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# to you under the Apache License, Version 2.0 (the
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#
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#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
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# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import hashlib
import importlib
import importlib.machinery
import importlib.util
import os
import sys
import textwrap
import traceback
import warnings
import zipfile
from datetime import datetime, timedelta
from typing import TYPE_CHECKING, Dict, List, NamedTuple, Optional, Union
from sqlalchemy.exc import OperationalError
from sqlalchemy.orm import Session
from tabulate import tabulate
from airflow import settings
from airflow.configuration import conf
from airflow.exceptions import (
AirflowClusterPolicyViolation,
AirflowDagCycleException,
AirflowDagDuplicatedIdException,
AirflowTimetableInvalid,
ParamValidationError,
)
from airflow.stats import Stats
from airflow.utils import timezone
from airflow.utils.dag_cycle_tester import check_cycle
from airflow.utils.docs import get_docs_url
from airflow.utils.file import correct_maybe_zipped, list_py_file_paths, might_contain_dag
from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.retries import MAX_DB_RETRIES, run_with_db_retries
from airflow.utils.session import provide_session
from airflow.utils.timeout import timeout
if TYPE_CHECKING:
import pathlib
[docs]class FileLoadStat(NamedTuple):
"""Information about single file"""
[docs]class DagBag(LoggingMixin):
"""
A dagbag is a collection of dags, parsed out of a folder tree and has high
level configuration settings, like what database to use as a backend and
what executor to use to fire off tasks. This makes it easier to run
distinct environments for say production and development, tests, or for
different teams or security profiles. What would have been system level
settings are now dagbag level so that one system can run multiple,
independent settings sets.
:param dag_folder: the folder to scan to find DAGs
:param include_examples: whether to include the examples that ship
with airflow or not
:param include_smart_sensor: whether to include the smart sensor native
DAGs that create the smart sensor operators for whole cluster
:param read_dags_from_db: Read DAGs from DB if ``True`` is passed.
If ``False`` DAGs are read from python files.
:param load_op_links: Should the extra operator link be loaded via plugins when
de-serializing the DAG? This flag is set to False in Scheduler so that Extra Operator links
are not loaded to not run User code in Scheduler.
"""
def __init__(
self,
dag_folder: Union[str, "pathlib.Path", None] = None,
include_examples: bool = conf.getboolean('core', 'LOAD_EXAMPLES'),
include_smart_sensor: bool = conf.getboolean('smart_sensor', 'USE_SMART_SENSOR'),
safe_mode: bool = conf.getboolean('core', 'DAG_DISCOVERY_SAFE_MODE'),
read_dags_from_db: bool = False,
store_serialized_dags: Optional[bool] = None,
load_op_links: bool = True,
):
# Avoid circular import
from airflow.models.dag import DAG
super().__init__()
if store_serialized_dags:
warnings.warn(
"The store_serialized_dags parameter has been deprecated. "
"You should pass the read_dags_from_db parameter.",
DeprecationWarning,
stacklevel=2,
)
read_dags_from_db = store_serialized_dags
dag_folder = dag_folder or settings.DAGS_FOLDER
self.dag_folder = dag_folder
self.dags: Dict[str, DAG] = {}
# the file's last modified timestamp when we last read it
self.file_last_changed: Dict[str, datetime] = {}
self.import_errors: Dict[str, str] = {}
self.has_logged = False
self.read_dags_from_db = read_dags_from_db
# Only used by read_dags_from_db=True
self.dags_last_fetched: Dict[str, datetime] = {}
# Only used by SchedulerJob to compare the dag_hash to identify change in DAGs
self.dags_hash: Dict[str, str] = {}
self.dagbag_import_error_tracebacks = conf.getboolean('core', 'dagbag_import_error_tracebacks')
self.dagbag_import_error_traceback_depth = conf.getint('core', 'dagbag_import_error_traceback_depth')
self.collect_dags(
dag_folder=dag_folder,
include_examples=include_examples,
include_smart_sensor=include_smart_sensor,
safe_mode=safe_mode,
)
