Akka Streams and Akka Actors are both parts of the Akka toolkit, but they serve different purposes and address different programming paradigms. Here’s a detailed comparison of the two:
Purpose and Focus :
-
Akka Actors:
- Designed for message-driven concurrency and distributed systems.
- The core concept is an actor, which encapsulates state and behavior, processes messages asynchronously, and communicates with other actors via message passing.
- Focuses on managing complex, concurrent workflows using actor-based programming.
-
Akka Streams:
- Built on top of Akka Actors but focuses on stream-based, reactive data processing.
- Provides a declarative, composable API for working with data streams (e.g., processing, transforming, and handling backpressure).
- Focuses on efficiently managing data flow in a pipeline-like manner.
Programming Model :
Akka Actors :
* Uses the actor model.
* Developers define actor behavior by implementing the receive method or similar constructs.
* State is managed within each actor, and operations are triggered by incoming messages.
Example :
class MyActor extends Actor {
def receive: Receive = {
case msg: String => println(s"Received message: $msg")
case _ => println("Unknown message")
}
}?
Akka Streams :
* Based on the Reactive Streams specification.
* Developers define flows, sources, and sinks to create a stream processing pipeline.
* Handles backpressure automatically to control the rate of data flow.
Example :
val source = Source(1 to 10)
val sink = Sink.foreach[Int](println)
val flow = source.to(sink)
flow.run()?
