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We released Phobos, our enterprise DevOps suite for Akka.NET only just last month and we’ve already had major Akka.NET users put it through its paces in development and production environments. Based on our users’ feedback and suggestions, we’ve developed Phobos v0.6.0 and made it available for immediate release to all of our evaluation and production users.

If you missed our Phobos announcement here’s the gist of what Phobos does for Akka.NET users:

Phobos instrurments, monitors, and traces all activity from actors inside large Akka.NET applications and exports it to common, off-the-shelf monitoring tools used by .NET enterprises; it works over Akka.Remote and Akka.Cluster; and it can do all of this without any explicit instrumentation code at all. Phobos can be entirely driven through configuration and works automatically behind the scenes.

If you’d like more background information about Phobos, we highly recommend that you visit the Phobos homepage.

Here’s what’s in the newest version of Phobos:

Expanded Tracing and Monitoring Integrations

The biggest addition to Phobos in v0.6.0 is the expanded set of Phobos drivers for working with various monitoring and tracing systems. Here’s what is included in Phobos v0.6.0:

  • Tracing: Jaeger - we’ve added first party support for the popular open source Jaeger tracing engine, which is now available via the Phobos.Tracing.Jaeger NuGet package.
  • Tracing: Microsoft Application Insights - we’ve added OpenTracing-compatible tracing support for Microsoft Azure’s managed Application Insights service via the Phobos.Tracing.ApplicationInsights package.
  • Monitoring: Microsoft Application Insights - in addition to adding tracing support for Application Insights, we’ve also added corresponding monitoring support via the Phobos.Monitoring.ApplicationInsights NuGet package.

If you’re already using the Microsoft Azure platform, we think you’ll find Phobos’ Application Insights integration to be of...

What Happens When Akka.NET Actors Restart

What Gets Lost, What Doesn't, and Why Restarts Help Build Better .NET Applications

One of the most powerful features of Akka.NET actors is their built-in fault tolerance. Actors are an extremely effective tool at isolating failures and preventing them from having side effects on other parts of your application.

However, one of the most frequently asked questions we hear when training .NET developers on the fundamentals of Akka.NET is: “what happens to my data when an actor restarts? Do I lose messages? State?”

We address those questions in this tutorial.

This video covers:

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How Distributed Tracing Solves One of the Worst Microservices Problems

Distributed Tracing Solves Some Big Pain Points with Microservices

Historically most web applications were developed as monolithic architectures. The entire application shipped as a single process implemented on a single runtime. Ultimately this architectural choice makes scaling software development extremely painstaking and tedious, because 100% of code changes submitted by members of any development team target a single code base. Under monolithic designs deployments are executed as “all or nothing” affairs - either you deploy all parts of your application at once or you deploy none of them.

It’s for painful reasons like this that the industry is moving away from monoliths and towards distributed architectures such as microservices. What microservices provide isn’t product scalability; they provide people scalability - the ability to easily partition your software development organization along the same lines as you partition your application with service boundaries.

An organization partitioned along its microservice boundaries

DevOps Implications of Microservices

Microservices provide enormous agility and flexibility to software development organizations. By partitioning our large applications into interdependent services which communicate via explicit network communication contracts, each team encapsulates their implementation from the others. This makes it possible, in theory, for each team to choose the best tools for the job - if Service 1’s requirements are best satisfied using Node.JS and Redis but Service 2’s are better handled with .NET and SQL Server, both of these teams can make those choices and develop / deploy their services independently of each other.

In practice, microservices are really a trade off for one set of organizational and technical problems for others. While the benefits of microservices amount to greater independence; clearer organizational boundaries and division of labor; and greater agility those benefits come with some distinct costs:

  1. Loss of coherence - now that the work to fulfill a single end-user request is now broken...