New alert service
This commit is contained in:
Urtzi Alfaro
435
services/training/app/consumers/training_event_consumer.py
Normal file
435
services/training/app/consumers/training_event_consumer.py
Normal file
@@ -0,0 +1,435 @@
|
||||
"""
|
||||
Training Event Consumer
|
||||
Processes ML model retraining requests from RabbitMQ
|
||||
Queues training jobs and manages model lifecycle
|
||||
"""
|
||||
import json
|
||||
import structlog
|
||||
from typing import Dict, Any, Optional
|
||||
from datetime import datetime
|
||||
from uuid import UUID
|
||||
|
||||
from shared.messaging import RabbitMQClient
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
from sqlalchemy import select
|
||||
|
||||
logger = structlog.get_logger()
|
||||
|
||||
|
||||
class TrainingEventConsumer:
|
||||
"""
|
||||
Consumes training retraining events and queues ML training jobs
|
||||
Ensures no duplicate training jobs and manages priorities
|
||||
"""
|
||||
|
||||
def __init__(self, db_session: AsyncSession):
|
||||
self.db_session = db_session
|
||||
|
||||
async def consume_training_events(
|
||||
self,
|
||||
rabbitmq_client: RabbitMQClient
|
||||
):
|
||||
"""
|
||||
Start consuming training events from RabbitMQ
|
||||
"""
|
||||
async def process_message(message):
|
||||
"""Process a single training event message"""
|
||||
try:
|
||||
async with message.process():
|
||||
# Parse event data
|
||||
event_data = json.loads(message.body.decode())
|
||||
logger.info(
|
||||
"Received training event",
|
||||
event_id=event_data.get('event_id'),
|
||||
event_type=event_data.get('event_type'),
|
||||
tenant_id=event_data.get('tenant_id')
|
||||
)
|
||||
|
||||
# Process the event
|
||||
await self.process_training_event(event_data)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"Error processing training event",
|
||||
error=str(e),
|
||||
exc_info=True
|
||||
)
|
||||
|
||||
# Start consuming events
|
||||
await rabbitmq_client.consume_events(
|
||||
exchange_name="training.events",
|
||||
queue_name="training.retraining.queue",
|
||||
routing_key="training.retrain.*",
|
||||
callback=process_message
|
||||
)
|
||||
|
||||
logger.info("Started consuming training events")
|
||||
|
||||
async def process_training_event(self, event_data: Dict[str, Any]) -> bool:
|
||||
"""
|
||||
Process a training event based on type
|
||||
|
||||
Args:
|
||||
event_data: Full event payload from RabbitMQ
|
||||
|
||||
Returns:
|
||||
bool: True if processed successfully
|
||||
"""
|
||||
try:
|
||||
event_type = event_data.get('event_type')
|
||||
data = event_data.get('data', {})
|
||||
tenant_id = event_data.get('tenant_id')
|
||||
|
||||
if not tenant_id:
|
||||
logger.warning("Training event missing tenant_id", event_data=event_data)
|
||||
return False
|
||||
|
||||
# Route to appropriate handler
|
||||
if event_type == 'training.retrain.requested':
|
||||
success = await self._handle_retrain_requested(tenant_id, data, event_data)
|
||||
elif event_type == 'training.retrain.scheduled':
|
||||
success = await self._handle_retrain_scheduled(tenant_id, data)
|
||||
else:
|
||||
logger.warning("Unknown training event type", event_type=event_type)
|
||||
success = True # Mark as processed to avoid retry
|
||||
|
||||
if success:
|
||||
logger.info(
|
||||
"Training event processed successfully",
|
||||
event_type=event_type,
|
||||
tenant_id=tenant_id
|
||||
)
|
||||
else:
|
||||
logger.error(
|
||||
"Training event processing failed",
|
||||
event_type=event_type,
|
||||
tenant_id=tenant_id
|
||||
)
|
||||
|
||||
return success
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"Error in process_training_event",
|
||||
error=str(e),
|
||||
event_id=event_data.get('event_id'),
|
||||
exc_info=True
|
||||
)
|
||||
return False
|
||||
|
||||
async def _handle_retrain_requested(
|
||||
self,
|
||||
tenant_id: str,
|
||||
data: Dict[str, Any],
|
||||
event_data: Dict[str, Any]
|
||||
) -> bool:
|
||||
"""
|
||||
Handle retraining request event
|
||||
|
||||
Validates model, checks for existing jobs, queues training job
|
||||
|
||||
Args:
|
||||
tenant_id: Tenant ID
|
||||
data: Retraining request data
|
||||
