Docker Containers and Kubernetes Mastery - Real World Projects

Goal: Build deep, first-principles understanding of containers and Kubernetes by connecting Linux kernel primitives, OCI standards, and Kubernetes control loops to practical systems work. You will stop treating Dockerfiles and YAML as magic and start reasoning from namespaces, cgroups, image manifests, reconciliation, and network datapaths. Across ten real projects, you will design, observe, stress, and harden containerized systems with measurable outcomes. By the end, you will be able to design production-grade container platforms, debug hard incidents, and explain tradeoffs clearly in interviews and architecture reviews.

Introduction

Docker packages and runs software as isolated processes. Kubernetes coordinates those processes at cluster scale through a declarative API and control loops. Together they solve a practical problem: shipping software consistently while keeping operations predictable under change, failure, and growth.

What you will build in this sprint:

A mini container runtime model and image/registry workflow
A scheduler and controller simulation environment
Networking, storage, and policy labs that mirror production incidents
A final GitOps-style platform capstone that combines all concepts

In scope:

Linux container internals, OCI image/runtime/distribution model
Kubernetes control plane, scheduling, reconciliation, networking, stateful operations
Security baselines, policy enforcement, and operational troubleshooting

Out of scope:

Managed cloud vendor specifics as the primary focus
Full service-mesh internals implementation
Writing a full production orchestrator from scratch

Big-picture system view:

Developer -> Source + Dockerfile -> OCI Image -> Registry -> Cluster Pull -> Pod Runtime
    |                                |                |             |
    |                                |                |             +-> kubelet + CRI + runtime
    |                                |                +-> auth, tags, digests
    |                                +-> layers, manifests, signatures
    +-> CI, tests, SBOM, policy gates

Kubernetes Control Plane:
  API Server <-> etcd
      |           |
      v           v
  Scheduler   Controllers ----> Desired state -> Actual state convergence
      |
      v
  Nodes (kubelet, CNI, CSI, runtime)

How to Use This Guide

Read the Theory Primer first; each project assumes the concepts are internalized.
Choose one learning path, but do Project 1 and Project 2 first no matter your path.
Before each project, answer the Core Question and Thinking Exercise on paper.
During implementation, capture observable evidence: logs, events, metrics, and deterministic command transcripts.
After each project, validate against Definition of Done and write a short incident-style postmortem.

Prerequisites & Background Knowledge

Essential Prerequisites (Must Have)

Linux command-line fluency (processes, files, networking basics)
One systems language or scripting language (Go, Rust, Python, or Bash)
Basic distributed systems vocabulary (leader, quorum, eventual vs strong consistency)
Recommended Reading: “The Linux Programming Interface” by Michael Kerrisk - chapters on processes, namespaces, and filesystems

Helpful But Not Required

Prior Docker and Kubernetes usage
Exposure to CI/CD and GitOps tooling
Prior cloud networking experience

Self-Assessment Questions

Can you explain what PID 1 inside a container means and why it matters?
Can you explain the difference between an image digest and a mutable tag?
Can you explain why Kubernetes controllers continuously reconcile instead of executing once?
Can you describe one realistic failure mode in Kubernetes networking?

Development Environment Setup

Required Tools:

Docker Engine or Docker Desktop (recent stable)
kubectl, kustomize, helm
Local cluster tool (kind or k3d)
crictl, jq, rg, stern, k9s (optional but recommended)

Recommended Tools:

prometheus + grafana local stack for observability
trivy or grype for image scanning
cosign for image signing exercises

Testing Your Setup:

$ docker version
Client: Docker Engine - Community
Server: Docker Engine - Community

$ kubectl version --client
Client Version: v1.xx.x

$ kind create cluster --name dkk-lab
Creating cluster "dkk-lab" ...
Cluster "dkk-lab" created

$ kubectl get nodes
NAME                 STATUS   ROLES           AGE   VERSION
dkk-lab-control-plane Ready   control-plane   1m    v1.xx.x

Time Investment

Simple projects: 4-8 hours each
Moderate projects: 10-20 hours each
Complex projects: 20-40 hours each
Total sprint: 3-5 months (part-time)

Important Reality Check

Kubernetes issues often look like app bugs but are control-plane, networking, or policy bugs.
Many failures are timing and state bugs; reproducibility requires disciplined observability.
The value comes from debugging and verification, not from copying manifests.

Big Picture / Mental Model

The entire stack is a sequence of contracts:

Kernel contract:
  namespaces + cgroups + capabilities + seccomp

OCI contract:
  image spec + runtime spec + distribution spec

Kubernetes contract:
  desired state in API -> controllers/scheduler -> converged running state

Operational contract:
  policy + observability + backup/restore + rollout/rollback

Control-loop mental model:

[User intent]
   |
   v
[kubectl apply]
   |
   v
[API object stored in etcd] ---> [event stream]
   |                                 |
   v                                 v
[controllers compare desired vs actual] --(actions)--> [runtime changes]
   ^                                                      |
   |                                                      v
   +------------------------- observe ---------------- [actual state]

Theory Primer

Chapter 1: Container Isolation Primitives and Runtime Execution Path

Fundamentals

Containers are isolated processes, not lightweight virtual machines. The kernel features that make containerization possible are namespaces (visibility isolation), cgroups (resource control), capabilities (privilege partitioning), and Linux security modules such as seccomp/AppArmor/SELinux. Namespaces define what a process can see: process IDs, network interfaces, mount points, and hostnames. Cgroups define what a process can consume: CPU time, memory, and I/O bandwidth. Capabilities split root into fine-grained powers so workloads do not need full host-level privilege. This model matters because Docker and Kubernetes are orchestration and packaging layers on top of these primitives, not replacements for them.

Deep Dive

At runtime, a container launch path is a chain of components. A client issues a command to a container engine; the engine communicates with a daemon, which then calls a lower-level runtime to realize the container process according to an OCI runtime bundle. The bundle describes process args, environment, mounts, and isolation settings. The runtime then asks the kernel to apply namespace boundaries and cgroup constraints before executing the target process.

The first non-obvious rule is that container boundaries are not absolute security boundaries by default. Isolation quality depends on configured privileges, host kernel state, and runtime hardening. A container that runs privileged, mounts host paths, or has dangerous capabilities can often escape intended isolation. This is why production baselines restrict host namespace sharing, disable privilege escalation, and narrow capabilities.

The second rule is lifecycle semantics. The process started as the container entrypoint becomes PID 1 inside that namespace. PID 1 has special signal-handling semantics, and poor signal handling is a frequent cause of stuck rollouts and long termination delays. In orchestrated systems, clean shutdown behavior is operationally critical because rolling updates, autoscaling, and disruption budgets all assume workloads terminate predictably.

The third rule is resource economics. Cgroups enforce quotas and limits, but the effective behavior depends on scheduler pressure and node-level contention. Memory limits can trigger out-of-memory kills when page cache and heap pressure interact. CPU throttling can produce high latency despite low average CPU usage. Practical mastery requires correlating kernel-level signals with application behavior, not just setting arbitrary limits.

Failure modes cluster around visibility assumptions and privilege assumptions. Teams often assume that if a process runs in a container it cannot affect host state; this is false if hostPath mounts or privileged mode are present. Teams also assume limits imply guaranteed resources; this is false in oversubscribed environments where noisy neighbors and scheduling policy dominate tail latency. Production reliability comes from explicitly modeling these constraints and testing under stress.

Operationally, you should treat runtime configuration as code and audit it. Minimal base images, explicit users, read-only filesystems where possible, and defense-in-depth security controls provide measurable risk reduction. Debugging and forensics become easier when runtime policies are deterministic and versioned.

How this fits on projects

Projects 1, 4, 8, and 9 depend directly on runtime isolation behavior.

Definitions & key terms

Namespace: Kernel isolation boundary for visibility scope.
Cgroup: Kernel resource accounting and limit mechanism.
Capability: Fine-grained privilege unit replacing full root powers.
PID 1: Init-like process inside a container namespace.
OCI runtime bundle: Files describing how to run a containerized process.

Mental model diagram

Host Kernel
+--------------------------------------------------------------+
| Namespaces: pid net mnt uts ipc user                         |
| Cgroups: cpu memory io pids                                  |
| LSM/seccomp: syscall and policy filters                      |
+-------------------------^------------------------------------+
                          |
                 OCI runtime applies config
                          |
Container Process Tree    |
PID 1 -> worker -> helper |

How it works

Build runtime config from image metadata plus launch overrides.
Create namespaces and cgroup assignments.
Mount root filesystem and required volumes.
Drop privileges and apply seccomp/profile constraints.
Execute entrypoint as PID 1.

Invariants:

Namespace boundaries define visibility, not absolute trust.
Resource limits are enforced relative to host capacity and policy.

Failure modes:

Stuck termination due to bad PID 1 behavior.
OOM kills under memory pressure despite normal averages.
Privilege misuse through broad capabilities.

Minimal concrete example

Pseudo-run sequence:
container run --image app:v1 --memory 256Mi --cpu 500m
  -> create PID + NET + MNT namespaces
  -> join cgroup path /kubepods/.../container-id
  -> mount overlay rootfs
  -> set no-new-privileges + seccomp profile
  -> exec /app/server

Common misconceptions

“Containers are VMs.” -> They share the host kernel.
“Non-root user means fully safe.” -> Other privileges can still be dangerous.
“Limits equal reservations.” -> Limits and requests serve different scheduling purposes.

Check-your-understanding questions

Why is PID 1 behavior important for rolling updates?
How can a container become risky even if it is non-root?
Why can CPU throttling increase tail latency without high average CPU?

Check-your-understanding answers

PID 1 receives termination signals first and must shut down children cleanly.
Dangerous capabilities, host mounts, and shared namespaces can bypass expectations.
Throttling interrupts work bursts and stretches completion time for latency-sensitive requests.

