Helm-Chart-Boilerplates
Kubernetes-Volume-Autoscaler
Helm-Chart-Boilerplates | Kubernetes-Volume-Autoscaler | |
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12 | 16 | |
8 | 248 | |
- | 1.2% | |
0.0 | 7.1 | |
over 1 year ago | 7 months ago | |
Makefile | Python | |
- | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Helm-Chart-Boilerplates
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Dedpulication standards of Helm Charts values file for a global chart with subcharts for our app. What's the right way to only need to specify a value once?
I would point you to what I call the "Universal Helm Charts" and some examples of how to use them.
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Monitoring many cluster k8s
Shameless Plug: Here's one of my dashboards I made for Ingress-Nginx, which is my recommended border router/gateway into all the services. It adds deep robust metrics and configurability, and if you've got years of experience with Nginx also, it allows you rich complex customization via nginx's configuration structure via kubernetes annotations. Besides that I have open-source helm charts which are easy to use, boilerplates showing how to use them, a volume autoscaler to automatically resize your disks as they get full, and a blog where I share various of my experience which is a companion blog to my upcoming book of the same name. Hope this helps! Feel free to ask if you have any further questions.
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Best way of managing Helm?
Here is an example of a repo that uses an sub-chart: https://github.com/DevOps-Nirvana/Helm-Chart-Boilerplates/tree/master/boilerplate-apache-with-configmap-template/deployment
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Helm makes it overly complex, or is it just me?
Use multi-values files with helm ALWAYS. Allowing an env-specific overlay to tweak your default values files. See: https://github.com/DevOps-Nirvana/Helm-Chart-Boilerplates/tree/master/boilerplate-echoserver/deployment/boilerplate-echoserver
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The Helmet is a Helm Library Chart that defines many chart templates like Deployment, Service, Ingress, etc which can used in other application charts.
Helm charts - https://github.com/DevOps-Nirvana/Universal-Kubernetes-Helm-Charts Example using helm charts as sub charts - https://github.com/DevOps-Nirvana/Helm-Chart-Boilerplates/tree/master/boilerplate-echoserver
- How do you guys manage your deployment pipelines?
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Monthly 'Shameless Self Promotion' thread - 2023/01
Helm Chart Boilerplates are examples of usage of the above Universal Helm Charts to help people understand how to use them more, a stop-gap until I add more documentation
- Deploying with Helm - extra manifests?
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Creating Kubernetes Templates
Helm Chart Usage Boilerplates (Examples of using these helm chart)
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Use Kubernetes to load test my product.
To help you on deploying your service, I've created open source generic/universal Helm Charts to make it easy to do the above. Here are the Universal Helm Charts and here's some boilerplate examples of using them. These built-in have support for HPAs, services, ingresses, etc, making it as easy as autoscaling.enable: true I haven't gotten around to documenting the helm charts yet, but there's lots of comments in the values.yaml file explaining everything.
Kubernetes-Volume-Autoscaler
- Toyota blames factory shutdown in Japan on ‘insufficient disk space’
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[AWS] EKS vs Self managed HA k3s running on 1x2 ec2 machines, for medium production workload
Additionally if you don't know, Kubernetes freshly setup, especially AWS's EKS is largely useless after you first set it up. You need to then install roughly a dozen other services into it to make it "do all the magic automatically". Services such as aws-ebs-csi-driver, (optional) aws-efs-csi-driver, (optional) aws-fsx-csi-driver, aws-load-balancer-controller, (optional) aws-node-termination-handler, cluster-autoscaler, (optional) external-dns, logs cascading engine (eg: fluentd-elasticsearch / fluent-bit-elasticsearch / datadog), grafana, prometheus, your ingress controller of choice (I prefer and recommend ingress-nginx), and the Kubernetes Volume Autoscaler to auto-scale up EBS volumes. (shameless plug: I wrote the volume-autoscaler)
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Monitoring many cluster k8s
Shameless Plug: Here's one of my dashboards I made for Ingress-Nginx, which is my recommended border router/gateway into all the services. It adds deep robust metrics and configurability, and if you've got years of experience with Nginx also, it allows you rich complex customization via nginx's configuration structure via kubernetes annotations. Besides that I have open-source helm charts which are easy to use, boilerplates showing how to use them, a volume autoscaler to automatically resize your disks as they get full, and a blog where I share various of my experience which is a companion blog to my upcoming book of the same name. Hope this helps! Feel free to ask if you have any further questions.
