See.

Most popular micro frontend strategy with Kubernetes:

The most popular micro frontend strategy for use with Kubernetes is to deploy each microfrontend as a separate container within a single pod. This allows for each microfrontend to be independently updated and managed, while still being able to share resources such as network and storage. The use of Kubernetes services, ingress controllers, and network policies can further assist in managing and securing communication between microfrontends.

Organizations who use this strategy

Many organizations are using micro frontend strategies with Kubernetes. Some well-known companies include Capital One, Walmart, GE Digital, and Zalando. The adoption of micro frontend strategies varies depending on the organization’s size, industry, and specific use cases.

As any typical enterprise, we have several micro front end (MFEs) apps. Each MFE being a docker image running an Express (nodejs) server and optionally, nginx inside them.

We were using a managed container orchestration platform offered by AWS called fargate. With Azure, the obvious choice was to move to AKS.

This was not going to be simple lift and shift. During the migration we wanted to explore solutions to create Canary deployment, A/B testing feature on the server side. We also wanted to have Blue Green strategy of deployment into our SDLC.

Cost was always a concern too. We din’t want to overshoot our cost. We wanted to remain within + or – 15% of the previous cost.

Steps

Here is a step by step guidance for infra migration from AWS to Azure.

Create an Azure account if you don’t have one already.
Create an Azure Container Registry (ACR) and upload your Docker images.
Create an Azure Kubernetes Service (AKS) cluster to host your micro front-end apps.
Use Helm charts or YAML manifests to define and deploy your apps to AKS.
To create a canary deployment, you can use the Azure Traffic Manager to route a portion of the traffic to a new version of your app, and gradually increase the traffic to it until it becomes the primary version.
For A/B testing, you can use the same approach as for canary deployment, by routing a portion of the traffic to different versions of your app and compare the results.
To implement a Blue-Green strategy, you can create two identical AKS clusters and deploy your app to both. Then, you can use the Azure Traffic Manager to route the traffic to one of the clusters at a time, and switch to the other cluster in case of an issue.
To save costs, you can use the Azure Reservations to reserve capacity and reduce the cost of your AKS nodes. You can also use the Azure Cost Management & Billing service to monitor your costs and identify areas for optimization.

Note: These steps are just high-level guidance, and additional steps may be needed depending on your specific requirements.

Phases to migrate MFEs in AWS Fargate to Azure AKS

Here are the general phases for migrating from AWS Fargate to Azure AKS:

Assessment: Evaluate the current micro frontend architecture, identify dependencies, and assess the compatibility with Azure AKS.
Plan: Define a clear migration plan, including the timeline, roles and responsibilities, and the technology stack.
Preparation: Prepare the environment for the migration, including setting up the necessary infrastructure, creating Azure AKS clusters, and configuring network security.
Data migration: Transfer data and stateful information from AWS Fargate to Azure AKS, including databases, secrets, and configuration files.
Application migration: Migrate the micro frontend applications and services to the new Azure AKS environment, ensuring that they are deployed and configured correctly.
Validation: Validate the migration by testing the applications, services, and infrastructure to ensure that everything is working as expected.

How to keep the cost constant?

To keep costs constant, you can consider the following improvements:

Utilize Azure Cost Management and Azure Reservations for cost optimization.
Automate the scaling and resource management of AKS clusters with Azure Autoscaling.
Use Azure DevOps for continuous integration and deployment.
Implement Azure Monitor and Azure Log Analytics for centralized log management and troubleshooting.
Utilize Azure Container Registry for image management and continuous deployment.

Advantages of migrating to Azure AKS from AWS Fargate

Azure AKS and AWS Fargate are both managed container orchestration services, and the choice between them will depend on a number of factors, including the specific requirements and goals of your organization. Here are some of the advantages of Azure AKS over AWS Fargate:

Integration with Azure Services: Azure AKS is deeply integrated with other Azure services, making it easier to manage and deploy applications that use multiple services.
Lower Costs: Azure AKS generally has lower compute costs compared to AWS Fargate, especially when using spot instances and discounts.
Managed Control Plane: Azure AKS includes a managed control plane, which simplifies the process of deploying, scaling and managing clusters.
Larger Community: Azure has a larger community of users, making it easier to find support and resources.
Hybrid Cloud Support: Azure AKS is part of the Azure ecosystem and supports hybrid cloud scenarios, allowing you to run your applications on-premises, in the cloud, or in other clouds.

When migrating from AWS Fargate to Azure AKS, there are a few things to consider. First, you need to assess your current infrastructure and determine what changes need to be made to support AKS. This might include updates to your network, security, and deployment pipelines. Second, you need to determine what tools and services you’ll use to manage and monitor your AKS cluster, and make sure you have the resources to support these tools. Finally, you need to plan your migration and test it thoroughly before making the switch to production.

How does good architecture help in striking right balance between tech priorities & business priorities?

To configure Akamai to route only 5% of traffic to a new Kubernetes cluster and 95% to the old AWS cluster, you can follow these steps:

Configure your Akamai content delivery network (CDN) to route traffic based on the host header or IP address.
Create two origin groups, one for the new Kubernetes cluster and one for the old AWS cluster.
Set the weight of the new Kubernetes origin group to 5% and the weight of the old AWS origin group to 95%.
Create a new property in Akamai to handle the routing of traffic.
Map the property to the origin groups and set the traffic distribution weights accordingly.

Note: The exact steps for implementing this configuration will depend on the specific version of Akamai you are using and the setup of your origin groups.

What are origin groups in Akamai?

In Akamai, Origin groups are a feature that enables you to define multiple origin servers (such as multiple instances of a web server, load balancer, or Content Delivery Network [CDN] edge server) as a single origin entity. This allows you to define a custom load balancing policy, manage failover, and provide backup origins to be used if the primary origin is unavailable. Origin groups provide a single point of configuration, enabling you to quickly add, remove, or modify the origin servers within a group, without the need to reconfigure multiple locations in your edge network.

Kubernetes Key Concepts

Some key concepts and terms related to Kubernetes that one needs to familiarize themselves before proceeding:

Nodes: The physical or virtual machines that run your applications and services.
Pods: The smallest and simplest unit in the Kubernetes object model, a pod represents a single instance of a running process in your cluster.
Replication Controllers: An object responsible for maintaining the correct number of replicas of your application or service.
Services: An abstraction that defines a logical set of pods and a policy to access them, usually via a network load balancer.
Labels: Key-value pairs used to organize and select objects in your cluster.
Volumes: Persistent, network-attached storage available to your pods.
Namespaces: A way to partition resources within a cluster, useful for separating different environments or teams.
Secrets: Objects that store sensitive information, such as passwords, keys, or certificates.
ConfigMaps: Objects that store configuration data as key-value pairs.
Ingress: A collection of rules that define how external traffic should be route to your services.

By understanding these concepts and terms, you can demonstrate a strong understanding of Kubernetes and how it works. Additionally, familiarizing yourself with common use cases, such as rolling updates, scaling, and resource management, can also help you exhibit your expertise.

Lifecyle of a Micro Frontend application in AKS

The lifecycle of a micro frontend app in AKS typically includes the following stages:

Development: Code is written and tested locally, and then committed to a version control system (e.g., Git).
Continuous Integration (CI): The code is built and tested automatically by a CI system (e.g., Jenkins) to ensure it meets certain quality standards.
Continuous Deployment (CD): The app is deployed automatically to a test environment in AKS.
Testing: The app is tested in the test environment to ensure it meets requirements and to catch any issues before deployment to production.
Deployment to production: The app is deployed to a production environment in AKS.
Monitoring and scaling: The app is monitored for performance and availability, and the number of replicas is adjusted as needed to ensure performance and availability.
Updates and rollbacks: The app is updated with bug fixes and new features, and previous versions can be rolled back if necessary.
Retirement: The app is decommissioned when it is no longer needed.

How to get started quickly with Kubernetes?

Hands-on experience: The best way to learn Kubernetes is by setting up a cluster and deploying applications to it. Try to experiment with different scenarios, such as scaling, rolling updates, and rollbacks.
Read the documentation: The Kubernetes documentation is comprehensive and well-written. It’s a great resource for learning the basics and advanced topics.
Attend online meetups and workshops: There are many online meetups and workshops that focus on Kubernetes. Attending these can help you learn from experts and other community members.
Join online forums and communities: Joining online forums and communities can provide you with a wealth of knowledge and resources, as well as opportunities to ask questions and get feedback from others.

