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Kubernetes Introduction

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An introduction to Kubernetes presentation.

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Kubernetes Introduction

  1. 1. Kubernetes Introduction Advanced Technology Group (ATG) for Open Source & Cloud August 2016
  2. 2. What is Kubernetes? 2 Κυβερνήτης — Greek: A nautical term meaning “helmsman” or “pilot” “K8s”
  3. 3. Kubernetes “Open Source Container Cluster Manager” • Google — Architect and creator. • Borg — Google’s internal cluster management software.  Kubernetes – complete rewrite, (in Go). • Google partnered with Linux Foundation to form:  Cloud Native Computing Foundation (CNCF)  offered Kubernetes as a seed technology 3
  4. 4. Kubernetes History 2013 2014 2015 2016 Apr 2015 Tectonic formed (commercial support) Apr 2015 The Borg Paper is published Sep 2014 Kubernetes announced in Wired magazine Jun 2014 Kubernetes 1st GitHub commit Mar 2013 Docker initial release Aug 2014 CoreOS introduces Flannel networking Oct 2013 CoreOS initial release 4 2008 …2006 2006 Google starts work on “Process Containers” (renamed “cgroups”) Jan 2008 cgroups merged into Linux (2.6.24) 2007 July 2015 CNCF Formed, K8s v1.0 released, donated to CNCF Borg development inside Google
  5. 5. Kubernetes Tech Specs Features • μService Architecture • Automatic Workload Placement (efficient) • Auto Remediating (self healing) • Horizontal Scaling • Load Balanced • Declarative Deployment • Service Discovery included • A/B & Canary Deployments (testing) Surrounding Ecosystem  Docker – the container “engine” on each host.  etcd (from CoreOS) – distributed K/V store.  CoreOS – the platform.  Flannel – overlay networking.  Hosted Service: Google Container Platform  GKE is the abbreviation. 5
  6. 6. 6 Network Client μService Programming Model — Cloud Native proxy μS … μS μS proxy μS … μS μS proxy μS … μS μS proxy μS … μS μS proxy μS … μS μS proxy μS … μS μS (HTTP)Route/Proxy Optional (nginx) Pod (container) Service “Load Balancer”
  7. 7. Kubernetes – Programming Model 7 • Filesystem – that the program uses. • Persistent – how state is saved beyond run-time. • Persistent Volumes are attached and live outside of the K8s cluster. Volumes & Persistent Volumes Pod • One (or more) containers “grouped” • Network (IP address): shared • Volumes: shared Service • Common API (behavior) replicated across the cluster. • Well Known Endpoint – a consistent IP address, regardless of changes in specific Pods underneath. Service proxy Host (“node” in K8s) Pod – different μS Pod Container(s) proxy Host (“node” in K8s) Pod Container(s) Volume, external to K8s Abstract (Common IP)
  8. 8. Kubernetes – Framework Architecture 8 Client Control Plane Workload *https://github.com/kubernetes/kubernetes/blob/release-1.3/docs/design/architecture.md
  9. 9. Kubernetes – Framework Architecture 9 • K8s is extensible • Storage Plugin(s) - NFS / iSCSI - AWS-EC2 / Google GCE - Ceph (RBD/CephFS) / Gluster - Cinder (OpenStack) • Other Extension Points - Logging - Access & Auth - Scheduler Control Plane Worker Node(s) Client Extension Points kubelet: local, control plane agent. Pod management using docker-engine. kube-proxy: internal service routing (i.e. TCP/UDP stream forwarding) docker-engine: container execution kube-apiserver: Client’s API access point. Routes requests to appropriate, internal components. kube-controller-manager: Embeds the core control loops. • Replication controller • Endpoints controller (proxies) • Namespace controller kube-scheduler: Workload (Pod) placement. Sophisticated, configurable, globally aware. etcd (from CoreOS): Distributed, watchable storage The k8s system state kubectl: CLI into K8s HTTP — RESTful protocol.
