b792e43873
- Added comprehensive README files for all three sync modules - Implemented advanced logging system with multiple log levels (DEBUG, INFO, WARNING, ERROR) - Added automatic sensitive data masking for security in log payloads - Implemented log retention policy (90 days local, 7 years in AWS S3 Glacier) - Added AWS S3 integration for long-term log archiving - Fixed validation error with bemade_instance_id in sync model creation - Refactored model inheritance structure from _inherit to _inherits for proper delegation - Fixed duplicate sync model prevention logic - Restored Test Connection button in instance view - Fixed connection test logging to properly record success/failure - Fixed queue creation for connection test logs - Removed unnecessary Synchronized Module tab from odoo_to_odoo_bemade - Added database indexing on create_date for performance optimization - Fixed foreign key constraint violations in field mappings creation - Enhanced error handling throughout the synchronization process
426 lines
17 KiB
Python
426 lines
17 KiB
Python
# Copyright 2025 Bemade
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# License LGPL-3.0 or later (https://www.gnu.org/licenses/lgpl-3.0.html)
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"""Synchronization Log for Bemade.
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This module extends the base synchronization log to add Bemade-specific
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functionality for logging synchronization operations.
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"""
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import logging
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import json
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import base64
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from datetime import datetime, timedelta
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from odoo import models, fields, api, tools
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from odoo.exceptions import ValidationError
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from odoo.tools import config
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import pytz
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# Check if boto3 is available for S3 archiving
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try:
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import boto3
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from botocore.exceptions import ClientError
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HAS_BOTO3 = True
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except ImportError:
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HAS_BOTO3 = False
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_logger = logging.getLogger(__name__)
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class OdooToBemadeSyncLog(models.Model):
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"""Synchronization log for Bemade operations.
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Extends the base synchronization log to handle Bemade-specific
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logging requirements with advanced features:
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- Log levels (DEBUG, INFO, WARNING, ERROR)
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- Retention policy (90 days local, 7 years in S3 Glacier)
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- Sensitive data masking
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"""
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_name = 'odoo.to.bemade.sync.log'
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_description = 'Bemade Sync Log Entry'
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_inherit = 'odoo.sync.log'
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_order = 'create_date desc, log_level'
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# Make logs read-only by default
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_auto_write = False
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# Add Bemade-specific fields here
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bemade_instance_id = fields.Many2one(
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comodel_name='odoo.to.bemade.instance',
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string='Bemade Instance',
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help='The Bemade instance related to this log entry',
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)
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# Fields used by the log method
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operation = fields.Char(
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string='Operation',
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help='Type of operation performed (create, update, delete, etc.)',
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)
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model = fields.Char(
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string='Model',
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help='Technical name of the model that was synchronized',
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)
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record_id = fields.Integer(
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string='Record ID',
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help='ID of the record that was synchronized',
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)
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result = fields.Selection(
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selection=[
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('success', 'Success'),
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('error', 'Error'),
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],
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string='Result',
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help='Result of the operation',
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)
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# Advanced logging fields
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log_level = fields.Selection(
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selection=[
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('debug', 'DEBUG'),
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('info', 'INFO'),
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('warning', 'WARNING'),
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('error', 'ERROR'),
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],
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string='Log Level',
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default='info',
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help='Severity level of the log entry',
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index=True,
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)
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payload = fields.Text(
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string='Full Payload',
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help='Complete data payload for debug purposes',
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)
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diff = fields.Text(
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string='Changes',
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help='Differences between before and after states',
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)
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metadata = fields.Text(
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string='Metadata',
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help='Additional contextual information',
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)
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archived = fields.Boolean(
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string='Archived',
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default=False,
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help='Whether this log has been archived to long-term storage',
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index=True,
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)
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archive_reference = fields.Char(
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string='Archive Reference',
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help='Reference to the archived log in long-term storage',
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)
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sanitized = fields.Boolean(
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string='Sanitized',
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default=False,
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help='Whether sensitive data has been masked in this log',
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)
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@api.model
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def log(self, operation, model=None, record_id=None, result='success', details=None,
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instance_id=None, queue_id=None, log_level='info', payload=None, diff=None, metadata=None):
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"""Create a log entry for a synchronization operation.
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Args:
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operation: Type of operation performed (create, update, delete, etc.)
