Intros

• Hello! I'm Jérôme (@jpetazzo, Enix SAS)

• The workshop will run from 9am to 5pm

• Lunch will be served at 12:30pm (in Rhinelander)

• Morning and afternoon breaks are scheduled at 10:30am and 3pm (in Sutton Foyer)

• Feel free to interrupt for questions at any time

• Especially when you see full screen container pictures!

• Live feedback, questions, help: Gitter

logistics.md

A brief introduction

• This was initially written by Jérôme Petazzoni to support in-person, instructor-led workshops and tutorials

• Credit is also due to multiple contributors — thank you!

• You can also follow along on your own, at your own pace

• We included as much information as possible in these slides

• We recommend having a mentor to help you ...

• ... Or be comfortable spending some time reading the Kubernetes documentation ...

• ... And looking for answers on StackOverflow and other outlets

k8s/intro.md

About these slides

• All the content is available in a public GitHub repository:

  https://github.com/jpetazzo/container.training

• You can get updated "builds" of the slides there:

  http://container.training/

Chapter 1

• Pre-requirements

• Our sample application

• Identifying bottlenecks

• Kubernetes concepts

• Declarative vs imperative

(auto-generated TOC)

Chapter 2

• Kubernetes network model

• First contact with kubectl

• Setting up Kubernetes

• Running our first containers on Kubernetes

• Exposing containers

(auto-generated TOC)

Chapter 3

• Deploying a self-hosted registry

• Exposing services internally

• Exposing services for external access

• Accessing the API with kubectl proxy

• Controlling the cluster remotely

• Accessing internal services

• The Kubernetes dashboard

• Security implications of kubectl apply

• Scaling a deployment

(auto-generated TOC)

Chapter 4

• Daemon sets

• Updating a service through labels and selectors

• Rolling updates

• Healthchecks

• Accessing logs from the CLI

• Centralized logging

(auto-generated TOC)

Chapter 5

• Managing stacks with Helm

• Namespaces

• Network policies

• Authentication and authorization

(auto-generated TOC)

Chapter 6

• Exposing HTTP services with Ingress resources

• Collecting metrics with Prometheus

(auto-generated TOC)

Chapter 7

• Volumes

• Building images with the Docker Engine

• Building images with Kaniko

• Managing configuration

(auto-generated TOC)

Chapter 8

• Owners and dependents

• Stateful sets

• Highly available Persistent Volumes

(auto-generated TOC)

Chapter 9

• Next steps

• Links and resources

• Final words

(auto-generated TOC)

shared/toc.md

Pre-requirements

• Be comfortable with the UNIX command line

  • navigating directories

  • editing files

  • a little bit of bash-fu (environment variables, loops)

• Some Docker knowledge

  • docker run, docker ps, docker build

  • ideally, you know how to write a Dockerfile and build it
    (even if it's a FROM line and a couple of RUN commands)

• It's totally OK if you are not a Docker expert!

shared/prereqs.md

Hands-on sections

• The whole workshop is hands-on

• We are going to build, ship, and run containers!

• You are invited to reproduce all the demos

• All hands-on sections are clearly identified, like the gray rectangle below

shared/prereqs.md

Doing or re-doing the workshop on your own?

• Use something like Play-With-Docker or Play-With-Kubernetes

  Zero setup effort; but environment are short-lived and might have limited resources

• Create your own cluster (local or cloud VMs)

  Small setup effort; small cost; flexible environments

• Create a bunch of clusters for you and your friends (instructions)

  Bigger setup effort; ideal for group training

shared/prereqs.md

We will (mostly) interact with node1 only

These remarks apply only when using multiple nodes, of course.

• Unless instructed, all commands must be run from the first VM, node1

• We will only checkout/copy the code on node1

• During normal operations, we do not need access to the other nodes

• If we had to troubleshoot issues, we would use a combination of:

  • SSH (to access system logs, daemon status...)

  • Docker API (to check running containers and container engine status)

shared/prereqs.md

Terminals

Once in a while, the instructions will say:
"Open a new terminal."

There are multiple ways to do this:

• create a new window or tab on your machine, and SSH into the VM;

• use screen or tmux on the VM and open a new window from there.

You are welcome to use the method that you feel the most comfortable with.

shared/prereqs.md

Tmux cheatsheet

Tmux is a terminal multiplexer like screen.

You don't have to use it or even know about it to follow along.
But some of us like to use it to switch between terminals.
It has been preinstalled on your workshop nodes.

• Ctrl-b c → creates a new window
• Ctrl-b n → go to next window
• Ctrl-b p → go to previous window
• Ctrl-b " → split window top/bottom
• Ctrl-b % → split window left/right
• Ctrl-b Alt-1 → rearrange windows in columns
• Ctrl-b Alt-2 → rearrange windows in rows
• Ctrl-b arrows → navigate to other windows
• Ctrl-b d → detach session
• tmux attach → reattach to session

shared/prereqs.md

Versions installed

• Kubernetes 1.12.0
• Docker Engine 18.06.1-ce
• Docker Compose 1.21.1

k8s/versions-k8s.md

Our sample application

• We will clone the GitHub repository onto our node1

• The repository also contains scripts and tools that we will use through the workshop

(You can also fork the repository on GitHub and clone your fork if you prefer that.)

shared/sampleapp.md

Downloading and running the application

Let's start this before we look around, as downloading will take a little time...

Compose tells Docker to build all container images (pulling the corresponding base images), then starts all containers, and displays aggregated logs.

shared/sampleapp.md

More detail on our sample application

• Visit the GitHub repository with all the materials of this workshop:
  https://github.com/jpetazzo/container.training

• The application is in the dockercoins subdirectory

• Let's look at the general layout of the source code:

  there is a Compose file docker-compose.yml ...

