GitOps Observability — Visualizing the journey of a container

In this blog, I’m going to talk about how I brought a simple web application to run on Kubernetes utilizing a GitOps architecture, and how it helped me understand the process. We’ll also talk about how this led our team to understand the need for visualizing the journey of a container — its creation, utilization in a K8s resource configuration, and approval to be deployed to a cluster, and so on.

Project Bedrock

The last few years have seen an incredible rise in popularity for Kubernetes, containers and Infrastructure as code. Kubernetes is created to help with automation, deployment and agile methodologies for building software applications.

My team at Microsoft started collecting our Kubernetes deployment experiences in a project called Bedrock. The effort provides a starting point for automating infrastructure management and establishing GitOps pipelines. Being a developer new to Kubernetes and GitOps, I had a huge learning curve — I first began by trying to understand how a simple web application would run on Kubernetes, without using GitOps. This involves creating the Kubernetes resource configuration from ground up, understand YAML configurations, helm charts and all of that.


One of the big shifts with the introduction of Kubernetes has been the move towards expressing infrastructure as YAML using a declarative model. The declarative-ness of Kubernetes makes it an excellent candidate for introducing the concept of GitOps.

GitOps is a method that helps with application delivery by using Git as a single source of truth and declarative infrastructure, such as Kubernetes. The idea behind GitOps is that engineers are already well versed in Git and can define the state of their application and infrastructure in a git repository, and the application running on Kubernetes is synced to a repository with the help of a tool such as Flux, which enables enables continuous delivery of container images.

GitOps has become increasingly popular with teams running large scale Kubernetes deployments due to its declarative nature, which works perfectly with Git. A git repository holds the Kubernetes resource manifests necessary for the cluster, and Flux constantly polls the repository to apply new changes. This method allows for easy disaster recovery with the help of reverting commits, and the audit trail it creates comes with it. Everything that comes out of the box with a git repository, such as security, history, branching is an added benefit to the GitOps approach.

This figure explains a simple GitOps scenario — a developer makes a code change to their source repo. The continuous integration pipeline runs to build a container for the application and publishes the new version in their docker registry. A cluster is deployed for this application that has flux installed on it, which is able to pull the latest docker image and apply the change to the cluster. The developer can now navigate to the URL and see their changes in action.

Trivia app

I have a very simple Trivia web application that I would like to host on Kubernetes using the GitOps pattern. It contains a simple React frontend and a Nodejs backend that queries existing APIs on the internet for fresh trivia questions.

Even in a simple application such as this one, there’s two microservices since we have a separate dockerfile for the frontend and backend. This would mean that I need to setup CI pipelines for these individually, and one common CD pipeline should be sufficient to deploy the changes to the cluster.

I’m following the 5 minutes GitOps pipeline with Bedrock guide which creates a skeleton for all my pipelines and a starter HLD. My current CI setup looks like this:

High Level Definition (HLD)

Kubernetes manifest files define the final configuration of the deployment on the cluster. Being extremely error prone, helm charts are a great tool for templating Kubernetes resource definitions. In any application, there are multiple microservices (n) and that leads to (n) helm chart configurations. Bedrock uses the concept of a High Level Definition (HLD) that allows you to define the components of your application at a higher level.

For example, in my Trivia application I need to include the frontend and backend microservices. But at an even higher level, I want to include Traefik in the cluster. I’m looking at having my high level definition at the root folder look something like below:

name: default-component
type: component
- name: hello-world-full-stack
type: component
method: local
path: ./hello-world-full-stack
- name: traefik2
method: git
path: definitions/traefik2

And the definition at the nested level to be:

name: hello-world-full-stack
type: component
- name: backend
type: component
method: local
path: ./backend
- name: frontend
type: component
method: local
path: ./frontend

Who doesn’t love recursion!

Fabrikate is the tool behind HLD that simplifies the GitOps workflow: it takes this high level description of the deployment, a target environment configuration (eg. QA or PROD) and renders the Kubernetes resource manifests for that deployment by utilizing templating tools such as Helm. As you may have already guessed, it runs as part of the CI/CD pipeline that connects the HLD repository to the final manifest configuration repository!

Deployment Rings

I want to setup two environments, let’s say dev and prod for my application. But I want to run them on the same cluster to cut cost! I also want to be able to test my new features for the Trivia app in their own separate environments before I merge into dev or prod. Several customers have expressed interest in being able to test their features within an existing cluster before they make merge it into their next release.

This scenario calls for the concept of Deployment Rings — an encapsulation of a DevOps strategy to group your users into cohorts based on the features of your application you wish to expose to them, similar to A/B or canary testing but in a more formalized manner. Rings allow multiple environments to live in a single cluster with the help of a service mesh, by setting a header on their ingress routes. Each developer in the team would be able to test their features in their own rings before merging it into the formal production branch.

In the figure above, a single cluster has multiple deployment rings, and each of these rings is running off a branch in the source repository. For example, the production ring is deployed against the master branch and the dev ring is deployed against the develop branch. When a developer is testing their features before merging into develop, they're able to deploy a ring into the cluster, let's say feature_ring_1 and get feedback from their team before merging it into develop. Once the feature gets into develop, they can go through the testing process, and make it to staging, and then finally into master for production.

Connecting all the pieces together

Using Bedrock CLI, I created a High Level Definition for my Trivia app and hooked up the CI/CD pipelines for the repositories together.

In this figure, there are several components.
Three repositories:

  • Source code: This is where the source code for the microservices lives, currently all in the same mono repo here
  • HLD repo: This is the high level definition repository, located here
  • Manifest repo: This is where the final Kubernetes manifests are stored and the cluster is synced to. Located here

Two pipelines:

  • CI Pipeline: This pipeline builds the docker image using the Dockerfile for the microservice(s) and writes the updated image tag to the HLD repository
  • Manifest generation: This pipeline uses Fabrikate and helm to generate the manifests for Kubernetes configuration, and pushes them to the manifest repository.

n Image registries:

  • api: The images for backend Nodejs app
  • client: The images for frontend Reactjs app

There are too many elements involved this GitOps configuration. In any real world application, it’s fair to conclude that we’re introducing a lot of complexity into the Ops pattern by going with this solution. Anyone looking at this can ask a lot of high level questions, such as:

  • How do we get a high level visual of what is happening in the state of things?
  • How do I know which developer I should contact when something breaks in production?
  • What version of microservice x is running in production?
  • Did the cluster sync successfully to the latest deployment using flux?
  • What branch runs against the feature ring and who is the author?

Spektate — A Customizable GitOps Observability tool

Spektate is a React based visualization tool that “observes” your entire GitOps process at a high level (and hence the name Spektate). GitOps Observability is currently a loosely defined term that aims to observe the GitOps process, it could include any of the following questions.

As we were building Spektate, we drew the line between the before and after — what happens before the desired state is updated and what happens after are two separate concerns. There are several tools for monitoring the “after” piece — Kubernetes dashboard, Prometheus, cAdvisor etc. The “after” piece needs direct access to the cluster(s). We wanted to build a tool that provides a full high level view of the “before” up until the point where Flux has synced with the cluster, which is a green signal for the developer/operator to know that their change is applied.

A quick glance at Spektate tells me that a recent change is being deployed into the dev ring, the dev cluster is currently synced to an older deployment, there are currently three rings (dev, master and securitybugfix) in the cluster running simultaneously, and I can click on any of the links to pipelines/code changes to get more details. If a deployment is pending approval, I can also navigate to that and hit approve.

Capturing Data

Spektate uses a storage table to capture the details of every deployment attempt in the GitOps process. When the first code change is made to the source code, it’s associated with a docker image, which creates a change in the HLD and eventually makes its way to the manifest when approved. All these details are captured in a storage table using bash scripts inserted into the CI/CD pipelines by bedrock-cli. For this Trivia app, when I used bedrock-cli to configure the pipelines, it created the bash scripts automatically along with a storage container in my Azure subscription.


Spektate can be easily extended to work with any other storage tables, although currently we are supporting Azure storage table. If you would like to use it with another CI/CD orchestrator other than Azure DevOps, we’ve support for GitHub Actions and Gitlab coming soon, and we can apply the same idea to any orchestrator as well.


Spektate can be deployed in a Kubernetes cluster by using the helm chart for Spektate.


Spektate does not access your cluster directly, it only needs access to your pipelines and repositories to gather information. The keys are stored securely using Kubernetes secrets.

What next?

We’re working to add support for Github Actions and Gitlab into Spektate. We’re also trying to make Spektate less dependent on external APIs by capturing all necessary data into the storage table. This will enable us to run Spektate on any ecosystem. We’re also looking to make Spektate columns and table more customizable.

The work in this area is ongoing and there’s a long journey ahead — feel free to reach out to us with feedback on Github or email.


I would like to thank the entire Bedrock team: Andre Briggs, Bhargav Nookala, Dennis Seah, Edaena Salinas Jasso, Evan Louie, Jhansi Reddy, James Spring, Michael Tarng, Nathaniel Rose, Sarath Pinninty, Tim Park and Yvonne Radsmikham. Special thanks to Andre Briggs for partnering on this blog post and our Kubecon presentation.

Related Links

Our presentation at Kubecon 2020 is available on YouTube.
Why GitOps?
5 Minute GitOps Pipeline with bedrock
Automated Kubernetes deployments with Bedrock
Deployment rings
Trivia sample repositories:
Source code
A sample Spektate dashboard

Software Engineer at Microsoft

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