N.B. For the purpose of clarity: “Akka” refers to Lightbend’s Scala / Java library and “Akka.NET” refers to the .NET Foundation library maintained by Petabridge. I have been very careful in my writing to ensure there is a little confusion as possible in this post.

TL;DR; Akka.NET is Not Impacted

Imagine my surprise this week: out of the blue one of the core committers to Akka.NET forwards me a link to a Lightbend blog post entitled “Why We Are Changing the License for Akka.”

Lightbend’s license change for the original Akka library has no impact on Akka.NET. All of Akka.NET’s source is still Apache 2.0 and anything we’ve ported from the original Akka library was also done under Apache 2.0 as well.

As of today, Phobos 2.0 - our fully OpenTelemetry-enabled instrumentation library for Akka.NET, is now available for production use with Akka.NET.

The key features of Phobos 2.0 are as follows:

1. Requires no instrumentation code on the part of the end-user;
2. Automatically creates and propagates OpenTelemetry traces during actor messaging, creation, Ask<T>, crashes, and restarts;
3. Automatically records OpenTelemetry metrics for message processing by message type / actor type, mailbox depth, message processing latency, log rates, error rates, and more;
4. Automatically records OpenTelemetry metrics for the state of the Akka.NET cluster - including the number of unreachable members, members by status, and so on;
5. Includes enhanced noise control for OpenTelemetry tracing via the ITraceFilter interface, which allows you to suppress the creation of unwanted trace data in order to reduce cost, noise, and resource consumption;
6. Measures latency on message-processing activity from the point in which the message is initially created, so in-flight time over Akka.Remote or time spent in queue can now be easily observed and monitored;
7. Uses Akka.Hosting to make Phobos a HOCONless installation experience;
8. Is high performance - tracing obviously produces some additional CPU, memory allocation, and bandwidth overhead but Phobos metrics are allocation-free and very performant; and lastly
9. Comes with ready-made Akka.NET dashboards for many popular metrics and data visualization platforms.

Phobos’ license fees are still the same as Phobos 1.0 - $4000 per organization per year, and you can buy Phobos instantly with a 30-day moneyback guarantee through Sdkbin.

In our Akka.NET Community Standup on March 9th, 2022 we presented for the very first time Akka.Hosting - a new approach to configuring Akka.NET and managing ActorSystems that requires zero HOCON, automatically enforces Akka.NET best practices, is type-checked, and makes it easy to pass IActorRefs via Microsoft.Extensions.DependencyInjection using the brand new ActorRegistry construct. Although Akka.Hosting is part of the current Akka.NET v1.5 development effort underway, it is already available for use with Akka.NET v1.4 and is ready for production use.

In this post and accompanying video we demonstrate how to use Akka.Hosting to streamline Akka.NET configuration, ActorSystem life-cycle management, actor instantiation, dependency injection, and more.

If you want to see the code samples that goes along with this video and blog post, you can find them here:

• https://github.com/akkadotnet/Akka.Hosting
• https://github.com/petabridge/akkadotnet-code-samples/tree/master/src/clustering/sharding-sqlserver

We’ve prepared a set of three professional-grade dotnet new templates (Petabridge.Templates) for use with your projects - these will help you get started right away with Akka.NET or developing your own NuGet libraries that you wish to distribute. We’ve built and maintained these for years in order to create our own internal and external projects here at Petabridge.

But first, I should probably tell you a personal story, about how I improved the professionalism on my projects with build automation. Before I had the opportunity to work on a build system, I have always known the advantage of them and envied projects that had them.
On Twitter I read tweets from different .NET developers about creating a powerful build automations; I felt like it was a rocket science and secretly wished to be doing something like that. I was stuck, I needed a boost to move me out of that state.

Many years ago I wrote an article entitled “How to Do Asynchronous I/O with Akka.NET Actors Using PipeTo” - back when await support was still very much a work in progress. I subsequently wrote a major update on that article in 2016 - talking mostly about the benefits of PipeTo from a structural point of view.

