Tutorial 6: Advanced Features - Coming Soon

🚧 Coming Soon

This tutorial is currently being developed and will be available soon!

What you’ll learn:

  • Advanced HAP protocol features and extensions

  • Custom protocol implementations

  • Performance optimization techniques

  • Production deployment and scaling

  • Integration with external systems

Expected completion: Next release

In the meantime, check out:

What This Tutorial Will Cover:

  1. 🔧 Protocol Extensions

    • Custom HAP protocol methods

    • Streaming responses and real-time updates

    • Protocol middleware and interceptors

  2. ⚡ Performance Optimization

    • Workflow compilation and caching

    • Parallel execution optimization

    • Memory and resource management

  3. 🚀 Production Deployment

    • Containerized HAP servers

    • Load balancing and high availability

    • Monitoring and observability

  4. 🌐 System Integration

    • External API integration

    • Database and storage backends

    • Event-driven architectures

Prerequisites: - Completed all previous tutorials (1-5) - Production system experience - Understanding of distributed systems concepts

Estimated Time: 60-90 minutes

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📚 Alternative Resources:

While waiting for this tutorial, explore these resources:

Preview - Advanced Features You’ll Learn:

# Custom Protocol Extension (Coming Soon)
class CustomHAPServer(HAPServer):
    """HAP server with custom protocol methods."""

    @rpc_method
    async def stream_workflow_progress(self, workflow_id: str):
        """Stream real-time workflow progress updates."""
        async for progress in self.runtime.stream_execution(workflow_id):
            yield {
                "workflow_id": workflow_id,
                "current_node": progress.current_node,
                "progress_percent": progress.completion_percentage,
                "estimated_remaining": progress.estimated_time_remaining
            }

# Performance Optimization (Coming Soon)
class OptimizedHAPRuntime(HAPRuntime):
    """HAP runtime with performance optimizations."""

    def __init__(self, graph: HAPGraph):
        super().__init__(graph)
        self.compiled_graph = self._compile_graph(graph)
        self.execution_cache = LRUCache(maxsize=1000)
        self.parallel_executor = ThreadPoolExecutor(max_workers=10)

# Production Deployment (Coming Soon)
# docker-compose.yml
version: '3.8'
services:
  hap-server:
    build: .
    ports:
      - "8080:8080"
    environment:
      - HAP_WORKERS=4
      - HAP_MAX_CONCURRENT_WORKFLOWS=100
    deploy:
      replicas: 3
      resources:
        limits:
          memory: 2G
          cpus: '1.0'

Stay Updated:

Follow the project repository for tutorial release announcements and updates.