mirror of
https://github.com/mealie-recipes/mealie.git
synced 2025-10-29 17:24:31 -04:00
7602c67449
* refactor normalized search migration to use dummy default * changed group slug migration to use raw SQL * updated comment * added tests with anonymized backups (currently failing) * typo * fixed LDAP enum in test data * fix for adding label settings across groups * add migration data fixes * fix shopping list label settings test * re-run db init instead of just running alembic migration, to include fixes * intentionally broke SQLAlchemy GUID handling * safely convert between GUID types in different databases * restore original test data after testing backup restores * added missing group name update to migration
75 lines
1.7 KiB
Python
75 lines
1.7 KiB
Python
import json
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import logging
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import random
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import string
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from datetime import datetime
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from uuid import UUID
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logger = logging.getLogger("anonymize_backups")
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def is_uuid4(value: str):
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try:
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UUID(value)
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return True
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except ValueError:
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return False
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def is_iso_datetime(value: str):
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try:
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datetime.fromisoformat(value)
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return True
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except ValueError:
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return False
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def random_string(length=10):
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return "".join(random.choice(string.ascii_lowercase) for _ in range(length))
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def clean_value(value):
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try:
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match value:
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# preserve non-strings
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case int(value) | float(value):
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return value
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case None:
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return value
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# preserve UUIDs and datetimes
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case str(value) if is_uuid4(value) or is_iso_datetime(value):
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return value
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# randomize strings
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case str(value):
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return random_string()
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case _:
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pass
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except Exception as e:
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logger.exception(e)
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logger.error(f"Failed to anonymize value: {value}")
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return value
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def walk_data_and_anonymize(data):
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for k, v in data.items():
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if isinstance(v, list):
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for item in v:
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walk_data_and_anonymize(item)
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else:
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# preserve alembic version number and enums
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if k in ["auth_method", "version_num"]:
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continue
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data[k] = clean_value(v)
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def anonymize_database_json(input_filepath: str, output_filepath: str):
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with open(input_filepath) as f:
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data = json.load(f)
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walk_data_and_anonymize(data)
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with open(output_filepath, "w") as f:
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json.dump(data, f)
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