Pods and deployments for services in Microservices - Architecture Diagram

System Overview - Pods and deployments for services

This system manages microservices using Pods and Deployments in a container orchestration platform like Kubernetes. It ensures services run reliably, can scale automatically, and recover from failures without downtime.

Architecture Diagram

User
  |
  v
Ingress Controller
  |
  v
Load Balancer
  |
  v
+-------------------+       +-------------------+
| Deployment A       |       | Deployment B       |
| +---------------+ |       | +---------------+ |
| | Pod A1        | |       | | Pod B1        | |
| +---------------+ |       | +---------------+ |
| +---------------+ |       | +---------------+ |
| | Pod A2        | |       | | Pod B2        | |
| +---------------+ |       | +---------------+ |
+-------------------+       +-------------------+
         |                           |
         v                           v
   Service A                    Service B
         |                           |
         v                           v
     Database                    Cache Layer

Components

User

client

Sends requests to the services

Ingress Controller

ingress

Manages external access to services inside the cluster

Load Balancer

load_balancer

Distributes incoming traffic evenly across Pods

Deployment A

deployment

Manages lifecycle and scaling of Pods for Service A

Deployment B

deployment

Manages lifecycle and scaling of Pods for Service B

Pod A1

pod

Runs an instance of Service A

Pod A2

pod

Runs another instance of Service A for redundancy and scaling

Pod B1

pod

Runs an instance of Service B

Pod B2

pod

Runs another instance of Service B for redundancy and scaling

Service A

service

Provides stable network endpoint for Pods of Service A

Service B

service

Provides stable network endpoint for Pods of Service B

Database

database

Stores persistent data for services

Cache Layer

cache

Speeds up data access for services

Request Flow - 11 Hops

User → Ingress Controller

Ingress Controller → Load Balancer

Load Balancer → Pod (e.g., Pod A1)

Pod (e.g., Pod A1) → Cache Layer

Cache Layer → Pod (e.g., Pod A1)

Pod (e.g., Pod A1) → Database

Database → Pod (e.g., Pod A1)

Pod (e.g., Pod A1) → Cache Layer

Pod (e.g., Pod A1) → Load Balancer

Load Balancer → Ingress Controller

Ingress Controller → User

Failure Scenario

Component Fails:Pod (e.g., Pod A1)

Impact:Requests routed to the failing Pod fail or timeout, causing degraded service availability

Mitigation:Deployment controller detects Pod failure and automatically creates a new Pod instance; Load Balancer stops sending traffic to the failed Pod

Architecture Quiz - 3 Questions

What component ensures that multiple Pod instances of a service run and recover automatically?

A Deployment

B Load Balancer

C Ingress Controller

D Cache Layer

Design Principle

This architecture uses Deployments to manage Pods, ensuring automatic scaling and self-healing. Services provide stable network endpoints, while Load Balancers distribute traffic evenly. The system separates concerns for reliability and scalability.

Practice

1. What is the main role of a Pod in a microservices architecture?

easy

A. To manage updates and scaling of containers

B. To run one or more containers together as a single unit

C. To route network traffic between services

D. To store persistent data for containers

Solution

1. Step 1: Understand what a Pod is

   A Pod is the smallest deployable unit in Kubernetes that runs one or more containers together.

2. Step 2: Differentiate Pod from other components

   Deployments manage Pods, Services route traffic, and persistent storage is handled separately.

3. Final Answer:

   To run one or more containers together as a single unit -> Option B

4. Quick Check:

   Pod = container unit [OK]

Hint: Pods run containers; deployments manage pods [OK]

Common Mistakes:

• Confusing Pods with Deployments
• Thinking Pods handle networking
• Assuming Pods store data

2. Which of the following is the correct YAML snippet to define a Deployment that runs 3 replicas of a Pod?

easy

A. kind: Pod\nreplicas: 3\nmetadata:\n name: my-pod

B. replicas: 3\nkind: Service\nmetadata:\n name: my-service

C. replicas: 3\nkind: Deployment\nmetadata:\n name: my-deployment

D. kind: Deployment\nmetadata:\n name: my-deployment\nreplicas: three

Solution

1. Step 1: Identify correct kind and replicas field

   Deployment kind is correct and replicas should be a number, here 3.

