Utsav Bhardwaj

💡 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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In my K8s Learning series, I have published 10 in-depth guides so far. These guides will lay a strong foundation for mastering Kubernetes. Also, understanding these core fundamentals will help you design better systems using K8s. They are well-organized with workflow illustrations for each concept. Each guide also includes a member discussion section. If you have any questions or something to add, feel free to post it there. Here’s the list of guides published so far. 1️⃣ Beyond YAML: Core IT for Kubernetes: https://lnkd.in/gh-Tywdb 2️⃣ Kubernetes Architecture: https://lnkd.in/gstZZaVF 3️⃣ Kubernetes API Server Explained: https://lnkd.in/eertpTc2 4️⃣ etcd in Kubernetes: https://lnkd.in/gdKxMWCg 5️⃣ Scheduler in Kubernetes: https://lnkd.in/g3qSCT2U 6️⃣ How the Scheduler Chooses a Node: https://lnkd.in/gX55UEX5 7️⃣ Kubernetes Custom Schedulers: https://lnkd.in/gR54AtGc 8️⃣ Kubernetes Bin Packing Strategies: https://lnkd.in/g_Gqgntb 9️⃣ Kube Controller Manager Guide: https://lnkd.in/gsRdy54k 🔟 Cloud Controller Manager (CCM) Guide: https://lnkd.in/gY45jbWy It’s a growing list and every week new in-depth guides get added. Over 7,900 subscribers are receiving notifications about new guides, insights, and trends in their email. So, don’t forget to subscribe! PS: ♻️ Repost and help community get better at k8s 🙂 Have any suggestions? Comment below. #devops #aws

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𝗥𝘂𝗻 𝗟𝗲𝗮𝗱𝗲𝗿 𝗘𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗔𝗺𝗼𝗻𝗴 𝗣𝗼𝗱𝘀 𝗶𝗻 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗟𝗲𝗮𝗱𝗲𝗿 𝗘𝗹𝗲𝗰𝘁𝗶𝗼𝗻: Some applications require specific processes to run only on a single Pod instance to avoid duplicate tasks. Kubernetes Deployments with multiple Pod replicas require a leader election process outside the Pods to ensure only one Pod performs these tasks. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲𝘀: 🔶  𝙎𝙘𝙝𝙚𝙙𝙪𝙡𝙚𝙙 𝙏𝙖𝙨𝙠𝙨: Only one Pod should run batch processing, cache updates, etc. 🔶  𝙇𝙚𝙖𝙙𝙚𝙧-𝙊𝙣𝙡𝙮 𝙎𝙚𝙧𝙫𝙞𝙘𝙚𝙨: Sending notifications or triggering specific APIs that shouldn't be called multiple times. 𝘒𝘶𝘣𝘦𝘳𝘯𝘦𝘵𝘦𝘴 𝗟𝗲𝗮𝘀𝗲 𝘖𝘣𝘫𝘦𝘤𝘵 𝘤𝘰𝘮𝘦𝘴 𝘵𝘰 𝘵𝘩𝘦 𝘳𝘦𝘴𝘤𝘶𝘦!! Here's how leader election works using Leases: 🔶  𝙇𝙚𝙖𝙨𝙚 𝘾𝙧𝙚𝙖𝙩𝙞𝙤𝙣: The first Pod creates a Lease object using configurations such as Lease name, Pod name & namespace and updates the current timestamp along with the Lease duration. 🔶  𝙇𝙚𝙖𝙨𝙚 𝙏𝙧𝙖𝙘𝙠𝙞𝙣𝙜: The Lease object keeps track of the current leader Pod's name. 🔶  𝙇𝙚𝙖𝙙𝙚𝙧 𝙍𝙚𝙣𝙚𝙬𝙖𝙡: If the Leader Pod is still active, it renews the lease by updating the timestamp before it expires. 🔶  𝙇𝙚𝙖𝙙𝙚𝙧 𝙀𝙡𝙚𝙘𝙩𝙞𝙤𝙣: If the timestamp isn't updated, inactive replicas race to acquire the lock with its identity and assume the leader role. This process leverages the 𝙖𝙩𝙤𝙢𝙞𝙘 𝙣𝙖𝙩𝙪𝙧𝙚 𝙤𝙛 𝙆𝙪𝙗𝙚𝙧𝙣𝙚𝙩𝙚𝙨 operations to ensure that no two replicas can acquire the Lease simultaneously by keeping track of the 𝘳𝘦𝘴𝘰𝘶𝘳𝘤𝘦𝘝𝘦𝘳𝘴𝘪𝘰𝘯 field. Read more and Code snippet: https://lnkd.in/gatVJGzP & https://lnkd.in/gWNGht3J #Kubernetes #LeaderElection #Containerization

