Managing harbor effectively is a crucial skill for any system administrator. This tutorial provides step-by-step instructions for registry configuration, along with best practices for production environments.
Prerequisites
- Basic familiarity with the Linux command line
- A Git repository for your project
- A VPS running Ubuntu 22.04 or later (2GB+ RAM recommended)
- Root or sudo access to the server
Pipeline Configuration
After applying these changes, monitor the server's resource usage for at least 24 hours to ensure stability. Tools like htop, iostat, and vmstat can provide real-time insights into system performance.
# .github/workflows/deploy.yml
name: Deploy to Production
on:
push:
branches: [main]
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Build
run: |
docker build -t myapp:latest .
- name: Deploy
run: |
ssh deploy@server 'cd /opt/myapp && docker compose pull && docker compose up -d'
Note that file paths may vary depending on your Linux distribution. The examples here are for Debian/Ubuntu; adjust paths accordingly for RHEL/CentOS-based systems.
Advanced Settings
Before making changes to the configuration, always create a backup of the existing files. This ensures you can quickly roll back if something goes wrong during the setup process.
- Profile before optimizing - measure first
- Implement caching at every appropriate layer
- Scale vertically before scaling horizontally
- Use connection pooling for database connections
- Start with the minimum required resources
Build and Test Setup
Performance benchmarks show that properly tuned harbor can handle significantly more concurrent connections than the default configuration. The key improvements come from adjusting worker processes and connection pooling.
# Set up deployment pipeline
mkdir -p /opt/myapp
cd /opt/myapp
# Create deployment script
cat << 'EOF' > deploy.sh
#!/bin/bash
set -euo pipefail
echo "Deploying version: $1"
docker pull myapp:$1
docker compose down
DOCKER_TAG=$1 docker compose up -d
echo "Deployment complete"
EOF
chmod +x deploy.sh
Make sure to restart the service after applying these changes. Some settings require a full restart rather than a reload to take effect.
Deployment Automation
The default configuration works well for development environments, but production servers require additional tuning. Pay particular attention to connection limits, timeout values, and logging settings.
# .github/workflows/deploy.yml
name: Deploy to Production
on:
push:
branches: [main]
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Build
run: |
docker build -t myapp:latest .
- name: Deploy
run: |
ssh deploy@server 'cd /opt/myapp && docker compose pull && docker compose up -d'
Each line in the configuration serves a specific purpose. The comments explain the reasoning behind each setting, making it easier to customize for your specific use case.
Security Implications
Performance benchmarks show that properly tuned harbor can handle significantly more concurrent connections than the default configuration. The key improvements come from adjusting worker processes and connection pooling.
Environment Management
When scaling this setup, consider vertical scaling (adding more RAM/CPU) first, as it's simpler to implement. Horizontal scaling adds complexity but may be necessary for high-traffic applications.
# Set up deployment pipeline
mkdir -p /opt/myapp
cd /opt/myapp
# Create deployment script
cat << 'EOF' > deploy.sh
#!/bin/bash
set -euo pipefail
echo "Deploying version: $1"
docker pull myapp:$1
docker compose down
DOCKER_TAG=$1 docker compose up -d
echo "Deployment complete"
EOF
chmod +x deploy.sh
This configuration provides a good balance between performance and resource usage. For high-traffic scenarios, you may need to increase the limits further.
Advanced Settings
The harbor configuration requires careful attention to resource limits and security settings. On a VPS with limited resources, it's important to tune these parameters according to your available RAM and CPU cores.
Next Steps
With harbor now set up and running, consider implementing monitoring to track performance metrics over time. Regularly review your configuration as your workload changes and scale resources accordingly.