What is happening when a
Deployment rolls out a change to your app?
What does it actually do when a
Pod crashes or is killed? What happens
Pod is re-labled so that it’s not targeted by the
Deployment is probably the most complex resource type in Kubernetes
Deployment specifies how changes should be rolled out over
ReplicaSets, which themselves specify how
Pods should be replicated
in a cluster.
In this post we continue our exploration of the Kubernetes API, cracking
Deployment open using
kubespy, a small tool we developed to observe
Kubernetes resources in real-time.
kubespy trace, for example, we can observe at a high level what
Deployment rolls out a new version of an application:
But this post also comes with a twist, because in addition to being
Deployment is also the most dynamic resource in Kubernetes
Pod can crash or be killed. A node can disappear. A user can
trigger a rollout. In each of these cases, some combination of the
Deployment controller, the
ReplicaSet controller, and the Kubelet
have to cope.
In other words, understanding
Deployment necessarily involves
understanding how it does this coping. We therefore will also use
kubespy trace to observe what happens when we kill and otherwise
kubespy repository comes with a simple example Kubernetes
which is used in each of these examples. If you want to try it out for
yourself the README contains detailed instructions.
You can use
kubespy CLI itself is powered by
the same code that underlies the core (OSS) Pulumi
engine. If you like this, and would
like to see information like it in CI/CD, we hope you’ll give it a
shot! To get a flavor of what this looks like in practice, you might
also check out my tweetstorm
on the subject.
What happens during a rollout?
The gif in the introduction shows what happens when:
- We deploy the example application.
- Then, later, change the image tag to
nginx:1.12-alpineand deploy again.
kubespy trace deploy nginx is run, it will watch for changes to
nginx, (1) the
ReplicaSets it controls,
and (3) the
Pods they control over time. The overall status is
aggregated and printed to the console as they change.
From this gif, we can see 3 distinct phases:
First, the initial state: the
Deployment is in a steady state. It
controls a single
ReplicaSet, which in turn controls three replicas of
Pod is marked as available. We can see
Deployment’s status that the application is on revision 2,
which means the application has been deployed twice — one initial
deployment, and one update.
Second: the user submits a change to the
Deployment, which triggers
a rollout. The
Deployment creates a new revision, revision 3. It
creates a new
ReplicaSet to represent this revision, and begins to
Pods with the new app version on it.
This gif is slowed down to show each of these individual changes — in the gif above, you can see these happen in very quick succession.
Third: the new version of the app comes online; the old
is killed. As the new
Pods in revision 3 come online and are
marked available, the
Deployment controller begins killing off
replicas from revision 2. Eventually it succeeds and the rollout is
What happens if we kill a Pod?
Now that we understand the semantics of
Deployment’s rollout, we can
see what happens when we use
kubectl delete pod <name> on one of the
Pods that is controlled by our
As expected, we can see that the
ReplicaSet controller notices the
Pod goes missing and spins up a new one. Note that it does not
trigger a rollout, and does not increment the revision.
Notice also that the
Pod that was destroyed hangs around for a few
seconds, even though the new one has been booted up and the
has been marked available.
What happens if we add or remove labels from a Pod?
kubectl edit to delete the labels on one of your
kubespy trace will show you something the following:
Unlike the “killing a
Pod” example above, the old
Pod seems to
disappear, replaced by a new
Pod that, when booted up, causes the
Deployment to be marked as available again.
What’s happening here? It turns out that if you remove the app labels
ReplicaSet controller notices, removes itself from
.metadata.ownerRef, and then treats the
Pod as if it’s been
deleted, spinning up a new one immediately.
This is useful: if you notice one of your
Pods is behaving strangely,
you can simply take it out of rotation by removing those labels, so that
you can pull it aside and test it.
The Kubernetes API is rich, packed with useful information about the
state of your cluster. It’s remarkable how little it is directly used
to make tools. Combine a little knowledge of
field with the Kubernetes
Watch API and a bit of terminal UI
programming, you’re most of your way to
kubespy trace deployment.
If you enjoyed this post, or are curious to see how this lifecycle is baked into the Pulumi CLI, please give us a shot! We’d love to hear your feedback.