2025-12-04 08:48:28
I'm starting my Kubernetes journey and wanted to document and blog about what I'm learning, to help myself get more acquainted with the content while I learn. I'm currently studying with Kodekloud's CKA course so I will be documenting my notes from there.
I don't have any clear idea of how I'll go about it - my main goal is to try to explain what I've learnt to help myself better understand and retain the information in the process of recapping what I've studied.
First day of this, I have notes from the first video on the core concepts in Kubernetes.
The video goes over an analogy of a ship operation, carrying cargo containers, to illustrate cluster architecture. The main idea is that there is a master/control plane node managing the cluster, while there are a number of worker nodes carrying the containers.
Here are some of the notes I have on this analogy, relating the different components like the controllers, etcd, the scheduler, the kube API server, and the kubelet, to the cargo ship operations analogy.
This is a really great way to get an overview of the main components in a kubernetes cluster, however I think I have a better idea of this after having already studied a couple things about kubernetes already - otherwise they can seem pretty abstract. So the more I go into the course and study more of the components individually, I'm sure these will all come together and become more and more familiar!
If you're also studying Kubernetes reach out to see if we'd have a good accountability buddy system!
- Catt
2025-12-04 08:46:12
So you've seen how database-driven automation works, maybe even tried the Killercoda demo, and now you're thinking: "cool, but I already have a hundred tenants running. I can't just blow everything away and start over."
Yeah. That's a real problem.
This post is about how to migrate existing resources managed by Terraform, Helm, Kustomize, or whatever else you're using, over to Lynq without deleting anything. The goal is zero downtime, no data loss, and a safe rollback path if things go sideways.
I'm assuming you've already:
If you haven't done that yet, check out the quickstart first. This guide is specifically about taking over existing resources, not creating new ones.
Here's the high-level approach:
Let's walk through each step.
This is crucial. Your LynqForm templates need to produce the exact same resource names that already exist in the cluster.
Say your existing deployment is named acme-corp-app in namespace acme-corp. Your template needs to render to exactly that:
deployments:
- id: app
nameTemplate: "{{ .uid }}-app"
namespaceTemplate: "{{ .uid }}"
spec:
apiVersion: apps/v1
kind: Deployment
# ...
If your database has uid = "acme-corp", this renders to acme-corp-app in namespace acme-corp. Perfect match.
Double check your naming conventions. If Terraform was using underscores and Lynq templates use dashes, you'll create duplicate resources instead of taking over the existing ones.
For migration, start with the most conservative settings:
deployments:
- id: app
nameTemplate: "{{ .uid }}-app"
conflictPolicy: Stuck # don't force takeover yet
deletionPolicy: Retain # never auto-delete, even if something goes wrong
creationPolicy: WhenNeeded
spec:
# ...
Why these settings?
conflictPolicy: Stuck is your early warning system. When Lynq tries to apply a resource that's already owned by something else (like Terraform or Helm), it will stop and emit an event instead of forcing through. This lets you verify that Lynq is actually targeting the right resources.
deletionPolicy: Retain is your safety net. Even if you accidentally delete a LynqNode or mess up the hub config, the actual kubernetes resources stay in the cluster. You can always recover.
Apply this to every resource in your template. Yes, all of them.
Don't migrate everything at once. Pick one tenant/node and try it first.
Insert or activate the row in your database:
UPDATE nodes SET is_active = true WHERE node_id = 'acme-corp';
Now watch what happens:
kubectl get lynqnodes -w
You should see a LynqNode created. Check its events:
kubectl describe lynqnode acme-corp-web-app
If your existing resources match the template output, you'll see ResourceConflict events. This is actually what we want at this stage. It confirms Lynq is finding and targeting the right resources.
The event message tells you exactly what's conflicting:
Resource conflict detected for acme-corp/acme-corp-app (Kind: Deployment, Policy: Stuck).
Another controller or user may be managing this resource. Consider using ConflictPolicy=Force
to take ownership or resolve the conflict manually.
