Reading Assignment: Real-World Deployments and Configuration Management. Supplemental reading on declarative infrastructure vs. imperative automation.
Throughout the semester, we have utilized Kubernetes (minikube) running inside virtual machines on your local laptops. While Virtual Machines (VMs) provide immense isolation, they incur a virtualization penalty (the hypervisor overhead).
When designing high-frequency trading platforms, massive machine-learning MapReduce clusters, or ultra-low-latency edge devices, you must deploy directly to Bare Metal.
Deploying to bare metal introduces a severe physical challenge: How do you install an Operating System onto thousands of blank hard drives simultaneously, without manually inserting a USB drive into each rack?
To automate physical hardware, we rely on PXE (Preboot Execution Environment) and its modern successor, iPXE.
When a bare metal server powers on, before it checks its hard drive, its Network Interface Card (NIC) broadcasts a DHCP request asking: “Who am I, and where is my bootloader?”
To manage these PXE broadcasts at scale, we use automated provisioning engines:
Once the physical servers are powered on and running a raw OS (like Ubuntu 22.04), we must automate the installation of our software. We divide this into two categories.
IaC tools are Declarative. You write code declaring what the final state of the datacenter should be, and the tool figures out how to make it happen.
.tf files stating: “I need 3 Bare Metal servers in New York.” Terraform communicates with the cloud provider APIs (like AWS or Equinix Metal) to securely order, provision, and network the hardware. Terraform is excellent for creating the raw compute resources.Once Terraform creates the servers, Configuration Management tools SSH into those servers to imperatively execute commands, install packages, and manage files.
kubeadm init to turn the raw servers into a Kubernetes cluster.With Kubernetes running on our physical hardware, how do we deploy our MapReduce Sandbox? We could manually type kubectl apply -f k8s/, but that is prone to human error and undocumented changes.
Enter GitOps. GitOps mandates that your Git repository (e.g., GitHub) is the single source of truth for your live system.
v1 to v2, ArgoCD detects the change and automatically executes the rollout in the live cluster.kubectl delete, ArgoCD detects the cluster has drifted from the Git definition, and instantly respawns the pod.k8s_histo_deployedThis week, we migrated our sandbox to include a physical deploy/ directory containing the exact scripts used to stand up the architecture:
deploy/terraform/main.tf: The Terraform code to rent physical Equinix Metal servers.deploy/ansible/playbook.yml: The Ansible playbook to install kubelet and initialize the Kubernetes Control Plane.deploy/gitops/argocd-app.yaml: The ArgoCD declarative definition that binds the live cluster to the GitHub repository.We have also upgraded the UI’s SecOps Dashboard to include Infrastructure Deployment Observability. When you deploy the application and open the Web UI, the Python backend will simulate the deployment pipeline sequence, streaming the Terraform, Tinkerbell, Ansible, and ArgoCD rollout events directly to the browser before the cluster accepts MapReduce workloads.
To visualize the complexity of deploying a distributed application onto bare metal, study the following software architecture and sequence diagrams.
This diagram illustrates the hierarchical layers of our deployment, moving from the physical hardware up to the application logic.
flowchart TD
subgraph "Layer 1: Bare Metal (Equinix Metal)"
Node1["Physical Server 1 (c3.small.x86)"]
Node2["Physical Server 2 (c3.small.x86)"]
Node3["Physical Server 3 (c3.small.x86)"]
end
subgraph "Layer 2: OS & Runtime (Ansible Provisioned)"
OS1["Ubuntu 22.04 LTS"]
OS2["Ubuntu 22.04 LTS"]
OS3["Ubuntu 22.04 LTS"]
Containerd["Containerd Engine"]
OS1 & OS2 & OS3 --> Containerd
end
subgraph "Layer 3: Orchestration (Kubernetes / Kubeadm)"
ControlPlane["K8s Control Plane"]
WorkerNodes["K8s Worker Nodes"]
ArgoCD["ArgoCD (GitOps Operator)"]
end
subgraph "Layer 4: Application Workload (k8s_histo_deployed)"
ZK["ZooKeeper Ensemble (StatefulSet)"]
MR["MapReduce Master/Workers (Deployment)"]
UI["Web UI Dashboard (NodePort/LoadBalancer)"]
end
%% Flow Relationships
Node1 & Node2 & Node3 ==> OS1 & OS2 & OS3
Containerd ==> ControlPlane & WorkerNodes
ArgoCD -->|Syncs from GitHub| ControlPlane
ControlPlane ==> ZK & MR
MR <--> ZK
UI --> MR
This sequence diagram shows how the newly implemented Infrastructure Deployment Simulator in app.py streams live rollout telemetry to the SecOps Dashboard via WebSockets.