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This Week I spend sometime to explore the usecase of Network Policy in kubernetes. Bydefault all the pods inside a cluster can access to each other irrespective of the namespace they're deployed. To test the networkpolicy let's do a small demo. 1. Deploying an NGINX Pod To get started with Kubernetes Network Policies, let’s first create a simple nginx pod. Run the following command: $ kubectl run nginx --image=nginx 2. Exposing the Pod as a ClusterIP Service Next, expose the nginx pod as a service to make it accessible within the cluster: $ kubectl expose pod/nginx --port=80 3. Testing Accessibility Without Restrictions To test if the nginx service is accessible by default, create a temporary curl pod and send a request to the nginx service: $ kubectl run --rm -i --tty curl --image=curlimages/curl:8.4.0 --restart=Never -- sh Once inside the curl pod, use: $ curl nginx.default.svc.cluster.local You’ll see a response from the nginx service, confirming that it is accessible to all pods in the cluster. Exit the pod: $ exit 4. Applying a Network Policy To restrict access to the nginx pod, we’ll create a Network Policy. This policy will allow access only from pods with specific labels. Here’s the configuration for the policy: $ cat <<EOF > networkpolicy.yaml apiVersion: networking.k8s.io/v1 kind: NetworkPolicy metadata: name: allow-nginx-access namespace: default spec: podSelector: matchLabels: run: nginx policyTypes: - Ingress ingress: - from: - podSelector: matchLabels: run: curl EOF Apply the policy. $ kubectl apply -f networkpolicy.yaml 5. Validating the Network Policy Test with Allowed Pods: Create a new curl pod with the run: curl label to test access: $ kubectl run --rm -i --tty curl --image=curlimages/curl:8.4.0 --restart=Never -- sh Inside the pod, verify access: $ curl nginx.default.svc.cluster.local You should receive a response from the nginx service. Test with Non-Allowed Pods: Now, test with a pod that does not match the label criteria (i.e. run:curl): $ kubectl run --rm -i --tty curl2 --image=curlimages/curl:8.4.0 --restart=Never -- sh Inside the pod, attempt to access nginx: $ curl nginx.default.svc.cluster.local The request will be blocked this time, demonstrating how the network policy enforces restricted access. Exit the pod: $ exit Summary With this setup, we’ve successfully implemented a Network Policy in Kubernetes to control traffic to an nginx pod. This simple example shows how you can use Network Policies to enhance security and isolate services within your Kubernetes cluster. Have you implemented Network Policies in your Kubernetes environment? Share your thoughts and experiences in the comments below! Thanks, James Spurin for the amazing course on Kubernetes. Loved it!

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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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🚀 **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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🛠️ **How to Attach Multiple EBS Volumes to an EC2 Instance in AWS** 🛠️ Attaching multiple Elastic Block Store (EBS) volumes to an EC2 instance can enhance performance and storage capabilities. Here’s a step-by-step guide to do it: **1. Launch Your EC2 Instance:**   - Start by launching your EC2 instance from the AWS Management Console.   - Select the desired instance type and configure other settings as needed. **2. Create EBS Volumes:**   - Navigate to the **EC2 Dashboard**.   - Click on **Volumes** under the **Elastic Block Store** section.   - Choose **Create Volume** and specify the size, type (e.g., gp2, io1), and availability zone (must match the instance's AZ).   - Repeat this step for each EBS volume you want to create. **3. Attach EBS Volumes to Your Instance:**   - In the **Volumes** section, select the volume you want to attach.   - Click on **Actions** and then **Attach Volume**.   - Select your EC2 instance from the list.   - Specify the device name (e.g., `/dev/sdf`, `/dev/sdg`, etc.).   - Click **Attach**. Repeat for additional volumes. **4. Format and Mount the Volumes (Linux Instances):**   - SSH into your EC2 instance.   - Use the `lsblk` command to list the attached volumes.   - Format the new volumes using a command like `sudo mkfs -t ext4 /dev/xvdf` (replace `/dev/xvdf` with your volume name).   - Create a mount point: `sudo mkdir /mnt/myvolume`.   - Mount the volume: `sudo mount /dev/xvdf /mnt/myvolume`.   - To make the mount persistent, add an entry in `/etc/fstab`. **5. Verify and Use the Volumes:**   - Check the mounted volumes with `df -h` to ensure everything is set up correctly.   - Now you can use the volumes for data storage, backups, or any other purpose! #AWS #EC2 #EBS #CloudComputing #DevOps #TechTips

