self[B]

Maps elements from the source using a function that can do asynchronous processing by means of scala.concurrent.Future.

Given a source observable, this function is basically the equivalent of doing:

observable.concatMap(a => Observable.fromFuture(f(a)))

However prefer this operator to concatMap because it is more clear and has better performance.

See also: mapTask for the version that can work with Task

def mapTask[B](f: (A) ⇒ Task[B]): Self[B]

Maps elements from the source using a function that can do asynchronous processing by means of Task.

Given a source observable, this function is basically the equivalent of doing:

observable.concatMap(a => Observable.fromTask(f(a)))

However prefer this operator to concatMap because it is more clear and has better performance.

See also: mapFuture for the version that can work with scala.concurrent.Future

def materialize: Self[Notification[A]]

Converts the source Observable that emits A into an Observable that emits Notification[A].

def maxByF[B](f: (A) ⇒ B)(implicit ev: Ordering[B]): Self[A]

Takes the elements of the source Observable and emits the element that has the maximum key value, where the key is generated by the given function f.

def maxF[B >: A](implicit ev: Ordering[B]): Self[B]

Takes the elements of the source Observable and emits the maximum value, after the source has completed.

def merge[B](implicit ev: <:<[A, Observable[B]], os: OverflowStrategy[B] = OverflowStrategy.Default): Self[B]

Merges the sequence of Observables emitted by the source into one Observable, without any transformation.

You can combine the items emitted by multiple Observables so that they act like a single Observable by using this operator.

returns: an Observable that emits items that are the result of flattening the items emitted by the Observables emitted by this.

Note: this operation needs to do buffering and by not specifying an OverflowStrategy, the default strategy is being used.

def mergeDelayErrors[B](implicit ev: <:<[A, Observable[B]], os: OverflowStrategy[B] = OverflowStrategy.Default): Self[B]

Merges the sequence of Observables emitted by the source into one Observable, without any transformation.

You can combine the items emitted by multiple Observables so that they act like a single Observable by using this operator.

This version is reserving onError notifications until all of the observables complete and only then passing the issued errors(s) downstream. Note that the streamed error is a CompositeException, since multiple errors from multiple streams can happen.

returns: an Observable that emits items that are the result of flattening the items emitted by the Observables emitted by this.

Note: this operation needs to do buffering and by not specifying an OverflowStrategy, the default strategy is being used.

def mergeMap[B](f: (A) ⇒ Observable[B])(implicit os: OverflowStrategy[B] = OverflowStrategy.Default): Self[B]

Creates a new observable by applying a function that you supply to each item emitted by the source observable, where that function returns an observable, and then merging those resulting observable and emitting the results of this merger.

The difference between this and concatMap is that concatMap cares about ordering of emitted items (e.g. all items emitted by the first observable in the sequence will come before the elements emitted by the second observable), whereas merge doesn't care about that (elements get emitted as they come). Because of back-pressure applied to observables, the concat operation is safe to use in all contexts, whereas merge requires buffering.

f: - the transformation function
returns: an observable that emits the result of applying the transformation function to each item emitted by the source observable and merging the results of the observables obtained from this transformation.

def mergeMapDelayErrors[B](f: (A) ⇒ Observable[B])(implicit os: OverflowStrategy[B] = OverflowStrategy.Default): Self[B]

Creates a new observable by applying a function that you supply to each item emitted by the source observable, where that function returns an observable, and then merging those resulting observable and emitting the results of this merger.

The difference between this and concatMap is that concatMap cares about ordering of emitted items (e.g. all items emitted by the first observable in the sequence will come before the elements emitted by the second observable), whereas merge doesn't care about that (elements get emitted as they come). Because of back-pressure applied to observables, the concat operation is safe to use in all contexts, whereas merge requires buffering.

This version is reserving onError notifications until all of the observables complete and only then passing the issued errors(s) downstream. Note that the streamed error is a CompositeException, since multiple errors from multiple streams can happen.

f: - the transformation function
returns: an observable that emits the result of applying the transformation function to each item emitted by the source observable and merging the results of the observables obtained from this transformation.

def minByF[B](f: (A) ⇒ B)(implicit ev: Ordering[B]): Self[A]

Takes the elements of the source Observable and emits the element that has the minimum key value, where the key is generated by the given function f.

def minF[B >: A](implicit ev: Ordering[B]): Self[B]

Takes the elements of the source Observable and emits the minimum value, after the source has completed.

final def ne(arg0: AnyRef): Boolean

Definition Classes: AnyRef

def nonEmptyF: Self[Boolean]

Returns an Observable that emits false if the source Observable is empty, otherwise true.

final def notify(): Unit

Definition Classes: AnyRef
Annotations: @native()

final def notifyAll(): Unit

Definition Classes: AnyRef
Annotations: @native()

def observeOn[B >: A](s: Scheduler, os: OverflowStrategy[B]): Self[B]

Operator that specifies a different Scheduler, on which subscribers will observe events, instead of the default one.

