Kubernetes

Random comments about Kubernetes

How to ensure high availability for pods in Kubernetes

I was thinking the other day about these Kubernetes pods, and how they’re like little spaceships floating around in the cluster. But what happens if one of those spaceships suddenly vanishes? Poof! Gone! That’s a real problem. So I started wondering, how can we ensure our pods are always there, ready to do their job, even if things go wrong? It’s like trying to keep a juggling act going while someone’s moving the floor around you…

Let me tell you about this tool called Karpenter. It’s like a super-efficient hotel manager for our Kubernetes worker nodes, always trying to arrange the “guests” (our applications) most cost-effectively. Sometimes, this means moving guests from one room to another to save on operating costs. In Kubernetes terminology, we call this “consolidation.”

The dancing pods challenge

Here’s the thing: We have this wonderful hotel manager (Karpenter) who’s doing a fantastic job, keeping costs down by constantly optimizing room assignments. But what about our guests (the applications)? They might get a bit dizzy with all this moving around, and sometimes, their important work gets disrupted.

So, the question is: How do we keep our applications running smoothly while still allowing Karpenter to do its magic? It’s like trying to keep a circus performance going while the stage crew rearranges the set in the middle of the act.

Understanding the moving parts

Before we explore the solutions, let’s take a peek behind the scenes and see what happens when Karpenter decides to relocate our applications. It’s quite a fascinating process:

First, Karpenter puts up a “Do Not Disturb” sign (technically called a taint) on the node it wants to clear. Then, it finds new accommodations for all the applications. Finally, it carefully moves each application to its new location.

Think of it as a well-choreographed dance where each step must be perfectly timed to avoid any missteps.

The art of high availability

Now, for the exciting part, we have some clever tricks up our sleeves to ensure our applications keep running smoothly:

The buddy system: The first rule of high availability is simple: never go it alone! Instead of running a single instance of your application, run at least two. It’s like having a backup singer, if one voice falters, the show goes on. In Kubernetes, we do this by setting replicas: 2 in our deployment configuration.
Strategic placement: Here’s a neat trick: we can tell Kubernetes to spread our application copies across different physical machines. It’s like not putting all your eggs in one basket. We use something called “Pod Topology Spread Constraints” for this. Here’s how it looks in practice:

topologySpreadConstraints:
  - maxSkew: 1
    topologyKey: kubernetes.io/hostname
    whenUnsatisfiable: DoNotSchedule
    labelSelector:
      matchLabels:
        app: your-app

Setting boundaries: Remember when your parents set rules about how many cookies you could eat? We do something similar in Kubernetes with PodDisruptionBudgets (PDB). We tell Kubernetes, “Hey, you must always keep at least 50% of my application instances running.” This prevents our hotel manager from getting too enthusiastic about rearranging things.
The “Do Not Disturb” sign: For those special cases where we absolutely don’t want an application to be moved, we can put up a permanent “Do Not Disturb” sign using the karpenter.sh/do-not-disrupt: “true” annotation. It’s like having a VIP guest who gets to keep their room no matter what.

The complete picture

The beauty of this system lies in how all the pieces work together. Think of it as a safety net with multiple layers:

Multiple instances ensure basic redundancy.
Strategic placement keeps instances separated.
PodDisruptionBudgets prevent too many moves at once.
And when necessary, we can completely prevent disruption.

A real example

Let me paint you a picture. Imagine you’re running a critical web service. Here’s how you might set it up:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: critical-web-service
spec:
  replicas: 2
  template:
    metadata:
      annotations:
        karpenter.sh/do-not-disrupt: "false"  # We allow movement, but with controls
    spec:
      topologySpreadConstraints:
        - maxSkew: 1
          topologyKey: kubernetes.io/hostname
          whenUnsatisfiable: DoNotSchedule
---
apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  name: critical-web-service-pdb
spec:
  minAvailable: 50%
  selector:
    matchLabels:
      app: critical-web-service

The result

With these patterns in place, our applications become incredibly resilient. They can handle node failures, scale smoothly, and even survive Karpenter’s optimization efforts without any downtime. It’s like having a self-healing system that keeps your services running no matter what happens behind the scenes.

High availability isn’t just about having multiple copies of our application, it’s about thoughtfully designing how those copies are managed and maintained. By understanding and implementing these patterns, we are not just running applications in Kubernetes; we are crafting reliable, resilient services that can weather any storm.

The next time you deploy an application to Kubernetes, think about these patterns. They might just save you from that dreaded 3 AM wake-up call about your service being down!

How to mount AWS EFS on EKS for scalable storage solutions

Suppose you need multiple applications to share files seamlessly, without worrying about running out of storage space or struggling with complex configurations. That’s where AWS Elastic File System (EFS) comes in. EFS is a fully managed, scalable file system that multiple AWS services or containers can access. In this guide, we’ll take a simple yet comprehensive journey through the process of mounting AWS EFS to an Amazon Elastic Kubernetes Service (EKS) cluster. I’ll make sure to keep it straightforward, so you can follow along regardless of your Kubernetes experience.

Why use EFS with EKS?

Before we go into the details, let’s consider why using EFS in a Kubernetes environment is beneficial. Imagine you have multiple applications (pods) that all need to access the same data—like a shared directory of documents. Instead of replicating data for each application, EFS provides a centralized storage solution that can be accessed by all pods, regardless of which node they’re running on.

Here’s what makes EFS a great choice for EKS:

Shared Storage: Multiple pods across different nodes can access the same files at the same time, making it perfect for workloads that require shared access.
Scalability: EFS automatically scales up or down as your data needs change, so you never have to worry about manually managing storage limits.
Durability and Availability: AWS ensures that your data is highly durable and accessible across multiple Availability Zones (AZs), which means your applications stay resilient even if there are hardware failures.

Typical use cases for using EFS with EKS include machine learning workloads, content management systems, or shared file storage for collaborative environments like JupyterHub.

Prerequisites

Before we start, make sure you have the following:

EKS Cluster: You need a running EKS cluster, and kubectl should be configured to access it.
EFS File System: An existing EFS file system in the same AWS region as your EKS cluster.
IAM Roles: Correct IAM roles and policies for your EKS nodes to interact with EFS.
Amazon EFS CSI Driver: This driver must be installed in your EKS cluster.

How to mount AWS EFS on EKS

Let’s take it step by step, so by the end, you’ll have a working setup where your Kubernetes pods can use EFS for shared, scalable storage.

Create an EFS file system

To begin, navigate to the EFS Management Console:

Create a New File System: Select the appropriate VPC and subnets—they should be in the same region as your EKS cluster.
File System ID: Note the File System ID; you’ll use it later.
Networking: Ensure that your security group allows inbound traffic from the EKS worker nodes. Think of this as permitting EKS to access your storage safely.

Set up IAM role for the EFS CSI driver

The Amazon EFS CSI driver manages the integration between EFS and Kubernetes. For this driver to work, you need to create an IAM role. It’s a bit like giving the CSI driver its set of keys to interact with EFS securely.