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How do you Manage bandwidth for ingress and egress traffic at the pod level ?🤔 Try Calico CNI Plugin next time: Your Gateway to Seamless Network Management! 🗼. with #calico we can optimize our Kubernetes clusters to its Potential performance and security. 🛡️ (It's For Pod Level) I Implemented bandwidth management to prioritize critical applications and ensure optimal performance and used the (kubernetes.io / ingress-bandwidth )and ( kubernetes.io / ingress-bandwidth ) annotations. 🎯The (kubernetes.io / ingress-bandwidth ) annotation sets the maximum allowed ingress bandwidth for the pod to 1 megabit per second (1Mbps). 🎯The (kubernetes.io / egress-bandwidth ) annotation sets the maximum allowed egress bandwidth for the pod to 1 megabit per second (1Mbps). Attaching Yaml Pic for the reference : ) Feel free to ask, if any doubt! 💥 #kubernetes #cloud #devops

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What is AWS S3? Amazon S3 is an object storage service that allows you to store and retrieve data anytime, anywhere. From hosting static websites to backing up critical data, S3 offers unmatched scalability, durability, and performance. Key Features 1. Scalability: • Start small and scale up to store terabytes or even petabytes of data seamlessly. • Pay only for the storage you use—no upfront costs. 2. Durability: • Provides 99.999999999% (11 nines) durability, ensuring your data is safe and recoverable even in the face of disasters. 3. Security: • Offers encryption at rest and in transit to protect sensitive information. • Integrated with AWS Identity and Access Management (IAM) for fine-grained access control. 4. Versatile Storage Classes: • From frequent access data (S3 Standard) to archival storage (S3 Glacier), S3 provides cost-effective options for all use cases. 5. Global Accessibility: • Data is accessible worldwide with low latency, thanks to AWS’s global infrastructure. Popular Use Cases 1. Backup and Disaster Recovery: • Use S3 to store backups of critical systems, ensuring quick recovery in case of an outage. 2. Big Data Analytics: • Store massive datasets in S3 and analyze them using AWS services like Athena, Redshift, or EMR. 3. Static Website Hosting: • Host static websites directly from S3, with scalability and high availability built-in. 4. Media Storage: • Store and deliver large media files, such as videos or images, to users worldwide. Why Choose AWS S3? 1. Flexibility: • Easily integrate with other AWS services like Lambda, EC2, and CloudFront. 2. Reliability: • Built on AWS’s robust global infrastructure for maximum uptime. 3. Cost-Effectiveness: • Save money by choosing the right storage class and optimizing data lifecycle policies. How to Get Started 1. Create an S3 Bucket: • Sign in to the AWS Management Console and create your first bucket. 2. Upload Data: • Use the web interface, AWS CLI, or SDKs to upload and manage your files. 3. Configure Permissions: • Set access controls and policies to manage who can view or modify your data. 4. Optimize Costs: • Use lifecycle rules to automatically transition data to cheaper storage classes as it ages. Pro Tip: Combine S3 with AWS CloudFront for faster data delivery to users globally or AWS Backup for centralized data management. 💡 Ready to transform your storage strategy? Start leveraging AWS S3 to unlock the power of secure, scalable, and cost-effective storage for your business! #AWS #S3 #CloudStorage #DataManagement #DevOps #CloudComputing #devops #sre #career #backup #storage #bucket #performance #career #openprofile #remote #apac #openforrole #development #software #automation #codexio_tech Codexio Tech

