Source Streaming

Akka HTTP supports completing a request with an Akka Source<T, ?>, which makes it possible to easily build and consume streaming end-to-end APIs which apply back-pressure throughout the entire stack.

It is possible to complete requests with raw Source<ByteString, ?>, however often it is more convenient to stream on an element-by-element basis, and allow Akka HTTP to handle the rendering internally - for example as a JSON array, or CSV stream (where each element is separated by a new-line).

In the following sections we investigate how to make use of the JSON Streaming infrastructure, however the general hints apply to any kind of element-by-element streaming you could imagine.

JSON Streaming

JSON Streaming is a term referring to streaming a (possibly infinite) stream of element as independent JSON objects as a continuous HTTP request or response. The elements are most often separated using newlines, however do not have to be. Concatenating elements side-by-side or emitting “very long” JSON array is also another use case.

In the below examples, we’ll be referring to the Tweet and Measurement case classes as our model, which are defined as:

private static final class JavaTweet {
  private int id; 
  private String message;

  public JavaTweet(int id, String message) { = id;
    this.message = message;

  public int getId() {
    return id;

  public void setId(int id) { = id;

  public void setMessage(String message) {
    this.message = message;

  public String getMessage() {
    return message;


Responding with JSON Streams

In this example we implement an API representing an infinite stream of tweets, very much like Twitter’s Streaming API.

Firstly, we’ll need to get some additional marshalling infrastructure set up, that is able to marshal to and from an Akka Streams Source<T, ?>. Here we’ll use the Jackson helper class from akka-http-jackson (a separate library that you should add as a dependency if you want to use Jackson with Akka HTTP).

First we enable JSON Streaming by making an implicit EntityStreamingSupport instance available (Step 1).

The default mode of rendering a Source is to represent it as an JSON Array. If you want to change this representation for example to use Twitter style new-line separated JSON objects, you can do so by configuring the support trait accordingly.

In Step 1.1. we demonstrate how to configure the rendering to be new-line separated, and also how parallel marshalling can be applied. We configure the Support object to render the JSON as series of new-line separated JSON objects, simply by providing the start, sep and end ByteStrings, which will be emitted at the appropriate places in the rendered stream. Although this format is not valid JSON, it is pretty popular since parsing it is relatively simple - clients need only to find the new-lines and apply JSON unmarshalling for an entire line of JSON.

The final step is simply completing a request using a Source of tweets, as simple as that:

// Step 1: Enable JSON streaming // we're not using this in the example, but it's the simplest way to start: // The default rendering is a JSON array: `[el, el, el , ...]` final JsonEntityStreamingSupport jsonStreaming = EntityStreamingSupport.json(); // Step 1.1: Enable and customise how we'll render the JSON, as a compact array: final ByteString start = ByteString.fromString("["); final ByteString between = ByteString.fromString(","); final ByteString end = ByteString.fromString("]"); final Flow<ByteString, ByteString, NotUsed> compactArrayRendering = Flow.of(ByteString.class).intersperse(start, between, end); final JsonEntityStreamingSupport compactJsonSupport = EntityStreamingSupport.json() .withFramingRendererFlow(compactArrayRendering); // Step 2: implement the route final Route responseStreaming = path("tweets", () -> get(() -> parameter(StringUnmarshallers.INTEGER, "n", n -> { final Source<JavaTweet, NotUsed> tws = Source.repeat(new JavaTweet(12, "Hello World!")).take(n); // Step 3: call complete* with your source, marshaller, and stream rendering mode return completeOKWithSource(tws, Jackson.marshaller(), compactJsonSupport); }) ) ); // tests: final TestRoute routes = testRoute(tweets()); // test happy path final Accept acceptApplication = Accept.create(MediaRanges.create(MediaTypes.APPLICATION_JSON));"/tweets?n=2").addHeader(acceptApplication)) .assertStatusCode(200) .assertEntity("[{\"id\":12,\"message\":\"Hello World!\"},{\"id\":12,\"message\":\"Hello World!\"}]"); // test responses to potential errors final Accept acceptText = Accept.create(MediaRanges.ALL_TEXT);"/tweets?n=3").addHeader(acceptText)) .assertStatusCode(StatusCodes.NOT_ACCEPTABLE) // 406 .assertEntity("Resource representation is only available with these types:\napplication/json"); // tests -------------------------------------------- final TestRoute routes = testRoute(csvTweets()); // test happy path final Accept acceptCsv = Accept.create(MediaRanges.create(MediaTypes.TEXT_CSV));"/tweets?n=2").addHeader(acceptCsv)) .assertStatusCode(200) .assertEntity("12,Hello World!\n" + "12,Hello World!"); // test responses to potential errors final Accept acceptText = Accept.create(MediaRanges.ALL_APPLICATION);"/tweets?n=3").addHeader(acceptText)) .assertStatusCode(StatusCodes.NOT_ACCEPTABLE) // 406 .assertEntity("Resource representation is only available with these types:\ntext/csv; charset=UTF-8");

Consuming JSON Streaming uploads

Sometimes the client may be sending a streaming request, for example an embedded device initiated a connection with the server and is feeding it with one line of measurement data.

In this example, we want to consume this data in a streaming fashion from the request entity, and also apply back-pressure to the underlying TCP connection, if the server can not cope with the rate of incoming data (back-pressure will be applied automatically thanks to using Akka HTTP/Streams).

final Unmarshaller<ByteString, JavaTweet> JavaTweets = Jackson.byteStringUnmarshaller(JavaTweet.class);
final Route incomingStreaming = path("tweets", () ->
  post(() ->
    extractMaterializer(mat -> {
      final JsonEntityStreamingSupport jsonSupport = EntityStreamingSupport.json();

      return entityAsSourceOf(JavaTweets, jsonSupport, sourceOfTweets -> {
          final CompletionStage<Integer> tweetsCount = sourceOfTweets.runFold(0, (acc, tweet) -> acc + 1, mat);
          return onComplete(tweetsCount, c -> complete("Total number of tweets: " + c));

Simple CSV streaming example

Akka HTTP provides another EntityStreamingSupport out of the box, namely csv (comma-separated values). For completeness, we demonstrate its usage in the below snippet. As you’ll notice, switching between streaming modes is fairly simple, one only has to make sure that an implicit Marshaller of the requested type is available, and that the streaming support operates on the same Content-Type as the rendered values. Otherwise you’ll see an error during runtime that the marshaller did not expose the expected content type and thus we can not render the streaming response).

final Marshaller<JavaTweet, ByteString> renderAsCsv = 
  Marshaller.withFixedContentType(ContentTypes.TEXT_CSV_UTF8, t -> 
    ByteString.fromString(t.getId() + "," + t.getMessage())

final CsvEntityStreamingSupport compactJsonSupport = EntityStreamingSupport.csv();

final Route responseStreaming = path("tweets", () ->
  get(() ->
    parameter(StringUnmarshallers.INTEGER, "n", n -> {
      final Source<JavaTweet, NotUsed> tws =
        Source.repeat(new JavaTweet(12, "Hello World!")).take(n);
      return completeWithSource(tws, renderAsCsv, compactJsonSupport);

Implementing custom EntityStreamingSupport traits

The EntityStreamingSupport infrastructure is open for extension and not bound to any single format, content type or marshalling library. The provided JSON support does not rely on Spray JSON directly, but uses Marshaller<T, ByteString> instances, which can be provided using any JSON marshalling library (such as Circe, Jawn or Play JSON).

When implementing a custom support trait, one should simply extend the EntityStreamingSupport abstract class, and implement all of it’s methods. It’s best to use the existing implementations as a guideline.