Why Building Your Own YAML Generator Might Be a Mistake

As Kubernetes adoption grows, so does the struggle with YAML configuration management. Many teams, frustrated with repetitive YAML tasks, decide to build internal YAML generators. While the intention is good, the reality is often a mix of technical debt and maintenance headaches.

Why YAML Generators Are a Common Trap

Internal YAML generators often aim to address these use cases:

  1. Quickly generating Kubernetes configurations for new services.
  2. Managing configurations for the same service across multiple environments (e.g., testing and production).

What starts as a small utility often becomes an unwieldy, critical tool that’s hard to debug and maintain. This creates friction between application developers and platform engineers.

Scenarios and Better Alternatives

Scenario 1: Just Starting with Kubernetes

If you’re new to Kubernetes or don’t anticipate frequent app launches, don’t over-engineer your setup. Instead:

  • Use tools like k8syaml.com to generate base YAML files.
  • Define separate YAML files for each environment.
  • Manage deployments with simple CI/CD pipelines that target specific namespaces or clusters.

Example Base YAML

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
  labels:
    app: web
spec:
  replicas: 1
  template:
    metadata:
      labels:
        app: web
    spec:
      containers:
        - name: nginx
          image: nginx
          ports:
            - containerPort: 80
          env:
            - name: DATABASE_URL
              value: testdata.example.com

This low-effort approach works for most small setups and avoids the need for unnecessary tooling.

Scenario 2: Managing Multiple Apps and Environments

When YAML maintenance becomes cumbersome, consider automation through existing tools, like yq or Kustomize.

Option 1: yq for YAML Manipulation

yq is a lightweight YAML processor, ideal for CI/CD pipelines.

Example: Updating Environment Variables

NAME=awesomedatabase
yq -i '.spec.template.spec.containers[0].env[0].value = strenv(NAME)' deployment.yaml

Before:

env:
  - name: DATABASE_URL
    value: testdata.example.com

After:

env:
  - name: DATABASE_URL
    value: awesomedatabase

Why yq?

  • Precise targeting of YAML structures.
  • Easy to integrate into CI/CD pipelines.
  • Safer than sed for production-critical files.

Option 2: Kustomize for Structured Management

Kustomize is built into kubectl and allows layering configurations for different environments without direct file manipulation.

Example Directory Structure

base/
  kustomization.yaml
  deployment.yaml
overlays/
  dev/
    kustomization.yaml
    patch.yaml
  prod/
    kustomization.yaml
    patch.yaml

Dev Patch (overlays/dev/patch.yaml):

spec:
  replicas: 2

Apply Configuration:

kubectl apply -k overlays/dev/

Scenario 3: Scaling Complexity with Kubernetes Client Libraries

For organizations deeply invested in Kubernetes, using official client libraries (e.g., Python, Go) offers unparalleled flexibility.

Advantages:

  • Full API control.
  • TDD-friendly workflows.
  • Automation of complex operations (e.g., dynamic namespace creation).

Example: Python Client

from kubernetes import client, config

config.load_kube_config()

v1 = client.CoreV1Api()
namespace = client.V1Namespace(metadata=client.V1ObjectMeta(name="new-namespace"))

v1.create_namespace(namespace)
print("Namespace created!")

Conclusion

Avoid the temptation to create internal Kubernetes YAML generators. Instead:

  1. Start small with tools like k8syaml.com.
  2. Scale intelligently with yq or Kustomize.
  3. Leverage Kubernetes client libraries for advanced scenarios.

Kubernetes YAML management doesn’t have to be a headache. With the right tools, you can balance simplicity, safety, and scalability without reinventing the wheel.