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28 Commits

Author SHA1 Message Date
Jerome Petazzoni
08fa37dace fix-redirects.sh: adding forced redirect 2020-04-07 16:57:19 -05:00
Jerome Petazzoni
807028cbf3 Remove WiFi warning 2019-06-13 10:51:44 -05:00
Jerome Petazzoni
dfde597cb9 Merge branch 'master' into sfsf-2019-06 2019-06-13 10:51:22 -05:00
Jerome Petazzoni
96419c6baf test→node 2019-06-12 21:35:12 -05:00
Jerome Petazzoni
12da011f21 Customize logistics etc 2019-06-12 21:13:00 -05:00
Jerome Petazzoni
fa1637fb7e Add Helm charts and reorg content 2019-06-12 21:07:55 -05:00
Jerome Petazzoni
fbe2251e21 Merge remote-tracking branch 'origin/make-chart' into sfsf-2019-06 2019-06-12 21:07:12 -05:00
Jerome Petazzoni
bac0d9febd Add a more meaningful exercise with Helm charts 2019-06-12 21:05:47 -05:00
Jerome Petazzoni
b4faf10581 merge 2019-06-12 16:43:24 -05:00
Jerome Petazzoni
0ef9c87f97 Merge branch 'master' into sfsf-2019-06 2019-06-12 16:04:36 -05:00
Jerome Petazzoni
addd14582a merge 2019-06-09 18:41:04 -05:00
Jerome Petazzoni
5299fbaab5 merge 2019-06-02 19:32:20 -05:00
Jerome Petazzoni
398ff5ee4f merge 2019-06-02 16:48:30 -05:00
Jerome Petazzoni
b883e6d557 Prepare SFSF training 2019-06-02 16:47:53 -05:00
Jerome Petazzoni
42ed6fc56a Tweaks 2019-06-02 09:55:50 -05:00
Jerome Petazzoni
2c0b4b15ba Remove bogus slide 2019-06-01 19:31:27 -05:00
Jerome Petazzoni
879e7f2ec9 Improve phrasing following Bridget's feedback 2019-05-31 21:06:17 -05:00
Jerome Petazzoni
68f35bd2ed Add info about zombies and exec probes (courtesy of @lbernail) 2019-05-27 19:11:04 -05:00
Jerome Petazzoni
f4ef2bd6d4 Add control plane auth info 2019-05-27 15:39:12 -05:00
Jerome Petazzoni
baf428ebdb Add note about operator reliability 2019-05-26 22:46:24 -05:00
Jerome Petazzoni
3a87183a66 Add bottom-us vs top-down approaches
Bottom-us is inspired by the Zalando ES operator
2019-05-26 22:39:11 -05:00
Jerome Petazzoni
3f70ee2c2a Add note about operator scaling 2019-05-26 22:17:20 -05:00
Jerome Petazzoni
4c55336079 automatons -> automata 2019-05-25 21:43:07 -05:00
Jerome Petazzoni
39027675d5 Add a whole chapter about operator design 2019-05-25 12:53:15 -05:00
Jerome Petazzoni
c565dad43c Fix typos and add precisions 2019-05-24 19:33:23 -05:00
Jerome Petazzoni
b0f01e018c Clarify healthchecks and dependencies 2019-05-24 18:44:41 -05:00
Jerome Petazzoni
69f9cee6c9 More on healthchecks! Exercises! Fun! Action! 2019-05-20 23:15:44 -05:00
Jerome Petazzoni
b69119eed4 Add operator chapter with nice ElasticSearch demo 2019-05-16 22:21:40 -05:00
22 changed files with 1809 additions and 807 deletions

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@@ -2,3 +2,4 @@
#/ /kube-halfday.yml.html 200
#/ /kube-fullday.yml.html 200
#/ /kube-twodays.yml.html 200
/ /sfsf.yml.html 200!

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@@ -1,63 +0,0 @@
title: |
Introduction
to Containers
chat: "[Slack](https://dockercommunity.slack.com/messages/C7GKACWDV)"
#chat: "[Gitter](https://gitter.im/jpetazzo/workshop-yyyymmdd-city)"
gitrepo: github.com/jpetazzo/container.training
slides: http://container.training/
exclude:
- self-paced
chapters:
- shared/title.md
- logistics.md
- containers/intro.md
- shared/about-slides.md
- shared/toc.md
- - containers/Docker_Overview.md
#- containers/Docker_History.md
- containers/Training_Environment.md
- containers/Installing_Docker.md
- containers/First_Containers.md
- containers/Background_Containers.md
- containers/Start_And_Attach.md
- - containers/Initial_Images.md
- containers/Building_Images_Interactively.md
- containers/Building_Images_With_Dockerfiles.md
- containers/Cmd_And_Entrypoint.md
- - containers/Copying_Files_During_Build.md
- containers/Exercise_Dockerfile_Basic.md
- containers/Multi_Stage_Builds.md
- containers/Publishing_To_Docker_Hub.md
- containers/Dockerfile_Tips.md
- containers/Exercise_Dockerfile_Advanced.md
- - containers/Naming_And_Inspecting.md
- containers/Labels.md
- containers/Getting_Inside.md
- containers/Resource_Limits.md
- - containers/Container_Networking_Basics.md
- containers/Network_Drivers.md
- containers/Container_Network_Model.md
#- containers/Connecting_Containers_With_Links.md
- containers/Ambassadors.md
- - containers/Local_Development_Workflow.md
- containers/Windows_Containers.md
- containers/Working_With_Volumes.md
- containers/Compose_For_Dev_Stacks.md
- containers/Exercise_Composefile.md
- - containers/Docker_Machine.md
- containers/Advanced_Dockerfiles.md
- containers/Application_Configuration.md
- containers/Logging.md
- - containers/Namespaces_Cgroups.md
- containers/Copy_On_Write.md
#- containers/Containers_From_Scratch.md
- - containers/Container_Engines.md
#- containers/Ecosystem.md
- containers/Orchestration_Overview.md
- shared/thankyou.md
- containers/links.md

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@@ -1,63 +0,0 @@
title: |
Introduction
to Containers
chat: "[Slack](https://dockercommunity.slack.com/messages/C7GKACWDV)"
#chat: "[Gitter](https://gitter.im/jpetazzo/workshop-yyyymmdd-city)"
gitrepo: github.com/jpetazzo/container.training
slides: http://container.training/
exclude:
- in-person
chapters:
- shared/title.md
# - shared/logistics.md
- containers/intro.md
- shared/about-slides.md
- shared/toc.md
- - containers/Docker_Overview.md
- containers/Docker_History.md
- containers/Training_Environment.md
- containers/Installing_Docker.md
- containers/First_Containers.md
- containers/Background_Containers.md
- containers/Start_And_Attach.md
- - containers/Initial_Images.md
- containers/Building_Images_Interactively.md
- containers/Building_Images_With_Dockerfiles.md
- containers/Cmd_And_Entrypoint.md
- containers/Copying_Files_During_Build.md
- containers/Exercise_Dockerfile_Basic.md
- - containers/Multi_Stage_Builds.md
- containers/Publishing_To_Docker_Hub.md
- containers/Dockerfile_Tips.md
- containers/Exercise_Dockerfile_Advanced.md
- - containers/Naming_And_Inspecting.md
- containers/Labels.md
- containers/Getting_Inside.md
- - containers/Container_Networking_Basics.md
- containers/Network_Drivers.md
- containers/Container_Network_Model.md
#- containers/Connecting_Containers_With_Links.md
- containers/Ambassadors.md
- - containers/Local_Development_Workflow.md
- containers/Windows_Containers.md
- containers/Working_With_Volumes.md
- containers/Compose_For_Dev_Stacks.md
- containers/Exercise_Composefile.md
- containers/Docker_Machine.md
- - containers/Advanced_Dockerfiles.md
- containers/Application_Configuration.md
- containers/Logging.md
- containers/Resource_Limits.md
- - containers/Namespaces_Cgroups.md
- containers/Copy_On_Write.md
#- containers/Containers_From_Scratch.md
- - containers/Container_Engines.md
- containers/Ecosystem.md
- containers/Orchestration_Overview.md
- shared/thankyou.md
- containers/links.md

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@@ -167,11 +167,13 @@ What does that mean?