  ... and 4 other services, each in its own directory:

  • rng = web service generating random bytes
  • hasher = web service computing hash of POSTed data
  • worker = background process using rng and hasher
  • webui = web interface to watch progress

shared/sampleapp.md

Service discovery in container-land

• We do not hard-code IP addresses in the code

• We do not hard-code FQDN in the code, either

• We just connect to a service name, and container-magic does the rest

  (And by container-magic, we mean "a crafty, dynamic, embedded DNS server")

shared/sampleapp.md

Example in worker/worker.py

redis = Redis("redis")
def get_random_bytes():
    r = requests.get("http://rng/32")
    return r.content
def hash_bytes(data):
    r = requests.post("http://hasher/",
                      data=data,
                      headers={"Content-Type": "application/octet-stream"})

(Full source code available here)

shared/sampleapp.md

What's this application?

Our application at work

• On the left-hand side, the "rainbow strip" shows the container names

• On the right-hand side, we see the output of our containers

• We can see the worker service making requests to rng and hasher

• For rng and hasher, we see HTTP access logs

shared/sampleapp.md

Connecting to the web UI

• "Logs are exciting and fun!" (No-one, ever)

• The webui container exposes a web dashboard; let's view it

A drawing area should show up, and after a few seconds, a blue graph will appear.

shared/sampleapp.md

Stopping the application

• If we interrupt Compose (with ^C), it will politely ask the Docker Engine to stop the app

• The Docker Engine will send a TERM signal to the containers

• If the containers do not exit in a timely manner, the Engine sends a KILL signal

Restarting in the background

• Many flags and commands of Compose are modeled after those of docker

docker-compose ps also shows the ports exposed by the application.

shared/composescale.md

Scaling up the application

• Our goal is to make that performance graph go up (without changing a line of code!)

Looking at resource usage

• Let's look at CPU, memory, and I/O usage

We have available resources.

• Why?
• How can we use them?

shared/composescale.md

Scaling workers on a single node

• Docker Compose supports scaling
• Let's scale worker and see what happens!

shared/composescale.md

Adding more workers

• Great, let's add more workers and call it a day, then!

shared/composescale.md

Identifying bottlenecks

• You should have seen a 3x speed bump (not 10x)

• Adding workers didn't result in linear improvement

• Something else is slowing us down

Accessing internal services

• rng and hasher are exposed on ports 8001 and 8002

• This is declared in the Compose file:

  ...
  rng:
    build: rng
    ports:
    - "8001:80"
  hasher:
    build: hasher
    ports:
    - "8002:80"
  ...

shared/composescale.md

Measuring latency under load

We will use httping.

rng has a much higher latency than hasher.

shared/composescale.md

Let's draw hasty conclusions

• The bottleneck seems to be rng

• What if we don't have enough entropy and can't generate enough random numbers?

• We need to scale out the rng service on multiple machines!

Note: this is a fiction! We have enough entropy. But we need a pretext to scale out.

(In fact, the code of rng uses /dev/urandom, which never runs out of entropy...
...and is just as good as /dev/random.)

shared/composescale.md

Clean up

• Before moving on, let's remove those containers

shared/composedown.md

Kubernetes concepts

• Kubernetes is a container management system

• It runs and manages containerized applications on a cluster

Basic things we can ask Kubernetes to do

Other things that Kubernetes can do for us

• Basic autoscaling

• Blue/green deployment, canary deployment

• Long running services, but also batch (one-off) jobs

• Overcommit our cluster and evict low-priority jobs

• Run services with stateful data (databases etc.)

• Fine-grained access control defining what can be done by whom on which resources

• Integrating third party services (service catalog)

• Automating complex tasks (operators)

k8s/concepts-k8s.md

Kubernetes architecture

k8s/concepts-k8s.md

Kubernetes architecture

• Ha ha ha ha

• OK, I was trying to scare you, it's much simpler than that ❤️

k8s/concepts-k8s.md

Credits

• The first schema is a Kubernetes cluster with storage backed by multi-path iSCSI

  (Courtesy of Yongbok Kim)

• The second one is a simplified representation of a Kubernetes cluster

  (Courtesy of Imesh Gunaratne)

k8s/concepts-k8s.md

Kubernetes architecture: the nodes

• The nodes executing our containers run a collection of services:

  • a container Engine (typically Docker)

  • kubelet (the "node agent")

  • kube-proxy (a necessary but not sufficient network component)

• Nodes were formerly called "minions"

  (You might see that word in older articles or documentation)

k8s/concepts-k8s.md

Kubernetes architecture: the control plane

• The Kubernetes logic (its "brains") is a collection of services:

  • the API server (our point of entry to everything!)

  • core services like the scheduler and controller manager

  • etcd (a highly available key/value store; the "database" of Kubernetes)

• Together, these services form the control plane of our cluster

• The control plane is also called the "master"

k8s/concepts-k8s.md

Running the control plane on special nodes

• It is common to reserve a dedicated node for the control plane

  (Except for single-node development clusters, like when using minikube)

• This node is then called a "master"

  (Yes, this is ambiguous: is the "master" a node, or the whole control plane?)

• Normal applications are restricted from running on this node

  (By using a mechanism called "taints")

• When high availability is required, each service of the control plane must be resilient

• The control plane is then replicated on multiple nodes

  (This is sometimes called a "multi-master" setup)

k8s/concepts-k8s.md

Running the control plane outside containers

• The services of the control plane can run in or out of containers

• For instance: since etcd is a critical service, some people deploy it directly on a dedicated cluster (without containers)

  (This is illustrated on the first "super complicated" schema)

• In some hosted Kubernetes offerings (e.g. AKS, GKE, EKS), the control plane is invisible

  (We only "see" a Kubernetes API endpoint)

• In that case, there is no "master node"

For this reason, it is more accurate to say "control plane" rather than "master".

k8s/concepts-k8s.md

Do we need to run Docker at all?

No!

Do we need to run Docker at all?

Yes!

Do we need to run Docker at all?

