It’s been a while since we’ve published an official roadmap update for Akka.NET. We are still on track to achieve the goals of the previous roadmap, but with a few minor changes that I will explain here.

Akka.NET 1.1 - Akka.Cluster Release to Market; Akka.Streams Beta

We’ve publicly committed to releasing Akka.NET 1.1 on June 14th, 2016:

Coming soon to @AkkaDotNET - Akka.Cluster stable release. June 14 target ship date, along with the to-do list :p pic.twitter.com/f4UKOASowR

— Aaron Stannard (@Aaronontheweb) May 23, 2016

This release has the following goals:

1. Officially releasing Akka.Cluster to market, signifying “it is ready for full-blown production use;”
2. Deploy Helios 2.0 transport to production, which is significantly faster, more memory efficient, and more reliable than the current Helios 1.4.1-based transport;
3. Releasing the MultinodeTestRunner and the Akka.Remote.TestKit, used for testing distributed systems built with Akka.NET; and
4. Releasing the very first beta of the new Akka.Streams module, which you can read more about here.

Akka.Cluster has been available as a beta package for nearly two years and has had thousands of users. It is currently serving production workloads both on Linux and on Windows for a variety of different types of customers. During this period we have collected lots of bug reports, feedback, and data that has been used to help improve its reliability and performance.

This will be a tremendous opportunity for Akka.NET users to build high availability systems of all shapes and sizes on any cloud they wish.

Akka.NET 1.5 - TLS, New Serializer, Faster Transports

The next major release we have planned following Akka.NET 1.1 is Akka.NET 1.5. This release will introduce some breaking changes at the dependency level.

We are making the following two important changes:

...

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Akka.NET is a .NET implementation of the actor model.

The actor model is an old technology, originating in 1973 as an approach to parallel computing at a time when it looked like the computers of the future might be constructed using thousands of small, low-powered CPUs. History didn’t turn out that way thanks to Moore’s Law; CPUs became faster and faster and modern machines were developed with a small number of very high-powered CPUs.

Despite that, the actor model is immensely popular and runs some of the world’s most important software today. Amazon’s SimpleDb, RabbitMQ, Riak, CouchBase, Goldman Sachs, Motorola, Blizzard Games, Cisco, eBay, Credit Suisse, AMN Healthcare, Bank of America, McGraw Hill Financial, and scores of other major organizations use implementations of the actor model to power mission-critical applications responsible for the world’s largest companies.

So why is the actor model so popular today? Why are so many businesses using it for mission-critical applications?

First Adopters of the Actor Model: Telecoms

The truth of the matter is, the actor model has been popular for a long time through the Erlang programming language. Erlang was the first large-scale, production usable implementation of the actor model - developed originally by Joe Armstrong as a proprietary language at Ericsson in 1986 (open sourced later) to build telephone exchanges. Today it’s used to power the GPRS, 3G, and LTE cellular networks that depend Ericsson’s products.

Although the actor model was originally developed as a means for running applications on types of computer hardware that never really took off, the emergence of electronic computer networks in the late 70s and early 80s gave the actor model an extremely viable commercial application: distributed and concurrent systems.

As the Internet grew and more...

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Back in December I released the first publicly available version of NBench - Petabridge’s automated .NET performance testing and benchmarking framework.

NBench has proven itself to be an invaluable part of our QA process for Akka.NET and scores of other projects, for one critical reason: performance is a mission critical feature for an increasingly large number of applications. And if you can’t measure performance, then you’re shipping a totally untested feature to your end-users.

The Impact of Poor Performance

A simple anecdote to illustrate the real-world impact of shipping non-performant software.

One of my favorite musicians, whom I have never seen live before, is coming to town and I wanted to purchase a ticket. I was out of the country the day tickets went on sale, so if I wanted to attend I’d need to buy a ticket from a secondary market.

I decided to give a new company I’d never purchased tickets through before, SeatGeek, a try. I quickly found two tickets for about $100 each, went through the checkout process, put in my payment information, and submitted payment. A few seconds later I get an error message back letting me know that the tickets were no longer available. So I repeat this process a few more times with progressively more expensive tickets with no success.

Eventually I just gave up and SeatGeek lost about $500 worth of revenue, because I lost any confidence that their reported ticket inventory was available. The crucial error was that the underlying software responsible for reporting inventory availability under-performed - it wasn’t able to keep up with the demand of actual customers, and as a result they nearly lost my business. I tried again on a whim immediately before writing this post and...

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This is a follow-up to our last post, “How to Guarantee Delivery of Messages in Akka.NET.” In that post we introduced the AtLeastOnceDeliveryActor and talked about strategies for retrying deliveries of messages in order to make sure they’re successfully procssed.

In this post we’re going to talk about “At Least Once” delivery’s big brother, “Exactly Once” delivery - and why you should try to avoid it if at all possible.

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A question we get asked about frequently with Akka.NET is “how can I guarantee that my messages will eventually be received and processed by another actor?” Most of the time it’s developers who work on finance applications who have this question, because each missed message means that someone’s accounting is off - but developers in lots of other domains have this issue too.

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After last month’s successful Akka.NET virtual meetup, we’re going to be coming right back with another on on February 29th at 18:00 UTC. RSVP Here.

Update: thanks to everyone who attended! You can view the meetup below!

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Petabridge does .NET open source software. One of the great things that’s happening in .NET-land right now is that scores of developers from traditionally more conservative, pure Microsoft stack companies are starting to become not just users of open source software, but also contributors!

Since lots of .NET developers are new to Github, having been previously raised on a steady diet of Perforce and Team Foundation Server, I put this video together illustrating how to do all of the Github workflows used by most major .NET OSS projects.

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2016 is going to be a big year for Akka.NET, and we’re starting things off right by hosting our second Akka.NET Virtual Meetup on Tuesday, January 26th @ 18:00UTC. RSVP Here.

Update: thanks to everyone who attended! You can view the meetup below!

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One of the most frequently asked questions I get is about how to create stateful actors that are also durable by default, meaning that the actor can recover its state from some sort of storage engine in the event that the entire Akka.NET process needs to be restarted.

Enter Akka.Persistence - an entire framework built into Akka.NET that’s designed to allow you to create actors with durable state that can be persisted on any database or storage system you want.

In this post we’re going to explore the concepts behind Akka.Persistence, how it works, and what some of the available storage options are.

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Not long ago in Akka.NET-land we had an issue occur where users noticed a dramatic drop in throughput in Akka.Remote’s message processing pipeline - and to make matters worse, this occurred in a production release of AKka.NET!

Yikes, how did that happen?

The answer is that although you can use unit tests and code reviews to detect functional problems with code changes and pull requests, using those same mechanisms to detect performance problems with code is utterly ineffective. Even skilled developers who have detailed knowledge about the internals of the .NET framework and CLR are unable to correctly predict how changes to code will impact its performance.

Hence why I developed NBench - a .NET performance-testing, stress-testing, and benchmarking framework for .NET applications that works and feels a lot like a unit test.

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