Real-world applications

Multi-tenant platform hardening
Runtime policy baselines for regulated workloads
Performance incident analysis under node contention

Where you’ll apply it

Project 1, Project 4, Project 8, Project 9

References

Kubernetes container runtimes docs: https://kubernetes.io/docs/setup/production-environment/container-runtimes/
Kubernetes CRI docs: https://kubernetes.io/docs/concepts/containers/cri/
Docker overview: https://docs.docker.com/get-started/docker-overview/

Key insights

Containers are a kernel-level process isolation contract with explicit security and resource tradeoffs.

Summary

Understanding namespaces, cgroups, and privileges is mandatory for reliable Docker/Kubernetes operations.

Homework/Exercises to practice the concept

Compare behavior of two identical workloads with different CPU and memory limits.
Test graceful shutdown behavior with and without proper signal handling.
Document capability requirements for one workload and remove all unnecessary ones.

Solutions to the homework/exercises

Record throttle and OOM events; compare p95 latency and restart counts.
Use termination tests and verify child processes exit before grace period ends.
Start from restricted baseline and add only capabilities proven necessary by workload tests.

Chapter 2: OCI Image Model, Build Graphs, and Registry Distribution

Fundamentals

OCI standards define portable image and runtime formats across tools. The image spec describes manifests, configuration objects, and layers. The distribution spec defines registry APIs for pushing and pulling content by digest. Modern builders such as BuildKit optimize builds through content-addressable caching and dependency graph execution. The practical result is reproducibility and portability: the same digest should represent the same content everywhere.

Deep Dive

An image is a directed set of immutable blobs plus metadata, not a monolithic file. Layers are content-addressed by digest, so registries can deduplicate and clients can verify integrity. Tags are mutable pointers to manifests; digests are immutable identifiers. Production policy should treat digests as deployment truth and tags as discovery convenience.

Build systems convert source context and instructions into a graph of filesystem transformations. BuildKit executes independent stages in parallel when possible, skips unused stages, and can export/import cache from registries. This makes build performance and reproducibility first-class concerns. The optimization target is not only speed but also determinism: identical inputs should produce identical artifact digests.

Registry interactions follow a predictable workflow: authenticate, check existence of blobs, upload missing blobs, upload manifest, and update tag references. Pulls reverse this flow. Failures usually involve auth scope mismatch, digest mismatch, manifest media-type compatibility, or transient network errors. Teams that understand this protocol can debug CI failures quickly instead of retrying blindly.

Supply chain security overlays this model. Signatures and attestations bind trust metadata to digests, while SBOMs expose component composition for vulnerability and license analysis. Security posture improves when policy gates promote only signed and scanned digests to runtime environments.

A common performance trap is cache invalidation at the wrong build step. If an early step is volatile, all downstream layers rebuild. Better layering strategy puts stable dependencies before rapidly changing application files, reducing cache churn. Another trap is pulling large base images for tiny services, inflating cold-start and network costs. Minimal base images and explicit dependency management improve both security and performance.

At scale, registry architecture becomes a platform concern: geo-replication, retention policies, immutable tag policies, and garbage collection all influence reliability and cost. Digest pinning combined with promotion pipelines yields auditable releases and safer rollbacks.

How this fits on projects

Projects 2, 3, 4, and 10 are image/registry heavy.

Definitions & key terms

Manifest: Object describing referenced config and layers.
Digest: Immutable hash-based content identifier.
Tag: Mutable human-friendly reference.
SBOM: Software bill of materials for component transparency.
Attestation: Signed metadata proving build/provenance claims.

Mental model diagram

Source -> Build Graph -> Layers + Config -> Manifest -> Registry
   |          |               |               |          |
   |          +-> cache keys  +-> digests     +-> tag -> pull by digest

How it works

Parse build instructions into dependency graph.
Execute graph nodes, producing layers and metadata.
Hash content to produce digests.
Push missing blobs and manifest to registry.
Deploy by immutable digest.

Invariants:

Same digest implies same content.
Tags can change over time.

Failure modes:

Digest mismatch from corruption or tooling incompatibility.
Cache misses from poor layer ordering.
Mutable tag drift causing unplanned runtime changes.

Minimal concrete example

Pseudo-manifest flow:
build app -> manifest digest sha256:AAA
push manifest sha256:AAA to registry
set tag app:prod -> sha256:AAA
deploy workload image@sha256:AAA (not tag-only)

Common misconceptions

“Tag equals immutable release.” -> Tags are mutable pointers.
“Fast build means good build.” -> Reproducibility and traceability matter equally.
“Registry is just storage.” -> It is a protocol and policy boundary.

Check-your-understanding questions

Why should production deploy by digest instead of tag?
How can BuildKit reduce CI cost in ephemeral runners?
Why are SBOMs useful even when no critical CVEs are present?

Check-your-understanding answers

Digests prevent drift from mutable tag updates.
External cache export/import avoids rebuilding unchanged graph nodes.
They support license/compliance and future vulnerability investigations.

Real-world applications

Reproducible CI/CD pipelines
Secure artifact promotion between environments
Registry cost and latency optimization

Where you’ll apply it

Project 2, Project 3, Project 4, Project 10

References

OCI image-spec release notices: https://opencontainers.org/release-notices/overview/
OCI distribution-spec release notices: https://opencontainers.org/release-notices/v1-1-1-distribution-spec/
Docker BuildKit docs: https://docs.docker.com/build/buildkit/
Docker cache guidance: https://docs.docker.com/build/cache/optimize/

Key insights

Treat image digests as immutable contracts and build graphs as reproducibility engines.

Summary

OCI standards plus disciplined build and promotion patterns eliminate many deployment surprises.

Homework/Exercises to practice the concept

Design a promotion workflow from dev to prod using digest pinning.
Compare build times with and without external cache.
Draft an SBOM and signature verification gate for pre-deploy checks.

Solutions to the homework/exercises

Promote manifest digests across repos or tags without rebuilding.
Measure warm/cold build delta and identify invalidation boundaries.
Block deploy if signature missing or vulnerability threshold exceeded.

Chapter 3: Kubernetes Control Plane, Scheduling, and Reconciliation

Fundamentals

Kubernetes is a distributed control system centered on desired state. Users and automation declare intent in API objects. The control plane stores that intent in etcd, then scheduler and controllers continuously work to make actual state match desired state. The architecture is explicitly event-driven and eventually convergent, not transactionally immediate.

Deep Dive

The API server is the front door for all state mutations. It validates requests, enforces admission policies, and persists objects. etcd is the source-of-truth data store behind the API server. Watches stream object changes to controllers and operators, enabling reactive automation. This design scales by decoupling declaration from execution.

Scheduler behavior is often misunderstood. Scheduling is a two-stage decision process: filtering identifies feasible nodes and scoring ranks candidates. Plugin chains support extensible policies (resource fit, affinity, topology spread, taints/tolerations, storage constraints). Once a node is selected, binding finalizes placement. Backoff and queue behavior influence pending pod latency under pressure.

Controllers are reconciliation loops. Each loop compares desired and observed state for a resource type, then issues minimal actions to reduce drift. Deployments manage ReplicaSets; ReplicaSets manage Pods; StatefulSets manage ordered identity and storage semantics. The power of Kubernetes comes from this layered composition of simple controllers, not from one monolithic scheduler brain.

Operationally, control-plane correctness depends on event ordering assumptions, retry semantics, and idempotent logic. Controllers must tolerate missed events by periodic resync and state listing. They must also tolerate transient errors and partial progress. This is why Kubernetes patterns emphasize level-triggered reconciliation over edge-triggered one-shot automation.

Failure modes include API server overload, etcd latency spikes, and pathological resync storms. Symptoms often appear as slow scheduling, delayed rollouts, and stale status fields. Observability should include API request latency, watch health, queue depth, reconcile duration, and error rate.

Design tradeoffs are explicit: strong consistency for API object storage through etcd quorum, but eventual convergence for higher-level workload state. This balance enables robust distributed operation without requiring global transactional workflows for every object mutation.

How this fits on projects

Projects 5, 6, 7, and 10 rely directly on scheduler and controller mental models.

Definitions & key terms

Reconciliation: Continuous convergence process from desired to actual state.
Admission: Validation/mutation step before object persistence.
Binding: Scheduler action that assigns a pod to a node.
Informer/watch: Event-driven cache+notification mechanism for API changes.
Idempotency: Safe repeated execution with same final result.

Mental model diagram

Intent (YAML) -> API Server -> etcd
                     |
                     +-> Scheduler queue -> filter -> score -> bind
                     |
                     +-> Controllers (Deployment/StatefulSet/custom)
                              |
                              v
                         Runtime changes on nodes

How it works

Persist desired object in API.
Notify scheduler/controller loops via watch streams.
Scheduler binds unscheduled pods.
Controllers create/update/delete lower-level objects.
Status updates report observed state.

Invariants:

etcd-backed API data is authoritative.
Controllers must be idempotent and retry-safe.

Failure modes:

Pending pods from unsatisfied constraints.
Slow reconciliation from API or controller backpressure.
Divergence from non-idempotent custom automation.

Minimal concrete example

Pseudo-control loop:
if desired replicas != observed ready pods:
  create or delete pods
requeue after event or periodic resync

Common misconceptions

“Apply means immediate execution.” -> Apply means desired state update.
“Scheduler alone guarantees reliability.” -> Controllers and probes are equally critical.
“One failed reconcile means broken system.” -> Retries are expected in distributed control loops.

Check-your-understanding questions

Why does Kubernetes prefer reconciliation loops over one-shot actions?
What does scheduler scoring add beyond feasibility filtering?
Why must controller actions be idempotent?

Check-your-understanding answers

Distributed systems need convergence under partial failures and retries.
It optimizes placement quality among many feasible nodes.
Retries and duplicate events are normal; non-idempotent actions cause drift or thrash.

Real-world applications

Capacity-aware scheduling strategies
Safe rollout automation
Building custom operators for domain-specific platforms

Where you’ll apply it

Project 5, Project 6, Project 7, Project 10

References

Kubernetes components: https://kubernetes.io/docs/concepts/overview/components
Scheduler configuration and framework: https://kubernetes.io/docs/reference/scheduling/config/
etcd operations in Kubernetes: https://kubernetes.io/docs/tasks/administer-cluster/configure-upgrade-etcd/

Key insights

Kubernetes reliability emerges from small, idempotent, event-driven control loops.