• On our development environments, CI pipelines ... :

  Yes, almost certainly

• On our production servers:

  Yes (today)

  Probably not (in the future)

More information about CRI on the Kubernetes blog

k8s/concepts-k8s.md

Kubernetes resources

• The Kubernetes API defines a lot of objects called resources

• These resources are organized by type, or Kind (in the API)

• A few common resource types are:

  • node (a machine — physical or virtual — in our cluster)
  • pod (group of containers running together on a node)
  • service (stable network endpoint to connect to one or multiple containers)
  • namespace (more-or-less isolated group of things)
  • secret (bundle of sensitive data to be passed to a container)

  And much more!

• We can see the full list by running kubectl api-resources

  (In Kubernetes 1.10 and prior, the command to list API resources was kubectl get)

k8s/concepts-k8s.md

Credits

• The first diagram is courtesy of Weave Works

  • a pod can have multiple containers working together

  • IP addresses are associated with pods, not with individual containers

• The second diagram is courtesy of Lucas Käldström, in this presentation

  • it's one of the best Kubernetes architecture diagrams available!

Both diagrams used with permission.

k8s/concepts-k8s.md

Declarative vs imperative

• Our container orchestrator puts a very strong emphasis on being declarative

• Declarative:

  I would like a cup of tea.

• Imperative:

  Boil some water. Pour it in a teapot. Add tea leaves. Steep for a while. Serve in a cup.

Declarative vs imperative

• What declarative would really be:

  I want a cup of tea, obtained by pouring an infusion¹ of tea leaves in a cup.

Declarative vs imperative

• Imperative systems:

  • simpler

  • if a task is interrupted, we have to restart from scratch

• Declarative systems:

  • if a task is interrupted (or if we show up to the party half-way through), we can figure out what's missing and do only what's necessary

  • we need to be able to observe the system

  • ... and compute a "diff" between what we have and what we want

shared/declarative.md

Declarative vs imperative in Kubernetes

• Virtually everything we create in Kubernetes is created from a spec

• Watch for the spec fields in the YAML files later!

• The spec describes how we want the thing to be

• Kubernetes will reconcile the current state with the spec
  (technically, this is done by a number of controllers)

• When we want to change some resource, we update the spec

• Kubernetes will then converge that resource

k8s/declarative.md

Kubernetes network model

• TL,DR:

  Our cluster (nodes and pods) is one big flat IP network.

Kubernetes network model: the good

• Everything can reach everything

• No address translation

• No port translation

• No new protocol

• Pods cannot move from a node to another and keep their IP address

• IP addresses don't have to be "portable" from a node to another

  (We can use e.g. a subnet per node and use a simple routed topology)

• The specification is simple enough to allow many various implementations

k8s/kubenet.md

Kubernetes network model: the less good

• Everything can reach everything

  • if you want security, you need to add network policies

  • the network implementation that you use needs to support them

• There are literally dozens of implementations out there

  (15 are listed in the Kubernetes documentation)

• Pods have level 3 (IP) connectivity, but services are level 4

  (Services map to a single UDP or TCP port; no port ranges or arbitrary IP packets)

• kube-proxy is on the data path when connecting to a pod or container,
  and it's not particularly fast (relies on userland proxying or iptables)

k8s/kubenet.md

Kubernetes network model: in practice

• The nodes that we are using have been set up to use Weave

• We don't endorse Weave in a particular way, it just Works For Us

• Don't worry about the warning about kube-proxy performance

• Unless you:

  • routinely saturate 10G network interfaces
  • count packet rates in millions per second
  • run high-traffic VOIP or gaming platforms
  • do weird things that involve millions of simultaneous connections
    (in which case you're already familiar with kernel tuning)

• If necessary, there are alternatives to kube-proxy; e.g. kube-router

k8s/kubenet.md

The Container Network Interface (CNI)

• The CNI has a well-defined specification for network plugins

• When a pod is created, Kubernetes delegates the network setup to CNI plugins

• Typically, a CNI plugin will:

  • allocate an IP address (by calling an IPAM plugin)

  • add a network interface into the pod's network namespace

  • configure the interface as well as required routes etc.

• Using multiple plugins can be done with "meta-plugins" like CNI-Genie or Multus

• Not all CNI plugins are equal

  (e.g. they don't all implement network policies, which are required to isolate pods)

k8s/kubenet.md

First contact with kubectl

• kubectl is (almost) the only tool we'll need to talk to Kubernetes

• It is a rich CLI tool around the Kubernetes API

  (Everything you can do with kubectl, you can do directly with the API)

• On our machines, there is a ~/.kube/config file with:

  • the Kubernetes API address

  • the path to our TLS certificates used to authenticate

• You can also use the --kubeconfig flag to pass a config file

• Or directly --server, --user, etc.

• kubectl can be pronounced "Cube C T L", "Cube cuttle", "Cube cuddle"...

k8s/kubectlget.md

kubectl get

• Let's look at our Node resources with kubectl get!

k8s/kubectlget.md

Obtaining machine-readable output

• kubectl get can output JSON, YAML, or be directly formatted

k8s/kubectlget.md

(Ab)using kubectl and jq

• It's super easy to build custom reports

k8s/kubectlget.md

What's available?

• kubectl has pretty good introspection facilities

• We can list all available resource types by running kubectl api-resources
  (In Kubernetes 1.10 and prior, this command used to be kubectl get)

• We can view details about a resource with:

  kubectl describe type/name
  kubectl describe type name

• We can view the definition for a resource type with:

  kubectl explain type

Each time, type can be singular, plural, or abbreviated type name.

k8s/kubectlget.md

Services

• A service is a stable endpoint to connect to "something"

  (In the initial proposal, they were called "portals")

ClusterIP services

• A ClusterIP service is internal, available from the cluster only

• This is useful for introspection from within containers

Listing running containers

• Containers are manipulated through pods

• A pod is a group of containers:

  • running together (on the same node)

  • sharing resources (RAM, CPU; but also network, volumes)

Namespaces

• Namespaces allow us to segregate resources

Accessing namespaces

• By default, kubectl uses the default namespace

• We can switch to a different namespace with the -n option

What are all these control plane pods?

