Random things learned building Akka.NET – Part 1

In this short post I will explain some of the things I’ve learned building Akka.NET.
I will describe some of the friction points I have noticed and why I personally don’t use features like Akka Cluster to build entire systems.
Some of these thoughts might be obvious, some might be naive, but they do reflect my current view on building distributed systems.

Location transparency and Message contracts

Actors are supposed to be location transparent.
DCom, Corba and .NET remoting were all based on a local model, trying to make remote calls appear as local, in process calls, all failed for this very reason.
Never try to make the explicit implicit.
The Actor Model is remote first and can be optimized when used locally.

We constantly fall into the trap of trying to make local message objects somehow become wire friendly.
Making something that is designed to work local only and try to shoehorn that into a world of network calls will result in problems.

Messages should be designed with a remote first mindset, explicit contracts that can support versioning and be maintained over time.

In Akka.NET we have tried very hard to make serialization just work magically for any message type, currently using Json.NET serializer and soon the Wire serializer.
This gives you low friction when getting started with Akka.NET, but it is the wrong design to use for real systems.

You should swap that out for a custom serializer using e.g. ProtoBuf, where your message contract are explicit and there are no magic or unexpected behavior involved.

Distributed monolith and the Unix philosophy

Actor model frameworks and languages does not play nicely between platforms.
Erlang, Pony, Akka, Akka.NET, Project Orleans, Service Fabric ActFab, Orbit, none of those can communicate with any of the others.
If you base your entire infrastructure on such framework or language, you are building a distributed monolith.

You pay a very high price when you decide to build your systems this way, none of the components or services in your systems can be replaced with another tech stack, you are forever bound to use the same stack until you replace the entire thing or surgically cut one of the parts out of it.

Instead, architect your systems as isolated islands, bounded contexts, and connect them using standard protocols and contracts, HTTP, JSON, XML, AMQP etc.
Then inside each of those isolated islands, feel free to use any of the above technologies.

The above does not just apply to actor model frameworks, it applies to any RPC or micro-service framework that have their own special service discovery, clustering support or protocols.

The Unix philosophy should be applied.
Use tools like Consul, Etcd, Zookeeper for service discovery, Docker using Swarm, Rancher, Kubernetes for deployment and clustering.
This gives you a lot more flexibility and options.
If it turns out that one of your choices didn’t work out, there are plenty of others to solve the same problem without completely redesigning your system.

There are of course cases where your system might have special requirements, such as extremely high throughput and/or latency requirements, then other rules apply.
Maybe you need to build a distributed monolith for such reasons, but it should not be the default when designing a new system.

More on this in the next post.


Building a framework – The early Akka.NET history

In this post, I will try to cover some of the early history of Akka.NET and how and why things turned out the way they did.
Akka.NET of course have some parallel histories going as there are many contributors on the project.
But the post is written from my own point of view and my reasons for getting involved in this.

The butterfly effect

Back in 2005, I attended an architecture workshop initiated by Jimmy Nilsson, hosted in Lillehammer Norway.
One of the attendees there was a Einar Landre, he worked for Statoil at the time, and he talked about how they used asynchronous systems and how you could build eventually consistent systems using message passing.
I was totally sold on the concepts and as soon as I got back from the workshop, I introduced the concepts at my work, and build my first asynchronous message passing application, which is actually still in use today, ten years later.
This had a huge impact on me, and changed how I came to reason about systems and integrations and why I almost a decade later thought it was a good idea to port Akka.

I also met Mats Helander, he had just started developing an Object Relatonal mapper called NPersist.
NPersist was based on code generation, so I showed him my framework for aspect oriented programming I was building at the time, and explained how that would be able to get rid of all the code generation and make NPersist persistence ignorant via POCOs.
Me and Mats started working on these two tools together, we packaged them under an umbrella project called Puzzle Framework.
Back then, our competitor NHibernate was in alpha stage and featurewise we were way ahead of them.

But as time passed by, it turned out that NHibernate would become the winner, not because it was better, but because it attracted a lot more people due to it’s well known sister project, Hibernate on the JVM.
Hibernate had a lot of learning material; books, videos and tutorials.
So having the same framework on .NET of-course ment that you could re-use existing knowledge or learn from the vast set of resources.

Eventually me and Mats dropped the development of NPersist, at this time, NHibernate was already the de-facto standard and Linq to SQL had just been released, there were simply no reason for us to keep the project alive any more.

The most important thing that I learned in this process, was that adoption will always outweigh features, that is; documentation, ease of use and familiarity are worth more than shiny features if no one knows how to use them.

