Krs Extension for Docker Desktop

Overview

This project is a Docker Desktop extension for KRS(Kubetools Recommender System) - a Gen-AI powered tool designed to recommend and manage tools for Kubernetes clusters. The extension provides a user-friendly interface for Kubernetes cluster operations such as initialization, scanning, recommendation, and healthcheck for tools, with support for different Kubernetes environments.

Table of Contents

1. Prerequisite
2. Build and Install the Docker Extension
3. Debug the Docker Extension
4. Dockerfile Explanation
5. User Interface (UI)
6. Running the KRS Commands
7. Handling Kubernetes Configurations
8. Handling Other Kubernetes Contexts

Prerequisite

Before you begin, ensure you have the following installed:

• Docker Desktop (for building and running Docker Desktop extensions)
• Node.js
• Go
• Python

Before you install the Krs Docker Extension, ensure that Kubernetes is enabled using Docker Desktop. Don't forget to select the "Show System containers" option under Docker Desktop > Settings > Kubernetes.

Build and Install the Docker Extension

Step 1: Build the Extension

In the project root, run the following commands to build the extension:

$ make build-extension

Step 2: Install the Extension in Docker Desktop

You can install the built extension using Docker Desktop's command-line interface:

$ make install-extension

After installation, you will see the KRS - Kubetools Recommender System extension under Docker Desktop's Extensions tab.

Additional Step: Update the Extension in Docker Desktop

Whenever you make a change to your extension, you only need to hit the following command. It will help you rebuild the extension and update it:

$ make update-extension

Debug the Docker Extension

Step 1: Debug the Extension

In the project root, run the following commands to debug the extension:

$ make debug-extension

Step 2: Reset the debug mode

You can turn off the debug mode by hitting this below command

$ make reset-extension

Dockerfile Explanation

The Dockerfile is divided into three main stages:

Stage 1: Backend Service (Go)

• Base Image: golang:1.21-alpine
• This stage builds the backend service from the Go source files.

Stage 2: Frontend (UI)

• Base Image: node:21.6-alpine
• This stage builds the frontend UI using Node.js.

Stage 3: KRS (Python)

• Base Image: python:3.12.5-alpine
• This stage sets up the KRS tool, installs required dependencies, and prepares the service to interact with Kubernetes.

User Interface (UI)

KRS Instruction Window

The Docker extension provides a graphical user interface (GUI) that allows users to initialize the Kubernetes environment and run various KRS commands.

• React-based Frontend: The UI is built using React and Material-UI.
• Buttons so far:
  • krs init: Initializes the services and loads the scanner. (WORKING) ✅
  • krs scan: Scans the cluster and extracts a list of tools that are currently used. (WORKING) ✅
  • krs recommend: Generates a table of recommended tools from our ranking database and their CNCF project status. (WORKING) ✅
  • krs namespaces: Lists all the namespaces. (WORKING) ✅
  • krs pods: Lists all the pods with namespaces, or lists pods under a specified namespace. (WORKING) ✅
  • krs health: Starts an interactive terminal using an LLM of your choice to detect and fix issues with your cluster. (WORKING) ✅
  • krs export: Exports pod info with logs and events. (WORKING) ✅
  • krs exit: Ends krs services safely and deletes all state files from system. Removes all cached data. (WORKING) ✅

Running the KRS Commands

Step 0: Specifying your Kubernetes Configuration path

This is the first step in starting your work with KRS. You will be asked to specify your local Kubernetes Configuration path. As a default, the path is ~/.kube/config. You would not need to specify the path if you did not make any changes.

• Default: ~/.kube/config
• Other: The path to your Kubernetes Configuration file.

Step 1: Starting the KRS-EXTENSION Container

The krs commands are executed inside a Docker container. This container is started in detached mode and can run commands via exec:

export const startKrsContainer = async (ddClient: v1.DockerDesktopClient) => {
  // Start container in detached mode with kube config mounted
};

Step 2: Running the krs init Command

This command initializes the services and loads the scanner.

export const initKRS = async (ddClient: v1.DockerDesktopClient) => {
  // Ensure the container is running, and execute "krs init" inside the container
};

Step 3: Running the krs scan Command

This command scans the cluster and extracts a list of tools that are currently used.

export const krsScan = async (ddClient: v1.DockerDesktopClient) => {
  // Ensure the container is running, and execute "krs scan" inside the container
};

Step 4: Running the krs recommend Command

This command scans the cluster and generates a table of recommended tools from our ranking database and their CNCF project status.

export const krsRecommend = async (ddClient: v1.DockerDesktopClient) => {
  // Ensure the container is running, and execute "krs recommend" inside the container
};

Step 5: Running the krs namespaces Command

This command lists all the namespaces.

export const krsNamespaces = async (ddClient: v1.DockerDesktopClient) => {
  // Ensure the container is running, and execute "krs namespaces" inside the container
};

Step 5: Running the krs pods Command

This command lists all the pods with namespaces, or lists pods under a specified namespace.

export const krsPods = async (ddClient: v1.DockerDesktopClient) => {
  // Ensure the container is running, and execute "krs pods" inside the container
};

Step 6: Running the krs health Command

This command starts an interactive terminal using an LLM of your choice to detect and fix issues with your cluster.

export const krsHealth = async (ddClient: v1.DockerDesktopClient) => {
  // Ensure the container is running, and execute "krs health" inside the container
};

This command will allow you to interact with your cluster.

Step 7: Running the krs export Command

This command exports pod info with logs and events.

export const krsExport = async (ddClient: v1.DockerDesktopClient) => {
  // Ensure the container is running, and execute "krs export" inside the container
};

This command will export the pod information with logs and events inside the running container and copy it to your home directory.

Step 8: Running the krs exit Command

This command ends krs services safely and deletes all state files from system. Removes all cached data.

export const krsExit = async (ddClient: v1.DockerDesktopClient) => {
  // Ensure the container is running, and execute "krs exit" inside the container
};

Handling Kubernetes Configurations

The Docker extension dynamically handles various Kubernetes configurations by mounting the ~/.kube/config file from the host system into the container. This file contains the necessary configurations to connect to the local Kubernetes clusters.

Handling Other Kubernetes Contexts

The KRS docker extension has been upgraded to support other k8s contexts besides docker-desktop such as Minikube and AWS EKS.

The following guidelines are the steps to setup Minikube:

1. Ensure Minikube installed
2. Start Minikube:

$ minikube start

3. Ensure Minikube is running:

$ minikube status

4. Select Minikube in a dropdown menu:

Whenever users select minikube context, the kube/config file will be updated to have minikube, it also requires some configurations for minikube which is located in the .minikube folder. Additionally, the KRS docker extension requires that folder to be able to run the tool. As a default, the minikube configuration path is ~/.minikube, but the path might be different in other platforms such as Windows. Therefore, users can select the Other button to specify it, otherwise, users can hit the Default button.

Minikube is scanned by KRS