text: #
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  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you under the Apache License, Version 2.0 (the
  # "License"); you may not use this file except in compliance
  # with the License.  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing,
  # software distributed under the License is distributed on an
  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # 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 croniter import CroniterBadCronError, CroniterBadDateError, CroniterNotAlphaError, croniter
  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, SerializedDagNotFound
  from airflow.stats import Stats
  from airflow.utils import timezone
  from airflow.utils.dag_cycle_tester import test_cycle
  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
  
  
  class FileLoadStat(NamedTuple):
      """Information about single file"""
  
      file: str
      duration: timedelta
      dag_num: int
      task_num: int
      dags: str
  
  
  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
      :type dag_folder: unicode
      :param include_examples: whether to include the examples that ship
          with airflow or not
      :type include_examples: bool
      :param include_smart_sensor: whether to include the smart sensor native
          DAGs that create the smart sensor operators for whole cluster
      :type include_smart_sensor: bool
      :param read_dags_from_db: Read DAGs from DB if ``True`` is passed.
          If ``False`` DAGs are read from python files.
      :type read_dags_from_db: bool
      :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.
      :type load_op_links: bool
      """
  
      DAGBAG_IMPORT_TIMEOUT = conf.getfloat('core', 'DAGBAG_IMPORT_TIMEOUT')
      SCHEDULER_ZOMBIE_TASK_THRESHOLD = conf.getint('scheduler', 'scheduler_zombie_task_threshold')
  
      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
  
      def size(self) -> int:
          """:return: the amount of dags contained in this dagbag"""
          return len(self.dags)
  
      @property
      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
      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
      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
          :type dag_id: str
          """
          # 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.
              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 sd_last_updated_datetime and 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.is_subdag:
                  root_dag_id = dag.parent_dag.dag_id  # type: ignore
  
          # 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_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:
              raise SerializedDagNotFound(f"DAG '{dag_id}' not found in serialized_dag table")
  
          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
  
      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:  # pylint: disable=broad-except
              self.log.exception(e)
              return []
  
          if 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]
  
          timeout_msg = f"DagBag import timeout for {filepath} after {self.DAGBAG_IMPORT_TIMEOUT}s"
          with timeout(self.DAGBAG_IMPORT_TIMEOUT, error_message=timeout_msg):
              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:  # pylint: disable=broad-except
                  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 []
  
      def _load_modules_from_zip(self, filepath, safe_mode):
          mods = []
          current_zip_file = zipfile.ZipFile(filepath)
          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:  # pylint: disable=broad-except
                  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 mods
  
      def _process_modules(self, filepath, mods, file_last_changed_on_disk):
          from airflow.models.dag import DAG  # Avoid circular import
  
          is_zipfile = zipfile.is_zipfile(filepath)
          top_level_dags = [o for m in mods for o in list(m.__dict__.values()) if isinstance(o, DAG)]
  
          found_dags = []
  
          for dag in top_level_dags:
              if not dag.full_filepath:
                  dag.full_filepath = filepath
                  if dag.fileloc != filepath and not is_zipfile:
                      dag.fileloc = filepath
              try:
                  dag.is_subdag = False
                  if isinstance(dag.normalized_schedule_interval, str):
                      croniter(dag.normalized_schedule_interval)
                  self.bag_dag(dag=dag, root_dag=dag)
                  found_dags.append(dag)
                  found_dags += dag.subdags
              except (CroniterBadCronError, CroniterBadDateError, CroniterNotAlphaError) as cron_e:
                  self.log.exception("Failed to bag_dag: %s", dag.full_filepath)
                  self.import_errors[dag.full_filepath] = f"Invalid Cron expression: {cron_e}"
                  self.file_last_changed[dag.full_filepath] = file_last_changed_on_disk
              except (AirflowDagCycleException, AirflowClusterPolicyViolation) as exception:
                  self.log.exception("Failed to bag_dag: %s", dag.full_filepath)
                  self.import_errors[dag.full_filepath] = str(exception)
                  self.file_last_changed[dag.full_filepath] = file_last_changed_on_disk
          return found_dags
  
      def bag_dag(self, dag, root_dag):
          """
          Adds the DAG into the bag, recurses into sub dags.
          Throws AirflowDagCycleException if a cycle is detected in this dag or its subdags
          """
          test_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:
              for subdag in subdags:
                  subdag.full_filepath = dag.full_filepath
                  subdag.parent_dag = dag
                  subdag.is_subdag = True
                  self.bag_dag(dag=subdag, root_dag=root_dag)
  
              self.dags[dag.dag_id] = dag
              self.log.debug('Loaded DAG %s', dag)
          except AirflowDagCycleException as cycle_exception:
              # 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 dag == root_dag:
                  for subdag in subdags:
                      if subdag.dag_id in self.dags:
                          del self.dags[subdag.dag_id]
              raise cycle_exception
  
      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 regex patterns specified
          in the file.
  
          **Note**: The patterns in .airflowignore are treated as
          un-anchored regexes, not shell-like glob patterns.
          """
          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:  # pylint: disable=broad-except
                  self.log.exception(e)
  
          self.dagbag_stats = sorted(stats, key=lambda x: x.duration, reverse=True)
  
      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)
  
      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
      def sync_to_db(self, session: Optional[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 cant 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:  # pylint: disable=broad-except
                  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: Optional[Session] = None):
          """Sync DAG specific permissions, if necessary"""
          from flask_appbuilder.security.sqla import models as sqla_models
  
          from airflow.security.permissions import DAG_PERMS, permission_name_for_dag
  
          def needs_perm_views(dag_id: str) -> bool:
              view_menu_name = permission_name_for_dag(dag_id)
              for permission_name in DAG_PERMS:
                  if not (
                      session.query(sqla_models.PermissionView)
                      .join(sqla_models.Permission)
                      .join(sqla_models.ViewMenu)
                      .filter(sqla_models.Permission.name == permission_name)
                      .filter(sqla_models.ViewMenu.name == view_menu_name)
                      .one_or_none()
                  ):
                      return True
              return False
  
          if dag.access_control or needs_perm_views(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)
  