Kubernetes intro public - kubernetes user group 4-21-2015

Daniel Smith <dbsmith@google.com> Robert Bailey <robertbailey@google.com> Kit Merker <kitm@google.com> 2015-04-21 Kubernetes: Introduction

Everything at Google runs in containers: • Gmail, Web Search, Maps, ... • MapReduce, batch, ... • GFS, Colossus, ... • Even GCE itself: VMs in containers We launch over 2 billion containers per week.

Kubernetes Greek for “Helmsman”; also the root of the word “Governor” • Container orchestration • Runs Docker containers • Supports multiple cloud and bare-metal environments • Inspired and informed by Google’s experiences and internal systems • Open source, written in Go Manage applications, not machines

Design principles Declarative > imperative: State your desired results, let the system actuate Control loops: Observe, rectify, repeat Simple > Complex: Try to do as little as possible Modularity: Components, interfaces, & plugins Legacy compatible: Requiring apps to change is a non-starter No grouping: Labels are the only groups Cattle > Pets: Manage your workload in bulk Open > Closed: Open Source, standards, REST, JSON, etc.

High level design CLI API UI apiserver users master kubelet kubelet kubelet nodes scheduler

Primary concepts Container: A sealed application package (Docker) Pod: A small group of tightly coupled Containers example: content syncer & web server Controller: A loop that drives current state towards desired state example: replication controller Service: A set of running pods that work together example: load-balanced backends Labels: Identifying metadata attached to other objects example: phase=canary vs. phase=prod Selector: A query against labels, producing a set result example: all pods where label phase == prod

Pods Small group of containers & volumes Tightly coupled The atom of cluster scheduling & placement Shared namespace • share IP address & localhost Ephemeral • can die and be replaced Example: data puller & web server Pod File Puller Web Server Volume Consumers Content Manager

Pod lifecycle Once scheduled to a node, pods do not move • restart policy means restart in-place Pods can be observed pending, running, succeeded, or failed • failed is really the end - no more restarts • no complex state machine logic Pods are not rescheduled by the scheduler or apiserver • even if a node dies • controllers are responsible for this • keeps the scheduler simple Apps should consider these rules • Services hide this • Makes pod-to-pod communication more formal

Labels Arbitrary metadata Attached to any API object Generally represent identity Queryable by selectors • think SQL ‘select ... where ...’ The only grouping mechanism • pods under a ReplicationController • pods in a Service • capabilities of a node (constraints) Example: “phase: canary” App: Nifty Phase: Dev Role: FE App: Nifty Phase: Dev Role: BE App: Nifty Phase: Test Role: FE App: Nifty Phase: Test Role: BE

Selectors App: Nifty Phase: Dev Role: FE App: Nifty Phase: Test Role: FE App: Nifty Phase: Dev Role: BE App: Nifty Phase: Test Role: BE

App == NiftyApp: Nifty Phase: Dev Role: FE App: Nifty Phase: Test Role: FE App: Nifty Phase: Dev Role: BE App: Nifty Phase: Test Role: BE Selectors

App == Nifty Role == FE App: Nifty Phase: Dev Role: FE App: Nifty Phase: Test Role: FE App: Nifty Phase: Dev Role: BE App: Nifty Phase: Test Role: BE Selectors

App == Nifty Role == BE App: Nifty Phase: Dev Role: FE App: Nifty Phase: Test Role: FE App: Nifty Phase: Dev Role: BE App: Nifty Phase: Test Role: BE Selectors

App == Nifty Phase == Dev App: Nifty Phase: Dev Role: FE App: Nifty Phase: Test Role: FE App: Nifty Phase: Dev Role: BE App: Nifty Phase: Test Role: BE Selectors

App == Nifty Phase == Test App: Nifty Phase: Dev Role: FE App: Nifty Phase: Test Role: FE App: Nifty Phase: Dev Role: BE App: Nifty Phase: Test Role: BE Selectors

Replication Controllers Canonical example of control loops Runs out-of-process wrt API server Have 1 job: ensure N copies of a pod • if too few, start new ones • if too many, kill some • group == selector Cleanly layered on top of the core • all access is by public APIs Replicated pods are fungible • No implied ordinality or identity Replication Controller - Name = “nifty-rc” - Selector = {“App”: “Nifty”} - PodTemplate = { ... } - NumReplicas = 4 API Server How many? 3 Start 1 more OK How many? 4

Replication Controllers node 1 f0118 node 3 node 4node 2 d9376 b0111 a1209 Replication Controller - Desired = 4 - Current = 4

Replication Controllers node 1 f0118 node 3 node 4node 2 Replication Controller - Desired = 4 - Current = 4 d9376 b0111 a1209

Replication Controllers node 1 f0118 node 3 node 4 Replication Controller - Desired = 4 - Current = 3 b0111 a1209

Replication Controllers node 1 f0118 node 3 node 4 Replication Controller - Desired = 4 - Current = 4 b0111 a1209 c9bad

Replication Controllers node 1 f0118 node 3 node 4node 2 Replication Controller - Desired = 4 - Current = 5 d9376 b0111 a1209 c9bad

Replication Controllers node 1 f0118 node 3 node 4node 2 Replication Controller - Desired = 4 - Current = 4 d9376 b0111 a1209 c9bad

Pod networking Pod IPs are routable • Docker default is private IP Pods can reach each other without NAT • even across nodes No brokering of port numbers This is a fundamental requirement • several SDN solutions

Services A group of pods that act as one == Service • group == selector Defines access policy • only “load balanced” for now Gets a stable virtual IP and port • called the service portal • also a DNS name VIP is captured by kube-proxy • watches the service constituency • updates when backends change Hide complexity - ideal for non-native apps Portal (VIP) Client

Services 10.0.0.1 : 9376 Client kube-proxy Service - Name = “nifty-svc” - Selector = {“App”: “Nifty”} - Port = 9376 - ContainerPort = 8080 Portal IP is assigned iptables DNAT TCP / UDP apiserver watch 10.240.2.2 : 808010.240.1.1 : 8080 10.240.3.3 : 8080 TCP / UDP

Events A central place for information about your cluster • filed by any component: kubelet, scheduler, etc Real-time information on the current state of your pod • kubectl describe pod foo Real-time information on the current state of your cluster • kubectl get --watch-only events • You can also ask only for events that mention some object you care about.

Monitoring Optional add-on to Kubernetes clusters Run cAdvisor as a pod on each node • gather stats from all containers • export via REST Run Heapster as a pod in the cluster • just another pod, no special access • aggregate stats Run Influx and Grafana in the cluster • more pods • alternately: store in Google Cloud Monitoring

Logging Optional add-on to Kubernetes clusters Run fluentd as a pod on each node • gather logs from all containers • export to elasticsearch Run Elasticsearch as a pod in the cluster • just another pod, no special access • aggregate logs Run Kibana in the cluster • yet another pod • alternately: store in Google Cloud Logging

Kubernetes is Open Source We want your help! http://kubernetes.io https://github.com/GoogleCloudPlatform/kubernetes irc.freenode.net #google-containers @kubernetesio

Questions? Images by Connie Zhou http://kubernetes.io

Kubernetes intro public - kubernetes user group 4-21-2015

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