To create the role:

Log in to the AWS Management Console and navigate to IAM.
Create a new role and set up a custom trust policy:

{
   "Version": "2012-10-17",
   "Statement": [
       {
           "Effect": "Allow",
           "Principal": {
               "Federated": "arn:aws:iam::<account-id>:oidc-provider/oidc.eks.<region>.amazonaws.com/id/<oidc-provider-id>"
           },
           "Action": "sts:AssumeRoleWithWebIdentity",
           "Condition": {
               "StringLike": {
                   "oidc.eks.<region>.amazonaws.com/id/<oidc-provider-id>:sub": "system:serviceaccount:kube-system:efs-csi-*"
               }
           }
       }
   ]
}

Make sure to attach the AmazonEFSCSIDriverPolicy to this role. This step ensures that the CSI driver has the necessary permissions to manage EFS volumes.

Install the Amazon EFS CSI driver

You can install the EFS CSI driver using either the EKS Add-ons feature or via Helm charts. I recommend the EKS Add-on method because it’s easier to manage and stays updated automatically.

Attach the IAM role you created to the EFS CSI add-on in your cluster.

(Optional) Create an EFS access point

Access points provide a way to manage and segregate access within an EFS file system. It’s like having different doors to different parts of the same warehouse, each with its key and permissions.

Go to the EFS Console and select your file system.
Create a new Access Point and note its ID for use in upcoming steps.

Configure an IAM Policy for worker nodes

To make sure your EKS worker nodes can access EFS, attach an IAM policy to their role. Here’s an example policy:

{
   "Version": "2012-10-17",
   "Statement": [
       {
           "Effect": "Allow",
           "Action": [
               "elasticfilesystem:DescribeAccessPoints",
               "elasticfilesystem:DescribeFileSystems",
               "elasticfilesystem:ClientMount",
               "elasticfilesystem:ClientWrite"
           ],
           "Resource": "*"
       }
   ]
}

This ensures your nodes can create and interact with the necessary resources.

Create a storage class for EFS

Next, create a Kubernetes StorageClass to provision Persistent Volumes (PVs) dynamically. Here’s an example YAML file:

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: efs-sc
provisioner: efs.csi.aws.com
parameters:
  fileSystemId: <file-system-id>
  directoryPerms: "700"
  basePath: "/dynamic_provisioning"
  ensureUniqueDirectory: "true"

Replace <file-system-id> with your EFS File System ID.

Apply the file:

kubectl apply -f efs-storage-class.yaml

Create a persistent volume claim (PVC)

Now, let’s request some storage by creating a PersistentVolumeClaim (PVC):

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: efs-pvc
spec:
  accessModes:
    - ReadWriteMany
  resources:
    requests:
      storage: 5Gi
  storageClassName: efs-sc

Apply the PVC:

kubectl apply -f efs-pvc.yaml

Use the EFS PVC in a pod

With the PVC created, you can now mount the EFS storage into a pod. Here’s a sample pod configuration:

apiVersion: v1
kind: Pod
metadata:
  name: efs-app
spec:
  containers:
  - name: app
    image: nginx
    volumeMounts:
    - mountPath: "/data"
      name: efs-volume
  volumes:
  - name: efs-volume
    persistentVolumeClaim:
      claimName: efs-pvc

Apply the configuration:

kubectl apply -f efs-pod.yaml

You can verify the setup by checking if the pod can access the mounted storage:

kubectl exec -it efs-app -- ls /data

A note on direct EFS mounting

You can mount EFS directly into pods without using a Persistent Volume (PV) or Persistent Volume Claim (PVC) by referencing the EFS file system directly in the pod’s configuration. This approach simplifies the setup but offers less flexibility compared to using dynamic provisioning with a StorageClass. Here’s how you can do it:

apiVersion: v1
kind: Pod
metadata:
  name: efs-mounted-app
  labels:
    app: efs-example
spec:
  containers:
  - name: nginx-container
    image: nginx:latest
    volumeMounts:
    - name: efs-storage
      mountPath: "/shared-data"
  volumes:
  - name: efs-storage
    csi:
      driver: efs.csi.aws.com
      volumeHandle: <file-system-id>
      readOnly: false

Replace <file-system-id> with your EFS File System ID. This method works well for simpler scenarios where direct access is all you need.

Final remarks

Mounting EFS to an EKS cluster gives you a powerful, shared storage solution for Kubernetes workloads. By following these steps, you can ensure that your applications have access to scalable, durable, and highly available storage without needing to worry about complex management or capacity issues.

As you can see, EFS acts like a giant, shared repository that all your applications can tap into. Whether you’re working on machine learning projects, collaborative tools, or any workload needing shared data, EFS and EKS together simplify the whole process.

Now that you’ve walked through mounting EFS on EKS, think about what other applications could benefit from this setup. It’s always fascinating to see how managed services can help reduce the time you spend on the nitty-gritty details, letting you focus on building great solutions.

December 5, 2024 by Fernando SRE Cloud stuff DevOps stuff Kubernetes SRE stuff 0

How many pods fit on an AWS EKS node?

Managing Kubernetes workloads on AWS EKS (Elastic Kubernetes Service) is much like managing a city, you need to know how many “tenants” (Pods) you can fit into your “buildings” (EC2 instances). This might sound straightforward, but a bit more is happening behind the scenes. Each type of instance has its characteristics, and understanding the limits is key to optimizing your deployments and avoiding resource headaches.

Why Is there a pod limit per node in AWS EKS?

Imagine you want to deploy several applications as Pods across several instances in AWS EKS. You might think, “Why not cram as many as possible onto each node?” Well, there’s a catch. Every EC2 instance in AWS has a limit on networking resources, which ultimately determines how many Pods it can support.

Each EC2 instance has a certain number of Elastic Network Interfaces (ENIs), and each ENI can hold a certain number of IPv4 addresses. But not all these IP addresses are available for Pods, AWS reserves some for essential services like the AWS CNI (Container Network Interface) and kube-proxy, which helps maintain connectivity and communication across your cluster.

Think of each ENI like an apartment building, and the IPv4 addresses as individual apartments. Not every apartment is available to your “tenants” (Pods), because AWS keeps some for maintenance. So, when calculating the maximum number of Pods for a specific instance type, you need to take this into account.

For example, a t3.medium instance has a maximum capacity of 17 Pods. A slightly bigger t3.large can handle up to 35 Pods. The difference depends on the number of ENIs and how many apartments (IPv4 addresses) each ENI can hold.

Formula to calculate Max pods per EC2 instance

To determine the maximum number of Pods that an instance type can support, you can use the following formula:

Max Pods = (Number of ENIs × IPv4 addresses per ENI) – Reserved IPs

Let’s apply this to a t2.medium instance:

Number of ENIs: 3
IPv4 addresses per ENI: 6

Using these values, we get:

Max Pods = (3 × 6) – 1

Max Pods = 18 – 1

Max Pods = 17

So, a t2.medium instance in EKS can support up to 17 Pods. It’s important to understand that this number isn’t arbitrary, it reflects the way AWS manages networking to keep your cluster running smoothly.

Why does this matter?

Knowing the limits of your EC2 instances can be crucial when planning your Kubernetes workloads. If you exceed the maximum number of Pods, some of your applications might fail to deploy, leading to errors and downtime. On the other hand, choosing an instance that’s too large might waste resources, costing you more than necessary.

