Streams Cookbook

Logging elements of a stream

Situation: During development it is sometimes helpful to see what happens in a particular section of a stream.

The simplest solution is to simply use a map operation and use println to print the elements received to the console. While this recipe is rather simplistic, it is often suitable for a quick debug session.

val loggedSource = mySource.map { elem => println(elem); elem }

Another approach to logging is to use log() operation which allows configuring logging for elements flowing through the stream as well as completion and erroring.

// customise log levels
mySource.log("before-map")
  .withAttributes(Attributes.logLevels(onElement = Logging.WarningLevel))
  .map(analyse)

// or provide custom logging adapter
implicit val adapter = Logging(system, "customLogger")
mySource.log("custom")

Flattening a stream of sequences

Situation: A stream is given as a stream of sequence of elements, but a stream of elements needed instead, streaming all the nested elements inside the sequences separately.

The mapConcat operation can be used to implement a one-to-many transformation of elements using a mapper function in the form of In => immutable.Seq[Out]. In this case we want to map a Seq of elements to the elements in the collection itself, so we can just call mapConcat(identity).

val myData: Source[List[Message], NotUsed] = someDataSource
val flattened: Source[Message, NotUsed] = myData.mapConcat(identity)

Draining a stream to a strict collection

Situation: A possibly unbounded sequence of elements is given as a stream, which needs to be collected into a Scala collection while ensuring boundedness

A common situation when working with streams is one where we need to collect incoming elements into a Scala collection. This operation is supported via Sink.seq which materializes into a Future[Seq[T]].

The function limit or take should always be used in conjunction in order to guarantee stream boundedness, thus preventing the program from running out of memory.

For example, this is best avoided:

// Dangerous: might produce a collection with 2 billion elements!
val f: Future[Seq[String]] = mySource.runWith(Sink.seq)

Rather, use limit or take to ensure that the resulting Seq will contain only up to max elements:

val MAX_ALLOWED_SIZE = 100

// OK. Future will fail with a `StreamLimitReachedException`
// if the number of incoming elements is larger than max
val limited: Future[Seq[String]] =
  mySource.limit(MAX_ALLOWED_SIZE).runWith(Sink.seq)

// OK. Collect up until max-th elements only, then cancel upstream
val ignoreOverflow: Future[Seq[String]] =
  mySource.take(MAX_ALLOWED_SIZE).runWith(Sink.seq)

Calculating the digest of a ByteString stream

Situation: A stream of bytes is given as a stream of ByteStrings and we want to calculate the cryptographic digest of the stream.

This recipe uses a GraphStage to host a mutable MessageDigest class (part of the Java Cryptography API) and update it with the bytes arriving from the stream. When the stream starts, the onPull handler of the stage is called, which just bubbles up the pull event to its upstream. As a response to this pull, a ByteString chunk will arrive (onPush) which we use to update the digest, then it will pull for the next chunk.

Eventually the stream of ByteStrings depletes and we get a notification about this event via onUpstreamFinish. At this point we want to emit the digest value, but we cannot do it with push in this handler directly since there may be no downstream demand. Instead we call emit which will temporarily replace the handlers, emit the provided value when demand comes in and then reset the stage state. It will then complete the stage.

import akka.stream.stage._
class DigestCalculator(algorithm: String) extends GraphStage[FlowShape[ByteString, ByteString]] {
  val in = Inlet[ByteString]("DigestCalculator.in")
  val out = Outlet[ByteString]("DigestCalculator.out")
  override val shape = FlowShape.of(in, out)

  override def createLogic(inheritedAttributes: Attributes): GraphStageLogic = new GraphStageLogic(shape) {
    val digest = MessageDigest.getInstance(algorithm)

    setHandler(out, new OutHandler {
      override def onPull(): Trigger = {
        pull(in)
      }
    })

    setHandler(in, new InHandler {
      override def onPush(): Trigger = {
        val chunk = grab(in)
        digest.update(chunk.toArray)
        pull(in)
      }

      override def onUpstreamFinish(): Unit = {
        emit(out, ByteString(digest.digest()))
        completeStage()
      }
    })

  }
}
val digest: Source[ByteString, NotUsed] = data.via(new DigestCalculator("SHA-256"))

Parsing lines from a stream of ByteStrings

Situation: A stream of bytes is given as a stream of ByteStrings containing lines terminated by line ending characters (or, alternatively, containing binary frames delimited by a special delimiter byte sequence) which needs to be parsed.

