Helm

Registry Access

You'll need credentials from our support team to access Membrane artifacts:

Registry Credentials

• Username format: robot$<your-company-name>
• Access to: harbor.integration.app

Setting Up Registry Access

1. Login to Helm registry

helm registry login harbor.integration.app \
  --username <registry-username> \
  --password <registry-password>

2. Pull and unpack the Integration.app Helm chart:

# See Versions section at the bottom of this page for available versions
helm pull oci://harbor.integration.app/helm/integration-app --version <version> --untar

Prerequisites

Before installing Membrane using Helm, ensure you have the following components set up:

Prometheus Stack

The kube-prometheus stack provides Prometheus, Grafana dashboards, and necessary Prometheus rules:

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update
kubectl create namespace monitoring
helm install prometheus-stack prometheus-community/kube-prometheus-stack --namespace monitoring

For advanced configuration options, refer to the kube-prometheus stack documentation.

KEDA

If you plan to use autoscaling features, install KEDA:

helm repo add kedacore https://kedacore.github.io/charts
helm repo update
kubectl create namespace keda
helm install keda kedacore/keda --namespace keda

For advanced KEDA configuration, consult the official KEDA documentation.

Installation

1. Configure Container Registry Access

Create a Docker registry secret using your container registry credentials:

kubectl create secret docker-registry integration-app-harbor \
  --namespace <your-namespace> \
  --docker-server=harbor.integration.app \
  --docker-username=<registry-username> \
  --docker-password=<registry-password>

2. Prepare Configuration

Populate or provide override to values.yaml file with the values for your setup:

serviceAccount:
  annotations:
    eks.amazonaws.com/role-arn: arn:aws:iam::<YOUR_AWS_ACCOUNT_ID>:role/<YOUR_ROLE_NAME>

config:
  NODE_ENV: production
  MONGO_URI: MONGO_URI
  REDIS_URI: REDIS_URI
  # URI where `api` service will be available
  API_URI: API_URI
  # URI where `ui` service will be available
  UI_URI: UI_URI
  # URI where `console` service will be available
  CONSOLE_URI: CONSOLE_URI
  # Bucket for storing custom connectors
  CONNECTORS_S3_BUCKET: CONNECTORS_S3_BUCKET
  # Bucket for storing temporary files (like logs)
  TMP_S3_BUCKET: TMP_S3_BUCKET
  # Buckets for storing static files that should be available in user's browser (like images)
  STATIC_S3_BUCKET: STATIC_S3_BUCKET
  # Base URI where files stored in STATIC_S3_BUCKET will be available
  BASE_STATIC_URI: BASE_STATIC_URI
  # Auth0 Settings
  AUTH0_DOMAIN: AUTH0_DOMAIN
  AUTH0_CLIENT_ID: AUTH0_CLIENT_ID
  AUTH0_CLIENT_SECRET: AUTH0_CLIENT_SECRET
  # Secret key used for signing JWT tokens
  SECRET: SECRET
  # Secret key used for encrypting data at rest
  ENCRYPTION_SECRET: ENCRYPTION_SECRET

3. Validate Chart

Before deploying, make sure that chart is rendering correctly:

helm template integration-app ./path-to-your-chart --namespace <your-namespace>

4. Select Cluster Context

Make sure to switch to desired cluster context:

kubectl config use-context <your-desired-context>

5. Deploy

Install the chart to cluster:

helm install integration-app ./path-to-this-folder --namespace <your-namespace> --create-namespace

To update an existing installation:

helm upgrade integration-app ./path-to-this-folder --namespace <your-namespace>

Autoscaling Configuration

The following components support autoscaling:

• API
• Instant Tasks Worker
• Queued Tasks Worker
• Custom Code Runner

Each component that supports autoscaling accepts these parameters:

These properties are part of KEDA's core functionality. For more detailed information, please refer to the official KEDA documentation.

Component-Specific Scaling

Each component has specific scaling parameters that control its autoscaling behavior:

Versions and Changelog

Latest Version: 0.2.1