• etcd is our etcd server

• kube-apiserver is the API server

• kube-controller-manager and kube-scheduler are other master components

• coredns provides DNS-based service discovery (replacing kube-dns as of 1.11)

• kube-proxy is the (per-node) component managing port mappings and such

• weave is the (per-node) component managing the network overlay

• the READY column indicates the number of containers in each pod

• the pods with a name ending with -node1 are the master components
  (they have been specifically "pinned" to the master node)

k8s/kubectlget.md

What about kube-public?

Setting up Kubernetes

• How did we set up these Kubernetes clusters that we're using?

kubeadm drawbacks

• Doesn't set up Docker or any other container engine

• Doesn't set up the overlay network

• Doesn't set up multi-master (no high availability)

Other deployment options

• If you are on Azure: AKS

• If you are on Google Cloud: GKE

• If you are on AWS: EKS or kops

• On a local machine: minikube, kubespawn, Docker4Mac

• If you want something customizable: kubicorn

  Probably the closest to a multi-cloud/hybrid solution so far, but in development

k8s/setup-k8s.md

Even more deployment options

• If you like Ansible: kubespray

• If you like Terraform: typhoon

• If you like Terraform and Puppet: tarmak

• You can also learn how to install every component manually, with the excellent tutorial Kubernetes The Hard Way

  Kubernetes The Hard Way is optimized for learning, which means taking the long route to ensure you understand each task required to bootstrap a Kubernetes cluster.

• There are also many commercial options available!

• For a longer list, check the Kubernetes documentation:
  it has a great guide to pick the right solution to set up Kubernetes.

k8s/setup-k8s.md

Running our first containers on Kubernetes

• First things first: we cannot run a container

Starting a simple pod with kubectl run

• We need to specify at least a name and the image we want to use

Behind the scenes of kubectl run

• Let's look at the resources that were created by kubectl run

What are these different things?

• A deployment is a high-level construct

  • allows scaling, rolling updates, rollbacks

  • multiple deployments can be used together to implement a canary deployment

  • delegates pods management to replica sets

• A replica set is a low-level construct

  • makes sure that a given number of identical pods are running

  • allows scaling

  • rarely used directly

• A replication controller is the (deprecated) predecessor of a replica set

k8s/kubectlrun.md

Our pingpong deployment

• kubectl run created a deployment, deployment.apps/pingpong

NAME                       DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/pingpong   1         1         1            1           10m

• That deployment created a replica set, replicaset.apps/pingpong-xxxxxxxxxx

NAME                                  DESIRED   CURRENT   READY     AGE
replicaset.apps/pingpong-7c8bbcd9bc   1         1         1         10m

• That replica set created a pod, pod/pingpong-xxxxxxxxxx-yyyyy

NAME                            READY     STATUS    RESTARTS   AGE
pod/pingpong-7c8bbcd9bc-6c9qz   1/1       Running   0          10m

• We'll see later how these folks play together for:

  • scaling, high availability, rolling updates

k8s/kubectlrun.md

Viewing container output

• Let's use the kubectl logs command

• We will pass either a pod name, or a type/name

  (E.g. if we specify a deployment or replica set, it will get the first pod in it)

• Unless specified otherwise, it will only show logs of the first container in the pod

  (Good thing there's only one in ours!)

k8s/kubectlrun.md

Streaming logs in real time

• Just like docker logs, kubectl logs supports convenient options:

  • -f/--follow to stream logs in real time (à la tail -f)

  • --tail to indicate how many lines you want to see (from the end)

  • --since to get logs only after a given timestamp

k8s/kubectlrun.md

Scaling our application

• We can create additional copies of our container (I mean, our pod) with kubectl scale

Note: what if we tried to scale replicaset.apps/pingpong-xxxxxxxxxx?

We could! But the deployment would notice it right away, and scale back to the initial level.

k8s/kubectlrun.md

Resilience

• The deployment pingpong watches its replica set

• The replica set ensures that the right number of pods are running

• What happens if pods disappear?

k8s/kubectlrun.md

What if we wanted something different?

• What if we wanted to start a "one-shot" container that doesn't get restarted?

• We could use kubectl run --restart=OnFailure or kubectl run --restart=Never

• These commands would create jobs or pods instead of deployments

• Under the hood, kubectl run invokes "generators" to create resource descriptions

• We could also write these resource descriptions ourselves (typically in YAML),
  and create them on the cluster with kubectl apply -f (discussed later)

• With kubectl run --schedule=..., we can also create cronjobs

k8s/kubectlrun.md

What about that deprecation warning?

• As we can see from the previous slide, kubectl run can do many things

• The exact type of resource created is not obvious

• To make things more explicit, it is better to use kubectl create:

  • kubectl create deployment to create a deployment

  • kubectl create job to create a job

• Eventually, kubectl run will be used only to start one-shot pods

  (see https://github.com/kubernetes/kubernetes/pull/68132)

k8s/kubectlrun.md

Various ways of creating resources

• kubectl run

  • easy way to get started
  • versatile

• kubectl create <resource>

  • explicit, but lacks some features
  • can't create a CronJob
  • can't pass command-line arguments to deployments

• kubectl create -f foo.yaml or kubectl apply -f foo.yaml

  • all features are available
  • requires writing YAML

k8s/kubectlrun.md

Viewing logs of multiple pods

• When we specify a deployment name, only one single pod's logs are shown

• We can view the logs of multiple pods by specifying a selector

• A selector is a logic expression using labels

• Conveniently, when you kubectl run somename, the associated objects have a run=somename label

Unfortunately, --follow cannot (yet) be used to stream the logs from multiple containers.

k8s/kubectlrun.md

Aren't we flooding 1.1.1.1?

• If you're wondering this, good question!

• Don't worry, though:

  APNIC's research group held the IP addresses 1.1.1.1 and 1.0.0.1. While the addresses were valid, so many people had entered them into various random systems that they were continuously overwhelmed by a flood of garbage traffic. APNIC wanted to study this garbage traffic but any time they'd tried to announce the IPs, the flood would overwhelm any conventional network.