Laying the foundation

Now fast forward to 2013.
I was doing a consultancy gig for a Swedish agency, that project contained a fair deal of concurrency, multiple systems integrating with each other, all touching the same data, possibly at the same time.
During this project, I got more and more frustrated with the lack of concurrency tools for .NET, I started reading up on this topic and eventually stumbled upon the actor model and Akka on the JVM.

As often when I find an interesting programming topic, I had to try to implement some of these concepts myself, as this is my way of learning.
I did some weekend hacking, first using F# with pattern matching, mailbox processors and all the goodness that exists there.
I played around with some proof of concept implementations of the core concepts of Akka, as a learning experience and with the intent to make something I might be able to use in my everyday work.

However, I knew that if I ever should have any chance to get to use any of this in my client projects, I would have to switch over to C#, and the same was true to a large extent for attracting contributors, simply because C# has a much larger market share than F# has.

I also remembered the lesson learned from NPersist vs NHbernate, there were already a handful of small hobby hacks or abandoned actor frameworks on .NET, but I knew that if I would contribute to one of those, or roll my own, the result would still be something new unproven, untrusted and it would be extremely hard to get any adoption of such effort.

Porting Akka

A few weeks passed and eventually I actually had something that worked pretty well, quite a few of the core Akka-Actor features and some rudimentary Akka-Remote support like remote deployment and a fairly complete HOCON configuration parser was now in place.
The code was published on Github and the project was named “Pigeon” in a lame attempt to play on carrier pigeons for message passing.
(The name Pigeon can still be seen in the Akka.NET source code, as the main configuration file is still called “Pigeon.conf”)

The networking layer was a problem, I didn’t have much experience writing low level networking code, so the first early attempts of Akka-Remote used SignalR for communication, which later was replaced with a very naive socket implementation.

First class support for F#

Even if I decided to go for C# as the language of implementation, I still wanted to involve the F# community.
F# has a truly awesome opensource community around it, and I had seen that there was a genuine interest in the actor model over at the F# camp.
So I sent out a few requests on the F# forums, looking for someone who could help me build an idiomatic F# API on top of the C# code.

The Co-Pilot

One day in February (2014), I got an email by a guy named Aaron.

This is the actual letter:

Hello Roger!

My name is Aaron Stannard – I’m the Founder of MarkedUp Analytics, a .NET startup in Los Angeles. We build analytics and marketing automation tools for developers who author native applications for Microsoft platforms, including native Windows.
I began my own port of Akka to C# beginning in early December, and took a break right around Christmas. I just got back to it this week and discovered Pigeon when I was researching some details about the TPL Dataflow! I wish you had started this project a few weeks earlier ;)
My implementation of Akka is right around the same stage / maturity as yours, but Pigeon offers much better performance (3.5-5x), is more simply designed than mine, and you’ve already made a lot of headway on features that I haven’t even started on like Remoting and Configuration.
Therefore, I would like to stop working on my own implementation of Akka and support Pigeon instead. I think that would be a much better use of my time than trying to invent it all on my own.
I’m an experienced .NET OSS contributor – I currently maintain FluentCassandra (popular C# Cassandra driver) and have a bunch of projects of my own that I’ve open-sourced.

I plan on using Pigeon in at least two of our services, both of which operate under high loads.

Please let me know how I can help!
Aaron Stannard • Founder • MarkedUp

“I plan on using Pigeon in at least two of our services, both of which operate under high loads.”

Say WAAT!?

It turned out that Aaron had created his own networking lib called Helios, which was exactly what Akka-Remote needed.
Aaron joined the effort and started working on the akka-remote bits while I focused mostly on akka-actor and akka-testkit.
We had some nice progress going, and we contacted Jonas Bonér of Typesafe to see if we could use the name Akka as we aimed to be a pure port, which we got an OK to do.

Lift off

Now the project started to gain some real attention.
Håkan Canberger joined the team and Jérémie Chassaing contributed the first seed of the F# API.

At the same time, my youngest son Theo was born, 10 weeks too early and 1195 grams small, so I spend the next two months full time in the hospital, managing pull requests and issues on my phone.

This turned out to be a good thing for the project, up until that point, I had seen the project as “mine”, which is not a good mindset to have when trying to run a community project.

Meanwhile we gained more users and contributors and Aaron and Håkan were busy pushing new features.
Now all of a sudden we have people like the F# language inventor Don Syme retweeting our tweets.

Bartosz joins the team and sets out to complete the F# API.
This results in even more attention from the F# community, and Don Syme even did a code review of one of the F# API pull requests.

From that point on, the project have been pretty much self sustaining, with new contributors stepping up and contributing entire modules or integrations.