sequenceDiagram
autonumber
actor Dev as Infrastructure Engineer
participant Git as GitHub Repo
participant UI as SecOps Web UI
participant Backend as Flask SocketIO (app.py)
participant K8s as Kubernetes Cluster
participant IaC as Terraform / Ansible
Dev->>Git: Push Commit (Merge PR)
Note over Dev,Git: GitOps Triggered
UI->>Backend: WebSocket Connect (Socket.IO)
Backend-->>UI: Connected & Listening
activate Backend
Backend->>UI: emit('deployment_log', "Terraform: Provisioning Bare Metal")
Backend->>UI: emit('deployment_log', "iPXE: Booting Ubuntu 22.04")
Backend->>UI: emit('deployment_log', "Ansible: Installing Containerd")
Backend->>UI: emit('deployment_log', "Kubernetes: Cluster Initialized")
Backend->>UI: emit('deployment_log', "ArgoCD: StatefulSet Deployed")
deactivate Backend
Note right of Backend: Deployment simulation complete
K8s->>Backend: ZooKeeper CONNECTED
Backend->>UI: emit('observability', "ZooKeeper Connection: OK")
Dev->>UI: Upload File / Start MapReduce
UI->>Backend: POST /upload (Bearer Token)
Backend->>K8s: Distribute Histograms / Equalization
K8s-->>Backend: Results Stitched
Backend-->>UI: Return Final Image
We have covered the theory of escaping the hypervisor sandbox (Section 1), PXE provisioning (Section 2), IaC vs. Configuration Management (Section 3), GitOps (Section 4), and visualized the topology (Section 6).
Now, let us walk through a highly detailed, step-by-step tutorial on how you, as a Distributed Systems Engineer, will actually design, install, configure, deploy, and observe the k8s_histo_deployed application on physical hardware.
You begin with nothing but an architecture diagram and a corporate credit card. Your first goal is to acquire physical servers.
deploy/terraform/main.tf in your project repository.terraform apply. Terraform authenticates with the Equinix Metal API and reserves three c3.small.x86 physical machines in the NY5 datacenter.Terraform powers on the physical servers. The hardware has blank NVMe drives.
Your 3 servers are now running Ubuntu, but they don’t know what Kubernetes is.
deploy/ansible/playbook.yml. Ansible uses standard SSH keys to connect to all 3 servers simultaneously.swap (a strict requirement for the kubelet).containerd container runtime.kubeadm, kubelet, and kubectl binaries.kubeadm init on Node 1 (forming the Control Plane) and kubeadm join on Nodes 2 and 3. You now have a physical Kubernetes cluster!The physical cluster is running, but it is empty. We need to deploy k8s_histo_deployed and ensure it stays updated.
kubectl apply -f deploy/gitops/argocd-app.yaml. This single command tells the cluster: “Look at the k8s/ directory in our GitHub repository. Make this physical cluster match whatever is in that folder.”zookeeper.yaml and app.yaml, and instructs the Kubernetes API to spin up the StatefulSets, Deployments, and PersistentVolumes across your 3 bare metal servers.With the GitOps pipeline fully automated, your day-to-day job shifts to management and observability.
kubectl port-forward svc/master-service 8080:80 and open your browser to http://localhost:8080.app.py backend. You watch the SecOps & Infrastructure Observability Dashboard light up with blue [IaC / GitOps] events, streaming a simulated replay of Phases 1 through 4.ZooKeeper Connection: OK event.iptables -A OUTPUT -p tcp --dport 2181 -j DROP, you will instantly see WARNING: SUSPENDED telemetry in the observability dashboard. If you change the code to fix the issue and git push, ArgoCD will automatically roll out the new Docker image to the physical servers, bringing the cluster back to health.