  (Source: https://blog.cloudflare.com/announcing-1111/)

• It's very unlikely that our concerted pings manage to produce even a modest blip at Cloudflare's NOC!

k8s/kubectlrun.md

Exposing containers

• kubectl expose creates a service for existing pods

• A service is a stable address for a pod (or a bunch of pods)

• If we want to connect to our pod(s), we need to create a service

• Once a service is created, CoreDNS will allow us to resolve it by name

  (i.e. after creating service hello, the name hello will resolve to something)

• There are different types of services, detailed on the following slides:

  ClusterIP, NodePort, LoadBalancer, ExternalName

k8s/kubectlexpose.md

Basic service types

• ClusterIP (default type)

  • a virtual IP address is allocated for the service (in an internal, private range)
  • this IP address is reachable only from within the cluster (nodes and pods)
  • our code can connect to the service using the original port number

• NodePort

  • a port is allocated for the service (by default, in the 30000-32768 range)
  • that port is made available on all our nodes and anybody can connect to it
  • our code must be changed to connect to that new port number

These service types are always available.

Under the hood: kube-proxy is using a userland proxy and a bunch of iptables rules.

k8s/kubectlexpose.md

More service types

• LoadBalancer

  • an external load balancer is allocated for the service
  • the load balancer is configured accordingly
    (e.g.: a NodePort service is created, and the load balancer sends traffic to that port)
  • available only when the underlying infrastructure provides some "load balancer as a service"
    (e.g. AWS, Azure, GCE, OpenStack...)

• ExternalName

  • the DNS entry managed by CoreDNS will just be a CNAME to a provided record
  • no port, no IP address, no nothing else is allocated

k8s/kubectlexpose.md

Running containers with open ports

• Since ping doesn't have anything to connect to, we'll have to run something else

The jpetazzo/httpenv image runs an HTTP server on port 8888.
It serves its environment variables in JSON format.

The -w option "watches" events happening on the specified resources.

k8s/kubectlexpose.md

Exposing our deployment

• We'll create a default ClusterIP service

k8s/kubectlexpose.md

Services are layer 4 constructs

• You can assign IP addresses to services, but they are still layer 4

  (i.e. a service is not an IP address; it's an IP address + protocol + port)

• This is caused by the current implementation of kube-proxy

  (it relies on mechanisms that don't support layer 3)

• As a result: you have to indicate the port number for your service

• Running services with arbitrary port (or port ranges) requires hacks

  (e.g. host networking mode)

k8s/kubectlexpose.md

Testing our service

• We will now send a few HTTP requests to our pods

What's on the menu?

In this part, we will:

• build images for our app,

• ship these images with a registry,

• run deployments using these images,

• expose these deployments so they can communicate with each other,

• expose the web UI so we can access it from outside.

k8s/ourapponkube.md

The plan

• Build on our control node (node1)

• Tag images so that they are named $REGISTRY/servicename

• Upload them to a registry

• Create deployments using the images

• Expose (with a ClusterIP) the services that need to communicate

• Expose (with a NodePort) the WebUI

k8s/ourapponkube.md

Which registry do we want to use?

• We could use the Docker Hub

• Or a service offered by our cloud provider (ACR, GCR, ECR...)

• Or we could just self-host that registry

We'll self-host the registry because it's the most generic solution for this workshop.

k8s/ourapponkube.md

Using the open source registry

• We need to run a registry container

• It will store images and layers to the local filesystem
  (but you can add a config file to use S3, Swift, etc.)

• Docker requires TLS when communicating with the registry

  • unless for registries on 127.0.0.0/8 (i.e. localhost)

  • or with the Engine flag --insecure-registry

• Our strategy: publish the registry container on a NodePort,
  so that it's available through 127.0.0.1:xxxxx on each node

k8s/ourapponkube.md

Deploying a self-hosted registry

• We will deploy a registry container, and expose it with a NodePort

k8s/ourapponkube.md

Connecting to our registry

• We need to find out which port has been allocated

k8s/ourapponkube.md

Testing our registry

• A convenient Docker registry API route to remember is /v2/_catalog

Testing our local registry

• We can retag a small image, and push it to the registry

k8s/ourapponkube.md

Checking again what's on our local registry

• Let's use the same endpoint as before

The curl command should now output:

{"repositories":["busybox"]}

k8s/ourapponkube.md

Building and pushing our images

• We are going to use a convenient feature of Docker Compose

Let's have a look at the dockercoins.yml file while this is building and pushing.

k8s/ourapponkube.md

version: "3"
services:
  rng:
    build: dockercoins/rng
    image: ${REGISTRY-127.0.0.1:5000}/rng:${TAG-latest}
    deploy:
      mode: global
  ...
  redis:
    image: redis
  ...
  worker:
    build: dockercoins/worker
    image: ${REGISTRY-127.0.0.1:5000}/worker:${TAG-latest}
    ...
    deploy:
      replicas: 10

Just in case you were wondering ... Docker "services" are not Kubernetes "services".

k8s/ourapponkube.md

Deploying all the things

• We can now deploy our code (as well as a redis instance)

k8s/ourapponkube.md

Is this working?

• After waiting for the deployment to complete, let's look at the logs!

  (Hint: use kubectl get deploy -w to watch deployment events)

Exposing services internally

• Three deployments need to be reachable by others: hasher, redis, rng

• worker doesn't need to be exposed

• webui will be dealt with later

k8s/ourapponkube.md

Is this working yet?

