Sagar Parkar.

Do not run Databases in Kubernetes Cluster (In Production)...!! Databases like MongoDB, Oracle, Postgres, MySQL, or any other DB's are okay to be run in K8s cluster for Dev, Pre-Production & PoC's and must not be used for production. Here is why: Few Facts to consider: - Databases need persistent storage and especially a performant storage (SSD or NVMe). I can't use an s3 or file systems or network storages. - Once you size your DB, the resources requirements (CPU, RAM & Disk) would be invariably high and no more suitable for k8s. - Kubernetes uses probes (readiness & liveliness) & resources (requests & limits). - Assuming you would be running a database cluster and not a standalone instance. - Database k8s operators can help reduce the complexity but there's a lot more than that. Why we should not run DB in K8s: - Pods in Kubernetes are ephemeral and can be rescheduled or restarted at any time. This can lead to higher chances of failover events and database restarts, which is not desirable for databases that require persistent state. - In the event of network failures let's say calico or dns failures or someone applying a network policy, it may result in disconnection between nodes of a cluster and eventually lead to data loss and chaos (if two nodes go down). - Running databases in containers can introduce performance overhead due to the additional layers of abstraction, impacting database response times and throughput. - Your DBA's, Deployment & SRE's need to understand K8s concepts better. - The complexity of configuring and managing databases in Kubernetes can be significantly higher compared to traditional deployment methods, including ensuring high availability, disaster recovery, and consistent performance. Until the above problems are sorted out, I would prefer DBaaS than deploying it on k8s be it using helm or operators. #kuberentes #databases #devops

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💡 Use 𝐫𝐞𝐜𝐮𝐫𝐬𝐢𝐯𝐞 flag along with 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐞𝐱𝐩𝐥𝐚𝐢𝐧 command to get all the fields available for that Kubernetes resource. Following is the example command for deployment resource. >>> 𝘬𝘶𝘣𝘦𝘤𝘵𝘭 𝘦𝘹𝘱𝘭𝘢𝘪𝘯 𝘥𝘦𝘱𝘭𝘰𝘺𝘮𝘦𝘯𝘵 --𝘳𝘦𝘤𝘶𝘳𝘴𝘪𝘷𝘦 and then, If you want to deep dive into any of the field, use following command along with field name. In this scenario, I am looking for 𝘥𝘦𝘱𝘭𝘰𝘺𝘮𝘦𝘯𝘵.𝘴𝘱𝘦𝘤.𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺 >>> 𝘬𝘶𝘣𝘦𝘤𝘵𝘭 𝘦𝘹𝘱𝘭𝘢𝘪𝘯 𝘥𝘦𝘱𝘭𝘰𝘺𝘮𝘦𝘯𝘵.𝘴𝘱𝘦𝘤.𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺 #kubernetes #devops #docker #sre #hackwithharsha #python #java