Suppose you’re running a city, and you need to decide how many tenants each building can support comfortably. You don’t want buildings overcrowded with tenants, nor do you want them half-empty. Similarly, you need to find the sweet spot in AWS EKS, enough Pods to maximize efficiency, but not so many that your node runs out of resources.

The apartment analogy

Consider an m5.large instance. Let’s say it has 4 ENIs, and each ENI can support 10 IP addresses. But, AWS reserves a few apartments (IPv4 addresses) in each building (ENI) for maintenance staff (essential services). Using our formula, we can estimate how many Pods (tenants) we can fit.

Number of ENIs: 4
IPv4 addresses per ENI: 10

Max Pods = (4 × 10) – 1

Max Pods = 40 – 1

Max Pods = 39

So, an m5.large can support 39 Pods. This limit helps ensure that the building (instance) doesn’t get overwhelmed and that the essential services can function without issues.

Automating the Calculation

Manually calculating these limits can be tedious, especially if you’re managing multiple instance types or scaling dynamically. Thankfully, AWS provides tools and scripts to help automate these calculations. You can use the kubectl describe node command to get insights into your node’s capacity or refer to AWS documentation for Pod limits by instance type. Automating this step saves time and helps you avoid deployment issues.

Best practices for scaling

When planning the architecture of your EKS cluster, consider these best practices:

Match instance type to workload needs: If your application requires many Pods, opt for an instance type with more ENIs and IPv4 capacity.
Consider cost efficiency: Sometimes, using fewer large instances can be more cost-effective than using many smaller ones, depending on your workload.
Leverage autoscaling: AWS allows you to set up autoscaling for both your Pods and your nodes. This can help ensure that you have the right amount of capacity during peak and off-peak times without manual intervention.

Key takeaways

Understanding the Pod limits per EC2 instance in AWS EKS is more than just a calculation, it’s about ensuring your Kubernetes workloads run smoothly and efficiently. By thinking of ENIs as buildings and IP addresses as apartments, you can simplify the complexity of AWS networking and better plan your deployments.

Like any good city planner, you want to make sure there’s enough room for everyone, but not so much that you’re wasting space. AWS gives you the tools, you just need to know how to use them.

November 24, 2024 by Fernando SRE Cloud stuff DevOps stuff Kubernetes SRE stuff 0

Container deployment in AWS with ECS, EKS, and Fargate

How do the apps you use daily get built, shipped, and scaled so smoothly? A lot of it has to do with the magic of containers. Think of containers like neat little LEGO blocks, self-contained, portable, and ready to snap together to build something awesome. In the tech world, these blocks hold all the essential bits and pieces of an application, making it super easy to move them around and run them anywhere.

Imagine you’ve got a bunch of these LEGO blocks, each representing a different part of your app. You’ll need a good way to organize them, right? That’s where container orchestration comes in. It’s like having a master builder who knows how to put those blocks together, make sure they’re all playing nicely, and even create more blocks when things get busy.

And guess what? AWS, the cloud superhero, has a whole toolkit to help you with this container adventure.

AWS container services toolkit

AWS offers a variety of services that work together like a well-oiled machine to help you build, deploy, and manage your containerized applications.

Amazon Elastic Container Registry (ECR) – Your container garage

Think of ECR as your very own garage for storing container images. It’s a fully managed service that allows you to store, share, and deploy your container images securely. ECR is like a safe and organized space where you keep all your valuable LEGO creations. You can easily control who has access to your images, making sure only the right people can use them. Plus, it integrates seamlessly with other AWS services, making it a breeze to include in your workflows.

Amazon Elastic Container Service (ECS) – Your container playground

Once you’ve got your container images stored safely in ECR, what’s next? Meet ECS, your container playground! ECS is a highly scalable and high-performance container orchestration service that allows you to run and manage your containers on a cluster of Amazon EC2 instances. It’s like having a dedicated play area where you can arrange your LEGO blocks, build amazing structures, and even add or remove blocks as needed. ECS takes care of all the heavy lifting, so you can focus on what matters most, building awesome applications.

Amazon Elastic Kubernetes Service (EKS) – Your Kubernetes command center

For those of you who prefer the Kubernetes way of doing things, AWS has you covered with EKS. It’s a managed Kubernetes service that makes it easy to run Kubernetes on AWS without having to worry about managing the underlying infrastructure. Kubernetes is like a super-sophisticated set of instructions for building complex LEGO structures. EKS takes care of all the complexities of managing Kubernetes so that you can focus on building and deploying your applications.

EC2 vs. Fargate – Choosing your foundation

Now, let’s talk about the foundation of your container playground. You have two main options: EC2 and Fargate.

EC2-based container deployment – The DIY approach

With EC2, you get full control over the underlying infrastructure. It’s like building your own LEGO table from scratch. You choose the size, shape, and color of the table, and you’re responsible for keeping it clean and tidy. This gives you a lot of flexibility, but it also means you have more responsibilities.

AWS Fargate – The hassle-free option

Fargate, on the other hand, is like having a magical LEGO table that appears whenever you need it. You don’t have to worry about building or maintaining the table; you just focus on playing with your LEGOs. Fargate is a serverless compute engine for containers, meaning you don’t have to manage any servers. It’s a great option if you want to simplify your operations and reduce your overhead.

Making the right choice

So, which option is right for you? Well, it depends on your specific needs and preferences. If you need full control over your infrastructure and want to optimize costs by managing your own servers, EC2 might be a good choice. But if you prefer a serverless approach and want to avoid the hassle of managing servers, Fargate is the way to go.

AWS Container Services Compared

To make things easier, here’s a quick comparison of ECS, EKS, and Fargate:

Service	Description	Use Case
ECS	Managed container orchestration for EC2 instances	Great for full control over infrastructure
EKS	Managed Kubernetes service	Ideal for teams with Kubernetes expertise
Fargate	Serverless compute engine for ECS or EKS	Simplifies operations, no infrastructure management

Best practices and security for building a secure and reliable playground

Just like any playground, your container environment needs to be safe and secure. AWS provides a range of tools and best practices to help you build a reliable and secure container playground.

Security best practices for keeping your LEGOs safe

AWS offers a variety of security features to help you protect your container environment. You can use IAM to control access to your resources, implement network security measures (like Security Groups and NACLs) to protect your containers from unauthorized access, and scan your container images for vulnerabilities with tools like Amazon Inspector.

High availability for ensuring your playground is always open

To ensure your applications are always available, you can use AWS’s high-availability features. This includes deploying your containers across multiple availability zones, configuring load balancing to distribute traffic across your containers, and implementing disaster recovery measures to protect your applications from unexpected events.

Monitoring and troubleshooting for keeping an eye on your playground

AWS provides comprehensive monitoring and troubleshooting tools to help you keep your container environment running smoothly. You can use CloudWatch to monitor your containers’ performance, set up detailed alarms to catch issues before they escalate, and use CloudWatch Logs to dive deep into the activity of your applications. Additionally, AWS X-Ray helps you trace requests as they travel through your application, giving you a granular view of where bottlenecks or failures may occur. These tools together allow for proactive monitoring, quick detection of anomalies, and effective root-cause analysis, ensuring that your container environment is always optimized and functioning properly.