Error: Apply failed with 1 conflict: conflict with "helm" using apps/v1: .spec.replicas
This tells you:
acme-corp/acme-corp-app
helm
.spec.replicas
Sometimes the conflict message reveals that your LynqForm doesn't quite match the existing resource. Maybe your template sets replicas: 2 but the existing deployment has replicas: 5 because of HPA.
You have a few options:
Option A: Update your template to match
If the difference is intentional (like HPA managing replicas), don't set that field in your template, or use ignoreFields to skip it during reconciliation.
Option B: Accept the difference
If you want Lynq to enforce a new value, that's fine. Just be aware the resource will change when you force takeover.
Option C: Update the database
If the value should come from your database, add it to extraValueMappings and use it in the template.
The key is understanding what will change before you flip the switch.
Now comes the actual migration. You need to tell your old tool to stop managing these resources without deleting them.
For Terraform:
# Remove from state without destroying
terraform state rm kubernetes_deployment.acme_corp_app
terraform state rm kubernetes_service.acme_corp_svc
# repeat for all resources
For Helm:
# Uninstall release but keep resources
helm uninstall acme-corp-release --keep-history
# Or if you want to be extra safe, just delete the release secret
kubectl delete secret -l owner=helm,name=acme-corp-release
For Kustomize/kubectl:
If you were just applying manifests directly, there's no state to remove. The resources exist, they're just not tracked by anything. Lynq can take over directly.
For ArgoCD/Flux:
Remove the Application or Kustomization CR, or exclude those resources from sync. The actual resources stay in cluster.
After this step, the resources exist in kubernetes but nothing is actively managing them. They're orphaned, which is exactly what we want temporarily.
Now update your LynqForm to force takeover:
deployments:
- id: app
nameTemplate: "{{ .uid }}-app"
conflictPolicy: Force # changed from Stuck
deletionPolicy: Retain # keep this for now
spec:
# ...
Apply the updated LynqForm:
kubectl apply -f lynqform.yaml
The next reconciliation will use Server-Side Apply with force=true to take ownership. Check the LynqNode status:
kubectl get lynqnode acme-corp-web-app -o yaml
You should see:
status:
desiredResources: 3
readyResources: 3
failedResources: 0
appliedResources:
- "Deployment/acme-corp/acme-corp-app@app"
- "Service/acme-corp/acme-corp-svc@svc"
No more conflicts. Lynq now owns these resources.
Verify by checking the resource's managedFields:
kubectl get deployment acme-corp-app -n acme-corp -o yaml | grep -A5 managedFields
You should see manager: lynq in there.
Once you've confirmed the first node works, migrate the rest. You can do this gradually:
-- Migrate in batches
UPDATE nodes SET is_active = true WHERE region = 'us-east-1';
-- Wait, verify
UPDATE nodes SET is_active = true WHERE region = 'us-west-2';
-- And so on
Monitor the LynqHub status to track progress:
kubectl get lynqhub my-hub -o yaml
status:
referencingTemplates: 1
desired: 150
ready: 148
failed: 2
Investigate any failures before continuing.
Once everything is migrated and stable:
--keep-history
After running stable for a while, you might want to adjust policies:
deployments:
- id: app
conflictPolicy: Stuck # back to Stuck for safety
deletionPolicy: Delete # now safe to auto-cleanup
Switching deletionPolicy back to Delete means when a node is deactivated, resources get cleaned up automatically. Only do this once you trust the system.
Keep conflictPolicy: Stuck for ongoing safety. Force was just for the migration.
Resource names don't match
If you see Lynq creating new resources instead of conflict events, your nameTemplate isn't producing the right names. Check the LynqNode spec to see what names it's trying to create.
Stuck on unexpected fields
The conflict message shows which fields conflict. Common culprits:
replicas (managed by HPA)annotations (added by other controllers)labels (injected by admission webhooks)Use ignoreFields in your resource definition to skip these during reconciliation.
Old tool still trying to manage
If Terraform or Helm is still running somewhere (CI pipeline, cron job), it might fight with Lynq for ownership. Make sure you've fully disabled the old automation before migration.
