modern_stack.py

"""Modern advanced orchestration stack primitives.

Implements a configurable orchestration profile that combines:
- FastAPI API layer
- Airflow/Temporal orchestrators
- Kafka eventing
- Kubernetes jobs for execution
- Ray for distributed compute
- PostgreSQL + object storage persistence
- Prometheus/Grafana/ELK observability
- JWT + RBAC + secrets manager security
"""
from __future__ import annotations

from dataclasses import dataclass
from datetime import datetime, timezone
from typing import Any, Dict, List
from uuid import uuid4

from config import settings


@dataclass
class StackComponent:
    name: str
    enabled: bool
    config: Dict[str, Any]


class ModernOrchestrationStack:
    """Service layer for the high-end orchestration stack."""

    def architecture(self) -> Dict[str, Any]:
        """Return stack topology and active configuration."""
        return {
            "api_layer": StackComponent(
                name="FastAPI",
                enabled=True,
                config={"base_path": settings.API_PREFIX},
            ).__dict__,
            "orchestration": StackComponent(
                name=settings.ORCHESTRATION_ENGINE,
                enabled=True,
                config={
                    "temporal_enabled": settings.TEMPORAL_ENABLED,
                    "temporal_address": settings.TEMPORAL_ADDRESS,
                    "namespace": settings.TEMPORAL_NAMESPACE,
                    "task_queue": settings.TEMPORAL_TASK_QUEUE,
                },
            ).__dict__,
            "event_layer": StackComponent(
                name="kafka",
                enabled=settings.KAFKA_ENABLED,
                config={
                    "bootstrap_servers": settings.KAFKA_BOOTSTRAP_SERVERS,
                    "topic": settings.KAFKA_EXECUTION_TOPIC,
                },
            ).__dict__,
            "execution": StackComponent(
                name="kubernetes-jobs",
                enabled=True,
                config={
                    "namespace": settings.KUBERNETES_NAMESPACE,
                    "default_image": settings.KUBERNETES_JOB_IMAGE,
                    "service_account": settings.KUBERNETES_SERVICE_ACCOUNT,
                },
            ).__dict__,
            "distributed_compute": StackComponent(
                name="ray",
                enabled=settings.RAY_ENABLED,
                config={
                    "dashboard_url": settings.RAY_DASHBOARD_URL,
                    "entrypoint": settings.RAY_JOB_ENTRYPOINT,
                },
            ).__dict__,
            "storage": {
                "database": "postgresql",
                "object_storage": {
                    "enabled": settings.OBJECT_STORAGE_ENABLED,
                    "bucket": settings.OBJECT_STORAGE_BUCKET,
                    "endpoint": settings.OBJECT_STORAGE_ENDPOINT,
                },
            },
            "monitoring": {
                "metrics": ["prometheus", "grafana"],
                "logs": ["elasticsearch", "logstash", "kibana"],
            },
            "security": {
                "auth": "jwt",
                "authorization": "rbac",
                "secrets_provider": settings.SECRETS_PROVIDER,
            },
        }

    def submit_execution(self, pipeline_id: str, payload: Dict[str, Any]) -> Dict[str, Any]:
        """Create a stack-aware execution command envelope.

        This API intentionally stays transport-agnostic and returns metadata that
        can be consumed by workers/connectors for Airflow/Temporal/Kubernetes/Ray.
        """
        execution_id = f"exec-{uuid4()}"
        now = datetime.now(tz=timezone.utc).isoformat()

        commands: List[Dict[str, Any]] = [
            {
                "layer": "orchestration",
                "engine": settings.ORCHESTRATION_ENGINE,
                "action": "start_workflow",
            },
            {
                "layer": "execution",
                "engine": "kubernetes-jobs",
                "action": "submit_job",
                "namespace": settings.KUBERNETES_NAMESPACE,
                "image": settings.KUBERNETES_JOB_IMAGE,
            },
        ]

        if settings.RAY_ENABLED:
            commands.append(
                {
                    "layer": "distributed_compute",
                    "engine": "ray",
                    "action": "submit_ray_job",
                    "dashboard": settings.RAY_DASHBOARD_URL,
                }
            )

        if settings.KAFKA_ENABLED:
            commands.append(
                {
                    "layer": "event_layer",
                    "engine": "kafka",
                    "action": "publish_event",
                    "topic": settings.KAFKA_EXECUTION_TOPIC,
                }
            )

        return {
            "execution_id": execution_id,
            "pipeline_id": pipeline_id,
            "submitted_at": now,
            "status": "accepted",
            "commands": commands,
            "payload": payload,
        }