azure

If you deployed a private volume claim using the managed-premium storage class, then ran out of space and now you are searching how to expand the disk to a larger disk, this is how you can do it from scratch: manage-premium storage class is a premium storage class that allows volume expansion: allowVolumeExpansion: true.

In my previous post AKS: Open Service Mesh & mTLS, I described how to deploy an AKS cluster with Open Service Mesh enabled, and how: Easy is to onboard applications onto the mesh by enabling automatic sidecar injection of Envoy proxy. OSM enables secure service to service communication. This time I’ll show you that Open Service Mesh (OSM) also provides a nice feature for controlling traffic between microservices: Traffic Access Control based on the SMI specifications.

Open Service Mesh (OSM) is a lightweight and extensible cloud native service mesh, easy to install and configure and with features as mTLS to secure your microservice environments. Now that Open Service Mesh (OSM) integration with Azure Kubernetes Service (AKS) is GA (Check the announcement) I’ll show you not only to deploy it but also how to add your microservices to the mesh so communication between them is encrypted.

Disclaimer: this is just a Proof of Concept. If you deploy Azure Kubernetes Service clusters with availability zones, you’ll probaly need a high available storage solution. In such situation you may use Azure Files as an external storage solution. But what if you need something that performs better? Or something running inside your cluster?

Today I’ll show you how to use Container Insights and Azure Monitor to check your AKS cluster for pods without requests and limits. You’ll need to use the following tables and fields: KubePodInventory: Table that stores kubernetes cluster’s Pod & container information ClusterName: ID of the kubernetes cluster from which the event was sourced Computer: Computer/node name in the cluster that has this pod/container. Namespace: Kubernetes Namespace for the pod/container ContainerName:This is in poduid/containername format. Perf: Performance counters from Windows and Linux agents that provide insight into the performance of hardware components operating systems and applications. ObjectName: Name of the performance object. CounterName: Name of the performance counter. CounterValue: The value of the counter And take a close look at the following Objects and Counters:

This post will show you how to deploy a Static Website on a Storage Account protected with Private Endpoint using Terraform: Define the terraform providers to use # Create a providers.tf file with the following contents: terraform { required_version = "> 0.12" required_providers { azurerm = { source = "azurerm" version = "~> 2.26" } } } provider "azurerm" { features {} skip_provider_registration = true } Define the variables # Create a variables.tf file with the following contents:

Some months ago a customer asked me if there was a way to deploy a Windows node pool with spot virtual machines and ephemeral disks in Azure Kubernetes Service (AKS). The idea was to create a cluster that could be used to run Windows batch workloads and minimize costs by deploying the following:

This post will show you the steps you’ll have to take to deploy an Azure Files Storage with a Private Endpoint and use it to create volumes for an Azure Kubernetes Service cluster: Create a bicep file to declare the Azure resources # You’ll have to declare the following resources:

When configuring Azure Kubernetes Service with Azure Container Network Interface (CNI), every pod gets an IP address of the subnet you’ve configured. So how do you plan you address space? What factors should you consider? Each node consumes one IP. Each pod consumes one IP. Each internal LoadBalancer Service you anticipate consumes one IP. Azure reserves 5 IP addresses within each subnet. The Max pods per node is 250. The Max pods per nodes lower limit is 10. 30 pods is the minimum per cluster. Max nodes per cluster is 1000. When a cluster is upgraded a new node is added as part of the process which requires a minimum of one additional block of IP addresses to be available. Your node count is then n + 1. When you scale a cluster an additional node is added. Your node count is then n + number-of-additional-scaled-nodes-you-anticipate + 1. With all that in mind the formula to calculate the number of IPs required for your cluster should look like this:

Microservices architectures are inherently distributed and building such solutions always bring interesting challenges to the table: resilient service invocation, distributed transactions, on-demand scaling, idempotent message processing and more. Deploying Microservices on Kubernetes doesn’t solve these problems and Developers need to learn and use SDK’s on top of frameworks such as .NET, while building distributed Microservices architectures.

Microservices architectures are inherently distributed and building such solutions always bring interesting challenges to the table: resilient service invocation, distributed transactions, on-demand scaling, idempotent message processing and more. Deploying Microservices on Kubernetes doesn’t solve these problems and Developers need to learn and use SDK’s on top of frameworks such as .NET, while building distributed Microservices architectures.

This morning I saw this tweet from Mr Brendan Burns: AKS Cost Monitoring and Governance With Kubecost https://t.co/OStwIBsuPp — brendandburns (@brendandburns) April 30, 2021 And I’m sure that once you also read through it, you’ll learn that you have to take several steps in order to achieve AKS Cost Monitoring and Governance With Kubecost.

A talk (Spanish) on Azure Landing Zones and Enterprise-scale Architecture

By default Cloud Shell sessions run inside a container inside a Microsoft network separate from any resources you may have deployed in Azure. So what happens when you want to access services you have deployed inside a Virtual Network such as a private AKS cluster, a Virtual Machine or Private Endpoint enabled services?

Azure Functions provides you with an event-driven programming model and Dapr a set of essential cloud-native building blocks that you can use together to create great solutions. Joins Carlos and create a solution that reads tweets, stores them in a database, access secrets, uses pub/sub between functions and much more!

A talk (Spanish) on Dapr and Azure Functions

Let’s see how Azure ARM, Terraform, Azure Service Operator for Kubernetes and other solutions compare to each other so you can choose the right weapon to win the Infrastructure as Code War!

In this workshop you’ll learn how to deploy, monitor, scale, secure and debug workloads in AKS: Deploy an aplication. Configure monitoring and health checks for your application. Scale your application to meet demand. Enable SSL/TLS with an ingress controller. Secret Management with AKS & Keyvault. Debugging your Kubernetes application.