LynqNode stuck in progressing
Check events: kubectl describe lynqnode <name>. Usually it's a dependency waiting for readiness or a template rendering error.
If something goes wrong:
deletionPolicy: Retain, resources are safekubectl delete lynqnode <name>
terraform import ...
helm upgrade --install ...
The retain policy gives you this escape hatch. Use it.
Migrating to database-driven automation doesn't have to be scary. The key is:
Take your time. There's no rush. The resources aren't going anywhere.
Questions? Drop them in the comments or open an issue on GitHub.
2025-12-04 08:27:10
(Think of it like Tony Stark + JARVIS — one thinks, the other keeps everything organized.)
Because they solve each other’s weaknesses.
AI is powerful but unaccountable.
It learns fast, but can hallucinate, hide how it made a decision, or be biased.
Blockchain is slow but trustworthy.
It records everything transparently and immutably.
Together:
🌱 For Beginners: What Does This Actually Mean?
Don’t worry — no technical wizardry needed.
Here’s the simple version:
AI = The brain 🧠
Blockchain = The memory + truth machine 📘
AI + blockchain creates apps that are:
Imagine an AI that can’t lie, can’t cheat, and can’t hide its source because everything it does is recorded.
That’s why this combo is blowing up.
🔥 Real Use Cases Beginners Will Understand
These are AIs that can:
Basically digital employees.
And blockchain ensures they spend exactly how they’re instructed — no funny business.
AI needs a lot of computing power — GPUs, servers, storage.
Traditionally only big tech giants (Google, Amazon, OpenAI) control that.
But now decentralized networks like:
Render
IO.NET
Akash
allow anyone to rent unused GPU power to AI developers.
It’s like Airbnb…
but for GPUs.
You rent out your hardware, get paid in crypto.
Think Amazon, but for AI tools:
A token-based marketplace lets creators:
Imagine knowing:
Blockchain provides:
So AI stops being a black box.
🚀 Why This Trend Matters for Founders
Founders love this combo because it unlocks new products that were impossible before.
A. AI Agents as Services
Startups can build:
These agents use blockchain wallets to operate autonomously.
B. Decentralized AI Clouds
Instead of paying Big Tech for compute, founders can:
This levels the playing field.
C. Trustable AI Products
With blockchain logs, founders can:
Prove their AI doesn’t steal data
Show how decisions were made
Build compliant AI for finance/health
Offer tamper-proof operations
This is huge for regulated industries.
D. Tokenized AI Economies
Tokens let founders bootstrap:
💰 Why Investors Are Paying Attention
Investors care because AI + blockchain unlocks new revenue categories.
Everyone needs compute — this is the new oil.
Projects like Render went from niche to mainstream fast.
Imagine investing early in the Mac App Store or Google Play Store.
AI agents will become the next app ecosystem.
The more people use the models, the more:
Finance, defence, healthcare, insurance — they must trust AI outputs.
Blockchain provides that trust.
This sector could be as big as the identity verification market… or bigger.
🏁 Final Takeaway
AI gives intelligence, speed, and automation.
Blockchain gives trust, transparency, and verifiable truth.
Together they’re building:
This is not the next trend…
This is the next version of the internet.
2025-12-04 08:26:06
Introduction:
The Problem: Briefly explain why traditional thread-per-connection models struggle with high concurrency (too many threads cause high resource overhead and context-switching costs).
The Rust Promise: Introduce the Rust solution: Asynchronous Programming using Futures, emphasizing that it delivers C-style performance with Rust's signature memory safety.
Definition: A Future is just a trait representing a value that might be ready at some point. It is the fundamental building block of Rust's async ecosystem.
Lazy Execution: Explain the crucial concept: Futures are inert. They do nothing until they are actively polled by an executor (the runtime). This is the "compute when needed" principle.
Code Example 1: A Simple (Manual) Future
Show a basic custom Future implementation (e.g., a simple counter that increments on each poll or a future that resolves after a fixed number of polls). This is to illustrate the poll method directly, even if it's not a real-world scenario.