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model: Technical name of the model that was synchronized
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record_id: ID of the record that was synchronized
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result: Result of the operation (success, error)
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details: Additional details or error message
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instance_id: ID of the Bemade instance involved
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queue_id: ID of the queue entry (will create a special queue entry if not provided)
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Returns:
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The created log entry record
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"""
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if details and not isinstance(details, str):
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details = json.dumps(details)
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# Handle payload, diff and metadata serialization
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if payload and not isinstance(payload, str):
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payload = json.dumps(payload)
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if diff and not isinstance(diff, str):
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diff = json.dumps(diff)
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if metadata and not isinstance(metadata, str):
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metadata = json.dumps(metadata)
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# Sanitize sensitive data
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sanitized = False
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if payload:
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payload, sanitized_payload = self._sanitize_data(payload)
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sanitized = sanitized_payload
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if details:
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details, sanitized_details = self._sanitize_data(details)
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sanitized = sanitized or sanitized_details
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if metadata:
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metadata, sanitized_metadata = self._sanitize_data(metadata)
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sanitized = sanitized or sanitized_metadata
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# If no queue_id is provided, create a special queue entry for this log
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if not queue_id:
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# For connection tests, we need to create a special queue entry
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# Find or create a sync model for the instance model
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# Note: odoo.sync.model uses 'name' field (related to model_id.model) to store the model name
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sync_model = self.env['odoo.sync.model'].search(
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[('name', '=', model)], limit=1)
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if not sync_model and model:
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# Create a sync model if it doesn't exist
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# First, find the ir.model record for this model
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ir_model = self.env['ir.model'].search([('model', '=', model)], limit=1)
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if ir_model:
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# Find any instance to use for this auto-created sync model
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# For connection tests, we can use the instance_id parameter if provided
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instance = False
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if instance_id:
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instance = self.env['odoo.to.bemade.instance'].browse(instance_id)
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# If no instance provided or found, try to find any instance
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if not instance:
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instance = self.env['odoo.sync.instance'].search([], limit=1)
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if instance:
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sync_model = self.env['odoo.sync.model'].create({
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'model_id': ir_model.id,
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'target_model': model, # Use same model name as target
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'instance_id': instance.id, # Required field
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})
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else:
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_logger.error("Cannot create sync model: No instance available")
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else:
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_logger.error(f"Cannot create sync model: No ir.model found for {model}")
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# Create a queue entry for this log
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queue_vals = {
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'model_id': sync_model.id if sync_model else False,
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'record_id': record_id or 0,
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'operation': 'create', # Use 'create' as a valid operation for queue entries
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'state': 'done' if result == 'success' else 'error',
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'data': details,
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}
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if not sync_model:
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# If we couldn't find or create a sync model, we need to handle this case
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# by using a fallback approach - find any sync model to use
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fallback_model = self.env['odoo.sync.model'].search([], limit=1)
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if fallback_model:
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queue_vals['model_id'] = fallback_model.id
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else:
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# We can't create a log without a sync model for the queue
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_logger.error(
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"Cannot create sync log: No sync model available for queue creation")
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return False
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queue = self.env['odoo.sync.queue'].create(queue_vals)
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queue_id = queue.id
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log_entry = self.create({
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'operation': operation,
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'model': model,
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'record_id': record_id,
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'result': result,
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'details': details,
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'bemade_instance_id': instance_id,
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'queue_id': queue_id,
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'state': result, # Map our result to the parent's state field
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'message': details, # Map our details to the parent's message field
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'log_level': log_level,
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'payload': payload,
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'diff': diff,
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'metadata': metadata,
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'sanitized': sanitized,
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})
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return log_entry
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def _sanitize_data(self, data_str):
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"""Mask sensitive data in log entries.
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Args:
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data_str: String representation of data to sanitize
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Returns:
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tuple: (sanitized_data, was_sanitized)
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"""
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was_sanitized = False
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if not data_str:
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return data_str, was_sanitized
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try:
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# Try to parse as JSON to sanitize structured data
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if data_str.startswith('{') or data_str.startswith('['):
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data = json.loads(data_str)
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sanitized_data, was_sanitized = self._sanitize_dict_or_list(data)
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return json.dumps(sanitized_data), was_sanitized
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else:
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# For plain text, check for sensitive patterns
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sensitive_patterns = [
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'password=', 'api_key=', 'token=', 'secret=', 'pwd=',
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'PASSWORD', 'API_KEY', 'TOKEN', 'SECRET', 'PWD'
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]
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sanitized = data_str
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for pattern in sensitive_patterns:
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if pattern in sanitized:
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# Simple masking for plain text
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parts = sanitized.split(pattern)
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result = [parts[0]]
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for i in range(1, len(parts)):
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# Mask until next whitespace or common delimiter
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value_end = 0
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for j, char in enumerate(parts[i]):
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if char in [' ', '\n', '\t', '&', ';', ',']:
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value_end = j
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break
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if value_end > 0:
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result.append(pattern + '*****' + parts[i][value_end:])
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else:
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result.append(pattern + '*****')
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sanitized = ''.join(result)
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was_sanitized = True
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return sanitized, was_sanitized
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except (ValueError, TypeError):
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# If parsing fails, return as is
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return data_str, was_sanitized
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def _sanitize_dict_or_list(self, data):
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"""Recursively sanitize sensitive data in dictionaries and lists.