• The worker has an infinite loop, that retries 10 seconds after an error

Exposing services for external access

• Now we would like to access the Web UI

• We will expose it with a NodePort

  (just like we did for the registry)

k8s/ourapponkube.md

Accessing the web UI

• We can now connect to any node, on the allocated node port, to view the web UI

Accessing the API with kubectl proxy

• The API requires us to authenticate¹

• There are many authentication methods available, including:

  • TLS client certificates
    (that's what we've used so far)

  • HTTP basic password authentication
    (from a static file; not recommended)

  • various token mechanisms
    (detailed in the documentation)

¹OK, we lied. If you don't authenticate, you are considered to be user system:anonymous, which doesn't have any access rights by default.

k8s/kubectlproxy.md

Accessing the API directly

• Let's see what happens if we try to access the API directly with curl

The API will tell us that user system:anonymous cannot access this path.

k8s/kubectlproxy.md

Authenticating to the API

If we wanted to talk to the API, we would need to:

• extract our TLS key and certificate information from ~/.kube/config

  (the information is in PEM format, encoded in base64)

• use that information to present our certificate when connecting

  (for instance, with openssl s_client -key ... -cert ... -connect ...)

• figure out exactly which credentials to use

  (once we start juggling multiple clusters)

• change that whole process if we're using another authentication method

🤔 There has to be a better way!

k8s/kubectlproxy.md

Using kubectl proxy for authentication

• kubectl proxy runs a proxy in the foreground

• This proxy lets us access the Kubernetes API without authentication

  (kubectl proxy adds our credentials on the fly to the requests)

• This proxy lets us access the Kubernetes API over plain HTTP

• This is a great tool to learn and experiment with the Kubernetes API

• ... And for serious usages as well (suitable for one-shot scripts)

• For unattended use, it is better to create a service account

k8s/kubectlproxy.md

Trying kubectl proxy

• Let's start kubectl proxy and then do a simple request with curl!

The output is a list of available API routes.

k8s/kubectlproxy.md

kubectl proxy is intended for local use

• By default, the proxy listens on port 8001

  (But this can be changed, or we can tell kubectl proxy to pick a port)

• By default, the proxy binds to 127.0.0.1

  (Making it unreachable from other machines, for security reasons)

• By default, the proxy only accepts connections from:

  ^localhost$,^127\.0\.0\.1$,^\[::1\]$

• This is great when running kubectl proxy locally

• Not-so-great when you want to connect to the proxy from a remote machine

k8s/kubectlproxy.md

Running kubectl proxy on a remote machine

• If we wanted to connect to the proxy from another machine, we would need to:

  • bind to INADDR_ANY instead of 127.0.0.1

  • accept connections from any address

• This is achieved with:

  kubectl proxy --port=8888 --address=0.0.0.0 --accept-hosts=.*

Do not do this on a real cluster: it opens full unauthenticated access!

k8s/kubectlproxy.md

Security considerations

• Running kubectl proxy openly is a huge security risk

• It is slightly better to run the proxy where you need it

  (and copy credentials, e.g. ~/.kube/config, to that place)

• It is even better to use a limited account with reduced permissions

k8s/kubectlproxy.md

Good to know ...

• kubectl proxy also gives access to all internal services

• Specifically, services are exposed as such:

  /api/v1/namespaces/<namespace>/services/<service>/proxy

• We can use kubectl proxy to access an internal service in a pinch

  (or, for non HTTP services, kubectl port-forward)

• This is not very useful when running kubectl directly on the cluster

  (since we could connect to the services directly anyway)

• But it is very powerful as soon as you run kubectl from a remote machine

k8s/kubectlproxy.md

Controlling the cluster remotely

• All the operations that we do with kubectl can be done remotely

• In this section, we are going to use kubectl from our local machine

k8s/localkubeconfig.md

Installing kubectl

• If you already have kubectl on your local machine, you can skip this

Note: if you are following along with a different platform (e.g. Linux on an architecture different from amd64, or with a phone or tablet), installing kubectl might be more complicated (or even impossible) so feel free to skip this section.

k8s/localkubeconfig.md

Testing kubectl

• Check that kubectl works correctly

  (before even trying to connect to a remote cluster!)

The output should look like this:

Client Version: version.Info{Major:"1", Minor:"11", GitVersion:"v1.11.2",
GitCommit:"bb9ffb1654d4a729bb4cec18ff088eacc153c239", GitTreeState:"clean",
BuildDate:"2018-08-07T23:17:28Z", GoVersion:"go1.10.3", Compiler:"gc",
Platform:"linux/amd64"}

k8s/localkubeconfig.md

Moving away the existing ~/.kube/config

• If you already have a ~/.kube/config file, move it away

  (we are going to overwrite it in the following slides!)

• If you never used kubectl on your machine before: nothing to do!

• If you already used kubectl to control a Kubernetes cluster before:

  • rename ~/.kube/config to e.g. ~/.kube/config.bak

k8s/localkubeconfig.md

Copying the configuration file from node1

• The ~/.kube/config file that is on node1 contains all the credentials we need

• Let's copy it over!

k8s/localkubeconfig.md

Updating the server address

• There is a good chance that we need to update the server address

• To know if it is necessary, run kubectl config view

• Look for the server: address:

  • if it matches the public IP address of node1, you're good!

  • if it is anything else (especially a private IP address), update it!

• To update the server address, run:

  kubectl config set-cluster kubernetes --server=https://X.X.X.X:6443
  kubectl config set-cluster kubernetes --insecure-skip-tls-verify
  # Make sure to replace X.X.X.X with the IP address of node1!

k8s/localkubeconfig.md

Checking that we can connect to the cluster

• We can now run a couple of trivial commands to check that all is well

We can now utilize the cluster exactly as we did before, ignoring that it's remote.

k8s/localkubeconfig.md

Accessing internal services

• When we are logged in on a cluster node, we can access internal services

  (by virtue of the Kubernetes network model: all nodes can reach all pods and services)

• When we are accessing a remote cluster, things are different

  (generally, our local machine won't have access to the cluster's internal subnet)

• How can we temporarily access a service without exposing it to everyone?

Suspension of disbelief

The exercises in this section assume that we have set up kubectl on our local machine in order to access a remote cluster.

We will therefore show how to access services and pods of the remote cluster, from our local machine.

You can also run these exercises directly on the cluster (if you haven't installed and set up kubectl locally).

