Error Handling in Streams

Dependency

To use Pekko Streams, add the module to your project:

sbt

val PekkoVersion = "1.1.2"
libraryDependencies += "org.apache.pekko" %% "pekko-stream" % PekkoVersion

Maven

<properties>
  <scala.binary.version>2.13</scala.binary.version>
</properties>
<dependencyManagement>
  <dependencies>
    <dependency>
      <groupId>org.apache.pekko</groupId>
      <artifactId>pekko-bom_${scala.binary.version}</artifactId>
      <version>1.1.2</version>
      <type>pom</type>
      <scope>import</scope>
    </dependency>
  </dependencies>
</dependencyManagement>
<dependencies>
  <dependency>
    <groupId>org.apache.pekko</groupId>
    <artifactId>pekko-stream_${scala.binary.version}</artifactId>
  </dependency>
</dependencies>

Gradle

def versions = [
  ScalaBinary: "2.13"
]
dependencies {
  implementation platform("org.apache.pekko:pekko-bom_${versions.ScalaBinary}:1.1.2")

  implementation "org.apache.pekko:pekko-stream_${versions.ScalaBinary}"
}

Introduction

When an operator in a stream fails this will normally lead to the entire stream being torn down. Each of the operators downstream gets informed about the failure and each upstream operator sees a cancellation.

In many cases you may want to avoid complete stream failure, this can be done in a few different ways:

• recoverrecover to emit a final element then complete the stream normally on upstream failure
• recoverWithRetriesrecoverWithRetries to create a new upstream and start consuming from that on failure
• Restarting sections of the stream after a backoff
• Using a supervision strategy for operators that support it

In addition to these built in tools for error handling, a common pattern is to wrap the stream inside an actor, and have the actor restart the entire stream on failure.

Logging errors

log()log() enables logging of a stream, which is typically useful for error logging. The below stream fails with ArithmeticException when the element 0 goes through the mapmap operator,

Scala

copysourceSource(-5 to 5)
  .map(1 / _) // throwing ArithmeticException: / by zero
  .log("error logging")
  .runWith(Sink.ignore)

Java

copysourceSource.from(Arrays.asList(-1, 0, 1))
    .map(x -> 1 / x) // throwing ArithmeticException: / by zero
    .log("error logging")
    .runWith(Sink.ignore(), system);

and error messages like below will be logged.

[error logging] Upstream failed.
java.lang.ArithmeticException: / by zero

If you want to control logging levels on each element, completion, and failure, you can find more details in Logging in streams.

Recover

recoverrecover allows you to emit a final element and then complete the stream on an upstream failure. Deciding which exceptions should be recovered is done through a PartialFunction. If an exception does not have a matching case match defined the stream is failed.

Recovering can be useful if you want to gracefully complete a stream on failure while letting downstream know that there was a failure.

Throwing an exception inside recover will be logged on ERROR level automatically.

More details in recover

Scala

copysourceSource(0 to 6)
  .map(n =>
    // assuming `4` and `5` are unexpected values that could throw exception
    if (List(4, 5).contains(n)) throw new RuntimeException(s"Boom! Bad value found: $n")
    else n.toString)
  .recover {
    case e: RuntimeException => e.getMessage
  }
  .runForeach(println)

Java

copysourceSource.from(Arrays.asList(0, 1, 2, 3, 4, 5, 6))
    .map(
        n -> {
          // assuming `4` and `5` are unexpected values that could throw exception
          if (Arrays.asList(4, 5).contains(n))
            throw new RuntimeException(String.format("Boom! Bad value found: %s", n));
          else return n.toString();
        })
    .recover(
        PFBuilder.<Throwable, String>create()
            .match(RuntimeException.class, Throwable::getMessage)
            .build())
    .runForeach(System.out::println, system);

This will output:

Scala

copysource0
1
2
3                         // last element before failure
Boom! Bad value found: 4  // first element on failure

Java

copysource0
1
2
3                         // last element before failure
Boom! Bad value found: 4  // first element on failure

Recover with retries

recoverWithRetriesrecoverWithRetries allows you to put a new upstream in place of the failed one, recovering stream failures up to a specified maximum number of times.

Deciding which exceptions should be recovered is done through a PartialFunction. If an exception does not have a matching case match defined the stream is failed.