// Example 1: A basic custom Future (for illustrative purposes)
use std::future::Future;
use std::pin::Pin;
use std::task::{Context, Poll};
use std::time::{Duration, Instant};
struct MyFuture {
start: Instant,
duration: Duration,
}
impl Future for MyFuture {
type Output = &'static str; // What this Future will produce
fn poll(self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll<Self::Output> {
if self.start.elapsed() >= self.duration {
println!("MyFuture is ready!");
Poll::Ready("Done waiting!")
} else {
// If not ready, register the current task to be woken up.
// In a real async runtime, this would involve registering with an event loop.
cx.waker().wake_by_ref(); // For this simple example, we'll just wake ourselves
// or a real runtime would ensure this gets polled again.
println!("MyFuture is pending...");
Poll::Pending
}
}
}
// How to create and run it (briefly, to show usage)
// Note: This won't run without an executor, but it shows the API.
async fn demonstrate_my_future() {
let fut = MyFuture { start: Instant::now(), duration: Duration::from_millis(10) };
println!("{}", fut.await); // This .await relies on an executor
}
The poll Method: (Crucial detail for experts) Briefly explain the signature of the poll method:
It returns Poll::Ready(T) (done) or Poll::Pending (not yet done).
The Waker is passed to wake the task when it's ready to be polled again.
async Block/Function: Explain that async fn is syntactic sugar for a function that returns an opaque type implementing the Future trait.
Analogy: It packages your code into a state machine.
await Operator: Explain that .await is the key mechanism. When you .await a Future:
If the Future is not ready, the task is yielded back to the executor.
This allows the single thread to go work on other tasks (Futures) instead of blocking.
Code Example 2: async/await in Action (Simple Task)
Show a basic async fn that does something simple, like tokio::time::sleep. This clearly demonstrates how await pauses execution without blocking the thread.
// Example 2: Simple async/await using Tokio
// Requires: `tokio = { version = "1", features = ["full"] }` in Cargo.toml
#[tokio::main] // This macro sets up the Tokio runtime
async fn main() {
println!("Hello from main!");
// Call an async function
say_hello_after_delay().await;
println!("Main function finished.");
}
async fn say_hello_after_delay() {
println!("Inside async function: About to wait...");
tokio::time::sleep(tokio::time::Duration::from_secs(1)).await; // .await pauses THIS task
println!("Inside async function: Waited for 1 second!");
}
The Executor's Role: A Future needs an executor (like Tokio or async-std) to drive its state machine forward.
The Event Loop: Describe how the runtime works: It takes pending Futures and efficiently schedules them onto a small pool of threads . When a task (Future) signals it's ready (via the Waker), the executor resumes polling that task.
Benefit: This model provides non-blocking I/O without the overhead of creating one operating system thread per connection, leading to high throughput.
Code Example 3: Concurrency with Multiple Async Tasks
Demonstrate tokio::spawn to run multiple Futures concurrently on a single (or small number of) thread(s), showing how the runtime interleaves their execution. This highlights the "non-blocking" nature.
// Example 3: Running multiple async tasks concurrently with Tokio
// Requires: `tokio = { version = "1", features = ["full"] }` in Cargo.toml
#[tokio::main]
async fn main() {
println!("Main starting...");
let task1 = tokio::spawn(async { // Spawn creates a Future and adds it to the executor
for i in 1..=3 {
println!("Task 1: {i}");
tokio::time::sleep(tokio::time::Duration::from_millis(100)).await;
}
"Task 1 complete!" // Return value of the spawned future
});
let task2 = tokio::spawn(async {
for i in 1..=2 {
println!(" Task 2: {i}");
tokio::time::sleep(tokio::time::Duration::from_millis(200)).await;
}
"Task 2 complete!"
});
// Await the results of the spawned tasks
let result1 = task1.await.unwrap(); // .await on JoinHandle blocks current task until spawned task finishes
let result2 = task2.await.unwrap();
println!("{result1}");
println!("{result2}");
println!("Main finished.");
}
Real-World Impact: Emphasize why this is crucial for blockchain and network services:
High Scalability: Handling thousands of concurrent connections efficiently.
