#kubernetes pod

[codeonedigest]

Kubernetes Node Tutorial for Beginners | Kubernetes Node Explained

Hi, a new #video on #kubernetesnode is published on #codeonedigest #youtube channel. Learn #kubernetes #node #kubectl #docker #controllermanager #programming #coding with codeonedigest

 #kubernetesnode #kubernetesnodeport #kubernetesnodeaffinity #kubernetesnodes #kubernetesnodesandpods #kubernetesnodeportvsclusterip #kubernetesnodenotready #kubernetesnodeaffinityvsnodeselector #kubernetesnodeselector #kubernetesnodetaint #kubernetesnodeexporter #kubernetesnodetutorial #kubernetesnodeexplained #kubernetesnodes #kubernetesnodesandpods #kubernetesnodesvspods #kubernetesnodesnotready #kubernetesnodesvscluster #kubernetesnodesvsnamespaces #kubernetesnodesnotreadystatus #kubernetesnodesstatusnotready

[karamathalip]

Introduction to Kubernetes

Kubernetes, often abbreviated as K8s, is an open-source platform designed to automate deploying, scaling, and operating application containers. Originally developed by Google, it is now maintained by the Cloud Native Computing Foundation (CNCF). Kubernetes has become the de facto standard for container orchestration, offering a robust framework for managing microservices architectures in production environments.

In today's rapidly evolving tech landscape, Kubernetes plays a crucial role in modern application development. It provides the necessary tools and capabilities to handle complex, distributed systems reliably and efficiently. From scaling applications seamlessly to ensuring high availability, Kubernetes is indispensable for organizations aiming to achieve agility and resilience in their software deployments.

History and Evolution of Kubernetes

The origins of Kubernetes trace back to Google's internal system called Borg, which managed large-scale containerized applications. Drawing from years of experience and lessons learned with Borg, Google introduced Kubernetes to the public in 2014. Since then, it has undergone significant development and community contributions, evolving into a comprehensive and flexible orchestration platform.

Some key milestones in the evolution of Kubernetes include its donation to the CNCF in 2015, the release of version 1.0 the same year, and the subsequent releases that brought enhanced features and stability. Today, Kubernetes is supported by a vast ecosystem of tools, extensions, and integrations, making it a cornerstone of cloud-native computing.

Key Concepts and Components

Nodes and Clusters

A Kubernetes cluster is a set of nodes, where each node can be either a physical or virtual machine. There are two types of nodes: master nodes, which manage the cluster, and worker nodes, which run the containerized applications.

Pods and Containers

At the core of Kubernetes is the concept of a Pod, the smallest deployable unit that can contain one or more containers. Pods encapsulate an application’s container(s), storage resources, a unique network IP, and options on how the container(s) should run.

Deployments and ReplicaSets

Deployments are used to manage and scale sets of identical Pods. A Deployment ensures that a specified number of Pods are running at all times, providing declarative updates to applications. ReplicaSets are a subset of Deployments that maintain a stable set of replica Pods running at any given time.

Services and Networking

Services in Kubernetes provide a stable IP address and DNS name to a set of Pods, facilitating seamless networking. They abstract the complexity of networking by enabling communication between Pods and other services without needing to manage individual Pod IP addresses.

Kubernetes Architecture

Master and Worker Nodes

The Kubernetes architecture is based on a master-worker model. The master node controls and manages the cluster, while the worker nodes run the applications. The master node’s key components include the API server, scheduler, and controller manager, which together manage the cluster’s state and lifecycle.

Control Plane Components

The control plane, primarily hosted on the master node, comprises several critical components:

API Server: The front-end for the Kubernetes control plane, handling all API requests for managing cluster resources.

etcd: A distributed key-value store that holds the cluster’s state data.

Scheduler: Assigns workloads to worker nodes based on resource availability and other constraints.

Controller Manager: Runs various controllers to regulate the state of the cluster, such as node controllers, replication controllers, and more.

Node Components

Each worker node hosts several essential components:

kubelet: An agent that runs on each node, ensuring containers are running in Pods.

kube-proxy: Maintains network rules on nodes, enabling communication to and from Pods.

Container Runtime: Software responsible for running the containers, such as Docker or containerd.