What are the differences between a master node and a worker node?

In a Kubernetes cluster, a master node is responsible for managing the cluster and coordinating the deployment, scaling, and maintenance of applications. A master node runs various components such as the API server, scheduler, and control manager.

A worker node, on the other hand, runs containers and is responsible for executing the tasks assigned to it by the master node. The worker nodes communicate with the master node to receive updates and send information about the state of containers. Worker nodes are where the pods run, and pods contain one or more containers.

What is the difference between controllers and operators?

Controllers and operators are both management components in Kubernetes.

Controllers are responsible for maintaining the desired state of the system, such as ensuring that a specified number of replicas of a particular deployment are running at any given time. Controllers continuously monitor the state of the cluster and take action as needed to ensure that the desired state is met.

Operators, on the other hand, are specialized controllers designed to manage and operate a specific application or component in the cluster. They go beyond the basic features of controllers by offering more advanced functionality, such as automatic updates, custom resource definitions, and lifecycle management. Operators are used to automate and manage complex, stateful applications in a Kubernetes cluster.

In summary, controllers are a basic component for maintaining the desired state of a cluster, while operators offer more advanced functionality for managing specific applications.

What does `kubectl` do?

kubectl is a command line tool used to interact with a Kubernetes cluster. It allows users to deploy, inspect, and manage applications and their components (such as pods, services, and deployments) on a cluster. Some common actions performed using kubectl include:

Deploying and updating applications
Scaling up or down the number of replicas
Viewing logs and resource utilization
Troubleshooting and debugging applications.

Service Mesh

A Service Mesh is used in microservice-based architectures to manage communication between service instances. It provides features like traffic management, service discovery, load balancing, and security, to name a few. Service Mesh helps to abstract and manage the network communication between services, freeing up developers to focus on building business logic and applications. This can lead to improved resiliency, reliability, and security of microservice-based systems.

Istio is a popular open source service mesh that provides various features like traffic management, security, and observability for microservices applications. Service mesh is a configurable infrastructure layer for microservices application that makes communication between service instances flexible, reliable, and fast.

Service Mesh and Istio are related but different concepts in cloud-native application development. Service Mesh refers to a configurable infrastructure layer for microservices application that makes communication between service instances flexible, reliable, and fast. Istio is an open-source service mesh that provides communication routing, traffic management, and security features for microservices. In other words, Istio is an implementation of a service mesh.

Some differences between Service Mesh and Istio include:

Functionality: Service Mesh provides an infrastructure layer to manage service-to-service communication, while Istio provides a full-featured service mesh with advanced traffic management, security, and observability capabilities.
Architecture: Service Mesh is a layer between the application and the network, while Istio is built on top of a service mesh and provides additional functionality.
Adoption: Istio is one of the most widely adopted open-source service meshes, while other service meshes are available and used in the industry.
Community: Istio is an open-source project with a strong community of contributors and users, while the level of community support for other service meshes may vary.

Whether to use Istio or not depends on the specific needs of your application and infrastructure. If you have a complex microservices architecture that requires advanced traffic management and security features, then Istio might be a good choice. If you have a simpler architecture and don’t require the features provided by Istio, then using a service mesh may not be necessary.

Ultimately, the decision to use Istio or a service mesh should be based on your specific requirements and the trade-offs between the features offered by Istio and the additional operational overhead that comes with using it.

Multiple Clusters vs Multiple Namespaces

We did want to create multiple clusters. The organization’s strategic direction was towards minimizing the number of clusters we used (so as to consolidate all of into a single cluster some day in the distance future). We wanted to see if there was a better way to separate deployments without using multiple clusters.

So, we found that we can use namespaces in a single AKS cluster to separate deployments without using multiple clusters.

Here’s how:

Create a namespace for each deployment in your AKS cluster using the kubectl create namespace command.
Deploy each micro front-end app to its corresponding namespace using Helm charts or YAML manifests.
Use network policies to isolate network traffic between the namespaces.
To implement a canary or A/B testing deployment, you can use the same approach as before with the Azure Traffic Manager, but this time you would route traffic to different namespaces within the same AKS cluster.
For a Blue-Green deployment, you can create two separate namespaces within the same AKS cluster and switch traffic between them, just like before, but this time you would use the Azure Traffic Manager to route traffic to the desired namespace.

From a cost perspective what is preferred – Multiple Clusters or Multiple Namespaces?

Multiple namespaces are preferred over multiple clusters for cost optimization in Azure, as they allow you to share a single cluster and its resources among multiple isolated environments. However, the decision depends on factors such as resource isolation requirements, security, and resource utilization, so it is important to consider all factors before making a decision. If you need complete isolation of resources, multiple clusters might be a better choice, but they will also come with increased cost and complexity.

What are the increased complexities?

Using multiple namespaces or multiple clusters can increase the complexity of the infrastructure in the following ways:

Complexity in resource management: Managing multiple namespaces or clusters can increase the complexity of managing resources like network policies, roles, and access controls.
Complexity in deployment and scaling: Deploying and scaling applications across multiple namespaces or clusters can increase the complexity of deployment and scaling processes.
Complexity in monitoring and logging: Monitoring and logging events in multiple namespaces or clusters can be complex and require additional setup and configurations.
Complexity in cost management: Managing costs in multiple namespaces or clusters can be complex as resources are distributed across multiple entities.

It’s important to weigh the benefits of cost optimization against the increased complexities when deciding on the right infrastructure configuration.

What are network policies?

Network policies in Kubernetes define access control rules for incoming and outgoing network traffic to Pods. They control which sources are allowed to communicate with Pods and can be used to secure communication within a cluster.

Example:

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-from-frontend
spec:
  podSelector:
    matchLabels:
      app: frontend
  policyTypes:
  - Ingress
  ingress:
  - from:
    - namespaceSelector:
        matchLabels:
          environment: production
    - podSelector:
        matchLabels:
          app: api-server
    ports:
    - protocol: TCP
      port: 80

This example creates a network policy named “allow-from-frontend” which allows incoming network traffic from Pods labeled as “frontend” in the same namespace or “api-server” in a different namespace labeled as “production”. The traffic allowed is only TCP traffic on port 80.

Deployments

IaC – How to use Terraform to deploy multiple micro frontends in AKS?

Infrastructure as Code (IaC) is a methodology for managing and provisioning infrastructure through code, rather than manual configuration.

A sample code for AKS deployment of multiple micro frontends using IaC can be done using a tool like Terraform. Here is an example configuration in Terraform for creating an AKS cluster and deploying a micro frontend:

provider "azurerm" {
  version = "2.0"
}

resource "azurerm_resource_group" "aks_group" {
  name     = "aks-group"
  location = "westus2"
}

module "aks" {
  source = "Azure/aks/azurerm"
  version = "2.0.0"
  
  cluster_name = "aks-cluster"
  resource_group_name = azurerm_resource_group.aks_group.name
  location = azurerm_resource_group.aks_group.location
  dns_prefix = "aks-cluster"
  node_count = 2
  linux_profile = {
    admin_username = "aksadmin"
  }
  service_principal = {
    client_id = "YOUR_CLIENT_ID"
    client_secret = "YOUR_CLIENT_SECRET"
  }
  tags = {
    Environment = "AKS"
  }
}

resource "kubernetes_deployment" "frontend" {
  metadata {
    name = "frontend-deployment"
    labels = {
      app = "frontend"
    }
  }
  spec {
    replicas = 2
    selector {
      match_labels = {
        app = "frontend"
      }
    }
    template {
      metadata {
        labels = {
          app = "frontend"
        }
      }
      spec {
        container {
          name  = "frontend"
          image = "YOUR_FRONTEND_IMAGE"
          ports {
            container_port = 80
          }
        }
      }
    }
  }
}

output "kubeconfig" {
  value = module.aks.kubeconfig_raw
}

In this example, Terraform creates a resource group and an AKS cluster, and then deploys a micro frontend using a Kubernetes deployment resource. You can modify the example to fit your specific needs, such as adding environment variables, secrets, or config maps.

How to add Config Maps, Environment Variables and Secrets in the Terraform script?