DevOps integration for automating your LEGO creations

AWS container services integrate seamlessly with your DevOps workflows, allowing you to automate deployments, ensure consistent environments, and streamline the entire development lifecycle. By integrating services like CodeBuild, CodeDeploy, and CodePipeline, AWS enables you to create end-to-end CI/CD pipelines that automate testing, building, and releasing your containerized applications. This integration helps teams release features faster, reduce errors due to manual processes, and maintain a high level of consistency across different environments.

CI/CD pipeline integration for building and deploying automatically

You can use AWS CodePipeline to create a continuous integration and continuous delivery (CI/CD) pipeline that automatically builds, tests, and deploys your containerized applications. This allows you to release new features and updates quickly and efficiently. Imagine using CodePipeline as an automated assembly line for your LEGO creations.

Cost optimization for saving money on your LEGOs

AWS offers a variety of cost optimization tools to help you save money on your container deployments. You can use ECR lifecycle policies to manage your container images efficiently, choose the right instance types for your workloads, and leverage AWS’s pricing models to optimize your costs. Additionally, AWS provides Savings Plans and Spot Instances, which allow you to significantly reduce costs when running containerized workloads with flexible scheduling. Utilizing the AWS Compute Optimizer can also help identify opportunities to downsize or modify your infrastructure to be more cost-effective, ensuring you’re always operating in a lean and optimized manner.

Real-world implementation for bringing your LEGO creations to life

Deploying containerized applications in a production environment requires careful planning and execution. This involves assessing your infrastructure, understanding resource requirements, and preparing for potential scaling needs. AWS provides a range of tools and best practices, such as infrastructure templates, automated deployment scripts, and monitoring solutions, to help ensure that your applications are deployed successfully. Additionally, AWS recommends using blue-green deployments to minimize downtime and risk, as well as leveraging autoscaling to maintain performance under varying loads.

Production deployment checklist for your Pre-flight check

Before deploying your applications, it’s important to consider a few key factors, such as your application’s requirements, your infrastructure needs, and your security and compliance requirements. AWS provides a comprehensive checklist to help you ensure your applications are ready for production.

Common challenges and solutions for troubleshooting your LEGO creations

Deploying and managing containerized applications can present some challenges, such as dealing with scaling complexities, managing network configurations, or troubleshooting performance bottlenecks. However, AWS provides a wealth of resources and support to help you overcome these challenges. You can find solutions to common problems, troubleshooting tips, and best practices in the AWS documentation, community forums, and even through AWS Support Plans, which offer access to technical experts. Additionally, tools like AWS Trusted Advisor can help identify potential issues before they impact your applications, while AWS Well-Architected Framework guides optimizing your container deployments for reliability, performance, and cost-efficiency.

Choosing the right tools for the job

AWS offers a comprehensive suite of container services to help you build, deploy, and manage your applications. By understanding the different services and their capabilities, you can choose the right tools for your specific needs and build a secure, reliable, and cost-effective container environment.

The key is to choose the right tools for the job and follow best practices to ensure your applications are secure, reliable, and scalable.

November 19, 2024 by Fernando SRE Cloud stuff Kubernetes 0

Helm or Kustomize for deploying to Kubernetes?

Choosing the right tool for continuous deployments is a big decision. It’s like picking the right vehicle for a road trip. Do you go for the thrill of a sports car or the reliability of a sturdy truck? In our world, the “cargo” is your application, and we want to ensure it reaches its destination smoothly and efficiently.

Two popular tools for this task are Helm and Kustomize. Both help you manage and deploy applications on Kubernetes, but they take different approaches. Let’s dive in, explore how they work, and help you decide which one might be your ideal travel buddy.

What is Helm?

Imagine Helm as a Kubernetes package manager, similar to apt or yum if you’ve worked with Linux before. It bundles all your application’s Kubernetes resources (like deployments, services, etc.) into a neat Helm chart package. This makes installing, upgrading, and even rolling back your application straightforward.

Think of a Helm chart as a blueprint for your application’s desired state in Kubernetes. Instead of manually configuring each element, you have a pre-built plan that tells Kubernetes exactly how to construct your environment. Helm provides a command-line tool, helm, to create these charts. You can start with a basic template and customize it to suit your needs, like a pre-fabricated house that you can modify to match your style. Here’s what a typical Helm chart looks like:

mychart/
  Chart.yaml        # Describes the chart
  templates/        # Contains template files
    deployment.yaml # Template for a Deployment
    service.yaml    # Template for a Service
  values.yaml       # Default configuration values

Helm makes it easy to reuse configurations across different projects and share your charts with others, providing a practical way to manage the complexity of Kubernetes applications.

What is Kustomize?

Now, let’s talk about Kustomize. Imagine Kustomize as a powerful customization tool for Kubernetes, a versatile toolkit designed to modify and adapt existing Kubernetes configurations. It provides a way to create variations of your deployment without having to rewrite or duplicate configurations. Think of it as having a set of advanced tools to tweak, fine-tune, and adapt everything you already have. Kustomize allows you to take a base configuration and apply overlays to create different variations for various environments, making it highly flexible for scenarios like development, staging, and production.

Kustomize works by applying patches and transformations to your base Kubernetes YAML files. Instead of duplicating the entire configuration for each environment, you define a base once, and then Kustomize helps you apply environment-specific changes on top. Imagine you have a basic configuration, and Kustomize is your stencil and spray paint set, letting you add layers of detail to suit different environments while keeping the base consistent. Here’s what a typical Kustomize project might look like:

base/
  deployment.yaml
  service.yaml

overlays/
  dev/
    kustomization.yaml
    patches/
      deployment.yaml
  prod/
    kustomization.yaml
    patches/
      deployment.yaml

The structure is straightforward: you have a base directory that contains your core configurations, and an overlays directory that includes different environment-specific customizations. This makes Kustomize particularly powerful when you need to maintain multiple versions of an application across different environments, like development, staging, and production, without duplicating configurations.

Kustomize shines when you need to maintain variations of the same application for multiple environments, such as development, staging, and production. This helps keep your configurations DRY (Don’t Repeat Yourself), reducing errors and simplifying maintenance. By keeping base definitions consistent and only modifying what’s necessary for each environment, you can ensure greater consistency and reliability in your deployments.

Helm vs Kustomize, different approaches

Helm uses templating to generate Kubernetes manifests. It takes your chart’s templates and values, combines them, and produces the final YAML files that Kubernetes needs. This templating mechanism allows for a high level of flexibility, but it also adds a level of complexity, especially when managing different environments or configurations. With Helm, the user must define various parameters in values.yaml files, which are then injected into templates, offering a powerful but sometimes intricate method of managing deployments.

Kustomize, by contrast, uses a patching approach, starting from a base configuration and applying layers of customizations. Instead of generating new YAML files from scratch, Kustomize allows you to define a consistent base once, and then apply overlays for different environments, such as development, staging, or production. This means you do not need to maintain separate full configurations for each environment, making it easier to ensure consistency and reduce duplication. Kustomize’s patching mechanism is particularly powerful for teams looking to maintain a DRY (Don’t Repeat Yourself) approach, where you only change what’s necessary for each environment without affecting the shared base configuration. This also helps minimize configuration drift, keeping environments aligned and easier to manage over time.