Resource Efficiency: Lower memory footprint compared to thread-heavy models.
Rust's Safety Guarantees: All of this performance without data races or memory errors, thanks to the borrow checker and type system.
Final Call to Action: Encourage readers to experiment with async/await and explore Rust's ecosyste
2025-12-04 08:25:57
2025-12-04 08:22:28
✅ 1.What is a Microservice?
A microservice is a small, independent, deployable unit of an application that performs one specific business function.
It has its own codebase
It runs as a separate process
It has its own database or data storage
It communicates with other services using REST APIs / gRPC / messaging
Example:
In an e-commerce app,
User Service → handles registration/login
Order Service → handles orders
Payment Service → handles payments
Inventory Service → handles products
Each is a microservice.
✅ 2.Why do we need Microservices?
Microservices solve the problems of monolithic applications.
Problems in Monolith
One code change → entire app must be redeployed
Hard to scale individual components
Large codebase → difficult to maintain
A bug in one module can crash whole system
Microservices Benefits
Independent deployment
Independent scaling
Technology freedom (Java service + Node.js service + Python service)
Fault isolation
Faster CI/CD
Team ownership
✅ 3.Why should a DevOps Engineer learn Microservices?
Because DevOps == deployment + automation + observability + scalability.
Microservices require:
Containers (Docker)
Orchestration (Kubernetes)
CI/CD pipelines
Service mesh
Logging + Monitoring
API gateways
Distributed tracing
If you are a DevOps engineer, you must know:
how they deploy
how they communicate
how to troubleshoot
how to scale
how to secure
A DevOps engineer is responsible for running 100s of microservices in production, so you must understand microservice architecture deeply.
✅ 4.Key Concepts in Microservices
Here are the core concepts every DevOps engineer must know:
A. Architecture Concepts
Service registry & discovery
API Gateway (Zuul, Kong, Nginx, Traefik)
Load Balancing
Circuit Breaker (Resilience4j/Hystrix)
Design for failure
B. Data Concepts
Database per service
Saga pattern
Event-driven communication
CQRS
Message queues (Kafka/RabbitMQ/SQS)
C. Deployment Concepts
Containerization
Kubernetes deployments
Service mesh (Istio/Linkerd)
Sidecar pattern
D. Observability
Central logging
Distributed tracing (Jaeger/Zipkin)
Metrics & dashboards (Prometheus + Grafana)
E. Security
API authentication (JWT/OAuth2)
Zero trust networking
mTLS
F. Reliability
Autoscaling
Health checks
Readiness/Liveness probes
Blue-Green / Canary deployments
✅ 5.Use cases showing WHY microservices are important in CI/CD pipelines
Use Case 1: Deploy Only What Changes
Situation: In a monolith, one small change → redeploy entire application.
Microservice Benefit:
If only the Order Service changes, deploy only that service.
➡️ Faster builds
➡️ Faster deployments
➡️ Higher productivity
Use Case 2: Parallel Builds in CI
Each microservice has its own CI pipeline:
Pay service → builds independently
User service → builds independently
Inventory service → builds independently
➡️ Multiple teams commit code at same time
➡️ CI/CD runs in parallel
➡️ Zero waiting for others
Use Case 3: Canary Deployment
Deploy version v2 of a microservice to 5% traffic.
If stable → move to 50%
If stable → 100%
➡️ Safer releases
➡️ Instant rollback
➡️ Zero downtime
Use Case 4: Rolling Deployments
Every microservice supports:
Rolling updates
Rolling back
No downtime
DevOps uses Kubernetes + CI/CD to automate.
Use Case 5: Automated Testing Per-Service
Each microservice has dedicated:
Unit tests
Integration tests
API tests
Contract tests
CI pipeline runs tests only for that microservice.
➡️ Testing becomes faster
➡️ Quality improves
Use Case 6: Independent Versioning
Microservices allow:
Payment v1.0
Payment v1.1
Payment v1.2
All running in production.
CI/CD handles version management effortlessly.
