Kubernetes request flow

 Let’s explain the super beginner level, with clear analogy, simple flow, and easy memory tricks.

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🧠 Big Picture First (Overview)

When you create a Pod, Kubernetes must decide:

👉 “On which worker machine should this Pod run?”

That decision is made by the Scheduler.

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🏫 Beginner Analogy: School Classroom Example

Imagine:

• 🧑‍🎓 Pod = A student

• 🏫 Node = A classroom

• 👨‍🏫 Scheduler = School principal

• 📋 API Server = School office register

When a new student joins:

1. Student arrives (Pod created).

2. Student has no classroom yet (nodeName = empty).

3. Principal checks:

   • Which classrooms have space?

   • Any classroom restricted?

   • Any special requirement?

4. Principal assigns best classroom.

5. Office writes it in register (Binding).

That’s it. That’s Kubernetes scheduling.

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🔁 Real Kubernetes Flow (Simple Version)

Step 1 — You create a Pod

kubectl apply -f pod.yaml

Pod is stored in cluster database (etcd).

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Step 2 — Pod has no node

Inside Pod object:

nodeName: ""

Meaning:

“I don’t know where to run yet.”

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Step 3 — Scheduler sees this

Scheduler constantly watches for:

Pods with nodeName empty.

When it finds one, it starts thinking:

“Which node can run this pod?”

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🧠 What Does Scheduler Check? (Filters Phase)

Think of this as elimination round.

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1️⃣ CPU & Memory Check

Beginner analogy:

Student needs 2 seats.
Classroom must have 2 free seats.

If Pod requests:

resources:
  requests:
    cpu: 500m
    memory: 256Mi

Scheduler checks:

• Does node have that much free CPU?

• Does node have that much free memory?

If NO → reject node ❌
If YES → keep node ✅

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2️⃣ Taints & Tolerations

Analogy:

Some classrooms have a board:

🚫 “Only Science students allowed”

That’s a taint.

If a student doesn’t have matching permission (toleration),
he cannot enter.

Node has:

kubectl taint nodes node1 dedicated=ml:NoSchedule

Pod must have:

tolerations:
- key: "dedicated"
  value: "ml"
  effect: "NoSchedule"

Otherwise → rejected ❌

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3️⃣ nodeSelector / Affinity

Analogy:

Student says:

“I want only AC classroom.”

Node label:

disktype=ssd

Pod:

nodeSelector:
  disktype: ssd

If node doesn't match → rejected ❌

Affinity is smarter version:

• Required → Must match

• Preferred → Try to match

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4️⃣ Topology Constraints

Analogy:

School rule:

“Don’t put all students in one building.”

In real world:
Don’t put all Pods in one zone.

Example:

topologySpreadConstraints:
  topologyKey: topology.kubernetes.io/zone

Scheduler tries to spread Pods across zones.

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🧮 After Filtering

Now scheduler has:

👉 List of nodes that CAN run the Pod.

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⭐ Scoring Phase (Picking Best Node)

Now scheduler asks:

“Among possible nodes, which one is BEST?”

It scores based on:

• Less loaded node

• Better spread

• Preferred affinity match

Highest score wins 🏆

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📝 Final Step — Binding

Scheduler writes decision via API Server:

Pod → node1

Now inside Pod:

nodeName: node1

This is called Binding.

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🚀 After Binding

Now:

1. Kubelet on node1 sees the Pod.

2. Pulls container image.

3. Starts container.

4. Pod becomes Running.

Scheduler’s job is done.

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📦 Full Flow (End-to-End Overview)

You create Pod
      ↓
API Server stores Pod
      ↓
nodeName = empty
      ↓
Scheduler sees unscheduled Pod
      ↓
Filter nodes:
   - CPU/Mem?
   - Taints?
   - nodeSelector?
   - Affinity?
   - Topology?
      ↓
Remaining nodes
      ↓
Score nodes
      ↓
Pick best
      ↓
Write Binding (Pod → Node)
      ↓
Kubelet runs container

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🧠 Memory Trick (Very Important)

Think:

Filter → Score → Bind → Run

Or even shorter:

Can it run? → Where best? → Assign → Start

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🎯 Ultra Simple One-Line Definition

Scheduler = Brain that decides where a Pod should run based on rules and resources.

