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backup-docker-to-local/src/baudolo/backup/dumps.py
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"""Database dumps taken before a volume's files are copied."""
from __future__ import annotations
import sys
import pandas
from pandas.errors import EmptyDataError
from baudolo.databases import COLUMNS, DELIMITER
from .db import backup_database
from .docker import has_tool, image_id
DUMP_TOOLS: tuple[tuple[str, str], ...] = (
("postgres", "pg_dumpall"),
("mariadb", "mariadb-dump"),
("mariadb", "mysqldump"),
)
_ENGINE_BY_IMAGE: dict[str, tuple[str, str] | None] = {}
def container_engine(container: str) -> tuple[str, str] | None:
"""The (engine, dump tool) a container can serve, or None for neither.
Asks the container what it can run instead of reading its image name. A
dedicated Postgres is tagged `<app>-database` or `postgis/postgis` and
carries no engine token at all, while a swarm registry host such as
`svc-db-mariadb-swarm-mgr-01:5000` carries the wrong one.
Args:
container: must be running - `docker exec` is the probe, and a
stopped container would be cached as "no engine" for its whole
image. The only caller feeds it `docker ps` output.
Returns:
The engine and the tool that dumps it, cached per image ID so that
replicas of one image are probed once.
"""
image = image_id(container)
if image not in _ENGINE_BY_IMAGE:
_ENGINE_BY_IMAGE[image] = next(
(
(engine, tool)
for engine, tool in DUMP_TOOLS
if has_tool(container, tool)
),
None,
)
return _ENGINE_BY_IMAGE[image]
def backup_mariadb_or_postgres(
*,
container: str,
volume_dir: str,
databases_df: pandas.DataFrame,
database_containers: list[str],
) -> tuple[bool, bool]:
"""
Returns (is_db_container, dumped_any)
"""
engine = container_engine(container)
if engine is None:
return False, False
db_type, dump_tool = engine
dumped = backup_database(
container=container,
volume_dir=volume_dir,
db_type=db_type,
dump_tool=dump_tool,
databases_df=databases_df,
database_containers=database_containers,
)
return True, dumped
def _empty_databases_df() -> pandas.DataFrame:
"""
Create an empty DataFrame with the expected schema for databases.csv.
This allows the backup to continue without DB dumps when the CSV is missing
or empty (pandas EmptyDataError).
"""
return pandas.DataFrame(columns=list(COLUMNS))
def load_databases_df(csv_path: str) -> pandas.DataFrame:
"""
Load databases.csv robustly.
- Missing file -> warn, continue with empty df
- Empty file -> warn, continue with empty df
- Valid CSV -> return dataframe
"""
try:
return pandas.read_csv(
csv_path, sep=DELIMITER, keep_default_na=False, dtype=str
)
except FileNotFoundError:
print(
f"WARNING: databases.csv not found: {csv_path}. Continuing without database dumps.",
file=sys.stderr,
flush=True,
)
return _empty_databases_df()
except EmptyDataError:
print(
f"WARNING: databases.csv exists but is empty: {csv_path}. Continuing without database dumps.",
file=sys.stderr,
flush=True,
)
return _empty_databases_df()
def backup_dumps_for_volume(
*,
containers: list[str],
vol_dir: str,
databases_df: pandas.DataFrame,
database_containers: list[str],
) -> tuple[bool, bool]:
"""
Returns (found_db_container, dumped_any)
"""
found_db = False
dumped_any = False
for c in containers:
is_db, dumped = backup_mariadb_or_postgres(
container=c,
volume_dir=vol_dir,
databases_df=databases_df,
database_containers=database_containers,
)
if is_db:
found_db = True
if dumped:
dumped_any = True
return found_db, dumped_any