Running commands locally will be less useful (since you could access services and pods directly), but keep in mind that these commands will work anywhere as long as you have installed and set up kubectl to communicate with your cluster.

k8s/accessinternal.md

kubectl proxy in theory

• Running kubectl proxy gives us access to the entire Kubernetes API

• The API includes routes to proxy HTTP traffic

• These routes look like the following:

  /api/v1/namespaces/<namespace>/services/<service>/proxy

• We just add the URI to the end of the request, for instance:

  /api/v1/namespaces/<namespace>/services/<service>/proxy/index.html

• We can access services and pods this way

k8s/accessinternal.md

kubectl proxy in practice

• Let's access the webui service through kubectl proxy

k8s/accessinternal.md

kubectl port-forward in theory

• What if we want to access a TCP service?

• We can use kubectl port-forward instead

• It will create a TCP relay to forward connections to a specific port

  (of a pod, service, deployment...)

• The syntax is:

  kubectl port-forward service/name_of_service local_port:remote_port

• If only one port number is specified, it is used for both local and remote ports

k8s/accessinternal.md

kubectl port-forward in practice

• Let's access our remote Redis server

k8s/accessinternal.md

The Kubernetes dashboard

• Kubernetes resources can also be viewed with a web dashboard

• We are going to deploy that dashboard with three commands:

  1) actually run the dashboard

  2) bypass SSL for the dashboard

  3) bypass authentication for the dashboard

1) Running the dashboard

• We need to create a deployment and a service for the dashboard

• But also a secret, a service account, a role and a role binding

• All these things can be defined in a YAML file and created with kubectl apply -f

k8s/dashboard.md

2) Bypassing SSL for the dashboard

• The Kubernetes dashboard uses HTTPS, but we don't have a certificate

• Recent versions of Chrome (63 and later) and Edge will refuse to connect

  (You won't even get the option to ignore a security warning!)

• We could (and should!) get a certificate, e.g. with Let's Encrypt

• ... But for convenience, for this workshop, we'll forward HTTP to HTTPS

Do not do this at home, or even worse, at work!

k8s/dashboard.md

Running the SSL unwrapper

• We are going to run socat, telling it to accept TCP connections and relay them over SSL

• Then we will expose that socat instance with a NodePort service

• For convenience, these steps are neatly encapsulated into another YAML file

All our dashboard traffic is now clear-text, including passwords!

k8s/dashboard.md

Connecting to the dashboard

You'll want the 3xxxx port.

The dashboard will then ask you which authentication you want to use.

k8s/dashboard.md

Dashboard authentication

• We have three authentication options at this point:

  • token (associated with a role that has appropriate permissions)

  • kubeconfig (e.g. using the ~/.kube/config file from node1)

  • "skip" (use the dashboard "service account")

• Let's use "skip": we get a bunch of warnings and don't see much

k8s/dashboard.md

3) Bypass authentication for the dashboard

• The dashboard documentation explains how to do this

• We just need to load another YAML file!

Exposing the dashboard over HTTPS

• We took a shortcut by forwarding HTTP to HTTPS inside the cluster

• Let's expose the dashboard over HTTPS!

• The dashboard is exposed through a ClusterIP service (internal traffic only)

• We will change that into a NodePort service (accepting outside traffic)

Editing the kubernetes-dashboard service

• If we look at the YAML that we loaded before, we'll get a hint

Running the Kubernetes dashboard securely

• The steps that we just showed you are for educational purposes only!

• If you do that on your production cluster, people can and will abuse it

• For an in-depth discussion about securing the dashboard,
  check this excellent post on Heptio's blog

k8s/dashboard.md

Security implications of kubectl apply

• When we do kubectl apply -f <URL>, we create arbitrary resources

• Resources can be evil; imagine a deployment that ...

kubectl apply is the new curl | sh

• curl | sh is convenient

• It's safe if you use HTTPS URLs from trusted sources

Scaling a deployment

• We will start with an easy one: the worker deployment

After a few seconds, the graph in the web UI should show up.
(And peak at 10 hashes/second, just like when we were running on a single one.)

k8s/kubectlscale.md

Daemon sets

• We want to scale rng in a way that is different from how we scaled worker

• We want one (and exactly one) instance of rng per node

• What if we just scale up deploy/rng to the number of nodes?

  • nothing guarantees that the rng containers will be distributed evenly

  • if we add nodes later, they will not automatically run a copy of rng

  • if we remove (or reboot) a node, one rng container will restart elsewhere

• Instead of a deployment, we will use a daemonset

k8s/daemonset.md

Daemon sets in practice

• Daemon sets are great for cluster-wide, per-node processes:

  • kube-proxy

  • weave (our overlay network)

  • monitoring agents

  • hardware management tools (e.g. SCSI/FC HBA agents)

  • etc.

• They can also be restricted to run only on some nodes

k8s/daemonset.md

Creating a daemon set

• Unfortunately, as of Kubernetes 1.12, the CLI cannot create daemon sets

Creating the YAML file for our daemon set

• Let's start with the YAML file for the current rng resource

Note: --export will remove "cluster-specific" information, i.e.:

• namespace (so that the resource is not tied to a specific namespace)
• status and creation timestamp (useless when creating a new resource)
• resourceVersion and uid (these would cause... interesting problems)

k8s/daemonset.md

"Casting" a resource to another

• What if we just changed the kind field?

  (It can't be that easy, right?)

Understanding the problem

• The core of the error is:

  error validating data:
  [ValidationError(DaemonSet.spec):
  unknown field "replicas" in io.k8s.api.extensions.v1beta1.DaemonSetSpec,
  ...