[jamercado]

Kubernetes: Control Plane and Workers

In Kubernetes, the control plane and worker nodes are two key components that together form the foundation of a Kubernetes cluster. They play distinct roles in managing and running containerized applications. Here’s an explanation of each component along with examples and YAML configurations where relevant: Control Plane The control plane is the brain of the Kubernetes cluster. It manages the…

[theskillpedia]

Session 9 Kubernetes Pods

[mp3monsterme]

OpenLens or Lens app

I wrote about how much I like the lens app K8s dashboard capability without needing to deploy K8s dashboard. Sadly recently, there has been some divergence from K8sLens being a pure open source to a licensed tool with an upstream open-source version called Open Lens (article here). It has fallen to individual contributors to maintain the open-lens binary (here) and made it available via…

[rajaniesh]

Unlock the benefits of Horizontal Autoscaling on Azure Kubernetes Services

Are you a developer or coder looking for ways to maximize efficiency when using Azure Kubernetes Service (AKS) for large-scale applications? If so, horizontal autoscaling might be just what you need! Horizontal autoscaling lets you maintain your existing architecture and enables your system to continually adjust the number of pods in response to changing workloads. This blog post will explore how…

[exeggcute]

someone should ask chatGPT what it's like to live inside a huge kubernetes cluster... is it dark in there, is it noisy, does it hurt when pods scale up/down, are replicas more like friends or rivals, etc etc

[govindhtech]

How To Use Llama 3.1 405B FP16 LLM On Google Kubernetes

How to set up and use large open models for multi-host generation AI over GKE

Access to open models is more important than ever for developers as generative AI grows rapidly due to developments in LLMs (Large Language Models). Open models are pre-trained foundational LLMs that are accessible to the general population. Data scientists, machine learning engineers, and application developers already have easy access to open models through platforms like Hugging Face, Kaggle, and Google Cloud’s Vertex AI.

How to use Llama 3.1 405B

Google is announcing today the ability to install and run open models like Llama 3.1 405B FP16 LLM over GKE (Google Kubernetes Engine), as some of these models demand robust infrastructure and deployment capabilities. With 405 billion parameters, Llama 3.1, published by Meta, shows notable gains in general knowledge, reasoning skills, and coding ability. To store and compute 405 billion parameters at FP (floating point) 16 precision, the model needs more than 750GB of GPU RAM for inference. The difficulty of deploying and serving such big models is lessened by the GKE method discussed in this article.

Customer Experience

You may locate the Llama 3.1 LLM as a Google Cloud customer by selecting the Llama 3.1 model tile in Vertex AI Model Garden.

Once the deploy button has been clicked, you can choose the Llama 3.1 405B FP16 model and select GKE.Image credit to Google Cloud

The automatically generated Kubernetes yaml and comprehensive deployment and serving instructions for Llama 3.1 405B FP16 are available on this page.

Deployment and servicing multiple hosts

Llama 3.1 405B FP16 LLM has significant deployment and service problems and demands over 750 GB of GPU memory. The total memory needs are influenced by a number of parameters, including the memory used by model weights, longer sequence length support, and KV (Key-Value) cache storage. Eight H100 Nvidia GPUs with 80 GB of HBM (High-Bandwidth Memory) apiece make up the A3 virtual machines, which are currently the most potent GPU option available on the Google Cloud platform. The only practical way to provide LLMs such as the FP16 Llama 3.1 405B model is to install and serve them across several hosts. To deploy over GKE, Google employs LeaderWorkerSet with Ray and vLLM.

LeaderWorkerSet

A deployment API called LeaderWorkerSet (LWS) was created especially to meet the workload demands of multi-host inference. It makes it easier to shard and run the model across numerous devices on numerous nodes. Built as a Kubernetes deployment API, LWS is compatible with both GPUs and TPUs and is independent of accelerators and the cloud. As shown here, LWS uses the upstream StatefulSet API as its core building piece.

A collection of pods is controlled as a single unit under the LWS architecture. Every pod in this group is given a distinct index between 0 and n-1, with the pod with number 0 being identified as the group leader. Every pod that is part of the group is created simultaneously and has the same lifecycle. At the group level, LWS makes rollout and rolling upgrades easier. For rolling updates, scaling, and mapping to a certain topology for placement, each group is treated as a single unit.

