Major Updates to VS Code Docker: Introducing Container Tools
The first, most obvious thing is the introduction of the Container Tools extension to broaden our focus and open new extensibility opportunities. The existing extension code (and MIT license) will be migrated to the Container Tools extension, and the Docker extension will become an extension pack that includes the Docker DX and Container Tools extensions. For you, this means the ability to customize the tooling to meet your needs - choose your preferred container runtime and only the functionality that you need in the extension settings. This major update marks a significant step forward in enhancing the development experience when working with containers. Please comment here with any questions or feedback and stay tuned to experiment with the new features! tl;dr The Docker extension is becoming the Container Tools extension Still free and open source Podman support is coming No action is requiredReference Architecture for a High Scale Moodle Environment on Azure
Introduction Moodle is an open-source learning platform that was developed in 1999 by Martin Dougiamas, a computer scientist and educator from Australia. Moodle stands for Modular Object-Oriented Dynamic Learning Environment, and it is written in PHP, a popular web programming language. Moodle aims to provide educators and learners with a flexible and customizable online environment for teaching and learning, where they can create and access courses, activities, resources, and assessments. Moodle also supports collaboration, communication, and feedback among users, as well as various plugins and integrations with other systems and tools. Moodle is widely used around the world by schools, universities, businesses, and other organizations, with over 100 million registered users and 250,000 registered sites as of 2020. Moodle is also supported by a large and active community of developers, educators, and users, who contribute to its development, documentation, translation, and support. [URL] is the official website of the Moodle project, where anyone can download the software, join the forums, access the documentation, participate in events, and find out more about Moodle. Goal The goal for this architecture is to have a Moodle environment that can handle 400k concurrent users and scale in and out its application resources according to usage. Using Azure managed services to minimize operational burden was a design premise because standard Moodle reference architectures are based on Virtual Machines that comes with a heavy operational cost. Challenges Being a monolith application, scaling Moodle in a modern cloud native environment is challenging. We choose to use Kubernetes as its computing provider due to the fact that it allow us to build a Moodle artifact in an immutable way that allows it to scale out and in when needed in a fast and automatic way and also recover from potential failures by simply recreating its Deployments without the need to maintain Virtual Machine resources, introducing the concept of pets vs cattle[1] to a scenario that at first glance wouldn't be feasible. Since Moodle is written in PHP it has no concept of database polling, creating a scenario where its underlying database is heavily impacted by new client requests, making it necessary to use an external database pooling solution that had to be custom tailored in order to handle the amount of connections for a heavy-traffic setup like this instead of using Azure Database for PostgreSQL's built-in pgbouncer. The same effect is also observed in its Redis implementation, where a custom Redis cluster had to be created, whereas using Azure Cache for Redis would incur prohibitive costs due to the way it is set up for a more general usage. 1 - https://learn.microsoft.com/en-us/dotnet/architecture/cloud-native/definition#the-cloud Architecture This architecture uses Azure managed (PaaS) components to minimize operational burden by using Azure Kubernetes Service to run Moodle, Azure Storage Account to host course content, Azure Database for PostgreSQL Flexible Server as its database and Azure Front Door to expose the application to the public as well as caching commonly used assets. The solution also leverages Azure Availability Zones to distribute its component across different zones in the region to optimize its availability. Provisioning the solution The provisioning has two parts: setting up the infrastructure and the application. The first part uses Terraform to deploy easily. The second part involves creating Moodle's database and configuring the application for optimal performance based on the templates, number of users, etc. and installing templates, courses, plugins etc. The following steps walk you through all tasks needed to have this job done. Clone the repository $ git clone https://github.com/Azure-Samples/moodle-high-scale Provision the infrastructure $ cd infra/ $ az login $ az group create --name moodle-high-scale --location <region> $ terraform init $ terraform plan -var moodle-environment=production $ terraform apply -var moodle-environment=production $ az aks get-credentials --name moodle-high-scale --resource-group moodle-high-scale Provision the Redis Cluster $ cd ../manifests/redis-cluster $ kubectl apply -f redis-configmap.yaml $ kubectl apply -f redis-cluster.yaml $ kubectl apply -f redis-service.yaml Wait for all the replicas to be running $ ./init.sh Type 'yes' when prompted. Deploy Moodle and its services Change image in moodle-service.yaml and also adjust the moodle data storage account name in the nfs-pv.yaml (see commented lines in the files) $ cd ../../images/moodle $ az acr build --registry