Evolving Infrastructure

Feb 4, 2019 · 847 words · 4 minute read

We’ve undergone a lot of infrastructure changes over the last few years at Monax. We actually submitted the very first DLT framework into Helm’s stable charts over a year ago. This allows anyone with a Kubernetes cluster to deploy a custom blockchain courtesy of Burrow (our contribution to the Hyperledger Greenhouse ). We’re a great believer in cloud first and open source technologies so not only is Kubernetes a great fit for what we do, but Helm extraordinarily simplifies the whole deployment process through Go templating.


Last year, as we began extending the size of our stack we realized a problem. Many of our charts had explicit dependencies on one another which forced us to overload our CI/CD systems with synchronous bash scripts. With each new addition, our deployment setup was starting to become slower and increasingly unmaintainable. Enter Terraform.

Terraform enables you to safely and predictably create, change, and improve infrastructure.

Terraform is a fantastic toolkit with the right amount of configurability to state management. It enables the definition of a global blueprint which can be managed by separate workspaces and modularized resources. When we first started experimenting with new configurations we discovered an official Terraform Helm provider which enabled us to reuse our existing charts and inject operational specific knowledge into each chart at deploy time. We could then quickly spin up new stacks in GKE with implicit dependencies between individual resources and closer reliance on upstream changes — all through better code reuse. This new deployment setup worked great at first but we soon discovered a problem.

King of State

Helm uses a thing called Tiller to manage what is deployed in your cluster.

Tiller is the in-cluster component of Helm. It interacts directly with the Kubernetes API server to install, upgrade, query, and remove Kubernetes resources. It also stores the objects that represent releases.

Note the final point in that description. Every time we communicate a change to Tiller, it consults its release store to form an opinion of the difference between the current state and the desired state. It will then instruct the Kubernetes API server to alter, delete or create new objects. With Terraform’s own state management, this meant storing two distinctly separate views of each deployment. Unfortunately, the Helm provider never actually compares its view of the stack to Tiller directly which means that it is never called until a drift has been detected in Terraform’s current configuration versus its previous state. This is great in a number of less stateful settings, but actually introduced more problems in this case than it solved.

Additionally, in its typical stance on lifecycle management, we also found that Terraform would non-deterministically delete resources before re-installation, instead of upgrading. This is less than ideal, especially when we’ve taken the effort to define rolling upgrade strategies in our Helm templates to ensure minimal downtime. It’s possible to plan these executions in advance but this requires significant overhead from operators and is simply not doable in automated CD setups.

New Direction

We essentially required a simple pipelining tool for Helm where we could define an agnostic but configurable buildflow for multiple environments. The lightbulb moment came a few weeks ago after playing around with a tool called bashful which provides a way to stitch together shell commands by way of a readable YAML specification:

    - name: Testing app
      cmd: go test .
      tags: test

    - name: Packaging app
      cmd: docker build -t my-build:latest .
      tags: build

    - name: Publishing image
      cmd: docker push my-build:latest
      tags: deploy

Much like Terraform, we could define separable tasks and resources with room for parallelization. However this alone would have reintroduced significant overhead for multi-environment installations, especially as we run around ten broadly similar stacks in our cloud infrastructure. A typical distinction for many could be staging vs production, where each setup may track different release streams.

Compass allows a Helm based infrastructure deployment to be modularized by different charts with an extra layer of templating on top. Each set of templated values are initially rendered based on a selection of environment specific variables then used in the typical sense by Helm to define application specific logic which form the Kubernetes release objects. Go routines enable concurrent deployments based on listed dependencies, optional charts can be triggered based on the environment specific inputs and each definition allows pre and post-deployment shell jobs. Using Go templates also enables us to do other interesting things. When a CI pipeline is triggered for a specific git branch tag, namely develop or master, it’s far easier for the CD to identify docker images based on the branch tag. Unfortunately, upstream tagged images can easily go stale in many docker based environments due to caching. This can lead to many painful hours debugging live containers, only to discover that the image is outdated. With a custom go templating function however, we can strongly version each tag based on the latest digest as given by the targeted API.

So, if you’re devOpsing for Helm or Kubernetes and have similarly complex deployment requirements, please check out Compass!