This overloaded version of observeOn takes an extra OverflowStrategy parameter specifying the behavior of the underlying buffer.

s: is the alternative Scheduler reference to use for observing events
os: is the OverflowStrategy to apply to the underlying buffer

See also: observeOn(Scheduler) for the version that does not take an OverflowStrategy parameter.

def observeOn(s: Scheduler): Self[A]

Operator that specifies a different Scheduler, on which subscribers will observe events, instead of the default one.

An Observable with an applied observeOn call will forward events into a buffer that uses the specified Scheduler reference to cycle through events and to make onNext calls to downstream listeners.

Example:

import monix.execution.Scheduler
val io = Scheduler.io("my-io")

source.map(_ + 1)
  .observeOn(io)
  .foreach(x => println(x))

In the above example the first map (whatever comes before the observeOn call) gets executed using the default Scheduler (might execute on the current thread even), however the foreach that's specified after observeOn will get executed on the indicated Scheduler.

NOTE: this operator does not guarantee that downstream listeners will actually use the specified Scheduler to process events, because this depends on the rest of the pipeline. E.g. this will not work OK:

source.observeOn(io).asyncBoundary(Unbounded)

This sample might not do what a user of observeOn would want. Indeed the implementation will use the provided io reference for calling onNext / onComplete / onError events, however because of the following asynchronous boundary created the actual listeners will probably end up being execute on a different Scheduler.

The underlying implementation uses a buffer to forward events. The OverflowStrategy being applied is the default one.

s: is the alternative Scheduler reference to use for observing events

See also: observeOn(Scheduler, OverflowStrategy) for the version that allows customizing the OverflowStrategy being used by the underlying buffer.

def onCancelTriggerError: Self[A]

If the connection is cancelled then trigger a CancellationException.

A connection can be cancelled with the help of the Cancelable returned on subscribe.

Because the cancellation is effectively concurrent with the signals the Observer receives and because we need to uphold the contract, this operator will effectively synchronize access to onNext, onComplete and onError. It will also watch out for asynchronous Stop events.

In other words, this operator does heavy synchronization, can prove to be inefficient and you should avoid using it because the signaled error can interfere with functionality from other operators that use cancellation internally and cancellation in general is a side-effecting operation that should be avoided, unless it's necessary.

def onErrorFallbackTo[B >: A](that: Observable[B]): Self[B]

Returns an Observable that mirrors the behavior of the source, unless the source is terminated with an onError, in which case the streaming of events continues with the specified backup sequence.

The created Observable mirrors the behavior of the source in case the source does not end with an error.

NOTE that compared with onErrorResumeNext from Rx.NET, the streaming is not resumed in case the source is terminated normally with an onComplete.

that: is a backup sequence that's being subscribed in case the source terminates with an error.

def onErrorHandle[B >: A](f: (Throwable) ⇒ B): Self[B]

Returns an observable that mirrors the behavior of the source, unless the source is terminated with an onError, in which case the streaming of events fallbacks to an observable emitting a single element generated by the backup function.

See onErrorRecover for the version that takes a partial function as a parameter.

f: - a function that matches errors with a backup element that is emitted when the source throws an error.

def onErrorHandleWith[B >: A](f: (Throwable) ⇒ Observable[B]): Self[B]

Returns an Observable that mirrors the behavior of the source, unless the source is terminated with an onError, in which case the streaming of events continues with the specified backup sequence generated by the given function.

See onErrorRecoverWith for the version that takes a partial function as a parameter.

f: is a function that matches errors with a backup throwable that is subscribed when the source throws an error.

def onErrorRecover[B >: A](pf: PartialFunction[Throwable, B]): Self[B]

Returns an observable that mirrors the behavior of the source, unless the source is terminated with an onError, in which case the streaming of events fallbacks to an observable emitting a single element generated by the backup function.