Scala

copysourceval planB = Source(List("five", "six", "seven", "eight"))

Source(0 to 10)
  .map(n =>
    if (n < 5) n.toString
    else throw new RuntimeException("Boom!"))
  .recoverWithRetries(attempts = 1,
    {
      case _: RuntimeException => planB
    })
  .runForeach(println)

Java

copysourceSource<String, NotUsed> planB = Source.from(Arrays.asList("five", "six", "seven", "eight"));

Source.from(Arrays.asList(0, 1, 2, 3, 4, 5, 6))
    .map(
        n -> {
          if (n < 5) return n.toString();
          else throw new RuntimeException("Boom!");
        })
    .recoverWithRetries(
        1, // max attempts
        PFBuilder.<Throwable, Source<String, NotUsed>>create()
            .match(RuntimeException.class, ex -> planB)
            .build())
    .runForeach(System.out::println, system);

This will output:

Scala

copysource0
1
2
3
4
five
six
seven
eight

Java

copysource0
1
2
3
4
five
six
seven
eight

Delayed restarts with a backoff operator

Pekko streams provides a RestartSourceRestartSource, RestartSinkRestartSink and RestartFlowRestartFlow for implementing the so-called exponential backoff supervision strategy, starting an operator again when it fails or completes, each time with a growing time delay between restarts.

This pattern is useful when the operator fails or completes because some external resource is not available and we need to give it some time to start-up again. One of the prime examples when this is useful is when a WebSocket connection fails due to the HTTP server it’s running on going down, perhaps because it is overloaded. By using an exponential backoff, we avoid going into a tight reconnect loop, which both gives the HTTP server some time to recover, and it avoids using needless resources on the client side.

The various restart shapes mentioned all expect an RestartSettingsRestartSettings which configures the restart behavior. Configurable parameters are:

• minBackoff is the initial duration until the underlying stream is restarted
• maxBackoff caps the exponential backoff
• randomFactor allows addition of a random delay following backoff calculation
• maxRestarts caps the total number of restarts
• maxRestartsWithin sets a timeframe during which restarts are counted towards the same total for maxRestarts

The following snippet shows how to create a backoff supervisor using RestartSourceRestartSource which will supervise the given SourceSource. The Source in this case is a stream of Server Sent Events, produced by pekko-http. If the stream fails or completes at any point, the request will be made again, in increasing intervals of 3, 6, 12, 24 and finally 30 seconds (at which point it will remain capped due to the maxBackoff parameter):

Scala

copysourceval settings = RestartSettings(
  minBackoff = 3.seconds,
  maxBackoff = 30.seconds,
  randomFactor = 0.2 // adds 20% "noise" to vary the intervals slightly
).withMaxRestarts(20, 5.minutes) // limits the amount of restarts to 20 within 5 minutes

val restartSource = RestartSource.withBackoff(settings) { () =>
  // Create a source from a future of a source
  Source.futureSource {
    // Make a single request with pekko-http
    Http()
      .singleRequest(HttpRequest(uri = "http://example.com/eventstream"))
      // Unmarshall it as a source of server sent events
      .flatMap(Unmarshal(_).to[Source[ServerSentEvent, NotUsed]])
  }
}

Java

copysourceRestartSettings settings =
    RestartSettings.create(
            Duration.ofSeconds(3), // min backoff
            Duration.ofSeconds(30), // max backoff
            0.2 // adds 20% "noise" to vary the intervals slightly
            )
        .withMaxRestarts(
            20, Duration.ofMinutes(5)); // limits the amount of restarts to 20 within 5 minutes

Source<ServerSentEvent, NotUsed> eventStream =
    RestartSource.withBackoff(
        settings,
        () ->
            // Create a source from a future of a source
            Source.completionStageSource(
                // Issue a GET request on the event stream
                Http.get(system)
                    .singleRequest(HttpRequest.create("http://example.com/eventstream"))
                    .thenCompose(
                        response ->
                            // Unmarshall it to a stream of ServerSentEvents
                            EventStreamUnmarshalling.fromEventStream()
                                .unmarshall(response, materializer))));

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. 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.

The above RestartSource will never terminate unless the SinkSink it’s fed into cancels. It will often be handy to use it in combination with a KillSwitch, so that you can terminate it when needed:

Scala

copysourceval killSwitch = restartSource
  .viaMat(KillSwitches.single)(Keep.right)
  .toMat(Sink.foreach(event => println(s"Got event: $event")))(Keep.left)
  .run()

doSomethingElse()

killSwitch.shutdown()

Java

copysourceKillSwitch killSwitch =
    eventStream
        .viaMat(KillSwitches.single(), Keep.right())
        .toMat(Sink.foreach(event -> System.out.println("Got event: " + event)), Keep.left())
        .run(materializer);

doSomethingElse();

killSwitch.shutdown();

Sinks and flows can also be supervised, using RestartSinkRestartSink and RestartFlowRestartFlow. 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.

