Files
sientia-dataops-model-manager/model_manager/activities/cleanup.py
vitor-aignosi c0bef2d688 feat: update values.yaml and refactor cleanup paths
- Changed project name in values.yaml from "sientia-dataops-model-manager" to "sientia-model-manager".
- Added new environment variables for GitHub repository and branch configuration.
- Refactored cleanup paths to use a centralized REPORTS_TEMP_DIR constant for consistency.
- Updated runtime configurations and adjusted volume mounts for better resource management.
- Enabled SSH access for the model manager and disabled Grafana dashboard creation.
- Updated tests to reflect changes in directory paths and environment variable usage.
2026-04-09 16:37:36 -03:00

222 lines
8.2 KiB
Python

"""
Cleanup activities for removing stale files from local filesystem.
This module provides activities for cleaning up temporary files and directories
that are older than the configured retention period. It operates independently
of the database, using timestamps embedded in filenames.
"""
from temporalio import activity, workflow
with workflow.unsafe.imports_passed_through():
import os
import re
import shutil
import traceback
from datetime import datetime, timedelta
from typing import Any
from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
from sientia_do.notifications.models import NotificationLevel
from sientia_do.observability.logger import Logger
from sientia_do.observability.metrics_controller import MetricsController
from sientia_do.observability.sientia_monitoring import SientiaMonitoring
from model_manager.metrics import ACTIVITY_EXECUTION_TOTAL, WORKFLOW_EXECUTION_TOTAL
from model_manager.runtime_paths import REPORTS_TEMP_DIR
RETENTION_HOURS = int(os.getenv('CLEANUP_RETENTION_HOURS', '24'))
DRY_RUN = os.getenv('CLEANUP_DRY_RUN', 'false').lower() == 'true'
class Cleanup(SientiaMonitoring):
"""
Activity for cleaning up stale files and directories.
This activity extends SientiaMonitoring and handles cleanup of:
- Local temporary directories with timestamp suffixes
"""
def __init__(
self,
logger: Logger,
notification_handler: NotificationHandler,
metrics_controller: MetricsController,
):
"""
Initialize Cleanup activity.
Args:
logger: Logger instance for observability
notification_handler: Handler for sending notifications
metrics_controller: Controller for metrics emission
"""
SientiaMonitoring.__init__(self, logger, notification_handler, metrics_controller)
# Configuration from environment variables
self.retention_hours = RETENTION_HOURS
self.dry_run = DRY_RUN
# Regex patterns for timestamp extraction
self.dir_timestamp_pattern = re.compile(
r'^(.+)_(\d{8}_\d{6}_\d{6})$'
) # name_YYYYMMDD_HHMMSS_microseconds
@activity.defn(name='cleanup_temp_directories')
def cleanup_temp_directories(self, input_data: dict[str, Any]) -> None:
"""
Clean up stale temporary directories based on timestamp in directory name.
This activity scans the reports/temp directory for subdirectories following
the pattern '{name}_{timestamp}' where timestamp is in YYYYMMDD_HHMMSS_microseconds format.
Directories older than the retention period are deleted.
Args:
input_data: Cleanup configuration containing:
- metadata (dict): Workflow execution metadata
- temp_path (str): Path to temp directory (optional, defaults to reports/temp)
Returns:
None: Results are logged and tracked via metrics
Raises:
Exception: If cleanup fails (after sending notification)
"""
metadata = input_data.get('metadata', {})
temp_path = input_data.get('temp_path', REPORTS_TEMP_DIR)
metrics_status = 'success'
cutoff_time = datetime.now() - timedelta(hours=self.retention_hours)
try:
self.info(
f'Starting local directory cleanup - Path: {temp_path}, '
f'Retention: {self.retention_hours}h, Dry run: {self.dry_run}',
metadata,
)
if not os.path.exists(temp_path):
self.warning(f'Temp directory does not exist: {temp_path}', metadata)
return
directories_scanned = 0
directories_deleted = 0
errors = []
for item_name in os.listdir(temp_path):
item_path = os.path.join(temp_path, item_name)
if not os.path.isdir(item_path):
continue
directories_scanned += 1
# Extract timestamp from directory name
match = self.dir_timestamp_pattern.match(item_name)
if not match:
self.debug(
f'Skipping directory without timestamp pattern: {item_name}', metadata
)
continue
timestamp_str = match.group(2)
try:
# Parse YYYYMMDD_HHMMSS_microseconds
dir_time = datetime.strptime(timestamp_str, '%Y%m%d_%H%M%S_%f')
if dir_time < cutoff_time:
age_hours = (datetime.now() - dir_time).total_seconds() / 3600
if self.dry_run:
self.info(
f'[DRY RUN] Would delete directory: {item_name} (age: {age_hours:.1f}h)',
metadata,
)
directories_deleted += 1
else:
try:
shutil.rmtree(item_path)
self.info(
f'Deleted stale directory: {item_name} (age: {age_hours:.1f}h)',
metadata,
)
directories_deleted += 1
except OSError as e:
error_msg = f'Failed to delete directory {item_name}: {str(e)}'
errors.append(error_msg)
self.error(error_msg, metadata)
else:
age_hours = (datetime.now() - dir_time).total_seconds() / 3600
self.debug(
f'Keeping recent directory: {item_name} (age: {age_hours:.1f}h)',
metadata,
)
except ValueError as e:
error_msg = f'Invalid timestamp format in directory {item_name}: {str(e)}'
errors.append(error_msg)
self.error(error_msg, metadata)
self.info(
f'Directory cleanup completed - Scanned: {directories_scanned}, '
f'Deleted: {directories_deleted}, Errors: {len(errors)}',
metadata,
)
except Exception as e:
metrics_status = 'error'
error_msg = f'Error in directory cleanup: {str(e)}'
trace = traceback.format_exc()
self.send_notification(
metadata=metadata,
notification_id='CLEANUP_DIRECTORIES_ERROR',
message=error_msg,
block='cleanup_temp_directories',
level=NotificationLevel.ERROR,
attachment_content=trace,
)
raise
finally:
self._emit_metrics(
metadata=metadata,
metrics_status=metrics_status,
activity_name='cleanup_temp_directories',
emit_workflow_metric=True,
)
def _emit_metrics(
self,
metadata: dict[str, Any],
metrics_status: str,
activity_name: str,
emit_workflow_metric: bool,
) -> None:
"""
Emit workflow and activity execution metrics.
Args:
metadata: Activity metadata containing pod_id and workflow_name
metrics_status: Execution status ('success' or 'error')
activity_name: Name of the activity being executed
"""
if emit_workflow_metric:
self.emit_metric_sync(
metric_object=WORKFLOW_EXECUTION_TOTAL,
tags={
'pod_id': metadata.get('pod_id'),
'workflow_name': metadata.get('workflow_name'),
'status': metrics_status,
},
)
self.emit_metric_sync(
metric_object=ACTIVITY_EXECUTION_TOTAL,
tags={
'pod_id': metadata.get('pod_id'),
'activity_name': activity_name,
'status': metrics_status,
},
)