Here is an example configuration in Terraform to create a ConfigMap and set environment variables for a deployment:

# ConfigMap
resource "kubernetes_config_map" "example_config_map" {
  metadata {
    name = "example-config-map"
  }

  data = {
    key1 = "value1"
    key2 = "value2"
  }
}

# Deployment
resource "kubernetes_deployment" "example_deployment" {
  metadata {
    name = "example-deployment"
  }

  spec {
    replicas = 1

    template {
      metadata {
        labels = {
          app = "example-app"
        }
      }

      spec {
        container {
          name  = "example-container"
          image = "nginx:alpine"

          env {
            name  = "EXAMPLE_ENV_VAR_1"
            value = "value1"
          }

          env_from {
            config_map_ref {
              name = kubernetes_config_map.example_config_map.metadata[0].name
            }
          }
        }
      }
    }
  }
}

Similarly, you can use Terraform to create a Secret and set environment variables for a deployment:

# Secret
resource "kubernetes_secret" "example_secret" {
  metadata {
    name = "example-secret"
  }

  data = {
    key1 = "ZW5jb2RlZC12YWx1ZTE="
    key2 = "ZW5jb2RlZC12YWx1ZTI="
  }
}

# Deployment
resource "kubernetes_deployment" "example_deployment" {
  metadata {
    name = "example-deployment"
  }

  spec {
    replicas = 1

    template {
      metadata {
        labels = {
          app = "example-app"
        }
      }

      spec {
        container {
          name  = "example-container"
          image = "nginx:alpine"

          env {
            name  = "EXAMPLE_ENV_VAR_1"
            value = "value1"
          }

          env_from {
            secret_ref {
              name = kubernetes_secret.example_secret.metadata[0].name
            }
          }
        }
      }
    }
  }
}

In the above example code, kubernetes_config_map and kubernetes_secret resources are used to create a ConfigMap and a Secret, respectively. The environment variables are then set for the container in the deployment using env and env_from blocks.

How to integrate Azure Key Vault?

Azure Key Vault integration with Kubernetes can be achieved through various ways, including:

Direct use of Azure Key Vault API: The applications running in a Kubernetes cluster can directly call the Azure Key Vault API to fetch the secrets stored in it.
Using Kubernetes Init Containers: Kubernetes init containers can be used to retrieve the secrets from Azure Key Vault and then mount them as environment variables or files in a volume, which the main application containers can access.
Using Helm Charts: The Helm package manager can be used to automate the creation of the Kubernetes manifests and configuration required to fetch the secrets from Azure Key Vault.
Using Kubernetes External Secrets: External Secrets is a Kubernetes controller that can be used to manage the lifecycle of secrets stored in Azure Key Vault and retrieve them as Kubernetes secrets in the cluster.
Using the Azure Key Vault FlexVolume: Azure Key Vault FlexVolume is a Kubernetes volume plugin that allows you to mount secrets stored in Azure Key Vault directly into your pods.

The choice of integration method will depend on your specific requirements and the complexity of your deployment.

How to use a sidecar to inject Vault Secrets Into Kubernetes Pods?

How to integrate Vault using the Rafay Kubernetes Management Cloud?

https://rafay.co/the-kubernetes-current/kubernetes-secrets-management-with-hashicorp-vault-and-rafay/

How to use Kubernetes Init containers for Azure Key Vault integration?

Kubernetes Init containers are run before the main container in a Pod is started. They can be used to perform setup tasks, such as pulling config files from a remote location, before the main application is started.

Here’s an example of a Init container in a Pod definition that pulls a config file from a remote location and stores it in a volume that is shared with the main container:

apiVersion: v1
kind: Pod
metadata:
  name: example-init-container
spec:
  containers:
  - name: main-container
    image: myimage
    volumeMounts:
    - name: config-volume
      mountPath: /config

  initContainers:
  - name: init-container
    image: busybox
    command: ["wget", "-O", "/config/myconfig.txt", "http://example.com/myconfig.txt"]
    volumeMounts:
    - name: config-volume
      mountPath: /config

  volumes:
  - name: config-volume
    emptyDir: {}

In this example, the Init container uses the wget command to download a config file from a remote location and stores it in a volume. The main container can then access the config file from the same volume.

What are the different possible ways to integrate Azure Key Vault?

Vault offers a comprehensive set of capabilities to manage and distribute secrets in a Kubernetes deployment.

However, the creation and deployment of an application securely as a set of microservices using Kubernetes touches application developers and DevOps personnel equally, and security is a top concern for both of these groups

Vault Integration with Kubernetes can be done with the following possible manners:

Kubernetes init containers
Side cars and more which introduces a learning curve.

What are helm charts?

Helm charts are packages of pre-configured Kubernetes resources that can be easily deployed and managed as a single unit. They are used to automate the deployment, management, and upgrade of complex applications in a Kubernetes cluster.

Helm charts are widely used in Kubernetes to package, distribute and deploy applications.

Some popular uses of Helm charts are:

Deploying complex microservices applications: Helm charts make it easy to manage the deployment of multiple interdependent microservices as a single, versioned release.
Reusability: Helm charts can be reused across different projects, enabling teams to package their applications into reusable components.
Application version management: Helm charts allow you to manage different versions of an application and roll back to previous releases if needed.
Consistent deployment: Helm charts ensure consistent deployment of applications, making it easy to manage the configuration of multiple instances of the same application.

Some lesser known uses of Helm charts are:

Upgrading legacy applications: Helm charts can be used to upgrade legacy applications, by encapsulating the upgrade process into a single, repeatable process.
Continuous Deployment: Helm charts can be integrated with CI/CD pipelines, enabling teams to continuously deploy their applications to multiple environments.
Automating test environments: Helm charts can be used to automate the creation of test environments, by specifying the required components in a chart and deploying them automatically.
Deploying tools and services: Helm charts can be used to deploy and manage a wide range of tools and services, such as databases, monitoring tools, and log aggregators, in a consistent and repeatable manner.

Here is a sample example of a Helm chart for deploying multiple micro frontends:

apiVersion: v2
name: frontend-app
description: A Helm chart for deploying multiple micro frontends
version: 0.1.0

# Define the resources to be deployed
resources:
  - name: frontend-1
    type: Deployment
    apiVersion: apps/v1
    metadata:
      name: frontend-1
      labels:
        app: frontend-1
    spec:
      replicas: 2
      selector:
        matchLabels:
          app: frontend-1
      template:
        metadata:
          labels:
            app: frontend-1
        spec:
          containers:
            - name: frontend-1
              image: <image-repository>/frontend-1:<version>
              ports:
                - containerPort: 80
  - name: frontend-2
    type: Deployment
    apiVersion: apps/v1
    metadata:
      name: frontend-2
      labels:
        app: frontend-2
    spec:
      replicas: 2
      selector:
        matchLabels:
          app: frontend-2
      template:
        metadata:
          labels:
            app: frontend-2
        spec:
          containers:
            - name: frontend-2
              image: <image-repository>/frontend-2:<version>
              ports:
                - containerPort: 80
  - name: frontend-3
    type: Deployment
    apiVersion: apps/v1
    metadata:
      name: frontend-3
      labels:
        app: frontend-3
    spec:
      replicas: 2
      selector:
        matchLabels:
          app: frontend-3
      template:
        metadata:
          labels:
            app: frontend-3
        spec:
          containers:
            - name: frontend-3
              image: <image-repository>/frontend-3:<version>
              ports:
                - containerPort: 80

# Define the dependencies for the resources
dependencies:
  - name: frontend-1
    version: 0.1.0
    repository: <repository-

How to use Helm Charts to automate Azure Key Vault integration?

Here is an example of using Helm Charts to automate Azure Key Vault integration:

First, you need to create a secrets.yaml file which includes the secrets you want to store in Azure Key Vault, for example:

yamlCopy codeapiVersion: v1
kind: Secret
metadata:
  name: secrets-example
data:
  password: cGFzc3dvcmQ=
  username: dXNlcm5hbWU=

Create a Helm Chart that includes a reference to the secrets.yaml file in its templates folder, for example:

# Chart.yaml
apiVersion: v2
name: keyvault-secrets-example
version: 0.1.0
description: A Helm Chart to automate Azure Key Vault integration.

# templates/secrets.yaml
{{- define "secrets.create" -}}
{{- include "secrets.yaml" . | nindent 4 -}}
{{- end -}}

Add a reference to the secrets.yaml file in the values.yaml file, for example:

# values.yaml
secrets:
  {{- include "secrets.create" . | nindent 2 }}

Finally, use Helm to install the chart with the following command:

$ helm install keyvault-secrets-example .

This will create the secrets in Azure Key Vault and store them as environment variables in your Kubernetes cluster, which can then be used by your application.

How to configure multiple namespaces in Azure Kubernetes Services?