Summary

Mastering scheduler and controller behavior is the shortest path to cluster debugging confidence.

Homework/Exercises to practice the concept

Trace one Deployment rollout from API update to ready pods.
Simulate node pressure and explain why specific pods stay pending.
Design reconcile pseudocode for a custom backup resource.

Solutions to the homework/exercises

Capture events and correlate generation/observedGeneration transitions.
Inspect scheduling events for resource, affinity, or taint constraints.
Ensure idempotent steps, status checkpoints, and retry-safe external calls.

Chapter 4: Kubernetes Networking and Traffic Delivery

Fundamentals

Kubernetes networking is built on a simple but strict model: each pod gets a cluster-routable IP, pod-to-pod communication should work without NAT in the pod network, and Services provide stable virtual addressing for dynamic pod backends. CNI plugins implement pod network plumbing, while kube-proxy or eBPF datapaths implement service routing.

Deep Dive

Networking in Kubernetes has three layers of responsibility. First, CNI attaches pod interfaces and routes. Second, Service abstractions map stable virtual identities to changing pod endpoints. Third, ingress/gateway layers expose traffic entry points and policy. Understanding where a failure sits among these layers accelerates incident response.

CNI behavior determines pod connectivity fundamentals: interface creation, IP allocation, route setup, and network policy enforcement support. A broken CNI deployment often manifests as pods that are Running but unreachable. Service routing issues, in contrast, usually appear as healthy pod-to-pod communication with broken virtual service endpoints.

Service resolution combines DNS and endpoint programming. Clients usually resolve service names to cluster IPs, then kube-proxy/ipvs/eBPF routes traffic to endpoint pods. Endpoint readiness governs whether pods receive traffic. Misconfigured probes can therefore look like network outages when the actual issue is readiness policy.

Gateway and ingress APIs define north-south traffic policy. Gateway API has matured into a more expressive model for multi-tenant and advanced routing use cases. Teams should treat routing configuration as code, with explicit ownership boundaries and test coverage for edge cases such as TLS mismatch, backend policy conflicts, and canary rules.

Common failure patterns include DNS timeouts, stale endpoints, policy-denied flows, MTU mismatches, and asymmetric routing across nodes. Effective troubleshooting starts at L3/L4 reachability, then progresses to service abstraction and L7 policy. Skipping layers leads to guesswork.

Performance tradeoffs also matter. Overly chatty service meshes, suboptimal connection reuse, and mis-scoped retries can amplify latency and load. Good architecture balances observability and policy richness with datapath simplicity.

How this fits on projects

Projects 8, 10, and parts of 6 and 7 depend on this network model.

Definitions & key terms

CNI: Standard interface for container network plugins.
Service: Stable virtual endpoint for a dynamic set of pods.
EndpointSlice: Scalable representation of service backends.
NetworkPolicy: Allow/deny traffic policy at pod level.
Gateway API: Kubernetes API model for advanced service networking.

Mental model diagram

Client Pod -> DNS -> Service VIP -> datapath (kube-proxy/eBPF) -> Endpoint Pod
    |                                                         |
    +-------------------- CNI routes and policy --------------+

North-South:
Internet -> Gateway/Ingress -> Service -> Pod

How it works

CNI assigns pod IP and node routes.
Service controller tracks matching pods.
EndpointSlices update as pod readiness changes.
Datapath routes service traffic to healthy endpoints.
Gateway/ingress applies entry policies and TLS handling.

Invariants:

Pod-level identity is IP-based in cluster network model.
Service identity is stable while backend membership changes.

Failure modes:

DNS or endpoint staleness causing intermittent failures.
Policy misconfiguration blocking expected flows.
MTU or conntrack pressure creating silent packet drops.

Minimal concrete example

Pseudo-debug sequence:
1) pod A cannot call service B
2) test pod A -> pod B IP directly
3) test DNS resolution for service B
4) inspect EndpointSlices and readiness
5) inspect NetworkPolicy allow rules

Common misconceptions

“Service equals load balancer process.” -> It is a virtual abstraction plus datapath rules.
“Running pod means traffic-ready.” -> Readiness controls endpoint participation.
“NetworkPolicy defaults to deny only for inbound.” -> Behavior depends on selected policy rules.

Check-your-understanding questions

Why can a Running pod still receive zero service traffic?
What distinguishes a CNI issue from a Service issue?
Why does endpoint readiness matter for rollout safety?

Check-your-understanding answers

Failed readiness probe keeps it out of endpoints.
CNI issues break direct pod reachability; service issues can preserve direct pod reachability.
It prevents routing to pods that are alive but not ready for requests.

Real-world applications

Multi-tenant cluster segmentation
Reliable service exposure and traffic shaping
Incident triage for packet loss and service brownouts

Where you’ll apply it

Project 8, Project 10 (and supporting work in Projects 6 and 7)

References

Kubernetes network model: https://kubernetes.io/docs/concepts/services-networking/
CNI project: https://github.com/containernetworking/cni
Gateway API v1.4 update: https://kubernetes.io/blog/2025/11/06/gateway-api-v1-4/

Key insights

Networking reliability comes from layered reasoning: CNI, Service endpoints, then L7 policy.

Summary

Treat networking as composable control planes, not a single black box.

Homework/Exercises to practice the concept

Create a failure matrix for DNS, endpoints, and network policy faults.
Run a controlled network policy deny test and validate expected blast radius.
Compare service latency with different endpoint counts and connection reuse patterns.

Solutions to the homework/exercises

Define symptoms and diagnostics per layer.
Start with audit mode labels, then enforce with explicit namespace scoping.
Track p50/p95 latency and connection metrics before and after adjustments.

Chapter 5: Stateful Workloads, Security Baselines, and GitOps Operations

Fundamentals

Stateless scheduling is only half the story; real systems need durable data, controlled access, and repeatable change management. Kubernetes addresses this with StatefulSets, persistent volumes, policy controls, and automation patterns such as GitOps. Production excellence depends on combining data safety, security defaults, and auditable deployment flow.

Deep Dive

Stateful workloads require stable identity and storage attachment semantics. StatefulSets provide ordered startup/shutdown and stable pod naming, while persistent volumes bind data beyond pod lifetime. This model supports databases, queues, and other stateful systems but introduces operational complexity: upgrades, failover, and backup/restore must be intentionally designed.

Storage reliability is an operational pipeline, not a single feature. You need backup cadence, restore drills, and integrity checks. A backup that has never been restored is an assumption, not a control. In practice, teams should treat restore time objective and data loss objective as testable metrics.

Security posture starts with least privilege and admission control. Pod Security Standards define escalating profiles from privileged to restricted. Namespace-level enforcement plus explicit exceptions provides a practical baseline. Complementary controls include image scanning, signature verification, secret management, and strict RBAC scoping.

Policy-as-code tools encode these rules as versioned constraints. This reduces drift and enables pre-merge validation. Good policies are explicit, explainable, and paired with developer feedback loops. Overly strict policies without migration paths cause platform friction; gradual enforcement with audit/warn stages is usually the better rollout strategy.

GitOps operationalizes desired state management by treating cluster config as source-controlled truth. Automated reconciliation from Git to cluster improves auditability and rollback safety, but only if repository structure, promotion flow, and secret handling are well designed. GitOps is not “just auto-apply”; it is a discipline of declarative operations, drift management, and release governance.

Failure modes in this chapter are typically organizational as much as technical: unclear ownership, untested restores, and policy exemptions with no expiry. Mature teams align platform, security, and application owners around shared controls and explicit service-level objectives.

How this fits on projects

Projects 7, 9, and 10 are centered on these production-grade concerns.

Definitions & key terms

StatefulSet: Workload API for ordered, identity-stable pod sets.
PVC/PV: Persistent volume claim and persistent volume abstraction.
Pod Security Standards: Baseline security profile model.
GitOps: Continuous reconciliation of declarative config from Git.
Drift: Difference between declared and actual running state.

Mental model diagram

Git (desired config) -> reconciler -> cluster state
        |                              |
        +-> policy checks              +-> runtime signals

Stateful workload:
identity + storage + backup + restore + failover drills

How it works

Define workload, storage, and policy manifests declaratively.
Enforce baseline admission and RBAC constraints.
Reconcile cluster from versioned source of truth.
Execute backup and restore drills on schedule.
Track drift and security exceptions over time.

Invariants:

State durability requires independent validation, not assumptions.
Security controls require explicit exception management.

Failure modes:

Data loss from untested restore path.
Policy bypass through broad namespace exemptions.
Config drift from manual cluster changes outside Git flow.

Minimal concrete example

Pseudo-release flow:
PR -> policy and security checks -> merge to main -> GitOps sync -> rollout
if health check fails:
  rollback to previous commit digest

Common misconceptions

“StatefulSet means data is safe by default.” -> Backup and restore strategy is separate.
“Policy engine alone secures cluster.” -> RBAC, runtime hardening, and supply chain controls are also required.
“GitOps removes need for incident response.” -> It improves recovery but does not prevent all failures.

Check-your-understanding questions

Why is restore testing more important than backup creation logs?
How does gradual policy enforcement reduce operational risk?
What problem does GitOps solve that CI pipelines alone do not?

Check-your-understanding answers

It validates end-to-end recoverability under realistic conditions.
It surfaces violations early without immediate production breakage.
It continuously enforces declared state and detects runtime drift.

Real-world applications

Database platform operations
Security compliance and audit readiness
Multi-team production release governance

Where you’ll apply it

Project 7, Project 9, Project 10

References

Pod Security Standards: https://kubernetes.io/docs/concepts/security/pod-security-standards/
Pod Security Admission: https://kubernetes.io/docs/tasks/configure-pod-container/enforce-standards-admission-controller
Kubernetes security checklist: https://kubernetes.io/docs/concepts/security/security-checklist/
NSA/CISA Kubernetes hardening notice: https://www.nsa.gov/Press-Room/News-Highlights/Article/Article/2716980/nsa-cisa-release-kubernetes-hardening-guidance/

Key insights

Production maturity is the intersection of durable state, enforceable policy, and declarative operations.

