External Scaler for KEDA

Introduction

A KEDA extenal scaler for Opensearch. Currently the ELasticsearch scaler in KEDA does not support Opensearch

Prerequisites

Before you begin, ensure you have met the following requirements:

• Kubernetes Cluster: A running Kubernetes cluster.
• KEDA: Installed KEDA in your Kubernetes cluster.
• Docker: Docker installed on your local machine.
• Go: Go programming language installed (if you plan to modify the scaler code).

How to Run Locally

To run the external scaler locally, follow these steps. For test purposes, you will have to set the environment variables loaded in main.go:27

1. Clone the Repository:

   git clone https://github.com/hatemmezlini/keda-opensearch-ext.git
   cd keda-opensearch-ext

2. **Install dependencies **:

   go mod tidy

3. Test the Application locally:

   go run main.go

How to Build and Run Docker Image

To build and run the external scaler as a Docker container, follow these steps:

1. Build the Docker Image:

   docker build -t your-username/external-scaler-for-keda:latest .

2. Run the Docker Container:

   docker run -d -p 8080:8080 -p 6000:6000 --name keda-opensearch-ext -e OPENSEARCH_URL="http://localhost:9200" -e OPENSEARCH_USERNAME="keda" -e OPENSEARCH_PASSWORD="keda" opensearch-keda-externalscaler your-username/external-scaler-for-keda:latest

Usage example

apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: your-scaledobject-name
  namespace: your-namespace
spec:
  scaleTargetRef:
    name: your-scaledobject-name
    # kind: Statefulset (defaults to Deployment)
  pollingInterval: 30
  cooldownPeriod: 43200
  triggers:
    - type: external
      metadata:
        scalerAddress: opensearch-externalscaler.your-namespace.svc.cluster.local:6000
        unsafeSSL: "true"  # default is false
        index: "your-index-name-*"
        searchTemplateName: "search_template_name"
        targetValue: "5000"
        parameters: "param1=value1,param2=value2"

Conclusion

Thank you for using the External Scaler for KEDA! This project aims to provide a flexible and powerful solution to extend KEDA's autoscaling capabilities. If you have any questions or encounter any issues, feel free to open an issue on the GitHub repository. Happy scaling!