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Part2 : Most frequently asked Kubernetes interview question in most of the companies: 8. what is the Kubernetes default strategies? ans: In Kubernetes, the default deployment strategy is called Rolling Update. This is the strategy used by Kubernetes to gradually replace old pods with new ones, ensuring there is minimal downtime and service disruption. Zero downtime: New pods are created and started before the old ones are terminated. Gradual replacement: we can control the rate of replacement by specifying parameters like maxUnavailable and maxSurge 9. what is CRD in Kubernetes? A CustomResourceDefinition (CRD) in Kubernetes is a powerful mechanism that allows you to extend the Kubernetes API by creating custom resources. These custom resources are tailored to fit specific application or infrastructure needs that aren't addressed by the built-in Kubernetes objects like Pods, Services, or Deployments. With CRDs, you can define your own custom resources and have them managed just like native Kubernetes resources. 10. Kubernetes storage class: A StorageClass in Kubernetes is a resource that defines how storage is provisioned dynamically for Persistent Volumes (PVs). It allows administrators to describe the types of storage (such as SSD or HDD) and the provisioning mechanism (e.g., NFS, AWS EBS, GCE Persistent Disk, etc.) that should be used when a PersistentVolumeClaim (PVC) requests storage. Key Components of a StorageClass: Provisioner Parameters ReclaimPolicy AllowVolumeExpansion VolumeBindingMode 11 . what is pod security policy in Kubernetes? A Pod Security Policy (PSP) in Kubernetes is a cluster-level resource that controls the security settings of pods running within the Kubernetes cluster. I 1. User privileges: Controlling which user the pod can run as (e.g., root or non-root users). 2. Network options: Restricting privileges like host networking or host ports. 3. Control over sensitive permissions 4. Admission control policy 12. difference between deployment and stateful set? 13. what are all the services in Kubernetes and explain each service ? 14. what is Kubernetes operators? 15. how to rollback/undo the Kubernetes to previous revision?  > kubectl rollout history deployment/<deployment-name> --> Check the deployment history  > kubectl rollout undo deployment/nginx-deployment ---> rollback to previous running version  > kubectl rollout undo deployment/nginx-deployment --to-revision=3  ---> Rollback to a specific revision (e.g., revision 3):  > kubectl rollout status deployment/nginx-deployment - Check the status 16. how To check pod CPU and memory usage in Kubernetes pod? 1. kubectl top pods --all-namespaces 2. Using Prometheus & Grafana (for detailed monitoring) 17. how to restart the Kubernetes pod? kubectl rollout restart deployment <deployment name> -n <namespace>

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📜 How Kubernetes Stores Data in etcd 📜 Kubernetes stores all its cluster resources (Pods, Services, Deployments, ConfigMaps, etc.) in etcd. Etcd is a distributed, fault-tolerant key-value store that serves as the source of truth for the entire cluster. ⚡ Protocol Buffers (Protobuf): etcd uses Protocol Buffers (protobuf) exclusively for data serialization. All Kubernetes resources are stored as protobuf data in etcd, ensuring compact and efficient storage. ⚡ Key Format in etcd: The keys in etcd are structured based on resource types and their scope: 🔹 Namespace-scoped resources (like Pods, Deployments, etc.) are stored as: /registry/{resource-type}/namespace/{resource-name} 🔹 Cluster-scoped resources (like ClusterRoles) are stored as: /registry/{resource-type}/{resource-name} 🔹 Custom Resource Definitions (CRD): For CRDs, the key format is: /registry/apiextensions. k8s. io/customresourcedefinitions/{crd-name} 🔄 Kubectl and the API Server: When we use kubectl or interact with the Kubernetes API, the API server is responsible for storing resources in etcd as protobuf data. It serializes and sends back the resource data in JSON format when responding to our requests. Here's how the etcd keys look like in my local "kind" cluster