## Let's experiment a bit!
- For the exercises in this section, connect to the first node of the `test` cluster
- For this section, we will use a cluster with 4 nodes
(named node1, node2, node3, node4)
.exercise[
- SSH to the first node of the test cluster
- SSH to the first node of the cluster
- Check that the cluster is operational:
```bash

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@@ -0,0 +1,265 @@
# Securing the control plane
- Many components accept connections (and requests) from others:
- API server
- etcd
- kubelet
- We must secure these connections:
- to deny unauthorized requests
- to prevent eavesdropping secrets, tokens, and other sensitive information
- Disabling authentication and/or authorization is **strongly discouraged**
(but it's possible to do it, e.g. for learning / troubleshooting purposes)
---
## Authentication and authorization
- Authentication (checking "who you are") is done with mutual TLS
(both the client and the server need to hold a valid certificate)
- Authorization (checking "what you can do") is done in different ways
- the API server implements a sophisticated permission logic (with RBAC)
- some services will defer authorization to the API server (through webhooks)
- some services require a certificate signed by a particular CA / sub-CA
---
## In practice
- We will review the various communication channels in the control plane
- We will describe how they are secured
- When TLS certificates are used, we will indicate:
- which CA signs them
- what their subject (CN) should be, when applicable
- We will indicate how to configure security (client- and server-side)
---
## etcd peers
- Replication and coordination of etcd happens on a dedicated port
(typically port 2380; the default port for normal client connections is 2379)
- Authentication uses TLS certificates with a separate sub-CA
(otherwise, anyone with a Kubernetes client certificate could access etcd!)
- The etcd command line flags involved are:
`--peer-client-cert-auth=true` to activate it
`--peer-cert-file`, `--peer-key-file`, `--peer-trusted-ca-file`
---
## etcd clients
- The only¹ thing that connects to etcd is the API server
- Authentication uses TLS certificates with a separate sub-CA
(for the same reasons as for etcd inter-peer authentication)
- The etcd command line flags involved are:
`--client-cert-auth=true` to activate it
`--trusted-ca-file`, `--cert-file`, `--key-file`
- The API server command line flags involved are:
`--etcd-cafile`, `--etcd-certfile`, `--etcd-keyfile`
.footnote[¹Technically, there is also the etcd healthcheck. Let's ignore it for now.]
---
## API server clients
- The API server has a sophisticated authentication and authorization system
- For connections coming from other components of the control plane:
- authentication uses certificates (trusting the certificates' subject or CN)
- authorization uses whatever mechanism is enabled (most oftentimes, RBAC)
- The relevant API server flags are:
`--client-ca-file`, `--tls-cert-file`, `--tls-private-key-file`
- Each component connecting to the API server takes a `--kubeconfig` flag
(to specify a kubeconfig file containing the CA cert, client key, and client cert)
- Yes, that kubeconfig file follows the same format as our `~/.kube/config` file!
---
## Kubelet and API server
- Communication between kubelet and API server can be established both ways
- Kubelet → API server:
- kubelet registers itself ("hi, I'm node42, do you have work for me?")
- connection is kept open and re-established if it breaks
- that's how the kubelet knows which pods to start/stop
- API server → kubelet:
- used to retrieve logs, exec, attach to containers
---
## Kubelet → API server
- Kubelet is started with `--kubeconfig` with API server information
- The client certificate of the kubelet will typically have:
`CN=system:node:<nodename>` and groups `O=system:nodes`
- Nothing special on the API server side
(it will authenticate like any other client)
---
## API server → kubelet
- Kubelet is started with the flag `--client-ca-file`
(typically using the same CA as the API server)
- API server will use a dedicated key pair when contacting kubelet
(specified with `--kubelet-client-certificate` and `--kubelet-client-key`)
- Authorization uses webhooks
(enabled with `--authorization-mode=Webhook` on kubelet)
- The webhook server is the API server itself
(the kubelet sends back a request to the API server to ask, "can this person do that?")
---
## Scheduler
- The scheduler connects to the API server like an ordinary client
- The certificate of the scheduler will have `CN=system:kube-scheduler`
---
## Controller manager
- The controller manager is also a normal client to the API server
- Its certificate will have `CN=system:kube-controller-manager`
- If we use the CSR API, the controller manager needs the CA cert and key
(passed with flags `--cluster-signing-cert-file` and `--cluster-signing-key-file`)
- We usually want the controller manager to generate tokens for service accounts
- These tokens deserve some details (on the next slide!)
---
## Service account tokens
- Each time we create a service account, the controller manager generates a token
- These tokens are JWT tokens, signed with a particular key
- These tokens are used for authentication with the API server
(and therefore, the API server needs to be able to verify their integrity)
- This uses another keypair:
- the private key (used for signature) is passed to the controller manager
<br/>(using flags `--service-account-private-key-file` and `--root-ca-file`)
- the public key (used for verification) is passed to the API server
<br/>(using flag `--service-account-key-file`)
---
## kube-proxy
- kube-proxy is "yet another API server client"
- In many clusters, it runs as a Daemon Set
- In that case, it will have its own Service Account and associated permissions
- It will authenticate using the token of that Service Account
---
## Webhooks
- We mentioned webhooks earlier; how does that really work?
- The Kubernetes API has special resource types to check permissions
- One of them is SubjectAccessReview
- To check if a particular user can do a particular action on a particular resource:
- we prepare a SubjectAccessReview object
- we send that object to the API server
- the API server responds with allow/deny (and optional explanations)
- Using webhooks for authorization = sending SAR to authorize each request
---
## Subject Access Review
Here is an example showing how to check if `jean.doe` can `get` some `pods` in `kube-system`:
```bash
kubectl -v9 create -f- <<EOF
apiVersion: authorization.k8s.io/v1beta1
kind: SubjectAccessReview
spec:
user: jean.doe
group:
- foo
- bar
resourceAttributes:
#group: blah.k8s.io
namespace: kube-system
resource: pods
verb: get
#name: web-xyz1234567-pqr89
EOF
```

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@@ -0,0 +1,367 @@
# Creating Helm charts
- We are going to create a generic Helm chart
- We will use that Helm chart to deploy DockerCoins
- Each component of DockerCoins will have its own *release*
- In other words, we will "install" that Helm chart multiple times
(one time per component of DockerCoins)
---
## Creating a generic chart
- Rather than starting from scratch, we will use `helm create`
- This will give us a basic chart that we will customize
.exercise[
- Create a basic chart:
```bash
cd ~
helm create helmcoins
```
]
This creates a basic chart in the directory `helmcoins`.
---
## What's in the basic chart?
- The basic chart will create a Deployment and a Service
- Optionally, it will also include an Ingress
- If we don't pass any values, it will deploy the `nginx` image
- We can override many things in that chart
- Let's try to deploy DockerCoins components with that chart!
---
## Writing `values.yaml` for our components
- We need to write one `values.yaml` file for each component
(hasher, redis, rng, webui, worker)
- We will start with the `values.yaml` of the chart, and remove what we don't need
- We will create 5 files:
hasher.yaml, redis.yaml, rng.yaml, webui.yaml, worker.yaml
---
## Getting started
- For component X, we want to use the image dockercoins/X:v0.1
(for instance, for rng, we want to use the image dockercoins/rng:v0.1)
- Exception: for redis, we want to use the official image redis:latest
.exercise[
- Write minimal YAML files for the 5 components, specifying only the image
]
--
*Hint: our YAML files should look like this.*
```yaml
### rng.yaml
image:
repository: dockercoins/`rng`
tag: v0.1
```
---
## Deploying DockerCoins components
- For convenience, let's work in a separate namespace
.exercise[
- Create a new namespace:
```bash
kubectl create namespace helmcoins
```
- Switch to that namespace:
```bash
kns helmcoins
```
]
---
## Deploying the chart
- To install a chart, we can use the following command:
```bash
helm install [--name `X`] <chart>
```
- We can also use the following command, which is idempotent:
```bash
helm upgrade --install `X` chart
```
.exercise[
- Install the 5 components of DockerCoins:
```bash
for COMPONENT in hasher redis rng webui worker; do
helm upgrade --install $COMPONENT helmcoins/ --values=$COMPONENT.yaml
done
```
]
---
## Checking what we've done
- Let's see if DockerCoins is working!
.exercise[
- Check the logs of the worker:
```bash
stern worker
```
- Look at the resources that were created:
```bash
kubectl get all
```
]
There are *many* issues to fix!