To configure multiple namespaces in Azure Kubernetes Service (AKS), follow these steps:

Create AKS cluster: You can create an AKS cluster using the Azure CLI or the Azure portal.
Create Namespaces: You can create multiple namespaces in AKS using the following command:

kubectl create namespace <namespace-name>

Deploy applications to namespaces: Once the namespaces are created, you can deploy your micro front-end applications to each namespace using kubectl or Helm charts.
Assign network policy: To isolate network traffic between namespaces, you can use Kubernetes network policies.
Monitor and manage: You can monitor and manage the resources in each namespace using kubectl or the Azure portal.

Note: To make it easier to manage multiple namespaces in AKS, consider using Kubernetes namespace labels and resource quotas.

What are Namespaces?

Namespaces allow you to group objects together in Kubernetes, so that you can filter them and control them as a unit. Some resources are namespaced (i.e. associated to a particular namespace), while other resources apply to the entire cluster.

Think of a namespace as a house. Inside a house, you have things like rooms, furniture, and people. Suppose that you have two houses: Mindy’s house, and Abdul’s house. They both have rooms, furniture, and people. Inside Mindy’s house, you refer to the couch as just “couch”. Similarly, inside Abdul’s house, you refer to the couch as “couch”. Outside of each home, however, we need to be able to distinguish which couch belongs to what house. We do this by saying “Mindy’s house’s couch” and “Abdul’s house’s couch”. That is, you’re qualifying the object (couch) as belonging to a particular house.

What are the default namespaces in K8s?

In Kubernetes, there are 4 namespaces that are created by default upon cluster creation:

default: Default dumping ground for objects. If you don’t specify a namespace, all objects go here.
kube-system: Reserved for Kubernetes system objects (e.g. kube-dns, kube-proxy). Also, add-ons that provide cluster-level features also go here (e.g. web UI dashboards, cluster-level logging, ingresses.
kube-public: Resources that should be made available to all users are created here. Any objects here are available without authentication.
kube-node-lease: Objects related to cluster scaling go here.

In addition to the 4 out-of-the box namespaces, we can also create custom namespaces. Namespace creation is typically only allowed by Kubernetes admins. With the proper security in place, namespaces can be set up so that only certain people have access to a particular namespace — just like having a key to a house. Only folks with the key can get in.

Note: When you create an object in Kubernetes, if you don’t specify a namespace, it will be automagically placed in the default namespace, so make sure you always specify a namespace!

What are some real life usecases of namespaces in K8s?

In real life, namespaces can be used for grouping:

Resources that are a part of the same application.
Resources that belong to a particular user. For example, I can create a namespace called adri, and create a bunch of resources in there as part of my Kubernetes experimentations.
Environment-specific resources. For example, rather than having a separate cluster for Dev and QA, you can simply create a dev namespace, and a qa namespace in the same cluster, and deploy resources to the appropriate namespace.

How to create namespaces in K8s?

You can create a namespace in Kubernetes using kubectl like this (if you have permission to do so):

kubectl create ns foo

Where foo is our namespace. You can choose to call your namespaces whatever your want, as long as they follow the k8s naming convention described earlier.

You can also create a namespace from a YAML file, like this:

---
apiVersion: v1
kind: Namespace
metadata:
  name: foo

To create the namespace in Kubernetes from the above file:

kubectl apply -f sample-k8s-namespace.yml

How to create namespaces dynamically from Jenkins?

To create namespaces dynamically from Jenkins, you can use the Jenkins Kubernetes Plugin and the Kubernetes CLI (kubectl) in your Jenkins pipeline. Here’s a rough outline of the steps:

Install the Jenkins Kubernetes Plugin: Go to the Manage Plugins section of your Jenkins instance, search for the “Kubernetes plugin,” and install it.
Configure Kubernetes CLI: You need to configure the Kubernetes CLI on the Jenkins agent. You can do this by downloading and installing kubectl on the agent or using a pre-installed version.
Set up credentials: You need to provide the Jenkins Kubernetes plugin with the credentials to access your AKS cluster. This can be done through the Jenkins Credentials Plugin.
Create a Jenkinsfile: In your Jenkinsfile, you will use the Jenkins Kubernetes Plugin and the kubectl CLI to create the namespace dynamically.

Example Jenkinsfile:

pipeline {
    agent {
        label 'my-agent'
    }
    stages {
        stage('Create namespace') {
            steps {
                script {
                    sh 'kubectl create namespace <namespace-name>'
                }
            }
        }
        stage('Deploy application') {
            steps {
                script {
                    sh 'kubectl apply -f <deployment-file>.yaml -n <namespace-name>'
                }
            }
        }
    }
}

In this example, the Jenkins pipeline creates a namespace using the kubectl create namespace command and then deploys the application to that namespace using kubectl apply. You can further customize this example to your specific requirements.

Advantages of using namespaces

Namespaces provide a way to divide cluster resources between multiple users. They allow you to isolate resources, like pods, services and secrets, so that they do not affect other users and also to avoid naming collisions. Some benefits of using namespaces in Kubernetes include:

Resource isolation: Each namespace provides a separate scope for resources, making it easier to manage and control access.
Multi-tenancy: Allows multiple teams to share the same cluster and still maintain isolation.
Improved resource management: Namespaces can be used to define and enforce resource quotas, ensuring that one team’s workloads don’t consume resources intended for another team.
Ease of administration: Namespaces make it easier to manage resources and delegate administration to different teams.
Improved security: Namespaces can be used to implement role-based access control (RBAC) policies, ensuring that only authorized users have access to the resources within the namespace.

What are some limitations with namespaces in Kubernetes?

Kubernetes namespaces have several limitations, including:

Resource Quotas: Kubernetes namespaces do not provide a way to enforce resource usage limits across multiple namespaces within a cluster.
Network Segregation: Namespaces do not provide an isolated network segment, which means communication between namespaces is not restricted by default.
Global Resources: Some Kubernetes resources like nodes, persistent volumes, and cluster roles cannot be scoped to a namespace and are available cluster-wide.
Cluster Level Resources: Some resources like network policies and cluster roles are cluster-level and cannot be scoped to a namespace.
Inter-Namespace Communication: Communication between namespaces can be challenging and may require additional configuration, such as network policies or service meshes.
Interoperability: Kubernetes namespaces are not compatible with all external services, such as some service meshes, and may require additional configuration or custom tooling.

What are the naming conventions that I need to follow while creating a new namespace?

It’s worth noting that objects created in Kubernetes must follow a specific naming convention. Kubernetes follows certain naming conventions for namespaces:

Namespaces must be lowercase alphanumeric characters and start with a letter.
They can contain hyphens (-), dots (.), and underscores (_).
Namespaces must be between 2 and 63 characters long.
The name of the namespace must be unique within the cluster.

It is also recommended to follow a consistent naming convention that is easy to understand and manage, such as using a prefix to indicate the environment (e.g. dev-, prod-) or the purpose of the namespace (e.g. app-, db-).

If you try to create objects that violate this naming convention, Kubernetes will complain.

How to apply a TTL (time to live) on this freshly created namespace?

To apply a TTL (time to live) on a freshly created namespace in AKS, you can use Kubernetes resource quotas and garbage collection. Here’s an outline of the steps:

Create a Resource Quota: A resource quota allows you to set limits on the amount of resources used in a namespace. You can set a limit on the number of days that a namespace can exist.

Example resource quota yaml:

apiVersion: v1
kind: ResourceQuota
metadata:
  name: ttl-quota
spec:
  hard:
    metadata.creationTimestamp: "2023-01-01T00:00:00Z"

Apply Garbage Collection: Garbage collection in Kubernetes automatically deletes resources that are no longer needed. You can use garbage collection to automatically delete namespaces when their TTL has expired.

Example garbage collection policy yaml:

apiVersion: policy/v1beta1
kind: PodDisruptionBudget
metadata:
  name: ttl-pdb
spec:
  selector:
    matchLabels:
      app: ttl-app
  maxUnavailable: 0

Automate with Jenkins: You can automate the deletion of namespaces using Jenkins by adding a step in your Jenkins pipeline to run the kubectl delete command. You can also schedule the pipeline using a cron job to run periodically and delete the namespaces.

Example Jenkinsfile for deletion:

codepipeline {
    agent {
        label 'my-agent'
    }
    stages {
        stage('Delete namespace') {
            steps {
                script {
                    sh 'kubectl delete namespace <namespace-name>'
                }
            }
        }
    }
}

In this example, the Jenkins pipeline runs a kubectl delete command to delete the namespace. You can further customize this example to your specific requirements.