Summary

Stateful reliability and platform security require recurring drills, clear ownership, and policy discipline.

Homework/Exercises to practice the concept

Define a backup and restore runbook with explicit RTO/RPO targets.
Create a phased Pod Security rollout plan (audit -> warn -> enforce).
Model a GitOps rollback scenario after a failed deployment.

Solutions to the homework/exercises

Include backup cadence, restore command sequence, verification checks, and escalation path.
Start with non-prod namespaces, then production with timed exception expiry.
Revert the Git commit, reconcile, and verify post-rollback service health and data integrity.

Glossary

Container: Isolated process with constrained kernel visibility and resources.
OCI: Open standards for container images, runtime configuration, and distribution APIs.
Digest: Immutable hash identifier for image content.
Control Loop: Continuous comparison of desired and actual state followed by corrective actions.
EndpointSlice: Kubernetes object describing service backend endpoints.
StatefulSet: Kubernetes workload API for identity-stable, ordered, stateful pods.
GitOps: Operational model where Git is source of truth and automated reconcilers apply state.
Drift: Configuration difference between declared and runtime state.
PSS: Pod Security Standards policy profiles.
CRI: Kubernetes interface between kubelet and container runtime.

Why Docker and Kubernetes Matters

Modern motivation:

Teams need faster release cycles without environment-specific surprises.
Platform teams need predictable operations under autoscaling, failures, and audits.
AI/ML and data workloads increasingly require standardized orchestration at scale.

Real-world statistics and impact:

CNCF Annual Cloud Native Survey (January 2026): 82% of container users report Kubernetes in production; 98% report adoption of cloud native techniques; 59% report much or nearly all development/deployment is cloud native.
Stack Overflow Developer Survey 2024: Docker appears in 53.9% of all-respondent “other tools” usage and 58.7% among professional developers; Kubernetes appears at 19.4% overall and 22% among professional developers.
Kubernetes v1.34 (August 27, 2025) shipped 58 enhancements, showing a fast-moving but stable ecosystem.
OCI standards continued active releases through 2025 (image-spec v1.1.1, distribution-spec v1.1.1, runtime-spec v1.3.0), reinforcing interoperability.

Context and evolution (after modern context):

Early container usage focused on developer portability.
The ecosystem then standardized around OCI artifacts and Kubernetes orchestration.
Current maturity emphasizes supply-chain trust, platform engineering, and AI workload operations.

Old vs new operating model:

Traditional VM-Centric Ops                Cloud-Native Container Ops
--------------------------------          --------------------------------
Manual host configuration                 Declarative desired state
Release by mutable server                 Release by immutable artifacts
Snowflake environments                    Reproducible images and policy
Ad-hoc rollback                           Versioned rollout and rollback
Limited workload portability              Runtime + OCI + Kubernetes portability

Concept Summary Table

Concept Cluster	What You Need to Internalize
Container Isolation Primitives	Containers are isolated processes using namespaces, cgroups, and privilege controls; these are the root cause surface for many runtime bugs.
OCI Image and Distribution Model	Images are immutable, content-addressed artifacts moved through standardized registry APIs; digest pinning and cache strategy are operational essentials.
Kubernetes Control Plane and Reconciliation	Kubernetes is event-driven desired-state convergence via scheduler and controllers, not immediate imperative execution.
Networking and Traffic Delivery	Reliable service behavior requires layered reasoning across CNI, Service endpoints, and L7 routing policy.
Stateful + Security + GitOps Operations	Durable systems require tested backup/restore, enforceable policy, and declarative change management with drift control.

Project-to-Concept Map

Project	Concepts Applied
Project 1	Container Isolation Primitives
Project 2	OCI Image and Distribution Model
Project 3	OCI Image and Distribution Model
Project 4	Container Isolation Primitives, OCI Image and Distribution Model
Project 5	Kubernetes Control Plane and Reconciliation
Project 6	Kubernetes Control Plane and Reconciliation
Project 7	Kubernetes Control Plane and Reconciliation, Stateful + Security + GitOps Operations
Project 8	Networking and Traffic Delivery
Project 9	Container Isolation Primitives, Stateful + Security + GitOps Operations
Project 10	All concept clusters

Deep Dive Reading by Concept

Concept	Book and Chapter	Why This Matters
Container Isolation Primitives	“The Linux Programming Interface” by Michael Kerrisk - process and namespace chapters	Grounds container behavior in kernel mechanics.
OCI Image and Distribution Model	“Docker Deep Dive” by Nigel Poulton - image, registry, and build chapters	Builds artifact literacy for reproducible delivery.
Kubernetes Control Plane and Reconciliation	“Programming Kubernetes” by Michael Hausenblas and Stefan Schimanski - controllers/operators chapters	Explains how desired state becomes running systems.
Networking and Traffic Delivery	“Kubernetes in Action” by Marko Luksa - networking and service chapters	Connects service abstractions to real packet paths.
Stateful + Security + GitOps Operations	“Kubernetes Patterns” by Bilgin Ibryam and Roland Huss - stateful, operational, and policy patterns	Provides operational blueprints for production reliability.

Quick Start: Your First 48 Hours

Day 1:

Read Theory Primer Chapters 1-3.
Build and run Project 1 baseline outcome.
Capture one page of notes: “What changed in my mental model?”

Day 2:

Read Theory Primer Chapters 4-5.
Complete Project 2 deterministic outcome transcript.
Verify both projects against Definition of Done checklists.

Recommended Learning Paths

Path 1: Platform Engineer Track

Project 1 -> Project 2 -> Project 5 -> Project 6 -> Project 7 -> Project 10

Path 2: DevOps/SRE Track

Project 2 -> Project 3 -> Project 8 -> Project 9 -> Project 10

Path 3: Security and Reliability Track

Project 1 -> Project 4 -> Project 7 -> Project 9 -> Project 10

Success Metrics

You can explain and diagnose at least 15 common Docker/Kubernetes failure patterns.
You can deploy by digest with policy gates and rollback safely.
You can trace one user request from gateway to pod and back with observable evidence.
You can execute a tested restore workflow for a stateful workload.
You can present a clear architecture tradeoff memo for your capstone platform.

Project Overview Table

#	Project	Core Focus	Difficulty	Time
1	Kernel-Level Container Runtime Lab	Namespaces, cgroups, process lifecycle	Advanced	10-16h
2	OCI Image Builder + Registry Workflow	Manifests, layers, digest workflows	Advanced	10-16h
3	BuildKit Cache and Reproducibility Lab	Build graph optimization, cache strategy	Intermediate	6-10h
4	CRI Runtime Observability Lab	kubelet-runtime boundaries, runtime debugging	Advanced	8-14h
5	Kubernetes Scheduler Simulator	Filter/score decisions, placement policy	Advanced	12-20h
6	Controller Reconciliation Lab	Idempotent control loops, drift correction	Advanced	12-20h
7	StatefulSet Failover + Backup Drills	durability, ordering, restore confidence	Advanced	14-24h
8	CNI + Service Network Troubleshooting	packet path reasoning and policy	Advanced	12-20h
9	Pod Security + Policy-as-Code	admission policy, least privilege	Intermediate-Advanced	8-14h
10	GitOps Platform Capstone	integrated production platform thinking	Expert	20-40h

Project List

The following projects move you from container internals to production-grade Kubernetes platform operations.

Project 1: Kernel-Level Container Runtime Lab

File: DOCKER_CONTAINERS_KUBERNETES_LEARNING_PROJECTS/P01-container-runtime-from-kernel-primitives.md
Main Programming Language: Go
Alternative Programming Languages: Rust, C
Coolness Level: Level 4 - Hardcore Tech Flex
Business Potential: 2. The “Operational Superpower”
Difficulty: Level 4 - Expert
Knowledge Area: Linux internals, process isolation
Software or Tool: namespaces, cgroups v2, runc concepts
Main Book: The Linux Programming Interface

What you will build: A minimal runtime lab that starts isolated processes with explicit namespace and cgroup constraints.

Why it teaches containers: It strips away Docker abstractions and shows the kernel-level reality.

Core challenges you will face:

PID and mount namespace correctness -> Container Isolation Primitives
Resource controls with cgroups v2 -> Container Isolation Primitives
Safe privilege model -> Stateful + Security + GitOps Operations

Real World Outcome

You run a deterministic scenario where a workload is launched with explicit constraints and verify:

isolated process view
constrained memory and CPU
predictable termination behavior

Expected transcript:

$ ./runtime-lab run --memory 256Mi --cpu 500m --cmd /bin/sh
[INFO] Created pid,mnt,uts,net namespaces
[INFO] Attached process to cgroup /lab/runtime/pod-001
[INFO] Applied seccomp baseline profile
[INFO] Process started as PID 1 inside container namespace

$ ./runtime-lab inspect pod-001
Namespaces: pid,mnt,uts,net
Limits: cpu=500m memory=256Mi
Capabilities: NET_BIND_SERVICE only

The Core Question You Are Answering

“What must be true at the kernel boundary for a process to behave like a container?”

This question forces you to reason about primitives, not CLI conveniences.

Concepts You Must Understand First

Linux namespaces
- Which resources are isolated and which are shared?
- Book Reference: The Linux Programming Interface - namespace chapters
cgroups v2
- How do throttling and OOM behavior surface in runtime metrics?
- Book Reference: Linux internals references in TLPI + kernel docs
Capabilities and no-new-privileges
- Which privileges are still dangerous under non-root users?
- Book Reference: Kubernetes security checklist + Linux capabilities docs

Questions to Guide Your Design

Isolation design
- Which namespaces are mandatory for this lab baseline?
- Which host resources must never be mounted inside the runtime sandbox?
Resource policy
- What are reasonable default quotas for deterministic tests?
- How will you observe throttle/OOM events quickly?

