Kubernetes Deployments and Services

Use the concept at the correct boundary

The difficult part of Kubernetes Deployments and Services is deciding where the responsibility belongs and how you will know it works. This lesson controls how a working change survives machines, environments, traffic and failure after it leaves a developer laptop.

Here, that decision supports a specific checkpoint: Package CourseFlow for a second CI platform, deploy it behind a reverse proxy, and write the evidence needed to operate or roll it back. A reviewable result should include a command transcript, CI result, deployment check and rollback note rather than a claim that the feature simply works.

A practical model for kubernetes deployments and services

Deploy a stateless API with health probes and resource boundaries, then decide honestly whether orchestration is warranted. The useful unit of understanding is the boundary: who owns the decision, which input crosses it, what result is visible and how a failure is reported.

• Pods: Explain the concept without framework jargon, then point to it in the working example.
• Deployments: Decide what belongs in code, configuration, data or documentation and explain why.
• Services: Name its input, observable result and most likely failure in this lesson.
• Health Probes: Locate this responsibility in CourseFlow and defend the boundary you chose.

Engineering decisions for Kubernetes Deployments and Services

These are the details that separate a working demonstration from a maintainable production decision.

• Kubernetes solves orchestration problems; it does not make an application stateless, secure or observable automatically.
• Pin immutable image versions, set requests and limits, and keep secrets outside manifests committed to Git.
• Separate liveness from readiness so a temporary dependency problem does not create a restart storm.

What the example proves

Use the sample to answer one question: does the implementation make pods easier to verify or merely harder to see?

apiVersion: apps/v1
kind: Deployment
metadata: { name: courseflow-api }
spec:
  replicas: 2
  selector: { matchLabels: { app: courseflow-api } }
  template:
    metadata: { labels: { app: courseflow-api } }
    spec:
      containers:
        - name: api
          image: registry.example/courseflow-api:1.0.0
          ports: [{ containerPort: 3000 }]
          readinessProbe:
            httpGet: { path: /ready, port: 3000 }
          resources:
            requests: { cpu: 100m, memory: 128Mi }
            limits: { memory: 256Mi }

Implement and verify one behavior

1. 1
   Pods

   Add this responsibility at the narrowest sensible boundary; do not pull an unrelated layer into the change.

2. 2
   Deployments

   Run the focused example and save the output, trace, query or screenshot that confirms the result.

3. 3
   Services

   Break one assumption on purpose, make recovery clear and record the trade-off you accepted.

4. 4
   Health Probes

   Name the caller and the owner of this behavior before changing the implementation.

Check your reasoning

What to carry into the next lesson

• Deploy a stateless API with health probes and resource boundaries, then decide honestly whether orchestration is warranted.
• Keep pods visible at the boundary where it can be tested.
• Use evidence from deployments before widening the implementation.

References and related reading

• Kubernetes documentation