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The coming weeks are are going to learn Terraform in 7.0 as it is one of the most common provisioning IAC in companies like Walmart Amazon Flipkart Atlassian etc. Terraform is a tool for building, changing, and versioning infrastructure safely and efficiently. Terraform can manage existing and popular service providers as well as custom in-house solutions. The key features of Terraform are: Infrastructure as Code: Infrastructure is described using a high-level configuration syntax. This allows a blueprint of your datacenter to be versioned and treated as you would any other code. Additionally, infrastructure can be shared and re-used. Execution Plans: Terraform has a "planning" step where it generates an execution plan. The execution plan shows what Terraform will do when you call apply. This lets you avoid any surprises when Terraform manipulates infrastructure. Resource Graph: Terraform builds a graph of all your resources, and parallelizes the creation and modification of any non-dependent resources. Because of this, Terraform builds infrastructure as efficiently as possible, and operators get insight into dependencies in their infrastructure. Change Automation: Complex changesets can be applied to your infrastructure with minimal human interaction. With the previously mentioned execution plan and resource graph, you know exactly what Terraform will change and in what order, avoiding many possible human errors. If you are a fresher/Experienced and looking to upskill yourself, then do checkout the PRIME batch7.0 - 𝟏𝟒+ 𝐑𝐞𝐚𝐥 𝐓𝐢𝐦𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 on DevOps and SRE - Highest CTC package of Rs. 𝟒𝟎 𝐥𝐚𝐤𝐡 𝐩𝐞𝐫 𝐚𝐧𝐧𝐮𝐦 in 6.0 batch - 𝟐𝟎𝟎+ 𝐑𝐞𝐚𝐥 𝐓𝐢𝐦𝐞 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 - Roles and Responsibilities data with 𝐉𝐨𝐛 𝐒𝐞𝐚𝐫𝐜𝐡 𝐏𝐨𝐫𝐭𝐚𝐥𝐬 - Mock Interviews and Resume Reviews And the best part? Anyone who is a Fresher/Intern/working professional regardless of the industry they are working in is eligible for 𝐏𝐫𝐢𝐦𝐞 𝐁𝐚𝐭𝐜𝐡𝟕.𝟎. 𝐄𝐧𝐫𝐨𝐥𝐥 𝐏𝐑𝐈𝐌𝐄 𝐁𝐀𝐓𝐂𝐇 𝟕.𝟎-https://lnkd.in/gaRDBD4y [ 70% OFF ] 𝐂𝐡𝐞𝐜𝐤 𝟏𝟒+ 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐚𝐧𝐝 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭𝐬 - https://lnkd.in/gQg42c5E

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🚀Recently asked 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐛𝐚𝐬𝐞𝐝 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 on 𝐀𝐖𝐒-𝐃𝐞𝐯𝐎𝐩𝐬: 🌐Designation- 𝗗𝗲𝘃𝗢𝗽𝘀/𝗖𝗹𝗼𝘂𝗱 🌐Experience- 𝟮-𝟱 𝒀𝙚𝒂𝙧𝒔 𝗧𝗼𝗽𝗶𝗰𝘀:  *** 𝗜𝗔𝗠, 𝗥𝗗𝗦, 𝗘𝗟𝗕, 𝗔𝘂𝘁𝗼 𝘀𝗰𝗮𝗹𝗶𝗻𝗴, 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗧𝗼𝘄𝗲𝗿, 𝗹𝗮𝗻𝗱𝗶𝗻𝗴 𝘇𝗼𝗻𝗲, 𝗔𝗪𝗦 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗥𝗼𝘂𝘁𝗲 𝟱𝟯, 𝗰𝗹𝗼𝘂𝗱𝘄𝗮𝘁𝗰𝗵, 𝗔𝗪𝗦 𝗕𝗲𝗱𝗿𝗼𝗰𝗸 *** 1. What is the Target group in ELB? and how does it work? 2. What are the listener rules in ELB? 3. What is the purpose of AWS Landing Zone, and how does it streamline multi-account setups? 4. What are Service Control Policies (SCPs), and how do they differ from IAM Policies? 5. How does AWS Control Tower enforce governance and compliance across multiple accounts? 6. What is AWS Organizations, and how does it assist in managing compliance across accounts? 7. How do you ensure compliance with industry regulations like GDPR or HIPAA in AWS? 8. Can you explain how auto-scaling policies are configured based on CloudWatch metrics? 9. What happens when all targets in a target group become unhealthy? 10. How can you implement cross-account access securely using IAM roles? 11. How does Amazon RDS differ from Amazon Aurora in terms of scalability? 12. What are common use cases for Amazon Elasticsearch/OpenSearch, and how do you secure it? 13. Explain the difference between latency-based routing and geolocation routing in Route 53. 14. How do you integrate Route 53 health checks with failover routing? 15. What is the difference between CloudWatch Logs and CloudWatch Metrics? 16. What are the challenges of migrating an existing multi-account setup to AWS Landing Zone, and how would you address them? 17. You have a high-traffic application, and users report intermittent 503 errors. How would you troubleshoot the Application Load Balancer and the target group's health checks? 18. What would you do if an auto-scaling group fails to scale up despite exceeding the defined CloudWatch alarm thresholds? 19. How would you configure predictive scaling in Auto Scaling to handle seasonal traffic surges for an B2B e-commerce application? 20. How would you design a temporary access solution using IAM policies for a third-party vendor requiring access to specific AWS resources? 21. How would you integrate AWS Bedrock with other AWS services to deliver real-time personalized recommendations using generative AI?

