Urtzi Alfaro 7ef85c1188 Add comprehensive SigNoz configuration guide and monitoring setup
Documentation includes:

1. OpAMP Root Cause Analysis:
   - Explains OpenAMP (Open Agent Management Protocol) functionality
   - Documents how OpAMP was overwriting config with "nop" receivers
   - Provides two solution paths:
     * Option 1: Disable OpAMP (current solution)
     * Option 2: Fix OpAMP server configuration (recommended for prod)
   - References: SigNoz architecture and OTel collector docs

2. Database Receivers Configuration:
   - PostgreSQL: Complete setup for 21 database instances
     * SQL commands to create monitoring users
     * Proper pg_monitor role permissions
     * Environment variable configuration
   - Redis: Configuration with/without TLS
     * Uses existing redis-secrets
     * Optional TLS certificate generation
   - RabbitMQ: Management API setup
     * Uses existing rabbitmq-secrets
     * Port 15672 management interface

3. Automation Script:
   - create-pg-monitoring-users.sh
   - Creates monitoring user in all 21 PostgreSQL databases
   - Generates secure random password
   - Verifies permissions
   - Provides next-step commands

Resources Referenced:
- PostgreSQL: https://signoz.io/docs/integrations/postgresql/
- Redis: https://signoz.io/blog/redis-opentelemetry/
- RabbitMQ: https://signoz.io/blog/opentelemetry-rabbitmq-metrics-monitoring/
- OpAMP: https://signoz.io/docs/operate/configuration/
- OTel Config: https://signoz.io/docs/opentelemetry-collection-agents/opentelemetry-collector/configuration/

Current Infrastructure Discovered:
- 21 PostgreSQL databases (all services have dedicated DBs)
- 1 Redis instance (password in redis-secrets)
- 1 RabbitMQ instance (credentials in rabbitmq-secrets)

Next Implementation Steps:
1. Run create-pg-monitoring-users.sh script
2. Create Kubernetes secrets for monitoring credentials
3. Update signoz-values-dev.yaml with receivers
4. Enable receivers in metrics pipeline
5. Test and verify metric collection

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
2026-01-09 12:15:58 +01:00
2026-01-09 07:26:11 +01:00
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2026-01-09 07:26:11 +01:00
2025-12-17 16:36:26 +01:00
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2026-01-09 07:26:11 +01:00
2026-01-02 21:33:23 +01:00

🍞 BakeWise - Multi-Service Architecture

Welcome to BakeWise, an advanced AI-powered platform for bakery management and optimization. This project implements a microservices architecture with multiple interconnected services to provide comprehensive bakery management solutions.

🚀 Quick Start

Prerequisites

  • Docker Desktop with Kubernetes enabled
  • Docker Compose
  • Node.js (for frontend development)

Running the Application

  1. Clone the repository:

    git clone <repository-url>
    cd bakery-ia
    
  2. Set up environment variables:

    cp .env.example .env
    # Edit .env with your specific configuration
    
  3. Run with Docker Compose:

    docker-compose up --build
    
  4. Or run with Kubernetes (Docker Desktop):

    # Enable Kubernetes in Docker Desktop
    # Run the setup script
    ./scripts/setup-kubernetes-dev.sh
    

🏗️ Architecture Overview

The project follows a microservices architecture with the following main components:

  • Frontend: React-based dashboard for user interaction
  • Gateway: API gateway handling authentication and routing
  • Services: Multiple microservices handling different business domains
  • Infrastructure: Redis, RabbitMQ, PostgreSQL databases

🐳 Kubernetes Infrastructure

🛠️ Services

The project includes multiple services:

  • Auth Service: Authentication and authorization
  • Tenant Service: Multi-tenancy management
  • Sales Service: Sales processing
  • External Service: Integration with external systems
  • Training Service: AI model training
  • Forecasting Service: Demand forecasting
  • Notification Service: Notifications and alerts
  • Inventory Service: Inventory management
  • Recipes Service: Recipe management
  • Suppliers Service: Supplier management
  • POS Service: Point of sale
  • Orders Service: Order management
  • Production Service: Production planning
  • Alert Processor: Background alert processing

📊 Monitoring

The system includes comprehensive monitoring with:

  • Prometheus for metrics collection
  • Grafana for visualization
  • ELK stack for logging (planned)

🚀 Production Deployment

For production deployment on clouding.io with Kubernetes:

  1. Set up your clouding.io Kubernetes cluster
  2. Update image references to your container registry
  3. Configure production-specific values
  4. Deploy using the production kustomization:
    kubectl apply -k infrastructure/kubernetes/environments/production/
    

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Submit a pull request

📄 License

This project is licensed under the MIT License.

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