The created Observable mirrors the behavior of the source in case the source does not end with an error or if the thrown Throwable is not matched.

See onErrorHandle for the version that takes a total function as a parameter.

pf: - a function that matches errors with a backup element that is emitted when the source throws an error.

def onErrorRecoverWith[B >: A](pf: PartialFunction[Throwable, Observable[B]]): Self[B]

Returns an Observable that mirrors the behavior of the source, unless the source is terminated with an onError, in which case the streaming of events continues with the specified backup sequence generated by the given function.

The created Observable mirrors the behavior of the source in case the source does not end with an error or if the thrown Throwable is not matched.

See onErrorHandleWith for the version that takes a total function as a parameter.

pf: is a function that matches errors with a backup throwable that is subscribed when the source throws an error.

def onErrorRestart(maxRetries: Long): Self[A]

Returns an Observable that mirrors the behavior of the source, unless the source is terminated with an onError, in which case it tries subscribing to the source again in the hope that it will complete without an error.

The number of retries is limited by the specified maxRetries parameter, so for an Observable that always ends in error the total number of subscriptions that will eventually happen is maxRetries + 1.

def onErrorRestartIf(p: (Throwable) ⇒ Boolean): Self[A]

Returns an Observable that mirrors the behavior of the source, unless the source is terminated with an onError, in which case it tries subscribing to the source again in the hope that it will complete without an error.

The given predicate establishes if the subscription should be retried or not.

def onErrorRestartUnlimited: Self[A]

Returns an Observable that mirrors the behavior of the source, unless the source is terminated with an onError, in which case it tries subscribing to the source again in the hope that it will complete without an error.

NOTE: The number of retries is unlimited, so something like Observable.error(new RuntimeException).onErrorRestartUnlimited will loop forever.

def pipeThrough[I >: A, B](pipe: Pipe[I, B]): Self[B]

Given a Pipe, transform the source observable with it.

def pipeThroughSelector[S >: A, B, R](pipe: Pipe[S, B], f: (Observable[B]) ⇒ Observable[R]): Self[R]

Returns an observable that emits the results of invoking a specified selector on items emitted by a ConnectableObservable, which shares a single subscription to the underlying sequence.

pipe: is the Pipe used to transform the source into a multicast (hot) observable that can be shared in the selector function
f: is a selector function that can use the multicasted source sequence as many times as needed, without causing multiple subscriptions to the source sequence. Observers to the given source will receive all notifications of the source from the time of the subscription forward.

def publishSelector[R](f: (Observable[A]) ⇒ Observable[R]): Self[R]

Returns an observable that emits the results of invoking a specified selector on items emitted by a ConnectableObservable, which shares a single subscription to the underlying sequence.

f: is a selector function that can use the multicasted source sequence as many times as needed, without causing multiple subscriptions to the source sequence. Observers to the given source will receive all notifications of the source from the time of the subscription forward.

def reduce[B >: A](op: (B, B) ⇒ B): Self[B]

Applies a binary operator to a start value and all elements of this Observable, going left to right and returns a new Observable that emits only one item before onComplete.

def repeat: Self[A]

Repeats the items emitted by the source continuously.

Repeats the items emitted by the source continuously. It caches the generated items until onComplete and repeats them forever.

It terminates either on error or if the source is empty.

def restartUntil(p: (A) ⇒ Boolean): Self[A]

Keeps restarting / resubscribing the source until the predicate returns true for the the first emitted element, after which it starts mirroring the source.

def sample(period: FiniteDuration): Self[A]

Emit the most recent items emitted by the source within periodic time intervals.

Use the sample operator to periodically look at an observable to see what item it has most recently emitted since the previous sampling. Note that if the source observable has emitted no items since the last time it was sampled, the observable that results from the sample operator will emit no item for that sampling period.

period: the timespan at which sampling occurs

See also: sampleRepeated for repeating the last value on silence
sampleBy for fine control

def sampleBy[B](sampler: Observable[B]): Self[A]

Returns an observable that, when the specified sampler emits an item or completes, emits the most recently emitted item (if any) emitted by the source since the previous emission from the sampler.

Use the sampleBy operator to periodically look at an observable to see what item it has most recently emitted since the previous sampling. Note that if the source observable has emitted no items since the last time it was sampled, the observable that results from the sampleBy operator will emit no item.

sampler: - the observable to use for sampling the source

See also: sampleRepeatedBy for repeating the last value on silence
sample for periodic sampling

def sampleRepeated(period: FiniteDuration): Self[A]

Emit the most recent items emitted by an observable within periodic time intervals.

