Make sure Global Configuration > GitOps is configured before moving ahead.

A deployment configuration is a manifest for the application. It defines the runtime behavior of the application.

Devtron includes deployment template for both default as well as custom charts created by a super admin.

To configure a deployment chart for your application:

• Go to Applications and create a new application.

• Go to App Configuration page and configure your application.

• On the Deployment Template page, select the drop-down under Chart type.

You can select a chart in one of the following ways:

Select chart by Devtron

1. Deployment (Recommended)

2. Rollout Deployment

3. Job and Cronjob

4. Knative

Select chart from custom charts

Custom charts are added by a super admin from the custom charts section.

Users can select the available custom charts from the drop-down list.

Upload custom chart

A custom chart can be uploaded by a super admin.

Application Metrics

Enable show application metrics toggle to view the application metrics on the App Details page.

IMPORTANT: Enabling Application metrics introduces a sidecar container to your main container which may require some additional configuration adjustments, we recommend you to do load test after enabling it in a non-prod environment before enabling it in production environment.

Select Save to save your configurations.

This chart creates a deployment that runs multiple replicas of your application and automatically replaces any instances that fail or become unresponsive. It does not support Blue/Green and Canary deployments.

This is the default deployment chart. You can select Deployment chart when you want to use only basic use cases which contain the following:

• Create a Deployment to rollout a ReplicaSet. The ReplicaSet creates Pods in the background. Check the status of the rollout to see if it succeeds or not.

• Declare the new state of the Pods. A new ReplicaSet is created and the Deployment manages moving the Pods from the old ReplicaSet to the new one at a controlled rate. Each new ReplicaSet updates the revision of the Deployment.

• Rollback to an earlier Deployment revision if the current state of the Deployment is not stable. Each rollback updates the revision of the Deployment.

• Scale up the Deployment to facilitate more load.

• Use the status of the Deployment as an indicator that a rollout has stuck.

• Clean up older ReplicaSets that you do not need anymore.

You can define application behavior by providing information in the following sections:

1. Job

A Job creates one or more Pods and will continue to retry execution of the Pods until a specified number of them successfully terminate. As pods successfully complete, the Job tracks the successful completions. When a specified number of successful completions is reached, the task (ie, Job) is complete. Deleting a Job will clean up the Pods it created. Suspeding a Job will delete its active Pods until the Job is resumed again.

Example:

kind: Job
jobConfigs:
    activeDeadlineSeconds: 120
    backoffLimit: 6
    completions: 1
    parallelism: 1
    suspend: false
    ttlSecondsAfterFinished: 100

2. Cronjob

A Cronjob creates Jobs on a repeating schedule , One Cronjob object is like one line of a crontab (cron table) file. It runs a job periodically on a given schedule, written in Cron format. Cronjobs are meant for performing regular scheduled actions such as backups, report generation, and so on. Each of those tasks should be configured to recur indefinitely (for example: once a day / week / month); you can define the point in time within that interval when the job should start.

Example:

kind: CronJob
cronjobConfigs:
    concurrencyPolicy: Allow
    failedJobsHistoryLimit: 1
    restartPolicy: OnFailure
    schedule: 32 8 * * *
    startingDeadlineSeconds: 100
    successfulJobsHistoryLimit: 3
    suspend: false

Deployment configuration is the Manifest for the application, it defines the runtime behavior of the application. You can define application behavior by providing information in three sections:

1. Chart Version

2. Yaml file

3. Show application metrics

1. Chart version

Devtron uses helm charts for the deployments. And we are having multiple chart versions based on features it is supporting with every chart version.

One can see multiple chart version options available in the drop-down. you can select any chart version as per your requirements. By default, the latest version of the helm chart is selected in the chart version option.

Every chart version has its own YAML file. Helm charts are used to provide specifications for your application. To make it easy to use, we have created templates for the YAML file and have added some variables inside the YAML. You can provide or change the values of these variables as per your requirement.

If you want to see Application Metrics (For example Status codes 2xx, 3xx, 5xx; throughput, and latency) for your application, then you need to select the latest chart version.

Application Metrics is not supported for Chart version older than 3.7 version.

