#akka

Manuel Bernhardt’s Akka anti-patterns series continues. This time, he takes a closer look at a very frequent anti-pattern that can be found in codebases written by developers who have just discovered the actor model; and that is to have too many actors. Whilst Akka is entirely capable and designed to run many actors, this isn’t always the best approach.

The Akka documentation discourages the use of Java serialization but performance is just one reason to not use Java serialization – there are stronger reasons to not engage with it, especially in the context of Akka. What to do instead? In this article, Manuel Bernhardt gives some alternatives.

What is the importance of actors and why stateless actors make little sense? In this article, Manuel Bernhardt briefly explains the main purpose of actors and their state.

It’s time for another programming pub quiz! This week, we’re testing your knowledge about Akka. Do you know everything there is to know about this toolkit for building concurrent applications on the JVM?

In this part of Manuel Bernhardt’s tour of Akka Cluster, we have a look at the test support and how it can be used to build production-quality systems.

In this part of Manuel Bernhardt’s series on Akka anti-patterns, we have a look at the things that you should keep in mind when using the ActorSelection mechanism.

The tour of Akka cluster continues! In the previous article, we learned how to use techniques for making a reactive payment processor resilient to failure using Akka Cluster. Here, Manuel Bernhardt takes a closer look at how to scale out the reactive payment processor application using cluster sharding.

Do you want to build a more stable system that’s able to cope with individual node crashes? Join Manuel Bernhardt in this tutorial as he shows us techniques for making a reactive payment processor resilient to failure using Akka Cluster.

How can we make building distributed systems easier? In this article, Manuel Bernhardt explores one useful tool in the Akka toolbox: Akka Cluster. Today, we’re taking a closer look at one module, Akka Distributed Data, and how it can be used to build an example reactive payment processor.

The fundamental building block of Akka actor systems is an actor. In a way, actors are analogous to Lego or Minecraft blocks: With the simple building blocks of Lego and Minecraft, it is possible to craft magnificent structures. In this article, Hugh McKee, developer advocate at Lightbend tells you everything you need to know about cluster aware actors.

While there is a wealth of documentation around Akka, based on Lightbend’s real-world experience with users and customers over the last few years, they realized that there is a need for tutorial-style tips that focuses on different aspects of Akka. In this post, Hugh McKee focuses on Akka clustering vs. Akka remoting.

Big Data is changing. Buzzwords such as Hadoop, Storm, Pig and Hive are not the darlings of the industry anymore —they are being replaced by a powerful duo: Fast Data and SMACK. Such a fast change in such a (relatively) young ecosystem begs the following question: What is wrong with the current approach? What is the difference between Fast and Big Data? And what is SMACK?

Last year, Akka won the JAX Innovation Award for Most Innovative Open Source Tech—we’re still very proud of this recognition!—and one thing I can say is that winning the award did not make us pause or slow down. A lot is happening in and around Akka as we will see in this whirlwind tour through the ecosystem.

Choosing Akka as a tool is often – if not always – driven by the need for good performance. Surely, the actor model itself is appealing as a means for organizing and reasoning about code, but this isn’t in itself a good reason enough to use the Akka toolkit.