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Hello LinkedIn folks, 🧑‍🤝‍🧑 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 scaling play's pivotal role in Kubernetes which allows us to adjust the resources allocated to our application workloads based on demand. let's deep dive some scaling works in Kubernetes 🚀 [1] 𝐇𝐨𝐫𝐢𝐳𝐨𝐧𝐭𝐚𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 (𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐎𝐮𝐭):- Horizontal scaling involves altering the number of pods available to the cluster to suit sudden changes in workload demands. As the scaling technique involves scaling pods instead of resources, it’s commonly a preferred approach to avoid resource deficits. [2] 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 (𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐔𝐩):- Contrary to horizontal scaling, a vertical scaling mechanism involves the dynamic provisioning of attributed resources such as RAM or CPU of cluster nodes to match application requirements. This is essentially achieved by tweaking the pod resource request parameters based on workload consumption metrics. [3] 𝐂𝐥𝐮𝐬𝐭𝐞𝐫/𝐌𝐮𝐥𝐭𝐢𝐝𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 :- Cluster scaling involves increasing or reducing the number of nodes in the cluster based on node utilization metrics and the existence of pending pods. The cluster autoscaling object typically interfaces with the chosen cloud provider so that it can request and deallocate nodes seamlessly as needed. [4] 𝐌𝐚𝐧𝐮𝐚𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 :- Manual scaling in Kubernetes involves adjusting the number of nodes or resources allocated to a cluster manually. This can be done by adding or removing nodes, adjusting resource requests and limits, and distributing workloads across nodes to optimize performance. [5] 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐢𝐧 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 :- Predictive scaling stands as a transformative approach in the orchestration of cloud-native applications, allowing Kubernetes to not just react to current demands but to anticipate future needs. This forward-looking strategy harnesses the power of data analysis and machine learning to create a more dynamic, efficient, and user-oriented scaling process. #aws #awscloud #awsdevopsengineer #docker #dockercontainer #dockercompose #dockerfile #dockerhub #linux #unix #Kubernetes #terraform #terraformcertified #linuxfoundation #jenkins #docker #kubernetes #terraform #grafana #aws #awscloud #awsdevops #devops #devopsengineer #devopscommunity #cloudcomputing #cloud #cloudengineer #repost #hiring #hiringnow #interestedcandidates #interviewpreparation #interview #devopsengineerhiring #devopsHr #devopshiring

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What is Amazon DynamoDB? Amazon DynamoDB is a fully managed key-value and document database designed to run high-performance applications. It delivers single-digit millisecond latency at any scale, making it ideal for applications that require consistent performance and dynamic scalability. Key Features of Amazon DynamoDB 1. Fully Managed Service: • No need to manage hardware, software, or infrastructure. AWS takes care of provisioning, patching, and maintenance. 2. Serverless and Auto-Scaling: • DynamoDB automatically scales up or down to meet your application’s workload, eliminating manual intervention. 3. High Availability and Durability: • Replicates data across multiple Availability Zones (AZs) for fault tolerance and ensures 99.99% uptime. 4. Low Latency: • Delivers single-digit millisecond response times, even under heavy workloads. 5. Global Tables for Multi-Region Replication: • Enables applications to deliver low-latency reads and writes globally with automatic replication. 6. Flexible Data Model: • Supports both key-value and document-based data models for flexibility in storing structured and unstructured data. 7. Built-In Security: • Includes encryption at rest and in transit, fine-grained access control with AWS IAM, and network isolation with VPC. 8. Backup and Restore: • Automated continuous backups and point-in-time recovery ensure data safety and easy recovery. 9. Event-Driven Architecture: • Seamlessly integrates with AWS Lambda, enabling real-time triggers and workflows. Why Choose DynamoDB? 1. Scalability Without Limits: • DynamoDB scales horizontally to handle millions of requests per second, making it perfect for applications with unpredictable workloads. 2. Pay-As-You-Go: • Only pay for the throughput and storage you use, optimizing costs for both small and large-scale applications. 3. Real-Time Performance: • Designed for high-speed applications like gaming, IoT, and real-time analytics. 4. Seamless Integration with AWS Ecosystem: • Works effortlessly with services like Lambda, API Gateway, S3, and CloudWatch. Use Cases for Amazon DynamoDB 1. E-Commerce Applications: • Power shopping carts, inventory systems, and session storage with real-time performance. 2. Gaming Platforms: • Store game states, leaderboards, and player profiles with low latency and high availability. 3. IoT Applications: • Manage and analyze time-series data from IoT devices. 4. Mobile and Web Apps: • Deliver responsive user experiences with fast reads and writes. 5. Real-Time Analytics: • Enable live dashboards and data analytics applications. Real-World Benefits • Unmatched Scalability: A global retail company handles millions of transactions per second during peak shopping periods using DynamoDB. #devops #sre #career #aws #database #dynamoDB #data #cloudops #development #bigdata #dataops #codexio_tech Codexio Tech