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QUESTION: What is the best way to learn kubernetes?
Do not waste your timesetting up your own Kubernetes cluster; use any cloud provider's fully managed Kubernetes cluster, and then learn how to configure everything on it to do everything you want. Typically, there are anywhere between 10-30 foundational services you'll want to install on it to make everything work. Things such as Cluster-Autoscaler, an ingress controller, a mesh network technology, various CSI volume provisioners, a runner for your chosen CI/CD platform, a disk volume autoscaler (shameless plug I wrote this) etc. Learn to deploy Helm charts on it, and learn to deploy some of your services onto it, exposing them to the internet. Learn to install and use Prometheus and Grafana on it to get in-depth metrics and visualization. Learn how to use Prometheus Alertmanager to trigger alerts to your email, webhooks, slack, etc. There's a lot to learn, and it may feel intimidating, but get the ball rolling and incrementally improve/expand your experience.
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How do you guys on Mac M1's get around the annoying port forwarding issues with k8s + docker?
References: I use docker and Kubernetes daily. I currently manage numerous clusters and maintain pipelines for hundreds of microservices as I type this. I've been converting microservices into Docker images for companies hundreds if not thousands of times by now over the last bunches of years. I am also an avid and passionate open-source evangelist and Kubernetes/DevOps consultant. I author some Kubernetes controllers such as the Volume Autoscaler and have a set of Open Source Helm Charts and I love to contribute code/fixes wherever I run into issues.
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Accessing the Underlying Node
Old justifications for this were to resize drives but all major cloud providers support handling the resizing operation for you now. You still need to trigger the resize. But with a controller like the Kubernetes Volume Autoscaler you don’t even need to do that!
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Monthly 'Shameless Self Promotion' thread - 2023/01
An new open-source Kubernetes controller, the Kubernetes Volume Autoscaler, which auto-resizes your Persistent Volumes when they get almost full
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Monthly 'Shameless Self Promotion' thread - 2022/11
Kubernetes Volume Autoscaler - An Kubernetes Controller to automatically scale up volumes (disks). I just recently released an update based on some feedback, adding Prometheus metrics support and fixing a few bugs
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How do you prevent overprovisioning
Autoscale everything. There’s no over provisioning if it just provisions as needed. HPA and Cluster Autoscaler and for disks I wrote and use the Volume Autoscaler. Nodes disappear as needed and appear as needed. I generally even do spot instances in production. All assuming you are using a cloud provider.
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What are some must-have, can’t-live-without 3rd party apps/tools you have installed in your k8s clusters?
Volume Autoscaler - Automatically scale up your disks size, keeping your costs low and allowing you to grow over time. Also making one less thing your sysops/devops person has to do. (Shameless plug, I wrote this)
What are some alternatives?
Universal-Kubernetes-Helm-Charts - Some universal helm charts used for deploying services onto Kubernetes. All-in-one best-practices
pvc-autoresizer - Auto-resize PersistentVolumeClaim objects based on Prometheus metrics
argocd-autopilot - Argo-CD Autopilot
Grafana-Dashboards - A variety of open-source Grafana dashboards typically for AWS and Kubernetes
helm-charts - A collection of Helm charts
SparrowCI - SparrowCI - super fun and flexible CI system with many programming languages support
helmfile - Declaratively deploy your Kubernetes manifests, Kustomize configs, and Charts as Helm releases. Generate all-in-one manifests for use with ArgoCD.
autoscaler - Autoscaling components for Kubernetes
eksctl - The official CLI for Amazon EKS
sparrowci_web - ci.sparrowhub.io website
featbit - A feature flags service written in .NET
win2s3 - Windows to S3 Backup, Restore, Point in Time, and File Permissions