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Now let’s continue exactly from where scheduler finished — beginner level, simple analogies, clear flow, same style.

We start from:

✅ Scheduler has written: nodeName: node1

Now what happens next?

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🏫 Same School Analogy (Continue Story)

Earlier:

• 👨‍🏫 Principal (Scheduler) assigned student to classroom (node1)

• 📋 Office wrote it in register (Binding)

Now:

👩‍🏫 Classroom teacher (Kubelet) sees new student assigned.

Teacher prepares everything and makes student sit.

That is what Kubelet does.

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🔁 Real Kubernetes Flow (After Binding)

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🟢 Step 1 — Kubelet on node1 sees the Pod

Each node runs a process called:

👉 Kubelet

Kubelet constantly asks API Server:

“Are there any Pods assigned to me?”

When scheduler sets:

nodeName: node1

Kubelet on node1 detects:

“Oh! I need to run this Pod.”

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Beginner analogy:

Teacher checks attendance register.

Sees:

New student assigned to my classroom.

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🟢 Step 2 — Kubelet Checks the Container Image

Pod definition contains:

containers:
  - name: app
    image: nginx:1.27

Kubelet now checks:

“Do I already have this image on my machine?”

If not…

It asks the Container Runtime (Docker / containerd):

“Please download this image.”

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Analogy:

Student needs a textbook.

Teacher checks:

• If book already in class → use it

• If not → order from library (Docker Hub)

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🟢 Step 3 — Pulling the Container Image

Container runtime contacts:

• Docker Hub

• ECR

• GCR

• Any private registry

Downloads image layers.

If imagePullPolicy is:

• Always → always download

• IfNotPresent → only if not local

• Never → don’t download

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🟢 Step 4 — Creating the Container

Now Kubelet says:

“Start container using this image.”

Container runtime:

1. Creates container sandbox

2. Sets up networking

3. Mounts volumes

4. Applies CPU/memory limits

5. Starts container process

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Analogy:

Teacher:

• Gives student a seat (CPU/memory)

• Gives notebook (volume)

• Connects to classroom network

• Tells student to start writing

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🟢 Step 5 — Networking Setup

Kubernetes gives Pod:

• IP address

• Connects to cluster network

Each Pod gets:

Its own IP inside cluster

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Analogy:

Student gets:

• Roll number

• Desk number

• Connected to classroom group

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🟢 Step 6 — Health Checks Begin

If Pod has:

livenessProbe:
readinessProbe:

Kubelet starts checking:

• Is container alive?

• Is container ready to accept traffic?

If probe fails:

• Restart container (if needed)

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🟢 Step 7 — Pod Status Changes

Initially:

Pending

After container starts:

ContainerCreating

Then:

Running

API Server updates status.

Now kubectl get pods shows:

demo-pod   Running

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🧠 Full Flow From Binding to Running

Scheduler assigns node
        ↓
API Server updates Pod (nodeName set)
        ↓
Kubelet on that node detects Pod
        ↓
Check image locally
        ↓
Pull image (if needed)
        ↓
Create container
        ↓
Setup network + volumes
        ↓
Apply CPU/Mem limits
        ↓
Start container process
        ↓
Run health checks
        ↓
Pod status = Running

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🔥 Who Does What (Clear Separation)

Component	Responsibility
Scheduler	Decides WHERE Pod runs
API Server	Stores decision
Kubelet	Actually runs the Pod
Container Runtime	Creates container
etcd	Stores cluster state

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🧠 Memory Trick

Think:

Scheduler = Brain 🧠
Kubelet = Worker 👷
Container Runtime = Machine 🏭

Brain decides → Worker executes → Machine builds

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🎯 One-Line Definition

Scheduler assigns the Pod, Kubelet brings it to life.

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💡 Very Important Production Understanding

Scheduler’s job is finished after binding.

If container crashes:

👉 Scheduler is NOT involved.

Kubelet restarts it.

If node dies:

👉 Controller creates new Pod
👉 Scheduler schedules again

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Now we complete the FULL Kubernetes Pod Life Cycle — from creation → running → scaling → failure → termination → deletion.