The Framing helper object contains a convenience method to parse messages from a stream of ByteStrings:

import akka.stream.scaladsl.Framing
val linesStream = rawData.via(Framing.delimiter(
  ByteString("\r\n"), maximumFrameLength = 100, allowTruncation = true))
  .map(_.utf8String)

Implementing reduce-by-key

Situation: Given a stream of elements, we want to calculate some aggregated value on different subgroups of the elements.

The "hello world" of reduce-by-key style operations is wordcount which we demonstrate below. Given a stream of words we first create a new stream that groups the words according to the identity function, i.e. now we have a stream of streams, where every substream will serve identical words.

To count the words, we need to process the stream of streams (the actual groups containing identical words). groupBy returns a SubFlow, which means that we transform the resulting substreams directly. In this case we use the reduce combinator to aggregate the word itself and the number of its occurrences within a tuple (String, Integer). Each substream will then emit one final value—precisely such a pair—when the overall input completes. As a last step we merge back these values from the substreams into one single output stream.

One noteworthy detail pertains to the MaximumDistinctWords parameter: this defines the breadth of the groupBy and merge operations. Akka Streams is focused on bounded resource consumption and the number of concurrently open inputs to the merge operator describes the amount of resources needed by the merge itself. Therefore only a finite number of substreams can be active at any given time. If the groupBy operator encounters more keys than this number then the stream cannot continue without violating its resource bound, in this case groupBy will terminate with a failure.

val counts: Source[(String, Int), NotUsed] = words
  // split the words into separate streams first
  .groupBy(MaximumDistinctWords, identity)
  //transform each element to pair with number of words in it
  .map(_ -> 1)
  // add counting logic to the streams
  .reduce((l, r) => (l._1, l._2 + r._2))
  // get a stream of word counts
  .mergeSubstreams

By extracting the parts specific to wordcount into

• a groupKey function that defines the groups
• a map map each element to value that is used by the reduce on the substream
• a reduce function that does the actual reduction

we get a generalized version below:

def reduceByKey[In, K, Out](
  maximumGroupSize: Int,
  groupKey:         (In) => K,
  map:              (In) => Out)(reduce: (Out, Out) => Out): Flow[In, (K, Out), NotUsed] = {

  Flow[In]
    .groupBy[K](maximumGroupSize, groupKey)
    .map(e => groupKey(e) -> map(e))
    .reduce((l, r) => l._1 -> reduce(l._2, r._2))
    .mergeSubstreams
}

val wordCounts = words.via(
  reduceByKey(
    MaximumDistinctWords,
    groupKey = (word: String) => word,
    map = (word: String) => 1)((left: Int, right: Int) => left + right))

Sorting elements to multiple groups with groupBy

Situation: The groupBy operation strictly partitions incoming elements, each element belongs to exactly one group. Sometimes we want to map elements into multiple groups simultaneously.

To achieve the desired result, we attack the problem in two steps:

• first, using a function topicMapper that gives a list of topics (groups) a message belongs to, we transform our stream of Message to a stream of (Message, Topic) where for each topic the message belongs to a separate pair will be emitted. This is achieved by using mapConcat
• Then we take this new stream of message topic pairs (containing a separate pair for each topic a given message belongs to) and feed it into groupBy, using the topic as the group key.

val topicMapper: (Message) => immutable.Seq[Topic] = extractTopics

val messageAndTopic: Source[(Message, Topic), NotUsed] = elems.mapConcat { msg: Message =>
  val topicsForMessage = topicMapper(msg)
  // Create a (Msg, Topic) pair for each of the topics
  // the message belongs to
  topicsForMessage.map(msg -> _)
}

val multiGroups = messageAndTopic
  .groupBy(2, _._2).map {
    case (msg, topic) =>
      // do what needs to be done
  }

Triggering the flow of elements programmatically

Situation: Given a stream of elements we want to control the emission of those elements according to a trigger signal. In other words, even if the stream would be able to flow (not being backpressured) we want to hold back elements until a trigger signal arrives.

