The machine was the easy part.
Now watch how software reaches it.

An image got built - of what, exactly? Pushed - to where, and what travelled? Applied - which is not the same as launched. Running - according to whom? Every course below takes one of those words apart. The armour is already loose.

A stack of diffs wearing a name

An image is not a copy of a machine. It is a stack of read-only layers, each recording only what changed from the one beneath. The base OS is pulled once and shared by every image on the host; your dependencies sit on it; your code - usually the smallest layer, always the most volatile - sits above that; and the writable layer is created fresh per container and thrown away when it dies. Nothing you write there survives.

Change a layer and every layer above it must be rebuilt.

The order is a caching decision

The builder caches layer by layer, and a cached layer survives only while everything beneath it is unchanged. Put COPY . . above your dependency install and you have told the builder to throw the dependency cache away every time one line of code changes. Dependencies first, code last - a Dockerfile is a cache policy that happens to build software.

Field note. A rebuild that takes twenty minutes and one that takes twenty seconds are usually the same Dockerfile with the lines swapped.

This stack explains the whole ecosystem above it: sharing explains why pulls are fast, immutability explains why a digest can name the exact bytes (Course VII), and the throwaway top layer explains why state needs volumes. One idea - frozen diffs - all the way down.

Pre-reads: none - start here  Further: Docker get-started · Docker build + layers

Desired against actual, forever

The habit underneath everything: the reconciliation loop - compare desired state against actual state, fix the difference, repeat. That kubectl apply didn't launch anything; it filed paperwork. The machine took it from there, and it never stops taking it from there: kill a pod and it returns, not because something noticed the crash but because the loop noticed the difference.

Kubernetes doesn't run your app - it reconciles it.

One half decides, one half runs

The split in the scene is the split that makes everything else possible: a control plane that decides - holds the truth, schedules, reconciles - and worker nodes that run pods. The workers are deliberately interchangeable: identical, replaceable, cattle from day one. Authority does not live where the work happens.

Field note. If you are SSHing into nodes to "fix" things, you are arm-wrestling the reconciler - and it does not get tired. Change the desired state instead.

Everything the delivery arc teaches from Course VI onward is this loop wearing bigger clothes - git as the desired state, whole fleets as reconciled objects. Learn the promise once; it repeats all the way up.

Pre-reads: C-I  Further: Kubernetes overview · cluster architecture

One component starts containers. One.

Every node runs a kubelet - and in the entire system it is the only component that actually starts your container. It speaks CRI to containerd or CRI-O, which pulls the image (through the mirror of Course VII) and launches it. A CNI plugin hands the pod a real IP, and kube-proxy - or an eBPF datapath replacing it - makes Service addresses route to real pods. The kubelet drives, the runtime executes, the CNI wires, the proxy routes.

The kubelet is the only component that actually starts your container.

Field note. Node NotReady? Check kubelet-to-API-server connectivity before anything else - a node that cannot phone home is presumed lost.

The control plane never touches your workload. It writes intent; the kubelet reads it and makes it flesh. Authority and execution meet exactly here, nowhere else.

Pre-reads: C-II  Further: cluster architecture · node components

The jacket, not the container

Everything inside the jacket shares a network namespace: one IP, localhost between friends, volumes mountable by all. initContainers run first, in order, to completion - gatekeepers under everything. Sidecars ride along with their own containers and their own jobs: proxy, logs, reload.

Three probes, three different jobs

startup owns warm-up, readiness gates traffic, liveness restarts the truly hung. Confusing them is how healthy pods get executed - a slow start killed by an impatient liveness probe looks exactly like a crash.

containerPort is documentation - the app still has to bind the port itself.

Field note. Exit 137 with reason Error is the kubelet's probe kill; OOMKilled is memory. Same exit code, different crimes - read the reason.

The pod is the smallest schedulable unit - the jacket, never the container. Once that distinction lands, half of Kubernetes networking stops being mysterious.

Pre-reads: C-III's node scene above  Further: pods · the three probes

The stable name in front of the churn

A Service is the fixed point: a ClusterIP inside, a LoadBalancer at the edge, Ingress or the Gateway API doing host- and path-routing above. Clients hold the name; the pods behind it come and go without anyone being told.

Readiness decides membership

A pod failing its readiness probe silently leaves the pool. No error, no event at the client - traffic simply stops arriving. That is the feature: broken instances remove themselves. It is also the first place to look when traffic "disappears".

Never talk to a pod; talk to a Service.

Field note. "The network is broken" after a deploy is usually readiness telling the truth about your app - not the network lying about your packets.

The dark pod in the scene is not an error state - it is the system working. Membership is earned per probe, continuously, forever.

Pre-reads: C-III's pod scene (readiness lives there)  Further: Services and networking

The doorman interviews every pod

The SCC is admission deciding what a pod may BE. The default, restricted-v2, runs your container as a random non-root UID - your image has to cope. Workloads that genuinely need privilege get a dedicated ServiceAccount bound to a minimal custom SCC, never the stock one.

The platform runs itself

Routes predate Ingress and still rule here. Operators run the platform's own machinery. The OS underneath is immutable - changed by MachineConfig, never by SSH. And SNO puts the entire promise on one box at the edge. At fleet scale the labels from Course VI decide which of these boxes runs what.

On OpenShift, admission is the interview - the SCC is the dress code.

Field note. Deployment stuck at 0/1 with no pod at all? The refusal happened above scheduling - read the ReplicaSet events. The error lives a level up.

Everything in the ring is opinion wrapped around the same core you already know. Learn the core once; the opinions are just house rules.