---
## Service names
- Our services should be named `rng`, `hasher`, etc., but they are named differently
- Look at the YAML template used for the services
- Does it look like we can override the name of the services?
--
- *Yes*, we can use `.Values.nameOverride`
- This means setting `nameOverride` in the values YAML file
---
## Setting service names
- Let's add `nameOverride: X` in each values YAML file!
(where X is hasher, redis, rng, etc.)
.exercise[
- Edit the 5 YAML files to add `nameOverride: X`
- Deploy the updated Chart:
```bash
for COMPONENT in hasher redis rng webui worker; do
helm upgrade --install $COMPONENT helmcoins/ --values=$COMPONENT.yaml
done
```
(Yes, this is exactly the same command as before!)
]
---
## Checking what we've done
.exercise[
- Check the service names:
```bash
kubectl get services
```
Great! (We have a useless service for `worker`, but let's ignore it for now.)
- Check the state of the pods:
```bash
kubectl get pods
```
Not so great... Some pods are *not ready.*
]
---
## Troubleshooting pods
- The easiest way to troubleshoot pods is to look at *events*
- We can look at all the events on the cluster (with `kubectl get events`)
- Or we can use `kubectl describe` on the objects that have problems
(`kubectl describe` will retrieve the events related to the object)
.exercise[
- Check the events for the redis pods:
```bash
kubectl describe pod -l app.kubernetes.io/name=redis
```
]
What's going on?
---
## Healthchecks
- The default chart defines healthchecks doing HTTP requests on port 80
- That won't work for redis and worker
(redis is not HTTP, and not on port 80; worker doesn't even listen)
--
- We could comment out the healthchecks
- We could also make them conditional
- This sounds more interesting, let's do that!
---
## Conditionals
- We need to enclose the healthcheck block with:
`{{ if CONDITION }}` at the beginning
`{{ end }}` at the end
- For the condition, we will use `.Values.healthcheck`
---
## Updating the deployment template
.exercise[
- Edit `helmcoins/templates/deployment.yaml`
- Before the healthchecks section (it starts with `livenessProbe:`), add:
`{{ if .Values.healthcheck }}`
- After the healthchecks section (just before `resources:`), add:
`{{ end }}`
- Edit `hasher.yaml`, `rng.yaml`, `webui.yaml` to add:
`healthcheck: true`
]
---
## Update the deployed charts
- We can now apply the new templates (and the new values)
.exercise[
- Use the same command as earlier to upgrade all five components
- Use `kubectl describe` to confirm that `redis` starts correctly
- Use `kubectl describe` to confirm that `hasher` still has healthchecks
]
---
## Is it working now?
- If we look at the worker logs, it appears that the worker is still stuck
- What could be happening?
--
- The redis service is not on port 80!
- We need to update the port number in redis.yaml
- We also need to update the port number in deployment.yaml
(it is hard-coded to 80 there)
---
## Setting the redis port
.exercise[
- Edit `redis.yaml` to add:
```yaml
service:
port: 6379
```
- Edit `helmcoins/templates/deployment.yaml`
- The line with `containerPort` should be:
```yaml
containerPort: {{ .Values.service.port }}
```
]
---
## Apply changes
- Re-run the for loop to execute `helm upgrade` one more time
- Check the worker logs
- This time, it should be working!
---
## Extra steps
- We don't need to create a service for the worker
- We can put the whole service block in a conditional
(this will require additional changes in other files referencing the service)
- We can set the webui to be a NodePort service
- We can change the number of workers with `replicaCount`
- And much more!

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@@ -0,0 +1,393 @@
## Questions to ask before adding healthchecks
- Do we want liveness, readiness, both?
(sometimes, we can use the same check, but with different failure thresholds)
- Do we have existing HTTP endpoints that we can use?
- Do we need to add new endpoints, or perhaps use something else?
- Are our healthchecks likely to use resources and/or slow down the app?
- Do they depend on additional services?
(this can be particularly tricky, see next slide)
---
## Healthchecks and dependencies
- A good healthcheck should always indicate the health of the service itself
- It should not be affected by the state of the service's dependencies
- Example: a web server requiring a database connection to operate
(make sure that the healthcheck can report "OK" even if the database is down;
<br/>
because it won't help us to restart the web server if the issue is with the DB!)
- Example: a microservice calling other microservices
- Example: a worker process
(these will generally require minor code changes to report health)
---
## Adding healthchecks to an app
- Let's add healthchecks to DockerCoins!
- We will examine the questions of the previous slide
- Then we will review each component individually to add healthchecks
---
## Liveness, readiness, or both?
- To answer that question, we need to see the app run for a while
- Do we get temporary, recoverable glitches?
→ then use readiness
- Or do we get hard lock-ups requiring a restart?
→ then use liveness
- In the case of DockerCoins, we don't know yet!
- Let's pick liveness
---
## Do we have HTTP endpoints that we can use?
- Each of the 3 web services (hasher, rng, webui) has a trivial route on `/`
- These routes:
- don't seem to perform anything complex or expensive
- don't seem to call other services
- Perfect!
(See next slides for individual details)
---
- [hasher.rb](https://github.com/jpetazzo/container.training/blob/master/dockercoins/hasher/hasher.rb)
```ruby
get '/' do
"HASHER running on #{Socket.gethostname}\n"
end
```
- [rng.py](https://github.com/jpetazzo/container.training/blob/master/dockercoins/rng/rng.py)
```python
@app.route("/")
def index():
return "RNG running on {}\n".format(hostname)
```
- [webui.js](https://github.com/jpetazzo/container.training/blob/master/dockercoins/webui/webui.js)
```javascript
app.get('/', function (req, res) {
res.redirect('/index.html');
});
```
---
## Running DockerCoins
- We will run DockerCoins in a new, separate namespace
- We will use a set of YAML manifests and pre-built images
- We will add our new liveness probe to the YAML of the `rng` DaemonSet
- Then, we will deploy the application
---
## Creating a new namespace
- This will make sure that we don't collide / conflict with previous exercises
.exercise[
- Create the yellow namespace:
```bash
kubectl create namespace yellow
```
- Switch to that namespace:
```bash
kns yellow
```
]
---
## Retrieving DockerCoins manifests
- All the manifests that we need are on a convenient repository:
https://github.com/jpetazzo/kubercoins
.exercise[
- Clone that repository:
```bash
cd ~
git clone https://github.com/jpetazzo/kubercoins
```
- Change directory to the repository:
```bash
cd kubercoins
```
]
---
## A simple HTTP liveness probe
This is what our liveness probe should look like:
```yaml
containers:
- name: ...
image: ...
livenessProbe:
httpGet:
path: /
port: 80
initialDelaySeconds: 30
periodSeconds: 5
```
This will give 30 seconds to the service to start. (Way more than necessary!)
<br/>
It will run the probe every 5 seconds.
<br/>
It will use the default timeout (1 second).
<br/>
It will use the default failure threshold (3 failed attempts = dead).
<br/>
It will use the default success threshold (1 successful attempt = alive).
---
## Adding the liveness probe
- Let's add the liveness probe, then deploy DockerCoins
.exercise[
- Edit `rng-daemonset.yaml` and add the liveness probe
```bash
vim rng-daemonset.yaml
```
- Load the YAML for all the resources of DockerCoins:
```bash
kubectl apply -f .
```
]
---
## Testing the liveness probe
- The rng service needs 100ms to process a request
(because it is single-threaded and sleeps 0.1s in each request)
- The probe timeout is set to 1 second
- If we send more than 10 requests per second per backend, it will break
- Let's generate traffic and see what happens!
