Quick guide to Docker, Kubernetes, and Service Mesh

October 30, 2025

A short guide to packaging, scheduling, and operating services without overcomplicating your platform.

Docker: standardize packaging, not architecture

Containers are a delivery primitive:

• Build reproducible images
• Keep images small and explicit
• Run as non-root where possible

Treat the Dockerfile as part of your supply chain, not a one-off script.

Kubernetes: adopt it for operational leverage

Kubernetes is worth it when you need:

• Multiple services with shared scheduling and scaling needs
• Strong isolation between workloads
• A consistent deployment and networking model

If you’re running one or two services, a managed container platform may be simpler.

The basics that keep clusters stable

Stable clusters come from discipline:

• Requests/limits so scheduling is predictable
• Readiness/liveness probes that reflect reality
• Pod disruption budgets for safe upgrades
• Resource-aware autoscaling

Example (declare requests/limits and a basic readiness probe):

resources:
  requests:
    cpu: "100m"
    memory: "128Mi"
  limits:
    cpu: "500m"
    memory: "512Mi"
readinessProbe:
  httpGet:
    path: /healthz
    port: 8080

Service mesh: earn it

A mesh can help with:

• Mutual TLS between services
• Fine-grained traffic control (canary, retries, timeouts)
• Uniform telemetry

It also adds complexity and failure modes. Adopt it when you have a real need for consistent security and traffic policy across many services.

Keep the platform observable

Prioritize:

• Cluster-level metrics (CPU/memory pressure, eviction rates)
• Workload health (restarts, readiness)
• Traces across ingress and service boundaries

If debugging requires guesswork, you’ll pay for it in incidents.

References

• Dockerfile best practices
• Kubernetes: Probes
• Istio documentation