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$ kubectl apply -f ckad-certificate.yaml -n bharath-b-s certificates/ckad-certificate-created I’m absolutely over the moon to announce that I’ve earned my first K8s certification! 🚀 A big thank you to #MumshadMannambeth and #KodeKloud for the course and all the dedicated CKAD challenges and labs. Also, big thanks to killer.sh and #Killercoda for their hands-on exercises that prepped me for the real deal. I’m excited to leverage this knowledge to drive innovation and efficiency in my projects. Onward to new challenges and opportunities in the Kubernetes ecosystem #cncf #kubernetes #devops #cloudnativecomputing #continouslearning #ckad #linuxfoundation #containers #cloud #k8scertified #cloudengineering #softwareengineering #cloudnativearchitecture #KodeKloud #MumshadMannambeth #TechJourney

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Hello, Connections In today's blog post, I am going to share with you a step-by-step process of how you can do "Jenkins Installation and Configuration on Amazon Linux" 🚀 Are you an aspiring DevOps engineer or a software developer looking to streamline your CI/CD pipeline? Look no further! Get ready to unlock the power of Jenkins and transform your development workflow! 🔧 We'll cover: 1. Setting up the Amazon Linux Environment 🐧 2. Updating the package manager and installing necessary dependencies 3. Jenkins Installation 🛠️ 4. Adding the Jenkins repository 5. Installing the latest version of Jenkins 6. Jenkins Configuration 🔧 7. Accessing the Jenkins web interface 8. Configuring the Jenkins environment Automating your build, test, and deployment processes By the end of this post, you'll be equipped with the knowledge and skills to set up a robust Jenkins environment on Amazon Linux, streamlining your software development lifecycle. 🎉 #Jenkins #Installation #Configuration #AmazonLinux #DevOps #ContinuousIntegration #Automation #SoftwareDevelopment #CloudComputing #AWS #Linux #ITInfrastructure #Deployment #CI/CD #CodePipeline #CodeBuild #JenkinsInstallation #DevOps #ContinuousIntegration #Automation #SoftwareDevelopment #CI/CD #BuildAutomation #Deployment #CodePipeline #DevOpsTools #InfrastructureAsCode

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#Kubernetes v1.31: Top 3 Changes 🚀#1. #NFTables Support for KubeProxy: You can now use NFTables instead of IPTables, improving Kubernetes networking performance. 🚀#2. #PersistentVolume Reclaim Policy: PV reclaim policy is respected even after the associated PVC is deleted, preventing volume leakage. 🚀#3. #Multiple Service CIDRs: Allows dynamic modification of Service CIDR ranges, solving IP exhaustion issues in large clusters with zero downtime.

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All the time favorite of interviewers- ..... ...... ........ ........... Understanding the Role of an API Gateway The API gateway is a crucial component in managing and routing API requests. Here's how it functions: Step 1: The client initiates an HTTP request to the API gateway. Step 2: The API gateway parses and validates the attributes of the HTTP request. Step 3: It performs allow-list and deny-list checks to ensure the request is authorized. Step 4: The API gateway communicates with an identity provider for authentication and authorization. Step 5: Rate limiting rules are applied. If the request exceeds the limit, it is rejected. Steps 6 and 7: After passing basic checks, the API gateway routes the request to the appropriate service using path matching. Step 8: The API gateway transforms the request into the correct protocol and forwards it to the backend microservices. Steps 9-12: The API gateway manages errors and handles faults that take longer to recover (circuit breaking). It leverages the ELK (Elastic-Logstash-Kibana) stack for logging and monitoring. Sometimes, data is cached in the API gateway for efficiency. Discussion Points: Difference between a Load Balancer and an API Gateway: What distinguishes these two components in a system architecture? API Gateway Usage: Should different API gateways be used for PC, mobile, and browser clients? gif credit: Alex Xu

