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https://github.com/yazanzebak/k8s-gke-cloudproject
https://github.com/yazanzebak/k8s-gke-cloudproject
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/yazanzebak/k8s-gke-cloudproject
- Owner: YazanZebak
- Created: 2024-12-03T08:09:12.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-12-03T19:54:07.000Z (about 2 months ago)
- Last Synced: 2024-12-03T20:34:53.241Z (about 2 months ago)
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# K8S-GKE-CloudProject
This project demonstrates deploying the "Online Boutique" demo application, a microservice-based application created by Google, on Google Kubernetes Engine (GKE). It also includes automated deployment of a load generator using Terraform and Ansible.
---
## Overview
The [**Online Boutique**](https://github.com/GoogleCloudPlatform/microservices-demo/) is a mock application simulating an online shop. It consists of 11 microservices, showcasing a cloud-native architecture. The application can be tested with a load generator to simulate real-world traffic.
---
## Prerequisites
1. **Google Cloud Platform (GCP)** account with a project set up.
2. **Tools**:
- `gcloud` CLI
- Terraform
- Ansible
- Docker
3. **SSH Key** for accessing the VM.
---
## Setup Instructions
### 1. Clone the Repository
```bash
git clone https://github.com/YazanZebak/K8S-GKE-CloudProject.git
cd K8S-GKE-CloudProject
```
### 2. Run the Setup Script
Specify your GCP project ID and region in the `setup.sh` script and execute it to configure the environment.
```bash
scripts/setup-env.sh
```
### 3. Configure Terraform and Ansible
Update the following:
- **`terraform.main`**: Add your SSH key and username in the `user_keys` variable.
```hcl
variable "user_keys" {
default = {
your_username = "ssh-rsa AAAAB3.....YOUR_SSH_KEY"
}
}
```
- **`inventory.ini`**: add new line with your useranme and key to your private key
```ini
{{LOAD_GENERATOR_IP}} ansible_user=your_username ansible_private_key_file=/path/to/ssh/private/key FRONTEND_ADDR={{FRONTEND_ADDR}}
```
### 4. Deploy the Application and Load Generator
Run the deployment script:
```bash
scripts/deploy-app.sh
```
This script will:
- Deploy the Online Boutique application on GKE.
- Set up a load generator in a Google Compute Engine (GCE) VM.
---
## Testing the Deployment
### 1. Access the Application
- Use the IP address of the load balancer (`frontend-external`) to access the application in a web browser:
```
http://
```
### 2. Verify the Load Generator
- SSH into the VM:
```bash
ssh -i /path/to/your/private-key @
```
- Check Docker logs to verify the load generator is running:
```bash
docker logs
```
### 3. Local Load Generator Deployment
- Use the provided Docker image to deploy the load generator locally make sure to specify the FRONTEND_ADDR so the load_generator works correctly.
```
docker run -d --name load-generator -e FRONTEND_ADDR="http://{{ frontend_addr }}" --network host yazanzk/loadgen_locust
```
---
## Clean-Up
To avoid incurring unnecessary costs, destroy the resources after completing your tests:
```bash
scripts/destroy-all.sh
```
---
## Additional Notes
### Autopilot vs. Standard Mode in GKE
- **Autopilot Mode**: Simplifies resource management and ensures the cluster has sufficient resources for all workloads, but it is more expensive and less flexible.
- **Standard Mode**: Allows finer control over cluster resources. To deploy in standard mode, use the following node configuration:
- Machine type: `e2-standard-2`
- Number of nodes: `4`
---
## Technologies Used
- **GKE**: Hosts the Online Boutique application.
- **Terraform**: Provisions GCP resources.
- **Ansible**: Configures the load generator VM.
- **Docker**: Deploys the load generator.
---
## Results
- Access the Online Boutique via the frontend load balancer IP (Step 1: Deploying the original application in GKE).
- Test the load generator locally using Docker (Step 3: Deploying the load generator on a local machine).
- View logs and monitor the load generator on the VM (Step 4: Deploying automatically the load generator in Google Cloud).