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🚀Mastering Kubernetes: Services vs Ingress for Production Applications 🚀 Kubernetes Services and Ingress Resources are critical components for networking in Kubernetes. Here's a breakdown of each and their use cases in production: 🔷 1. Kubernetes Services A Kubernetes Service is an abstraction that defines a logical set of Pods and a policy for accessing them. It ensures reliable communication between different components in your cluster. Types of Services: ✅ ClusterIP (Default): Exposes the service within the cluster using an internal IP. Use Case: Internal communication between microservices. ✅ NodePort: Exposes the service on each Node’s IP at a static port. Use Case: Debugging or quick external access in development environments. ✅ LoadBalancer: Creates an external load balancer (e.g., AWS ELB, Azure Load Balancer). Use Case: Exposing services to the internet in a production environment with managed cloud load balancers. ✅ ExternalName: Maps the service to an external DNS name. Use Case: Proxying traffic to external services (e.g., legacy systems or external APIs). 🟣 Production Use Cases for Services: Backend APIs communicate with frontend services using ClusterIP. Exposing applications to the internet with LoadBalancer in cloud environments. 🔷 2. Ingress Resources An Ingress is a Kubernetes API object that manages external HTTP/S access to services within a cluster. It operates at the application layer (Layer 7). Key Features: Path-based and host-based routing. SSL termination (using certificates). Load balancing. URL rewrites and redirects. Ingress Controller: You need an Ingress Controller (e.g., NGINX, Traefik, AWS ALB) to use Ingress. The controller processes the Ingress resource and routes traffic accordingly. 🟣 Production Use Cases for Ingress: Path-Based Routing: Example: /api routes to the API service, /dashboard routes to the dashboard service. Use Case: Exposing multiple services under a single domain. Host-Based Routing: Ex: api.abc.com routes to the API service, www.abc.com routes to the frontend. Use Case: Hosting multiple subdomains with a single Ingress. TLS/SSL Termination: Secure communication with HTTPS using certificates. Use Case: Securing traffic for production applications. Integration with ALB: Use the AWS ALB Ingress Controller for automatic ALB provisioning and advanced routing. Use Case: Production environments in AWS. ❇️ Choosing Between Services and Ingress in Production: ⏩ Use Services: For internal communication between microservices. When you need simple, cluster-internal connectivity. For quick, direct exposure of services via LoadBalancer (e.g., for a single app). ⏩ Use Ingress: When hosting multiple services under one domain. For advanced routing, SSL termination, and better traffic management. To reduce costs by avoiding multiple LoadBalancer services. Follow Abdul Rahman UK for more DevOps Content. HAPPY LEARNING 📌 👑 DevOps 👑 CLOUD #DevOps #Cloud #Kubernetes #Services #Ingress #AWS

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HA and zero downtime in Kubernetes ... 😎 Ensuring high availability (HA) and zero downtime in Kubernetes is crucial for maintaining service reliability and user satisfaction. Here are key strategies and solutions to achieve these goals: 1. Cluster Configuration for High Availability   - Multi-Master Setup: Deploy multiple control plane nodes (masters) to prevent a single point of failure. Use tools like **kubeadm** to set up a HA cluster.   - Etcd Clustering: Ensure that your etcd database is also highly available by running it in a cluster mode with an odd number of nodes (e.g., 3, 5) to maintain quorum. 2. Pod Management   - ReplicaSets: Use ReplicaSets to ensure that a specified number of pod replicas are always running. This helps in maintaining availability if a pod fails.   - Deployment Strategy: Implement rolling updates for Deployments. This allows you to update applications without downtime by gradually replacing old pods with new ones. 3. Load Balancing   - Service Types: Use Kubernetes Services (ClusterIP, NodePort, LoadBalancer) to distribute traffic among pods. An external load balancer can be used for incoming traffic, ensuring that requests are routed to healthy pods.   - Ingress Controllers: Utilize Ingress resources with Ingress controllers to manage external access to services, providing features like SSL termination and path-based routing. 4. Health Checks   - Liveness and Readiness Probes: Implement liveness and readiness probes for your pods. Liveness probes restart unhealthy pods, while readiness probes ensure that traffic is only sent to pods that are ready to serve requests. 5. Stateful Applications   - For stateful applications, use StatefulSets which provide stable network identities and persistent storage. 6. Persistent Storage   - Use Persistent Volumes and Persistent Volume Claims to ensure that data is not lost when pods are rescheduled. Choose storage solutions that support HA, such as cloud provider storage classes or distributed file systems.  7. Monitoring and Alerts   - Implement monitoring solutions like Prometheus and Grafana to keep track of the health and performance of your applications and infrastructure. Set up alerts for unusual behaviors or failures. 8. Disaster Recovery   - Regularly back up your configurations and persistent data. Use tools like Velero for backing up Kubernetes resources and persistent volumes, ensuring you can recover quickly from failures. 9. Network Policies   - Use network policies to control the traffic flow between pods. This enhances security and can prevent cascading failures in case of a compromised service. 10. Testing and Staging   - Always test updates in a staging environment before deploying to production. #DevOps #DevOps_engineer #k8S #kubernetes #cloud #AWS #AZURE #Cloud_Prividers #GCP #CICD #PIPELINE #GIT #GITLAB #PODS #KUBE #AUTOMATION #ANSIBLE #CHEFF #NEXUS #DOCKER #MONITORING #Prometheus #Grafana #Monitoring_Tools