Supervision Strategies

The error handling strategies are inspired by actor supervision strategies, but the semantics have been adapted to the domain of stream processing. The most important difference is that supervision is not automatically applied to stream operators but instead something that each operator has to implement explicitly.

For many operators it may not even make sense to implement support for supervision strategies, this is especially true for operators connecting to external technologies where for example a failed connection will likely still fail if a new connection is tried immediately (see Restart with back off for such scenarios).

For operators that do implement supervision, the strategies for how to handle exceptions from processing stream elements can be selected when materializing the stream through use of an attribute.

There are three ways to handle exceptions from application code:

• StopSupervision.stop() - The stream is completed with failure.
• ResumeSupervision.resume() - The element is dropped and the stream continues.
• RestartSupervision.restart() - The element is dropped and the stream continues after restarting the operator. Restarting an operator means that any accumulated state is cleared. This is typically performed by creating a new instance of the operator.

By default the stopping strategy is used for all exceptions, i.e. the stream will be completed with failure when an exception is thrown.

Scala

copysourceval source = Source(0 to 5).map(100 / _)
val result = source.runWith(Sink.fold(0)(_ + _))
// division by zero will fail the stream and the
// result here will be a Future completed with Failure(ArithmeticException)

Java

copysourcefinal Source<Integer, NotUsed> source =
    Source.from(Arrays.asList(0, 1, 2, 3, 4, 5)).map(elem -> 100 / elem);
final Sink<Integer, CompletionStage<Integer>> fold =
    Sink.<Integer, Integer>fold(0, (acc, elem) -> acc + elem);
final CompletionStage<Integer> result = source.runWith(fold, system);
// division by zero will fail the stream and the
// result here will be a CompletionStage failed with ArithmeticException

The default supervision strategy for a stream can be defined on the complete RunnableGraphRunnableGraph.

Scala

copysourceval decider: Supervision.Decider = {
  case _: ArithmeticException => Supervision.Resume
  case _                      => Supervision.Stop
}
val source = Source(0 to 5).map(100 / _)
val runnableGraph =
  source.toMat(Sink.fold(0)(_ + _))(Keep.right)

val withCustomSupervision = runnableGraph.withAttributes(ActorAttributes.supervisionStrategy(decider))

val result = withCustomSupervision.run()
// the element causing division by zero will be dropped
// result here will be a Future completed with Success(228)

Java

copysourcefinal Function<Throwable, Supervision.Directive> decider =
    exc -> {
      if (exc instanceof ArithmeticException) return Supervision.resume();
      else return Supervision.stop();
    };
final Source<Integer, NotUsed> source =
    Source.from(Arrays.asList(0, 1, 2, 3, 4, 5))
        .map(elem -> 100 / elem)
        .withAttributes(ActorAttributes.withSupervisionStrategy(decider));
final Sink<Integer, CompletionStage<Integer>> fold = Sink.fold(0, (acc, elem) -> acc + elem);

final RunnableGraph<CompletionStage<Integer>> runnableGraph = source.toMat(fold, Keep.right());

final RunnableGraph<CompletionStage<Integer>> withCustomSupervision =
    runnableGraph.withAttributes(ActorAttributes.withSupervisionStrategy(decider));

final CompletionStage<Integer> result = withCustomSupervision.run(system);
// the element causing division by zero will be dropped
// result here will be a CompletionStage completed with 228

Here you can see that all ArithmeticException will resume the processing, i.e. the elements that cause the division by zero are effectively dropped.

The supervision strategy can also be defined for all operators of a flow.

Scala

copysourceval decider: Supervision.Decider = {
  case _: ArithmeticException => Supervision.Resume
  case _                      => Supervision.Stop
}
val flow = Flow[Int]
  .filter(100 / _ < 50)
  .map(elem => 100 / (5 - elem))
  .withAttributes(ActorAttributes.supervisionStrategy(decider))
val source = Source(0 to 5).via(flow)

val result = source.runWith(Sink.fold(0)(_ + _))
// the elements causing division by zero will be dropped
// result here will be a Future completed with Success(150)

