Files
dify/api/tasks/trigger_processing_tasks.py
Harry 1562d00037 feat(trigger): implement trigger debugging functionality
- Added DraftWorkflowTriggerNodeApi and DraftWorkflowTriggerRunApi for debugging trigger nodes and workflows.
- Enhanced TriggerDebugService to manage trigger debugging sessions and event listening.
- Introduced structured event responses for trigger debugging, including listening started, received, node finished, and workflow started events.
- Updated Queue and Stream entities to support new trigger debug events.
- Refactored trigger input handling to streamline the process of creating inputs from trigger data.

This implementation improves the debugging capabilities for trigger nodes and workflows, providing clearer event handling and structured responses.
2025-09-11 16:55:58 +08:00

152 lines
5.4 KiB
Python

"""
Celery tasks for async trigger processing.
These tasks handle trigger workflow execution asynchronously
to avoid blocking the main request thread.
"""
import logging
from celery import shared_task
from sqlalchemy.orm import Session
from core.plugin.entities.plugin import TriggerProviderID
from core.trigger.trigger_manager import TriggerManager
from extensions.ext_database import db
from extensions.ext_storage import storage
from models.trigger import TriggerSubscription
from services.trigger_service import TriggerService
logger = logging.getLogger(__name__)
# Use workflow queue for trigger processing
TRIGGER_QUEUE = "triggered_workflow_dispatcher"
@shared_task(queue=TRIGGER_QUEUE, bind=True, max_retries=3)
def dispatch_triggered_workflows_async(
self,
endpoint_id: str,
provider_id: str,
subscription_id: str,
triggers: list[str],
request_id: str,
) -> dict:
"""
Dispatch triggers asynchronously.
Args:
endpoint_id: Endpoint ID
provider_id: Provider ID
subscription_id: Subscription ID
triggers: List of triggers to dispatch
request_id: Unique ID of the stored request
Returns:
dict: Execution result with status and dispatched trigger count
"""
try:
logger.info(
"Starting async trigger dispatching for endpoint=%s, triggers=%s, request_id=%s",
endpoint_id,
triggers,
request_id,
)
# Verify request exists in storage
try:
serialized_request = storage.load_once(f"triggers/{request_id}")
# Just verify it exists, we don't need to deserialize it here
if not serialized_request:
raise ValueError("Request not found in storage")
except Exception as e:
logger.exception("Failed to load request %s", request_id, exc_info=e)
return {"status": "failed", "error": f"Failed to load request: {str(e)}"}
with Session(db.engine) as session:
# Get subscription
subscription = session.query(TriggerSubscription).filter_by(id=subscription_id).first()
if not subscription:
logger.error("Subscription not found: %s", subscription_id)
return {"status": "failed", "error": "Subscription not found"}
# Get controller
controller = TriggerManager.get_trigger_provider(subscription.tenant_id, TriggerProviderID(provider_id))
if not controller:
logger.error("Controller not found for provider: %s", provider_id)
return {"status": "failed", "error": "Controller not found"}
# Dispatch each trigger
dispatched_count = 0
for trigger in triggers:
try:
trigger = controller.get_trigger(trigger)
if trigger is None:
logger.error(
"Trigger '%s' not found in provider '%s'",
trigger,
provider_id,
)
continue
dispatched_count += TriggerService.dispatch_triggered_workflows(
subscription=subscription,
trigger=trigger,
request_id=request_id,
)
except Exception:
logger.exception(
"Failed to dispatch trigger '%s' for subscription %s",
trigger,
subscription_id,
)
# Continue processing other triggers even if one fails
continue
# Dispatch to debug sessions after processing all triggers
try:
debug_dispatched = TriggerService.dispatch_debugging_sessions(
subscription_id=subscription_id,
triggers=triggers,
request_id=request_id,
)
except Exception:
# Silent failure for debug dispatch
logger.exception("Failed to dispatch to debug sessions")
logger.info(
"Completed async trigger dispatching: processed %d/%d triggers",
dispatched_count,
len(triggers),
)
# Note: Stored request is not deleted here. It should be handled by:
# 1. Storage system's lifecycle policy (e.g., S3 lifecycle rules for triggers/* prefix)
# 2. Or periodic cleanup job if using local/persistent storage
# This ensures request data is available for debugging/retry purposes
return {
"status": "completed",
"total_count": len(triggers),
"dispatched_count": dispatched_count,
"debug_dispatched_count": debug_dispatched,
}
except Exception as e:
logger.exception(
"Error in async trigger dispatching for endpoint %s",
endpoint_id,
)
# Retry the task if not exceeded max retries
if self.request.retries < self.max_retries:
raise self.retry(exc=e, countdown=60 * (self.request.retries + 1))
# Note: Stored request is not deleted even on failure. See comment above for cleanup strategy.
return {
"status": "failed",
"error": str(e),
"retries": self.request.retries,
}