Use Case 7: Auto-scaling in Production
CI/CD integrates with Kubernetes to scale:
Inventory service: 2 → 20 replicas
Payment service: 3 → 10 replicas
Based on:
CPU
Memory
Request count
➡️ Perfect for high-traffic apps (E-commerce, Banking, OTT)
Use Case 8: Immediate Bug Fix Deployment
Small microservice → small build time → fast deployment.
If a bug is found in Production:
Fix code
Run tests
Deploy microservice
Users see fix in minutes
Here’s a DevOps-level microservices roadmap tailored for you.
0.Baseline (you probably already have)
Linux, networking (TCP/IP, DNS, HTTP, TLS)
Git, branching, PR workflow
Docker: images, layers, networking, volumes, security
Basic CI (GitHub Actions / GitLab / Jenkins)
If any of these feel shaky, fix them first; microservices will amplify gaps.
Microservices Road Map
1.Microservices Fundamentals (Architecture + DevOps view)
Goals
Understand when microservices make sense (and when they don’t).
Be able to read and design a simple microservice architecture diagram.
Key topics
Monolith vs SOA vs Microservices
Bounded context, domain-driven design (just enough for DevOps)
12-Factor App principles from an ops perspective (config, logs, disposability, etc.)
Synchronous vs asynchronous communication (REST/gRPC vs events)
Hands-on
Take a simple monolith (e.g., demo e-commerce) and logically split into:
user-service, order-service, inventory-service, payment-service
Draw:
Client → API Gateway → Services → DB per service
Where logs, metrics, tracing go
2.Containerization Patterns for Microservices
Goals
Build production-grade Docker images per service.
Standardise image patterns across the org.
Key topics
Multi-stage builds
Image tagging strategy (service:git-sha, :release-x.y, :latest)
Security:
Minimal base images (distroless, alpine)
Scanning (Trivy/Grype)
Non-root containers, capabilities
Hands-on
For each service:
Write an optimized Dockerfile (multi-stage, non-root).
Push to registry with CI (you already have a GitHub Actions YAML).
3.Kubernetes for Microservices (Core DevOps Skill)
Goals
Deploy and operate 5–10 services on K8s confidently.
Key topics
Deployments, ReplicaSets, Pods
Services (ClusterIP, NodePort, LoadBalancer)
Ingress / API Gateway (Nginx, Traefik, or cloud LB)
ConfigMap, Secret, downward API
Probes:
liveness, readiness, startup
Rolling updates & rollbacks
HPA (CPU/Memory + custom metrics)
Hands-on project
Deploy your 3–4 microservices to:
local: kind/minikube
remote: managed cluster (EKS/AKS/GKE if possible)
Add:
Ingress + path-based routing
ConfigMaps + Secrets for DB creds
HPA for at least one service
4.Communication, Service Discovery & API Gateway
Goals
Design how services talk and how traffic enters the cluster.
Key topics
REST vs gRPC (pros/cons)
API Gateway patterns:
routing, auth, rate limiting, request/response transform
Service discovery:
K8s DNS
If using service mesh: additional discovery/proxy layer
Client-side vs server-side load balancing
Hands-on
Put an API Gateway in front (Ingress + Nginx, or Kong/Traefik).
Route:
/api/users → user-service
/api/orders → order-service
Implement:
simple rate-limit
request logging at gateway
5.Data & Transaction Patterns (DevOps view)
Goals
Understand why DB per service and how to deal with consistency.
Key topics
Database per service
Shared DB anti-pattern (when it’s tolerated in real life)
Saga pattern (choreography vs orchestration)
Event-driven architecture (Kafka/RabbitMQ)
Outbox pattern, idempotency
Hands-on
Give each service its own DB (e.g., Postgres instances).
Implement a simple event flow:
order-service publishes event → inventory-service consumes and updates stock.
Deploy Kafka or RabbitMQ in your cluster (even for lab use).
6.CI/CD for Microservices (where you shine)
Goals
Design scalable pipelines for 10+ services without chaos.
Key decisions
Repo strategy:
Monorepo (all services) vs multi-repo (one per service).