.exercise[
- Get the ClusterIP address of the rng service:
```bash
kubectl get svc rng
```
]
---
## Monitoring the rng service
- Each command below will show us what's happening on a different level
.exercise[
- In one window, monitor cluster events:
```bash
kubectl get events -w
```
- In another window, monitor the response time of rng:
```bash
httping `<ClusterIP>`
```
- In another window, monitor pods status:
```bash
kubectl get pods -w
```
]
---
## Generating traffic
- Let's use `ab` to send concurrent requests to rng
.exercise[
- In yet another window, generate traffic:
```bash
ab -c 10 -n 1000 http://`<ClusterIP>`/1
```
- Experiment with higher values of `-c` and see what happens
]
- The `-c` parameter indicates the number of concurrent requests
- The final `/1` is important to generate actual traffic
(otherwise we would use the ping endpoint, which doesn't sleep 0.1s per request)
---
## Discussion
- Above a given threshold, the liveness probe starts failing
(about 10 concurrent requests per backend should be plenty enough)
- When the liveness probe fails 3 times in a row, the container is restarted
- During the restart, there is *less* capacity available
- ... Meaning that the other backends are likely to timeout as well
- ... Eventually causing all backends to be restarted
- ... And each fresh backend gets restarted, too
- This goes on until the load goes down, or we add capacity
*This wouldn't be a good healthcheck in a real application!*
---
## Better healthchecks
- We need to make sure that the healthcheck doesn't trip when
performance degrades due to external pressure
- Using a readiness check would have lesser effects
(but it still would be an imperfect solution)
- A possible combination:
- readiness check with a short timeout / low failure threshold
- liveness check with a longer timeout / higher failure treshold
---
## Healthchecks for redis
- A liveness probe is enough
(it's not useful to remove a backend from rotation when it's the only one)
- We could use an exec probe running `redis-cli ping`
---
class: extra-details
## Exec probes and zombies
- When using exec probes, we should make sure that we have a *zombie reaper*
🤔🧐🧟 Wait, what?
- When a process terminates, its parent must call `wait()`/`waitpid()`
(this is how the parent process retrieves the child's exit status)
- In the meantime, the process is in *zombie* state
(the process state will show as `Z` in `ps`, `top` ...)
- When a process is killed, its children are *orphaned* and attached to PID 1
- PID 1 has the responsibility if *reaping* these processes when they terminate
- OK, but how does that affect us?
---
class: extra-details
## PID 1 in containers
- On ordinary systems, PID 1 (`/sbin/init`) has logic to reap processes
- In containers, PID 1 is typically our application process
(e.g. Apache, the JVM, NGINX, Redis ...)
- These *do not* take care of reaping orphans
- If we use exec probes, we need to add a process reaper
- We can add [tini](https://github.com/krallin/tini) to our images
- Or [share the PID namespace between containers of a pod](https://kubernetes.io/docs/tasks/configure-pod-container/share-process-namespace/)
(and have gcr.io/pause take care of the reaping)
---
## Healthchecks for worker
- Readiness isn't useful
(because worker isn't a backend for a service)
- Liveness may help us to restart a broken worker, but how can we check it?
- Embedding an HTTP server is an option
(but it has a high potential for unwanted side-effects and false positives)
- Using a "lease" file can be relatively easy:
- touch a file during each iteration of the main loop
- check the timestamp of that file from an exec probe
- Writing logs (and checking them from the probe) also works

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## What does it take to write an operator?
- Writing a quick-and-dirty operator, or a POC/MVP, is easy
- Writing a robust operator is hard
- We will describe the general idea
- We will identify some of the associated challenges
- We will list a few tools that can help us
---
## Top-down vs. bottom-up
- Both approaches are possible
- Let's see what they entail, and their respective pros and cons
---
## Top-down approach
- Start with high-level design (see next slide)
- Pros:
- can yield cleaner design that will be more robust
- Cons:
- must be able to anticipate all the events that might happen
- design will be better only to the extend of what we anticipated
- hard to anticipate if we don't have production experience
---
## High-level design
- What are we solving?
(e.g.: geographic databases backed by PostGIS with Redis caches)
- What are our use-cases, stories?
(e.g.: adding/resizing caches and read replicas; load balancing queries)
- What kind of outage do we want to address?
(e.g.: loss of individual node, pod, volume)
- What are our *non-features*, the things we don't want to address?
(e.g.: loss of datacenter/zone; differentiating between read and write queries;
<br/>
cache invalidation; upgrading to newer major versions of Redis, PostGIS, PostgreSQL)
---
## Low-level design
- What Custom Resource Definitions do we need?
(one, many?)
- How will we store configuration information?
(part of the CRD spec fields, annotations, other?)
- Do we need to store state? If so, where?
- state that is small and doesn't change much can be stored via the Kubernetes API
<br/>
(e.g.: leader information, configuration, credentials)
- things that are big and/or change a lot should go elsewhere
<br/>
(e.g.: metrics, bigger configuration file like GeoIP)
---
class: extra-details
## What can we store via the Kubernetes API?
- The API server stores most Kubernetes resources into etcd
- Etcd is designed for reliability, not for performance
- If our storage needs exceed what etcd can offer, we need to use something else:
- either directly
- or by extending the API server
<br/>(for instance by using the agregation layer, like [metrics server](https://github.com/kubernetes-incubator/metrics-server) does)
---
## Bottom-up approach
- Start with existing Kubernetes resources (Deployment, Stateful Set...)
- Run the system in production
- Add scripts, automation, to facilitate day-to-day operations
- Turn the scripts into an operator
- Pros: simpler to get started; reflects actual use-cases
- Cons: can result in convoluted designs requiring extensive refactor
---
## General idea
- Our operator will watch its CRDs *and associated resources*
- Drawing state diagrams and finite state automata helps a lot
- It's OK if some transitions lead to a big catch-all "human intervention"
- Over time, we will learn about new failure modes and add to these diagrams
- It's OK to start with CRD creation / deletion and prevent any modification
(that's the easy POC/MVP we were talking about)
- *Presentation* and *validation* will help our users
(more on that later)
---
## Challenges
- Reacting to infrastructure disruption can seem hard at first
- Kubernetes gives us a lot of primitives to help:
- Pods and Persistent Volumes will *eventually* recover
- Stateful Sets give us easy ways to "add N copies" of a thing
- The real challenges come with configuration changes
(i.e., what to do when our users update our CRDs)
- Keep in mind that [some] of the [largest] cloud [outages] haven't been caused by [natural catastrophes], or even code bugs, but by configuration changes
[some]: https://www.datacenterdynamics.com/news/gcp-outage-mainone-leaked-google-cloudflare-ip-addresses-china-telecom/
[largest]: https://aws.amazon.com/message/41926/
[outages]: https://aws.amazon.com/message/65648/
[natural catastrophes]: https://www.datacenterknowledge.com/amazon/aws-says-it-s-never-seen-whole-data-center-go-down
---
## Configuration changes
- It is helpful to analyze and understand how Kubernetes controllers work:
- watch resource for modifications
- compare desired state (CRD) and current state
- issue actions to converge state
- Configuration changes will probably require *another* state diagram or FSA
- Again, it's OK to have transitions labeled as "unsupported"
(i.e. reject some modifications because we can't execute them)
---
## Tools
- CoreOS / RedHat Operator Framework
[GitHub](https://github.com/operator-framework)
|
[Blog](https://developers.redhat.com/blog/2018/12/18/introduction-to-the-kubernetes-operator-framework/)
|
[Intro talk](https://www.youtube.com/watch?v=8k_ayO1VRXE)
|
[Deep dive talk](https://www.youtube.com/watch?v=fu7ecA2rXmc)
- Zalando Kubernetes Operator Pythonic Framework (KOPF)
[GitHub](https://github.com/zalando-incubator/kopf)
|
[Docs](https://kopf.readthedocs.io/)
|
[Step-by-step tutorial](https://kopf.readthedocs.io/en/stable/walkthrough/problem/)
- Mesosphere Kubernetes Universal Declarative Operator (KUDO)
[GitHub](https://github.com/kudobuilder/kudo)
|
[Blog](https://mesosphere.com/blog/announcing-maestro-a-declarative-no-code-approach-to-kubernetes-day-2-operators/)
|
[Docs](https://kudo.dev/)
|
[Zookeeper example](https://github.com/kudobuilder/frameworks/tree/master/repo/stable/zookeeper)
---
## Validation
- By default, a CRD is "free form"
(we can put pretty much anything we want in it)
- When creating a CRD, we can provide an OpenAPI v3 schema
([Example](https://github.com/amaizfinance/redis-operator/blob/master/deploy/crds/k8s_v1alpha1_redis_crd.yaml#L34))
- The API server will then validate resources created/edited with this schema
- If we need a stronger validation, we can use a Validating Admission Webhook:
- run an [admission webhook server](https://kubernetes.io/docs/reference/access-authn-authz/extensible-admission-controllers/#write-an-admission-webhook-server) to receive validation requests
- register the webhook by creating a [ValidatingWebhookConfiguration](https://kubernetes.io/docs/reference/access-authn-authz/extensible-admission-controllers/#configure-admission-webhooks-on-the-fly)
- each time the API server receives a request matching the configuration,
<br/>the request is sent to our server for validation
---
## Presentation
- By default, `kubectl get mycustomresource` won't display much information
(just the name and age of each resource)
- When creating a CRD, we can specify additional columns to print
([Example](https://github.com/amaizfinance/redis-operator/blob/master/deploy/crds/k8s_v1alpha1_redis_crd.yaml#L6),
[Docs](https://kubernetes.io/docs/tasks/access-kubernetes-api/custom-resources/custom-resource-definitions/#additional-printer-columns))
- By default, `kubectl describe mycustomresource` will also be generic
- `kubectl describe` can show events related to our custom resources
(for that, we need to create Event resources, and fill the `involvedObject` field)
- For scalable resources, we can define a `scale` sub-resource
- This will enable the use of `kubectl scale` and other scaling-related operations
---
## About scaling
- It is possible to use the HPA (Horizontal Pod Autoscaler) with CRDs
- But it is not always desirable
- The HPA works very well for homogenous, stateless workloads
- For other workloads, your mileage may vary
- Some systems can scale across multiple dimensions
(for instance: increase number of replicas, or number of shards?)
