Rollout Deployment
Last updated
Last updated
The Rollout Deployment chart deploys an advanced version of deployment that supports Blue/Green and Canary deployments. For functioning, it requires a rollout controller to run inside the cluster.
You can define application behavior by providing information in the following sections:
| Key | Descriptions |
|---|---|
Super-admins can lock keys in rollout deployment template to prevent non-super-admins from modifying those locked keys. Refer Lock Deployment Configuration to know more.
This defines the ports on which application services will be exposed to other services.
EnvVariables provide run-time information to containers and allow to customize how the application works and the behavior of the applications on the system.
Here we can pass the list of env variables , every record is an object which contain the name of variable along with value.
To set environment variables for the containers that run in the Pod.
IMP Docker image should have env variables, whatever we want to set.
But ConfigMap and Secret are the preferred way to inject env variables. You can create this in App Configuration Section.
It is a centralized storage, specific to k8s namespace where key-value pairs are stored in plain text.
It is a centralized storage, specific to k8s namespace where we can store the key-value pairs in plain text as well as in encrypted(Base64) form.
IMP All key-values of Secret and CofigMap will reflect to your application.
If this check fails, kubernetes restarts the pod. This should return error code in case of non-recoverable error.
The maximum number of pods that can be unavailable during the update process. The value of "MaxUnavailable: " can be an absolute number or percentage of the replicas count. The default value of "MaxUnavailable: " is 25%.
The maximum number of pods that can be created over the desired number of pods. For "MaxSurge: " also, the value can be an absolute number or percentage of the replicas count. The default value of "MaxSurge: " is 25%.
This specifies the minimum number of seconds for which a newly created Pod should be ready without any of its containers crashing, for it to be considered available. This defaults to 0 (the Pod will be considered available as soon as it is ready).
If this check fails, kubernetes stops sending traffic to the application. This should return error code in case of errors which can be recovered from if traffic is stopped.
Startup Probe in Kubernetes is a type of probe used to determine when a container within a pod is ready to start accepting traffic. It is specifically designed for applications that have a longer startup time.
This is connected to HPA and controls scaling up and down in response to request load.
fullnameOverride replaces the release fullname created by default by devtron, which is used to construct Kubernetes object names. By default, devtron uses {app-name}-{environment-name} as release fullname.
Image is used to access images in kubernetes, pullpolicy is used to define the instances calling the image, here the image is pulled when the image is not present,it can also be set as "Always".
imagePullSecrets contains the docker credentials that are used for accessing a registry.
regcred is the secret that contains the docker credentials that are used for accessing a registry. Devtron will not create this secret automatically, you'll have to create this secret using dt-secrets helm chart in the App store or create one using kubectl. You can follow this documentation Pull an Image from a Private Registry https://kubernetes.io/docs/tasks/configure-pod-container/pull-image-private-registry/ .
the hostAliases field is used in a Pod specification to associate additional hostnames with the Pod's IP address. This can be helpful in scenarios where you need to resolve specific hostnames to the Pod's IP within the Pod itself.
This allows public access to the url. Please ensure you are using the right nginx annotation for nginx class. The default value is nginx.
Legacy deployment-template ingress format
This allows private access to the url, please ensure you are using right nginx annotation for nginx class, its default value is nginx
Specialized containers that run before app containers in a Pod. Init containers can contain utilities or setup scripts not present in an app image. One can use base image inside initContainer by setting the reuseContainerImage flag to true.
To wait for given period of time before switch active the container.
These define minimum and maximum RAM and CPU available to the application.
Resources are required to set CPU and memory usage.
Limits make sure a container never goes above a certain value. The container is only allowed to go up to the limit, and then it is restricted.
Requests are what the container is guaranteed to get.
This defines annotations and the type of service, optionally can define name also.
Note - If loadBalancerSourceRanges is not set, Kubernetes allows traffic from 0.0.0.0/0 to the LoadBalancer / Node Security Group(s).
It is required when some values need to be read from or written to an external disk.
It is used to provide mounts to the volume.
Spec is used to define the desire state of the given container.
Node Affinity allows you to constrain which nodes your pod is eligible to schedule on, based on labels of the node.
Inter-pod affinity allow you to constrain which nodes your pod is eligible to be scheduled based on labels on pods.
Key part of the label for node selection, this should be same as that on node. Please confirm with devops team.
Value part of the label for node selection, this should be same as that on node. Please confirm with devops team.
Taints are the opposite, they allow a node to repel a set of pods.
A given pod can access the given node and avoid the given taint only if the given pod satisfies a given taint.
Taints and tolerations are a mechanism which work together that allows you to ensure that pods are not placed on inappropriate nodes. Taints are added to nodes, while tolerations are defined in the pod specification. When you taint a node, it will repel all the pods except those that have a toleration for that taint. A node can have one or many taints associated with it.
This is used to give arguments to command.
It contains the commands to run inside the container.