Ease of use

Helm can be a bit intimidating at first due to its templating language and chart structure. It’s like jumping straight onto a motorcycle, whereas Kustomize might feel more like learning to ride a bike with training wheels. Kustomize is generally easier to pick up if you are already familiar with standard Kubernetes YAML files.

Packaging and reusability

Helm excels when it comes to packaging and distributing applications. Helm charts can be shared, reused, and maintained, making them perfect for complex applications with many dependencies. Kustomize, on the other hand, is focused on customizing existing configurations rather than packaging them for distribution.

Integration with kubectl

Both tools integrate well with Kubernetes’ command-line tool, kubectl. Helm has its own CLI, helm, which extends kubectl capabilities, while Kustomize can be directly used with kubectl via the -k flag.

Declarative vs. Imperative

Kustomize follows a declarative mode, you describe what you want, and it figures out how to get there. Helm can be used both declaratively and imperatively, offering more flexibility but also more complexity if you want to take a hands-on approach.

Release history management

Helm provides built-in release management, keeping track of the history of your deployments so you can easily roll back to a previous version if needed. Kustomize lacks this feature, which means you need to handle versioning and rollback strategies separately.

CI/CD integration

Both Helm and Kustomize can be integrated into your CI/CD pipelines, but their roles and strengths differ slightly. Helm is frequently chosen for its ability to package and deploy entire applications. Its charts encapsulate all necessary components, making it a great fit for automated, repeatable deployments where consistency and simplicity are key. Helm also provides versioning, which allows you to manage releases effectively and roll back if something goes wrong, which is extremely useful for CI/CD scenarios.

Kustomize, on the other hand, excels at adapting deployments to fit different environments without altering the original base configurations. It allows you to easily apply changes based on the environment, such as development, staging, or production, by layering customizations on top of the base YAML files. This makes Kustomize a valuable tool for teams that need flexibility across multiple environments, ensuring that you maintain a consistent base while making targeted adjustments as needed.

In practice, many DevOps teams find that combining both tools provides the best of both worlds: Helm for packaging and managing releases, and Kustomize for environment-specific customizations. By leveraging their unique capabilities, you can build a more robust, flexible CI/CD pipeline that meets the diverse needs of your application deployment processes.

Helm and Kustomize together

Here’s an interesting twist: you can use Helm and Kustomize together! For instance, you can use Helm to package your base application, and then apply Kustomize overlays for environment-specific customizations. This combo allows for the best of both worlds, standardized base configurations from Helm and flexible customizations from Kustomize.

Use cases for combining Helm and Kustomize

Environment-Specific customizations: Use Kustomize to apply environment-specific configurations to a Helm chart. This allows you to maintain a single base chart while still customizing for development, staging, and production environments.
Third-Party Helm charts: Instead of forking a third-party Helm chart to make changes, Kustomize lets you apply those changes directly on top, making it a cleaner and more maintainable solution.
Secrets and ConfigMaps management: Kustomize allows you to manage sensitive data, such as secrets and ConfigMaps, separately from Helm charts, which can help improve both security and maintainability.

Final thoughts

So, which tool should you choose? The answer depends on your needs and preferences. If you’re looking for a comprehensive solution to package and manage complex Kubernetes applications, Helm might be the way to go. On the other hand, if you want a simpler way to tweak configurations for different environments without diving into templating languages, Kustomize may be your best bet.

My advice? If the application is for internal use within your organization, use Kustomize. If the application is to be distributed to third parties, use Helm.

November 17, 2024 by Fernando SRE DevOps stuff Kubernetes SRE stuff 0

Simplifying Kubernetes with Operators, What Are They and Why Do You Need Them?

We’re about to look into the fascinating world of Kubernetes Operators. But before we get to the main course, let’s start with a little appetizer to set the stage

A Quick Refresher on Kubernetes

You’ve probably heard of Kubernetes, right? It’s like a super-smart traffic controller for your containerized applications. These are self-contained environments that package everything your app needs to run, from code to libraries and dependencies. Imagine a busy airport where planes (your containers) are constantly taking off and landing. Kubernetes is the air traffic control system that makes sure everything runs smoothly, efficiently, and safely.

The Challenge. Managing Complex Applications

Now, picture this: You’re not just managing a small regional airport anymore. Suddenly, you’re in charge of a massive international hub with hundreds of flights, different types of aircraft, and complex schedules. That’s what it’s like trying to manage modern, distributed, cloud-native applications in Kubernetes manually. Especially when you’re dealing with stateful applications or distributed systems that require fine-tuned coordination, things can get overwhelming pretty quickly.

Enter the Kubernetes Operator. Your Application’s Autopilot

This is where Kubernetes Operators come in. Think of them as highly skilled pilots who know everything about a specific type of aircraft. They can handle all the complex maneuvers, respond to changing conditions, and ensure a smooth flight from takeoff to landing. That’s exactly what an Operator does for your application in Kubernetes.

What Exactly is a Kubernetes Operator?

Let’s break it down in simple terms:

Definition: An Operator is like a custom-built robot that extends Kubernetes’ abilities. It’s programmed to understand and manage a specific application’s entire lifecycle.
Analogy: Imagine you have a pet robot that knows everything about taking care of your house plants. It waters them, adjusts their sunlight, repots them when needed, and even diagnoses plant diseases. That’s what an Operator does for your application in Kubernetes.
Controller: The Operator’s logic is embedded in a Controller. This is essentially a loop that constantly checks the desired state versus the current state of your application and acts to reconcile any differences. If the current state deviates from what it should be, the Controller steps in and makes the necessary adjustments.

Key Components:

Custom Resource Definitions (CRDs): These are like new vocabulary words that teach Kubernetes about your specific application. They extend the Kubernetes API, allowing you to define and manage resources that represent your application’s needs as if Kubernetes natively understood them.
Reconciliation Logic: This is the “brain” of the Operator, constantly monitoring the state of your application and taking action to maintain it in the desired condition.

Why Do We Need Operators?

They’re Expert Multitaskers: Operators can handle complex tasks like installation, updates, backups, and scaling, all on their own.
They’re Lifecycle Managers: Just like how a good parent knows exactly what their child needs at different stages of growth, Operators understand your application’s needs throughout its lifecycle, adjusting resources and configurations accordingly.
They Simplify Things: Instead of you having to speak “Kubernetes” to manage your app, the Operator translates your simple commands into complex Kubernetes actions. They take Kubernetes’ declarative model to the next level by constantly monitoring and reconciling the desired state of your app.
They’re Domain Experts: Each Operator is like a specialist doctor for a specific type of application. They know all the ins and outs of how it should behave, handle its quirks, and optimize its performance.