Use the --force, Luke

• We could also tell Kubernetes to ignore these errors and try anyway

• The --force flag's actual name is --validate=false

Checking what we've done

• Did we transform our deployment into a daemonset?

deploy/rng and ds/rng

• You can have different resource types with the same name

  (i.e. a deployment and a daemon set both named rng)

• We still have the old rng deployment

  NAME                       DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
  deployment.apps/rng        1         1         1            1           18m

• But now we have the new rng daemon set as well

  NAME                DESIRED  CURRENT  READY  UP-TO-DATE  AVAILABLE  NODE SELECTOR  AGE
  daemonset.apps/rng  2        2        2      2           2          <none>         9s

k8s/daemonset.md

Too many pods

• If we check with kubectl get pods, we see:

  • one pod for the deployment (named rng-xxxxxxxxxx-yyyyy)

  • one pod per node for the daemon set (named rng-zzzzz)

  NAME                        READY     STATUS    RESTARTS   AGE
  rng-54f57d4d49-7pt82        1/1       Running   0          11m
  rng-b85tm                   1/1       Running   0          25s
  rng-hfbrr                   1/1       Running   0          25s
  [...]

What are all these pods doing?

• Let's check the logs of all these rng pods

• All these pods have a run=rng label:

  • the first pod, because that's what kubectl run does
  • the other ones (in the daemon set), because we copied the spec from the first one

• Therefore, we can query everybody's logs using that run=rng selector

The magic of selectors

• The rng service is load balancing requests to a set of pods

• This set of pods is defined as "pods having the label run=rng"

When we created additional pods with this label, they were automatically detected by svc/rng and added as endpoints to the associated load balancer.

k8s/daemonset.md

Removing the first pod from the load balancer

• What would happen if we removed that pod, with kubectl delete pod ...?

Deep dive into selectors

• Let's look at the selectors for the rng deployment and the associated replica set

Updating a service through labels and selectors

• What if we want to drop the rng deployment from the load balancer?

• Option 1:

  • destroy it

• Option 2:

  • add an extra label to the daemon set

  • update the service selector to refer to that label

Add an extra label to the daemon set

• We will update the daemon set "spec"

• Option 1:

  • edit the rng.yml file that we used earlier

  • load the new definition with kubectl apply

• Option 2:

  • use kubectl edit

We've put resources in your resources

• Reminder: a daemon set is a resource that creates more resources!

• There is a difference between:

  • the label(s) of a resource (in the metadata block in the beginning)

  • the selector of a resource (in the spec block)

  • the label(s) of the resource(s) created by the first resource (in the template block)

• You need to update the selector and the template (metadata labels are not mandatory)

• The template must match the selector

  (i.e. the resource will refuse to create resources that it will not select)

k8s/daemonset.md

Adding our label

• Let's add a label isactive: yes

• In YAML, yes should be quoted; i.e. isactive: "yes"

k8s/daemonset.md

Checking what we've done

The timestamps should give us a hint about how many pods are currently receiving traffic.

k8s/daemonset.md

Cleaning up

• The pods of the deployment and the "old" daemon set are still running

• We are going to identify them programmatically

k8s/daemonset.md

Cleaning up stale pods

$ kubectl get pods
NAME                        READY     STATUS        RESTARTS   AGE
rng-54f57d4d49-7pt82        1/1       Terminating   0          51m
rng-54f57d4d49-vgz9h        1/1       Running       0          22s
rng-b85tm                   1/1       Terminating   0          39m
rng-hfbrr                   1/1       Terminating   0          39m
rng-vplmj                   1/1       Running       0          7m
rng-xbpvg                   1/1       Running       0          7m
[...]

• The extra pods (noted Terminating above) are going away

• ... But a new one (rng-54f57d4d49-vgz9h above) was restarted immediately!

Deleting a deployment

Avoiding extra pods

• When we changed the definition of the daemon set, it immediately created new pods. We had to remove the old ones manually.

• How could we have avoided this?

Labels and debugging

• When a pod is misbehaving, we can delete it: another one will be recreated

• But we can also change its labels

• It will be removed from the load balancer (it won't receive traffic anymore)

• Another pod will be recreated immediately

• But the problematic pod is still here, and we can inspect and debug it

• We can even re-add it to the rotation if necessary

  (Very useful to troubleshoot intermittent and elusive bugs)

k8s/daemonset.md

Labels and advanced rollout control

• Conversely, we can add pods matching a service's selector

• These pods will then receive requests and serve traffic

• Examples:

  • one-shot pod with all debug flags enabled, to collect logs

  • pods created automatically, but added to rotation in a second step
    (by setting their label accordingly)

• This gives us building blocks for canary and blue/green deployments

k8s/daemonset.md

Rolling updates

• By default (without rolling updates), when a scaled resource is updated:

  • new pods are created

  • old pods are terminated

  • ... all at the same time

  • if something goes wrong, ¯\_(ツ)_/¯

k8s/rollout.md

Rolling updates

• With rolling updates, when a resource is updated, it happens progressively

• Two parameters determine the pace of the rollout: maxUnavailable and maxSurge

• They can be specified in absolute number of pods, or percentage of the replicas count

• At any given time ...

  • there will always be at least replicas-maxUnavailable pods available

  • there will never be more than replicas+maxSurge pods in total

  • there will therefore be up to maxUnavailable+maxSurge pods being updated

• We have the possibility to rollback to the previous version
  (if the update fails or is unsatisfactory in any way)

k8s/rollout.md

Checking current rollout parameters

• Recall how we build custom reports with kubectl and jq:

k8s/rollout.md

Rolling updates in practice

• As of Kubernetes 1.8, we can do rolling updates with:

  deployments, daemonsets, statefulsets

• Editing one of these resources will automatically result in a rolling update

• Rolling updates can be monitored with the kubectl rollout subcommand

k8s/rollout.md

Building a new version of the worker service

k8s/rollout.md

Rolling out the new worker service

Give it some time

• At first, it looks like nothing is happening (the graph remains at the same level)

• According to kubectl get deploy -w, the deployment was updated really quickly

• But kubectl get pods -w tells a different story

• The old pods are still here, and they stay in Terminating state for a while

• Eventually, they are terminated; and then the graph decreases significantly

• This delay is due to the fact that our worker doesn't handle signals

• Kubernetes sends a "polite" shutdown request to the worker, which ignores it

• After a grace period, Kubernetes gets impatient and kills the container

  (The grace period is 30 seconds, but can be changed if needed)