I’ll explain:

• Beginner level

• Clear analogies

• Step-by-step flow

• Complete lifecycle

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🌱 FULL POD LIFE CYCLE (Simple Overview)

Create → Schedule → Start → Running → Serving
       → Scaling → Failure Handling → Termination → Deleted

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🏫 Master Analogy: Student Full School Life

Pod = Student
Node = Classroom
Scheduler = Principal
Kubelet = Teacher
Controller = School Management

We already covered:

✔ Principal assigns classroom
✔ Teacher starts class

Now let’s continue full life.

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🟢 PHASE 1 — Running & Serving Traffic

Once Pod becomes Running:

• It gets IP

• It joins Service (if defined)

• It can receive traffic

If behind a Service:

Client → Service → Pod IP

Analogy:

Student is now attending class and answering questions.

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🟢 PHASE 2 — Readiness & Liveness Monitoring

Kubelet keeps checking:

🟢 Liveness Probe

"Is student alive?"

If fails → restart container.

🟢 Readiness Probe

"Is student ready to answer?"

If fails → stop sending traffic (but don’t restart).

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🟢 PHASE 3 — Scaling Happens

If Deployment has:

replicas: 3

Controller ensures 3 Pods always exist.

If traffic increases:

• HPA (Horizontal Pod Autoscaler) increases replicas

Flow:

More CPU usage
     ↓
HPA increases replicas
     ↓
New Pods created
     ↓
Scheduler schedules them

Analogy:

More students enroll → school opens more classrooms.

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🟢 PHASE 4 — Pod Crash Scenario

If container crashes:

• Kubelet detects exit

• Restarts container (based on restartPolicy)

RestartPolicy options:

• Always

• OnFailure

• Never

Important:

Scheduler NOT involved.

Analogy:

Student faints → teacher wakes him up.

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🟢 PHASE 5 — Node Failure Scenario

If entire node crashes:

• Node becomes NotReady

• Controller detects Pods unavailable

• New Pods created

• Scheduler reschedules to other nodes

Analogy:

Entire classroom building collapses → principal assigns new classrooms.

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🟢 PHASE 6 — Rolling Update (Deployment)

When you update image:

image: nginx:1.28

Deployment does:

1. Create new Pod

2. Wait until ready

3. Delete old Pod

4. Continue gradually

This is:

👉 Zero downtime deployment

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🟢 PHASE 7 — Pod Termination

When Pod is deleted:

kubectl delete pod demo

Flow:

1. API server marks Pod as "Terminating"

2. Kubelet sends SIGTERM to container

3. Waits for terminationGracePeriodSeconds (default 30 sec)

4. Container stops

5. Pod removed

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SIGTERM vs SIGKILL

• SIGTERM → polite shutdown

• SIGKILL → forced kill

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Analogy:

Teacher tells student:

“Class over, pack and leave.”

Waits 30 seconds.
If student doesn’t leave → force remove.

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🟢 PHASE 8 — PreStop Hook (Optional)

Pod can define:

lifecycle:
  preStop:
    exec:
      command: ["sleep", "10"]

Used for:

• Finish requests

• Close connections

• Save state

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🟢 PHASE 9 — Pod Deleted

After termination:

• Removed from etcd

• Removed from Service endpoints

• Gone from cluster

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🟢 PHASE 10 — Garbage Collection

If controlled by Deployment:

• ReplicaSet ensures desired count maintained

If standalone Pod:

• Fully gone

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🔁 COMPLETE FLOW (Full Technical Diagram)

kubectl apply
      ↓
API Server
      ↓
Pod created (Pending)
      ↓
Scheduler assigns node
      ↓
Kubelet pulls image
      ↓
Container starts
      ↓
Pod Running
      ↓
Readiness OK
      ↓
Serving traffic
      ↓
Scaling / Restart / Monitoring
      ↓
Crash? → Restart
      ↓
Node fail? → Reschedule
      ↓
Update? → Rolling update
      ↓
Delete? → SIGTERM → Grace period
      ↓
Pod terminated
      ↓
Removed from cluster

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📊 Pod Status Lifecycle States

Phase	Meaning
Pending	Waiting for scheduling
ContainerCreating	Pulling image
Running	Container active
Succeeded	Completed successfully
Failed	Error
Terminating	Being deleted