The Perks of Using Operators

Fewer Oops Moments: By reducing manual tasks, Operators help prevent those facepalm-worthy human errors that can bring down applications.
More Time for Coffee Breaks: Okay, maybe not just coffee breaks, but automating repetitive tasks frees you up for more strategic work. Additionally, Operators integrate seamlessly with GitOps methodologies, allowing for full end-to-end automation of your infrastructure and applications.
Growth Without Growing Pains: Operators can manage applications at a massive scale without breaking a sweat. As your system grows, Operators ensure it scales efficiently and reliably.
Tougher Apps: With Operators constantly monitoring and adjusting, your applications become more resilient and recover faster from issues, often without any intervention from you.

Real-World Examples of Operator Magic

Database Whisperers: Operators can set up, configure, scale, and backup databases like PostgreSQL, MySQL, or MongoDB without you having to remember all those pesky command-line instructions. For instance, the PostgreSQL Operator can automate everything from provisioning to scaling and backup.
Messaging System Maestros: They can juggle complex messaging clusters, like Apache Kafka or RabbitMQ, handling partitions, replication, and scaling with ease.
Observability Ninjas: Take the Prometheus Operator, for example. It automates the deployment and management of Prometheus, allowing dynamic service discovery and gathering metrics without manual intervention.
Jack of All Trades: Really, any application with a complex lifecycle can benefit from having its own personal Operator. Whether it’s storage systems, machine learning platforms, or even CI/CD pipelines, Operators are there to make your life easier.

To see just how easy it is, here’s a simple YAML example to deploy Prometheus using the Prometheus Operator:

apiVersion: monitoring.coreos.com/v1
kind: Prometheus
metadata:
  name: example-prometheus
  labels:
    prometheus: example
spec:
  serviceAccountName: prometheus
  serviceMonitorSelector:
    matchLabels:
      team: frontend
  resources:
    requests:
      memory: 400Mi
  alerting:
    alertmanagers:
    - namespace: monitoring
      name: alertmanager
      port: web
  ruleSelector:
    matchLabels:
      role: prometheus-rulefiles
  storage:
    volumeClaimTemplate:
      spec:
        storageClassName: gp2
        resources:
          requests:
            storage: 10Gi

In this example:

We’re defining a Prometheus custom resource (thanks to the Prometheus Operator).
It specifies how Prometheus should be deployed, including memory requests, storage, and alerting configurations.
The serviceMonitorSelector ensures that only services with specific labels (in this case, team: frontend) are monitored.
Storage is defined using persistent volumes, ensuring that Prometheus data is retained even if the pod is restarted.

This YAML configuration is just the beginning. The Prometheus Operator allows for more advanced setups, automating otherwise complex tasks like monitoring service discovery, setting up persistent storage, and integrating alert managers, all with minimal manual intervention.

Wrapping Up

So, there you have it! Kubernetes Operators are like having a team of expert, tireless assistants managing your applications. They automate complex tasks, understand your app’s specific needs, and keep everything running smoothly.

As Kubernetes evolves towards more self-healing and automated systems, Operators play a crucial role in driving that transformation. They’re not just a cool feature, they’re the backbone of modern cloud-native architectures.

So, why not give Operators a try in your next project? Who knows, you might just find your new favorite Kubernetes sidekick.

September 11, 2024 by Fernando SRE DevOps stuff Kubernetes SRE stuff 0

How Does etcd Work in Kubernetes?

Kubernetes has emerged as a dominant player in the container orchestration world, providing robust solutions for managing containerized applications. At the heart of Kubernetes lies etcd, an essential component often compared to the “brain” of the system. This comparison is appropriate, as etcd plays a crucial role in maintaining a Kubernetes cluster’s overall state and health. Understanding how etcd works within Kubernetes is key to grasping the fundamentals of Kubernetes itself.

The Core Function of etcd in Kubernetes

Etcd is a distributed key-value store that serves as the primary data store for Kubernetes. Its main function is to store all the cluster data, such as configuration data, secrets, service discovery information, and the state of all the resources in the cluster. This centralized data store acts as the single source of truth for the entire cluster, ensuring consistency and reliability in the information that Kubernetes needs to operate efficiently.

Cluster Data Storage

In Kubernetes, etcd stores all the persistent data of the cluster. This includes:

Cluster configuration: All the configuration settings required to manage the cluster.
State of the cluster: Information about all the nodes, pods, services, and other resources.
Service discovery: Data that helps in the discovery of services within the cluster.
Secrets: Sensitive information like passwords, tokens, and keys.

By acting as the only source of truth, etcd ensures that the cluster’s state is accurately maintained and can be reliably queried and updated as needed.

Consistency and Availability

Etcd achieves high consistency and availability through the use of the Raft consensus algorithm. Raft is designed to ensure that even in the presence of failures, etcd can maintain a consistent state across all nodes. This is crucial for Kubernetes, as it relies on etcd to provide a consistent view of the cluster’s state.

The Raft Consensus Algorithm

Raft works by electing a leader among the etcd nodes, which then manages all write operations. The leader replicates these changes to the follower nodes, ensuring that all nodes have the same data. If the leader fails, a new leader is elected from the follower nodes. This process ensures that etcd remains available and consistent, even in the face of node failures.

Interaction with the Kubernetes API

When users or administrators interact with Kubernetes through its API, any changes made to resources (such as creating or modifying pods, services, or deployments) are stored in etcd. The Kubernetes API server communicates directly with etcd to persist these changes. This interaction is fundamental to Kubernetes’ ability to maintain and manage the cluster’s desired state.

The “Watch” Functionality

One of the powerful features of etcd is its ability to watch for changes in the data it stores. Kubernetes leverages this functionality to detect changes in the cluster’s state quickly and efficiently. When a change occurs, etcd notifies Kubernetes, which can then take appropriate actions to ensure the cluster’s desired state is maintained.

Deployment of etcd in Kubernetes

In a typical Kubernetes setup, etcd is deployed on the control plane nodes. For production environments, it is recommended to use a dedicated etcd cluster. This approach enhances the reliability and availability of etcd, as it reduces the risk of resource contention with other control plane components.

Best Practices for Deployment

Dedicated etcd cluster: Ensures high availability and performance.
High availability setup: Deploying etcd in a highly available configuration with multiple nodes.
Regular backups: Ensuring that regular backups of the etcd data are taken to safeguard against data loss.

Security Considerations

Security is a critical aspect of etcd deployment in Kubernetes. Typically, etcd is configured with mutual TLS (mTLS) authentication to secure communication between etcd nodes and between etcd and other Kubernetes components. This ensures that only authenticated and authorized entities can access the sensitive data stored in etcd.

Backup and Recovery

Given that etcd contains all the critical data of a Kubernetes cluster, regular backups are essential. In the event of a failure or data corruption, having recent backups allows administrators to restore the cluster to a known good state. Kubernetes provides tools and best practices for performing regular backups of etcd data.

