:mod:`airflow.models.dagbag`
============================

.. py:module:: airflow.models.dagbag


Module Contents
---------------

.. py:class:: DagBag(dag_folder=None, executor=None, include_examples=conf.getboolean('core', 'LOAD_EXAMPLES'), safe_mode=conf.getboolean('core', 'DAG_DISCOVERY_SAFE_MODE'), store_serialized_dags=False)

   Bases: :class:`airflow.dag.base_dag.BaseDagBag`, :class:`airflow.utils.log.logging_mixin.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 executor: the executor to use when executing task instances
       in this DagBag
   :param include_examples: whether to include the examples that ship
       with airflow or not
   :type include_examples: bool
   :param has_logged: an instance boolean that gets flipped from False to True after a
       file has been skipped. This is to prevent overloading the user with logging
       messages about skipped files. Therefore only once per DagBag is a file logged
       being skipped.
   :param store_serialized_dags: Read DAGs from DB if store_serialized_dags is ``True``.
       If ``False`` DAGs are read from python files.
   :type store_serialized_dags: bool

   .. attribute:: CYCLE_NEW
      :annotation: = 0

      
   .. attribute:: CYCLE_IN_PROGRESS
      :annotation: = 1

      
   .. attribute:: CYCLE_DONE
      :annotation: = 2

      
   .. attribute:: DAGBAG_IMPORT_TIMEOUT
      

   .. attribute:: UNIT_TEST_MODE
      

   .. attribute:: SCHEDULER_ZOMBIE_TASK_THRESHOLD
      

   .. attribute:: dag_ids
      

   .. method:: size(self)

      :return: the amount of dags contained in this dagbag


   .. method:: get_dag(self, dag_id, from_file_only=False)

      Gets the DAG out of the dictionary, and refreshes it if expired

      :param dag_id: DAG Id
      :type dag_id: str
      :param from_file_only: returns a DAG loaded from file.
      :type from_file_only: bool


   .. method:: 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.


   .. method:: kill_zombies(self, zombies, session=None)

      Fail given zombie tasks, which are tasks that haven't
      had a heartbeat for too long, in the current DagBag.

      :param zombies: zombie task instances to kill.
      :type zombies: airflow.utils.dag_processing.SimpleTaskInstance
      :param session: DB session.
      :type session: sqlalchemy.orm.session.Session


   .. method:: bag_dag(self, dag, parent_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


   .. method:: collect_dags(self, dag_folder=None, only_if_updated=True, include_examples=conf.getboolean('core', 'LOAD_EXAMPLES'), safe_mode=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.


   .. method:: collect_dags_from_db(self)

      Collects DAGs from database.


   .. method:: dagbag_report(self)

      Prints a report around DagBag loading stats