Emit the most recent items emitted by an observable within periodic time intervals. If no new value has been emitted since the last time it was sampled, it signals the last emitted value anyway.

period: the timespan at which sampling occurs

See also: sampleRepeatedBy for fine control
sample for a variant that doesn't repeat the last value on silence

def sampleRepeatedBy[B](sampler: Observable[B]): Self[A]

Returns an observable that, when the specified sampler observable emits an item or completes, emits the most recently emitted item (if any) emitted by the source Observable since the previous emission from the sampler observable.

Returns an observable that, when the specified sampler observable emits an item or completes, emits the most recently emitted item (if any) emitted by the source Observable since the previous emission from the sampler observable. If no new value has been emitted since the last time it was sampled, it signals the last emitted value anyway.

sampler: - the Observable to use for sampling the source Observable

See also: sampleRepeated for a periodic sampling
sampleBy for a variant that doesn't repeat the last value on silence

def scan[R](initial: ⇒ R)(f: (R, A) ⇒ R): Self[R]

Applies a binary operator to a start value and all elements of this Observable, going left to right and returns a new Observable that emits on each step the result of the applied function.

Similar to foldLeftF, but emits the state on each step. Useful for modeling finite state machines.

def startWith[B >: A](elems: Seq[B]): Self[B]

Creates a new Observable that emits the given elements and then it also emits the events of the source (prepend operation).

def subscribeOn(scheduler: Scheduler): Self[A]

Returns a new Observable that uses the specified Scheduler for initiating the subscription.

def sumF[B >: A](implicit arg0: Numeric[B]): Self[B]

Given a source that emits numeric values, the sum operator sums up all values and at onComplete it emits the total.

def switch[B](implicit ev: <:<[A, Observable[B]]): Self[B]

Convert an observable that emits observables into a single observable that emits the items emitted by the most-recently-emitted of those observables.

def switchIfEmpty[B >: A](backup: Observable[B]): Self[B]

In case the source is empty, switch to the given backup.

def switchMap[B](f: (A) ⇒ Observable[B]): Self[B]

Returns a new observable that emits the items emitted by the observable most recently generated by the mapping function.

final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes: AnyRef

def tail: Self[A]

Drops the first element of the source observable, emitting the rest.

def take(n: Long): Self[A]

Selects the first n elements (from the start).

n: the number of elements to take
returns: a new Observable that emits only the first n elements from the source

def takeByTimespan(timespan: FiniteDuration): Self[A]

Creates a new Observable that emits the events of the source, only for the specified timestamp, after which it completes.

timespan: the window of time during which the new Observable is allowed to emit the events of the source

def takeEveryNth(n: Int): Self[A]

Creates a new Observable that emits every n-th event from the source, dropping intermediary events.

def takeLast(n: Int): Self[A]

Creates a new observable that only emits the last n elements emitted by the source.

In case the source triggers an error, then the underlying buffer gets dropped and the error gets emitted immediately.

def takeUntil(trigger: Observable[Any]): Self[A]

Creates a new observable that mirrors the source until the given trigger emits either an element or onComplete, after which it is completed.

The resulting observable is completed as soon as trigger emits either an onNext or onComplete. If trigger emits an onError, then the resulting observable is also completed with error.

trigger: is an observable that will cancel the streaming as soon as it emits an event

def takeWhile(p: (A) ⇒ Boolean): Self[A]

Takes longest prefix of elements that satisfy the given predicate and returns a new Observable that emits those elements.

def takeWhileNotCanceled(c: BooleanCancelable): Self[A]

Takes longest prefix of elements that satisfy the given predicate and returns a new Observable that emits those elements.

def throttleFirst(interval: FiniteDuration): Self[A]

Returns an Observable that emits only the first item emitted by the source Observable during sequential time windows of a specified duration.

This differs from Observable!.throttleLast in that this only tracks passage of time whereas throttleLast ticks at scheduled intervals.

interval: time to wait before emitting another item after emitting the last item

def throttleLast(period: FiniteDuration): Self[A]

Emit the most recent items emitted by the source within periodic time intervals.