This recipe solves the problem by simply zipping the stream of Message elements with the stream of Trigger signals. Since Zip produces pairs, we simply map the output stream selecting the first element of the pair.

val graph = RunnableGraph.fromGraph(GraphDSL.create() { implicit builder =>
  import GraphDSL.Implicits._
  val zip = builder.add(Zip[Message, Trigger]())
  elements ~> zip.in0
  triggerSource ~> zip.in1
  zip.out ~> Flow[(Message, Trigger)].map { case (msg, trigger) => msg } ~> sink
  ClosedShape
})

Alternatively, instead of using a Zip, and then using map to get the first element of the pairs, we can avoid creating the pairs in the first place by using ZipWith which takes a two argument function to produce the output element. If this function would return a pair of the two argument it would be exactly the behavior of Zip so ZipWith is a generalization of zipping.

val graph = RunnableGraph.fromGraph(GraphDSL.create() { implicit builder =>
  import GraphDSL.Implicits._
  val zip = builder.add(ZipWith((msg: Message, trigger: Trigger) => msg))

  elements ~> zip.in0
  triggerSource ~> zip.in1
  zip.out ~> sink
  ClosedShape
})

Balancing jobs to a fixed pool of workers

Situation: Given a stream of jobs and a worker process expressed as a Flow create a pool of workers that automatically balances incoming jobs to available workers, then merges the results.

We will express our solution as a function that takes a worker flow and the number of workers to be allocated and gives a flow that internally contains a pool of these workers. To achieve the desired result we will create a Flow from a graph.

The graph consists of a Balance node which is a special fan-out operation that tries to route elements to available downstream consumers. In a for loop we wire all of our desired workers as outputs of this balancer element, then we wire the outputs of these workers to a Merge element that will collect the results from the workers.

To make the worker stages run in parallel we mark them as asynchronous with async.

def balancer[In, Out](worker: Flow[In, Out, Any], workerCount: Int): Flow[In, Out, NotUsed] = {
  import GraphDSL.Implicits._

  Flow.fromGraph(GraphDSL.create() { implicit b =>
    val balancer = b.add(Balance[In](workerCount, waitForAllDownstreams = true))
    val merge = b.add(Merge[Out](workerCount))

    for (_ <- 1 to workerCount) {
      // for each worker, add an edge from the balancer to the worker, then wire
      // it to the merge element
      balancer ~> worker.async ~> merge
    }

    FlowShape(balancer.in, merge.out)
  })
}

val processedJobs: Source[Result, NotUsed] = myJobs.via(balancer(worker, 3))

Dropping elements

Situation: Given a fast producer and a slow consumer, we want to drop elements if necessary to not slow down the producer too much.

This can be solved by using a versatile rate-transforming operation, conflate. Conflate can be thought as a special reduce operation that collapses multiple upstream elements into one aggregate element if needed to keep the speed of the upstream unaffected by the downstream.

When the upstream is faster, the reducing process of the conflate starts. Our reducer function simply takes the freshest element. This in a simple dropping operation.

val droppyStream: Flow[Message, Message, NotUsed] =
  Flow[Message].conflate((lastMessage, newMessage) => newMessage)

There is a more general version of conflate named conflateWithSeed that allows to express more complex aggregations, more similar to a fold.

Dropping broadcast

Situation: The default Broadcast graph element is properly backpressured, but that means that a slow downstream consumer can hold back the other downstream consumers resulting in lowered throughput. In other words the rate of Broadcast is the rate of its slowest downstream consumer. In certain cases it is desirable to allow faster consumers to progress independently of their slower siblings by dropping elements if necessary.