event_data: Full event payload
|
||||
|
||||
Returns:
|
||||
bool: True if handled successfully
|
||||
"""
|
||||
try:
|
||||
model_id = data.get('model_id')
|
||||
product_id = data.get('product_id')
|
||||
trigger_reason = data.get('trigger_reason', 'unknown')
|
||||
priority = data.get('priority', 'normal')
|
||||
event_id = event_data.get('event_id')
|
||||
|
||||
if not model_id:
|
||||
logger.warning("Retraining request missing model_id", data=data)
|
||||
return False
|
||||
|
||||
# Validate model exists
|
||||
from app.models import TrainedModel
|
||||
|
||||
stmt = select(TrainedModel).where(
|
||||
TrainedModel.id == UUID(model_id),
|
||||
TrainedModel.tenant_id == UUID(tenant_id)
|
||||
)
|
||||
result = await self.db_session.execute(stmt)
|
||||
model = result.scalar_one_or_none()
|
||||
|
||||
if not model:
|
||||
logger.error(
|
||||
"Model not found for retraining",
|
||||
model_id=model_id,
|
||||
tenant_id=tenant_id
|
||||
)
|
||||
return False
|
||||
|
||||
# Check if model is already in training
|
||||
if model.status in ['training', 'retraining_queued']:
|
||||
logger.info(
|
||||
"Model already in training, skipping duplicate request",
|
||||
model_id=model_id,
|
||||
current_status=model.status
|
||||
)
|
||||
return True # Consider successful (idempotent)
|
||||
|
||||
# Check for existing job in queue
|
||||
from app.models import TrainingJobQueue
|
||||
|
||||
existing_job_stmt = select(TrainingJobQueue).where(
|
||||
TrainingJobQueue.model_id == UUID(model_id),
|
||||
TrainingJobQueue.status.in_(['pending', 'running'])
|
||||
)
|
||||
existing_job_result = await self.db_session.execute(existing_job_stmt)
|
||||
existing_job = existing_job_result.scalar_one_or_none()
|
||||
|
||||
if existing_job:
|
||||
logger.info(
|
||||
"Training job already queued, skipping duplicate",
|
||||
model_id=model_id,
|
||||
job_id=str(existing_job.id)
|
||||
)
|
||||
return True # Idempotent
|
||||
|
||||
# Queue training job
|
||||
job_id = await self._queue_training_job(
|
||||
tenant_id=tenant_id,
|
||||
model_id=model_id,
|
||||
product_id=product_id,
|
||||
trigger_reason=trigger_reason,
|
||||
priority=priority,
|
||||
event_id=event_id,
|
||||
metadata=data
|
||||
)
|
||||
|
||||
if not job_id:
|
||||
logger.error("Failed to queue training job", model_id=model_id)
|
||||
return False
|
||||
|
||||
# Update model status
|
||||
model.status = 'retraining_queued'
|
||||
model.updated_at = datetime.utcnow()
|
||||
await self.db_session.commit()
|
||||
|
||||
# Publish job queued event
|
||||
await self._publish_job_queued_event(
|
||||
tenant_id=tenant_id,
|
||||
model_id=model_id,
|
||||
job_id=job_id,
|
||||
priority=priority
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"Retraining job queued successfully",
|
||||
model_id=model_id,
|
||||
job_id=job_id,
|
||||
trigger_reason=trigger_reason,
|
||||
priority=priority
|
||||
)
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
await self.db_session.rollback()
|
||||
logger.error(
|
||||
"Error handling retrain requested",
|
||||
error=str(e),
|
||||
model_id=data.get('model_id'),
|
||||
exc_info=True
|
||||
)
|
||||
return False
|
||||
|
||||
async def _handle_retrain_scheduled(
|
||||
self,
|
||||
tenant_id: str,
|
||||
data: Dict[str, Any]
|
||||
) -> bool:
|
||||
"""
|
||||
Handle scheduled retraining event
|
||||
|
||||
Similar to retrain_requested but for scheduled/batch retraining
|
||||
|
||||
Args:
|
||||
tenant_id: Tenant ID
|
||||
data: Scheduled retraining data
|
||||
|
||||
Returns:
|
||||
bool: True if handled successfully
|
||||
"""
|
||||
try:
|
||||
# Similar logic to _handle_retrain_requested
|
||||
# but may have different priority or batching logic
|
||||
logger.info(
|
||||
"Handling scheduled retraining",
|
||||
tenant_id=tenant_id,
|
||||
model_count=len(data.get('models', []))
|
||||
)
|
||||
|
||||
# For now, redirect to retrain_requested handler
|
||||
success_count = 0
|
||||
for model_data in data.get('models', []):
|
||||
if await self._handle_retrain_requested(
|
||||
tenant_id,
|
||||
model_data,
|
||||
{'event_id': data.get('schedule_id'), 'tenant_id': tenant_id}
|
||||
):
|
||||
success_count += 1