Pre-reads: C-II  Further: OpenShift documentation · Red Hat OpenShift

The loop you already know, one level up

C-II taught the reconciliation loop: desired versus actual, fix the difference, repeat. ArgoCD is the same habit applied to delivery. An Application names a repo, a path and a revision - watch this branch of this repository - and the controller renders what it finds there, compares it against the live cluster, and syncs the difference. The deploy button is a git commit; the audit log is git log; code review is change control.

kubectl apply is for archaeology, not for change.

Pull, not push - the security inversion

The cluster pulls. No CI system, no laptop, no build pipeline holds a credential that can touch it - the agent inside holds a read-only deploy key, and the trust arrow points out. Compromise the build system and you can propose a change, which is visible; you cannot reach into production. Hand-edit a live object and the controller flags it OutOfSync - selfHeal puts it back, and what leaves git leaves the cluster. Rollback is git revert, which is why commit hygiene is an operational skill.

Field note. Fighting the reconciler over SSH is arm-wrestling a machine that does not get tired - C-II's warning, now with a face. The fix is always upstream, in git.

At fleet scale, the label is the deploy button

Run many OpenShift clusters under a hub - RHACM, with edge clusters arriving through zero-touch provisioning - and nobody applies apps to clusters by hand. Each app carries a Placement that selects cluster labels; the hub matches placements against the labels a cluster wears, and the chosen cluster pulls its assignment. Labelling the cluster is the deploy action: attach the label and the app follows, remove it and the app leaves. The same pull model as above, one level bigger - a cluster's labels are its entitlements, reconciled like everything else.

Label the cluster; the app follows.

The one thing git never holds

Git holds everything except secrets - a secret in git is published, forever, to everyone who ever clones. So the pattern splits the reference from the value: git carries an ExternalSecret naming a logical key; a vault - Azure Key Vault in the worked example - holds the value; an operator inside the cluster exchanges one for the other at runtime. Rotation happens in the vault, never as a commit. That is why the vault floats apart in the scene above: it is never absorbed into the pipeline.

Git holds the shape of the secret. The vault holds the secret.

Delivery stops being an event and becomes a property: the cluster is always converging on what the repository says. "Who deployed this?" becomes "who merged this?" - and that question always has an answer.

Pre-reads: C-II · Kubernetes concepts · git + pull requests  Further: Argo CD · OpenShift GitOps · External Secrets · Azure Key Vault

Say the name properly

Three ways to name an image, in rising order of honesty: :latest (a moving target), :1.4.2 (a promise somebody keeps, until they re-push it), and name:1.4.2@sha256:... - a fact. The tag stays for human eyes; the digest does the pulling. Pin by digest and "what is running?" has exactly one answer.

Field note. :latest is how two nodes run different code from one manifest - the second node pulled an hour later, after a re-push. Nobody changed the YAML.

Why a fleet pulls once

Between the build and the node sits the registry chain. Upstream, a managed registry - Azure Container Registry in the worked example - holds what CI built. In front of the cluster sits a mirror: a pull-through cache like zot. The fleet asks the mirror, the mirror asks upstream once, everything after is local. Rate limits, egress cost, disconnected sites, control - one place to gate and audit what enters. OpenShift formalises the re-route with image mirror rules, and carries a sharp edge: digest-mirror rules rewrite digest pulls only, so a by-tag pull silently skips the mirror. The pinning habit is what makes the mirror actually catch the traffic.

At real fleet scale the mirror itself tiers: a central mirror in the cloud fronts upstream once, and every site's mirror pulls from the centre rather than from upstream directly. A new image ripples outward in layers - upstream to the centre, centre to each site as it asks, site to its nodes over the LAN - instead of every site hammering upstream at the same moment. Upstream sees one consumer; each site sees one hop; the nodes never leave the building.

Mirrors tier: the load fans out in layers, never all at once.

Build once, promote by copy

Every rebuild is a different artefact - different digest, untested by the stages before it. So build once, then promote the same digest through environments by copying, registry to registry - dev proves the exact bytes prod will run. Human tags ride along; the digest is the through-line.

If the digest changed, it is not a promotion - it is a new candidate.

Names that can move are convenient exactly until they move. Address content by what it is, and the supply chain stops being a chain of trust and becomes a chain of arithmetic.

Pre-reads: C-I · Kubernetes images  Further: zot · Azure Container Registry · OpenShift image mirroring · skopeo

It's Go under the braces

Helm templates are Go text/template: {{ .Values.device.address }} is a pipeline walking a values object, _helpers.tpl holds the named functions every manifest calls. You are not writing YAML - you are writing a program whose output is YAML. So render locally, read the output, and lint what came out, not what went in.

Review the render, not just the template.

Field note. ArgoCD deploys charts by running helm template - a pure render. Anything needing a live cluster or an install lifecycle (lookup, hooks) is silently dead there. Render the way your deployer renders.

Contexts: the cluster's whole voice is one small file

The chart owns everything structural - resources, probes, security, policy. Each cluster owns one values file: names, addresses, sizes, flags. The context is deliberately values-only; the moment it carries its own manifests there are two owners for one object, and they will disagree. One value can feed many rendered artefacts - an address appearing in the app config, the network attachment and two policies renders from one field, so the copies cannot diverge - the lived version is on the blog.

The chart owns the shape. The context owns the numbers.

Make the template refuse

A template that renders whatever it is given just moves the failure downstream. The grown-up chart carries a values.schema.json: a context missing a required value fails at render time, in the pipeline, with a message naming the field - not months later as enforcement pointed at nothing.

Field note. The failure you want is the render that refuses. It costs a red pipeline. The alternative reports healthy the whole time.

Fleet consistency is not a discipline problem, it is a construction problem. Divergence stops being something you police and becomes something the tooling cannot express.

Pre-reads: C-II · Kubernetes objects  Further: Helm docs · chart template guide · Go text/template · Helm on OpenShift