How to destroy a dynamically created namespace via a cron job or jenkins?

You can automate the deletion of namespaces using Jenkins by adding a step in your Jenkins pipeline to run the kubectl delete command. You can also schedule the pipeline using a cron job to run periodically and delete the namespaces.

Example Jenkinsfile for deletion:

pipeline {
    agent {
        label 'my-agent'
    }
    stages {
        stage('Delete namespace') {
            steps {
                script {
                    sh 'kubectl delete namespace <namespace-name>'
                }
            }
        }
    }
}

In this example, the Jenkins pipeline runs a kubectl delete command to delete the namespace. You can further customize this example to your specific requirements.

How to configure different domain names for different namespaces?

To configure different domain names for different namespaces in Kubernetes, you can use a LoadBalancer Service or Ingress.

LoadBalancer Service:

Create a LoadBalancer Service for each namespace that needs a different domain name.
Each Service will have its own external IP, which can be associated with a different domain name using DNS.

Ingress:

An Ingress resource allows you to define rules for accessing multiple Services in a cluster using the same IP address and DNS name.
You can create separate Ingress resources for each namespace and configure rules to direct traffic to the appropriate Service based on the host name or path.

To assign a domain name to an IP address, you’ll need to use a DNS provider or configure your own DNS server to map the domain names to the IP addresses of the Services.

How to configure different sub-domains for different namespaces?

To configure different sub-domains for different namespaces in Kubernetes, you can use an Ingress resource.

Here’s a sample Ingress YAML configuration that maps different sub-domains to different Services based on the branch name:

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: my-ingress
spec:
  rules:
  - host: feature-{branch-name}.myapp.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: feature-{branch-name}
            port:
              name: http
  - host: production.myapp.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: production
            port:
              name: http

In this example, the Ingress resource maps sub-domains “feature-{branch-name}.myapp.com” to a Service named “feature-{branch-name}”, where {branch-name} is replaced with the actual Git branch name. Similarly, the sub-domain “production.myapp.com” is mapped to the Service named “production”.

You’ll need to create separate Services for each branch in your Git repository, and make sure each Service is created with the correct name as specified in the Ingress resource. Additionally, you’ll need to configure your DNS provider or DNS server to map the sub-domains to the IP address of the Ingress resource.

How long will it typically take to create a new namespace and subdomains dynamically?

The time it takes to create a new namespace and set subdomains dynamically can vary depending on several factors such as the size of the infrastructure, the complexity of the setup, and the amount of resources available. On average, it can take anywhere from a few hours to several days to complete the process.

We have ~15 micro frontend applications. Each requires about 5 to 10 minutes of time to build and deploy.

If you have 15 micro frontends that each take 5 to 10 minutes to build and deploy, the total time to create dynamic namespaces for all of them would be approximately 2 to 4 hours (15 * 10 minutes = 150 to 300 minutes = 2.5 to 5 hours).

It’s important to note that this estimate is based on ideal conditions and does not account for any additional time needed for infrastructure setup or any unexpected issues that may arise during the process.

How to separate the build and deployment processes?

To separate the build and deployment processes, you can use the following steps:

Build: You can build the micro frontend applications in a continuous integration (CI) environment such as Jenkins or TravisCI. The build process typically involves compiling the code, creating an executable or Docker image, and storing it in a repository such as Docker Hub or AWS ECR.
Deployment: Once the build is complete, you can trigger the deployment process. In this process, you can use tools such as Helm, Kustomize, or kubectl to deploy the Docker images to your cluster. You can use a deployment strategy such as rolling updates, blue/green deployment, or canary deployment to deploy the updated images to your cluster.

By separating the build and deployment processes, you can make the deployment process more efficient and ensure that the deployment environment remains isolated from the build environment. This also helps to ensure that the deployment process is consistent, repeatable, and reliable.

How to configure rolling updates, blue/green deployment, and canary deployment in AKS?

To configure rolling updates, blue/green deployment, and canary deployment in AKS, you can use Kubernetes tools and techniques such as:

Rolling updates: To perform rolling updates, you can use the kubectl rollout command, or you can use the Kubernetes Deployment resource, which automatically manages and performs rolling updates.
Blue/Green deployment: Blue/green deployment is a technique to deploy a new version of an application without downtime, by deploying the new version alongside the old version and then switching traffic to the new version once it’s ready. This can be achieved by using a load balancer or ingress controller that can route traffic to the desired version of the application.
Canary deployment: Canary deployment is a technique to deploy a new version of an application to a subset of users, and then gradually rolling it out to more users based on the performance and stability of the new version. This can be achieved by using a load balancer or ingress controller that can route traffic to the desired version of the application.

In AKS, you can use ingress controllers such as Nginx or Traefik to configure routing and traffic management. You can also use tools such as Istio or Linkerd to manage traffic routing, perform canary deployments, and implement blue/green deployments.

Multi-tenancy for multiple-countries with Namespaces

I have 4 nations e.g. Cz, Sk, UK, and ROI. They speak 4 different languages. I want to create a separate name spaces for each country spread out across country-specific regions in Azure. How will my namespace configuration look like?

You can create separate namespaces in your Kubernetes cluster for each country, e.g. “cz”, “sk”, “uk”, and “roi”. You can then use resource quotas, network policies, and security contexts to isolate each namespace and restrict access to the resources within it. Additionally, you can configure your Azure regions to ensure that each namespace is deployed in the appropriate region for the country it represents. The configuration would vary based on your specific needs, but a basic configuration could look like the following:

apiVersion: v1
kind: Namespace
metadata:
  name: cz
---
apiVersion: v1
kind: Namespace
metadata:
  name: sk
---
apiVersion: v1
kind: Namespace
metadata:
  name: uk
---
apiVersion: v1
kind: Namespace
metadata:
  name: roi

You can apply this configuration to your cluster using the kubectl apply command. To ensure that each namespace is deployed in the appropriate region, you would need to set up your Azure resources accordingly, such as by creating separate resource groups for each namespace and setting the region for each resource group.

While deploying one microservice, say M1, how to deploy all of the other microservices (e.g. M2, M3, M4) that M1 depends upon?

To ensure that all microservices that M1 depends upon are deployed, you can use Kubernetes deployment objects. A deployment object provides declarative updates for pods and replica sets. You can define the desired state for your microservices and the deployment controller will manage the actual state. For example, you can define a deployment for each of the microservices (M2, M3, M4) and make M1 depend on them. The deployment controller will ensure that these microservices are running and available before M1 is deployed.

Additionally, you can use Kubernetes init containers to execute setup scripts before the main containers start, or use a sidecar container to deploy and manage the dependencies alongside the main container.

For example, if M1 depends on M2, M3, and M4, you can create separate Deployments for each of these microservices and use a config file to specify the dependencies. The config file can be in YAML or JSON format, and it will define the desired state of your microservices.

Here’s an example of a YAML file for deploying M2:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: m2-deployment
spec:
  selector:
    matchLabels:
      app: m2
  replicas: 3
  template:
    metadata:
      labels:
        app: m2
    spec:
      containers:
      - name: m2
        image: m2:v1
        ports:
        - containerPort: 80

Yes, you can use Kubernetes Deployments to manage the deployment of all the microservices that M1 depends upon. For example, if M1 depends on M2, M3, and M4, you can create separate Deployments for each of these microservices and use a config file to specify the dependencies. The config file can be in YAML or JSON format, and it will define the desired state of your microservices.

Here’s an example of a YAML file for deploying M2:

yamlCopy codeapiVersion: apps/v1
kind: Deployment
metadata:
  name: m2-deployment
spec:
  selector:
    matchLabels:
      app: m2
  replicas: 3
  template:
    metadata:
      labels:
        app: m2
    spec:
      containers:
      - name: m2
        image: m2:v1
        ports:
        - containerPort: 80

You can repeat this process for M3 and M4, and then use Kubernetes Services to expose the endpoints of these microservices. Once all the microservices are deployed, you can use Kubernetes Network Policy to secure the communication between the microservices.