- If autoscaling is desired, the operator will have to take complex decisions
(example: Zalando's Elasticsearch Operator ([Video](https://www.youtube.com/watch?v=lprE0J0kAq0)))
---
## Versioning
- As our operator evolves over time, we may have to change the CRD
(add, remove, change fields)
- Like every other resource in Kubernetes, [custom resources are versioned](https://kubernetes.io/docs/tasks/access-kubernetes-api/custom-resources/custom-resource-definition-versioning/
)
- When creating a CRD, we need to specify a *list* of versions
- Versions can be marked as `stored` and/or `served`
---
## Stored version
- Exactly one version has to be marked as the `stored` version
- As the name implies, it is the one that will be stored in etcd
- Resources in storage are never converted automatically
(we need to read and re-write them ourselves)
- Yes, this means that we can have different versions in etcd at any time
- Our code needs to handle all the versions that still exist in storage
---
## Served versions
- By default, the Kubernetes API will serve resources "as-is"
(using their stored version)
- It will assume that all versions are compatible storage-wise
(i.e. that the spec and fields are compatible between versions)
- We can provide [conversion webhooks](https://kubernetes.io/docs/tasks/access-kubernetes-api/custom-resources/custom-resource-definition-versioning/#webhook-conversion) to "translate" requests
(the alternative is to upgrade all stored resources and stop serving old versions)
---
## Operator reliability
- Remember that the operator itself must be resilient
(e.g.: the node running it can fail)
- Our operator must be able to restart and recover gracefully
- Do not store state locally
(unless we can reconstruct that state when we restart)
- As indicated earlier, we can use the Kubernetes API to store data:
- in the custom resources themselves
- in other resources' annotations
---
## Beyond CRDs
- CRDs cannot use custom storage (e.g. for time series data)
- CRDs cannot support arbitrary subresources (like logs or exec for Pods)
- CRDs cannot support protobuf (for faster, more efficient communication)
- If we need these things, we can use the [aggregation layer](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/) instead
- The aggregation layer proxies all requests below a specific path to another server
(this is used e.g. by the metrics server)
- [This documentation page](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/#choosing-a-method-for-adding-custom-resources) compares the features of CRDs and API aggregation

389
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# Operators
- Operators are one of the many ways to extend Kubernetes
- We will define operators
- We will see how they work
- We will install a specific operator (for ElasticSearch)
- We will use it to provision an ElasticSearch cluster
---
## What are operators?
*An operator represents **human operational knowledge in software,**
<br/>
to reliably manage an application.
— [CoreOS](https://coreos.com/blog/introducing-operators.html)*
Examples:
- Deploying and configuring replication with MySQL, PostgreSQL ...
- Setting up Elasticsearch, Kafka, RabbitMQ, Zookeeper ...
- Reacting to failures when intervention is needed
- Scaling up and down these systems
---
## What are they made from?
- Operators combine two things:
- Custom Resource Definitions
- controller code watching the corresponding resources and acting upon them
- A given operator can define one or multiple CRDs
- The controller code (control loop) typically runs within the cluster
(running as a Deployment with 1 replica is a common scenario)
- But it could also run elsewhere
(nothing mandates that the code run on the cluster, as long as it has API access)
---
## Why use operators?
- Kubernetes gives us Deployments, StatefulSets, Services ...
- These mechanisms give us building blocks to deploy applications
- They work great for services that are made of *N* identical containers
(like stateless ones)
- They also work great for some stateful applications like Consul, etcd ...
(with the help of highly persistent volumes)
- They're not enough for complex services:
- where different containers have different roles
- where extra steps have to be taken when scaling or replacing containers
---
## Use-cases for operators
- Systems with primary/secondary replication
Examples: MariaDB, MySQL, PostgreSQL, Redis ...
- Systems where different groups of nodes have different roles
Examples: ElasticSearch, MongoDB ...
- Systems with complex dependencies (that are themselves managed with operators)
Examples: Flink or Kafka, which both depend on Zookeeper
---
## More use-cases
- Representing and managing external resources
(Example: [AWS Service Operator](https://operatorhub.io/operator/alpha/aws-service-operator.v0.0.1))
- Managing complex cluster add-ons
(Example: [Istio operator](https://operatorhub.io/operator/beta/istio-operator.0.1.6))
- Deploying and managing our applications' lifecycles
(more on that later)
---
## How operators work
- An operator creates one or more CRDs
(i.e., it creates new "Kinds" of resources on our cluster)
- The operator also runs a *controller* that will watch its resources
- Each time we create/update/delete a resource, the controller is notified
(we could write our own cheap controller with `kubectl get --watch`)
---
## One operator in action
- We will install the UPMC Enterprises ElasticSearch operator
- This operator requires PersistentVolumes
- We will install Rancher's [local path storage provisioner](https://github.com/rancher/local-path-provisioner) to automatically create these
- Then, we will create an ElasticSearch resource
- The operator will detect that resource and provision the cluster
---
## Installing a Persistent Volume provisioner
(This step can be skipped if you already have a dynamic volume provisioner.)
- This provisioner creates Persistent Volumes backed by `hostPath`
(local directories on our nodes)
- It doesn't require anything special ...
- ... But losing a node = losing the volumes on that node!
.exercise[
- Install the local path storage provisioner:
```bash
kubectl apply -f ~/container.training/k8s/local-path-storage.yaml
```
]
---
## Making sure we have a default StorageClass
- The ElasticSearch operator will create StatefulSets
- These StatefulSets will instantiate PersistentVolumeClaims
- These PVCs need to be explicitly associated with a StorageClass
- Or we need to tag a StorageClass to be used as the default one
.exercise[
- List StorageClasses:
```bash
kubectl get storageclasses
```
]
We should see the `local-path` StorageClass.
---
## Setting a default StorageClass
- This is done by adding an annotation to the StorageClass:
`storageclass.kubernetes.io/is-default-class: true`
.exercise[
- Tag the StorageClass so that it's the default one:
```bash
kubectl annotate storageclass local-path \
storageclass.kubernetes.io/is-default-class=true
```
- Check the result:
```bash
kubectl get storageclasses
```
]
Now, the StorageClass should have `(default)` next to its name.
---
## Install the ElasticSearch operator
- The operator needs:
- a Deployment for its controller
- a ServiceAccount, ClusterRole, ClusterRoleBinding for permissions
- a Namespace
- We have grouped all the definitions for these resources in a YAML file
.exercise[
- Install the operator:
```bash
kubectl apply -f ~/container.training/k8s/elasticsearch-operator.yaml
```
]
---
## Wait for the operator to be ready
- Some operators require to create their CRDs separately
- This operator will create its CRD itself
(i.e. the CRD is not listed in the YAML that we applied earlier)
.exercise[
- Wait until the `elasticsearchclusters` CRD shows up:
```bash
kubectl get crds
```
]
---
## Create an ElasticSearch resource
- We can now create a resource with `kind: ElasticsearchCluster`
- The YAML for that resource will specify all the desired parameters:
- how many nodes do we want of each type (client, master, data)
- image to use
- add-ons (kibana, cerebro, ...)
- whether to use TLS or not
- etc.