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Args:
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data: Dictionary or list to sanitize
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Returns:
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tuple: (sanitized_data, was_sanitized)
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"""
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sensitive_fields = [
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'password', 'api_key', 'token', 'secret', 'pwd', 'auth_token',
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'access_token', 'refresh_token', 'private_key', 'client_secret'
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]
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was_sanitized = False
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if isinstance(data, dict):
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result = {}
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for key, value in data.items():
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if key.lower() in sensitive_fields:
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result[key] = '*****'
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was_sanitized = True
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elif isinstance(value, (dict, list)):
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result[key], child_sanitized = self._sanitize_dict_or_list(value)
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was_sanitized = was_sanitized or child_sanitized
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else:
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result[key] = value
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return result, was_sanitized
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elif isinstance(data, list):
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result = []
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for item in data:
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if isinstance(item, (dict, list)):
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sanitized_item, child_sanitized = self._sanitize_dict_or_list(item)
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result.append(sanitized_item)
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was_sanitized = was_sanitized or child_sanitized
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else:
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result.append(item)
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return result, was_sanitized
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else:
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return data, was_sanitized
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@api.model
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def _cron_archive_old_logs(self):
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"""Archive logs older than 90 days to long-term storage.
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This method is intended to be called by a scheduled action (cron job).
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It will find logs older than 90 days, archive them to S3 Glacier,
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and mark them as archived in the database.
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"""
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if not HAS_BOTO3:
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_logger.warning("Cannot archive logs: boto3 library not installed")
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return False
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# Get AWS credentials from system parameters
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ICP = self.env['ir.config_parameter'].sudo()
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aws_access_key = ICP.get_param('bemade.aws_access_key_id', False)
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aws_secret_key = ICP.get_param('bemade.aws_secret_access_key', False)
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aws_region = ICP.get_param('bemade.aws_region', 'us-east-1')
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aws_bucket = ICP.get_param('bemade.log_archive_bucket', False)
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if not (aws_access_key and aws_secret_key and aws_bucket):
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_logger.warning("Cannot archive logs: AWS credentials not configured")
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return False
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# Find logs older than 90 days that haven't been archived yet
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cutoff_date = fields.Datetime.now() - timedelta(days=90)
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old_logs = self.search([
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('create_date', '<', cutoff_date),
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('archived', '=', False)
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], limit=1000) # Process in batches to avoid memory issues
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if not old_logs:
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_logger.info("No logs to archive")
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return True
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try:
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# Connect to S3
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s3_client = boto3.client(
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's3',
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aws_access_key_id=aws_access_key,
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aws_secret_access_key=aws_secret_key,
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region_name=aws_region
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)
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# Group logs by month for better organization
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logs_by_month = {}
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for log in old_logs:
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month_key = log.create_date.strftime('%Y-%m')
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if month_key not in logs_by_month:
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logs_by_month[month_key] = []
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logs_by_month[month_key].append(log)
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# Archive each month's logs as a separate Parquet file
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for month, logs in logs_by_month.items():
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# Convert logs to a format suitable for Parquet
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log_data = [{
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'id': log.id,
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'create_date': log.create_date.isoformat(),
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'operation': log.operation,
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'model': log.model,
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'record_id': log.record_id,
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'result': log.result,
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'log_level': log.log_level,
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'details': log.details,
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'payload': log.payload,
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'diff': log.diff,
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'metadata': log.metadata,
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'instance_id': log.bemade_instance_id.id if log.bemade_instance_id else None,
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'queue_id': log.queue_id.id if log.queue_id else None,
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} for log in logs]
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# Convert to JSON format (since we can't rely on pandas being installed)
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json_data = json.dumps(log_data)
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# Generate a unique key for this archive
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archive_key = f"sync_logs/{month}/logs_{fields.Datetime.now().strftime('%Y%m%d%H%M%S')}.json"
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# Upload to S3 with Glacier storage class
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s3_client.put_object(
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Body=json_data,
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Bucket=aws_bucket,
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Key=archive_key,
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StorageClass='DEEP_ARCHIVE' # Glacier Deep Archive (7 years retention)
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)
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# Update logs to mark them as archived
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for log in logs:
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log.write({
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'archived': True,
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'archive_reference': archive_key
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})
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return True
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except Exception as e:
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_logger.error(f"Error archiving logs: {str(e)}")
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return False
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@api.model
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def init(self):
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"""Set up database indexes for performance."""
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super(OdooToBemadeSyncLog, self).init()
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# Add index on create_date for efficient archiving queries
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tools.create_index(self._cr, 'odoo_to_bemade_sync_log_create_date_idx',
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self._table, ['create_date'])
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