¿Para qué se utilizan las corrientes Akka?

What is Akka streams used for?

Akka Streams is a library to process and transfer a sequence of elements using bounded buffer space. This latter property is what we refer to as boundedness, and it is the defining feature of Akka Streams.

What is HTTP Dynamic Streaming? HDS, or HTTP Dynamic Streaming, is Adobe’s method for adaptive bitrate streaming for Flash Video. This method of streaming enables on-demand and live adaptive bitrate video delivery of MP4 media over regular HTTP connections.

Akka Streams operators (be it simple operators on Flows and Sources or graph junctions) are “fused” together and executed sequentially by default. This avoids the overhead of events crossing asynchronous boundaries but limits the flow to execute at most one operator at any given time.

Kafka is designed for use in distributed systems. Akka operates within the confines of a single process. It may be applied in a distributed context when using a cluster; this effectively aggregates multiple actor systems into a single actor address space.

It provides an open-source platform for building reactive applications for the JVM, consisting of the Play Framework, Akka middleware and Scala programming language, with additional supporting products and development tools such as the Slick database library for Scala and the sbt build tool.

A meandering stream has a single channel that winds snakelike through its valley, so that the distance ‘as the stream flows’ is greater than ‘as the crow flies. ‘ As water flows around these curves, the outer edge of water is moving faster than the inner.

The Akka Streams library calls them materialized values. That’s because, when you plug components together, you have an inert graph, but when you call the run method, the graph comes alive, or is materialized. The Jedi value returned by materializing a graph is called a materialized value.

Akka is a toolkit and runtime for building highly concurrent, distributed, and resilient message-driven applications on the JVM. On the other hand, Kafka is detailed as “Distributed, fault tolerant, high throughput pub-sub messaging system”. Kafka is a distributed, partitioned, replicated commit log service.

Advantages of Reactive Programming a lot simpler to do async / threaded work. a lot of operators that simplify work. very simple to compose streams of data. complex threading becomes very easy.

Reactive Extensions (Rx) are similar to Future. Future can return an independent element, while Rx returns a stream that can be subscribed to. The same set of specifications is supported on different platforms. The same API can be called asynchronously and synchronously.

The Akka Streams API can be combined with Akka Actors, therefore, to distribute workloads in powerful ways. To demonstrate, contemplate simulating one-hundred thousand wind-turbines in order to test the measurement collection and aggregation service at scale.

ActorRef [T]; sending messages to it will emit them on the stream only if they are of the same type as the stream. ActorRef [T]; sending messages to it will emit them on the stream. The source acknowledges reception after emitting a message, to provide back pressure from the source.

The two concurrency patterns that we demonstrated as means to increase throughput are not exclusive. In fact, it is rather simple to combine the two approaches and streams provide a nice unifying language to express and compose them. First, let’s look at how we can parallelize pipelined operators.

Success ( CompletionStrategy. draining ()), ActorRef. noSender ()); ActorRef [T]; sending messages to it will emit them on the stream only if they are of the same type as the stream. ActorRef [T]; sending messages to it will emit them on the stream.

Akka Streams is a module built on top of Akka Actors to make the ingestion and processing of streams easy. It provides easy-to-use APIs to create streams that leverage the power of the Akka toolkit without explicitly defining actor behaviors and messages.

Akka actors are asynchronous from the beginning. Reactive programs can be implemented in either way, synchronous and asynchronous.

In this article, we will be looking at the akka-streams library that is built atop of the Akka actor framework, which adheres to the reactive streams manifesto. The Akka Streams API allows us to easily compose data transformation flows from independent steps. Moreover, all processing is done in a reactive, non-blocking, and asynchronous way. 2.

The Akka Streams API is completely decoupled from the Reactive Streams interfaces. While Akka Streams focus on the formulation of transformations on data streams the scope of Reactive Streams is to define a common mechanism of how to move data across an asynchronous boundary without losses, buffering or resource exhaustion.

In the terminology of Akka Streams this is called a Sink. IOResult is a type that IO operations return in Akka Streams in order to tell you how many bytes or elements were consumed and whether the stream terminated normally or exceptionally. Here is another example that you can edit and run in the browser:

To use Akka Streams, add the module to your project: Both the Java and Scala DSLs of Akka Streams are bundled in the same JAR. For a smooth development experience, when using an IDE such as Eclipse or IntelliJ, you can disable the auto-importer from suggesting javadsl imports when working in Scala, or viceversa. See IDE Tips.