# Should the extra operator link be loaded via plugins?
# This flag is set to False in Scheduler so that Extra Operator links are not loaded
self.load_op_links = load_op_links
[docs] def size(self) -> int:
""":return: the amount of dags contained in this dagbag"""
return len(self.dags)
@property
[docs] def store_serialized_dags(self) -> bool:
"""Whether or not to read dags from DB"""
warnings.warn(
"The store_serialized_dags property has been deprecated. Use read_dags_from_db instead.",
DeprecationWarning,
stacklevel=2,
)
return self.read_dags_from_db
@property
[docs] def dag_ids(self) -> List[str]:
"""
:return: a list of DAG IDs in this bag
:rtype: List[unicode]
"""
return list(self.dags.keys())
@provide_session
[docs] def get_dag(self, dag_id, session: Session = None):
"""
Gets the DAG out of the dictionary, and refreshes it if expired
:param dag_id: DAG Id
"""
# Avoid circular import
from airflow.models.dag import DagModel
if self.read_dags_from_db:
# Import here so that serialized dag is only imported when serialization is enabled
from airflow.models.serialized_dag import SerializedDagModel
if dag_id not in self.dags:
# Load from DB if not (yet) in the bag
self._add_dag_from_db(dag_id=dag_id, session=session)
return self.dags.get(dag_id)
# If DAG is in the DagBag, check the following
# 1. if time has come to check if DAG is updated (controlled by min_serialized_dag_fetch_secs)
# 2. check the last_updated column in SerializedDag table to see if Serialized DAG is updated
# 3. if (2) is yes, fetch the Serialized DAG.
# 4. if (2) returns None (i.e. Serialized DAG is deleted), remove dag from dagbag
# if it exists and return None.
min_serialized_dag_fetch_secs = timedelta(seconds=settings.MIN_SERIALIZED_DAG_FETCH_INTERVAL)
if (
dag_id in self.dags_last_fetched
and timezone.utcnow() > self.dags_last_fetched[dag_id] + min_serialized_dag_fetch_secs
):
sd_last_updated_datetime = SerializedDagModel.get_last_updated_datetime(
dag_id=dag_id,
session=session,
)
if not sd_last_updated_datetime:
self.log.warning("Serialized DAG %s no longer exists", dag_id)
del self.dags[dag_id]
del self.dags_last_fetched[dag_id]
del self.dags_hash[dag_id]
return None
if sd_last_updated_datetime > self.dags_last_fetched[dag_id]:
self._add_dag_from_db(dag_id=dag_id, session=session)
return self.dags.get(dag_id)
# If asking for a known subdag, we want to refresh the parent
dag = None
root_dag_id = dag_id
if dag_id in self.dags:
dag = self.dags[dag_id]
if dag.parent_dag:
root_dag_id = dag.parent_dag.dag_id
# If DAG Model is absent, we can't check last_expired property. Is the DAG not yet synchronized?
orm_dag = DagModel.get_current(root_dag_id, session=session)
if not orm_dag:
return self.dags.get(dag_id)
# If the dag corresponding to root_dag_id is absent or expired
is_missing = root_dag_id not in self.dags
is_expired = orm_dag.last_expired and dag and dag.last_loaded < orm_dag.last_expired
if is_expired:
# Remove associated dags so we can re-add them.
self.dags = {
key: dag
for key, dag in self.dags.items()
if root_dag_id != key and not (dag.parent_dag and root_dag_id == dag.parent_dag.dag_id)
}
if is_missing or is_expired:
# Reprocess source file.
found_dags = self.process_file(
filepath=correct_maybe_zipped(orm_dag.fileloc), only_if_updated=False
)
# If the source file no longer exports `dag_id`, delete it from self.dags
if found_dags and dag_id in [found_dag.dag_id for found_dag in found_dags]:
return self.dags[dag_id]
elif dag_id in self.dags:
del self.dags[dag_id]
return self.dags.get(dag_id)
def _add_dag_from_db(self, dag_id: str, session: Session):
"""Add DAG to DagBag from DB"""
from airflow.models.serialized_dag import SerializedDagModel
row = SerializedDagModel.get(dag_id, session)
if not row:
return None
row.load_op_links = self.load_op_links
dag = row.dag
for subdag in dag.subdags:
self.dags[subdag.dag_id] = subdag
self.dags[dag.dag_id] = dag
self.dags_last_fetched[dag.dag_id] = timezone.utcnow()
self.dags_hash[dag.dag_id] = row.dag_hash
[docs] def process_file(self, filepath, only_if_updated=True, safe_mode=True):
"""
Given a path to a python module or zip file, this method imports
the module and look for dag objects within it.