Each group’s upgrade procedure is carried out as a single, cohesive entity, guaranteeing that every pod in the group receives an update at the same time. While topology-aware placement is optional, it is acceptable for all pods in the same group to co-locate in the same topology. With optional all-or-nothing restart support, the group is also handled as a single entity when addressing failures. When enabled, if one pod in the group fails or if one container within any of the pods is restarted, all of the pods in the group will be recreated.

In the LWS framework, a group including a single leader and a group of workers is referred to as a replica. Two templates are supported by LWS: one for the workers and one for the leader. By offering a scale endpoint for HPA, LWS makes it possible to dynamically scale the number of replicas.

Deploying multiple hosts using vLLM and LWS

vLLM is a well-known open source model server that uses pipeline and tensor parallelism to provide multi-node multi-GPU inference. Using Megatron-LM’s tensor parallel technique, vLLM facilitates distributed tensor parallelism. With Ray for multi-node inferencing, vLLM controls the distributed runtime for pipeline parallelism.

By dividing the model horizontally across several GPUs, tensor parallelism makes the tensor parallel size equal to the number of GPUs at each node. It is crucial to remember that this method requires quick network connectivity between the GPUs.

However, pipeline parallelism does not require continuous connection between GPUs and divides the model vertically per layer. This usually equates to the quantity of nodes used for multi-host serving.

In order to support the complete Llama 3.1 405B FP16 paradigm, several parallelism techniques must be combined. To meet the model’s 750 GB memory requirement, two A3 nodes with eight H100 GPUs each will have a combined memory capacity of 1280 GB. Along with supporting lengthy context lengths, this setup will supply the buffer memory required for the key-value (KV) cache. The pipeline parallel size is set to two for this LWS deployment, while the tensor parallel size is set to eight.

In brief

We discussed in this blog how LWS provides you with the necessary features for multi-host serving. This method maximizes price-to-performance ratios and can also be used with smaller models, such as the Llama 3.1 405B FP8, on more affordable devices. Check out its Github to learn more and make direct contributions to LWS, which is open-sourced and has a vibrant community.

You can visit Vertex AI Model Garden to deploy and serve open models via managed Vertex AI backends or GKE DIY (Do It Yourself) clusters, as the Google Cloud Platform assists clients in embracing a gen AI workload. Multi-host deployment and serving is one example of how it aims to provide a flawless customer experience.

Read more on Govindhtech.com

[haroldfinch07]

Load Balancing Web Sockets with K8s/Istio

When load balancing WebSockets in a Kubernetes (K8s) environment with Istio, there are several considerations to ensure persistent, low-latency connections. WebSockets require special handling because they are long-lived, bidirectional connections, which are different from standard HTTP request-response communication. Here’s a guide to implementing load balancing for WebSockets using Istio.

1. Enable WebSocket Support in Istio

By default, Istio supports WebSocket connections, but certain configurations may need tweaking. You should ensure that:

Destination rules and VirtualServices are configured appropriately to allow WebSocket traffic.

Example VirtualService Configuration.

Here, websocketUpgrade: true explicitly allows WebSocket traffic and ensures that Istio won’t downgrade the WebSocket connection to HTTP.

2. Session Affinity (Sticky Sessions)

In WebSocket applications, sticky sessions or session affinity is often necessary to keep long-running WebSocket connections tied to the same backend pod. Without session affinity, WebSocket connections can be terminated if the load balancer routes the traffic to a different pod.

Implementing Session Affinity in Istio.

Session affinity is typically achieved by setting the sessionAffinity field to ClientIP at the Kubernetes service level.

In Istio, you might also control affinity using headers. For example, Istio can route traffic based on headers by configuring a VirtualService to ensure connections stay on the same backend.

3. Load Balancing Strategy

Since WebSocket connections are long-lived, round-robin or random load balancing strategies can lead to unbalanced workloads across pods. To address this, you may consider using least connection or consistent hashing algorithms to ensure that existing connections are efficiently distributed.

Load Balancer Configuration in Istio.

Istio allows you to specify different load balancing strategies in the DestinationRule for your services. For WebSockets, the LEAST_CONN strategy may be more appropriate.

Alternatively, you could use consistent hashing for a more sticky routing based on connection properties like the user session ID.

This configuration ensures that connections with the same session ID go to the same pod.

4. Scaling Considerations

WebSocket applications can handle a large number of concurrent connections, so you’ll need to ensure that your Kubernetes cluster can scale appropriately.

