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configure-custom-values-envoy.md

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Caution

The contents of the docs folder have been moved to the docs-internal-orchestration repository. Please update this documentation in that repository instead.

To view the Helm Charts documentation, visit the documentation site for the product you are using:

Configure a custom values file for Scalar Envoy

This document explains how to create your custom values file for the Scalar Envoy chart. If you want to know the details of the parameters, please refer to the README of the Scalar Envoy chart.

Configure custom values for Scalar Envoy chart

The Scalar Envoy chart is used via other charts (scalardb, scalardb-cluster, scalardl, and scalardl-audit), so you don't need to create a custom values file for the Scalar Envoy chart. If you want to configure Scalar Envoy, you need to add the envoy.* configuration to the other charts.

For example, if you want to configure the Scalar Envoy for ScalarDB Server, you can configure some Scalar Envoy configurations in the custom values file of ScalarDB as follows.

  • Example (scalardb-custom-values.yaml) 
    envoy:
  configurationsForScalarEnvoy: 
    ...

scalardb:
  configurationsForScalarDB: 
     ...

Required configurations

Service configurations

You must set envoy.service.type to specify the Service resource type of Kubernetes.

If you accept client requests from inside of the Kubernetes cluster only (for example, if you deploy your client applications on the same Kubernetes cluster as Scalar products), you can set envoy.service.type to ClusterIP. This configuration doesn't create any load balancers provided by cloud service providers.

envoy:
  service:
    type: ClusterIP

If you want to use a load balancer provided by a cloud service provider to accept client requests from outside of the Kubernetes cluster, you need to set envoy.service.type to LoadBalancer.

envoy:
  service:
    type: LoadBalancer

If you want to configure the load balancer via annotations, you can also set annotations to envoy.service.annotations.

envoy:
  service:
    type: LoadBalancer
    annotations:
      service.beta.kubernetes.io/aws-load-balancer-internal: "true"
      service.beta.kubernetes.io/aws-load-balancer-type: "nlb"

Optional configurations

Resource configurations (Recommended in the production environment)

If you want to control pod resources using the requests and limits of Kubernetes, you can use envoy.resources.

You can configure them using the same syntax as the requests and limits of Kubernetes. So, please refer to the official document Resource Management for Pods and Containers for more details on the requests and limits of Kubernetes.

envoy:
  resources:
    requests:
      cpu: 1000m
      memory: 2Gi
    limits:
      cpu: 2000m
      memory: 4Gi

Affinity configurations (Recommended in the production environment)

If you want to control pod deployment using the affinity and anti-affinity of Kubernetes, you can use envoy.affinity.

You can configure them using the same syntax as the affinity of Kubernetes. So, please refer to the official document Assigning Pods to Nodes for more details on the affinity configuration of Kubernetes.

envoy:
  affinity:
    podAntiAffinity:
      preferredDuringSchedulingIgnoredDuringExecution:
        - podAffinityTerm:
            labelSelector:
              matchExpressions:
                - key: app.kubernetes.io/name
                  operator: In
                  values:
                    - scalardb-cluster
                - key: app.kubernetes.io/app
                  operator: In
                  values:
                    - envoy
            topologyKey: kubernetes.io/hostname
          weight: 50

Prometheus and Grafana configurations (Recommended in production environments)

If you want to monitor Scalar Envoy pods using kube-prometheus-stack, you can deploy a ConfigMap, a ServiceMonitor, and a PrometheusRule resource for kube-prometheus-stack using envoy.grafanaDashboard.enabled, envoy.serviceMonitor.enabled, and envoy.prometheusRule.enabled.

envoy:
  grafanaDashboard:
    enabled: true
    namespace: monitoring
  serviceMonitor:
    enabled: true
    namespace: monitoring
    interval: 15s
  prometheusRule:
    enabled: true
    namespace: monitoring

SecurityContext configurations (Default value is recommended)

If you want to set SecurityContext and PodSecurityContext for Scalar Envoy pods, you can use envoy.securityContext and envoy.podSecurityContext.

You can configure them using the same syntax as SecurityContext and PodSecurityContext of Kubernetes. So, please refer to the official document Configure a Security Context for a Pod or Container for more details on the SecurityContext and PodSecurityContext configurations of Kubernetes.

envoy:
  podSecurityContext:
    seccompProfile:
      type: RuntimeDefault
  securityContext:
    capabilities:
      drop:
        - ALL
    runAsNonRoot: true
    allowPrivilegeEscalation: false

Image configurations (Default value is recommended)

If you want to change the image repository and version, you can use envoy.image.repository to specify the container repository information of the Scalar Envoy container image that you want to pull.

envoy:
  image:
    repository: <SCALAR_ENVOY_CONTAINER_IMAGE>

If you're using AWS or Azure, please refer to the following documents for more details:

Replica configurations (Optional based on your environment)

You can specify the number of replicas (pods) of Scalar Envoy using envoy.replicaCount.

envoy:
  replicaCount: 3

Taint and toleration configurations (Optional based on your environment)

If you want to control pod deployment by using the taints and tolerations in Kubernetes, you can use envoy.tolerations.

You can configure taints and tolerations by using the same syntax as the tolerations in Kubernetes. For details on configuring tolerations in Kubernetes, see the official Kubernetes documentation Taints and Tolerations.

envoy:
  tolerations:
    - effect: NoSchedule
      key: scalar-labs.com/dedicated-node
      operator: Equal
      value: scalardb