One solution to this problem is to append a buffer element in front of all of the downstream consumers defining a dropping strategy instead of the default Backpressure. This allows small temporary rate differences between the different consumers (the buffer smooths out small rate variances), but also allows faster consumers to progress by dropping from the buffer of the slow consumers if necessary.

val graph = RunnableGraph.fromGraph(GraphDSL.create(mySink1, mySink2, mySink3)((_, _, _)) { implicit b => (sink1, sink2, sink3) =>
  import GraphDSL.Implicits._

  val bcast = b.add(Broadcast[Int](3))
  myElements ~> bcast

  bcast.buffer(10, OverflowStrategy.dropHead) ~> sink1
  bcast.buffer(10, OverflowStrategy.dropHead) ~> sink2
  bcast.buffer(10, OverflowStrategy.dropHead) ~> sink3
  ClosedShape
})

Collecting missed ticks

Situation: Given a regular (stream) source of ticks, instead of trying to backpressure the producer of the ticks we want to keep a counter of the missed ticks instead and pass it down when possible.

We will use conflateWithSeed to solve the problem. The seed version of conflate takes two functions:

• A seed function that produces the zero element for the folding process that happens when the upstream is faster than the downstream. In our case the seed function is a constant function that returns 0 since there were no missed ticks at that point.
• A fold function that is invoked when multiple upstream messages needs to be collapsed to an aggregate value due to the insufficient processing rate of the downstream. Our folding function simply increments the currently stored count of the missed ticks so far.

As a result, we have a flow of Int where the number represents the missed ticks. A number 0 means that we were able to consume the tick fast enough (i.e. zero means: 1 non-missed tick + 0 missed ticks)

val missedTicks: Flow[Tick, Int, NotUsed] =
  Flow[Tick].conflateWithSeed(seed = (_) => 0)(
    (missedTicks, tick) => missedTicks + 1)

Create a stream processor that repeats the last element seen

Situation: Given a producer and consumer, where the rate of neither is known in advance, we want to ensure that none of them is slowing down the other by dropping earlier unconsumed elements from the upstream if necessary, and repeating the last value for the downstream if necessary.

We have two options to implement this feature. In both cases we will use GraphStage to build our custom element. In the first version we will use a provided initial value initial that will be used to feed the downstream if no upstream element is ready yet. In the onPush() handler we just overwrite the currentValue variable and immediately relieve the upstream by calling pull(). The downstream onPull handler is very similar, we immediately relieve the downstream by emitting currentValue.

import akka.stream._
import akka.stream.stage._
final class HoldWithInitial[T](initial: T) extends GraphStage[FlowShape[T, T]] {
  val in = Inlet[T]("HoldWithInitial.in")
  val out = Outlet[T]("HoldWithInitial.out")

  override val shape = FlowShape.of(in, out)

  override def createLogic(inheritedAttributes: Attributes): GraphStageLogic = new GraphStageLogic(shape) {
    private var currentValue: T = initial

    setHandlers(in, out, new InHandler with OutHandler {
      override def onPush(): Unit = {
        currentValue = grab(in)
        pull(in)
      }

      override def onPull(): Unit = {
        push(out, currentValue)
      }
    })

    override def preStart(): Unit = {
      pull(in)
    }
  }

}

While it is relatively simple, the drawback of the first version is that it needs an arbitrary initial element which is not always possible to provide. Hence, we create a second version where the downstream might need to wait in one single case: if the very first element is not yet available.

We introduce a boolean variable waitingFirstValue to denote whether the first element has been provided or not (alternatively an Option can be used for currentValue or if the element type is a subclass of AnyRef a null can be used with the same purpose). In the downstream onPull() handler the difference from the previous version is that we check if we have received the first value and only emit if we have. This leads to that when the first element comes in we must check if there possibly already was demand from downstream so that we in that case can push the element directly.

import akka.stream._
import akka.stream.stage._
final class HoldWithWait[T] extends GraphStage[FlowShape[T, T]] {
  val in = Inlet[T]("HoldWithWait.in")
  val out = Outlet[T]("HoldWithWait.out")

  override val shape = FlowShape.of(in, out)

  override def createLogic(inheritedAttributes: Attributes): GraphStageLogic = new GraphStageLogic(shape) {
    private var currentValue: T = _
    private var waitingFirstValue = true

    setHandlers(in, out, new InHandler with OutHandler {
      override def onPush(): Unit = {
        currentValue = grab(in)
        if (waitingFirstValue) {
          waitingFirstValue = false
          if (isAvailable(out)) push(out, currentValue)
        }
        pull(in)
      }

      override def onPull(): Unit = {
        if (!waitingFirstValue) push(out, currentValue)
      }
    })

    override def preStart(): Unit = {
      pull(in)
    }
  }
}

Globally limiting the rate of a set of streams

Situation: Given a set of independent streams that we cannot merge, we want to globally limit the aggregate throughput of the set of streams.