|
||||
|
||||
logger.info(
|
||||
"Scheduled retraining processed",
|
||||
tenant_id=tenant_id,
|
||||
successful=success_count,
|
||||
total=len(data.get('models', []))
|
||||
)
|
||||
|
||||
return success_count > 0
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"Error handling retrain scheduled",
|
||||
error=str(e),
|
||||
tenant_id=tenant_id,
|
||||
exc_info=True
|
||||
)
|
||||
return False
|
||||
|
||||
async def _queue_training_job(
|
||||
self,
|
||||
tenant_id: str,
|
||||
model_id: str,
|
||||
product_id: str,
|
||||
trigger_reason: str,
|
||||
priority: str,
|
||||
event_id: str,
|
||||
metadata: Dict[str, Any]
|
||||
) -> Optional[str]:
|
||||
"""
|
||||
Queue a training job in the database
|
||||
|
||||
Args:
|
||||
tenant_id: Tenant ID
|
||||
model_id: Model ID to retrain
|
||||
product_id: Product ID
|
||||
trigger_reason: Why retraining was triggered
|
||||
priority: Job priority (low, normal, high)
|
||||
event_id: Originating event ID
|
||||
metadata: Additional job metadata
|
||||
|
||||
Returns:
|
||||
Job ID if successful, None otherwise
|
||||
"""
|
||||
try:
|
||||
from app.models import TrainingJobQueue
|
||||
import uuid
|
||||
|
||||
# Map priority to numeric value for sorting
|
||||
priority_map = {
|
||||
'low': 1,
|
||||
'normal': 2,
|
||||
'high': 3,
|
||||
'critical': 4
|
||||
}
|
||||
|
||||
job = TrainingJobQueue(
|
||||
id=uuid.uuid4(),
|
||||
tenant_id=UUID(tenant_id),
|
||||
model_id=UUID(model_id),
|
||||
product_id=UUID(product_id) if product_id else None,
|
||||
job_type='retrain',
|
||||
status='pending',
|
||||
priority=priority,
|
||||
priority_score=priority_map.get(priority, 2),
|
||||
trigger_reason=trigger_reason,
|
||||
event_id=event_id,
|
||||
metadata=metadata,
|
||||
created_at=datetime.utcnow(),
|
||||
scheduled_at=datetime.utcnow()
|
||||
)
|
||||
|
||||
self.db_session.add(job)
|
||||
await self.db_session.commit()
|
||||
|
||||
logger.info(
|
||||
"Training job created",
|
||||
job_id=str(job.id),
|
||||
model_id=model_id,
|
||||
priority=priority,
|
||||
trigger_reason=trigger_reason
|
||||
)
|
||||
|
||||
return str(job.id)
|
||||
|
||||
except Exception as e:
|
||||
await self.db_session.rollback()
|
||||
logger.error(
|
||||
"Failed to queue training job",
|
||||
model_id=model_id,
|
||||
error=str(e),
|
||||
exc_info=True
|
||||
)
|
||||
return None
|
||||
|
||||
async def _publish_job_queued_event(
|
||||
self,
|
||||
tenant_id: str,
|
||||
model_id: str,
|
||||
job_id: str,
|
||||
priority: str
|
||||
):
|
||||
"""
|
||||
Publish event that training job was queued
|
||||
|
||||
Args:
|
||||
tenant_id: Tenant ID
|
||||
model_id: Model ID
|
||||
job_id: Training job ID
|
||||
priority: Job priority
|
||||
"""
|
||||
try:
|
||||
from shared.messaging import get_rabbitmq_client
|
||||
import uuid
|
||||
|
||||
rabbitmq_client = get_rabbitmq_client()
|
||||
if not rabbitmq_client:
|
||||
logger.warning("RabbitMQ client not available for event publishing")
|
||||
return
|
||||
|
||||
event_payload = {
|
||||
"event_id": str(uuid.uuid4()),
|
||||
"event_type": "training.retrain.queued",
|
||||
"timestamp": datetime.utcnow().isoformat(),
|
||||
"tenant_id": tenant_id,
|
||||
"data": {
|
||||
"job_id": job_id,
|
||||
"model_id": model_id,
|
||||
"priority": priority,
|
||||
"status": "queued"
|
||||
}
|
||||
}
|
||||
|
||||
await rabbitmq_client.publish_event(
|
||||
exchange_name="training.events",
|
||||
routing_key="training.retrain.queued",
|
||||
event_data=event_payload
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"Published job queued event",
|
||||
job_id=job_id,
|
||||
event_id=event_payload["event_id"]
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"Failed to publish job queued event",
|
||||
job_id=job_id,
|
||||
error=str(e)
|
||||
)
|
||||
# Don't fail the main operation if event publishing fails
|
||||
|
||||
|
||||
# Factory function for creating consumer instance
|
||||
def create_training_event_consumer(db_session: AsyncSession) -> TrainingEventConsumer:
|
||||
"""Create training event consumer instance"""
|
||||
return TrainingEventConsumer(db_session)
|
||||
Reference in New Issue
Block a user