Horizontal Pod Autoscaler (HPA): Use an HPA to automatically scale your pods based on metrics like CPU, memory, or custom metrics such as open WebSocket connections.

Istio Autoscaler: You may also scale Istio itself to handle the increased load on the control plane as WebSocket connections increase.

5. Connection Timeouts and Keep-Alive

Ensure that both your WebSocket clients and the Istio proxy (Envoy) are configured for long-lived connections. Some settings that need attention:

Timeouts: In VirtualService, make sure there are no aggressive timeout settings that would prematurely close WebSocket connections.

Keep-Alive Settings: You can also adjust the keep-alive settings at the Envoy level if necessary. Envoy, the proxy used by Istio, supports long-lived WebSocket connections out-of-the-box, but custom keep-alive policies can be configured.

6. Ingress Gateway Configuration

If you're using an Istio Ingress Gateway, ensure that it is configured to handle WebSocket traffic. The gateway should allow for WebSocket connections on the relevant port.

This configuration ensures that the Ingress Gateway can handle WebSocket upgrades and correctly route them to the backend service.

Summary of Key Steps

Enable WebSocket support in Istio’s VirtualService.

Use session affinity to tie WebSocket connections to the same backend pod.

Choose an appropriate load balancing strategy, such as least connection or consistent hashing.

Set timeouts and keep-alive policies to ensure long-lived WebSocket connections.

Configure the Ingress Gateway to handle WebSocket traffic.

By properly configuring Istio, Kubernetes, and your WebSocket service, you can efficiently load balance WebSocket connections in a microservices architecture.

[andmaybegayer]

It looks like some of the cronjobs that would normally maintain NextCloud Memories are not set up by the kube pod that they use for apps so I am learning things about Kubernetes against my will. Committing crimes by running shells inside pods.

When I learned about Docker against my will I also turned out to think that was pretty neat so, you know. Kubernetes can use Docker but this one doesn't.

[labexio]

Mastering Kubernetes: A Comprehensive Guide to Kubernetes Skill Tree Free

Kubernetes has become an essential tool for modern developers and IT professionals aiming to manage containerized applications effectively. With its robust features and scalability, Kubernetes empowers organizations to automate deployments, manage workloads, and optimize resource utilization. Leveraging the Kubernetes Skill Tree can be a game-changer for mastering Kubernetes concepts and achieving seamless Kubernetes integration in your projects.

Why Kubernetes Matters

Kubernetes, also known as K8s, is an open-source platform designed to manage containerized workloads and services. It automates deployment, scaling, and operations, providing the flexibility needed for dynamic environments. Whether you're running a small project or managing large-scale enterprise applications, Kubernetes offers unmatched reliability and control.

Navigating the Kubernetes Skill Tree

The Kubernetes Skill Tree is an innovative approach to structured learning, breaking down complex topics into manageable, progressive steps. It allows learners to advance through foundational, intermediate, and advanced concepts at their own pace. Key areas of focus in the Kubernetes Skill Tree include:

Foundational Concepts

Understanding Kubernetes architecture and components.

Learning about nodes, pods, and clusters.

Basics of YAML files for deployment configuration.

Core Operations

Deploying applications with Kubernetes.

Managing scaling and resource allocation.

Monitoring and maintaining workloads.

Advanced Techniques

Setting up CI/CD pipelines with Kubernetes.

Leveraging Helm charts for application management.

Implementing security best practices.

This structured approach helps learners build a strong foundation while gradually mastering advanced Kubernetes capabilities.

Exploring the Kubernetes Playground

Hands-on practice is critical to understanding Kubernetes, and the Kubernetes Playground provides an ideal environment for experimentation. This interactive platform allows developers to test configurations, deploy applications, and debug issues without affecting production systems.

Benefits of the Kubernetes Playground include:

Safe Experimentation: Try new ideas without fear of breaking live systems.

Real-World Scenarios: Simulate deployment and scaling challenges in a controlled environment.

Collaboration: Work alongside team members to solve problems and share knowledge.

By incorporating regular practice in the Kubernetes Playground, learners can reinforce their understanding of concepts and gain confidence in applying them to real-world projects.

Streamlining Kubernetes Integration

One of the most critical aspects of Kubernetes adoption is ensuring seamless Kubernetes integration with existing systems and workflows. Integration can involve connecting Kubernetes with cloud services, on-premise systems, or third-party tools.