2. Yaml file

Container Ports

This defines ports on which application services will be exposed to other services

ContainerPort:
  - envoyPort: 8799
    envoyTimeout: 15s
    idleTimeout:
    name: app
    port: 8080
    servicePort: 80
    supportStreaming: true
    useHTTP2: true

EnvVariables

EnvVariables: []

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.

Example of env variables

IMP Docker image should have env variables, whatever we want to set.

EnvVariables: 
  - name: HOSTNAME
    value: www.xyz.com
  - name: DB_NAME
    value: mydb
  - name: USER_NAME
    value: xyz

But ConfigMap and Secret are the prefered way to inject env variables. So we can create this in App Configuration Section

ConfigMap

It is a centralized storage, specific to k8s namespace where key-value pairs are stored in plain text.

Secret

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.

Liveness Probe

If this check fails, kubernetes restarts the pod. This should return error code in case of non-recoverable error.

LivenessProbe:
  Path: ""
  port: 8080
  initialDelaySeconds: 20
  periodSeconds: 10
  successThreshold: 1
  timeoutSeconds: 5
  failureThreshold: 3
  command:
    - python
    - /etc/app/healthcheck.py
  httpHeaders:
    - name: Custom-Header
      value: abc
  scheme: ""
  tcp: true

MaxUnavailable

  MaxUnavailable: 0

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%.

MaxSurge

MaxSurge: 1

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%.

Min Ready Seconds

MinReadySeconds: 60

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).

Readiness Probe

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.

ReadinessProbe:
  Path: ""
  port: 8080
  initialDelaySeconds: 20
  periodSeconds: 10
  successThreshold: 1
  timeoutSeconds: 5
  failureThreshold: 3
  command:
    - python
    - /etc/app/healthcheck.py
  httpHeaders:
    - name: Custom-Header
      value: abc
  scheme: ""
  tcp: true

Autoscaling

This is connected to HPA and controls scaling up and down in response to request load.

autoscaling:
  enabled: false
  MinReplicas: 1
  MaxReplicas: 2
  TargetCPUUtilizationPercentage: 90
  TargetMemoryUtilizationPercentage: 80
  extraMetrics: []

Fullname Override

fullnameOverride: app-name

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

image:
  pullPolicy: IfNotPresent

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

imagePullSecrets contains the docker credentials that are used for accessing a registry.

imagePullSecrets:
  - regcred

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/ .

Ingress

This allows public access to the url, please ensure you are using right nginx annotation for nginx class, its default value is nginx

ingress:
  enabled: false
  # For K8s 1.19 and above use ingressClassName instead of annotation kubernetes.io/ingress.class:
  className: nginx
  annotations: {}
  hosts:
      - host: example1.com
        pathType: "ImplementationSpecific"
        paths:
            - /example
      - host: example2.com
        pathType: "ImplementationSpecific"
        paths:
            - /example2
            - /example2/healthz
  tls: []

Legacy deployment-template ingress format

ingress:
  enabled: false
  # For K8s 1.19 and above use ingressClassName instead of annotation kubernetes.io/ingress.class:
  ingressClassName: nginx-internal
  annotations: {}
  path: ""
  host: ""
  tls: []

Ingress Internal

This allows private access to the url, please ensure you are using right nginx annotation for nginx class, its default value is nginx

ingressInternal:
  enabled: false
  # For K8s 1.19 and above use ingressClassName instead of annotation kubernetes.io/ingress.class:
  ingressClassName: nginx-internal
  annotations: {}
  hosts:
      - host: example1.com
        pathType: "ImplementationSpecific"
        paths:
            - /example
      - host: example2.com
        pathType: "ImplementationSpecific"
        paths:
            - /example2
            - /example2/healthz
  tls: []

Init Containers

initContainers: 
  - reuseContainerImage: true
    securityContext:
      runAsUser: 1000
      runAsGroup: 3000
      fsGroup: 2000
    volumeMounts:
     - mountPath: /etc/ls-oms
       name: ls-oms-cm-vol
   command:
     - flyway
     - -configFiles=/etc/ls-oms/flyway.conf
     - migrate

  - name: nginx
    image: nginx:1.14.2
    securityContext:
      privileged: true
    ports:
    - containerPort: 80
    command: ["/usr/local/bin/nginx"]
    args: ["-g", "daemon off;"]

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.