Tools for etcd Backup

Several tools can be used to back up etcd:

etcdctl: This is the official command-line tool for interacting with etcd. It allows you to perform backups and restores with the following commands:

.– To make a backup:

ETCDCTL_API=3 etcdctl snapshot save <backup-file-path> \
  --endpoints=<etcd-endpoint> \
  --cacert=<path-to-cafile> \
  --cert=<path-to-certfile> \
  --key=<path-to-keyfile>

.– To restore from a backup:

ETCDCTL_API=3 etcdctl snapshot restore <backup-file-path> \
  --data-dir=<new-data-dir>

Velero: An open-source tool primarily used for backing up and restoring Kubernetes resources, but it can also be configured to back up etcd data. Velero is popular in production environments due to its efficient and automated backup management capabilities.
- To use Velero with etcd, a specific plugin can be configured to back up etcd data alongside Kubernetes resources.
Kubernetes Operator: Some Kubernetes operators are designed specifically for managing etcd and may include backup and restore functionalities. For example, the etcd-operator by CoreOS provides advanced management capabilities for etcd, including automated backups.
Kubernetes CronJobs: CronJobs can be set up in Kubernetes to execute etcdctl commands at regular intervals, automating periodic backups.

Best Practices for Backup

Backup Frequency: Perform regular backups, ideally daily, and before making any significant changes to the cluster.
Secure Storage: Store backups in secure and redundant locations, such as cloud storage with appropriate retention policies.
Recovery Testing: Periodically test the recovery process to ensure that backups are valid and can be restored correctly.

By incorporating these practices and tools, administrators can ensure that critical etcd data is protected and can be effectively restored in the event of a disaster.

Performance Characteristics

Etcd is designed to handle high volumes of write operations, making it well-suited for the dynamic nature of Kubernetes clusters. It can manage thousands of writes per second, ensuring that even in large-scale deployments, etcd can keep up with the demands of the cluster.

End Note

Etcd acts as the brain of Kubernetes, storing and managing all the critical information about the cluster. Its distributed, consistent, and highly available design makes it an ideal choice for this role. By understanding how etcd works and its importance in the Kubernetes ecosystem, administrators and developers can better appreciate the robustness and reliability of Kubernetes, ensuring smooth and efficient operation even at scale.

July 16, 2024 by Fernando SRE DevOps stuff Kubernetes SRE stuff 0

The Power of Event-Driven Scaling in Kubernetes: KEDA

Kubernetes is a compelling platform for managing containerized applications but can be complex. One area where Kubernetes shines is its ability to scale applications based on demand. However, traditional scaling methods in Kubernetes might not always be the most efficient, especially when dealing with event-driven workloads. This is where KEDA (Kubernetes Event-Driven Autoscaling) comes into play.

What is KEDA?

KEDA stands for Kubernetes Event-Driven Autoscaling. It is an open-source component that allows Kubernetes to scale applications based on events. This means that instead of only scaling your applications based on metrics like CPU or memory usage, you can scale them based on specific events or external metrics such as the number of messages in a queue, the rate of requests to an endpoint, or custom metrics from various sources.

Key Features and Functionalities

Event-Driven Scaling: KEDA enables scaling based on the number of events that need to be processed, rather than just CPU or memory metrics.
Lightweight Component: KEDA is designed to be a lightweight addition to your Kubernetes cluster, ensuring it doesn’t interfere with other components.
Flexibility: It integrates seamlessly with Kubernetes’ Horizontal Pod Autoscaler (HPA), extending its functionality without overwriting or duplicating it.
Built-In Scalers: KEDA comes with over 50 built-in scalers for various platforms, including cloud services, databases, messaging systems, telemetry systems, CI/CD tools, and more.
Support for Multiple Workloads: It can scale various types of workloads, including deployments, jobs, and custom resources.
Scaling to Zero: KEDA allows scaling down to zero pods when there are no events to process, optimizing resource usage and reducing costs.
Extensibility: You can use community-maintained or custom scalers to support unique event sources.
Provider-Agnostic: KEDA supports event triggers from a wide range of cloud providers and products.
Azure Functions Integration: It allows you to run and scale Azure Functions in Kubernetes for production workloads.
Resource Optimization: KEDA helps build sustainable platforms by optimizing workload scheduling and scaling to zero when not needed.

Advantages of Using KEDA

Efficiency: By scaling based on actual events, KEDA ensures that your application only uses the resources it needs, improving efficiency and potentially reducing costs.
Flexibility: With support for a wide range of event sources and integration with HPA, KEDA provides a flexible scaling solution.
Simplicity: It simplifies the configuration of event-driven scaling in Kubernetes, abstracting the complexities of integrating different event sources.
Seamless Integration: KEDA works well with existing Kubernetes components and can be easily integrated into your current infrastructure.

Optimizing a Retail Application

Imagine you are managing an online retail application. During normal hours, traffic is relatively steady, but during sales events, the number of orders can spike dramatically. Here’s how KEDA can help:

Order Processing: Your application uses a message queue to handle order processing. Normally, the queue has a manageable number of messages, but during a sale, the number of messages can skyrocket.
Scaling with KEDA: KEDA can monitor the message queue and automatically scale the order processing service based on the number of messages. This ensures that as more orders come in, additional instances of the service are started to handle the load, preventing delays and improving customer experience.
Cost Management: Once the sale is over and the message count drops, KEDA will scale down the service, ensuring that you are not paying for unused resources.
Scaling to Zero: When there are no orders to process, KEDA can scale the order processing service down to zero pods, further reducing costs.

In a few words

KEDA is a powerful tool that brings the benefits of event-driven scaling to Kubernetes. Its ability to scale applications based on events makes it an ideal choice for dynamic workloads. By integrating with a variety of event sources and providing a simple yet flexible way to configure scaling, KEDA helps optimize resource usage, enhance performance, and manage costs effectively. Whether you’re running an e-commerce platform, processing data streams, or managing microservices, KEDA can help ensure your applications are always running efficiently.

In essence, KEDA is about making your applications responsive to real-world events, ensuring they are always ready to meet demand without wasting resources. It’s a valuable addition to any Kubernetes toolkit, offering a smarter, more efficient way to handle scaling.

July 11, 2024 by Fernando SRE DevOps stuff Kubernetes SRE stuff 0

Important Kubernetes Concepts. A Friendly Guide for Beginners

In this guide, we’ll embark on a journey into the heart of Kubernetes, unraveling its essential concepts and demystifying its inner workings. Whether you’re a complete beginner or have dipped your toes into the container orchestration waters, fear not! We’ll break down the complexities into bite-sized, easy-to-digest pieces, ensuring you grasp the fundamentals with confidence.

What is Kubernetes, anyway?

Before we jump into the nitty-gritty, let’s quickly recap what Kubernetes is. Imagine you’re running a big restaurant. Kubernetes is like the head chef who manages the kitchen, making sure all the dishes are prepared correctly, on time, and served to the right tables. In the world of software, Kubernetes does the same for your applications, ensuring they run smoothly across multiple computers.

Now, let’s explore some key Kubernetes concepts:

1. Kubelet: The Kitchen Porter

The Kubelet is like the kitchen porter in our restaurant analogy. It’s a small program that runs on each node (computer) in your Kubernetes cluster. Its job is to make sure that containers are running in a Pod. Think of it as the person who makes sure each cooking station has all the necessary ingredients and utensils.

2. Pod: The Cooking Station

A Pod is the smallest deployable unit in Kubernetes. It’s like a cooking station in our kitchen. Just as a cooking station might have a stove, a cutting board, and some utensils, a Pod can contain one or more containers that work together.

