raboof/pekko
1
0
Fork 0
Code Issues Pull requests Projects Releases 6 Packages Wiki Activity Actions 11

Add missed apidoc links in stream/stream-quickstart.md (#22904) (#31391)

Browse source
This commit is contained in:
Andrei Arlou 2022-05-10 13:35:30 +03:00 • committed by GitHub
parent 1ab4e9854c
commit 0a12885e2a
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
1 changed files with 5 additions and 5 deletions
Download patch file Download diff file Expand all files Collapse all files

10
akka-docs/src/main/paradox/stream/stream-quickstart.md
View file

@ -408,20 +408,20 @@ prepared Sink using `toMat`]@java[`Sink.fold` will sum all `Integer` elements of
a `CompletionStage<Integer>`. Next we use the `map` method of `tweets` `Source` which will change each incoming tweet
into an integer value `1`. Finally we connect the Flow to the previously prepared Sink using `toMat`].
Remember those mysterious `Mat` type parameters on @scala[`Source[+Out, +Mat]`, `Flow[-In, +Out, +Mat]` and `Sink[-In, +Mat]`]@java[`Source<Out, Mat>`, `Flow<In, Out, Mat>` and `Sink<In, Mat>`]?
Remember those mysterious `Mat` type parameters on @scala[@scaladoc[Source](akka.stream.scaladsl.Source)\[+Out, +Mat\], @scaladoc[Flow](akka.stream.scaladsl.Flow)\[-In, +Out, +Mat\] and @scaladoc[Sink](akka.stream.scaladsl.Sink)\[-In, +Mat\]]@java[@javadoc[Source](akka.stream.javadsl.Source)<Out, Mat>, @javadoc[Flow](akka.stream.javadsl.Flow)<In, Out, Mat> and @javadoc[Sink](akka.stream.javadsl.Sink)<In, Mat>]?
They represent the type of values these processing parts return when materialized. When you chain these together,
you can explicitly combine their materialized values. In our example we used the @scala[`Keep.right`]@java[`Keep.right()`] predefined function,
you can explicitly combine their materialized values. In our example we used the @scala[@scaladoc[Keep.right](akka.stream.scaladsl.Keep$#right[L,R]:(L,R)=%3ER)]@java[@javadoc[Keep.right()](akka.stream.javadsl.Keep$#right())] predefined function,
which tells the implementation to only care about the materialized type of the operator currently appended to the right.
The materialized type of `sumSink` is @scala[`Future[Int]`]@java[`CompletionStage<Integer>`] and because of using @scala[`Keep.right`]@java[`Keep.right()`], the resulting `RunnableGraph`
The materialized type of `sumSink` is @scala[@scaladoc[Future](scala.concurrent.Future)\[Int\]]@java[@javadoc[CompletionStage](java.util.concurrent.CompletionStage)<Integer>] and because of using @scala[`Keep.right`]@java[`Keep.right()`], the resulting @apidoc[akka.stream.*.RunnableGraph]
has also a type parameter of @scala[`Future[Int]`]@java[`CompletionStage<Integer>`].
This step does *not* yet materialize the
processing pipeline, it merely prepares the description of the Flow, which is now connected to a Sink, and therefore can
be `run()`, as indicated by its type: @scala[`RunnableGraph[Future[Int]]`]@java[`RunnableGraph<CompletionStage<Integer>>`]. Next we call `run()` which materializes and runs the Flow. The value returned by calling `run()` on a @scala[`RunnableGraph[T]`]@java[`RunnableGraph<T>`] is of type `T`.
be `run()`, as indicated by its type: @scala[`RunnableGraph[Future[Int]]`]@java[`RunnableGraph<CompletionStage<Integer>>`]. Next we call @apidoc[run()](akka.stream.*.RunnableGraph) {scala="#run()(implicitmaterializer:akka.stream.Materializer):Mat" java="#run(akka.stream.Materializer)"} which materializes and runs the Flow. The value returned by calling `run()` on a @scala[`RunnableGraph[T]`]@java[`RunnableGraph<T>`] is of type `T`.
In our case this type is @scala[`Future[Int]`]@java[`CompletionStage<Integer>`] which, when completed, will contain the total length of our `tweets` stream.
In case of the stream failing, this future would complete with a Failure.
A `RunnableGraph` may be reused
A @apidoc[akka.stream.*.RunnableGraph] may be reused
and materialized multiple times, because it is only the "blueprint" of the stream. This means that if we materialize a stream,
for example one that consumes a live stream of tweets within a minute, the materialized values for those two materializations
will be different, as illustrated by this example: