(Part 0) Deploying Kubernetes' Applications: The Problem

(Part 0) Deploying Kubernetes' Applications: The Problem

Over the holiday break, I spent a lot of my leisure coding time rethinking the way we deploy applications to Kubernetes. The blog series this post kicks off will explore how we migrated from an overly simplistic deploy strategy to one giving us the flexibility we need to deploy more complex applications. To ensure a solid foundation, in this first post, we’ll define our requirements for deploying Kubernetes’ applications and evaluate whether our previous systems and strategies met these requirements (spoiler alert… it didn’t).

Requirements for how we deploy Kubernetes’ applications

As we considered the applications (NextCloud, Gitlab, etc.) we’re hoping to deploy to Kubernetes in 2019, we realized they necessitated certain functionality in our strategy for deploying Kubernetes’ applications.

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To start, these applications would require different configuration when deploying to development than when deploying to production. To reflect this reality, we must be able to write a manifest once, and deploy it in multiple environments with minimal effort and duplication. A simple example of this variance is the creation of persistent volumes. In development, we want these to be 1GB max, while in production they will be much larger.

In a similar vein, these applications are suitably complex that we want to be able to define multiple, tunable parameters and share them across all the manifests comprising an application.

Additionally, these applications will make substantial usage of secret values (i.e. password for the Gitlab admin account), and we want interactions with secrets to be secure and simple.

These complex applications also involve several different components (i.e. a web application and a database). Our deployment strategy must support grouping these components together conceptually without unnecessary duplication and sharing variables between them.

Finally, we want the ability to leverage already existing community investments to deploy applications via Kubernetes. Constantly looking to learn from the community, and utilize its solutions, will ensure our investments in Kubernetes are in areas where we can contribute unique value.

And with these definitions, we know the rules to which our deployment strategies and systems must conform.

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Our Previous system and its shortcomings

Before exploring the new system, we must explore the previous system and understand where it fell short.

Our previous system for deploying Kubernetes’ applications was about as simple as it gets: write our Kubernetes manifests as static .yaml files and then deploy them using kubectl apply -f.

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Its pretty much what every “Intro to Kubernetes” tutorial uses for deploying applications, and undoubtedly offers advantages as it encourages direct interaction with Kubernetes, without any potentially confusing abstractions. It is also helpful in that the template file in source control maps directly to what is deployed on Kubernetes.

This deployment system worked fine for a while, as evidenced by the five or so Kubernetes’ applications we deployed using it. However, it definitely does not fulfill the requirements we listed in the preceding section.

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Deploying static files with kubectl apply -f does not support changing variables in the manifest based on the environment in which we’re deploying the application. For example, when running Prometheus in production, we want to create a StorageClass specifying the use of Amazon EBS volumes of the gp2 type. When we were defining said StorageClass via a static manifest file, it was cumbersome to ensure we didn’t deploy it when deploying our application to our dev Minikube cluster.

Additionally, with static files, we had no ability to specify which parts of the manifest were “boilerplate” and which parts of the manifest were tunable parameters. Application version, which is likely to change frequently over the applications lifespan, was in the exact same file as volume mount paths which will likely never be change. Similarly, we had no ability to share variables across the different manifests used to deploy the different application components. For example, for many of our applications, the service-account.yaml manifest would hard code the ServiceAccount’s name field, and then we would also need to hard code that same value in the deployment.yaml manifest when indicating under which service account the pod should run.

Secrets were in a similarly difficult position. We had no way to delineate all secrets for an application in a singular file separated from other manifests, which we could easily exclude from source control.

We also couldn’t cleanly support applications with multiple related components. For example, suppose we have two different web applications, and for each one we want to deploy a web server component and a postgres component. If we’re using only static files, then we’re replicating the postgres manifests in two different directories. We also continue to struggle with our manifests for related components being unable to share variables, which only grows more problematic as our applications have more and more components with more and more complex relationships.

Finally, leveraging 3rd party applications when deploying static manifest files leaves much to be desired. To leverage a community solution for deploying an application, we just copy the static manifests into our own collection of manifest files. However, when we do so, we take on the burden for ensuring these static manifest files stay current and working. To compound the difficulty, we must perform this maintenance without any form of versioning.

Disappointingly, our system of deploying static manifest files via kubectl apply -f fails to meet pretty much all of our listed requirements.

Conclusion

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From our analysis above, its clear that deploying Kubernetes’ applications via static manifest files will not scale to the more complex applications we hope to tackle in the new year. In my next post, we’ll explore the new strategy we’re using for deploying applications and how it meets our stated requirements.

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