A lot more have of course happened since then, which may be the subject of another post, but I hope this post gives some insight into why Akka.NET came to be and why some of the early design choices was made.

With that being said, I’m sure the other developers have some interesting stories to share on why they got involved and what lead them down this route.


Akka.NET + Azure: Azure ServiceBus integration

I know that there is some confusion out there on how Akka.NET relates to products like NServiceBus and Azure ServiceBus, I think that Akka.NET Co-founder Aaron Stannard said it the best;

they’re very complimentary Akka.NET makes a great consumer or producer for NServiceBus

Another closely related question that comes up from time to time is how to integrate Akka.NET actors with service buses.

How can we pull messages from a service bus and pass those to a number of worker actors w/o message loss?

One approach we can use to solve this is since actors in Akka.NET support the Ask operator.
We can pass a message to an actor and expect a response, this response will be delivered in form of a Task.

As the response is a task, we can pipe this task into a continuation and depending on if the response represents a processing success or failure from the worker actor, we can then decide what we want to do with the service bus message.

In this case, we might want to Ack the service bus message, telling the service bus that we are done with this message and it can be removed from the queue.

If the response was a failure, just ignore the failure and continue processing other messages.
As we haven’t acked the message to the service bus, the service bus will try to re-deliver the message to our client and we get the chance to try again some time later.

A simple implementation of this approach using Azure Service bus could look something like this:

namespace ConsoleApplication13
    //define your worker actor
    public class MyBusinessActor : ReceiveActor
        public MyBusinessActor()
            //here is where you should receive your business messages
            //apply domain logic, store to DB etc.
            Receive<string>(str =>
                Console.WriteLine("{0} Processed {1}", Self.Path, s);

                //reply to the sender that everything went well
                //in this example, we pass back the message we received in a built in `Success` message
                //you can send back a Status.Failure incase of exceptions if you desire too
                //or just let it fail by timeout as we do in this example
                Sender.Tell(new Status.Success(s));

    internal class Program
        private static void Main(string[] args)

            using (var system = ActorSystem.CreateSystem("mysys"))
                //spin up our workers
                //this should be done via config, but here we use a
                //hardcoded setup for simplicity

                //Do note that the workers can be spread across multiple
                //servers using Akka.Remote or Akka.Cluster
                var businessActor =
                       .WithRouter(new ConsistentHashingPool(10)));

                //start the message processor

                //wait for user to end the application

        private static async void ProcessMessages(IActorRef myBusinessActor)
            //set up a azure SB subscription client
            //(or use a Queue client, or whatever client your specific MQ supports)
            var subscriptionClient = SubscriptionClient.Create("service1","service1");

            while (true)
                //fetch a batch of messages
                var batch = await subscriptionClient.ReceiveBatchAsync(100, TimeSpan.FromSeconds(1));

                //transform the messages into a list of tasks
                //the tasks will either be successful and ack the MQ message
                //or they will timeout and do nothing
                var tasks = (
                    from res in batch
                    let importantMessage = res.GetBody<string>()
                    let ask = myBusinessActor
                        .Ask<Status.Success>(new ConsistentHashableEnvelope(importantMessage,
                    let done = ask.ContinueWith(t =>;
                        if (t.IsCanceled)
                            Console.WriteLine("Failed to ack {0}", importantMessage);
                            Console.WriteLine("Completed {0}", importantMessage);
                    select done).ToList();

                //wait for all messages to either succeed or timeout
                await Task.WhenAll(tasks);
                Console.WriteLine("All messages handled (acked or failed)");
                //continue with the next batch

        //dummy method only used to prefill the msgqueue with data for this example
        private static void CreateMessages()
            var client = TopicClient.Create("service1");

            for (var i = 0; i < 100; i++)
                client.SendAsync(new BrokeredMessage("hello" + i)
                    MessageId = Guid.NewGuid().ToString()

But do note that when applying this pattern, we now go from the default Akka.NET “At most once” deliver to “At least once”.


Because if we fail to ack the message back to the service bus, we will eventually receive the same message again at a later time.

It could be that our worker actor have processed the message correctly, stored it in some persistent store, but the ack back to the client might have failed, network problems, timeout or something similar.

Thus, the wervice bus meesage will not be removed from the queue and the client will receive it as soon as whatever locking mechanism is in place frees the message again.

One extremely nice feature in Akka.NET is the cluster support. cluster nodes can be added or removed to a live application, so we can easily spread our load over multiple worker actors on remove nodes.

Completely w/o writing any special code for this, we just need to configure our actor system to be part of a Akka.NET cluster.