Using Sidecars for Dependency Management

Deploying a sample NodeJS app along with dependency management:

Here’s an example code in NodeJS for building and deploying dependencies for a container using Docker and a package manager such as npm:

# Dockerfile
FROM node:12
WORKDIR /app
COPY package*.json ./
RUN npm install
COPY . .
CMD ["npm", "start"]

# package.json
{
  "name": "my-app",
  "version": "1.0.0",
  "dependencies": {
    "express": "^4.17.1",
    "request-promise": "^4.2.5"
  },
  "scripts": {
    "start": "node index.js"
  }
}

# index.js
const express = require('express')
const request = require('request-promise')
const app = express()

app.get('/', (req, res) => {
  request('https://www.google.com')
    .then(html => {
      res.send(html)
    })
    .catch(err => {
      res.send(err)
    })
})

app.listen(3000, () => {
  console.log('App listening on port 3000!')
})

In this example, the dependencies are listed in the package.json file and installed during the build process. The main application is defined in the index.js file, which uses the express and request-promise packages to proxy requests to an external website. The app is started using the npm start script in the package.json file.

To build and deploy this container, you can use the following commands:

$ docker build -t my-app .
$ docker run -p 3000:3000 my-app

Security in Kubernetes

Ensuring security in Kubernetes involves several aspects, including certificate management, setting up mutual TLS (MTLS), securing network communication, and controlling access to resources.

For certificate management, you can use tools like kubeadm, which helps you bootstrap a secure Kubernetes cluster, or cert-manager, which automates the management and issuance of SSL/TLS certificates.

To set up MTLS, you can use a tool like Istio, which provides a flexible and easy-to-use solution for securing service-to-service communication.

To secure network communication, you can use network policies to define rules for inbound and outbound traffic. For example, you can allow traffic only from trusted sources or block traffic from untrusted sources.

To control access to resources, you can use Role-Based Access Control (RBAC) and Kubernetes admission controllers. RBAC allows you to define roles and assign permissions to users and service accounts, while admission controllers provide a way to enforce security policies before resources are created or updated in the cluster.

Certs

Example code for using cert-manager:

# 1. Install cert-manager
$ kubectl apply -f https://github.com/jetstack/cert-manager/releases/download/v1.1.0/cert-manager.yaml

# 2. Create a CertificateRequest resource
apiVersion: cert-manager.io/v1alpha2
kind: CertificateRequest
metadata:
  name: myapp-cert
spec:
  issuerRef:
    name: letsencrypt-prod
  dnsNames:
  - myapp.example.com

# 3. Create a ClusterIssuer resource
apiVersion: cert-manager.io/v1alpha2
kind: ClusterIssuer
metadata:
  name: letsencrypt-prod
spec:
  acme:
    server: https://acme-v02.api.letsencrypt.org/directory
    email: myemail@example.com
    privateKeySecretRef:
      name: letsencrypt-prod
    solvers:
    - dns01:
        cloudflare:
          email: myemail@example.com
          apiKeySecretRef:
            name: cloudflare-api-key
            key: api-key

MTLS with Istio

Example code for using Istio to set up MTLS:

# 1. Install Istio
$ kubectl apply -f https://raw.githubusercontent.com/istio/istio/main/install/kubernetes/operator/profiles/default.yaml

# 2. Enable MTLS
$ kubectl apply -f - <<EOF
apiVersion: "security.istio.io/v1beta1"
kind: "PeerAuthentication"
metadata:
  name: "default"
  namespace: "istio-system"
spec:
  mtls:
    mode: STRICT
EOF

# 3. Verify that MTLS is working
$ kubectl exec "$(kubectl get pod -l istio=ingressgateway -n istio-system -o jsonpath='{.items[0].metadata.name}')" -c istio-proxy -- curl -I https://istio-ingress.istio-system.svc.cluster.local/

What are some least known but great features of kubernetes?

StatefulSets: Ability to manage stateful applications and provide unique network identities to pods.
Custom Resource Definitions (CRDs): Ability to extend the Kubernetes API to accommodate custom resource types.
Init Containers: Ability to run a separate container before the main container starts, useful for pre-requisites and setup.
Auto-scaling based on custom metrics: Ability to scale applications based on custom metrics, not just CPU and memory usage.
Horizontal Pod Autoscaling (HPA): Ability to automatically scale pods based on CPU and memory utilization.
DaemonSets: Ability to run a single pod on all nodes or a subset of nodes, useful for infrastructure-level services.
Jobs and CronJobs: Ability to run one-off or scheduled tasks within the cluster.
NetworkPolicy: Ability to define and enforce network access rules within a namespace.
Affinity and Anti-Affinity rules: Ability to constrain or spread pods across nodes based on attributes or labels.
Persistent Volumes and Persistent Volume Claims: Ability to provide durable storage for pods and manage storage resources.

Labels and Annotations in Kubernets

Labels and annotations are metadata that can be attached to objects such as pods, services, or namespaces in Kubernetes. Some good use cases of labels and annotations are:

Labels for object selection: Labels can be used to group objects together for operations like rolling updates, scaling, or monitoring.
Annotations for operational information: Annotations can be used to store operational information such as version, creation time, or owner.
Labels for environment separation: Labels can be used to separate resources into different environments like development, staging, and production.
Labels for team ownership: Labels can be used to indicate which team is responsible for a particular set of resources.
Annotations for custom resource validation: Custom resource validation can be performed using annotations, making it easier to validate resource specifications.
Labels for disaster recovery: Labels can be used to define disaster recovery policies for specific resources.

Using Sidecars

A sidecar container is a secondary container that runs in the same pod as the main container and helps manage dependencies that the main container needs. To use a sidecar container, you’ll need to create a pod specification in Kubernetes that includes both the main container and the sidecar container. The pod specification will define how the containers run, including network, storage, and environmental requirements. The sidecar container will be responsible for managing the dependencies, while the main container runs the main application. Once the pod specification is defined, you can use kubectl to create and manage the pod. The sidecar container will automatically be deployed and managed alongside the main container, ensuring that dependencies are always available when needed.

Here’s an example YAML configuration for deploying a sidecar container:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-deployment
spec:
  replicas: 1
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
      - name: main-container
        image: my-app:latest
        ports:
        - containerPort: 80
      - name: sidecar-container
        image: my-sidecar:latest
        ports:
        - containerPort: 8080

In this example, the main container and the sidecar container are deployed together in the same pod, allowing them to share the same network namespace and communicate with each other directly via localhost. The sidecar container can perform tasks such as logging, monitoring, or proxying requests to the main container.

Here’s an example code for a sidecar container in NodeJS that proxies requests to the main container:

const express = require('express');
const request = require('request');
const app = express();

// URL of the main container
const mainContainerURL = 'http://main-container:3000';

// Route for proxying requests to the main container
app.use('/', (req, res) => {
  req.pipe(request(mainContainerURL + req.url)).pipe(res);
});

app.listen(3000, () => {
  console.log('Sidecar container started on port 3000');
});

In this example, the sidecar container is using the express and request npm packages to set up a basic NodeJS server and proxy requests. The server listens on port 3000 and routes all incoming requests to the URL of the main container (mainContainerURL). The req.pipe method is used to stream the incoming request to the main container and the response back to the client.

Configs and Secret

How to dynamically create a config map and secrets?

You can create a script or a program in the language of your choice that makes a call to the API, reads the configurations, and dynamically creates config maps and secrets. The script can be run as a part of a CI/CD pipeline or triggered manually.

For example, in a script written in Python, you can use the requests library to call the API and retrieve the configurations, then use the kubernetes library to create the config maps and secrets. The example code might look something like this:

import requests
from kubernetes import client, config

# Call the API to retrieve the configurations
response = requests.get("https://api.example.com/configurations")
configurations = response.json()

# Load the kubernetes config from the default location
config.load_kube_config()

# Create a ConfigMap object
config_map = client.V1ConfigMap(
    metadata={
        "name": "example-config-map",
        "namespace": "default"
    },
    data=configurations
)

# Create the config map
api_instance = client.CoreV1Api()
api_instance.create_namespaced_config_map(namespace="default", body=config_map)

# Create a Secret object
secret = client.V1Secret(
    metadata={
        "name": "example-secret",
        "namespace": "default"
    },
    string_data=configurations
)

# Create the secret
api_instance.create_namespaced_secret(namespace="default", body=secret)

Disaster Recovery

Disaster recovery strategies in Kubernetes typically include:

Cluster redundancy: Having multiple active clusters in different geographic locations helps ensure availability of services in case of a disaster.
Backup and restore: Regularly backing up cluster state and application data, which can be restored in case of a disaster.
Load balancing across multiple clusters: Configuring load balancers to direct traffic to healthy clusters, if a disaster impacts one of them.
Resource allocation: Pre-planning resource allocation and scaling policies in advance to minimize the impact of a disaster.
Health checks and auto-recovery: Monitoring the health of the cluster and using auto-recovery mechanisms, such as auto-scaling, to keep the system running even during a disaster.
Rollback: Keeping a history of deployments and being able to quickly rollback to a previous version in case of a failure during a deployment.
Network resiliency: Ensuring network resiliency through redundancy, load balancing, and failover mechanisms.