Pause For Seconds Before Switch Active

pauseForSecondsBeforeSwitchActive: 30

To wait for given period of time before switch active the container.

Resources

These define minimum and maximum RAM and CPU available to the application.

resources:
  limits:
    cpu: "1"
    memory: "200Mi"
  requests:
    cpu: "0.10"
    memory: "100Mi"

Resources are required to set CPU and memory usage.

Limits

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

Requests are what the container is guaranteed to get.

Service

This defines annotations and the type of service, optionally can define name also.

  service:
    type: ClusterIP
    annotations: {}

Note - If loadBalancerSourceRanges is not set, Kubernetes allows traffic from 0.0.0.0/0 to the LoadBalancer / Node Security Group(s).

Volumes

volumes:
  - name: log-volume
    emptyDir: {}
  - name: logpv
    persistentVolumeClaim:
      claimName: logpvc

It is required when some values need to be read from or written to an external disk.

Volume Mounts

volumeMounts:
  - mountPath: /var/log/nginx/
    name: log-volume 
  - mountPath: /mnt/logs
    name: logpvc
    subPath: employee  

It is used to provide mounts to the volume.

Affinity and anti-affinity

Spec:
  Affinity:
    Key:
    Values:

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

Key part of the label for node selection, this should be same as that on node. Please confirm with devops team.

Values

Value part of the label for node selection, this should be same as that on node. Please confirm with devops team.

Tolerations

tolerations:
 - key: "key"
   operator: "Equal"
   value: "value"
   effect: "NoSchedule|PreferNoSchedule|NoExecute(1.6 only)"

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.

Arguments

args:
  enabled: false
  value: []

This is used to give arguments to command.

Command

command:
  enabled: false
  value: []
  workingDir: {}

It contains the commands to run inside the container.

Containers

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.

    containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80
        command: ["/usr/local/bin/nginx"]
        args: ["-g", "daemon off;"]

Prometheus

  prometheus:
    release: monitoring

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.

rawYaml

rawYaml: 
  - apiVersion: v1
    kind: Service
    metadata:
      name: my-service
    spec:
      selector:
        app: MyApp
      ports:
        - protocol: TCP
          port: 80
          targetPort: 9376
      type: ClusterIP

Accepts an array of Kubernetes objects. You can specify any kubernetes yaml here and it will be applied when your app gets deployed.

Grace Period

GracePeriod: 30

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.

Server

server:
  deployment:
    image_tag: 1-95a53
    image: ""

It is used for providing server configurations.

Deployment

It gives the details for deployment.

Service Monitor

servicemonitor:
      enabled: true
      path: /abc
      scheme: 'http'
      interval: 30s
      scrapeTimeout: 20s
      metricRelabelings:
        - sourceLabels: [namespace]
          regex: '(.*)'
          replacement: myapp
          targetLabel: target_namespace

It gives the set of targets to be monitored.

Db Migration Config

dbMigrationConfig:
  enabled: false

It is used to configure database migration.

Application Metrics

Application metrics can be enabled to see your application's metrics-CPUService Monito usage,Memory Usage,Status,Throughput and Latency.

Deployment Metrics

It gives the realtime metrics of the deployed applications

Addon features in Deployment Template Chart version 3.9.0

Service Account

serviceAccountName: orchestrator

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.

Pod Disruption Budget

podDisruptionBudget: {}
     minAvailable: 1
     maxUnavailable: 1

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.

You can specify maxUnavailable and minAvailable in a PodDisruptionBudget.

With minAvailable of 1, evictions are allowed as long as they leave behind 1 or more healthy pods of the total number of desired replicas.

With maxAvailable of 1, evictions are allowed as long as at most 1 unhealthy replica among the total number of desired replicas.

Application metrics Envoy Configurations

envoyproxy:
  image: envoyproxy/envoy:v1.14.1
  configMapName: ""
  resources:
    limits:
      cpu: "50m"
      memory: "50Mi"
    requests:
      cpu: "50m"
      memory: "50Mi"

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.

Prometheus Rule

prometheusRule:
  enabled: true
  additionalLabels: {}
  namespace: ""
  rules:
    - alert: TooMany500s
      expr: 100 * ( sum( nginx_ingress_controller_requests{status=~"5.+"} ) / sum(nginx_ingress_controller_requests) ) > 5
      for: 1m
      labels:
        severity: critical
      annotations:
        description: Too many 5XXs
        summary: More than 5% of the all requests did return 5XX, this require your attention

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.