Here’s a simple example of a Pod definition in YAML:

apiVersion: v1
kind: Pod
metadata:
  name: my-pod
spec:
  containers:
  - name: my-container
    image: nginx:latest

3. Container: The Chef’s Tools

Containers are like the chef’s tools at each cooking station. They’re packaged versions of your application, including all the ingredients (code, runtime, libraries) needed to run it. In Kubernetes, containers live inside Pods.

4. Deployment: The Recipe Book

A Deployment in Kubernetes is like a recipe book. It describes how many replicas of a Pod should be running at any given time. If a Pod fails, the Deployment ensures a new one is created to maintain the desired number.

Here’s an example of a Deployment:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
      - name: my-container
        image: my-app:v1

5. Service: The Waiter

A Service in Kubernetes is like a waiter in our restaurant. It provides a stable “address” for a set of Pods, allowing other parts of the application to find and communicate with them. Even if Pods come and go, the Service ensures that requests are always directed to the right place.

Here’s a simple Service definition:

apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  selector:
    app: my-app
  ports:
    - protocol: TCP
      port: 80
      targetPort: 9376

6. Namespace: The Different Kitchens

Namespaces are like different kitchens in a large restaurant complex. They allow you to divide your cluster resources between multiple users or projects. This helps in organizing and isolating workloads.

7. ReplicationController: The Old-School Recipe Manager

The ReplicationController is an older way of ensuring a specified number of pod replicas are running at any given time. It’s like an old-school recipe manager that makes sure you always have a certain number of dishes ready. While it’s still used, Deployments are generally preferred for their additional features.

8. StatefulSet: The Specialized Kitchen Equipment

StatefulSets are used for applications that require stable, unique network identifiers, stable storage, and ordered deployment and scaling. Think of them as specialized kitchen equipment that needs to be set up in a specific order and maintained carefully.

9. Ingress: The Restaurant’s Front Door

An Ingress is like the front door of our restaurant. It manages external access to the services in a cluster, typically HTTP. Ingress can provide load balancing, SSL termination, and name-based virtual hosting.

10. ConfigMap: The Recipe Variations

ConfigMaps are used to store non-confidential data in key-value pairs. They’re like recipe variations that different dishes can use. For example, you might use a ConfigMap to store application configuration data.

Here’s a simple ConfigMap example:

apiVersion: v1
kind: ConfigMap
metadata:
  name: game-config
data:
  player_initial_lives: "3"
  ui_properties_file_name: "user-interface.properties"

11. Secret: The Secret Sauce

Secrets are similar to ConfigMaps but are specifically designed to hold sensitive information, like passwords or API keys. They’re like the secret sauce recipes that only trusted chefs have access to.

And there you have it! These are some of the most important concepts in Kubernetes. Remember, mastering Kubernetes takes time and practice like learning to cook in a professional kitchen. Don’t worry if it seems overwhelming at first, keep experimenting, and you’ll get the hang of it.

July 4, 2024 by Fernando SRE DevOps stuff Kubernetes SRE stuff 0

Storage Classes in Kubernetes, Let’s Manage Persistent Data

One essential aspect in Kubernetes is how to handle persistent storage, and this is where Kubernetes Storage Classes come into play. In this article, we’ll explore what Storage Classes are, their key components, and how to use them effectively with practical examples.
If you’re working with applications that need to store data persistently (like databases, file systems, or even just configuration files), you’ll want to understand how these work.

What is a Storage Class?

Imagine you’re running a library (that’s our Kubernetes cluster). Now, you need different types of shelves for different kinds of books, some for heavy encyclopedias, some for delicate rare books, and others for popular paperbacks. In Kubernetes, Storage Classes are like these different types of shelves. They define the types of storage available in your cluster.

Storage Classes allow you to dynamically provision storage resources based on the needs of your applications. It’s like having a librarian who can create the perfect shelf for each book as soon as it arrives.

Key Components of a Storage Class

Let’s break down the main parts of a Storage Class:

Provisioner: This is the system that will create the actual storage. It’s like our librarian who creates the shelves.
Parameters: These are specific instructions for the provisioner. For example, “Make this shelf extra sturdy” or “This shelf should be fireproof”.
Reclaim Policy: This determines what happens to the storage when it’s no longer needed. Do we keep the shelf (Retain) or dismantle it (Delete)?
Volume Binding Mode: This decides when the actual storage is created. It’s like choosing between having shelves ready in advance or building them only when a book arrives.

Creating a Storage Class

Now, let’s create our first Storage Class. We’ll use AWS EBS (Elastic Block Store) as an example. Don’t worry if you’re unfamiliar with AWS, the concepts are similar for other cloud providers.

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: fast-storage
provisioner: ebs.csi.aws.com
parameters:
  type: gp3
reclaimPolicy: Delete
volumeBindingMode: WaitForFirstConsumer

Let’s break this down:

name: fast-storage: This is the name we’re giving our Storage Class.
provisioner: ebs.csi.aws.com: This tells Kubernetes to use the AWS EBS CSI driver to create the storage.
parameters: type: gp3: This specifies that we want to use gp3 EBS volumes, which are a type of fast SSD storage in AWS.
reclaimPolicy: Delete: This means the storage will be deleted when it’s no longer needed.
volumeBindingMode: WaitForFirstConsumer: This tells Kubernetes to wait until a Pod actually needs the storage before creating it.

Using a Storage Class

Now that we have our Storage Class, how do we use it? We use it when creating a Persistent Volume Claim (PVC). A PVC is like a request for storage from an application.

Here’s an example of a PVC that uses our Storage Class:

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: my-app-storage
spec:
  accessModes:
    - ReadWriteOnce
  storageClassName: fast-storage
  resources:
    requests:
      storage: 5Gi

Let’s break this down too:

name: my-app-storage: This is the name of our PVC.
accessModes: – ReadWriteOnce: This means a single node can mount the storage as read-write.
storageClassName: fast-storage: This is where we specify which Storage Class to use, it matches the name we gave our Storage Class earlier.
storage: 5Gi: This is requesting 5 gigabytes of storage.

Real-World Use Case

Let’s imagine we’re running a photo-sharing application. We need fast storage for the database that stores user information and slower, cheaper storage for the actual photos.

We could create two Storage Classes:

A “fast-storage” class (like the one we created above) for the database.
A “bulk-storage” class for the photos, perhaps using a different type of EBS volume that’s cheaper but slower.

Then, we’d create two PVCs (Persistent Volume Claim), one for each Storage Class. Our database Pod would use the PVC with the “fast-storage” class, while our photo storage Pod would use the PVC with the “bulk-storage” class.

This way, we’re optimizing our storage usage (and costs) based on the needs of different parts of our application.

In Summary

Storage Classes in Kubernetes provide a flexible and powerful way to manage different types of storage for your applications. By understanding and using Storage Classes, you can ensure your applications have the storage they need while keeping your infrastructure efficient and cost-effective.

Whether you’re working with AWS EBS, Google Cloud Persistent Disk, or any other storage backend, Storage Classes are an essential tool in your Kubernetes toolkit.

June 24, 2024 by Fernando SRE DevOps stuff Kubernetes SRE stuff 0