2. Step 2: Check metadata and syntax

   Metadata name is valid; 'replicas: three' is invalid because replicas must be numeric.

3. Final Answer:

   replicas: 3\nkind: Deployment\nmetadata:\n name: my-deployment -> Option C

4. Quick Check:

   Deployment with numeric replicas = correct YAML [OK]

Hint: Deployments use 'kind: Deployment' and numeric replicas [OK]

Common Mistakes:

• Using 'kind: Pod' instead of Deployment
• Setting replicas as a word instead of number
• Confusing Service with Deployment

3. Given this Deployment YAML snippet, how many Pods will be running after applying it?

apiVersion: apps/v1
kind: Deployment
metadata:
  name: web-app
spec:
  replicas: 4
  selector:
    matchLabels:
      app: web
  template:
    metadata:
      labels:
        app: web
    spec:
      containers:
      - name: web-container
        image: nginx

medium

A. 4 Pods

B. 0 Pods until manually started

C. 1 Pod

D. Depends on the number of nodes

Solution

1. Step 1: Read replicas count in Deployment spec

   The replicas field is set to 4, meaning Kubernetes will maintain 4 Pods.

2. Step 2: Understand Deployment behavior

   Deployment automatically creates and manages the specified number of Pods.

3. Final Answer:

   4 Pods -> Option A

4. Quick Check:

   replicas = 4 Pods running [OK]

Hint: replicas number = Pods count after deployment [OK]

Common Mistakes:

• Assuming only 1 Pod runs by default
• Thinking Pods need manual start
• Confusing nodes with Pod count

4. You applied a Deployment YAML but notice no Pods are running. Which is the most likely cause?
apiVersion: apps/v1 kind: Deployment metadata: name: api-server spec: replicas: 3 selector: matchLabels: app: api template: metadata: labels: app: backend spec: containers: - name: api-container image: myapi:latest

medium

A. The Deployment kind is incorrect

B. The replicas count is too high for the cluster

C. The container image name is invalid

D. The selector labels do not match the Pod template labels

Solution

1. Step 1: Compare selector and template labels

   The selector uses label 'app: api' but the Pod template labels 'app: backend' which do not match.

2. Step 2: Understand label matching importance

   Deployment uses selector to manage Pods; mismatch means no Pods are controlled or created.

3. Final Answer:

   The selector labels do not match the Pod template labels -> Option D

4. Quick Check:

   Selector labels must match Pod labels [OK]

Hint: Selector and Pod labels must match exactly [OK]

Common Mistakes:

• Ignoring label mismatch
• Assuming image name causes no Pods
• Thinking replicas count blocks Pod creation

5. You want to update a microservice with zero downtime using Kubernetes. Which approach best uses Pods and Deployments to achieve this?

hard

A. Update the Deployment with a new image version; Kubernetes creates new Pods and gradually replaces old ones

B. Delete all old Pods manually and then create new Pods with the updated image

C. Scale down the Deployment to zero replicas, then scale up with the new image

D. Create a new Deployment with the updated image and delete the old Deployment immediately

Solution

1. Step 1: Understand Deployment update strategy

   Deployments support rolling updates that create new Pods and remove old Pods gradually.

2. Step 2: Compare options for zero downtime

   Manual deletion or scaling down causes downtime; creating new Deployment causes conflicts.

3. Final Answer:

   Update the Deployment with a new image version; Kubernetes creates new Pods and gradually replaces old ones -> Option A

4. Quick Check:

   Rolling update = zero downtime update [OK]

Hint: Use Deployment rolling updates for zero downtime [OK]

Common Mistakes:

• Deleting Pods manually causing downtime
• Scaling to zero causes service interruption
• Creating new Deployment causes conflicts