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I am always on the look out for Postgres 'Internals' Developers , especially in Europe and the USA -( Sorry no VISA sponsorship) But what is a Postgres '#Internals' Developer and how does is it different from a DBA or software engineer/software developer? A #Postgres Internals Developer focuses on the internal workings of the #PostgreSQL database system. They work on the core features, such as the query planner, execution engine, storage mechanisms, and concurrency control. This role is quite different from other developers, who might focus on using the database for building applications or managing databases at a higher level. When it comes to databases, many of us think of writing queries or managing data. But there's a whole world behind the scenes, where Postgres Internals Developers operate. These experts dive deep into PostgreSQL's source code to enhance and maintain the robustness of this powerful open-source relational database system. Different from Other Developers? While application developers use PostgreSQL as a tool to build software, Postgres Internals Developers are more like mechanics fine-tuning the engine. They don't just write #SQL queries; they enhance the very system that interprets and executes these queries. Their work requires a deep understanding of database theory, #Cprogramming, and the PostgreSQL codebase. This is a stark contrast to application developers, who primarily focus on leveraging databases to support their applications, often using high-level SQL queries and tools. Postgres Internals Developers, on the other hand, are more focused on performance and how to maximize PostgreSQL’s capabilities at its core. Skills Needed to Thrive in Postgres Internals Development To succeed as a #PostgresInternalsDeveloper, you need a mix of low-level programming and database knowledge. Here are some key skills: 1. C Programming Proficiency: Since PostgreSQL is written in C, you’ll need to be comfortable with this language. 2. Database Theory: Understanding the principles of relational databases, indexing, and query planning is essential. 3. System Architecture Knowledge: Familiarity with operating systems and memory management helps, as these factors influence database performance. 4. Debugging Expertise: You’ll often work with complex, low-level code, requiring strong debugging and profiling skills to identify bottlenecks and issues. Why Postgres Internals Development Matters Postgres Internals Developers are crucial to the open-source community #pgconfEU. Their contributions drive PostgreSQL’s reputation as a high-performance, reliable database solution. Their improvements help everyone who relies on PostgreSQL If you are one, you probably know me! #ThePostgresRecruiter #PostgresDevelopers open to a new gig? DM me here

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#Terraform Interview Questions:- 1. Can you describe what workspaces are in Terraform and how they assist with infrastructure management? 2. What are the best practices for managing secrets or sensitive information within Terraform configurations? 3. Could you explain the differences between the `count` and `for_each` meta-arguments in Terraform? 4. How do you manage dependencies between different resources in Terraform configurations? 5. How does Terraform manage state, and why is state management crucial? 6. What role do providers play in Terraform, and how do they aid in managing infrastructure? 7. What techniques can be used to enable parallelism in Terraform operations and enhance performance? 8. What are remote backends in Terraform, and what are the benefits of using them? 9. How can Terraform modules be effectively managed in a large-scale infrastructure setup? 10. What methods are available to prevent concurrent modifications to Terraform state? 11. Can you explain the differences between the `local-exec` and `remote-exec` provisioners in Terraform? 12. How can Terraform state be securely managed across multiple environments or teams? 13. What is the difference between the `taint` and `import` commands in Terraform? 14. How do you detect and address drift in Terraform-managed infrastructure? 15. What are some best practices for organizing Terraform configurations to ensure they are modular and reusable?

