Akka Streams allows you to easily create a stream leveraging the power of the Akka framework without explicitly defining actor behaviors and messages. Every stream will have at least one Source
(origin of the data) and at least one Sink
(destination of the data).
import akka.actor.ActorSystem
import akka.stream.ActorMaterializer
import akka.stream.scaladsl.{Sink, Source}
import java.io.File
val stream = Source(Seq("test1.txt", "test2.txt", "test3.txt"))
.map(new File(_))
.filter(_.exists())
.filter(_.length() != 0)
.to(Sink.foreach(f => println(s"Absolute path: ${f.getAbsolutePath}")))
In this quick example we have a Seq
of filenames that we input into the stream. First we map them to a File
, then we filter out files which don't exist, then files which length is 0. If a file went through the filters, it gets printed into the stdout
.
Akka streams also allows you to do streams in a modular way. You can create Flow
s with the partial modules of your stream. If we take the same example we could also do:
import akka.actor.ActorSystem
import akka.stream.ActorMaterializer
import akka.stream.scaladsl.{Sink, Source}
import java.io.File
implicit val actorSystem = ActorSystem("system")
implicit val actorMaterializer = ActorMaterializer()
val source = Source(List("test1.txt", "test2.txt", "test3.txt"))
val mapper = Flow[String].map(new File(_))
val existsFilter = Flow[File].filter(_.exists())
val lengthZeroFilter = Flow[File].filter(_.length() != 0)
val sink = Sink.foreach[File](f => println(s"Absolute path: ${f.getAbsolutePath}"))
val stream = source
.via(mapper)
.via(existsFilter)
.via(lengthZeroFilter)
.to(sink)
stream.run()
In this second version we can see that mapper
, existsFilter
, lengthZeroFilter
are Flow
s. You can compose them in stream by using the method via
. This capability would allow you to reuse your pieces of code. One important thing to mention is that Flow
s can be stateless or stateful. In the case of stateful, you need to be careful when reusing them.
You can also think about streams as Graphs
. Akka Streams also provides a powerful GraphDSL
to define complicated streams in a simple way. Following with the same example we could do:
import java.io.File
import akka.actor.ActorSystem
import akka.stream.{ActorMaterializer, ClosedShape}
import akka.stream.scaladsl.{Flow, GraphDSL, RunnableGraph, Sink, Source}
implicit val actorSystem = ActorSystem("system")
implicit val actorMaterializer = ActorMaterializer()
val graph = RunnableGraph.fromGraph(GraphDSL.create() { implicit b =>
import GraphDSL.Implicits._
val source = Source(List("test1.txt", "test2.txt", "test3.txt"))
val mapper = Flow[String].map(new File(_))
val existsFilter = Flow[File].filter(_.exists())
val lengthZeroFilter = Flow[File].filter(_.length() != 0)
val sink = Sink.foreach[File](f => println(s"Absolute path: ${f.getAbsolutePath}"))
source ~> mapper ~> existsFilter ~> lengthZeroFilter ~> sink
ClosedShape
})
graph.run()
It is also possible to create aggregated flow using the GraphDSL
. For example, if we would like to combine the mapper and two filters in one we could do:
val combinedFlow = Flow.fromGraph(GraphDSL.create() { implicit builder =>
import GraphDSL.Implicits._
val mapper = builder.add(Flow[String].map(new File(_)))
val existsFilter = builder.add(Flow[File].filter(_.exists()))
val lengthZeroFilter = builder.add(Flow[File].filter(_.length() != 0))
mapper ~> existsFilter ~> lengthZeroFilter
FlowShape(mapper.in, lengthZeroFilter.out)
})
And then use it as a individual block. combinedFlow
would be a FlowShape
or a PartialGraph
. We can us for example with via
:
val stream = source
.via(combinedFlow)
.to(sink)
stream.run()
Or using the GraphDSL
:
val graph = RunnableGraph.fromGraph(GraphDSL.create() { implicit b =>
import GraphDSL.Implicits._
val source = Source(List("test1.txt", "test2.txt", "test3.txt"))
val sink = Sink.foreach[File](f => println(s"Absolute path: ${f.getAbsolutePath}"))
source ~> combinedFlow ~> sink
ClosedShape
})
graph.run()