The most important difference is that supervision is not automatically applied to stream operators but instead something that each operator has to implement explicitly. The error handling strategies are inspired by actor supervision strategies, but the semantics have been adapted to the domain of stream processing. The operators that support supervision strategies are explicitly documented to do so, if there is nothing in the documentation of an operator saying that it adheres to the supervision strategy it means it fails rather than applies supervision. The RestartSink is restarted when it cancels, while the RestartFlow is restarted when either the in port cancels, the out port completes, or the out port sends an error. Sinks and flows can also be supervised, using and and. toMat(Sink.foreach(event -> ('Got event: ' + event)), Keep.left()) viaMat(KillSwitches.single(), Keep.right()) KillSwitch.shutdown() Java copy source KillSwitch killSwitch = toMat(Sink.foreach(event => println(s'Got event: $event')))(Keep.left) It will often be handy to use it in combination with a KillSwitch, so that you can terminate it when needed: Scala copy source val killSwitch = restartSource The above RestartSource will never terminate unless the Sink it’s fed into cancels.

By adding additional randomness to the re-start intervals the streams will start in slightly different points in time, thus avoiding large spikes of traffic hitting the recovering server or other resource that they all need to contact. Using a randomFactor to add a little bit of additional variance to the backoff intervals is highly recommended, in order to avoid multiple streams re-start at the exact same point in time, for example because they were stopped due to a shared resource such as the same server going down and re-starting after the same configured interval. Unmarshall it to a stream of ServerSentEventsĮomEventStream() Issue a GET request on the event stream Create a source from a future of a source Implementation platform(':akka-bom_$ Java copy source RestartSettings settings =Ġ.2 // adds 20% 'noise' to vary the intervals slightlyĢ0, Duration.ofMinutes(5)) // limits the amount of restarts to 20 within 5 minutes LibraryDependencies += '' %% 'akka-stream' % AkkaVersion Maven What would be a good approach to propagate the errors to an external destination? (Let's say for example, a Kafka topic).To use Akka Streams, add the module to your project: sbt val AkkaVersion = '2.6.19'.

What does specifically do or wthat is the feature that this particular annotation provide?.Based on this, I have the following questions, I have a use case where an external listener process will be consuming the errors published into a particular Kafka error-topic. I have also followed this whole thread about error channel creation, where it confirms that having a annotation will not create an external destination to propagate the errors. This means that the handlerįor such destination must be defined using one of the SI handler NOT have an associated external destination, such channel is a Given that the destination specific error channel does The use of annotation is intended specifically toĭefine bindings that bridge internal channels and externalĭestinations. Regarding to what the documentation says about it, when you want to catch the errors, you might want to use This does not have an associated external destination. Throw new = Processor.INPUT + '.myGroup.errors') //channel name '' An Error Output Stream is a special purpose stream that serves as application-level throw mechanism for runtime errors caught by the applications Error. I have been reading the spring-cloud-stream documentation, specifically error handling: // destination name 'input.myGroup'

Streamcloud error