Alias for sample.

period: duration of windows within which the last item emitted by the source Observable will be emitted

def throttleWithTimeout(timeout: FiniteDuration): Self[A]

Only emit an item from an observable if a particular timespan has passed without it emitting another item.

Note: If the source observable keeps emitting items more frequently than the length of the time window, then no items will be emitted by the resulting observable.

Alias for debounce.

timeout: the length of the window of time that must pass after the emission of an item from the source observable in which that observable emits no items in order for the item to be emitted by the resulting observable

See also: echoOnce for a similar operator that also mirrors the source observable

def timeoutOnSlowDownstream(timeout: FiniteDuration): Self[A]

Returns an observable that mirrors the source but that will trigger a DownstreamTimeoutException in case the downstream subscriber takes more than the given timespan to process an onNext message.

Note that this ignores the time it takes for the upstream to send onNext messages. For detecting slow producers see timeoutOnSlowUpstream.

timeout: maximum duration for onNext.

def timeoutOnSlowUpstream(timeout: FiniteDuration): Self[A]

Returns an observable that mirrors the source but applies a timeout for each emitted item by the upstream.

Returns an observable that mirrors the source but applies a timeout for each emitted item by the upstream. If the next item isn't emitted within the specified timeout duration starting from its predecessor, the resulting Observable terminates and notifies observers of a TimeoutException.

Note that this ignores the time it takes to process onNext. If dealing with a slow consumer, see timeoutOnSlowDownstream.

timeout: maximum duration between emitted items before a timeout occurs (ignoring the time it takes to process onNext)

def timeoutOnSlowUpstreamTo[B >: A](timeout: FiniteDuration, backup: Observable[B]): Self[B]

Returns an observable that mirrors the source but applies a timeout for each emitted item by the upstream.

Returns an observable that mirrors the source but applies a timeout for each emitted item by the upstream. If the next item isn't emitted within the specified timeout duration starting from its predecessor, the source is terminated and the downstream gets subscribed to the given backup.

Note that this ignores the time it takes to process onNext. If dealing with a slow consumer, see timeoutOnSlowDownstream.

timeout: maximum duration between emitted items before a timeout occurs (ignoring the time it takes to process onNext)
backup: is the alternative data source to subscribe to on timeout

def toString(): String

Definition Classes: AnyRef → Any

final def wait(): Unit

Definition Classes: AnyRef
Annotations: @throws( ... )

final def wait(arg0: Long, arg1: Int): Unit

Definition Classes: AnyRef
Annotations: @throws( ... )

final def wait(arg0: Long): Unit

Definition Classes: AnyRef
Annotations: @native() @throws( ... )

def whileBusyBuffer[B >: A](overflowStrategy: Synchronous[B]): Self[B]

While the destination observer is busy, buffers events, applying the given overflowStrategy.

overflowStrategy: - the overflow strategy used for buffering, which specifies what to do in case we're dealing with a slow consumer - should an unbounded buffer be used, should back-pressure be applied, should the pipeline drop newer or older events, should it drop the whole buffer? See OverflowStrategy for more details.

def whileBusyDropEvents: Self[A]

While the destination observer is busy, drop the incoming events.

def whileBusyDropEventsAndSignal[B >: A](onOverflow: (Long) ⇒ B): Self[B]

While the destination observer is busy, drop the incoming events.

While the destination observer is busy, drop the incoming events. When the downstream recovers, we can signal a special event meant to inform the downstream observer how many events where dropped.

onOverflow: - a function that is used for signaling a special event used to inform the consumers that an overflow event happened, function that receives the number of dropped events as a parameter (see OverflowStrategy.Evicted)

def withLatestFrom[B, R](other: Observable[B])(f: (A, B) ⇒ R): Self[R]

Combines the elements emitted by the source with the latest element emitted by another observable.

Similar with combineLatest, but only emits items when the single source emits an item (not when any of the Observables that are passed to the operator do, as combineLatest does).

other: is an observable that gets paired with the source
f: is a mapping function over the generated pairs

def withLatestFrom2[B1, B2, R](o1: Observable[B1], o2: Observable[B2])(f: (A, B1, B2) ⇒ R): Self[R]

Combines the elements emitted by the source with the latest elements emitted by two observables.

