A Kubernetes Deployment allows you to declaratively create pods and ReplicaSets. You can define a desired state, and a Deployment Controller continuously monitors the current state of the relevant resources, and deploys pods to match the desired state. It plays a central role in Kubernetes autoscaling.
A Deployment strategy defines how to create, upgrade, or downgrade different versions of Kubernetes applications. In a traditional software environment, deployments or upgrades to applications result in downtime and disruption of service. Kubernetes can help avoid this, providing several Deployment strategies that let you perform rolling updates to multiple application instances, and avoid or minimize downtime.
In this article, you will learn about the following Kubernetes Deployment strategies:
A rolling deployment is the default deployment strategy in Kubernetes. It replaces the existing version of pods with a new version, updating pods slowly one by one, without cluster downtime.
The rolling update uses a readiness probe to check if a new pod is ready, before starting to scale down pods with the old version. If there is a problem, you can stop an update and roll it back, without stopping the entire cluster.
To perform a rolling update, simply update the image of your pods using kubectl set image. This will automatically trigger a rolling update.
To refine your deployment strategy, change the parameters in the spec:strategy section of your manifest file. There are two optional parameters—maxSurge and maxUnavailable:
At least one of these parameters must be larger than zero. By changing the values of these parameters, you can define other deployment strategies, as shown below.
This is a basic deployment pattern which simply shuts down all the old pods and replaces them with new ones. You define it by setting the spec:strategy:type section of your manifest to Recreate, like this:
The Recreate strategy can result in downtime, because old pods are deleted before ensuring that new pods are rolled out with the new version of the application.
A ramped rollout updates pods gradually, by creating new replicas while removing old ones. You can choose the number of replicas to roll out each time. You also need to make sure that no pods become unavailable.
The difference between this strategy and a regular rolling deployment is you can control the pace at which new replicas are rolled out. For example, you can define that only 1 or 2 nodes should be updated at any one time, to reduce the risk of an update.
To define this behavior, set maxSurge to 1 and maxUnvailable to 0. This means the Deployment will roll one pod at a time, while ensuring no pods are unavailable. So, for example, if there are ten pods, the Deployment will ensure at least ten pods are available at one time.
The following Deployment YAML file performs a ramped rollout. This configuration, and the one shown in the next section, was shared by Gaurav Agarwal.
The downside of a ramped rollout is that it takes time to roll out the application, especially at large scale. An alternative is a “best-effort controlled rollout”. This enables a faster rollout, but with a tradeoff of higher risk, by tolerating a certain percentage of downtime among your nodes.
This has the effect of rapidly replacing pods, as quickly as possible, while ensuring a limited number of pods down at any given time.
Canary deployments are typically used to test some new features on the backend of an application. Two or more services or versions of an application are deployed in parallel, one running an existing version, and one with new features. Users are gradually shifted to the new version, allowing the new version to be validated by exposing it to real users. If no errors are reported, one of the new versions can be gradually deployed to all users.
See this detailed tutorial on the Kubernetes blog showing how to deploy an existing version and a new canary version, route between them using Gloo subset routing, and then shift all traffic to the new version.
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