Steps to effective Kubernetes integration include:

Assessing Requirements: Identify the systems and services to integrate with Kubernetes.

Configuring Networking: Ensure proper communication between Kubernetes clusters and external services.

Automating Workflows: Use tools like Jenkins, GitLab CI/CD, and Terraform for automated deployments.

Monitoring Performance: Implement tools such as Prometheus and Grafana for real-time monitoring and alerts.

Successful integration not only enhances operational efficiency but also unlocks Kubernetes’ full potential for managing complex applications.

Reinforcing Knowledge with Kubernetes Exercises

Learning Kubernetes isn’t just about theoretical knowledge; it’s about applying concepts to solve real-world problems. Kubernetes exercises offer practical scenarios that challenge learners to deploy, scale, and troubleshoot applications.

Examples of valuable Kubernetes exercises include:

Deploying a multi-container application.

Scaling a web application based on traffic spikes.

Implementing role-based access control (RBAC).

Debugging a failed deployment.

These exercises simulate real challenges faced by developers and operations teams, ensuring learners are well-prepared for professional environments.

The Future of Kubernetes

As cloud-native technologies evolve, Kubernetes continues to grow in importance. Organizations increasingly rely on it for flexibility, scalability, and innovation. By mastering the Kubernetes Skill Tree, leveraging the Kubernetes Playground, and performing hands-on Kubernetes exercises, professionals can stay ahead of the curve.

Whether you're an aspiring developer or an experienced IT professional, Kubernetes provides endless opportunities to enhance your skill set and contribute to cutting-edge projects. Begin your journey today and unlock the power of Kubernetes for modern application management.

[codeonedigest]

Kubernetes API Tutorial with Examples for Devops Beginners and Students

Hi, a new #video on #kubernetesapi is published on #codeonedigest #youtube channel. Learn #kubernetes #api #kubectl  #node #docker #container #cloud #aws #azure #programming #coding with #codeonedigest

@java #java #awscloud @awscloud #aws @AWSCloudIndia #Cloud #CloudComputing @YouTube #youtube #azure #msazure #microsoftazure  #kubernetes #kubernetestutorial #kubernetestutorialforbeginners #kubernetesinstallation #kubernetesinterviewquestions #kubernetesexplained #kubernetesorchestrationtutorial #kubernetesoperator #kubernetesoverview #kubernetesnetworkpolicy #kubernetesnetworkpolicyexplained #kubernetesnetworkpolicytutorial #kubernetesnetworkpolicyexample #containernetworkinterface #containernetworkinterfaceKubernetes #containernetworkinterfaceplugin #containernetworkinterfaceazure #containernetworkinterfaceaws #azure #aws #azurecloud #awscloud #orchestration #kubernetesapi #Kubernetesapiserver #Kubernetesapigateway #Kubernetesapipython #Kubernetesapiauthentication #Kubernetesapiversion #Kubernetesapijavaclient #Kubernetesapiclient

[fabzen123]

Scalable and Reliable: Kubernetes Deployment Strategies for EC2

Introduction:

Kubernetes has emerged as a leading container orchestration platform, offering scalable and reliable deployment options for managing containerized workloads on AWS EC2 instances. In this article, powered by Cloudzenia expertise, we'll explore Kubernetes deployment strategies tailored for EC2 environments, focusing on scalability, reliability, and best practices for optimal performance.

Understanding Kubernetes Deployment on EC2:

Kubernetes Components: Kubernetes orchestrates containerized applications using a master-slave architecture consisting of master nodes (control plane) and worker nodes (EC2 instances) where containers are deployed and managed.

EC2 Integration: Kubernetes seamlessly integrates with AWS EC2 instances, allowing organizations to leverage EC2's scalability, reliability, and networking capabilities for hosting Kubernetes clusters and running containerized workloads.

Deployment Strategies for EC2:

Static Pods: Deploying static pods directly on EC2 instances allows for simple and lightweight deployment of containers without relying on the Kubernetes control plane, suitable for stateless applications or specialized workloads.

Self-Managed Kubernetes: Running a self-managed Kubernetes cluster on EC2 instances provides full control over cluster configuration, node management, and workload scheduling, offering flexibility and customization options for specific requirements.

Managed Kubernetes Services: Leveraging managed Kubernetes on ec2 services such as Amazon EKS (Elastic Kubernetes Service) simplifies cluster provisioning, management, and scaling, abstracting away infrastructure complexities and enabling organizations to focus on application development and deployment.

Scaling Strategies for EC2-based Kubernetes Clusters:

Horizontal Pod Autoscaling (HPA): Implement HPA to automatically scale the number of Kubernetes pods based on CPU or custom metrics, allowing applications to dynamically adjust resources based on workload demand and optimize resource utilization.

Node Autoscaling: Utilize EC2 Auto Scaling groups to automatically scale the number of EC2 instances in Kubernetes worker nodes based on CPU, memory, or custom metrics, ensuring that the cluster can handle varying workload demands and maintain availability.

Cluster Autoscaler: Deploy the Kubernetes Cluster Autoscaler to automatically adjust the size of Kubernetes clusters by adding or removing EC2 instances based on pending pod requests, optimizing resource allocation and minimizing costs.

Reliability and High Availability Considerations:

Multi-AZ Deployment: Distribute Kubernetes worker nodes across multiple Availability Zones (AZs) to achieve fault tolerance and high availability, ensuring that applications remain resilient to AZ failures and maintain continuous operation.

Pod Disruption Budgets (PDBs): Implement PDBs to define policies for pod eviction during node maintenance or cluster scaling events, ensuring that critical pods are not disrupted and maintaining application availability and reliability.

Stateful Workloads: Handle stateful workloads on Kubernetes by leveraging Persistent Volumes (PVs) and Persistent Volume Claims (PVCs) to ensure data persistence and integrity across node failures or cluster scaling activities.

Best Practices for Kubernetes Deployment on EC2:

Infrastructure as Code (IaC): Use tools like Terraform or AWS CloudFormation to provision and manage EC2 instances, networking, and Kubernetes cluster configurations as code, enabling infrastructure automation and repeatability.

Monitoring and Observability: Implement comprehensive monitoring and observability solutions using tools like Prometheus, Grafana, and AWS CloudWatch to track Kubernetes cluster health, resource utilization, and application performance, enabling proactive issue detection and troubleshooting.

Continuous Integration and Deployment (CI/CD): Implement CI/CD pipelines with tools like Jenkins, GitLab CI, or AWS CodePipeline to automate application builds, testing, and deployment to Kubernetes clusters on EC2, ensuring fast and reliable delivery of updates and enhancements.

Conclusion:

Kubernetes deployment on AWS EC2, utilizing cutting-edge cloud technology, offers scalable, reliable, and flexible options for hosting containerized workloads in the cloud. By adopting appropriate deployment strategies, scaling mechanisms, reliability measures, and best practices, organizations can build robust Kubernetes environments on EC2, ensuring high availability, optimal performance, and efficient resource utilization for their applications in today's dynamic and demanding cloud landscape.

[ericvanderburg]

Troubleshooting Kubernetes Pod Crashes: Common Causes and Effective Solutions

http://securitytc.com/TH7Zh2

[fromdevcom]

Introduction Too much monitoring and alert fatigue is a serious issue for today's engineering teams. Nowadays, there are several open-source and third-party solutions available to help you sort through the noise. It always seems too good to be true, and it probably is. However, as Kubernetes deployments have grown in complexity and size, performance optimization and observability have become critical to guaranteeing optimal resource usage and early issue identification. Kubernetes events give unique and unambiguous information about cluster health and performance. And in these days of too much data, they also give clear insight with minimal noise. In this article, we will learn about Kubernetes events and their importance, their types, and how to access them. What is a Kubernetes Event? A Kubernetes event is an object that displays what is going on inside a cluster, node, pod, or container. These items are typically created in reaction to changes that occur inside your K8s system. The Kubernetes API Server allows all key components to generate these events. In general, each event includes a log message. However, they are quite different and have no other effect on one another. Importance of Kubernetes Events When any of the resources that Kubernetes manages changes, it broadcasts an event. These events frequently provide crucial metadata about the object that caused them, such as the event category (Normal, Warning, Error), as well as the reason. This data is often saved in etcd and made available by running specific kubectl commands. These events help us understand what happened behind the scenes when an entity entered a given state. You may also obtain an aggregated list of all events by running kubectl get events. Events are produced by every part of a cluster, therefore as your Kubernetes environment grows, so will the amount of events your system produces. Furthermore, every change in your system generates events, and even healthy and normal operations require changes in a perfectly running system. This means that a big proportion of the events created by your clusters are purely informative and may not be relevant when debugging an issue. Monitoring Kubernetes Events Monitoring Kubernetes events can help you identify issues with pod scheduling, resource limits, access to external volumes, and other elements of your Kubernetes setup. Events give rich contextual hints that will assist you in troubleshooting these issues and ensuring system health, allowing you to keep your Kubernetes-based apps and infrastructure stable, reliable, and efficient. How to Identify Which Kubernetes Events are Important Naturally, there are a variety of events that may be relevant to your Kubernetes setup, and various issues may arise when Kubernetes or your cloud platform executes basic functions. Let's get into each main event. Failed Events The kube-scheduler in Kubernetes schedules pods, which contain containers that operate your application on available nodes. The kubelet monitors the node's resource use and guarantees that containers execute as intended. The building of the underlying container fails when the kube-scheduler fails to schedule a pod, causing the kubelet to generate a warning event. Eviction Events Eviction events are another crucial event to keep track of since they indicate when a node removes running pods. The most typical reason for an eviction event is a node's insufficient incompressible resources, such as RAM or storage. The kubelet generates resource-exhaustion eviction events on the affected node. In case Kubernetes determines that a pod is utilizing more incompressible resources than what its runtime permits, it can remove the pod from its node and arrange for a new time slot. Volume Events A directory holding data (like an external library) that a pod may access and expose to its containers so they can carry out their workloads with any necessary dependencies is known as a Kubernetes volume.

Separating this linked data from the pod offers a failsafe way for retaining information if the pod breaks, as well as facilitating data exchange amongst containers on the same pod. When Kubernetes assigns a volume to a new pod, it first detaches it from the node it is presently on, attaches it to the required node, and then mounts it onto a pod. Unready Node Events Node readiness is one of the requirements that the node's kubelet consistently returns as true or false. The kubelet creates unready node events when a node transitions from ready to not ready, indicating that it is not ready for pod scheduling.  How to Access Kubernetes Events Metrics, logs, and events may be exported from Kubernetes for observability. With a variety of methods at your fingertips, events may be a valuable source of information about what's going on in your services. Kubernetes does not have built-in functionality for accessing, storing, or forwarding long-term events. It stores it for a brief period of time before cleaning it up. However, Kubernetes event logs may be retrieved directly from the cluster using Kubectl and collected or monitored using a logging tool. Running the kubectl describe command on a given cluster resource will provide a list of its events. A more general approach is to use the kubectl get events command, which lists the events of specified resources or the whole cluster. Many free and commercial third-party solutions assist in providing visibility and reporting Kubernetes cluster events. Let's look at some free, open-source tools and how they may be used to monitor your Kubernetes installation: KubeWatch KubeWatch is an excellent open-source solution for monitoring and broadcasting K8s events to third-party applications and webhooks. You may set it up to deliver notifications to Slack channels when major status changes occur. You may also use it to transmit events to analytics and alerting systems such as Prometheus. Events Exporter The Kubernetes Events Exporter is a good alternative to K8s' native observing mechanisms. It allows you to constantly monitor K8s events and list them as needed. It also extracts a number of metrics from the data it collects, such as event counts and unique event counts, and offers a simple monitoring configuration. EventRouter EventRouter is another excellent open-source solution for gathering Kubernetes events. It is simple to build up and seeks to stream Kubernetes events to numerous sources, as described in its documentation. However, like KubeWatch, it does not have querying or persistent capabilities. To get the full experience, you should link it to a third-party storage and analysis tool. Conclusion Kubernetes events provide an excellent approach to monitor and improve the performance of your K8s clusters. They become more effective when combined with realistic tactics and vast toolsets. I hope this article helps you to understand the importance of Kubernetes events and how to get the most out of them.

[dockerdummy]

Kubernetes InPlacePodVerticalScaling Feature: Changing CPU and Memory Reservations and Limits without POD Restart

In Kubernetes v1.27, the InPlacePodVerticalScaling feature was introduced as an alpha capability, allowing you to adjust CPU and memory resources of running pods without restarting them. This feature can reduce downtime and make resource scaling more efficient. In this guide, we’ll explore how to test this feature on Killerkoda, a free Kubernetes playground for hands-on…