One possible solution uses a shared actor as the global limiter combined with mapAsync to create a reusable Flow that can be plugged into a stream to limit its rate.

As the first step we define an actor that will do the accounting for the global rate limit. The actor maintains a timer, a counter for pending permit tokens and a queue for possibly waiting participants. The actor has an open and closed state. The actor is in the open state while it has still pending permits. Whenever a request for permit arrives as a WantToPass message to the actor the number of available permits is decremented and we notify the sender that it can pass by answering with a MayPass message. If the amount of permits reaches zero, the actor transitions to the closed state. In this state requests are not immediately answered, instead the reference of the sender is added to a queue. Once the timer for replenishing the pending permits fires by sending a ReplenishTokens message, we increment the pending permits counter and send a reply to each of the waiting senders. If there are more waiting senders than permits available we will stay in the closed state.

object Limiter {
  case object WantToPass
  case object MayPass

  case object ReplenishTokens

  def props(maxAvailableTokens: Int, tokenRefreshPeriod: FiniteDuration,
            tokenRefreshAmount: Int): Props =
    Props(new Limiter(maxAvailableTokens, tokenRefreshPeriod, tokenRefreshAmount))
}

class Limiter(
  val maxAvailableTokens: Int,
  val tokenRefreshPeriod: FiniteDuration,
  val tokenRefreshAmount: Int) extends Actor {
  import Limiter._
  import context.dispatcher
  import akka.actor.Status

  private var waitQueue = immutable.Queue.empty[ActorRef]
  private var permitTokens = maxAvailableTokens
  private val replenishTimer = system.scheduler.schedule(
    initialDelay = tokenRefreshPeriod,
    interval = tokenRefreshPeriod,
    receiver = self,
    ReplenishTokens)

  override def receive: Receive = open

  val open: Receive = {
    case ReplenishTokens =>
      permitTokens = math.min(permitTokens + tokenRefreshAmount, maxAvailableTokens)
    case WantToPass =>
      permitTokens -= 1
      sender() ! MayPass
      if (permitTokens == 0) context.become(closed)
  }

  val closed: Receive = {
    case ReplenishTokens =>
      permitTokens = math.min(permitTokens + tokenRefreshAmount, maxAvailableTokens)
      releaseWaiting()
    case WantToPass =>
      waitQueue = waitQueue.enqueue(sender())
  }

  private def releaseWaiting(): Unit = {
    val (toBeReleased, remainingQueue) = waitQueue.splitAt(permitTokens)
    waitQueue = remainingQueue
    permitTokens -= toBeReleased.size
    toBeReleased foreach (_ ! MayPass)
    if (permitTokens > 0) context.become(open)
  }

  override def postStop(): Unit = {
    replenishTimer.cancel()
    waitQueue foreach (_ ! Status.Failure(new IllegalStateException("limiter stopped")))
  }
}

To create a Flow that uses this global limiter actor we use the mapAsync function with the combination of the ask pattern. We also define a timeout, so if a reply is not received during the configured maximum wait period the returned future from ask will fail, which will fail the corresponding stream as well.

def limitGlobal[T](limiter: ActorRef, maxAllowedWait: FiniteDuration): Flow[T, T, NotUsed] = {
  import akka.pattern.ask
  import akka.util.Timeout
  Flow[T].mapAsync(4)((element: T) => {
    import system.dispatcher
    implicit val triggerTimeout = Timeout(maxAllowedWait)
    val limiterTriggerFuture = limiter ? Limiter.WantToPass
    limiterTriggerFuture.map((_) => element)
  })

}

Chunking up a stream of ByteStrings into limited size ByteStrings

Situation: Given a stream of ByteStrings we want to produce a stream of ByteStrings containing the same bytes in the same sequence, but capping the size of ByteStrings. In other words we want to slice up ByteStrings into smaller chunks if they exceed a size threshold.

