Task Master - Scalable Job Processing System

A background job processing system that handles heavy or time-consuming tasks—like sending emails, processing images, or automating workflows—so your main app stays fast and responsive.

🚀 Quick Start

# Start development environment
docker compose -f docker-compose.dev.yml up --build

# Run database migrations
docker exec -i postgres-db psql -U admin -d taskmaster < db/schema.sql

# Run tests
go test ./... -v

📋 Implementation Progress

1. Core Infrastructure ✅

• Project structure and module setup
• PostgreSQL database integration
• Redis for state management
• Kafka message queue setup
• Docker containerization
• Basic authentication (JWT)
• Configuration management
• gRPC API implementation

2. Job Processing System 🚧

Basic Features ✅

• REST API endpoints (Fiber)
• Job creation and storage
• Kafka producer implementation
• Worker service setup
• Basic job status tracking
• Simple retry mechanism

Core Processing Features 🚧

• Job type registry system
• Payload validation
• Configurable retry policies
• Job timeout handling
• Progress tracking
• Job dependencies
• Job cancellation
• Priority queues
• Dead-letter queue
• Result storage

Advanced Processing Features 🚧

• Distributed locking
• Job batching
• Workflow engine
• Cron scheduling
• Rate limiting
• Job routing
• Job chaining
• Recovery system

3. Developer Experience 🚧

Documentation & Tools

• Job type documentation
• Debugging tools
• Testing/simulation tools
• Job templates
• Payload transformation
• Hooks/middleware system

Web Dashboard

• React + Tailwind UI
• Real-time updates (WebSocket)
• Job filtering and search
• Analytics dashboard
• Retry controls
• Logs viewer
• Worker management

4. Operations & Monitoring 🚧

Observability

• Structured logging (slog)
• Prometheus metrics
• Grafana dashboards
• OpenTelemetry tracing
• Health checks
• Resource monitoring

Operational Tools

• Job archival
• Cleanup policies
• Audit logging
• Cost tracking
• Quota management
• Statistics collection
• Alerts/notifications

5. Production Deployment 🚧

Infrastructure

• Kubernetes setup
• Helm charts
• Worker auto-scaling
• Multi-region support
• Backup/restore system
• Blue-green deployments

Cloud Integration

• Terraform configurations
• AWS/GCP deployment
• Cost optimization
• Security hardening

🔄 System Architecture

Components

1. API Service

   • REST/gRPC endpoints
   • Request validation
   • Job creation & queuing

2. Message Queue

   • Kafka-based processing
   • Job distribution
   • Order guarantee

3. Worker Service

   • Job execution
   • Status management
   • Error handling

4. Storage Layer

   • PostgreSQL: Job data
   • Redis: Status cache
   • Kafka: Message queue

Basic Job Flow

1. Submit job via API
2. Store in PostgreSQL
3. Queue in Kafka
4. Process via Worker
5. Update status
6. Store results

🛠 Development

Prerequisites

• Go 1.23+
• Docker & Docker Compose
• Make (optional)

Environment Setup

DATABASE_URL=postgres://admin:admin@postgres-db:5432/taskmaster?sslmode=disable
KAFKA_BROKER=kafka:9092
REDIS_ADDR=redis:6379
JWT_SECRET=supersecretkey

API Examples

# Authentication
POST /api/login
{
    "username": "admin",
    "password": "password"
}

# Job Management
POST /api/jobs
{
    "type": "sendEmail",
    "payload": {
        "to": "user@example.com",
        "subject": "Welcome!",
        "template": "welcome_email"
    }
}

GET /api/jobs/:id
GET /api/jobs

📝 Contributing

1. Fork repository
2. Create feature branch
3. Commit changes
4. Push to branch
5. Open Pull Request

📄 License

MIT License