Thinking Exercise

Execution Path Trace

Draw the runtime flow from command invocation to exec and list where each security/resource control is applied.

Questions to answer:

Which step is irreversible if misconfigured?
Which failures are visible only after workload pressure increases?

The Interview Questions They Will Ask

“Explain namespaces vs cgroups in one minute.”
“Why is PID 1 behavior critical in containers?”
“How do capabilities differ from running as root?”
“How would you debug frequent OOM kills in production?”
“Why is privileged mode risky even in internal environments?”

Hints in Layers

Hint 1: Starting Point

Treat each isolation feature as a separate checkpoint, not one monolithic step.

Hint 2: Next Level

Validate process view (ps), filesystem view (mount), and resource counters separately.

Hint 3: Technical Details

Use pseudocode checkpoints: create namespaces -> assign cgroup -> apply security profile -> exec.

Hint 4: Tools/Debugging

Use strace, cat /sys/fs/cgroup/..., and structured logs per lifecycle phase.

Books That Will Help

Topic	Book	Chapter
Process isolation	The Linux Programming Interface	process + namespace chapters
Runtime internals	Docker Deep Dive	runtime architecture sections
Security posture	Kubernetes security docs	pod security + hardening sections

Common Pitfalls and Debugging

Problem 1: “Process never exits cleanly”

Why: PID 1 signal handling missing.
Fix: Add explicit shutdown and child reap handling policy.
Quick test: Launch and terminate 10 times; verify zero zombie processes.

Problem 2: “Latency spikes under low average CPU”

Why: CPU throttling bursts.
Fix: Tune requests/limits with workload profile.
Quick test: Compare p95 latency before and after throttling adjustment.

Definition of Done

Namespace and cgroup constraints are verifiably applied.
Termination behavior is deterministic under repeated tests.
Security baseline is documented and enforced.
Failure signals (OOM/throttle) are observable and explained.

Project 2: OCI Image Builder and Registry Workflow

File: DOCKER_CONTAINERS_KUBERNETES_LEARNING_PROJECTS/P02-oci-image-builder-registry-workflow.md
Main Programming Language: Go
Alternative Programming Languages: Python, Rust
Coolness Level: Level 3 - Genuinely Clever
Business Potential: 3. The “Platform Service”
Difficulty: Level 3 - Advanced
Knowledge Area: Artifact packaging and distribution protocols
Software or Tool: OCI image/distribution specs
Main Book: Docker Deep Dive

What you will build: An educational workflow that creates OCI-style artifacts and pushes/pulls them with digest integrity checks.

Why it teaches containers: It exposes the real contract behind docker build and docker push.

Core challenges you will face:

Manifest and layer model comprehension -> OCI Image and Distribution Model
Digest-first deployment discipline -> OCI Image and Distribution Model
Promotion policy and immutability -> Stateful + Security + GitOps Operations

Real World Outcome

$ ./oci-lab build ./demo-app
[INFO] Generated layer digests:
  sha256:1a2b...
  sha256:3c4d...
[INFO] Manifest digest: sha256:9f9e...

$ ./oci-lab push registry.local/demo-app:lab
[INFO] Uploaded 2 new blobs
[INFO] Uploaded manifest sha256:9f9e...
[INFO] Tag demo-app:lab -> sha256:9f9e...

$ ./oci-lab pull registry.local/demo-app@sha256:9f9e...
[INFO] Digest verified

The Core Question You Are Answering

“How do we guarantee that what we built is exactly what we run?”

Concepts You Must Understand First

Content-addressable storage
- Why digests matter for integrity and reproducibility.
- Book Reference: Designing Data-Intensive Applications - data integrity and replication concepts
OCI artifact model
- Manifest, config, and layer relationship.
- Book Reference: Docker Deep Dive - image internals chapters
Registry API behavior
- Why missing blob checks and retries matter.
- Book Reference: OCI distribution spec docs

Questions to Guide Your Design

Artifact identity
- Which workflows should permit tags, and which must require digests?
- How do you prevent accidental mutable-tag deployments?
Promotion flow
- Will you rebuild per environment or promote immutable digest artifacts?
- How do you audit release provenance?

Thinking Exercise

Tag Drift Drill

Model a scenario where app:prod is retagged without deployment review.

Which controls would detect this?
Which controls would prevent it?

The Interview Questions They Will Ask

“What is the difference between tag and digest?”
“Why do registries deduplicate layers?”
“How would you design secure image promotion across environments?”
“What causes digest mismatch errors?”
“How do SBOM and signatures relate to image trust?”

Hints in Layers

Hint 1: Starting Point

Build a tiny artifact and inspect manifest structure first.

Hint 2: Next Level

Compare push behavior when blobs already exist in registry.

Hint 3: Technical Details

Model push as: auth -> upload missing blobs -> upload manifest -> assign tag.

Hint 4: Tools/Debugging

Use registry API inspection and manifest tooling to validate media types and digests.

Books That Will Help

Topic	Book	Chapter
Docker image internals	Docker Deep Dive	image + registry chapters
Data integrity	Designing Data-Intensive Applications	data model and integrity chapters
OCI standards	OCI docs	image/distribution specifications

Common Pitfalls and Debugging

Problem 1: “Deployment uses wrong binary despite successful build”

Why: Tag drift.
Fix: Enforce digest pinning in deploy manifests.
Quick test: Compare deployed digest to build digest in release metadata.

Problem 2: “CI build times keep increasing”

Why: Layer cache invalidation strategy is poor.
Fix: Reorder build graph to isolate volatile steps.
Quick test: Run warm cache builds three times and compare.

Definition of Done

OCI manifest/layer model is documented with real digests.
Push/pull by digest works with verification.
Promotion flow uses immutable references.
Failure paths are tested (auth failure, missing blob, digest mismatch).

Project 3: BuildKit Cache and Reproducibility Lab

File: DOCKER_CONTAINERS_KUBERNETES_LEARNING_PROJECTS/P03-buildkit-multi-stage-cache-lab.md
Main Programming Language: Dockerfile + shell
Alternative Programming Languages: Make, Python, Go
Coolness Level: Level 2 - Practical but High ROI
Business Potential: 4. The “Cost and Velocity Win”
Difficulty: Level 2 - Intermediate
Knowledge Area: CI/CD performance engineering
Software or Tool: BuildKit, buildx, external cache backends
Main Book: Docker Deep Dive

What you will build: A benchmarked CI build pipeline with deterministic cache behavior and measurable speedups.

Why it teaches containers: It connects artifact theory to practical build throughput and cost.

Core challenges you will face:

Cache key stability -> OCI Image and Distribution Model
Multi-stage dependency planning -> OCI Image and Distribution Model
Deterministic builds under ephemeral runners -> Stateful + Security + GitOps Operations

Real World Outcome

$ ./build-lab run --scenario cold
[RESULT] Build duration: 4m12s
[RESULT] Cache hit ratio: 0%

$ ./build-lab run --scenario warm
[RESULT] Build duration: 1m03s
[RESULT] Cache hit ratio: 78%

$ ./build-lab report
[SUMMARY] Mean speedup: 3.9x
[SUMMARY] Reproducibility: digest stable across 5 runs

The Core Question You Are Answering

“How do we make container builds both fast and reproducible in ephemeral CI?”

Concepts You Must Understand First

Build graph dependencies
- Which input changes invalidate which layers?
- Book Reference: Docker build docs and BuildKit overview
External cache backends
- Why internal builder cache is insufficient for ephemeral CI.
- Book Reference: Docker cache backend docs
Deterministic artifact strategy
- How to separate volatile from stable build inputs.
- Book Reference: Docker Deep Dive

Questions to Guide Your Design

Which build stage changes most often, and how can it be isolated?
Which cache export/import strategy best fits your CI runner model?

Thinking Exercise

Cache Invalidation Map

Draw a stage graph and mark which files invalidate each stage. Estimate build-time impact for each invalidation edge.

The Interview Questions They Will Ask

“What is BuildKit and why does it improve builds?”
“How do you debug persistent cache misses?”
“When can cache optimization hurt correctness?”
“Why might digest reproducibility fail across runners?”

Hints in Layers

Hint 1: Starting Point

Measure baseline cold/warm builds before tuning anything.

Hint 2: Next Level

Move dependency installation earlier, source copy later.

Hint 3: Technical Details

Use registry-backed cache with explicit cache-to/cache-from policy.

Hint 4: Tools/Debugging

Inspect BuildKit output for cache hit/miss reasons per step.

Books That Will Help

Topic	Book	Chapter
Build internals	Docker Deep Dive	build and layer chapters
BuildKit behavior	Docker docs	BuildKit and cache sections
CI optimization	SRE/DevOps references	pipeline optimization chapters

Common Pitfalls and Debugging

Problem 1: “Warm build not faster than cold build”

Why: Non-deterministic step invalidates early stage.
Fix: Reorder volatile inputs and stabilize build args.
Quick test: Change one file per stage and measure invalidation scope.

Problem 2: “Digest differs across runners”

Why: Environment-specific timestamps or toolchain drift.
Fix: Pin toolchain and normalize build metadata.
Quick test: Run same commit on two runners and compare digest outputs.

Definition of Done

Cold vs warm build benchmark captured.
External cache configured and verified.
Reproducibility validated across repeated builds.
Optimization decisions documented with measured evidence.

Project 4: CRI Runtime Observability with crictl and Events

File: DOCKER_CONTAINERS_KUBERNETES_LEARNING_PROJECTS/P04-cri-observability-with-crictl-and-events.md
Main Programming Language: Shell + Go (optional)
Alternative Programming Languages: Python, Rust
Coolness Level: Level 3 - Incident-Ready
Business Potential: 2. The “Ops Differentiator”
Difficulty: Level 3 - Advanced
Knowledge Area: Node-level debugging
Software or Tool: kubelet, CRI, crictl, container runtime logs
Main Book: Kubernetes in Action

What you will build: A runtime observability playbook and tooling workflow to diagnose pod lifecycle failures from node to API.