  10. 10. Kubernetes – Deployment Model A Declarative Model 10 Manifest File(s) Labels PodSpec clause – within most descriptors Replication Controller descriptor • Optional only in trivial cases. • (trivial = CLI only possible) • YAML (or JSON) format. • Key/Value “tags” – placed on any deployable object. • Selectable – by actions and other declarations. • Configuration Flexibility • Labeled • allows versioning • other constraint application • Container(s) • very Dockerfile / docker-compose like. • Image location, (including image version) • Volume requirements • Ports exposed • “template/spec” clause declares PodSpec configuration. • “replica” clause declares sizing of the service. • Rolling-updates & canary deploys are a supported pattern. Descriptor Types (partial list) • Replication Controller • Deployment • Pod • Job • Service
  11. 11. Running a Kubernetes Cluster 11 “There’s more than one way to do it” – Larry Wall
  12. 12. Kubernetes in Public Cloud 12 Hosted Solution — Google Cloud Platform Google Container Engine (GKE) • Kubernetes Getting Started Guide “101” • Hello World Walkthrough https://cloud.google.com/container-engine/ http://kubernetes.io/docs/hellonode/ Turn-key Solutions Amazon Web Services (AWS) EC2 http://kubernetes.io/docs/getting-started-guides/aws/ Azure http://kubernetes.io/docs/getting-started-guides/azure/ Free Trial — 60 days $300 credit
  13. 13. Kubernetes Run Locally 13 On a Laptop / Desktop Minikube • K8s recommended method for single node deploy http://kubernetes.io/docs/getting-started-guides/minikube/ Vagrant — superseded by Minikube, still usable. http://kubernetes.io/docs/getting-started-guides/vagrant/ kube-up.sh — another previous “#1” method by k8s http://containertutorials.com/get_started_kubernetes/index.html Easy Kubernetes Cluster for macOS • Recently discovered and recommended by our team (ATG). https://github.com/TheNewNormal/kube-cluster-osx Multi-host / Lab CoreOS w/ Fleet • https://github.com/CaptTofu/kubernetes-cluster-fleet • https://github.com/coreos/coreos-vagrant • https://github.com/mhamrah/kubernetes-coreos-units
  14. 14. A Kubernetes Application 14
  15. 15. Kubernetes Application – minimalist application – 15 1. Construct • Create a standard Docker application, a μService. • Package it as a Docker Image. 2. Deploy • Deploy the Docker Image to a Docker Repository. 3. Run • kubectl run … --image=<Image-Repository-Path>
  16. 16. K8s App — Construct 16 app.py* from flask import Flask app = Flask(__name__) @app.route('/') def hello_world(): return '-- Hello Flask Dockerized --n' if __name__ == '__main__': app.run(debug=True, host='0.0.0.0') Dockerfile* FROM ubuntu:latest RUN apt-get update -y RUN apt-get install -y python-pip python-dev build-essential COPY . /apt WORKDIR /apt RUN pip install -r requirements.txt ENTRYPOINT ["python"] CMD ["app.py"] *https://github.com/egustafson/ex-py-docker-flask Build Run Verify (in a separate console) # docker build –t ex-py-docker-flask . ... ...<many lines of output> ... Successfully built 0fb21b16f3dd # # docker run –p 5000:5000 ex-py-docker-flask * Running on http://0.0.0.0:5000/ (Press CTRL+C to quit) * Restarting with stat * Debugger is active! * Debugger pin code: 236-035-556 # curl http://localhost:5000 -- Hello Flask Dockerized –- # run outside localhost (default port: 5000)
