Skip to main content

Release trains are NOT the solution

One of the techniques that you see in bigger organizations that struggle to move to an Agile mindset, is the introduction of Release trains:

A release train tries to align the vision, planning, and interdependencies of many teams by providing cross-team synchronization based on a common cadence. A release train focuses on fast, flexible flow at the level of a larger product.

It is especially used in SAFe(Scaled Agile Framework) organizations. Their definition of a release train is:

Agile Release Trains align teams to a shared business and technology mission. Each is a virtual organization (typically 50 – 125 people) that plans, commits, develops, and deploys together. ARTs are organized around the Enterprise’s significant Value Streams and exist solely to realize the promise of that value by building Solutions that deliver benefit to the end-user.

Based on the definitions above you would think that a release train is something you should aim for, an end goal you should achieve to become ‘Agile’. I don’t agree with that. Although release train can help you get in the right direction, they are a temporary workaround for an organization moving to continuous delivery. THAT is what you should aim for. Release trains are a remedial technique to get out of the mess that the release process in most organizations is today.

The biggest risk is that release trains bring you on the path of distributed monoliths. As you get used to the fact that all your services are deployed together, your architecture will start to depend on it. Now you have all the complexity of a distributed system, but also the downsides of a single unit of deployment as well.

Every part of your architecture should be independently deployable.

Intrigued? Read following article: https://rollout.io/blog/release-train-crashed/

I’ll leave you with one quote:

“If you have a release engineer/manager chances are you have a distributed monolith”

Popular posts from this blog

Azure DevOps/ GitHub emoji

I’m really bad at remembering emoji’s. So here is cheat sheet with all emoji’s that can be used in tools that support the github emoji markdown markup: All credits go to rcaviers who created this list.

Kubernetes–Limit your environmental impact

Reducing the carbon footprint and CO2 emission of our (cloud) workloads, is a responsibility of all of us. If you are running a Kubernetes cluster, have a look at Kube-Green . kube-green is a simple Kubernetes operator that automatically shuts down (some of) your pods when you don't need them. A single pod produces about 11 Kg CO2eq per year( here the calculation). Reason enough to give it a try! Installing kube-green in your cluster The easiest way to install the operator in your cluster is through kubectl. We first need to install a cert-manager: kubectl apply -f https://github.com/cert-manager/cert-manager/releases/download/v1.14.5/cert-manager.yaml Remark: Wait a minute before you continue as it can take some time before the cert-manager is up & running inside your cluster. Now we can install the kube-green operator: kubectl apply -f https://github.com/kube-green/kube-green/releases/latest/download/kube-green.yaml Now in the namespace where we want t...

Podman– Command execution failed with exit code 125

After updating WSL on one of the developer machines, Podman failed to work. When we took a look through Podman Desktop, we noticed that Podman had stopped running and returned the following error message: Error: Command execution failed with exit code 125 Here are the steps we tried to fix the issue: We started by running podman info to get some extra details on what could be wrong: >podman info OS: windows/amd64 provider: wsl version: 5.3.1 Cannot connect to Podman. Please verify your connection to the Linux system using `podman system connection list`, or try `podman machine init` and `podman machine start` to manage a new Linux VM Error: unable to connect to Podman socket: failed to connect: dial tcp 127.0.0.1:2655: connectex: No connection could be made because the target machine actively refused it. That makes sense as the podman VM was not running. Let’s check the VM: >podman machine list NAME         ...