Why it teaches containers/Kubernetes: It reveals the exact kubelet-to-runtime boundary where many production incidents occur.

Core challenges you will face:

Correlating events across layers -> Kubernetes Control Plane and Reconciliation
Runtime introspection at CRI boundary -> Container Isolation Primitives
Separating app failures from platform failures -> OCI Image and Distribution Model

Real World Outcome

$ ./cri-lab diagnose pod/web-7d8f9
[STEP] API status collected
[STEP] Node runtime queried via CRI
[STEP] Pulled event timeline for last 15m
[FINDING] Image pull backoff caused by registry auth scope mismatch
[REMEDIATION] Updated imagePullSecret and redeployed
[VERIFY] Pod transitioned to Ready in 42s

The Core Question You Are Answering

“When a pod fails, where exactly did the failure happen in the control path?”

Concepts You Must Understand First

Pod lifecycle and container states
- Pending vs Running vs CrashLoopBackOff interpretation.
- Book Reference: Kubernetes docs on pod lifecycle
CRI contract
- How kubelet and runtime communicate.
- Book Reference: Kubernetes CRI docs
Image pull and auth flow
- Which failures happen before container start.
- Book Reference: OCI distribution and registry behavior

Questions to Guide Your Design

How will you time-correlate API events and node runtime events?
Which minimum fields must every incident timeline include?

Thinking Exercise

Failure Taxonomy Exercise

Create a table mapping common statuses (ImagePullBackOff, CrashLoopBackOff, CreateContainerError) to likely failure layers and first diagnostic command.

The Interview Questions They Will Ask

“How do you debug CrashLoopBackOff systematically?”
“What does CRI do in Kubernetes?”
“How do you tell image pull problems from runtime start problems?”
“What is the first node-level command you run in a pod lifecycle incident?”

Hints in Layers

Hint 1: Starting Point

Start with event timeline, not assumptions.

Hint 2: Next Level

Build a single timeline that combines API, kubelet, and runtime entries.

Hint 3: Technical Details

Categorize failure as pre-schedule, post-schedule pre-start, or post-start.

Hint 4: Tools/Debugging

Use kubectl describe, crictl ps/images/inspect, and node journal logs.

Books That Will Help

Topic	Book	Chapter
Kubernetes troubleshooting	Kubernetes in Action	operations chapters
Runtime architecture	Programming Kubernetes	node and runtime sections
Incident practice	SRE workbook resources	incident response chapters

Common Pitfalls and Debugging

Problem 1: “Blaming app code for image pull failures”

Why: Failure happens before process start.
Fix: Verify registry auth and manifest availability first.
Quick test: Pull same digest manually with runtime credentials.

Problem 2: “Confusing pending scheduling with runtime failure”

Why: Node assignment never happened.
Fix: Check scheduler events and constraints.
Quick test: Inspect pod events for FailedScheduling markers.

Definition of Done

End-to-end failure taxonomy documented.
Deterministic timeline generation workflow implemented.
At least three failure scenarios diagnosed and resolved.
Post-incident checklist created for recurring use.

Project 5: Kubernetes Scheduler Simulator

File: DOCKER_CONTAINERS_KUBERNETES_LEARNING_PROJECTS/P05-kubernetes-scheduler-simulator.md
Main Programming Language: Go
Alternative Programming Languages: Python, Rust
Coolness Level: Level 4 - System Design Depth
Business Potential: 3. The “Platform Brain”
Difficulty: Level 4 - Expert
Knowledge Area: Scheduling and capacity strategy
Software or Tool: scheduler framework concepts
Main Book: Programming Kubernetes

What you will build: A simulator that models Kubernetes-like filter and score placement decisions for pod scheduling.

Why it teaches Kubernetes: Scheduling policy becomes concrete when you implement and test it under synthetic workloads.

Core challenges you will face:

Filter and score plugin design -> Kubernetes Control Plane and Reconciliation
Fairness vs efficiency tradeoffs -> Kubernetes Control Plane and Reconciliation
Constraint explosion under real workload mixes -> Stateful + Security + GitOps Operations

Real World Outcome

$ ./sched-lab simulate --workload workload-a.json --nodes nodes.json
[INFO] Pending pods: 120
[INFO] Feasible candidates after filter: 38 avg per pod
[INFO] Final placement complete: 112 bound, 8 unschedulable
[REPORT] Top unschedulable reason: insufficient memory (6), taint mismatch (2)

The Core Question You Are Answering

“What policy produces stable, fair, and efficient placement under changing constraints?”

Concepts You Must Understand First

Scheduler filter/score lifecycle
- Book Reference: Kubernetes scheduler framework docs
Resource requests/limits semantics
- Book Reference: Kubernetes in Action scheduling chapters
Affinity, taints, topology spread
- Book Reference: Programming Kubernetes scheduling content

Questions to Guide Your Design

Which scoring weights align with your workload goals?
How will you explain unschedulable decisions in human-readable form?

Thinking Exercise

Policy Tradeoff Matrix

Evaluate bin-packing-heavy vs spread-heavy policies across three workload distributions.

The Interview Questions They Will Ask

“How does Kubernetes scheduler choose a node?”
“What causes pods to remain Pending?”
“How would you customize scheduling for GPU and latency workloads?”
“What tradeoff exists between utilization and resilience?”

Hints in Layers

Hint 1: Starting Point

Implement explainability first (why not scheduled), then optimize scoring.

Hint 2: Next Level

Add configurable policy weights and compare outputs.

Hint 3: Technical Details

Separate hard constraints (filter) from preference weights (score).

Hint 4: Tools/Debugging

Produce per-pod decision traces for post-analysis.

Books That Will Help

Topic	Book	Chapter
Scheduler internals	Programming Kubernetes	scheduling chapters
Cluster operations	Kubernetes in Action	scheduling and resources
Capacity planning	SRE literature	capacity and reliability chapters

Common Pitfalls and Debugging

Problem 1: “Great utilization, poor failure tolerance”

Why: Overly aggressive bin packing.
Fix: Add spread and anti-affinity weighting.
Quick test: Simulate one-node loss and compare disruption.

Problem 2: “Placement looks random”

Why: Missing deterministic tie-break logic.
Fix: Add stable ranking and explicit tie-break rules.
Quick test: Re-run same workload and verify deterministic output.

Definition of Done

Simulator supports filter and score phases with explainable output.
At least three policy profiles benchmarked.
Unschedulable diagnostics are deterministic and human-readable.
Tradeoff report includes utilization and resilience metrics.

Project 6: Controller Reconciliation Lab

File: DOCKER_CONTAINERS_KUBERNETES_LEARNING_PROJECTS/P06-controller-reconciliation-lab.md
Main Programming Language: Go
Alternative Programming Languages: Python, Java
Coolness Level: Level 4 - Platform Craft
Business Potential: 3. The “Automation Engine”
Difficulty: Level 4 - Expert
Knowledge Area: Declarative automation
Software or Tool: Custom resources, informers, reconcile loops
Main Book: Programming Kubernetes

What you will build: A controller lab that reconciles a custom resource into child workloads with idempotent actions and status reporting.

Why it teaches Kubernetes: Reconciliation is the core Kubernetes design pattern.

Core challenges you will face:

Idempotent reconcile design -> Kubernetes Control Plane and Reconciliation
Status and error semantics -> Kubernetes Control Plane and Reconciliation
Safe retries and backoff -> Stateful + Security + GitOps Operations

Real World Outcome

$ ./reconcile-lab apply examples/cache-cluster.yaml
[INFO] Custom resource accepted
[INFO] Reconcile iteration #1 -> created StatefulSet and Service
[INFO] Reconcile iteration #2 -> status ReadyReplicas=3
[INFO] Drift detected after manual edit -> corrected in 9s

The Core Question You Are Answering

“How do we automate desired state safely in the presence of retries, failures, and drift?”

Concepts You Must Understand First

Informer/watch mechanics
- Book Reference: Programming Kubernetes controller chapters
Idempotent API mutation patterns
- Book Reference: Kubernetes patterns/operator design literature
Status conditions and observedGeneration
- Book Reference: Kubernetes API conventions

Questions to Guide Your Design

Which reconcile steps are safe to repeat without side effects?
How do you represent partial progress in status fields?

Thinking Exercise

Retry Safety Walkthrough

For each reconcile step, write the expected behavior if the step runs five times due to transient errors.

The Interview Questions They Will Ask

“What makes a reconcile loop idempotent?”
“How do you avoid hot loops in failing controllers?”
“How do status conditions help operations teams?”
“Why is eventual consistency acceptable here?”

Hints in Layers

Hint 1: Starting Point

Define desired child resources as pure functions of spec.

Hint 2: Next Level

Separate read/compare and mutate phases clearly.

Hint 3: Technical Details

Use generation checks and condition updates for progress visibility.

Hint 4: Tools/Debugging

Track reconcile duration, requeue reasons, and error class counts.

Books That Will Help

Topic	Book	Chapter
Operator/controller design	Programming Kubernetes	operator chapters
Kubernetes patterns	Kubernetes Patterns	controller and operational patterns
API conventions	Kubernetes docs	API conventions references

Common Pitfalls and Debugging

Problem 1: “Controller thrashes API server”

Why: Reconcile loop requeues immediately on non-critical conditions.
Fix: Use bounded backoff and event-driven requeues.
Quick test: Inject transient errors and verify stable request rates.

Problem 2: “Status says Ready but resources are unhealthy”

Why: Status update logic not tied to observed child health.
Fix: Gate Ready on concrete health checks and generation sync.
Quick test: Force child failure and confirm status flips promptly.

Definition of Done

Reconcile logic is idempotent and retry-safe.
Status conditions communicate progress and failure clearly.
Drift correction is demonstrated with deterministic timing.
Metrics/logging support incident diagnosis.