  17. 17. K8s App — Deploy 17 Hosted K8s – Google Container Engine Local “laptop” – Minikube... (from the construct stage … mostly) ... # docker build –t gcr.io/<my-proj-id>/ex-py-flask:v1 . ... # gcloud docker push gcr.io/<my-proj-id>/ex-py-flask:v1 # minikube start Starting local Kubernetes cluster... Kubernetes is available at https://192.168.99.100:8443. Kubectl is now configured to use the cluster. # eval $(minikube docker-env) # docker build –t library/ex-py-docker-flask . Caveat: the method used above is a bit of a “hack”. Using the ‘docker-env’ combined with ‘docker build’ works because Minikube only deploys into a single host. As a consequence the Docker image will be available in the local Docker repository. If Minikube ran across two or more hosts then the node Kubernetes choses to run the Pod (container) on may not match where it was built. *http://kubernetes.io/docs/hellonode/ GCR Convention (alternate)
  18. 18. K8s App — Run 18 Hosted K8s – Google Container Engine Local “laptop” – Minikube # kubectl run flask-node -–image=gcr.io/<my-proj-id>/ex-py-flask:v1 --port=5000 Deployment “flask-node” created # kubectl get pods NAME READY STATUS RESTARTS AGE flask-node-714049816-ztzrb 1/1 Running 0 6m # kubectl expose deployment flask-node -–type=“LoadBalancer” # kubectl get services flask-node NAME CLUSTER_IP EXTERNAL_IP PORT(S) AGE hello-node 10.3.246.12 23.251.159.72 5000/TCP 2m Run Verify Run Verify # curl http://23.251.159.72:5000 -- Hello Flask Dockerized – # 1. 2. 3. 4. # kubectl run flask-node -–image=library/ex-py-docker-flask --port=5000 Deployment “flask-node” created # kubectl get pods NAME READY STATUS RESTARTS AGE flask-node-714049816-ztzrb 1/1 Running 0 6m # kubectl expose deployment flask-node -–type=“NodePort” 1. 2. 3. # minikube service flask-node –-url http://192.168.99.100:31992 # curl $(minikube service flask-node –-url) -- Hello Flask Dockerized – #
  19. 19. Getting Involved 19 Community http://kubernetes.io/community/ GitHub http://github.com/kubernetes Project Page & Documents http://kubernetes.io Slack (chat) (sign-up: http://slack.k8s.io/) https://kubernetes.slack.com Special Interest Groups (SIGs) (+20 topics) Community Page  SIGs (https://github.com/kubernetes/community/blob/master/README.md#special-interest-groups-sig)
  20. 20. Demo https://github.com/egustafson/ex-gke-webdrop 20 https://github.com/egustafson/webdrop-py
  21. 21. Thank you Advanced Technology Group for Open Source and Cloud Eric Gustafson gustafson@hpe.com Patrick Galbraith patg@hpe.com Clare Springer clarissa.springer@hpe.com 21
  22. 22. Backup Slides (Kubernetes Introduction) 22
  23. 23. Advanced Technology Group for Open Source & Cloud HPE's Advanced Technology Group for Open Source & Cloud embraces a vision that is two steps ahead of today's solutions. We use this vision to drive product adoption and incubate technologies to advance HPE. Through open source initiatives we foster collaboration across HPE and beyond. 23 Patrick Galbraith patg@hpe.com http://patg.net/ Interests: Kubernetes, Ansible, MySQL projects New Hampshire, USA Eric Gustafson gustafson@hpe.com http://egustafson.github.io/ Interests: Monitoring, Networking, Embedded/IoT Colorado, USA Brian Aker, Fellow Yazz Atlas, Principle Engineer Hillary Cirimele, Executive Assistant Matt Farina, Principle Engineer Patrick Galbraith, Principle Engineer Eric Gustafson, Principle Engineer Clare Springer, Program Manager
  24. 24. References – Kubernetes Introduction • “Large-scale cluster management at Google with Borg” • https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43438.pdf • “Omega: flexible, scalable schedulers for large compute clusters” • https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41684.pdf • “Borg, Omega, and Kubernetes” • https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/44843.pdf • “Jupiter Rising: A Decade of Clos Topologies and Centralized Control in Google’s Datacenter Network” • http://conferences.sigcomm.org/sigcomm/2015/pdf/papers/p183.pdf 24

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