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https://github.com/sylvester-francis/ota-deploy-tracker

Progressive K8s deployment manager with FastAPI, Streamlit UI, and Prometheus monitoring.
https://github.com/sylvester-francis/ota-deploy-tracker

ci-cd container-orchestration deployment fastapi kubernetes microservices ota-updates prometheus python3 streamlit

Last synced: 2 months ago
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Progressive K8s deployment manager with FastAPI, Streamlit UI, and Prometheus monitoring.

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README 

          
# Kubernetes Deployment Manager



> **Professional-grade progressive deployment management for Kubernetes environments**



A comprehensive, enterprise-ready platform for managing safe, controlled software deployments across Kubernetes clusters with real-time monitoring, multiple rollout strategies, and instant rollback capabilities.



![Version](https://img.shields.io/badge/version-2.0.0-blue.svg)

![License](https://img.shields.io/badge/license-MIT-green.svg)

![Kubernetes](https://img.shields.io/badge/kubernetes-1.20+-brightgreen.svg)

![Python](https://img.shields.io/badge/python-3.9+-blue.svg)

![Prometheus](https://img.shields.io/badge/prometheus-monitoring-orange.svg)

![Grafana](https://img.shields.io/badge/grafana-dashboards-yellow.svg)

![FastAPI](https://img.shields.io/badge/fastapi-0.104+-green.svg)



## 🏗️ Architecture Overview



```mermaid

graph TB

    subgraph "External Clients"

        CLI[CLI Tool
cli/client.py]

        WebUI[Web Dashboard
dashboard/ota_dashboard.py]

        API_Client[External API Client
CI/CD Systems]

    end

    

    subgraph "Core Platform"

        API[FastAPI Backend
backend/main.py
Port: 8000]

        JobRunner[Job Runner
cli/job_runner.py
Background Processing]

        DB[(SQLite Database
deployment.db)]

    end

    

    subgraph "Kubernetes Cluster"

        K8S_API[Kubernetes API Server]

        Pods[Application Pods
k8s/application.yaml]

        Services[Services & Deployments
k8s/deployment.yaml]

    end

    

    subgraph "Monitoring & Metrics"

        Prometheus[Prometheus Server
Port: 9090]

        Grafana[Grafana Dashboard
Port: 3000]

        Metrics[Metrics Endpoint
/metrics]

        Logs[Application Logs
Structured Logging]

    end

    

    %% Client connections

    CLI -->|HTTP Requests| API

    WebUI -->|Streamlit Server| API

    API_Client -->|REST API| API

    

    %% Core platform flow

    API -->|Job Queue| DB

    DB -->|Pending Jobs| JobRunner

    JobRunner -->|kubectl Operations| K8S_API

    

    %% Kubernetes operations

    K8S_API -->|Manage| Pods

    K8S_API -->|Deploy| Services

    

    %% Monitoring flow

    API -->|Expose| Metrics

    Metrics -->|Scrape| Prometheus

    Prometheus -->|Data Source| Grafana

    JobRunner -->|Generate| Logs

    K8S_API -->|Status Updates| JobRunner

    

    %% Deployment strategies

    JobRunner -.->|Canary
1 Pod| Pods

    JobRunner -.->|Blue
Subset| Pods

    JobRunner -.->|Green
All Pods| Pods

    

    %% Rollback flow

    JobRunner -.->|Rollback
Previous Version| Pods

    

    style CLI fill:#e1f5fe

    style WebUI fill:#e8f5e8

    style API fill:#fff3e0

    style JobRunner fill:#fce4ec

    style K8S_API fill:#f3e5f5

    style Prometheus fill:#e0f2f1

```



---



## 🎯 Why Choose Kubernetes Deployment Manager?



**For DevOps Teams:**

- Reduce deployment risks with progressive rollout strategies

- Gain instant visibility into deployment status across all environments

- Automate complex deployment workflows with simple CLI commands

- Integrate seamlessly with existing CI/CD pipelines



**For Platform Engineers:**

- Enterprise-grade observability with Prometheus metrics

- Kubernetes-native integration with no external dependencies

- Scalable architecture supporting clusters of any size

- Production-tested rollback mechanisms for instant recovery



**For Development Teams:**

- Simple, intuitive interface for deployment management

- Real-time dashboard for monitoring application health

- Consistent deployment processes across all environments

- Reduced time-to-production with automated workflows



---



## ✨ Key Features



### 🚀 Progressive Deployment Strategies

- **Canary**: Test with minimal risk (1 pod)

- **Blue**: Controlled subset rollout (configurable)

- **Green**: Full cluster deployment

- **Custom**: Define your own deployment patterns



### 🔄 Instant Rollback

- One-command rollback to any previous version

- Granular rollback control (canary, blue, green scopes)