"""
# if the source file no longer exists in the DB or in the filesystem,
# return an empty list
# todo: raise exception?
if filepath is None or not os.path.isfile(filepath):
return []
try:
# This failed before in what may have been a git sync
# race condition
file_last_changed_on_disk = datetime.fromtimestamp(os.path.getmtime(filepath))
if (
only_if_updated
and filepath in self.file_last_changed
and file_last_changed_on_disk == self.file_last_changed[filepath]
):
return []
except Exception as e:
self.log.exception(e)
return []
if filepath.endswith(".py") or not zipfile.is_zipfile(filepath):
mods = self._load_modules_from_file(filepath, safe_mode)
else:
mods = self._load_modules_from_zip(filepath, safe_mode)
found_dags = self._process_modules(filepath, mods, file_last_changed_on_disk)
self.file_last_changed[filepath] = file_last_changed_on_disk
return found_dags
def _load_modules_from_file(self, filepath, safe_mode):
if not might_contain_dag(filepath, safe_mode):
# Don't want to spam user with skip messages
if not self.has_logged:
self.has_logged = True
self.log.info("File %s assumed to contain no DAGs. Skipping.", filepath)
return []
self.log.debug("Importing %s", filepath)
org_mod_name, _ = os.path.splitext(os.path.split(filepath)[-1])
path_hash = hashlib.sha1(filepath.encode('utf-8')).hexdigest()
mod_name = f'unusual_prefix_{path_hash}_{org_mod_name}'
if mod_name in sys.modules:
del sys.modules[mod_name]
def parse(mod_name, filepath):
try:
loader = importlib.machinery.SourceFileLoader(mod_name, filepath)
spec = importlib.util.spec_from_loader(mod_name, loader)
new_module = importlib.util.module_from_spec(spec)
sys.modules[spec.name] = new_module
loader.exec_module(new_module)
return [new_module]
except Exception as e:
self.log.exception("Failed to import: %s", filepath)
if self.dagbag_import_error_tracebacks:
self.import_errors[filepath] = traceback.format_exc(
limit=-self.dagbag_import_error_traceback_depth
)
else:
self.import_errors[filepath] = str(e)
return []
dagbag_import_timeout = settings.get_dagbag_import_timeout(filepath)
if not isinstance(dagbag_import_timeout, (int, float)):
raise TypeError(
f'Value ({dagbag_import_timeout}) from get_dagbag_import_timeout must be int or float'
)
if dagbag_import_timeout <= 0: # no parsing timeout
return parse(mod_name, filepath)
timeout_msg = (
f"DagBag import timeout for {filepath} after {dagbag_import_timeout}s.\n"
"Please take a look at these docs to improve your DAG import time:\n"
f"* {get_docs_url('best-practices.html#top-level-python-code')}\n"
f"* {get_docs_url('best-practices.html#reducing-dag-complexity')}"
)
with timeout(dagbag_import_timeout, error_message=timeout_msg):
return parse(mod_name, filepath)
def _load_modules_from_zip(self, filepath, safe_mode):
mods = []
with zipfile.ZipFile(filepath) as current_zip_file:
for zip_info in current_zip_file.infolist():
head, _ = os.path.split(zip_info.filename)
mod_name, ext = os.path.splitext(zip_info.filename)
if ext not in [".py", ".pyc"]:
continue
if head:
continue
if mod_name == '__init__':
self.log.warning("Found __init__.%s at root of %s", ext, filepath)
self.log.debug("Reading %s from %s", zip_info.filename, filepath)
if not might_contain_dag(zip_info.filename, safe_mode, current_zip_file):
# todo: create ignore list
# Don't want to spam user with skip messages
if not self.has_logged:
self.has_logged = True
self.log.info(
"File %s:%s assumed to contain no DAGs. Skipping.", filepath, zip_info.filename
)
continue
if mod_name in sys.modules:
del sys.modules[mod_name]
try:
sys.path.insert(0, filepath)
current_module = importlib.import_module(mod_name)
mods.append(current_module)
except Exception as e:
fileloc = os.path.join(filepath, zip_info.filename)
self.log.exception("Failed to import: %s", fileloc)
if self.dagbag_import_error_tracebacks:
self.import_errors[fileloc] = traceback.format_exc(
limit=-self.dagbag_import_error_traceback_depth
)
else:
self.import_errors[fileloc] = str(e)
finally:
if sys.path[0] == filepath:
del sys.path[0]
return mods
def _process_modules(self, filepath, mods, file_last_changed_on_disk):
from airflow.models.dag import DAG # Avoid circular import
top_level_dags = ((o, m) for m in mods for o in m.__dict__.values() if isinstance(o, DAG))
found_dags = []
for (dag, mod) in top_level_dags:
dag.fileloc = mod.__file__
try:
dag.timetable.validate()
# validate dag params
dag.params.validate()
self.bag_dag(dag=dag, root_dag=dag)
found_dags.append(dag)
found_dags += dag.subdags
except AirflowTimetableInvalid as exception:
self.log.exception("Failed to bag_dag: %s", dag.fileloc)
self.import_errors[dag.fileloc] = f"Invalid timetable expression: {exception}"
self.file_last_changed[dag.fileloc] = file_last_changed_on_disk
except (
AirflowDagCycleException,
AirflowDagDuplicatedIdException,
AirflowClusterPolicyViolation,
ParamValidationError,
) as exception:
self.log.exception("Failed to bag_dag: %s", dag.fileloc)
self.import_errors[dag.fileloc] = str(exception)
self.file_last_changed[dag.fileloc] = file_last_changed_on_disk
return found_dags
[docs] def bag_dag(self, dag, root_dag):
"""
Adds the DAG into the bag, recurses into sub dags.