.exercise[
- Create our ElasticSearch cluster:
```bash
kubectl apply -f ~/container.training/k8s/elasticsearch-cluster.yaml
```
]
---
## Operator in action
- Over the next minutes, the operator will create:
- StatefulSets (one for master nodes, one for data nodes)
- Deployments (for client nodes; and for add-ons like cerebro and kibana)
- Services (for all these pods)
.exercise[
- Wait for all the StatefulSets to be fully up and running:
```bash
kubectl get statefulsets -w
```
]
---
## Connecting to our cluster
- Since connecting directly to the ElasticSearch API is a bit raw,
<br/>we'll connect to the cerebro frontend instead
.exercise[
- Edit the cerebro service to change its type from ClusterIP to NodePort:
```bash
kubectl patch svc cerebro-es -p "spec: { type: NodePort }"
```
- Retrieve the NodePort that was allocated:
```bash
kubectl get svc cerebreo-es
```
- Connect to that port with a browser
]
---
## (Bonus) Setup filebeat
- Let's send some data to our brand new ElasticSearch cluster!
- We'll deploy a filebeat DaemonSet to collect node logs
.exercise[
- Deploy filebeat:
```bash
kubectl apply -f ~/container.training/k8s/filebeat.yaml
```
]
We should see at least one index being created in cerebro.
---
## (Bonus) Access log data with kibana
- Let's expose kibana (by making kibana-es a NodePort too)
- Then access kibana
- We'll need to configure kibana indexes
---
## Deploying our apps with operators
- It is very simple to deploy with `kubectl run` / `kubectl expose`
- We can unlock more features by writing YAML and using `kubectl apply`
- Kustomize or Helm let us deploy in multiple environments
(and adjust/tweak parameters in each environment)
- We can also use an operator to deploy our application
---
## Pros and cons of deploying with operators
- The app definition and configuration is persisted in the Kubernetes API
- Multiple instances of the app can be manipulated with `kubectl get`
- We can add labels, annotations to the app instances
- Our controller can execute custom code for any lifecycle event
- However, we need to write this controller
- We need to be careful about changes
(what happens when the resource `spec` is updated?)
---
## Operators are not magic
- Look at the ElasticSearch resource definition
(`~/container.training/k8s/elasticsearch-cluster.yaml`)
- What should happen if we flip the `use-tls` flag? Twice?
- What should happen if we remove / re-add the kibana or cerebro sections?
- What should happen if we change the number of nodes?
- What if we want different images or parameters for the different nodes?
*Operators can be very powerful, iff we know exactly the scenarios that they can handle.*

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# Highly available Persistent Volumes
# Highly available Persistent Volumes (extra material)
- How can we achieve true durability?

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@@ -1,44 +0,0 @@
title: |
Kubernetes
for Admins and Ops
#chat: "[Slack](https://dockercommunity.slack.com/messages/C7GKACWDV)"
#chat: "[Gitter](https://gitter.im/jpetazzo/workshop-yyyymmdd-city)"
chat: "In person!"
gitrepo: github.com/jpetazzo/container.training
slides: http://container.training/
exclude:
- self-paced
- static-pods-exercise
chapters:
- shared/title.md
- logistics.md
- k8s/intro.md
- shared/about-slides.md
- shared/toc.md
- - k8s/prereqs-admin.md
- k8s/architecture.md
- k8s/dmuc.md
- - k8s/multinode.md
- k8s/cni.md
- k8s/apilb.md
#FIXME: check le talk de Laurent Corbes pour voir s'il y a d'autres choses utiles à mentionner
#BONUS: intégration CoreDNS pour résoudre les noms des clusters des voisins
- - k8s/setup-managed.md
- k8s/setup-selfhosted.md
- k8s/cluster-upgrade.md
- k8s/staticpods.md
- k8s/cluster-backup.md
- k8s/cloud-controller-manager.md
- k8s/bootstrap.md
- - k8s/resource-limits.md
- k8s/metrics-server.md
- k8s/cluster-sizing.md
- k8s/horizontal-pod-autoscaler.md
- - k8s/lastwords-admin.md
- k8s/links.md
- shared/thankyou.md

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@@ -1,77 +0,0 @@
title: |
Deploying and Scaling Microservices
with Kubernetes
#chat: "[Slack](https://dockercommunity.slack.com/messages/C7GKACWDV)"
#chat: "[Gitter](https://gitter.im/jpetazzo/workshop-yyyymmdd-city)"
chat: "In person!"
gitrepo: github.com/jpetazzo/container.training
slides: http://container.training/
exclude:
- self-paced
chapters:
- shared/title.md
- logistics.md
- k8s/intro.md
- shared/about-slides.md
- shared/toc.md
- - shared/prereqs.md
- shared/connecting.md
- k8s/versions-k8s.md
- shared/sampleapp.md
#- shared/composescale.md
#- shared/hastyconclusions.md
- shared/composedown.md
- k8s/concepts-k8s.md
- shared/declarative.md
- k8s/declarative.md
- k8s/kubenet.md
- - k8s/kubectlget.md
- k8s/setup-k8s.md
- k8s/kubectlrun.md
- k8s/deploymentslideshow.md
- k8s/kubectlexpose.md
- - k8s/shippingimages.md
#- k8s/buildshiprun-selfhosted.md
- k8s/buildshiprun-dockerhub.md
- k8s/ourapponkube.md
#- k8s/kubectlproxy.md
#- k8s/localkubeconfig.md
#- k8s/accessinternal.md
- k8s/dashboard.md
#- k8s/kubectlscale.md
- k8s/scalingdockercoins.md
- shared/hastyconclusions.md
- k8s/daemonset.md
- - k8s/rollout.md
- k8s/namespaces.md
#- k8s/kustomize.md
#- k8s/helm.md
#- k8s/create-chart.md
#- k8s/healthchecks.md
- k8s/logs-cli.md
- k8s/logs-centralized.md
#- k8s/netpol.md
#- k8s/authn-authz.md
#- k8s/csr-api.md
#- k8s/podsecuritypolicy.md
#- k8s/ingress.md
#- k8s/gitworkflows.md
- k8s/prometheus.md
#- k8s/volumes.md
#- k8s/build-with-docker.md
#- k8s/build-with-kaniko.md
#- k8s/configuration.md
#- k8s/owners-and-dependents.md
#- k8s/extending-api.md
#- k8s/statefulsets.md
#- k8s/local-persistent-volumes.md
#- k8s/portworx.md
#- k8s/staticpods.md
- - k8s/whatsnext.md
- k8s/links.md
- shared/thankyou.md

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@@ -1,66 +0,0 @@
title: |
Kubernetes 101
#chat: "[Slack](https://dockercommunity.slack.com/messages/C7GKACWDV)"
#chat: "[Gitter](https://gitter.im/jpetazzo/training-20180413-paris)"
chat: "In person!"
