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.

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