How to do Rate Limiting in Akka Streams

How to do Rate Limiting in Akka Streams

In certain scenarios it is important to limit the number of concurrent requests to other services. For example, to avoid overwhelming the services and avoid performance degradation, or to maintain service level agreements. This is particularly important when streams are unbounded and the message rates are dynamic.

No matter the scenario, the Akka Streams API provides a seamless way to do this through back pressure applied upstream.

The following example shows how to batch elements, then asynchronously write the batched elements to a database, limiting the number of outstanding requests to only 10.

  implicit val system = ActorSystem()
  implicit val materializer = ActorMaterializer()
  implicit val ec = system.dispatcher

  def writeToDB(batch: Seq[Int])= Future {
    println(s"Writing ${batch.size} elements to the DB using thread '${Thread.currentThread().getName}'")

  val rateLimitedGraph = Source(1 to 100000)
    .groupedWithin(100, 100.millis)
    .onComplete(_ => system.terminate())

The above example preserves the order of the elements downstream, which can be important depending on the application. If downstream order of elements is not important the Akka Streams API provides mapAsyncUnordered.

For more on mapAsync see the Java or Scala documentation.
For more on mapAsyncUnordered see the Java or Scala documentation.

    • Related Articles

    • How to do Throttling in Akka Streams

      When building a streaming application you may find the need to throttle the upstream so as to avoid exceeding a specified rate. Akka Stream's provides the capability to either fail the stream or shape it by applying back pressure. This is simply done ...
    • Error handling and recovery in Akka Streams

      When developing applications you should assume that there will be unexpected issues. For this, Akka provides a set of supervision strategies to deal with errors within your actors. Akka streams is no different, in fact its error handling strategies ...
    • How to implement batching logic in Akka Streams

      A common request we see with streaming data is the need to take the stream of elements and group them together (i.e. committing data to a database, a message queue or disk). Batching is usually a more efficient and performant solution than writing a ...
    • Terminating a stream

      Streams do not run on the caller thread, instead they run on a different background thread. This is done to avoid blocking the caller. Therefore, once the stream completes, you need to terminate the underlying actor system to completely end the ...
    • How to stream multiple stream actions concurrently

      To construct efficient, scalable and low-latency data streams, it is often important to perform tasks concurrently. However Akka Streams executes stages sequentially on a single thread, so you must explicitly request this concurrency. Inserting these ...