Pod Labels

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.

podLabels:
  severity: critical

Pod Annotations

Pod Annotations are widely used to attach metadata and configs in Kubernetes.

podAnnotations:
  fluentbit.io/exclude: "true"

Custom Metrics in HPA

autoscaling:
  enabled: true
  MinReplicas: 1
  MaxReplicas: 2
  TargetCPUUtilizationPercentage: 90
  TargetMemoryUtilizationPercentage: 80
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Percent
        value: 100
        periodSeconds: 15
    scaleUp:
      stabilizationWindowSeconds: 0
      policies:
      - type: Percent
        value: 100
        periodSeconds: 15
      - type: Pods
        value: 4
        periodSeconds: 15
      selectPolicy: Max

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 Seconds Before Scaling Down

waitForSecondsBeforeScalingDown: 30

Wait for given period of time before scaling down the container.

3. Show Application Metrics

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 Table

Helm Chart json schema is used to validate the deployment template values.

Other Validations in Json Schema

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.

resources.limits.cpu >= resources.requests.cpu
resources.limits.memory >= resources.requests.memory
envoyproxy.resources.limits.cpu >= envoyproxy.resources.requests.cpu
envoyproxy.resources.limits.memory >= envoyproxy.resources.requests.memory

Addon features in Deployment Template Chart version 4.11.0

KEDA Autoscaling

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 autosccaling with KEDA using Prometheus metrics is given below:

kedaAutoscaling:
  enabled: true
  minReplicaCount: 1
  maxReplicaCount: 2
  idleReplicaCount: 0
  pollingInterval: 30
  advanced:
    restoreToOriginalReplicaCount: true
    horizontalPodAutoscalerConfig:
      behavior:
        scaleDown:
          stabilizationWindowSeconds: 300
          policies:
          - type: Percent
            value: 100
            periodSeconds: 15
  triggers: 
    - type: prometheus
      metadata:
        serverAddress:  http://<prometheus-host>:9090
        metricName: http_request_total
        query: envoy_cluster_upstream_rq{appId="300", cluster_name="300-0", container="envoy",}
        threshold: "50"
  triggerAuthentication:
    enabled: false
    name:
    spec: {}
  authenticationRef: {}

Example for autosccaling with KEDA based on kafka is given below :

kedaAutoscaling:
  enabled: true
  minReplicaCount: 1
  maxReplicaCount: 2
  idleReplicaCount: 0
  pollingInterval: 30
  advanced: {}
  triggers: 
    - type: kafka
      metadata:
        bootstrapServers: b-2.kafka-msk-dev.example.c2.kafka.ap-southeast-1.amazonaws.com:9092,b-3.kafka-msk-dev.example.c2.kafka.ap-southeast-1.amazonaws.com:9092,b-1.kafka-msk-dev.example.c2.kafka.ap-southeast-1.amazonaws.com:9092
        topic: Orders-Service-ESP.info
        lagThreshold: "100"
        consumerGroup: oders-remove-delivered-packages
        allowIdleConsumers: "true"
  triggerAuthentication:
    enabled: true
    name: keda-trigger-auth-kafka-credential
    spec:
      secretTargetRef:
        - parameter: sasl
          name: keda-kafka-secrets
          key: sasl
        - parameter: username
          name: keda-kafka-secrets
          key: username
  authenticationRef: 
    name: keda-trigger-auth-kafka-credential

Security Context

A security context defines privilege and access control settings for a Pod or Container.

To add a security context for main container:

containerSecurityContext:
  allowPrivilegeEscalation: false

To add a security context on pod level:

podSecurityContext:
  runAsUser: 1000
  runAsGroup: 3000
  fsGroup: 2000

Topology Spread Constraints

You can use topology spread constraints to control how Pods are spread across your cluster among failure-domains such as regions, zones, nodes, and other user-defined topology domains. This can help to achieve high availability as well as efficient resource utilization.

topologySpreadConstraints:
  - maxSkew: 1
    topologyKey: zone
    whenUnsatisfiable: DoNotSchedule
    autoLabelSelector: true
    customLabelSelector: {}