Disaster Recovery with cluster redundancy

Disaster recovery with cluster redundancy across multiple regions can be achieved using multi-cluster deployment in Kubernetes.

An example configuration of disaster recovery with cluster redundancy across multiple regions can be as follows:

Set up two separate Kubernetes clusters in different regions, e.g. one in region A and another in region B.
Configure a solution for replicating data between the two clusters, such as an NFS server or a database cluster like Galera.
Configure the applications deployed in one cluster to be easily deployed in the other cluster. For example, use Kubernetes manifests to describe the application deployment, so that they can be easily recreated in a different cluster.
Use a load balancer or a Global Traffic Manager (GTM) to direct traffic to the appropriate cluster based on availability or performance. For example, you can use the Azure Traffic Manager or the Amazon Route 53.
Implement a monitoring solution to monitor the health of the clusters and trigger a failover to the secondary cluster in case of a disaster.

This is a high-level example configuration, and the actual implementation would vary based on the specific requirements of your setup.

Here’s a slighly low level example configuration with recommendation for tools to be used:

Create two or more separate Kubernetes clusters in different regions.
Use a tool such as kubefed or federation v2 to federate the clusters into a single control plane.
Deploy your applications in each cluster, making sure to label them with appropriate information such as region and environment.
Use a tool such as Flagger to manage cross-cluster traffic routing, failover, and rollbacks.
Configure disaster recovery policies such as automatic failover in case of cluster downtime, and automatic replication of data across regions.

This configuration will allow you to distribute your applications across multiple regions, ensuring that your applications are still available even in the case of a disaster.

Using KubeFed

To use kubefed to federate clusters into a single control plane, you would typically perform the following steps:

Install the kubefed CLI on a machine with access to the Kubernetes clusters you want to federate.
Initialize the federation control plane by running the following command:

kubefed init myfederation --host-cluster-context=<host-cluster-context>

Initialize the federation control plane by running the following command:

kubefed init myfederation --host-cluster-context=<host-cluster-context>

Join the target clusters to the federation control plane by running the following command:

kubefed join <cluster-name> --cluster-context=<cluster-context> --host-cluster-context=<host-cluster-context>

Once all the target clusters have been joined, you can view the federated resources by running:

kubectl --context=myfederation get <resource-type>

Note: Replace <host-cluster-context> with the context of the cluster you want to use as the host for the federation control plane. Replace <cluster-name> with a unique name for the target cluster, and replace <cluster-context> with the context of the target cluster.

Here’s a sample configuration file to create a federated deployment in myfederation namespace:

apiVersion: types.federation.k8s.io/v1
kind: FederatedDeployment
metadata:
  name: example-federated-deployment
  namespace: myfederation
spec:
  template:
    metadata:
      labels:
        app: example-app
    spec:
      replicas: 3
      selector:
        matchLabels:
          app: example-app
      template:
        metadata:
          labels:
            app: example-app
        spec:
          containers:
          - name: example-container
            image: example-image
            ports:
            - containerPort: 80

Disaster Recovery with a single kubernetes cluster across multiple regions

You can set up a single Kubernetes cluster across multiple regions for disaster recovery. This is typically achieved through the use of multi-zone or multi-region cluster deployment.

In this setup, the nodes of the cluster are distributed across multiple geographic locations, providing higher availability and disaster recovery capabilities.

The state of the cluster is kept in sync across all regions through the use of a centralized control plane, such as a Global Control Plane. This ensures that in the event of a failure in one region, the cluster can automatically fail over to another region. However, it’s important to consider the network latency and data transfer costs associated with multi-region deployments.

Create multiple Kubernetes clusters in different regions and ensure they are properly networked and have access to necessary resources.
Use tools such as kubefed to federate the clusters into a single control plane. kubefed allows you to manage multiple clusters as a single unit, with a common set of API objects, including namespaces, labels, and annotations.
Use disaster recovery tools such as Velocity, Spinnaker, or KubeDR to manage the disaster recovery process. These tools can help you automate the process of creating disaster recovery clusters and ensuring that the primary cluster is running correctly.
Use labeling and annotations in Kubernetes to define disaster recovery policies. For example, you can create a label for your disaster recovery namespace, and apply it to all the objects in the namespace. You can also use annotations to specify the disaster recovery policies for individual objects.
Use deployment strategies such as blue/green deployment or canary deployment to ensure a smooth transition from one cluster to another in the event of a disaster.
Monitor your disaster recovery setup and perform regular testing to ensure it is working as expected.

Note that this is just a high-level overview and the specific implementation details would depend on the tools and resources available in your organization.

Something else for Disaster Recovery

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How to use Labels for Disaster Recovery?

Labels in Kubernetes can be used to define disaster recovery policies by attaching labels to resources such as pods, nodes, and namespaces. These labels can then be used to define rules for disaster recovery behavior.

For example, you can attach a label “DR-policy” to pods and define the value of the label as “backup” for pods that need to be backed up in the event of a disaster. In the disaster recovery plan, you can use these labels to determine which pods need to be backed up and how the backup should be performed. You can also use the labels to determine which resources should be recreated and where the resources should be recreated in the event of a disaster.

This approach allows you to specify disaster recovery behavior at a granular level, making it easier to manage and maintain your disaster recovery plan. The use of labels in disaster recovery also makes it easier to automate disaster recovery procedures, reducing the risk of human error and increasing the reliability of the disaster recovery plan.

Labels in Kubernetes can be used to create disaster recovery policies by adding specific labels to your resources such as Pods, Services, etc. For example, you can add a label “dr-policy: on” to a Pod, which represents that this Pod should be part of the disaster recovery process.

Here’s an example using kubectl to add a label to a Pod:

kubectl label pod <pod-name> dr-policy=on

You can then use the label in your disaster recovery policy to select the resources that should be part of the disaster recovery process. For example, you can use the label selector in a Deployment to specify which Pods should be part of the disaster recovery process:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-deployment
spec:
  selector:
    matchLabels:
      dr-policy: on
  template:
    metadata:
      labels:
        dr-policy: on
    spec:
      containers:
        - name: my-container
          image: my-image

Note that this is just one way to use labels in a disaster recovery policy. The exact implementation will depend on your specific requirements and the tools you are using in your environment.

State Management

How to use the Global Control Plane to keep the states in sync?

In order to use a Global Control Plane to keep the states in sync, you need to use a tool like kubefed. kubefed allows you to federate multiple Kubernetes clusters into a single, cohesive control plane. The Global Control Plane uses the API server of the federated cluster to keep the state of the federated clusters in sync. You can use the kubefed CLI to manage the state of the federated clusters, including applying changes to the configuration, updating and rolling back resources, and monitoring the health of the cluster.

Example configuration:

Install kubefed on the host machine

$ curl https://dl.k8s.io/release/stable.txt
$ v=<version>
$ curl -LO https://dl.k8s.io/$v/kubernetes-client-linux-amd64.tar.gz
$ tar -xzf kubernetes-client-linux-amd64.tar.gz
$ cd kubernetes/client/bin
$ sudo mv kubefed /usr/local/bin/

Create a cluster registry and set the API endpoint

$ kubefed init mycluster --host-cluster-context=<context-name> --dns-provider=<provider> --dns-zone-name=<zone-name>

Join additional clusters to the federation

$ kubefed join mycluster --cluster-context=<context-name>

Manage the federated resources using the kubefed CLI

$ kubefed get federatedresources
$ kubefed update federatedresources

With this configuration, you can manage the state of the federated clusters using the kubefed CLI and ensure that the state of the clusters is kept in sync using the Global Control Plane.

H2

H3

Rollbacks

How do I rollback to a previous version in case of a failure during a deployment?

In Kubernetes, you can use kubectl rollout undo command to rollback to a previous version in case of a failure during a deployment. This command will undo the latest rollout and revert the deployment to the previous version.

Here’s an example usage:

kubectl rollout undo deployment/mydeployment

You can also specify a specific revision by using the --to-revision option:

kubectl rollout undo deployment/mydeployment --to-revision=2

Note: Before rolling back to a previous version, make sure you have a backup of your data and ensure that the previous version of the deployment is still available and working properly.