Project 7: StatefulSet Failover and Backup Drills

File: DOCKER_CONTAINERS_KUBERNETES_LEARNING_PROJECTS/P07-statefulset-failover-backup-drills.md
Main Programming Language: YAML + shell automation
Alternative Programming Languages: Go, Python
Coolness Level: Level 3 - Production Realism
Business Potential: 4. The “Reliability Multiplier”
Difficulty: Level 4 - Expert
Knowledge Area: Stateful reliability engineering
Software or Tool: StatefulSets, PVCs, backup/restore tooling
Main Book: Kubernetes Patterns

What you will build: A stateful workload lab with controlled failover, backup schedules, and tested restore procedures.

Why it teaches production Kubernetes: Data safety and recoverability are where real platform maturity is proven.

Core challenges you will face:

Identity and storage lifecycle reasoning -> Stateful + Security + GitOps Operations
Failure-domain aware design -> Kubernetes Control Plane and Reconciliation
Restore confidence and RTO/RPO validation -> Stateful + Security + GitOps Operations

Real World Outcome

$ ./state-lab inject-failure --node worker-1
[INFO] Primary pod rescheduled with persistent volume reattach
[INFO] Service remained available with 1 retry spike

$ ./state-lab backup --job nightly-001
[INFO] Backup completed: snapshot-2026-02-11T02:00Z

$ ./state-lab restore --snapshot snapshot-2026-02-11T02:00Z
[INFO] Restore verified by checksum and application consistency checks
[RESULT] RTO=7m12s RPO<=5m

The Core Question You Are Answering

“Can this workload fail and recover without violating availability and data guarantees?”

Concepts You Must Understand First

StatefulSet semantics
- Book Reference: Kubernetes in Action stateful chapters
Persistent volume lifecycle
- Book Reference: Kubernetes storage docs and patterns
RTO/RPO engineering
- Book Reference: SRE and disaster recovery playbooks

Questions to Guide Your Design

Which failure scenarios are most likely and most expensive?
How will you prove restore integrity beyond process startup?

Thinking Exercise

Disaster Timeline Drill

Model a control-plane healthy but node-failure scenario and map detection, failover, restore, and validation checkpoints.

The Interview Questions They Will Ask

“How do StatefulSets differ from Deployments?”
“What is the difference between backup completion and restore confidence?”
“How do you choose RPO and RTO targets?”
“What is a common anti-pattern in stateful Kubernetes operations?”

Hints in Layers

Hint 1: Starting Point

Define success criteria before running failure drills.

Hint 2: Next Level

Test with realistic write load, not idle workloads.

Hint 3: Technical Details

Verify logical integrity (application checks) in addition to snapshot status.

Hint 4: Tools/Debugging

Use timeline logs and metrics to calculate RTO/RPO accurately.

Books That Will Help

Topic	Book	Chapter
Stateful patterns	Kubernetes Patterns	stateful service chapters
Storage behavior	Kubernetes in Action	storage chapters
Reliability targets	Site Reliability Engineering	availability and DR sections

Common Pitfalls and Debugging

Problem 1: “Restore works in test but fails in incident”

Why: Unvalidated dependencies and order-of-operations mismatch.
Fix: Rehearse full runbook in environment parity.
Quick test: Quarterly full restore game day with timeboxed objectives.

Problem 2: “Data corruption after failover”

Why: Consistency checkpoints missing before backup/restore.
Fix: Add pre/post consistency validation gates.
Quick test: Compare checksums and application-level invariants.

Definition of Done

Failover scenario completed with measured service impact.
Backup and restore runbook executed end-to-end.
RTO/RPO measured and documented.
Integrity checks prove data correctness post-restore.

Project 8: CNI and Service Network Troubleshooting Lab

File: DOCKER_CONTAINERS_KUBERNETES_LEARNING_PROJECTS/P08-cni-service-network-troubleshooting.md
Main Programming Language: Shell + YAML
Alternative Programming Languages: Go, Python
Coolness Level: Level 4 - Incident Hero
Business Potential: 3. The “Platform Reliability Service”
Difficulty: Level 4 - Expert
Knowledge Area: Cluster networking and incident response
Software or Tool: CNI, Service, NetworkPolicy, Gateway API
Main Book: Kubernetes in Action

What you will build: A reproducible networking incident lab with scripted fault injection and layered diagnosis workflows.

Why it teaches Kubernetes networking: You learn to diagnose by layer instead of guessing.

Core challenges you will face:

Layered fault isolation -> Networking and Traffic Delivery
Readiness vs reachability distinction -> Networking and Traffic Delivery
Policy and routing interactions -> Stateful + Security + GitOps Operations

Real World Outcome

$ ./net-lab run-scenario policy-block
[SYMPTOM] checkout-service timeout from frontend
[DIAG] pod-to-pod direct IP works
[DIAG] Service endpoint list healthy
[DIAG] NetworkPolicy denies namespace path
[FIX] Applied allow rule for frontend -> checkout
[VERIFY] p95 latency returned to baseline in 90s

The Core Question You Are Answering

“At which network layer is the failure actually happening, and how can I prove it quickly?”

Concepts You Must Understand First

Kubernetes network model
- Book Reference: Kubernetes services/networking docs
Service and EndpointSlice mechanics
- Book Reference: Kubernetes in Action networking chapters
NetworkPolicy behavior
- Book Reference: Kubernetes security/network policy docs

Questions to Guide Your Design

What is the minimum diagnostic sequence that avoids false conclusions?
Which metrics/logs prove recovery, not just temporary symptom relief?

Thinking Exercise

Fault Tree Construction

Create a fault tree for “service timeout” with branch probabilities and first diagnostic probe per branch.

The Interview Questions They Will Ask

“How do you troubleshoot Kubernetes networking in a structured way?”
“Why can a service fail while pods remain healthy?”
“What role does EndpointSlice play?”
“How do you test NetworkPolicy safely in production-like environments?”

Hints in Layers

Hint 1: Starting Point

Test direct pod IP reachability before service abstraction.

Hint 2: Next Level

Validate endpoint readiness and DNS resolution separately.

Hint 3: Technical Details

Record packet path checkpoints across source pod, node datapath, and destination pod.

Hint 4: Tools/Debugging

Use targeted captures and policy audit outputs with timestamps.

Books That Will Help

Topic	Book	Chapter
Kubernetes networking	Kubernetes in Action	networking chapters
Service architecture	Kubernetes docs	services/network model
Policy hardening	Kubernetes security docs	policy and admission sections

Common Pitfalls and Debugging

Problem 1: “Assuming DNS issue for every timeout”

Why: Endpoint or policy issue often looks identical from app logs.
Fix: Follow layered diagnostics strictly.
Quick test: Direct pod IP and EndpointSlice check before DNS deep dive.

Problem 2: “NetworkPolicy change fixed one path but broke another”

Why: Incomplete namespace/selector modeling.
Fix: Build explicit traffic matrix before policy edits.
Quick test: Run matrix verification against all service dependencies.

Definition of Done

At least three network fault scenarios reproduced and resolved.
Layered diagnostic playbook documented.
Mean time to root cause measured and improved.
Post-fix validation includes latency and error budget checks.

Project 9: Pod Security and Policy-as-Code Enforcement

File: DOCKER_CONTAINERS_KUBERNETES_LEARNING_PROJECTS/P09-policy-as-code-and-pod-security.md
Main Programming Language: YAML policy + shell
Alternative Programming Languages: Rego, CEL, Go
Coolness Level: Level 3 - Security-Forward Platforming
Business Potential: 4. The “Compliance Accelerator”
Difficulty: Level 3 - Advanced
Knowledge Area: Admission control and workload security
Software or Tool: Pod Security Standards, admission policies, image policy gates
Main Book: Kubernetes Patterns

What you will build: A staged policy rollout that enforces baseline and restricted controls with measurable developer impact.

Why it teaches production security: You convert abstract guidance into operational controls and exception workflows.

Core challenges you will face:

Policy correctness vs developer usability -> Stateful + Security + GitOps Operations
Least privilege enforcement -> Container Isolation Primitives
Exception governance -> Stateful + Security + GitOps Operations

Real World Outcome

$ ./policy-lab evaluate --mode audit
[RESULT] 37 workloads evaluated
[RESULT] 9 violations: privileged=true (3), hostPath (2), runAsNonRoot missing (4)

$ ./policy-lab enforce --namespace payments
[INFO] Enforcement active: restricted profile
[VERIFY] compliant workloads deploy, violating workloads blocked with clear reason

The Core Question You Are Answering

“How do we enforce secure defaults without freezing delivery velocity?”

Concepts You Must Understand First

Pod Security Standards profiles
- Book Reference: Kubernetes pod security standards docs
Admission control flow
- Book Reference: Kubernetes admission controller docs
Exception lifecycle management
- Book Reference: security governance practices

Questions to Guide Your Design

Which rules should be enforced immediately, and which should phase in?
What exception metadata is mandatory (owner, expiry, risk rationale)?

Thinking Exercise

Policy Rollout Plan

Design a 30-day rollout from audit to enforce for two namespaces with different workload maturity.

The Interview Questions They Will Ask

“What are Baseline and Restricted Pod Security profiles?”
“How do you roll out policy safely in production?”
“What metrics show whether policy rollout is healthy?”
“How do you prevent exception sprawl?”

Hints in Layers

Hint 1: Starting Point

Start in audit mode and classify violations by risk and fix complexity.

Hint 2: Next Level

Add actionable remediation text to policy failures.

Hint 3: Technical Details

Require exception expiry and approval metadata.

Hint 4: Tools/Debugging

Track violation counts, time-to-fix, and blocked deployment rates.

Books That Will Help

Topic	Book	Chapter
Policy patterns	Kubernetes Patterns	governance and policy chapters
Security standards	Kubernetes docs	pod security and admission sections
Hardening guidance	NSA/CISA guidance	recommendations summary

Common Pitfalls and Debugging

Problem 1: “Policy rollout triggered mass deployment failures”

Why: No audit phase and no exception process.
Fix: Stage rollout and publish migration guides.
Quick test: Pilot namespace before broad enforcement.

