I found the workaround in discussion of this Google issue, and it is to create separate DAG with task which loads all the data needed and stores that data in airflow variable: t("pipeline_config", config, serialize_json=True) Machine type, which can lead to DAG parsing failures if the parse-timeĪnd I was trying to load configuration from external source (which actually took negligible amount of time comparing to other operations to create DAG, but still broke something, because webserver of Airflow in composer runs on App Engine, which has strange behaviours). Have greater CPU and memory capacity, the webserver uses a fixed Worker and scheduler nodes, whose machine types can be customized to There is section about dag failures on webserver in troubleshooting documentation for Composer, which says:Īvoid running heavyweight computation at DAG parse time. I have a theory about possible cause of this issue in Google Composer. Thereafter you may end up with the (non)-issue that Priyank points, but that is expected behavior (state: paused or not) depending on the configuration you have in your installation. airflow backfill to reload the cacheĪs Dmitri suggests, running airflow backfill '' -s '' -e '' for the same start and end date can sometimes help. If you are using an older version of airflow, don't use builder functions. Some reports of this issue on github 2 and JIRA 3 led to a fix released with in airflow v1.9. That is: when a builder or factory pattern is used. Prior to airflow v1.9 this occurs when a dag is instantiated by a function which is imported into the file where instantiation happens. " Attempt removing DagBag caching for the web server" remains on the official TODO as part of the roadmap, indicating that this bug may not yet be fully resolved, but here are some suggestions on how to proceed: only use builders in airflow v1.9+ The web server is caching the DagBag in a way that you cannot really use it as expected. collect_dags_from_db ( self ) ¶Ĭollects DAGs from database.It is not you nor it is correct or expected behavior. Un-anchored regexes, not shell-like glob patterns. Ignoring files that match any of the regex patterns specified The directory, it will behave much like a. airflowignore file is found while processing Imports them and adds them to the dagbag collection. Given a file path or a folder, this method looks for python modules, Throws AirflowDagCycleException if a cycle is detected in this dag or its subdags 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') ) ¶ Session ( ) – DB session.īag_dag ( self, dag, parent_dag, root_dag ) ¶Īdds the DAG into the bag, recurses into sub dags. Zombies ( _processing.SimpleTaskInstance) – zombie task instances to kill. Had a heartbeat for too long, in the current DagBag. kill_zombies ( self, zombies, session = None ) ¶įail given zombie tasks, which are tasks that haven’t The module and look for dag objects within it. Given a path to a python module or zip file, this method imports Gets the DAG out of the dictionary, and refreshes it if expired Parametersĭag_id ( str) – DAG Id process_file ( self, filepath, only_if_updated = True, safe_mode = True ) ¶ The amount of dags contained in this dagbag get_dag ( self, dag_id ) ¶ If False DAGs are read from python files.ĬYCLE_NEW = 0 ¶ CYCLE_IN_PROGRESS = 1 ¶ CYCLE_DONE = 2 ¶ DAGBAG_IMPORT_TIMEOUT ¶ UNIT_TEST_MODE ¶ SCHEDULER_ZOMBIE_TASK_THRESHOLD ¶ dag_ids ¶ size ( self ) ¶ Returns Store_serialized_dags ( bool) – Read DAGs from DB if store_serialized_dags is True. Therefore only once per DagBag is a file logged This is to prevent overloading the user with logging Has_logged – an instance boolean that gets flipped from False to True after aįile has been skipped. Include_examples ( bool) – whether to include the examples that ship Settings are now dagbag level so that one system can run multiple,ĭag_folder ( unicode) – the folder to scan to find DAGsĮxecutor – the executor to use when executing task instances This makes it easier to runĭistinct environments for say production and development, tests, or forĭifferent teams or security profiles. Level configuration settings, like what database to use as a backend and 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 ) ¶īases: _dag.BaseDagBag, _mixin.LoggingMixinĪ dagbag is a collection of dags, parsed out of a folder tree and has high
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