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🚀 **Day 25 of 30-Day DevOps Interview Prep: Managing Kubernetes Clusters with Infrastructure as Code (Part 1)** 🚀 Today, we’re diving into managing Kubernetes clusters with Infrastructure as Code (IaC). IaC brings automation, repeatability, and version control to your infrastructure, making it easier to scale and maintain. Here’s a Q&A to guide you through best practices: 1️⃣ **What is Infrastructure as Code (IaC), and how does it apply to Kubernetes?** IaC is a practice where infrastructure is defined and managed using code. For Kubernetes, IaC tools like Terraform, Helm, and Kustomize allow you to declare your cluster configuration and application deployments in code. This approach brings consistency, version control, and automation to your Kubernetes environments, ensuring that infrastructure is managed in a repeatable and scalable way. 2️⃣ **What are the key benefits of using IaC for managing Kubernetes clusters?** IaC enables you to automate the provisioning, updating, and scaling of your Kubernetes clusters. It reduces manual errors by ensuring that your infrastructure is defined in code and versioned in Git. This makes rollbacks easy and allows you to replicate environments across different regions or stages (e.g., dev, staging, prod) with consistency. 3️⃣ **What tools are commonly used for managing Kubernetes with IaC?** Terraform, Helm, and Kustomize are popular IaC tools for Kubernetes. Terraform allows you to manage cloud infrastructure and Kubernetes resources from a single configuration file. Helm simplifies the deployment of Kubernetes applications using pre-packaged templates (charts). Kustomize lets you customize and manage Kubernetes manifests without needing to template files, making it easier to manage different environments. 4️⃣ **How do you use Terraform to manage Kubernetes clusters?** Terraform can be used to provision and manage Kubernetes clusters on various cloud providers (e.g., AWS, GCP, Azure). You define your Kubernetes infrastructure in a Terraform configuration file, which Terraform uses to create, update, or destroy resources. Terraform’s state management ensures that your infrastructure is always in sync with your code. 5️⃣ **What are the best practices for managing Kubernetes with Helm and Kustomize?** For Helm, use version-controlled Helm charts to deploy and update applications in Kubernetes. Helm’s templating system makes it easy to customize deployments. With Kustomize, you can overlay and modify existing Kubernetes manifests for different environments, making it easier to maintain consistent deployments across dev, staging, and production environments. 👉 **Stay tuned for Part 2**, where we’ll explore advanced strategies for scaling IaC, integrating IaC with CI/CD pipelines, and managing multi-cluster Kubernetes environments! #DevOps #Kubernetes #IaC #InfrastructureAsCode #Containers #InterviewPreparation #Learning

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✨ Engineering, specially deploying and managing applications at scale is a very big challenging task. 🤔 Managed Kubernetes - EKS, AKS and GKE only solves half of the problem of managing the control plane of Kubernetes. It's still the work of your Devops / SRE / Backend engineers to do all these - - Upgrade K8s and node versions frequently - Build, manage, update Helm Charts as needed - Maintain and monitor your application status, debug any node/infra/network failures - Build and maintain a full logging, monitoring stack Devops is costly, either hire experienced devops engineers, or make your backend engineers learn all this (developer time cost) 💰💰

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