Jenkins Setup and Configuration#

Jenkins is a self-hosted automation server. Unlike managed CI services, you own the infrastructure, which means you control everything from plugin versions to executor capacity. This guide covers the three main installation methods and the configuration patterns that make Jenkins manageable at scale.

Installation with Docker#

The fastest way to run Jenkins locally or in a VM:

docker run -d \
  --name jenkins \
  -p 8080:8080 \
  -p 50000:50000 \
  -v jenkins_home:/var/jenkins_home \
  jenkins/jenkins:lts-jdk17

Port 8080 is the web UI. Port 50000 is the JNLP agent port for inbound agent connections. The volume mount is critical – without it, all configuration and build history is lost when the container restarts.

kind Validation Templates#

kind (Kubernetes IN Docker) runs Kubernetes clusters using Docker containers as nodes. It was designed for testing Kubernetes itself, which makes it an excellent tool for validating infrastructure changes. It starts fast, uses fewer resources than minikube, and is disposable by design.

This article provides copy-paste cluster configurations and complete lifecycle scripts for common validation scenarios.

Cluster Configuration Templates#

Basic Single-Node#

The simplest configuration. One container acts as both control plane and worker. Sufficient for validating that deployments, services, ConfigMaps, and Secrets work correctly.

Knative: Serverless on Kubernetes#

Knative brings serverless capabilities to any Kubernetes cluster. Unlike managed serverless platforms, you own the cluster – Knative adds autoscaling to zero, revision-based deployments, and event-driven invocation on top of standard Kubernetes primitives. This gives you the serverless developer experience without vendor lock-in.

Knative has two independent components: Serving (request-driven compute that scales to zero) and Eventing (event routing and delivery). You can install either or both.

kubectl Debugging#

When something breaks in Kubernetes, you need to move through a specific sequence of commands. Here is every debugging command you will reach for, plus a step-by-step workflow for a pod that will not start.

Logs#

kubectl logs <pod-name> -n <namespace>                           # basic
kubectl logs <pod-name> -c <container-name> -n <namespace>       # specific container
kubectl logs <pod-name> --previous -n <namespace>                # previous crash (essential for CrashLoopBackOff)
kubectl logs -f <pod-name> -n <namespace>                        # stream in real-time
kubectl logs --since=5m <pod-name> -n <namespace>                # last 5 minutes
kubectl logs -l app=payments-api -n payments-prod --all-containers  # all pods matching label

The --previous flag is critical for crash-looping pods where the current container has no logs yet. The --all-containers flag captures init containers and sidecars.

Kubernetes API Deprecation Guide#

Kubernetes deprecates and removes API versions on a predictable schedule. When an API version is removed, any manifests or Helm charts using the old version will fail to apply on the upgraded cluster. Workloads already running are not affected – they continue to run – but you cannot create, update, or redeploy them until the manifests are updated. This guide walks through the complete workflow for detecting and fixing deprecated APIs before an upgrade.

Kubernetes API Server: Architecture, Authentication, Authorization, and Debugging#

The API server (kube-apiserver) is the front door to your Kubernetes cluster. Every interaction – kubectl commands, controller reconciliation loops, kubelet status updates, admission webhooks – goes through the API server. It is the only component that reads from and writes to etcd. If the API server is down, the cluster is unmanageable. Everything else (scheduler, controllers, kubelets) can tolerate brief API server outages because they cache state locally, but no mutations happen until the API server is back.

Kubernetes Audit Logging#

Kubernetes audit logging records every request to the API server: who made the request, what they asked for, and what happened. Without audit logging, you have no visibility into who accessed secrets, who changed RBAC roles, or who exec’d into a production pod. It is the foundation of security monitoring in Kubernetes.

Audit Policy#

The audit policy defines which events to record and at what detail level. There are four levels:

Kubernetes Audit Logging: Tracking API Activity for Security and Compliance#

Audit logging records every request to the Kubernetes API server. Every kubectl command, every controller reconciliation, every kubelet heartbeat, every admission webhook call – all of it can be captured with the requester’s identity, the target resource, the timestamp, and optionally the full request and response bodies. Without audit logging, you have no record of who did what in your cluster. With it, you can trace security incidents, satisfy compliance requirements, and debug access control issues.

Kubernetes Controllers: Reconciliation Loops, the Controller Manager, and Custom Controllers#

Kubernetes is a declarative system. You tell it what you want (a Deployment with 3 replicas), and controllers make it happen. Controllers are the engines that continuously reconcile desired state with actual state. Without controllers, your YAML manifests would be inert data in etcd.

The Controller Pattern#

Every controller follows the same loop:

1. Watch the API server for changes to a specific resource type
2. For each change, compare desired state (spec) to actual state (status)
3. Take action to bring actual state closer to desired state
4. Update status to reflect current actual state
5. Repeat

This is a level-triggered model, not edge-triggered. A controller does not just react to changes – it reconciles the entire state on each pass. If a controller crashes and restarts, it re-reads all objects and converges to the correct state without needing to replay missed events. This makes controllers resilient to transient failures.

Kubernetes Cost Audit and Reduction#

Kubernetes clusters accumulate cost waste silently. Resource requests padded “just in case” during initial deployment never get revisited. Load balancers created for debugging stay running. PVCs from deleted applications persist. Over six months, a cluster originally running at $5,000/month can drift to $12,000 with no corresponding increase in actual workload.

This operational plan works through cost reduction systematically, starting with visibility (you cannot cut what you cannot see), moving through quick wins, then tackling the larger structural optimizations that require data collection and careful rollout.