Rollback to a previous version using Jenkins

Here is an example Jenkins job script that demonstrates a rollback in case of a deployment failure:

pipeline {
    agent any

    stages {
        stage('Build and Deploy') {
            steps {
                sh 'kubectl apply -f my_deployment.yml'
                sh 'kubectl rollout status deployment/my_deployment'
            }
        }
        stage('Rollback') {
            steps {
                script {
                    try {
                        sh 'kubectl rollout status deployment/my_deployment'
                    } catch (error) {
                        sh 'kubectl rollout undo deployment/my_deployment'
                    }
                }
            }
        }
    }
}

In this example, the first stage performs a deployment using the command kubectl apply on a file named my_deployment.yml. The next stage, called “Rollback,” uses a try-catch block to monitor the status of the deployment. If the deployment fails, the catch block triggers a rollback using the kubectl rollout undo command on the same deployment.

Scaling

The typical open rate for marketing emails varies, but typically falls in the range of 15-25%. The open rate depends on many factors such as the subject line, sender reputation, personalization, etc.

Auto-scaling: What are the scaling strategies?

During festive seasons, we often see huge traffic surges for a limited duration e.g. immediately after the banners, or email comms or push notifications are sent.

Autoscaling: Set autoscaling rules based on the resource utilization or incoming request rate. This way, your system will automatically add more instances during high traffic periods and remove them when the traffic decreases.
Predictive Scaling: Predictive scaling uses machine learning algorithms to predict future traffic patterns and scale resources accordingly. This allows you to prepare for seasonal spikes in traffic before they happen.
Scheduled Scaling: You can schedule the scaling of your resources in advance based on known traffic patterns. This way, you can ensure that you have the necessary resources in place when you need them.
Spot instances: In the cloud, spot instances allow you to bid on spare compute capacity, resulting in cost savings. You can use spot instances during periods of low traffic to save on costs, and switch to on-demand instances during high traffic periods.
Load Balancing: Implementing a load balancing strategy, such as round-robin or least connections, can help distribute incoming traffic across multiple instances and improve overall system performance.

Note: Cost optimization should be done in parallel with the scaling strategies, using cost-effective instance types, using reserved instances, and proper instance utilization monitoring.

How to enable Scheduled scaling?

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Interesting facts about Replication Controllers:

Replication Controllers were the original mechanism for ensuring that a specified number of replicas of a pod were running in a cluster.
Replication Controllers were replaced by Deployments in Kubernetes version 1.2, but Replication Controllers are still supported for backwards compatibility.
Replication Controllers can be updated without creating new replicas, which makes it easier to roll out changes to your applications.
Replication Controllers ensure that the desired number of replicas are always running, even in the event of node failures.
Replication Controllers are a fundamental building block of Kubernetes and play a critical role in ensuring the availability and scalability of your applications.

How do I determine the right size for the infra?

Determining the right size for your infrastructure requires consideration of several factors, including:

Resource Requirements: Evaluate the resource requirements of your applications, such as CPU, memory, and storage. This will help you determine the minimum specifications for your infrastructure.
Traffic and Load: Consider the expected traffic and load on your applications and adjust the infrastructure accordingly. For example, if your applications are expected to handle high traffic, you may need more powerful nodes or additional nodes in your cluster.
Budget: The size of your infrastructure will also depend on your budget. Larger infrastructure will be more expensive to set up and maintain.
Scalability: Consider how easily your infrastructure can be scaled up or down as your resource requirements change. For example, if your applications are expected to grow rapidly, you may want to choose a solution that makes it easy to add more nodes to your cluster.
Compliance Requirements: If you are subject to specific compliance requirements, such as data privacy or security regulations, make sure your infrastructure meets these requirements.

You can use tools like benchmarking and load testing to determine the optimal size for your infrastructure. These tools can simulate real-world usage scenarios and help you identify performance bottlenecks or areas that need improvement.

How do you perform benchmarking and load testing for micro front end apps?

To perform benchmarking and load testing for micro front end apps, you can follow these steps:

Choose a tool: There are several tools available for benchmarking and load testing, such as Apache JMeter, Gatling, and LoadRunner. Choose a tool that fits your specific needs and requirements.
Prepare test scenarios: Define the test scenarios that you want to run, including the number of users, the types of requests, and the expected response times. This will help you determine the resource requirements for your infrastructure.
Configure test environment: Set up a test environment that is separate from your production environment. This will allow you to safely run the tests without affecting your users.
Run the tests: Use the tool you selected to run the tests you prepared. You can run the tests from a single machine or from multiple machines to simulate a distributed load.
Analyze results: Analyze the results of the tests to identify bottlenecks and areas for improvement. For example, you may identify that your infrastructure needs more resources or that certain parts of your application are not optimized for performance.
Repeat tests: Repeat the tests as needed to validate your changes and ensure that your infrastructure is optimized for performance.

Where do I run the tests from?

It is recommended to run these tests in non-production environments first, such as a staging or a development environment, to safely determine the optimal size for your infrastructure. This will allow you to make changes and improvements without affecting your users.

How can I do it in the lower environments i.e. non production environments to safely determine the infra?

TBC

How do I indentify bottlenecks?

To identify bottlenecks in a system, you can follow these steps:

Monitor system performance: Use tools such as Prometheus, Grafana, or Datadog to monitor your system’s performance in real-time. These tools can provide information on resource utilization, response times, and other key performance metrics.
Analyze logs: Review logs generated by your applications and infrastructure to identify any errors or performance issues. This can help you pinpoint specific areas that may be causing bottlenecks.
Monitor network traffic: Use network monitoring tools to analyze network traffic and identify any bottlenecks or issues with network connectivity.
Monitor database performance: If your system includes a database, monitor its performance to identify any bottlenecks or issues with database operations.
Isolate the problem: Once you have identified a bottleneck, isolate the problem by running tests or performing additional monitoring. This will help you determine the root cause of the bottleneck and find the most effective solution.
Implement solutions: Implement solutions to address the bottlenecks you have identified, such as adding more resources, optimizing your applications, or improving network connectivity.

It is important to regularly monitor your system and identify bottlenecks early, as this can help you avoid more serious performance issues and ensure that your infrastructure is optimized for performance.

What to focus on the Azure Dashboard during and after migration

Community Examples

Here are some examples from the community that you can refer to:

AKS namespaces and network policies: https://github.com/Azure/AKS/tree/main/examples/network-policy
Deploying microservices to AKS using Helm charts: https://github.com/Azure/AKS/tree/main/examples/microservices
Azure Traffic Manager with AKS: https://github.com/Azure/AKS/tree/main/examples/traffic-manager
Cost optimization in AKS: https://github.com/Azure/AKS/tree/main/examples/cost-optimization

These examples provide practical implementation details and code snippets that you can use as a reference while migrating your infrastructure

Examples from the community, written by users of these tools:

AKS namespaces and network policies: https://blog.alexellis.io/kubernetes-network-policy-primer/
Deploying microservices to AKS using Helm charts: https://thenewstack.io/how-to-deploy-microservices-to-kubernetes-with-helm/
Azure Traffic Manager with AKS: https://medium.com/@gaurav.aggarwal/configuring-traffic-manager-with-aks-cluster-b9c0b9f7c0c5
Cost optimization in AKS: https://medium.com/faun/cost-optimization-in-azure-kubernetes-service-aks-7f1b8f8f0b83

References

Here are some references you can use for the recommendations we just gave:

Namespaces in AKS: https://docs.microsoft.com/en-us/azure/aks/concepts-namespaces
Deploying microservices to AKS: https://docs.microsoft.com/en-us/azure/aks/tutorial-kubernetes-deploy-microservices
Using Helm charts in AKS: https://docs.microsoft.com/en-us/azure/aks/use-helm
Network policies in AKS: https://docs.microsoft.com/en-us/azure/aks/use-network-policies
Azure Traffic Manager: https://docs.microsoft.com/en-us/azure/traffic-manager/traffic-manager-overview
Azure Cost Management & Billing: https://docs.microsoft.com/en-us/azure/cost-management-billing/

These references provide detailed information and examples on how to implement the various components of the infrastructure migration from AWS to Azure.

UML Diagram Tools

Links to resources for schematic diagrams and UML diagrams:

Draw.io: https://app.diagrams.net/
Lucidchart: https://www.lucidchart.com/
Gliffy: https://www.gliffy.com/
Visio: https://products.office.com/en-us/visio/flow