:raises: AirflowDagCycleException if a cycle is detected in this dag or its subdags.
:raises: AirflowDagDuplicatedIdException if this dag or its subdags already exists in the bag.
"""
self._bag_dag(dag=dag, root_dag=root_dag, recursive=True)
def _bag_dag(self, *, dag, root_dag, recursive):
"""Actual implementation of bagging a dag.
The only purpose of this is to avoid exposing ``recursive`` in ``bag_dag()``,
intended to only be used by the ``_bag_dag()`` implementation.
"""
check_cycle(dag) # throws if a task cycle is found
dag.resolve_template_files()
dag.last_loaded = timezone.utcnow()
# Check policies
settings.dag_policy(dag)
for task in dag.tasks:
settings.task_policy(task)
subdags = dag.subdags
try:
# DAG.subdags automatically performs DFS search, so we don't recurse
# into further _bag_dag() calls.
if recursive:
for subdag in subdags:
subdag.fileloc = dag.fileloc
subdag.parent_dag = dag
self._bag_dag(dag=subdag, root_dag=root_dag, recursive=False)
prev_dag = self.dags.get(dag.dag_id)
if prev_dag and prev_dag.fileloc != dag.fileloc:
raise AirflowDagDuplicatedIdException(
dag_id=dag.dag_id,
incoming=dag.fileloc,
existing=self.dags[dag.dag_id].fileloc,
)
self.dags[dag.dag_id] = dag
self.log.debug('Loaded DAG %s', dag)
except (AirflowDagCycleException, AirflowDagDuplicatedIdException):
# There was an error in bagging the dag. Remove it from the list of dags
self.log.exception('Exception bagging dag: %s', dag.dag_id)
# Only necessary at the root level since DAG.subdags automatically
# performs DFS to search through all subdags
if recursive:
for subdag in subdags:
if subdag.dag_id in self.dags:
del self.dags[subdag.dag_id]
raise
[docs] def collect_dags(
self,
dag_folder: Union[str, "pathlib.Path", None] = None,
only_if_updated: bool = True,
include_examples: bool = conf.getboolean('core', 'LOAD_EXAMPLES'),
include_smart_sensor: bool = conf.getboolean('smart_sensor', 'USE_SMART_SENSOR'),
safe_mode: bool = conf.getboolean('core', 'DAG_DISCOVERY_SAFE_MODE'),
):
"""
Given a file path or a folder, this method looks for python modules,
imports them and adds them to the dagbag collection.
Note that if a ``.airflowignore`` file is found while processing
the directory, it will behave much like a ``.gitignore``,
ignoring files that match any of the patterns specified
in the file.
**Note**: The patterns in ``.airflowignore`` are interpreted as either
un-anchored regexes or gitignore-like glob expressions, depending on
the ``DAG_IGNORE_FILE_SYNTAX`` configuration parameter.
"""
if self.read_dags_from_db:
return
self.log.info("Filling up the DagBag from %s", dag_folder)
dag_folder = dag_folder or self.dag_folder
# Used to store stats around DagBag processing
stats = []
# Ensure dag_folder is a str -- it may have been a pathlib.Path
dag_folder = correct_maybe_zipped(str(dag_folder))
for filepath in list_py_file_paths(
dag_folder,
safe_mode=safe_mode,
include_examples=include_examples,
include_smart_sensor=include_smart_sensor,
):
try:
file_parse_start_dttm = timezone.utcnow()
found_dags = self.process_file(filepath, only_if_updated=only_if_updated, safe_mode=safe_mode)
file_parse_end_dttm = timezone.utcnow()
stats.append(
FileLoadStat(
file=filepath.replace(settings.DAGS_FOLDER, ''),
duration=file_parse_end_dttm - file_parse_start_dttm,
dag_num=len(found_dags),
task_num=sum(len(dag.tasks) for dag in found_dags),
dags=str([dag.dag_id for dag in found_dags]),
)
)
except Exception as e:
self.log.exception(e)
self.dagbag_stats = sorted(stats, key=lambda x: x.duration, reverse=True)
[docs] def collect_dags_from_db(self):
"""Collects DAGs from database."""