gitrepo: github.com/jpetazzo/container.training
slides: http://container.training/
exclude:
- self-paced
chapters:
- shared/title.md
#- logistics.md
# Bridget-specific; others use logistics.md
- logistics-bridget.md
- k8s/intro.md
- shared/about-slides.md
- shared/toc.md
- - shared/prereqs.md
- shared/connecting.md
- k8s/versions-k8s.md
- shared/sampleapp.md
# Bridget doesn't go into as much depth with compose
#- shared/composescale.md
#- shared/hastyconclusions.md
- shared/composedown.md
- k8s/concepts-k8s.md
- shared/declarative.md
- k8s/declarative.md
- k8s/kubenet.md
- k8s/kubectlget.md
- k8s/setup-k8s.md
- - k8s/kubectlrun.md
- k8s/deploymentslideshow.md
- k8s/kubectlexpose.md
- k8s/shippingimages.md
#- k8s/buildshiprun-selfhosted.md
- k8s/buildshiprun-dockerhub.md
- k8s/ourapponkube.md
#- k8s/kubectlproxy.md
#- k8s/localkubeconfig.md
#- k8s/accessinternal.md
- - k8s/dashboard.md
#- k8s/kubectlscale.md
- k8s/scalingdockercoins.md
- shared/hastyconclusions.md
- k8s/daemonset.md
- k8s/rollout.md
- - k8s/logs-cli.md
# Bridget hasn't added EFK yet
#- k8s/logs-centralized.md
- k8s/namespaces.md
- k8s/helm.md
- k8s/create-chart.md
#- k8s/kustomize.md
#- k8s/netpol.md
- k8s/whatsnext.md
# - k8s/links.md
# Bridget-specific
- k8s/links-bridget.md
- shared/thankyou.md

View File

@@ -1,77 +0,0 @@
title: |
Deploying and Scaling Microservices
with Docker and Kubernetes
chat: "[Slack](https://dockercommunity.slack.com/messages/C7GKACWDV)"
#chat: "[Gitter](https://gitter.im/jpetazzo/workshop-yyyymmdd-city)"
gitrepo: github.com/jpetazzo/container.training
slides: http://container.training/
exclude:
- in-person
chapters:
- shared/title.md
#- logistics.md
- k8s/intro.md
- shared/about-slides.md
- shared/toc.md
- - shared/prereqs.md
- shared/connecting.md
- k8s/versions-k8s.md
- shared/sampleapp.md
- shared/composescale.md
- shared/hastyconclusions.md
- shared/composedown.md
- k8s/concepts-k8s.md
- shared/declarative.md
- k8s/declarative.md
- - k8s/kubenet.md
- k8s/kubectlget.md
- k8s/setup-k8s.md
- k8s/kubectlrun.md
- k8s/deploymentslideshow.md
- - k8s/kubectlexpose.md
- k8s/shippingimages.md
- k8s/buildshiprun-selfhosted.md
- k8s/buildshiprun-dockerhub.md
- k8s/ourapponkube.md
- k8s/kubectlproxy.md
- k8s/localkubeconfig.md
- k8s/accessinternal.md
- k8s/dashboard.md
- - k8s/kubectlscale.md
# - k8s/scalingdockercoins.md
# - shared/hastyconclusions.md
- k8s/daemonset.md
- k8s/rollout.md
- k8s/namespaces.md
- - k8s/kustomize.md
- k8s/helm.md
- k8s/create-chart.md
- k8s/healthchecks.md
- k8s/logs-cli.md
- k8s/logs-centralized.md
- - k8s/netpol.md
- k8s/authn-authz.md
- k8s/csr-api.md
- k8s/podsecuritypolicy.md
- - k8s/ingress.md
- k8s/gitworkflows.md
- k8s/prometheus.md
- - k8s/volumes.md
- k8s/build-with-docker.md
- k8s/build-with-kaniko.md
- k8s/configuration.md
- - k8s/owners-and-dependents.md
- k8s/extending-api.md
- - k8s/statefulsets.md
- k8s/local-persistent-volumes.md
- k8s/portworx.md
- k8s/staticpods.md
- - k8s/whatsnext.md
- k8s/links.md
- shared/thankyou.md

View File

@@ -1,77 +0,0 @@
title: |
Deploying and Scaling Microservices
with Kubernetes
#chat: "[Slack](https://dockercommunity.slack.com/messages/C7GKACWDV)"
#chat: "[Gitter](https://gitter.im/jpetazzo/workshop-yyyymmdd-city)"
chat: "In person!"
gitrepo: github.com/jpetazzo/container.training
slides: http://container.training/
exclude:
- self-paced
chapters:
- shared/title.md
- logistics.md
- k8s/intro.md
- shared/about-slides.md
- shared/toc.md
- - shared/prereqs.md
- shared/connecting.md
- k8s/versions-k8s.md
- shared/sampleapp.md
#- shared/composescale.md
#- shared/hastyconclusions.md
- shared/composedown.md
- k8s/concepts-k8s.md
- shared/declarative.md
- k8s/declarative.md
- k8s/kubenet.md
- - k8s/kubectlget.md
- k8s/setup-k8s.md
- k8s/kubectlrun.md
- k8s/deploymentslideshow.md
- k8s/kubectlexpose.md
- - k8s/shippingimages.md
#- k8s/buildshiprun-selfhosted.md
- k8s/buildshiprun-dockerhub.md
- k8s/ourapponkube.md
- k8s/kubectlproxy.md
- k8s/localkubeconfig.md
- k8s/accessinternal.md
- k8s/dashboard.md
#- k8s/kubectlscale.md
- k8s/scalingdockercoins.md
- shared/hastyconclusions.md
- - k8s/daemonset.md
- k8s/rollout.md
- k8s/namespaces.md
- k8s/kustomize.md
#- k8s/helm.md
#- k8s/create-chart.md
- - k8s/healthchecks.md
- k8s/logs-cli.md
- k8s/logs-centralized.md
#- k8s/netpol.md
- k8s/authn-authz.md
- k8s/csr-api.md
- k8s/podsecuritypolicy.md
- - k8s/ingress.md
#- k8s/gitworkflows.md
- k8s/prometheus.md
- - k8s/volumes.md
#- k8s/build-with-docker.md
#- k8s/build-with-kaniko.md
- k8s/configuration.md
#- k8s/owners-and-dependents.md
#- k8s/extending-api.md
- - k8s/statefulsets.md
- k8s/local-persistent-volumes.md
- k8s/portworx.md
#- k8s/staticpods.md
- - k8s/whatsnext.md
- k8s/links.md
- shared/thankyou.md

View File

@@ -1,30 +1,14 @@
## Intros
- This slide should be customized by the tutorial instructor(s).
- Hello! We are:
- .emoji[👩🏻‍🏫] Ann O'Nymous ([@...](https://twitter.com/...), Megacorp Inc)
- .emoji[👷🏻‍♀️] AJ ([@s0ulshake](https://twitter.com/s0ulshake), Tiny Shell Script LLC)
- .emoji[👨🏾‍🎓] Stu Dent ([@...](https://twitter.com/...), University of Wakanda)
- .emoji[🐳] Jérôme ([@jpetazzo](https://twitter.com/jpetazzo), Ardan Labs LLC)
<!-- .dummy[
- The training will run from 9am to 5pm
- .emoji[👷🏻‍♀️] AJ ([@s0ulshake](https://twitter.com/s0ulshake), Travis CI)
- .emoji[🚁] Alexandre ([@alexbuisine](https://twitter.com/alexbuisine), Enix SAS)
- .emoji[🐳] Jérôme ([@jpetazzo](https://twitter.com/jpetazzo), Enix SAS)
- .emoji[⛵] Jérémy ([@jeremygarrouste](twitter.com/jeremygarrouste), Inpiwee)
- .emoji[🎧] Romain ([@rdegez](https://twitter.com/rdegez), Enix SAS)
] -->
- The workshop will run from ...
- There will be a lunch break at ...
- There will be a lunch break
(And coffee breaks!)

View File

@@ -1,15 +1,15 @@
title: |
Kubernetes
for administrators
and operators
Advanced
Training
#chat: "[Slack](https://dockercommunity.slack.com/messages/C7GKACWDV)"
#chat: "[Gitter](https://gitter.im/jpetazzo/workshop-yyyymmdd-city)"
chat: "In person!"
chat: "[Gitter](https://gitter.im/jpetazzo/training-20190613-sanfrancisco)"
#chat: "In person!"
