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Sparrow API Helm Chart

This repository contains the Helm chart for deploying the sparrow-api application. The chart allows for easy deployment, scaling, and configuration of the API on Kubernetes clusters.

Prerequisites

Before installing the sparrow-api Helm chart, make sure the following dependencies are set up:

  1. MongoDB: Install MongoDB using Helm.

    helm repo add bitnami https://charts.bitnami.com/bitnami
helm install sparrow-mongodb bitnami/mongodb --namespace sparrow-dev

  2. Kafka: Install Kafka using Helm.

    helm repo add bitnami https://charts.bitnami.com/bitnami
helm install sparrow-kafka bitnami/kafka --namespace sparrow-dev

Ensure that MongoDB and Kafka services are running and retrieve the connection URLs for each service, as they are required to configure the sparrow-api chart.

Installing the Chart

  1. Add the Sparrow Helm Repository (if hosted externally):

    helm repo add sparrow-api https://sparrowapp-dev.github.io/Sparrow-api-helm-chart
helm repo update

  2. Install the Chart: Replace <namespace> with your desired namespace (e.g., sparrow-dev).

    helm upgrade --install sparrow-api sparrow-api/sparrow-api --namespace <namespace>

Configuring MongoDB and Kafka URLs

To connect the chart to MongoDB and Kafka, provide their URLs using the --set flag or by updating the values.yaml file.

Example with --set

helm install sparrow-api sparrow-api/sparrow-api --namespace sparrow-dev   --set secrets.DB_URL="mongodb://<username>:<password>@mongodb-service/"   --set secrets.KAFKA_BROKER="<KAFKA_URL>:9092"

Configuration

The following table lists common configurable parameters of the sparrow-api chart and their default values in values.yaml:

Parameter Description Default
replicaCount Number of replicas for the API 1
autoscaling.enabled Enable horizontal pod autoscaling true
autoscaling.minReplicas Minimum number of replicas 1
autoscaling.maxReplicas Maximum number of replicas 10
autoscaling.targetCPUUtilizationPercentage Target CPU usage percentage for scaling 70
image.repository Docker image repository for sparrow-api sparrowapi/sparrow-api
image.tag Image tag to use v1
service.port Port on which the service is exposed 80
service.targetPort Port the container listens on 9000
secrets.DB_URL MongoDB connection URL (empty)
secrets.KAFKA_BROKER Kafka broker URL (empty)
resources.requests.memory Minimum memory guaranteed for the pod 1024Mi
resources.limits.memory Maximum memory allowed for the pod 1648Mi

Example values.yaml

Here's an example values.yaml configuration file:

replicaCount: 1

autoscaling:
  enabled: true
  minReplicas: 1
  maxReplicas: 10
  targetCPUUtilizationPercentage: 70

image:
  repository: sparrowapi/sparrow-api
  pullPolicy: IfNotPresent
  tag: v1

service:
  type: ClusterIP
  port: 80
  targetPort: 9000

secrets:
  DB_URL: "mongodb://<username>:<password>@mongodb-service/"
  KAFKA_BROKER: "<KAFKA_URL>:9092"

resources:
  requests:
    memory: 560Mi
  limits:
    memory: 1024Mi

Accessing the Application

  1. Using Port Forwarding (for ClusterIP services):

    export POD_NAME=$(kubectl get pods --namespace sparrow-dev -l "app.kubernetes.io/name=sparrow-api" -o jsonpath="{.items[0].metadata.name}")
kubectl port-forward $POD_NAME 8080:9000 --namespace sparrow-dev

  2. Using LoadBalancer IP (for LoadBalancer services):

    kubectl get svc --namespace sparrow-dev sparrow-api -o jsonpath="{.status.loadBalancer.ingress[0].ip}"

    Copy the IP address and access your application at http://<LoadBalancer_IP>:80.

Uninstalling the Chart

To remove the sparrow-api release, use the following command:

helm uninstall sparrow-api --namespace sparrow-dev

This will remove all resources associated with the Helm release.

Feel free to reach out for any additional guidance or information!