Problem 2: “Rules are enforced but risk remains high”

Why: Coverage gaps (RBAC, image trust, secret handling).
Fix: Expand controls beyond pod spec checks.
Quick test: Map controls against threat scenarios and run gap review.

Definition of Done

Policy baseline enforced in at least one production-like namespace.
Exception workflow documented and time-bounded.
Security and delivery impact metrics collected.
Rollout playbook reusable for new namespaces.

Project 10: GitOps Platform Capstone

File: DOCKER_CONTAINERS_KUBERNETES_LEARNING_PROJECTS/P10-gitops-platform-capstone.md
Main Programming Language: YAML + automation scripts
Alternative Programming Languages: Go, Python
Coolness Level: Level 4 - Full-System Mastery
Business Potential: 5. The “Platform Product”
Difficulty: Level 4 - Expert
Knowledge Area: End-to-end platform architecture
Software or Tool: GitOps reconciler, policy gates, observability stack
Main Book: Kubernetes Patterns + Programming Kubernetes

What you will build: A cohesive platform blueprint that integrates image promotion, policy, scheduling, networking, and stateful reliability into one operating model.

Why it teaches mastery: It forces concept integration across the full lifecycle from build to recovery.

Core challenges you will face:

Cross-domain architecture decisions -> All concept clusters
Operational governance and ownership boundaries -> Stateful + Security + GitOps Operations
Reliable rollback and incident response -> Kubernetes Control Plane and Reconciliation

Real World Outcome

$ ./platform-capstone promote release-2026-02-11
[CHECK] Image digest signed and scanned
[CHECK] Policy gates passed
[CHECK] Canary rollout healthy at 10% then 50% then 100%
[CHECK] SLO guardrails respected
[RESULT] Release completed in 18m with full audit trail

$ ./platform-capstone drill --scenario node-failure-and-rollback
[RESULT] Auto-heal completed in 4m
[RESULT] Rollback validated to previous digest in 2m

The Core Question You Are Answering

“Can I run a multi-team platform where change is fast, safe, observable, and recoverable?”

Concepts You Must Understand First

Artifact trust and promotion
- Book Reference: Docker Deep Dive + OCI docs
Reconciliation and rollout mechanics
- Book Reference: Programming Kubernetes
Policy and security baselines
- Book Reference: Kubernetes security docs
Stateful and network reliability
- Book Reference: Kubernetes Patterns

Questions to Guide Your Design

Where are the hard gates and where are advisory gates?
Which metrics decide rollback automatically vs manually?

Thinking Exercise

Platform Operating Model Diagram

Draw ownership boundaries for app teams, platform team, and security team. Mark handoff points and escalation paths.

The Interview Questions They Will Ask

“How would you design a production-grade Kubernetes platform?”
“What are your non-negotiable release gates and why?”
“How do you balance developer velocity and security controls?”
“Describe your rollback strategy for stateful and stateless workloads.”
“What does good platform observability look like?”

Hints in Layers

Hint 1: Starting Point

Begin with a minimal golden path and explicit non-goals.

Hint 2: Next Level

Add staged promotion with measurable quality gates.

Hint 3: Technical Details

Define a policy matrix: build-time, admission-time, runtime controls.

Hint 4: Tools/Debugging

Implement dashboards for rollout health, error budget, and drift detection.

Books That Will Help

Topic	Book	Chapter
Platform design	Kubernetes Patterns	platform and operational patterns
Control loops	Programming Kubernetes	controller/operator chapters
Reliability	Site Reliability Engineering	release and incident chapters

Common Pitfalls and Debugging

Problem 1: “Too many tools, no coherent operating model”

Why: Tool-first design without ownership boundaries.
Fix: Define platform contracts and responsibilities first.
Quick test: New team onboarding exercise completes without ad-hoc tribal knowledge.

Problem 2: “Rollbacks are documented but not trusted”

Why: No recurring rollback drills.
Fix: Schedule and score game days.
Quick test: Timeboxed quarterly rollback exercise with pass/fail criteria.

Definition of Done

End-to-end release flow includes digest trust, policy gates, and observability.
Rollback and failover drills validated with measured outcomes.
Ownership model and runbooks are documented.
Architecture tradeoffs and future roadmap are explicit.

Project Comparison Table

Project	Difficulty	Time	Depth of Understanding	Fun Factor
1. Kernel-Level Container Runtime Lab	Expert	10-16h	Very High	4/5
2. OCI Image Builder and Registry Workflow	Advanced	10-16h	High	4/5
3. BuildKit Cache and Reproducibility Lab	Intermediate	6-10h	Medium-High	3/5
4. CRI Runtime Observability Lab	Advanced	8-14h	High	4/5
5. Scheduler Simulator	Expert	12-20h	Very High	5/5
6. Controller Reconciliation Lab	Expert	12-20h	Very High	5/5
7. StatefulSet Failover and Backup Drills	Expert	14-24h	Very High	4/5
8. CNI and Service Network Troubleshooting Lab	Expert	12-20h	Very High	5/5
9. Pod Security and Policy-as-Code Enforcement	Advanced	8-14h	High	4/5
10. GitOps Platform Capstone	Expert	20-40h	Maximum	5/5

Recommendation

If you are new to this topic: Start with Project 1, then Project 2, then Project 3 to build a strong artifact/runtime base.

If you are a platform engineer: Start with Project 5, Project 6, and then integrate with Project 10.

If you want security and reliability leadership: Prioritize Project 7, Project 8, and Project 9, then complete Project 10.

Final Overall Project: Platform Reliability and Delivery Program

The Goal: Combine Projects 2, 6, 7, 8, 9, and 10 into a production-like platform operating model.

Build immutable image promotion and trust gates.
Implement reconciliation-driven deployment and rollback automation.
Add network fault injection and stateful restore drills with SLO guardrails.

Success Criteria: You can execute one full release and one disaster drill with measured success and clear postmortem evidence.

From Learning to Production: What Is Next

Your Project	Production Equivalent	Gap to Fill
Project 1	Hardened runtime baseline	enterprise policy integration
Project 2	Artifact registry platform	organization-wide provenance and signing
Project 3	CI optimization program	cross-team standardization and governance
Project 4	SRE runtime incident runbook	24/7 alerting and escalation integration
Project 5	Scheduling policy tuning	live workload and cost optimization loops
Project 6	Operator/controller platform	formal API lifecycle and versioning
Project 7	Stateful reliability program	multi-region DR and compliance audits
Project 8	Network reliability engineering	advanced traffic policy and eBPF observability
Project 9	Security policy platform	full threat model and audit evidence automation
Project 10	Internal developer platform	productization and multi-team onboarding

Summary

This learning path covers Docker and Kubernetes through 10 hands-on projects that start from kernel-level container behavior and end with platform-scale operational excellence.

#	Project Name	Main Language	Difficulty	Time Estimate
1	Kernel-Level Container Runtime Lab	Go	Expert	10-16h
2	OCI Image Builder and Registry Workflow	Go	Advanced	10-16h
3	BuildKit Cache and Reproducibility Lab	Dockerfile/Shell	Intermediate	6-10h
4	CRI Runtime Observability Lab	Shell	Advanced	8-14h
5	Kubernetes Scheduler Simulator	Go	Expert	12-20h
6	Controller Reconciliation Lab	Go	Expert	12-20h
7	StatefulSet Failover and Backup Drills	YAML/Shell	Expert	14-24h
8	CNI and Service Network Troubleshooting Lab	Shell	Expert	12-20h
9	Pod Security and Policy-as-Code Enforcement	YAML	Advanced	8-14h
10	GitOps Platform Capstone	YAML/Shell	Expert	20-40h

Expected Outcomes

You can reason about container and Kubernetes behavior from first principles.
You can build and operate digest-trusted, policy-enforced release pipelines.
You can diagnose and recover from real production failure patterns with confidence.

Additional Resources and References

Standards and Specifications

OCI release notices overview: https://opencontainers.org/release-notices/overview/
OCI image-spec v1.1.1 notice: https://opencontainers.org/release-notices/v1-1-1-image-spec/
OCI distribution-spec v1.1.1 notice: https://opencontainers.org/release-notices/v1-1-1-distribution-spec/
OCI runtime-spec v1.3.0 notice: https://opencontainers.org/release-notices/v1-3-0-runtime-spec/

Official Documentation

Docker overview: https://docs.docker.com/get-started/docker-overview/
Docker BuildKit docs: https://docs.docker.com/build/buildkit/
Docker cache optimization: https://docs.docker.com/build/cache/optimize/
Kubernetes components: https://kubernetes.io/docs/concepts/overview/components
Kubernetes CRI: https://kubernetes.io/docs/concepts/containers/cri/
Kubernetes container runtimes: https://kubernetes.io/docs/setup/production-environment/container-runtimes/
Kubernetes network model: https://kubernetes.io/docs/concepts/services-networking/
Kubernetes pod lifecycle: https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/
Kubernetes scheduler configuration: https://kubernetes.io/docs/reference/scheduling/config/
Kubernetes operating etcd: https://kubernetes.io/docs/tasks/administer-cluster/configure-upgrade-etcd/
Kubernetes pod security standards: https://kubernetes.io/docs/concepts/security/pod-security-standards/
Kubernetes security checklist: https://kubernetes.io/docs/concepts/security/security-checklist/

Industry Data and Adoption Context

CNCF Annual Cloud Native Survey announcement (Jan 20, 2026): https://www.cncf.io/announcements/2026/01/20/kubernetes-established-as-the-de-facto-operating-system-for-ai-as-production-use-hits-82-in-2025-cncf-annual-cloud-native-survey/
CNCF Annual Survey 2023: https://www.cncf.io/reports/cncf-annual-survey-2023/
Stack Overflow Developer Survey 2024: https://survey.stackoverflow.co/2024/
Stack Overflow 2024 Technology breakdown: https://survey.stackoverflow.co/2024/technology

Security Guidance

NSA/CISA Kubernetes hardening guidance notice: https://www.nsa.gov/Press-Room/News-Highlights/Article/Article/2716980/nsa-cisa-release-kubernetes-hardening-guidance/