gitrepo: github.com/jpetazzo/container.training
slides: http://container.training/
slides: http://sfsf-2019-06.container.training/
exclude:
- self-paced
@@ -27,43 +27,35 @@ chapters:
- k8s/dmuc.md
- - k8s/multinode.md
- k8s/cni.md
- - k8s/apilb.md
- k8s/setup-managed.md
- k8s/setup-selfhosted.md
- k8s/cluster-upgrade.md
- k8s/staticpods.md
- - k8s/cluster-backup.md
- k8s/cloud-controller-manager.md
- k8s/healthchecks.md
- k8s/healthchecks-more.md
# DAY 2
- k8s/apilb.md
- - k8s/kubercoins.md
- k8s/logs-cli.md
- k8s/logs-centralized.md
- k8s/authn-authz.md
- k8s/csr-api.md
- - k8s/openid-connect.md
- k8s/control-plane-auth.md
###- k8s/bootstrap.md
- k8s/netpol.md
- k8s/podsecuritypolicy.md
- - k8s/resource-limits.md
- k8s/metrics-server.md
- k8s/cluster-sizing.md
- k8s/horizontal-pod-autoscaler.md
- - k8s/prometheus.md
- k8s/extending-api.md
- k8s/operators.md
###- k8s/operators-design.md
# CONCLUSION
- - k8s/lastwords-admin.md
- k8s/links.md
- shared/thankyou.md
- |
# (All content after this slide is bonus material)
# EXTRA
- k8s/healthchecks.md
- k8s/healthchecks-more.md
- - k8s/volumes.md
- k8s/configuration.md
- k8s/statefulsets.md
- k8s/local-persistent-volumes.md
- k8s/portworx.md
# DAY 2
- - k8s/namespaces.md
- k8s/kustomize.md
- k8s/helm.md
- k8s/create-chart.md
- k8s/create-more-charts.md
- - k8s/extending-api.md
- k8s/operators.md
- k8s/operators-design.md
- k8s/owners-and-dependents.md
- - k8s/authn-authz.md
- k8s/control-plane-auth.md
- k8s/prometheus.md
- - k8s/resource-limits.md
- k8s/metrics-server.md
- k8s/cluster-sizing.md
- k8s/horizontal-pod-autoscaler.md
# CONCLUSION
- - k8s/lastwords-admin.md
- k8s/links.md
- shared/thankyou.md

View File

@@ -11,11 +11,5 @@ class: title, in-person
@@TITLE@@<br/></br>
.footnote[
**Be kind to the WiFi!**<br/>
<!-- *Use the 5G network.* -->
*Don't use your hotspot.*<br/>
*Don't stream videos or download big files during the workshop[.](https://www.youtube.com/watch?v=h16zyxiwDLY)*<br/>
*Thank you!*
**Slides: @@SLIDES@@**
]

View File

@@ -1,65 +0,0 @@
title: |
Container Orchestration
with Docker and Swarm
chat: "[Slack](https://dockercommunity.slack.com/messages/C7GKACWDV)"
#chat: "[Gitter](https://gitter.im/jpetazzo/workshop-yyyymmdd-city)"
gitrepo: github.com/jpetazzo/container.training
slides: http://container.training/
exclude:
- self-paced
- snap
- btp-auto
- benchmarking
- elk-manual
- prom-manual
chapters:
- shared/title.md
- logistics.md
- swarm/intro.md
- shared/about-slides.md
- shared/toc.md
- - shared/prereqs.md
- shared/connecting.md
- swarm/versions.md
- shared/sampleapp.md
- shared/composescale.md
- shared/hastyconclusions.md
- shared/composedown.md
- swarm/swarmkit.md
- shared/declarative.md
- swarm/swarmmode.md
- swarm/creatingswarm.md
#- swarm/machine.md
- swarm/morenodes.md
- - swarm/firstservice.md
- swarm/ourapponswarm.md
- swarm/hostingregistry.md
- swarm/testingregistry.md
- swarm/btp-manual.md
- swarm/swarmready.md
- swarm/stacks.md
- swarm/cicd.md
- swarm/updatingservices.md
- swarm/rollingupdates.md
- swarm/healthchecks.md
- - swarm/operatingswarm.md
- swarm/netshoot.md
- swarm/ipsec.md
- swarm/swarmtools.md
- swarm/security.md
- swarm/secrets.md
- swarm/encryptionatrest.md
- swarm/leastprivilege.md
- swarm/apiscope.md
- - swarm/logging.md
- swarm/metrics.md
- swarm/gui.md
- swarm/stateful.md
- swarm/extratips.md
- shared/thankyou.md
- swarm/links.md

View File

@@ -1,64 +0,0 @@
title: |
Container Orchestration
with Docker and Swarm
chat: "[Slack](https://dockercommunity.slack.com/messages/C7GKACWDV)"
#chat: "[Gitter](https://gitter.im/jpetazzo/workshop-yyyymmdd-city)"
gitrepo: github.com/jpetazzo/container.training
slides: http://container.training/
exclude:
- self-paced
- snap
- btp-manual
- benchmarking
- elk-manual
- prom-manual
chapters:
- shared/title.md
- logistics.md
- swarm/intro.md
- shared/about-slides.md
- shared/toc.md
- - shared/prereqs.md
- shared/connecting.md
- swarm/versions.md
- shared/sampleapp.md
- shared/composescale.md
- shared/hastyconclusions.md
- shared/composedown.md
- swarm/swarmkit.md
- shared/declarative.md
- swarm/swarmmode.md
- swarm/creatingswarm.md
#- swarm/machine.md
- swarm/morenodes.md
- - swarm/firstservice.md
- swarm/ourapponswarm.md
#- swarm/hostingregistry.md
#- swarm/testingregistry.md
#- swarm/btp-manual.md
#- swarm/swarmready.md
- swarm/stacks.md
- swarm/cicd.md
- swarm/updatingservices.md
#- swarm/rollingupdates.md
#- swarm/healthchecks.md
- - swarm/operatingswarm.md
#- swarm/netshoot.md
#- swarm/ipsec.md
#- swarm/swarmtools.md
- swarm/security.md
#- swarm/secrets.md
#- swarm/encryptionatrest.md
- swarm/leastprivilege.md
- swarm/apiscope.md
- swarm/logging.md
- swarm/metrics.md
#- swarm/stateful.md
#- swarm/extratips.md
- shared/thankyou.md
- swarm/links.md

View File

@@ -1,73 +0,0 @@
title: |
Container Orchestration
with Docker and Swarm
chat: "[Slack](https://dockercommunity.slack.com/messages/C7GKACWDV)"
gitrepo: github.com/jpetazzo/container.training
slides: http://container.training/
exclude:
- in-person
- btp-auto
chapters:
- shared/title.md
#- shared/logistics.md
- swarm/intro.md
- shared/about-slides.md
- shared/toc.md
- - shared/prereqs.md
- shared/connecting.md
- swarm/versions.md
- |
name: part-1
class: title, self-paced
Part 1
- shared/sampleapp.md
- shared/composescale.md
- shared/hastyconclusions.md
- shared/composedown.md
- swarm/swarmkit.md
- shared/declarative.md
- swarm/swarmmode.md
- swarm/creatingswarm.md
#- swarm/machine.md
- swarm/morenodes.md
- - swarm/firstservice.md
- swarm/ourapponswarm.md
- swarm/hostingregistry.md
- swarm/testingregistry.md
- swarm/btp-manual.md
- swarm/swarmready.md
- swarm/stacks.md
- swarm/cicd.md
- |
name: part-2
class: title, self-paced
Part 2
- - swarm/operatingswarm.md
- swarm/netshoot.md
- swarm/swarmnbt.md
- swarm/ipsec.md
- swarm/updatingservices.md
- swarm/rollingupdates.md
- swarm/healthchecks.md
- swarm/nodeinfo.md
- swarm/swarmtools.md
- - swarm/security.md
- swarm/secrets.md
- swarm/encryptionatrest.md
- swarm/leastprivilege.md
- swarm/apiscope.md
- swarm/logging.md
- swarm/metrics.md
- swarm/stateful.md
- swarm/extratips.md
- shared/thankyou.md
- swarm/links.md

View File

@@ -1,72 +0,0 @@
title: |
Container Orchestration
with Docker and Swarm
chat: "[Slack](https://dockercommunity.slack.com/messages/C7GKACWDV)"
gitrepo: github.com/jpetazzo/container.training
slides: http://container.training/
exclude:
- in-person
- btp-auto
chapters:
- shared/title.md
#- shared/logistics.md
- swarm/intro.md
- shared/about-slides.md
- shared/toc.md
- - shared/prereqs.md
- shared/connecting.md
- swarm/versions.md
- |
name: part-1
class: title, self-paced
Part 1
- shared/sampleapp.md
- shared/composescale.md
- shared/hastyconclusions.md
- shared/composedown.md
- swarm/swarmkit.md
- shared/declarative.md
- swarm/swarmmode.md
- swarm/creatingswarm.md
#- swarm/machine.md
- swarm/morenodes.md
- - swarm/firstservice.md
- swarm/ourapponswarm.md
- swarm/hostingregistry.md
- swarm/testingregistry.md
- swarm/btp-manual.md
- swarm/swarmready.md
- swarm/stacks.md
- |
name: part-2
class: title, self-paced
Part 2
- - swarm/operatingswarm.md
#- swarm/netshoot.md
#- swarm/swarmnbt.md
- swarm/ipsec.md
- swarm/updatingservices.md
- swarm/rollingupdates.md
#- swarm/healthchecks.md
- swarm/nodeinfo.md
- swarm/swarmtools.md
- - swarm/security.md
- swarm/secrets.md
- swarm/encryptionatrest.md
- swarm/leastprivilege.md
- swarm/apiscope.md
#- swarm/logging.md
#- swarm/metrics.md
- swarm/stateful.md
- swarm/extratips.md
- shared/thankyou.md
- swarm/links.md