Java

copysourcefinal Function<Throwable, Supervision.Directive> decider =
    exc -> {
      if (exc instanceof ArithmeticException) return Supervision.resume();
      else return Supervision.stop();
    };
final Flow<Integer, Integer, NotUsed> flow =
    Flow.of(Integer.class)
        .filter(elem -> 100 / elem < 50)
        .map(elem -> 100 / (5 - elem))
        .withAttributes(ActorAttributes.withSupervisionStrategy(decider));
final Source<Integer, NotUsed> source = Source.from(Arrays.asList(0, 1, 2, 3, 4, 5)).via(flow);
final Sink<Integer, CompletionStage<Integer>> fold =
    Sink.<Integer, Integer>fold(0, (acc, elem) -> acc + elem);
final CompletionStage<Integer> result = source.runWith(fold, system);
// the elements causing division by zero will be dropped
// result here will be a Future completed with 150

RestartSupervision.restart() works in a similar way as ResumeSupervision.resume() with the addition that accumulated state, if any, of the failing processing operator will be reset.

Scala

copysourceval decider: Supervision.Decider = {
  case _: IllegalArgumentException => Supervision.Restart
  case _                           => Supervision.Stop
}
val flow = Flow[Int]
  .scan(0) { (acc, elem) =>
    if (elem < 0) throw new IllegalArgumentException("negative not allowed")
    else acc + elem
  }
  .withAttributes(ActorAttributes.supervisionStrategy(decider))
val source = Source(List(1, 3, -1, 5, 7)).via(flow)
val result = source.limit(1000).runWith(Sink.seq)
// the negative element cause the scan stage to be restarted,
// i.e. start from 0 again
// result here will be a Future completed with Success(Vector(0, 1, 4, 0, 5, 12))

Java

copysourcefinal Function<Throwable, Supervision.Directive> decider =
    exc -> {
      if (exc instanceof IllegalArgumentException) return Supervision.restart();
      else return Supervision.stop();
    };
final Flow<Integer, Integer, NotUsed> flow =
    Flow.of(Integer.class)
        .scan(
            0,
            (acc, elem) -> {
              if (elem < 0) throw new IllegalArgumentException("negative not allowed");
              else return acc + elem;
            })
        .withAttributes(ActorAttributes.withSupervisionStrategy(decider));
final Source<Integer, NotUsed> source = Source.from(Arrays.asList(1, 3, -1, 5, 7)).via(flow);
final CompletionStage<List<Integer>> result =
    source.grouped(1000).runWith(Sink.<List<Integer>>head(), system);
// the negative element cause the scan stage to be restarted,
// i.e. start from 0 again
// result here will be a Future completed with List(0, 1, 4, 0, 5, 12)

Errors from mapAsync

Stream supervision can also be applied to the futures of mapAsyncmapAsync and mapAsyncUnorderedmapAsyncUnordered even if such failures happen in the future rather than inside the operator itself.

Let’s say that we use an external service to lookup email addresses and we would like to discard those that cannot be found.

We start with the tweet stream of authors:

Scala

copysourceval authors: Source[Author, NotUsed] =
  tweets.filter(_.hashtags.contains(pekkoTag)).map(_.author)

Java

copysourcefinal Source<Author, NotUsed> authors =
    tweets.filter(t -> t.hashtags().contains(PEKKO)).map(t -> t.author);

Assume that we can lookup their email address using:

Scala

copysourcedef lookupEmail(handle: String): Future[String] =

Java

copysourcepublic CompletionStage<String> lookupEmail(String handle)

The Future CompletionStage is completed with Failure normally if the email is not found.

Transforming the stream of authors to a stream of email addresses by using the lookupEmail service can be done with mapAsyncmapAsync and we use Supervision.resumingDecider Supervision.getResumingDecider() to drop unknown email addresses:

Scala

copysourceimport ActorAttributes.supervisionStrategy
import Supervision.resumingDecider

val emailAddresses: Source[String, NotUsed] =
  authors.via(
    Flow[Author]
      .mapAsync(4)(author => addressSystem.lookupEmail(author.handle))
      .withAttributes(supervisionStrategy(resumingDecider)))

Java

copysourcefinal Attributes resumeAttrib =
    ActorAttributes.withSupervisionStrategy(Supervision.getResumingDecider());
final Flow<Author, String, NotUsed> lookupEmail =
    Flow.of(Author.class)
        .mapAsync(4, author -> addressSystem.lookupEmail(author.handle))
        .withAttributes(resumeAttrib);
final Source<String, NotUsed> emailAddresses = authors.via(lookupEmail);

If we would not use ResumeSupervision.resume() the default stopping strategy would complete the stream with failure on the first Future CompletionStage that was completed with Failureexceptionally.