from airflow.models.serialized_dag import SerializedDagModel
with Stats.timer('collect_db_dags'):
self.log.info("Filling up the DagBag from database")
# The dagbag contains all rows in serialized_dag table. Deleted DAGs are deleted
# from the table by the scheduler job.
self.dags = SerializedDagModel.read_all_dags()
# Adds subdags.
# DAG post-processing steps such as self.bag_dag and croniter are not needed as
# they are done by scheduler before serialization.
subdags = {}
for dag in self.dags.values():
for subdag in dag.subdags:
subdags[subdag.dag_id] = subdag
self.dags.update(subdags)
[docs] def dagbag_report(self):
"""Prints a report around DagBag loading stats"""
stats = self.dagbag_stats
dag_folder = self.dag_folder
duration = sum((o.duration for o in stats), timedelta()).total_seconds()
dag_num = sum(o.dag_num for o in stats)
task_num = sum(o.task_num for o in stats)
table = tabulate(stats, headers="keys")
report = textwrap.dedent(
f"""\n
-------------------------------------------------------------------
DagBag loading stats for {dag_folder}
-------------------------------------------------------------------
Number of DAGs: {dag_num}
Total task number: {task_num}
DagBag parsing time: {duration}
{table}
"""
)
return report
@provide_session
[docs] def sync_to_db(self, session: Session = None):
"""Save attributes about list of DAG to the DB."""
# To avoid circular import - airflow.models.dagbag -> airflow.models.dag -> airflow.models.dagbag
from airflow.models.dag import DAG
from airflow.models.serialized_dag import SerializedDagModel
def _serialize_dag_capturing_errors(dag, session):
"""
Try to serialize the dag to the DB, but make a note of any errors.
We can't place them directly in import_errors, as this may be retried, and work the next time
"""
if dag.is_subdag:
return []
try:
# We can't use bulk_write_to_db as we want to capture each error individually
dag_was_updated = SerializedDagModel.write_dag(
dag,
min_update_interval=settings.MIN_SERIALIZED_DAG_UPDATE_INTERVAL,
session=session,
)
if dag_was_updated:
self._sync_perm_for_dag(dag, session=session)
return []
except OperationalError:
raise
except Exception:
self.log.exception("Failed to write serialized DAG: %s", dag.full_filepath)
return [(dag.fileloc, traceback.format_exc(limit=-self.dagbag_import_error_traceback_depth))]
# Retry 'DAG.bulk_write_to_db' & 'SerializedDagModel.bulk_sync_to_db' in case
# of any Operational Errors
# In case of failures, provide_session handles rollback
for attempt in run_with_db_retries(logger=self.log):
with attempt:
serialize_errors = []
self.log.debug(
"Running dagbag.sync_to_db with retries. Try %d of %d",
attempt.retry_state.attempt_number,
MAX_DB_RETRIES,
)
self.log.debug("Calling the DAG.bulk_sync_to_db method")
try:
# Write Serialized DAGs to DB, capturing errors
for dag in self.dags.values():
serialize_errors.extend(_serialize_dag_capturing_errors(dag, session))
DAG.bulk_write_to_db(self.dags.values(), session=session)
except OperationalError:
session.rollback()
raise
# Only now we are "complete" do we update import_errors - don't want to record errors from
# previous failed attempts
self.import_errors.update(dict(serialize_errors))
@provide_session
def _sync_perm_for_dag(self, dag, session: Session = None):
"""Sync DAG specific permissions, if necessary"""
from airflow.security.permissions import DAG_ACTIONS, resource_name_for_dag
from airflow.www.fab_security.sqla.models import Action, Permission, Resource
def needs_perms(dag_id: str) -> bool:
dag_resource_name = resource_name_for_dag(dag_id)
for permission_name in DAG_ACTIONS:
if not (
session.query(Permission)
.join(Action)
.join(Resource)
.filter(Action.name == permission_name)
.filter(Resource.name == dag_resource_name)
.one_or_none()
):
return True
return False
if dag.access_control or needs_perms(dag.dag_id):
self.log.debug("Syncing DAG permissions: %s to the DB", dag.dag_id)
from airflow.www.security import ApplessAirflowSecurityManager
security_manager = ApplessAirflowSecurityManager(session=session)
security_manager.sync_perm_for_dag(dag.dag_id, dag.access_control)
