Kubernetes Patterns - Part 2

TechOps Examples

Hey — It's Govardhana MK 👋

Along with a use case deep dive, we identify the remote job opportunities, top news, tools, and articles in the TechOps industry.

👋 Before we begin... a big thank you to today's sponsor SUPERHUMAN AI

Find out why 1M+ professionals read Superhuman AI daily.

In 2 years you will be working for AI

Or an AI will be working for you

Here's how you can future-proof yourself:

1. Join the Superhuman AI newsletter – read by 1M+ people at top companies

2. Master AI tools, tutorials, and news in just 3 minutes a day

3. Become 10X more productive using AI

Join 1,000,000+ pros at companies like Google, Meta, and Amazon that are using AI to get ahead.

Sign up and start learning AI

IN TODAY'S EDITION

🧠 Use Case

• Kubernetes Patterns - Part 2

🚀 Top News

• Hugging Face's new course on how to build AI models like Deepseek R1 – it's FREE and CERTIFIED

👀 Remote Jobs

• Automatiq is hiring a Junior DevOps Engineer

  Remote Location: Worldwide

• SS&C is hiring a Cloud Operations Engineer (Kubernetes & AWS)

  Remote Location: India

📚️ Resources

• Guide to KEDA (Kubernetes Event-Driven Autoscaler)

• Four ways to grant cross-account access in AWS

• How to use Docker and Jenkins together to build and maintain efficient pipelines

📢 Reddit Threads

• Words of my CEO - Why hire juniors when single senior with AI can do work of 10-20 of juniors

• AWS Emerging Talent Community Offering 100% Discounted AWS Certification Vouchers Starting February 28, 2025

🛠️ TOOL OF THE DAY

finders keypers - An open source tool to discover the usage and blast radius of encryption keys in AWS.

Supports both AWS Customer Managed KMS Keys and AWS Managed KMS Keys.

🧠 USE CASE

Kubernetes Patterns - Part 2

Few editions ago, we discussed Kubernetes Patterns - Part 1 covering

Init Container, Sidecar, Ambassador, Adapter, Controller, Operator

Let’s look into a few more crucial patterns every Kubernetes practitioner should know in this Part 2.

Download a high resolution copy of this diagram here for future reference.

1. Singleton Application

Some applications require that only one instance is active at a time, with others remaining in a passive or standby state.

Use cases: Leader-election apps, primary-replica DBs

Implementation approach:

• Use Kubernetes Lease API (coordination.k8s.io/v1) for leader election

• Store lock state in etcd/Zookeeper to prevent split-brain scenarios

• Define PodAntiAffinity to distribute instances across nodes

2. Batch Job

Batch workloads are non continuous tasks that run to completion. Kubernetes provides Jobs and CronJobs to manage these workloads by ensuring execution, retries, and cleanup.

Use cases: Data processing, ETL, backups

Implementation approach:

• Use Job (batch/v1) for one-time execution

• Use CronJob (batch/v1) for scheduled tasks

• Set backoffLimit to control retry attempts

• Use ttlSecondsAfterFinished for automatic cleanup

3. Service Discovery

Microservices running in Kubernetes need a reliable way to communicate with each other. Kubernetes Service objects provide built-in discovery mechanisms for internal and external traffic routing.

Use cases: Microservices, backend APIs

Implementation approach:

• Use ClusterIP Service for internal traffic (cluster.local DNS resolution)

• Use Headless Service (clusterIP: None) for pod-to-pod direct communication

• Deploy an Ingress Controller (NGINX, Traefik) for external routing

4. Stateful Service

Unlike stateless applications, stateful workloads require persistent storage and stable network identities. Kubernetes StatefulSet ensures ordered deployment, unique pod names, and persistent storage attachments.

Use cases: Databases (PostgreSQL, MongoDB), Kafka

Implementation approach:

• Deploy StatefulSets (apps/v1) for ordered scaling and stable pod names

• Use PersistentVolumes (PVs) with volumeClaimTemplates for per-pod storage

• Define Headless Services for direct pod addressing

5. Self Healing Systems

Kubernetes continuously monitors the state of pods and ensures they match the declared state by replacing failed instances automatically.

Use cases: High-availability applications, Multi replica workloads

Implementation approach:

• Define Liveness Probes to restart stuck containers

• Use Readiness Probes to ensure traffic is routed only to healthy pods

• Leverage ReplicaSets and Deployments for automated recovery

• Configure PodDisruptionBudgets (PDBs) to limit voluntary disruptions

6. Health Probe

Determines pod health and take corrective actions such as restarting or removing it from service endpoints.

Use cases: Prevent routing to failing pods

Implementation approach:

• Use Liveness Probe (/health) to restart unresponsive containers

• Use Readiness Probe (/ready) to control traffic exposure

• Use Startup Probe for applications with long boot times

These patterns provide structured approaches to solving common challenges in distributed systems.

Select the right pattern based on your workload characteristics to realize optimal performance, resilience, and scalability.

Looking to promote your company, product, service, or event to 40,000+ Cloud Native Professionals? Let's work together.