- Automated failure detection and recovery

- Zero-downtime rollback operations



### 📊 Enterprise Observability

- Prometheus-compatible metrics out of the box

- Real-time deployment status tracking

- Comprehensive logging and audit trails

- Grafana dashboard templates included



### 🎛️ Multiple Interfaces

- **CLI**: Perfect for CI/CD integration and automation

- **Web Dashboard**: Visual monitoring and management

- **REST API**: Programmatic access for custom integrations

- **Kubernetes Native**: Direct kubectl compatibility



---



## 🚀 Quick Start



### Prerequisites

- **Kubernetes cluster** (v1.20+)

- **Python 3.8+**

- **kubectl** configured and connected

- **Docker** (for containerized deployment)



### 1. Installation



#### Option A: Quick Setup (Recommended)

```bash

# Clone the repository

git clone https://github.com/sylvester-francis/ota-deploy-tracker.git

cd ota-deploy-tracker



# Install dependencies

pip install -r requirements.txt



# Deploy sample applications with logging

kubectl apply -f k8s/application.yaml



# Start the system

docker-compose up -d

```



#### Option B: Local Development

```bash

# Clone and setup virtual environment

git clone https://github.com/sylvester-francis/ota-deploy-tracker.git

cd ota-deploy-tracker

python -m venv venv

source venv/bin/activate  # Windows: venv\Scripts\activate



# Install dependencies

pip install -r requirements.txt



# Start components separately

uvicorn backend.main:app --reload &

streamlit run dashboard/ota_dashboard.py &

python -m cli.job_runner &

```



### 2. Access Your Dashboard



- **Web Dashboard**: http://localhost:8501

- **API Documentation**: http://localhost:8000/docs

- **Metrics Endpoint**: http://localhost:8000/metrics



### 3. Start Monitoring (Optional)



```bash

# Launch Prometheus + Grafana monitoring stack

./start-monitoring.sh



# Access monitoring:

# - Prometheus: http://localhost:9090

# - Grafana: http://localhost:3000 (admin/admin)

```



### 4. Your First Deployment



```bash

# Deploy version 2.0.0 using canary strategy

python -m cli.client deploy 2.0.0 --wave canary



# Monitor deployment status

python -m cli.client list



# Scale to blue wave (partial rollout)

python -m cli.client deploy 2.0.0 --wave blue



# Full deployment

python -m cli.client deploy 2.0.0 --wave green



# Rollback if needed

python -m cli.client rollback 1.0.0 --wave green

```



---



## 💻 Usage Examples



### CLI Operations



```bash

# Deployment Management

python -m cli.client deploy 3.1.4 --wave canary    # Canary deployment

python -m cli.client deploy 3.1.4 --wave blue      # Partial rollout

python -m cli.client deploy 3.1.4 --wave green     # Full deployment



# Monitoring

python -m cli.client list                           # List all jobs



# Rollback Operations

python -m cli.client rollback 3.1.3 --wave canary  # Canary rollback

python -m cli.client rollback 3.1.3 --wave green   # Full rollback



# Direct Pod Updates (Advanced)

python -m cli.client update 3.1.4 --wave blue      # Direct update

```



### Docker Operations



```bash

# Production deployment

docker-compose up -d



# View logs

docker-compose logs -f api

docker-compose logs -f dashboard

docker-compose logs -f job-runner



# Execute CLI commands

docker-compose exec api python -m cli.client deploy 1.5.0 --wave canary

docker-compose exec api python -m cli.client list

docker-compose exec api python -m cli.client rollback 1.4.0



# Scale services

docker-compose up -d --scale job-runner=3

```



---



## 🧪 Development & Testing



### Running Tests



```bash

# Install test dependencies

pip install pytest pytest-cov



# Run all tests

pytest



# Run with coverage

pytest --cov=backend --cov=cli --cov=dashboard --cov-report=html



# Run specific test categories

pytest tests/test_api.py          # API tests

pytest tests/test_cli.py          # CLI tests

pytest tests/test_job_runner.py   # Job runner tests

```



### Code Quality



```bash

# Linting and formatting

ruff check --fix backend/ cli/ dashboard/ tests/

ruff format backend/ cli/ dashboard/ tests/

```



---



## 🧪 Sample Applications



The platform includes sample applications (`app-1` and `app-2`) that demonstrate deployment management capabilities:



### Application Features

- **Realistic Logging**: Continuous application logs with startup sequences and health checks

- **Simulated Metrics**: Random memory usage and connection statistics

- **Version Tracking**: Applications tagged with `sw_version` labels for deployment tracking