Containers section can be used to run side-car containers along with your main container within same pod. Containers running within same pod can share volumes and IP Address and can address each other @localhost. We can use base image inside container by setting the reuseContainerImage flag to true.
It is a kubernetes monitoring tool and the name of the file to be monitored as monitoring in the given case.It describes the state of the prometheus.
Accepts an array of Kubernetes objects. You can specify any kubernetes yaml here and it will be applied when your app gets deployed.
Kubernetes waits for the specified time called the termination grace period before terminating the pods. By default, this is 30 seconds. If your pod usually takes longer than 30 seconds to shut down gracefully, make sure you increase the GracePeriod.
A Graceful termination in practice means that your application needs to handle the SIGTERM message and begin shutting down when it receives it. This means saving all data that needs to be saved, closing down network connections, finishing any work that is left, and other similar tasks.
There are many reasons why Kubernetes might terminate a perfectly healthy container. If you update your deployment with a rolling update, Kubernetes slowly terminates old pods while spinning up new ones. If you drain a node, Kubernetes terminates all pods on that node. If a node runs out of resources, Kubernetes terminates pods to free those resources. It’s important that your application handle termination gracefully so that there is minimal impact on the end user and the time-to-recovery is as fast as possible.
It is used for providing server configurations.
It gives the details for deployment.
It gives the set of targets to be monitored.
It is used to configure database migration.
These Istio configurations collectively provide a comprehensive set of tools for controlling access, authenticating requests, enforcing security policies, and configuring traffic behavior within a microservices architecture. The specific settings you choose would depend on your security and traffic management requirements.
These Istio configurations collectively provide a comprehensive set of tools for controlling access, authenticating requests, enforcing security policies, and configuring traffic behavior within a microservices architecture. The specific settings you choose would depend on your security and traffic management requirements.
Application metrics can be enabled to see your application's metrics-CPU Service Monitor usage, Memory Usage, Status, Throughput and Latency.
It gives the realtime metrics of the deployed applications
A service account provides an identity for the processes that run in a Pod.
When you access the cluster, you are authenticated by the API server as a particular User Account. Processes in containers inside pod can also contact the API server. When you are authenticated as a particular Service Account.
When you create a pod, if you do not create a service account, it is automatically assigned the default service account in the namespace.
You can create PodDisruptionBudget for each application. A PDB limits the number of pods of a replicated application that are down simultaneously from voluntary disruptions. For example, an application would like to ensure the number of replicas running is never brought below the certain number.
or
You can specify either maxUnavailable or minAvailable in a PodDisruptionBudget and it can be expressed as integers or as a percentage.
Envoy is attached as a sidecar to the application container to collect metrics like 4XX, 5XX, Throughput and latency. You can now configure the envoy settings such as idleTimeout, resources etc.
Alerting rules allow you to define alert conditions based on Prometheus expressions and to send notifications about firing alerts to an external service.
In this case, Prometheus will check that the alert continues to be active during each evaluation for 1 minute before firing the alert. Elements that are active, but not firing yet, are in the pending state.
Labels are key/value pairs that are attached to pods. Labels are intended to be used to specify identifying attributes of objects that are meaningful and relevant to users, but do not directly imply semantics to the core system. Labels can be used to organize and to select subsets of objects.
Pod Annotations are widely used to attach metadata and configs in Kubernetes.
HPA, by default is configured to work with CPU and Memory metrics. These metrics are useful for internal cluster sizing, but you might want to configure wider set of metrics like service latency, I/O load etc. The custom metrics in HPA can help you to achieve this.
Wait for given period of time before scaling down the container.
If you want to see application metrics like different HTTP status codes metrics, application throughput, latency, response time. Enable the Application metrics from below the deployment template Save button. After enabling it, you should be able to see all metrics on App detail page. By default it remains disabled.
Once all the Deployment template configurations are done, click on Save to save your deployment configuration. Now you are ready to create Workflow to do CI/CD.
Helm Chart json schema is used to validate the deployment template values.
The values of CPU and Memory in limits must be greater than or equal to in requests respectively. Similarly, In case of envoyproxy, the values of limits are greater than or equal to requests as mentioned below.
Prerequisite: KEDA controller should be installed in the cluster. To install KEDA controller using Helm, navigate to chart store and search for keda chart and deploy it. You can follow this documentation for deploying a Helm chart on Devtron.
KEDA Helm repo : https://kedacore.github.io/charts
KEDA is a Kubernetes-based Event Driven Autoscaler. With KEDA, you can drive the scaling of any container in Kubernetes based on the number of events needing to be processed. KEDA can be installed into any Kubernetes cluster and can work alongside standard Kubernetes components like the Horizontal Pod Autoscaler(HPA).
Example for autoscaling with KEDA using Prometheus metrics is given below:
Example for autosccaling with KEDA based on kafka is given below :
Kubernetes NetworkPolicies control pod communication by defining rules for incoming and outgoing traffic.