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Interviewer : If your Kubernetes application is experiencing slow response times. How would you diagnose and resolve the performance issues in this Kubernetes application? Initially i will check the resource usage of your pods to see if any are using too much CPU or memory, which might be causing the slowdown. Post that , il look at the logs of the affected pods for any errors or warnings that could explain the issue. Il make sure that the deployment settings are correct, such as the number of replicas and resource allocations. Also, review the service configuration to confirm that it's properly set up and connecting to the right pods. On the other side il also use monitoring tools to check the overall health of your cluster and nodes to identify any bottlenecks. Finally, consider scaling up by increasing the number of pod replicas or adding more resources to better handle the load. I’m posting a Kubernetes Task on "How to create and configure Kubernetes users with TLS certificates, set up roles, and merge Kubernetes config files for access management" Catch it on my YouTube channel at 8:00 PM here: https://lnkd.in/ghMzDF5Q. #Kubernetes #KubernetesTutorial #TLSCertificates #KubernetesRoles #DevOps #CloudComputing #ITInfrastructure #KubernetesConfiguration #KubernetesSecurity #TechTutorial #SysAdmin #InfrastructureAsCode #CloudSecurity #K8s #TechEducation

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Yesterday's class covers Kubernetes Architecture compared with the MNC company environment 😊 A big shout out to Shubham Londhe Sir TrainWithShubham for explaining kubernetes architecture in a crystal clear language🚀 🚀 ✨ Master Node Components ✨ 1. etcd A consistent and highly available key-value store used as Kubernetes backing store for all cluster data. Stores the entire state of the cluster, including configuration, metadata, and status information. 2. api server Acts as the front end for the Kubernetes control plane. Handles all RESTful API requests for managing Kubernetes objects (like pods, services, deployments). 3. scheduler Assigns newly created pods to nodes based on various factors like resource requirements, policy constraints, and affinity/anti-affinity specifications. Ensures efficient and balanced distribution of workloads across the cluster. 4. controller manager Runs controller processes that regulate the state of the cluster by making sure the desired state matches the actual state. ✨ Worker Node Components ✨ 1. kubelet An agent that runs on each worker node and communicates with the Kubernetes control plane. 2. kube proxy Manages network routing and load balancing for services on each node. 3. Container Runtime The software responsible for running containers (e.g., Docker, containerd, CRI-O. Manages container lifecycle, including starting, stopping, and managing the state of containers. ✨ Kubectl ✨ kubectl is the command-line interface (CLI) tool for interacting with Kubernetes clusters. It allows users to deploy applications, inspect and manage cluster resources, and view logs and perform troubleshooting. #90daysofdevops #trainwithshubham #learninpublic Savinder Puri Abhishek Veeramalla Dheeraj Yadav #kubernetes #kubectl #kubelet #kubeproxy #eks #docker #linux #dockercompose #dockerswarm #git #github #gitlab #cicd #jenkins #devopsengineer #cloudengineer #devopsconsultant #debopsevengalist #sre #softwaredeveloper #softwaredevelopment

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👋 Hello Everyone, it's day 15! 📚 Below are the learnings for today: 🗄️ Storage Classes in K8s In Kubernetes, Storage Classes define types of storage available in a cluster. They enable: 1. 🔄 Dynamic provisioning of Persistent Volumes (PVs) 2. 🚀 Automatic creation of PVs when needed 3. 🎛️ Different storage options for various use cases Sample YAML for Storage Class: apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: standard provisioner: kubernetes.io/aws-ebs parameters: type: gp2 Explanation of the YAML: - 📜 apiVersion and kind: Specify this is a StorageClass resource - 🏷️ metadata.name: Name of the StorageClass ("standard") - 🔧 provisioner: Defines who will provision the storage (AWS EBS in this case) - ⚙️ parameters: Specific options for the provisioner (gp2 type for AWS EBS) 🔍 What does "provisioner" mean? The provisioner is like a storage manager for Kubernetes. It's responsible for: - 🏗️ Creating new storage volumes - 🗑️ Deleting volumes when they're no longer needed - 🔗 Attaching volumes to nodes In simple terms, when you ask for storage, the provisioner makes it happen in the background, whether it's on AWS, Google Cloud, or any other supported storage system. 💡 To check created storage classes: kubectl get storageclass 🔗 Using Storage Classes in PVCs: In your PVC (PersistentVolumeClaim), you can specify the storage class under the spec section: spec: storageClassName: standard 📦 Using PVC in Pods: You can use the PVC in a pod by providing the PVC claim name under the volumes section of the pod specification. 🔑 Key Points: - Storage Classes help in automating storage management - They provide a way to define different storage tiers - PVCs can request specific Storage Classes - Pods can then use these PVCs for persistent storage - The provisioner handles the actual creation and management of storage resources This setup allows for flexible and automated storage management in Kubernetes, adapting to different cloud providers and storage systems. That's all for today, Stay tuned for further updates 🎉 #Kubernetes #aws #DevOps #googlecloud #azure #microsoft #tech #technology #learning #techtips #CloudNative

