karpenter-provider-aws VS dapr

Implement Instance Autoscaling: Configure autoscaling for worker nodes by using Karpenter to adjust resources based on demand dynamically.

A point worth noting is that using the AWS Load Balancer Controller decouples your node management with your cluster management. Let’s say we wanted to use Karpenter for autoscaling instead of the defacto cluster-autoscaler. Karpenter will not use AWS AutoScalingGroups but will instead create standalone EC2 instances based on the Provisioners you define. This means our previous approach of attaching AutoScalingGroups with TargetGroups will not work as the EC2 instances Karpenter manages will not belong to the AutoScalingGroup and therefore not be automatically attached to the TargetGroup. The AWS Load Balancer Controller doesn’t care how the nodes are created; only that they belong to the cluster and match the label selectors defined. Probably we will look into Karpenter again in the near future for our project now that it supports pod anti-affinity, as this was previously a blocker for us.

Built in AWS, Karpenter is a high-performance, flexible, open-source Kubernetes cluster auto-scaler. One of its key features is the ability to launch EC2 instances based on specific workload requirements such as storage, compute, acceleration, and scheduling needs.

Documentación Oficial de Karpenter Post Community AWS - Christian Melendez (AWS) Video Explicativo Karpenter Workshop Karpenter (AWS)

Karpenter is a high-performance Kubernetes cluster autoscaler that dynamically provisions worker nodes to meet the resource demands of unscheduled pods.

Karpenter: https://karpenter.sh/

Dan: Argo CD is the first tool I install. For AWS, I will add Karpenter to manage costs. I will also use Longhorn for on-prem storage solutions, though I'd need ingress. Depending on the situation, I will install Argo CD first and then one of those other two.

If you’re not familiar with Karpenter — watch my quick intro. But in a nutshell, Karpenter is a better node autoscaler for Kubernetes (say goodbye to wasted compute resources). It is open-source and built by the AWS team. Qovery is an Internal Developer Platform I’m a co-founder) that we’ll use to spin up our EKS cluster with Karpenter.

In this blog, we will explore how the open-source Dapr (Distributed Application Runtime) can assist us in building reliable and secure distributed applications. Dapr provides a set of building blocks for common microservice patterns, such as service invocation (calling services), state management (handling data), and pub/sub messaging (publish/subscribe communication), which can significantly reduce the development effort.

I've been playing with this thing recently called Dapr (you can blame @marcduiker for me finding out about the project).

In the demo application architecture deployed into Azure Container Apps, we leverage Dapr for its distributed application runtime capabilities. Before diving into Dapr, let's refresh one of the design patterns called the Sidecar pattern, as Dapr is deployed as a sidecar. For more details, you can visit the Dapr website.

dapr.io project

The sidecar pattern in Kubernetes describes a single pod containing a container in which a main app sits. A helper container (the sidecar) is deployed alongside a main app container within the same pod. This pattern allows each container to focus on a single aspect of the overall functionality, improving the maintainability and scalability of apps deployed in Kubernetes environments. From gathering metrics to connecting to data sources (a la Dapr), sidecars have found a notable place in the cloud-native developer’s toolbox. Sidecars are designed to run alongside your apps continuously and do not scale down to zero. Wouldn't it be great if they did? In this article, we introduce scaling sidecars to zero in Kubernetes.

Dapr provides a set of building blocks that abstract concepts commonly used in distributed systems. This includes secured synchronous and asynchronous communication between services, caching, workflows, resiliency, secret management and much more. Not having to implement these features yourself eliminates boilerplate, reduce complexity and allows you to focus on developing your business features.

Diagrid Catalyst is a Developer API platform providing a brand-new approach to distributed application development. Using the Catalyst APIs, powered by the Dapr open source project, developers can overcome the complexity of rewriting common software patterns and achieve higher productivity by offloading infrastructure concerns from their code to Catalyst.

The following two examples are open-source projects maintained by Fermyon with contributions from companies like Microsoft and SUSE. The first is Spin, which allows us to use WebAssembly to create Serverless applications. The second, SpinKube, combines some of the topics I'm most excited about these days: WebAssembly and Kubernetes Operators :) The official website says, "By running applications in the Wasm abstraction layer, SpinKube offers developers a more powerful, efficient, and scalable way to optimize application delivery on Kubernetes." By the way, this post shows how to integrate SpinKube with Dapr, another technology I'm very interested in, and I should write some posts soon.

Speaking of this has anyone had much experience with Dapr (https://dapr.io/) before?

I always thought this was a particularly interesting approach from Microsoft where they use this pattern to essentially take the complexity of micro services and instead try and keep it as simple as a normal .NET application but (and I think this is the clever part) in both a vendor and language neutral way.

But all of a sudden it means you can start removing all kinds of cruft and random SDKs from your codebase and push almost all of your interactions with the outside world into something like this .