- **Health Monitoring**: Periodic health check logs every 30 seconds



### Sample Log Output

```bash

Mon Jun 16 06:23:20 UTC 2025 [INFO] Application app-1 starting up...

Mon Jun 16 06:23:20 UTC 2025 [INFO] Loading configuration for version 1.0.0

Mon Jun 16 06:23:20 UTC 2025 [INFO] Database connection established

Mon Jun 16 06:23:20 UTC 2025 [INFO] Server listening on port 8080

Mon Jun 16 06:23:20 UTC 2025 [INFO] Application ready to serve requests

Mon Jun 16 06:23:20 UTC 2025 [INFO] Health check passed - app-1 running normally

Mon Jun 16 06:23:20 UTC 2025 [DEBUG] Memory usage: 73MB

Mon Jun 16 06:23:20 UTC 2025 [DEBUG] Active connections: 5

```



### Dashboard Features

- **Real-time Pod Status**: View current application versions and status

- **Log Streaming**: Access live application logs through the web interface

- **Auto-refresh**: Optional 30-second auto-refresh for real-time monitoring

- **Manual Refresh**: Instant updates with the refresh button



---



## 📊 Monitoring & Observability



### Quick Start Monitoring Stack



Launch the complete monitoring solution with one command:



```bash

# Start Prometheus + Grafana

./start-monitoring.sh



# Access URLs:

# - Prometheus UI: http://localhost:9090

# - Grafana Dashboard: http://localhost:3000 (admin/admin)

# - Raw Metrics: http://localhost:8000/metrics

```



### Prometheus Metrics



The platform exports comprehensive metrics for monitoring:



```bash

# Deployment Metrics

ota_updated_pods_total                    # Total pods updated

ota_last_run_timestamp_seconds           # Last deployment timestamp

ota_jobs_pending                         # Pending deployment jobs

ota_jobs_successful                      # Successful deployments

ota_jobs_total                          # Total deployment jobs



# Rollback Metrics

ota_rollback_pods_total                  # Total pods rolled back

ota_last_rollback_timestamp_seconds     # Last rollback timestamp

```



### Grafana Dashboard



Pre-configured dashboard includes:



- **📈 Real-time Metrics**: Live deployment statistics and trends

- **📊 Visual Charts**: Deployment timeline and rollback activity

- **🎯 Key Performance Indicators**: Success rates and system health

- **⏰ Historical Data**: Track deployment patterns over time



### Manual Monitoring Setup



```bash

# Start monitoring stack manually

cd monitoring

docker-compose -f docker-compose.monitoring.yml up -d



# Stop monitoring stack

docker-compose -f docker-compose.monitoring.yml down



# View raw metrics

curl http://localhost:8000/metrics

```



### Custom Queries



Example Prometheus queries for advanced monitoring:



```promql

# Deployment success rate

(ota_jobs_successful / ota_jobs_total) * 100



# Average deployment frequency (per hour)

rate(ota_jobs_total[1h]) * 3600



# Time since last deployment

time() - ota_last_run_timestamp_seconds



# Rollback frequency

rate(ota_rollback_pods_total[24h])

```



---



## 📄 API Reference



### Deployment Endpoints



| Method | Endpoint | Description |

|--------|----------|-------------|

| `POST` | `/ota/deploy` | Create new deployment |

| `GET` | `/ota/jobs` | List all jobs |

| `POST` | `/ota/update_status` | Update job status |

| `POST` | `/ota/rollback` | Trigger rollback |

| `GET` | `/metrics` | Prometheus metrics |



### CLI Commands



```bash

# Deployment commands

cli.client deploy  [--wave ]

cli.client update  [--wave ]

cli.client rollback  [--wave ]

cli.client list



# Management commands

cli.job_runner                    # Start job runner

```



---



## 🤝 Contributing



We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.



### Development Workflow



1. Fork the repository

2. Create a feature branch: `git checkout -b feature/amazing-feature`

3. Make your changes and add tests

4. Run the test suite: `pytest`

5. Commit your changes: `git commit -m 'Add amazing feature'`

6. Push to the branch: `git push origin feature/amazing-feature`

7. Open a Pull Request



---



## 📝 License



This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.



---



## 🙏 Acknowledgments



- Kubernetes community for the robust container orchestration platform

- FastAPI team for the excellent Python web framework

- Streamlit team for the intuitive dashboard framework

- Contributors and users who make this project better



---



## 🤝 Connect & Follow



Ready to collaborate or learn more about cutting-edge tech solutions?



[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue?style=for-the-badge&logo=linkedin)](https://www.linkedin.com/in/sylvesterranjith/)

[![Medium](https://img.shields.io/badge/Medium-Follow-black?style=for-the-badge&logo=medium)](https://medium.com/@sylvesterranjithfrancis)

[![Instagram](https://img.shields.io/badge/Instagram-@techwithsyl-purple?style=for-the-badge&logo=instagram)](https://instagram.com/techwithsyl)



**Let's build the future of deployment management together!**



---



**Ready to transform your deployment process?** [Get started now](#-quick-start) and experience professional-grade Kubernetes deployment management!