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Exploring Terraform: Infrastructure as Code (IaC) 🏗️ Today, I dived into Terraform, a powerful open-source tool for managing infrastructure as code (IaC). It simplifies the provisioning and management of infrastructure across various cloud providers like AWS, Azure, and GCP. 🛠️ Core Concepts: 1️⃣ Core: The Terraform Core is the engine responsible for creating, updating, and managing infrastructure resources based on the configuration files. 2️⃣ Provider: Providers are plugins that enable Terraform to interact with various cloud services. For example, the AWS provider allows Terraform to manage AWS resources. 📜 Terraform Language: Terraform uses its own declarative language called HashiCorp Configuration Language (HCL), which is simple and human-readable for defining infrastructure. 🔗 Key Components: Variables: Used to make the configuration flexible and reusable. Dynamic Blocks: Allow for creating multiple resources dynamically based on variable input, reducing code repetition. 💻 Commands: Here are some essential Terraform commands: # terraform init – Initializes the configuration directory. # terraform plan – Shows the changes Terraform will make to the infrastructure. # terraform apply – Applies the changes to create or modify the infrastructure. # terraform destroy – Destroys the infrastructure created by Terraform. 🚀 Building Infrastructure on AWS: Today, I also created a simple infrastructure on AWS using Terraform. The process of defining and managing resources through code is seamless and efficient, making Terraform a key tool in modern DevOps workflows. #Terraform #IaC #DevOps #CloudComputing #AWS #InfrastructureAsCode #LearningJourney

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👉 Mastering Kubernetes: Step-by-Step Guide to Managing ReplicaSets A ReplicaSet in #Kubernetes is a key controller to ensures a specified number of identical #Pod replicas are running at any given time. It helps maintain application #availability and #scalability by automatically replacing failed #Pods or #scaling them based on requirements. 🚀 Important aspect: 📌 Pod Replication: Ensures the desired number of Pod replicas are running. If a Pod crashes, the ReplicaSet creates a new one. 📌 Self-Healing: Automatically replaces #Pods that fail or are deleted unintentionally. 📌 Scaling: You can scale the number of #replicas up or down dynamically. 📌 Selectors: Uses label selectors to identify the Pods it manages. Pods with matching labels are controlled by the ReplicaSet. 🫵 Create a ReplicaSet ➡️ 💻 kubectl apply -f replicaset.yaml 🫵 View ReplicaSets ➡️ 💻 kubectl get rs 🫵 Describe a ReplicaSet ➡️ 💻 kubectl describe rs <replicaset-name> 🫵 Scale Up ReplicaSet ➡️ 💻 kubectl scale rs <replicaset-name> --replicas=5 🫵 Scale down ReplicaSet ➡️ 💻 kubectl scale rs <replicaset-name> --replicas=3 🫵 Delete ReplicaSet ➡️ 💻 kubectl delete -f replicaset.yaml In summary, a ReplicaSet ensures high availability and reliability by maintaining the desired number of Pods, making it a critical part of Kubernetes' self-healing and scaling capabilities. Read full blog here. ⬇️⬇️ https://lnkd.in/gW7KF-2v 🫵 A Simple Guide to Understanding Kubernetes Pods ⬇️⬇️ https://lnkd.in/gXT-BusQ 🫵 A Simple Guide to Understanding Pod Management with Kubectl commands ⬇️⬇️ https://lnkd.in/g82M6gZw 𝐒𝐭𝐚𝐲 𝐓𝐮𝐧𝐞𝐝 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞! & Happy Learning 😇 If you liked this post: 💾 Save this post for future reference 🤝 Have questions or insights? Share in the comments below! ♻️ Repost if this helped you—let’s keep supporting the DevOps community! Have anything to add? #Kubernetes #DevOps #DevOpsTraining #CKA2025 #CloudNative #Containerization #Automation #ContinuousIntegration #ContinuousDelivery #Microservices #InfrastructureAsCode #CloudComputing #Docker #ContainerOrchestration #CloudDeployment #CloudManagement #CloudInfrastructure #CloudNativeDevelopment #DevOpsEngineer #DevOpsSkills #DevOpsCertification #CloudSkills #CloudTraining #CloudCareer #CloudJobs #CloudTechnology 💻 ReplicaSet Manifest file