Similar with combineLatest, but only emits items when the single source emits an item (not when any of the Observables that are passed to the operator do, as combineLatest does).

o1: is the first observable that gets paired with the source
o2: is the second observable that gets paired with the source
f: is a mapping function over the generated pairs

def withLatestFrom3[B1, B2, B3, R](o1: Observable[B1], o2: Observable[B2], o3: Observable[B3])(f: (A, B1, B2, B3) ⇒ R): Self[R]

Combines the elements emitted by the source with the latest elements emitted by three observables.

Similar with combineLatest, but only emits items when the single source emits an item (not when any of the Observables that are passed to the operator do, as combineLatest does).

o1: is the first observable that gets paired with the source
o2: is the second observable that gets paired with the source
o3: is the third observable that gets paired with the source
f: is a mapping function over the generated pairs

def withLatestFrom4[B1, B2, B3, B4, R](o1: Observable[B1], o2: Observable[B2], o3: Observable[B3], o4: Observable[B4])(f: (A, B1, B2, B3, B4) ⇒ R): Self[R]

Combines the elements emitted by the source with the latest elements emitted by four observables.

Similar with combineLatest, but only emits items when the single source emits an item (not when any of the Observables that are passed to the operator do, as combineLatest does).

o1: is the first observable that gets paired with the source
o2: is the second observable that gets paired with the source
o3: is the third observable that gets paired with the source
o4: is the fourth observable that gets paired with the source
f: is a mapping function over the generated pairs

def withLatestFrom5[B1, B2, B3, B4, B5, R](o1: Observable[B1], o2: Observable[B2], o3: Observable[B3], o4: Observable[B4], o5: Observable[B5])(f: (A, B1, B2, B3, B4, B5) ⇒ R): Self[R]

Combines the elements emitted by the source with the latest elements emitted by five observables.

Similar with combineLatest, but only emits items when the single source emits an item (not when any of the Observables that are passed to the operator do, as combineLatest does).

o1: is the first observable that gets paired with the source
o2: is the second observable that gets paired with the source
o3: is the third observable that gets paired with the source
o4: is the fourth observable that gets paired with the source
o5: is the fifth observable that gets paired with the source
f: is a mapping function over the generated pairs

def withLatestFrom6[B1, B2, B3, B4, B5, B6, R](o1: Observable[B1], o2: Observable[B2], o3: Observable[B3], o4: Observable[B4], o5: Observable[B5], o6: Observable[B6])(f: (A, B1, B2, B3, B4, B5, B6) ⇒ R): Self[R]

Combines the elements emitted by the source with the latest elements emitted by six observables.

Similar with combineLatest, but only emits items when the single source emits an item (not when any of the Observables that are passed to the operator do, as combineLatest does).

o1: is the first observable that gets paired with the source
o2: is the second observable that gets paired with the source
o3: is the third observable that gets paired with the source
o4: is the fourth observable that gets paired with the source
o5: is the fifth observable that gets paired with the source
o6: is the sixth observable that gets paired with the source
f: is a mapping function over the generated pairs

def zip[B](other: Observable[B]): Self[(A, B)]

Creates a new observable from this observable and another given observable by combining their items in pairs in a strict sequence.

So the first item emitted by the new observable will be the tuple of the first items emitted by each of the source observables; the second item emitted by the new observable will be a tuple with the second items emitted by each of those observables; and so forth.

See combineLatest for a more relaxed alternative that doesn't combine items in strict sequence.

other: is an observable that gets paired with the source
returns: a new observable sequence that emits the paired items of the source observables

def zipMap[B, R](other: Observable[B])(f: (A, B) ⇒ R): Self[R]

Creates a new observable from this observable and another given observable by combining their items in pairs in a strict sequence.

So the first item emitted by the new observable will be the result of the function applied to the first item emitted by each of the source observables; the second item emitted by the new observable will be the result of the function applied to the second item emitted by each of those observables; and so forth.

See combineLatestMap for a more relaxed alternative that doesn't combine items in strict sequence.

other: is an observable that gets paired with the source
f: is a mapping function over the generated pairs

def zipWithIndex: Self[(A, Long)]

Zips the emitted elements of the source with their indices.

Packages

Package Overview

ObservableLike

Companion object ObservableLike

trait ObservableLike[+A, Self[+T] <: ObservableLike[T, Self]] extends Serializable

Abstract Value Members

Concrete Value Members

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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Packages

Package Overview

ObservableLike 

Companion object ObservableLike

trait ObservableLike[+A, Self[+T] <: ObservableLike[T, Self]] extends Serializable

Abstract Value Members

Concrete Value Members

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped

ObservableLike