# 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)\_/¯
---
## Rolling updates
- With rolling updates, when a Deployment is updated, it happens progressively
- The Deployment controls multiple Replica Sets
- Each Replica Set is a group of identical Pods
(with the same image, arguments, parameters ...)
- During the rolling update, we have at least two Replica Sets:
- the "new" set (corresponding to the "target" version)
- at least one "old" set
- We can have multiple "old" sets
(if we start another update before the first one is done)
---
## Update strategy
- 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 of rolling back to the previous version
(if the update fails or is unsatisfactory in any way)
---
## Checking current rollout parameters
- Recall how we build custom reports with `kubectl` and `jq`:
.exercise[
- Show the rollout plan for our deployments:
```bash
kubectl get deploy -o json |
jq ".items[] | {name:.metadata.name} + .spec.strategy.rollingUpdate"
```
]
---
## 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
---
## Rolling out the new `worker` service
.exercise[
- Let's monitor what's going on by opening a few terminals, and run:
```bash
kubectl get pods -w
kubectl get replicasets -w
kubectl get deployments -w
```
- Update `worker` either with `kubectl edit`, or by running:
```bash
kubectl set image deploy worker worker=dockercoins/worker:v0.2
```
]
--
That rollout should be pretty quick. What shows in the web UI?
---
## 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](https://kubernetes.io/docs/concepts/workloads/pods/pod/#termination-of-pods) if needed)
---
## Rolling out something invalid
- What happens if we make a mistake?
.exercise[
- Update `worker` by specifying a non-existent image:
```bash
kubectl set image deploy worker worker=dockercoins/worker:v0.3
```
- Check what's going on:
```bash
kubectl rollout status deploy worker
```
]
--
Our rollout is stuck. However, the app is not dead.
(After a minute, it will stabilize to be 20-25% slower.)
---
## What's going on with our rollout?
- Why is our app a bit slower?
- Because `MaxUnavailable=25%`
... So the rollout terminated 2 replicas out of 10 available
- Okay, but why do we see 5 new replicas being rolled out?
- Because `MaxSurge=25%`
... So in addition to replacing 2 replicas, the rollout is also starting 3 more
- It rounded down the number of MaxUnavailable pods conservatively,
but the total number of pods being rolled out is allowed to be 25+25=50%
---
class: extra-details
## The nitty-gritty details
- We start with 10 pods running for the `worker` deployment
- Current settings: MaxUnavailable=25% and MaxSurge=25%
- When we start the rollout:
- two replicas are taken down (as per MaxUnavailable=25%)
- two others are created (with the new version) to replace them
- three others are created (with the new version) per MaxSurge=25%)
- Now we have 8 replicas up and running, and 5 being deployed
- Our rollout is stuck at this point!
---
## Checking the dashboard during the bad rollout
If you didn't deploy the Kubernetes dashboard earlier, just skip this slide.
.exercise[
- Connect to the dashboard that we deployed earlier
- Check that we have failures in Deployments, Pods, and Replica Sets
- Can we see the reason for the failure?
]
---
## Recovering from a bad rollout
- We could push some `v0.3` image
(the pod retry logic will eventually catch it and the rollout will proceed)
- Or we could invoke a manual rollback
.exercise[
- Cancel the deployment and wait for the dust to settle:
```bash
kubectl rollout undo deploy worker
kubectl rollout status deploy worker
```
]
---
## Rolling back to an older version
- We reverted to `v0.2`
- But this version still has a performance problem
- How can we get back to the previous version?
---
## Multiple "undos"
- What happens if we try `kubectl rollout undo` again?
.exercise[
- Try it:
```bash
kubectl rollout undo deployment worker
```
- Check the web UI, the list of pods ...
]
🤔 That didn't work.
---
## Multiple "undos" don't work
- If we see successive versions as a stack:
- `kubectl rollout undo` doesn't "pop" the last element from the stack
- it copies the N-1th element to the top
- Multiple "undos" just swap back and forth between the last two versions!
.exercise[
- Go back to v0.2 again:
```bash
kubectl rollout undo deployment worker
```
]
---
## In this specific scenario
- Our version numbers are easy to guess
- What if we had used git hashes?
- What if we had changed other parameters in the Pod spec?
---
## Listing versions
- We can list successive versions of a Deployment with `kubectl rollout history`
.exercise[
- Look at our successive versions:
```bash
kubectl rollout history deployment worker
```
]
We don't see *all* revisions.
We might see something like 1, 4, 5.
(Depending on how many "undos" we did before.)
---
## Explaining deployment revisions
- These revisions correspond to our Replica Sets
- This information is stored in the Replica Set annotations
.exercise[
- Check the annotations for our replica sets:
```bash
kubectl describe replicasets -l app=worker | grep -A3 ^Annotations
```
]
---
class: extra-details
## What about the missing revisions?
- The missing revisions are stored in another annotation:
`deployment.kubernetes.io/revision-history`
- These are not shown in `kubectl rollout history`
- We could easily reconstruct the full list with a script
(if we wanted to!)
---
## Rolling back to an older version
- `kubectl rollout undo` can work with a revision number
.exercise[
- Roll back to the "known good" deployment version:
```bash
kubectl rollout undo deployment worker --to-revision=1
```
- Check the web UI or the list of pods
]
---
class: extra-details
## Changing rollout parameters
- We want to:
- revert to `v0.1`
- be conservative on availability (always have desired number of available workers)
- go slow on rollout speed (update only one pod at a time)
- give some time to our workers to "warm up" before starting more
The corresponding changes can be expressed in the following YAML snippet:
.small[
```yaml
spec:
template:
spec:
containers:
- name: worker
image: dockercoins/worker:v0.1
strategy:
rollingUpdate:
maxUnavailable: 0
maxSurge: 1
minReadySeconds: 10
```
]
---
class: extra-details
## Applying changes through a YAML patch
- We could use `kubectl edit deployment worker`
- But we could also use `kubectl patch` with the exact YAML shown before
.exercise[
.small[
- Apply all our changes and wait for them to take effect:
```bash
kubectl patch deployment worker -p "
spec:
template:
spec:
containers:
- name: worker
image: dockercoins/worker:v0.1
strategy:
rollingUpdate:
maxUnavailable: 0
maxSurge: 1
minReadySeconds: 10
"
kubectl rollout status deployment worker
kubectl get deploy -o json worker |
jq "{name:.metadata.name} + .spec.strategy.rollingUpdate"
```
]
]