moodlehighscale<suffix> -t moodle:v0.1 --file Dockerfile . $ cd ../../manifests $ kubectl apply -f pgbouncer-deployment.yaml $ kubectl apply -f nfs-pv.yaml $ kubectl apply -f nfs-pvc.yaml $ kubectl apply -f moodle-service.yaml $ kubectl -n moodle get svc –watch Provision the frontend configuration that will be used to expose Moodle and its assets publicly $ cd ../frontend $ terraform init $ terraform plan $ terraform apply Approve the private endpoint connection request from Frontdoor in moodle-svc-pls resource. Private Link Services > moodle-svc-pls > Private Endpoint Connections > Select the request from Front Door and click on Approve. Install database $ kubectl -n moodle exec -it deployment/moodle-deployment -- /bin/bash $ php /var/www/html/admin/cli/install_database.php --adminuser=admin_user --adminpass=admin_pass --agree-license Deploy Moodle Cron Change image in moodle-cron.yaml $ cd ../manifests $ kubectl apply -f moodle-cron.yaml Your Moodle installation is now ready to use! Conclusion You can create a Moodle environment that is scalable and reliable in minutes with a very simple approach, without having to deal with the hassle of operating its parts that normally comes with standard Moodle installations.Azure Kubernetes Service Baseline - The Hard Way
Are you ready to tackle Kubernetes on Azure like a pro? Embark on the “AKS Baseline - The Hard Way” and prepare for a journey that’s likely to be a mix of command line, detective work and revelations. This is a serious endeavour that will equip you with deep insights and substantial knowledge. As you navigate through the intricacies of Azure, you’ll not only face challenges but also accumulate a wealth of learning that will sharpen your skills and broaden your understanding of cloud infrastructure. Get set for an enriching experience that’s all about mastering the ins and outs of Azure Kubernetes Service!Cloud Rendering Adobe After Effects Video with Windows Docker Container
Since I run Newbie Homemade Mashup Lab, I always have video render needs for After Effects. When there are many videos, my personal computer will spend a lot of time rendering them. During this time, I cannot do anything else. So, I came up with the idea of Cloud Rendering. This article will guide you to build your own After Effects Docker image and ultimately try rendering on Azure App Service.Extend the capabilities of your AKS deployments with Kubernetes Apps on Azure Marketplace
We’re excited to announce that Kubernetes Apps in the Azure Marketplace is now Generally Available. Azure Kubernetes Service (AKS) provides a robust and scalable managed Kubernetes platform for organizations running their most mission-critical applications on Azure. With Kubernetes Apps, teams can further extend the capabilities of their AKS deployments with a vibrant ecosystem of tested and transactable third-party solutions from industry-leading partners and popular open-source offerings.
Getting started with Azure Fleet Manager
Why? A solution to manage multiple Azure Kubernetes Service (AKS) clusters at scale. A secure and compliant solution to streamline operations, maintenance, improve performance, and ensure efficient resource utilization. Addresses the challenges of multi-cluster scenarios orchestrating cluster updates propagating Kubernetes resources balancing multi-cluster loads Pointers Orchestrates application updates and upgrades across multiple clusters Certain Kubernetes objects to deploy on all or certain set of clusters Clusters can be the same subscription or different subscription or even in different region but should be under the same tenant Upgrade group: Updates are applied in parallel Make sure member cluster should be in the running state before joining them to Fleet There are two fleet options available, with Hub and without the Hub Supports private clusters CRP is a cluster-scoped resource HOW? Run the below commands with Azure CLI Step 1: Add the fleet extension az extension add -n fleet Step 2: Create a fleet manager az fleet create --resource-group <name of the resource group> --name <name of the fleet> --location <region> --enable-hub --enable-private-cluster --enable-managed-identity --agent-subnet-id <subent ID> --vm-size <vm size> Step 3: An AKS cluster with 1 node gets created. Get into this Hub cluster az fleet get-credentials --resource-group <name of the resource group> --name <name of the fleet> Step 4: Add and view member clusters az fleet member create --resource-group <name of the resource group> --fleet-name <name of the fleet> --name <name of the membercluster> --member-cluster-id <resource ID of the member cluster> az fleet member list --resource-group <name of the resource group> --fleet-name <name of the fleet> -o table Sample CRP deployment kubectl label membercluster <name of the member cluster> <name of the label>=<value of the label> --overwrite apiVersion: placement.kubernetes-fleet.io/v1beta1 kind: ClusterResourcePlacement metadata: name: crp-asd-prod spec: policy: placementType: PickAll affinity: clusterAffinity: requiredDuringSchedulingIgnoredDuringExecution: clusterSelectorTerms: - labelSelector: matchLabels: crp: prod resourceSelectors: - group: "" kind: Namespace name: dev version: v1 - group: "" kind: Namespace name: qa version: v1 Utilization Best Practices Plan to integrate with your DevOps platform to manage the dynamic nature of workloads. The integration is extremely helpful and easy to adopt for the dev teams, as deployments were synced across other clusters. The service is a viable candidate for multi-cluster management, disaster recovery and migration strategy. Happy Learning 🙂 Reference link: fleet/docs at main · Azure/fleet · GitHub