This can be achieved with a single GraphStage. The main logic of our stage is in emitChunk() which implements the following logic:

• if the buffer is empty, and upstream is not closed we pull for more bytes, if it is closed we complete
• if the buffer is nonEmpty, we split it according to the chunkSize. This will give a next chunk that we will emit, and an empty or nonempty remaining buffer.

Both onPush() and onPull() calls emitChunk() the only difference is that the push handler also stores the incoming chunk by appending to the end of the buffer.

import akka.stream.stage._

class Chunker(val chunkSize: Int) extends GraphStage[FlowShape[ByteString, ByteString]] {
  val in = Inlet[ByteString]("Chunker.in")
  val out = Outlet[ByteString]("Chunker.out")
  override val shape = FlowShape.of(in, out)

  override def createLogic(inheritedAttributes: Attributes): GraphStageLogic = new GraphStageLogic(shape) {
    private var buffer = ByteString.empty

    setHandler(out, new OutHandler {
      override def onPull(): Unit = {
        if (isClosed(in)) emitChunk()
        else pull(in)
      }
    })
    setHandler(in, new InHandler {
      override def onPush(): Unit = {
        val elem = grab(in)
        buffer ++= elem
        emitChunk()
      }

      override def onUpstreamFinish(): Unit = {
        if (buffer.isEmpty) completeStage()
        else {
          // There are elements left in buffer, so
          // we keep accepting downstream pulls and push from buffer until emptied.
          //
          // It might be though, that the upstream finished while it was pulled, in which
          // case we will not get an onPull from the downstream, because we already had one.
          // In that case we need to emit from the buffer.
          if (isAvailable(out)) emitChunk()
        }
      }
    })

    private def emitChunk(): Unit = {
      if (buffer.isEmpty) {
        if (isClosed(in)) completeStage()
        else pull(in)
      } else {
        val (chunk, nextBuffer) = buffer.splitAt(chunkSize)
        buffer = nextBuffer
        push(out, chunk)
      }
    }

  }
}

val chunksStream = rawBytes.via(new Chunker(ChunkLimit))

Limit the number of bytes passing through a stream of ByteStrings

Situation: Given a stream of ByteStrings we want to fail the stream if more than a given maximum of bytes has been consumed.

This recipe uses a GraphStage to implement the desired feature. In the only handler we override, onPush() we just update a counter and see if it gets larger than maximumBytes. If a violation happens we signal failure, otherwise we forward the chunk we have received.

import akka.stream.stage._
class ByteLimiter(val maximumBytes: Long) extends GraphStage[FlowShape[ByteString, ByteString]] {
  val in = Inlet[ByteString]("ByteLimiter.in")
  val out = Outlet[ByteString]("ByteLimiter.out")
  override val shape = FlowShape.of(in, out)

  override def createLogic(inheritedAttributes: Attributes): GraphStageLogic = new GraphStageLogic(shape) {
    private var count = 0

    setHandlers(in, out, new InHandler with OutHandler {

      override def onPull(): Unit = {
        pull(in)
      }

      override def onPush(): Unit = {
        val chunk = grab(in)
        count += chunk.size
        if (count > maximumBytes) failStage(new IllegalStateException("Too much bytes"))
        else push(out, chunk)
      }
    })
  }
}

val limiter = Flow[ByteString].via(new ByteLimiter(SizeLimit))

Compact ByteStrings in a stream of ByteStrings

Situation: After a long stream of transformations, due to their immutable, structural sharing nature ByteStrings may refer to multiple original ByteString instances unnecessarily retaining memory. As the final step of a transformation chain we want to have clean copies that are no longer referencing the original ByteStrings.

The recipe is a simple use of map, calling the compact() method of the ByteString elements. This does copying of the underlying arrays, so this should be the last element of a long chain if used.

val compacted: Source[ByteString, NotUsed] = data.map(_.compact)

Injecting keep-alive messages into a stream of ByteStrings

Situation: Given a communication channel expressed as a stream of ByteStrings we want to inject keep-alive messages but only if this does not interfere with normal traffic.

There is a built-in operation that allows to do this directly:

import scala.concurrent.duration._
val injectKeepAlive: Flow[ByteString, ByteString, NotUsed] =
  Flow[ByteString].keepAlive(1.second, () => keepaliveMessage)