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🚀 **Day 26 of 30-Day DevOps Interview Prep: Advanced Autoscaling Strategies in Kubernetes (Part 1)** 🚀 Today, we’re diving into advanced autoscaling strategies in Kubernetes. Autoscaling helps your applications handle fluctuating workloads efficiently, improving performance and resource utilization. Here’s a Q&A to guide you through the essential concepts: 1️⃣ **What is Horizontal Pod Autoscaling (HPA), and how does it work in Kubernetes?** HPA automatically scales the number of Pods in a deployment or replica set based on observed metrics like CPU and memory usage. It dynamically adjusts the number of running Pods to match the current workload, ensuring that your application has the resources it needs to handle traffic spikes while scaling down during low-traffic periods to save resources. 2️⃣ **What is Vertical Pod Autoscaling (VPA), and how does it complement HPA?** VPA automatically adjusts the resource requests and limits (e.g., CPU and memory) of Pods based on their actual usage. While HPA scales the number of Pods horizontally, VPA optimizes each Pod’s resource allocation vertically. This helps ensure that Pods are neither under- nor over-provisioned, improving resource efficiency and performance. 3️⃣ **How does Cluster Autoscaling work in Kubernetes?** Cluster Autoscaling automatically adjusts the number of nodes in your Kubernetes cluster based on the overall resource demands. When Pods cannot be scheduled due to insufficient resources, Cluster Autoscaling adds new nodes to the cluster. Conversely, when resources are underutilized, it scales down by removing unnecessary nodes. This helps optimize infrastructure costs and resource utilization. 4️⃣ **What are the best practices for combining HPA, VPA, and Cluster Autoscaling?** Combining HPA, VPA, and Cluster Autoscaling allows you to scale your applications and infrastructure efficiently. HPA scales Pods based on workload, VPA optimizes Pod resources, and Cluster Autoscaling adjusts the cluster size. Ensure that your resource requests and limits are configured properly to avoid conflicts between HPA and VPA. Additionally, monitor your scaling policies to ensure they respond effectively to traffic changes. 5️⃣ **How do you monitor autoscaling in Kubernetes, and why is it important?** Monitoring autoscaling is crucial for ensuring that your scaling strategies are working effectively. Use tools like Prometheus and Grafana to track metrics such as CPU usage, memory usage, and Pod scaling events. Set up alerts for scaling failures or unusual resource usage patterns, and regularly review your autoscaling logs to identify and resolve any issues. 👉 **Stay tuned for Part 2**, where we’ll cover advanced autoscaling strategies, including scaling for stateful applications, optimizing cost efficiency, and handling large-scale workloads in Kubernetes! #DevOps #Kubernetes #Autoscaling #Containers #InterviewPreparation #Learning

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