Per-service pipeline vs central orchestrator pipeline.
Promotion model:
dev → test → stage → prod
automatic vs manual approval gates
Pipeline capabilities
Build + unit tests per service
Container build & push
Static analysis & SCA:
SonarQube, Trivy, Snyk, etc.
Deployments:
kubectl or Helm or Kustomize
Strategies:
Rolling update
Blue/Green
Canary (via service mesh or gateway)
Hands-on
Extend the GitHub Actions pipeline I gave you:
matrix by service
add Trivy scan
deploy via Helm charts in helm//
Add environments in GitHub/GitLab:
DEV, QA, PROD with approvals for PROD.
7.Observability: Logs, Metrics, Tracing
Goals
From any incident, you can answer:
What broke? (logs)
How much & how bad? (metrics)
Where in the request path? (traces)
Key topics
Centralized logging:
EFK/ELK or Loki + Promtail
Metrics:
Prometheus + Grafana
RED/USE/Golden Signals
Tracing:
OpenTelemetry basics
Jaeger or Tempo/Zipkin
SLOs, SLIs, error budgets (SRE view)
Hands-on
Instrument each service:
HTTP latency, error rate, request count
Create Grafana dashboards for:
per-service metrics
overall system health
Enable distributed tracing:
trace from API gateway → each service → DB.
8.Security & Compliance for Microservices
Goals
Build secure-by-default pipelines and clusters.
Key topics
API security:
OAuth2/OIDC, JWT
API keys (when acceptable)
Network security:
mTLS (usually via service mesh)
NetworkPolicies in K8s
Secrets management:
K8s Secrets + external (Vault, cloud KMS)
Supply-chain security:
Image signing (Cosign)
SBOM (Syft)
Policy as code (OPA/Gatekeeper, Kyverno)
Hands-on
Introduce Keycloak/any IdP for auth, secure API gateway.
Apply basic NetworkPolicies (allow only necessary east-west traffic).
Add image scan + policy gate:
block images with critical CVEs.
9.Resilience, Scaling & Chaos
Goals
Make the system resilient under failure and load.
Key topics
Resilience patterns:
timeouts, retries with backoff
circuit breaker
bulkhead, rate limiting
Capacity planning, autoscaling strategies
Chaos engineering:
pod kill, node kill
latency injection, network loss
Hands-on
Configure:
HPA on at least one “hot” service.
If using service mesh (Istio/Linkerd):
test circuit breaker / retry at mesh layer.
Run chaos experiments:
randomly kill pods
verify system recovers and SLOs respected.
10.Service Mesh & Advanced Traffic Control (Optional but powerful)
Goals
Manage cross-cutting concerns via mesh instead of app code.
Key topics
Sidecar pattern (Envoy)
Traffic management:
canary releases, mirroring, A/B
mTLS, zero-trust networking
Telemetry built into mesh
Hands-on
Install Istio/Linkerd on your cluster.
Do:
canary deploy of new version of order-service (10% → 50% → 100%).
traffic mirroring for testing in prod.
11.GitOps & Platform Engineering
Goals
Move from ad-hoc operations to a productized platform.
Key topics
GitOps principles:
desired state in Git
Argo CD / Flux
Internal Developer Platform concepts:
golden templates (Helm charts, pipelines)
self-service onboarding
Hands-on
Put all K8s/Helm manifests in a separate infra repo.
Use Argo CD to sync:
infra/dev, infra/stage, infra/prod folders.
Create a reusable app template for “new microservice”:
Dockerfile
Helm chart
CI pipeline skeleton
12.Final Capstone (End-to-End DevOps + Microservices)
Build one serious project (even if only for yourself):
5–8 microservices (Java/Spring Boot + maybe one Node/Go).
Each with:
its own DB
Dockerfile
Helm chart
CI/CD:
build, test, scan, push, deploy
GitOps for environments
Platform:
K8s + API Gateway + service mesh
centralized logs, metrics, tracing
feature flags and canary deploys
Non-functional:
SLOs defined
alerts firing into Slack/Email