I recently learned that this article has many of our own users under the impression that Akka.NET actors don’t support await semantics properly and that PipeTo is the only “blessed” pathway for working with asynchronous operations inside actors. This is not the case - await has had first class support inside actors for many years now.

That’s what this post and video cover: async, await, and PipeTo - how do they work differently, when should you consider using one over the other, and what are the “gotchas” that you need to know?

If you want to see the code sample that goes along with this video and blog post, you can find that here: https://github.com/Aaronontheweb/Akka.AsyncAwait

Since announcing Phobos 1.0 a year ago we’ve made many significant changes to it that should add many useful new ways to observe the run-time behavior of your Akka.NET applications in production.

Phobos is a commercial add-on for Akka.NET that allows developers to automatically record distributed traces and metrics from their actors without the need for any manual instrumentation code.

In this post we’re going to cover:

• Improvements that have been made to Phobos all the way up through the v1.3.2 version released on December 21st, 2021;
• Phobos 2.0, which will be built entirely upon OpenTelemetry and should be in beta soon; and
• Our ongoing support plan for Phobos 1.x and 2.x over the next two years.

A concept I’ve been trying to put a name to is software application architectures that inherently lend themselves to a lower rate of technical debt accumulation than others. This post is my first attempt to do that.

Technical Debt

Technical debt is a term that is used frequently in our industry and while its meaning is commonly understood among experienced technologists, it’s not clearly defined.

In the abstract:

• Technical debt is cost incurred from software design and implementation choices made in the past;
• Interest on that technical debt accrues and compounds as a result of subsequent decisions that are layered onto the original set of choices; and
• The full cost of technical debt is not known until, at some point in the future, there is a need to modify the software system which forces the development team to modify the original choices built into the existing system and calculate the level of effort needed to change them safely.

On this blog before we’ve covered a high level introduction to Akka.Streams for Akka.NET, but in this post we go into some more detail on:

1. What is Akka.Streams;
2. The motivations for using Akka.Streams; and
3. When should you use Akka.Streams?

In the recent past we blogged about hosting headless Akka.NET services using IHostedService and we thought we’d extend that train of thought to cover ASP.NET Core. .NET’s web ecosystem has changed a lot since 2015, when we last wrote about hosting Akka.NET with ASP.NET MVC and even NancyFX on .NET Framework 4.5 - so we’re due for an update.

So, we went ahead and put together a video: “Best Practices for Integrating Akka.NET and ASP.NET Core” which demonstrates how to work with Akka.NET and ASP.NET Core inside a single process.

This blog post accompanies that video and expands on some of the advice we give in it!

NOTE: Also, please check out our Petabridge.App.Web dotnet new template to use these patterns straight away inside your own applications!

Vertech is an industrial automation and information solutions company based out of the United States that develops solutions for a broad range of industries.

This case study was authored by John Wolnisty, a Solutions Architect at Vertech who, in the context of this case study, used Akka.NET to help test, simulate, and build solutions for airport baggage handling systems.

What does Vertech do?

Based in Phoenix, Arizona, Vertech designs and implements industrial automation solutions including traditional automation systems, SCADA systems, MES solutions, and OT networks. One of our industry focuses is airport baggage handling systems. A large airport can have hundreds of conveyors that need to be integrated and controlled by the system. Airport security requirements add layers of complexity requiring control systems that account for every bag on a conveyor and track suspicious activities, such as a bag suddenly disappearing or a new bag appearing from the middle of a conveyor. At Vertech we provide comprehensive solutions that handle all these details by personally writing everything from automation code in PLCs to the high-level systems that interface with airline data feeds.

What problem were you trying to solve?

A PLC (Programmable Logic Controller) is an industrial computer repetitively running logic that reads input sensors, implements operational behaviors and controls outputs. Inputs are control panel switches, push buttons, photo-eyes and the like. Outputs are motors, lights, warning horns and other devices. The PLC programmers have a problem, especially with a large system, of being unable to develop and test their code without the physical hardware being present. We had a need for a system that could simulate the conveyor hardware, provide relatively seamless connections with the PLCs, display the output conditions, and log what was going on without having to set up...