Source code for airflow.models.dagbag

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from __future__ import division
from __future__ import unicode_literals

import hashlib
import imp
import importlib
import os
import sys
import textwrap
import zipfile
from collections import namedtuple
from datetime import datetime

from croniter import CroniterBadCronError, CroniterBadDateError, CroniterNotAlphaError, croniter
import six

from airflow import settings
from airflow.configuration import conf
from airflow.dag.base_dag import BaseDagBag
from airflow.exceptions import AirflowDagCycleException
from airflow.executors import get_default_executor
from airflow.settings import Stats
from airflow.utils import timezone
from airflow.utils.dag_processing import list_py_file_paths, correct_maybe_zipped
from airflow.utils.db import provide_session
from airflow.utils.helpers import pprinttable
from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.timeout import timeout

[docs]class DagBag(BaseDagBag, 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 """ # static class variables to detetct dag cycle
[docs] CYCLE_NEW = 0
[docs] CYCLE_DONE = 2
[docs] UNIT_TEST_MODE = conf.getboolean('core', 'UNIT_TEST_MODE')
[docs] SCHEDULER_ZOMBIE_TASK_THRESHOLD = conf.getint('scheduler', 'scheduler_zombie_task_threshold')
def __init__( self, 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, ): # do not use default arg in signature, to fix import cycle on plugin load if executor is None: executor = get_default_executor() dag_folder = dag_folder or settings.DAGS_FOLDER self.dag_folder = dag_folder self.dags = {} # the file's last modified timestamp when we last read it self.file_last_changed = {} self.executor = executor self.import_errors = {} self.has_logged = False self.store_serialized_dags = store_serialized_dags self.collect_dags( dag_folder=dag_folder, include_examples=include_examples, safe_mode=safe_mode)
[docs] def size(self): """ :return: the amount of dags contained in this dagbag """ return len(self.dags)
[docs] def dag_ids(self): return self.dags.keys()
[docs] def 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 """ from airflow.models.dag import DagModel # Avoid circular import # Only read DAGs from DB if this dagbag is store_serialized_dags. # from_file_only is an exception, currently it is for renderring templates # in UI only. Because functions are gone in serialized DAGs, DAGs must be # imported from files. # FIXME: this exception should be removed in future, then webserver can be # decoupled from DAG files. if self.store_serialized_dags and not from_file_only: # 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 row = SerializedDagModel.get(dag_id) if not row: return None dag = row.dag for subdag in dag.subdags: self.dags[subdag.dag_id] = subdag self.dags[dag.dag_id] = dag 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 # Needs to load from file for a store_serialized_dags dagbag. enforce_from_file = False if self.store_serialized_dags and dag is not None: from airflow.serialization.serialized_objects import SerializedDAG enforce_from_file = isinstance(dag, SerializedDAG) # If the dag corresponding to root_dag_id is absent or expired orm_dag = DagModel.get_current(root_dag_id) if (orm_dag and ( root_dag_id not in self.dags or ( orm_dag.last_expired and dag.last_loaded < orm_dag.last_expired ) )) or enforce_from_file: # 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)
[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. """ from airflow.models.dag import DAG # Avoid circular import found_dags = [] # 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 found_dags 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 found_dags except Exception as e: self.log.exception(e) return found_dags mods = [] is_zipfile = zipfile.is_zipfile(filepath) if not is_zipfile: if safe_mode: with open(filepath, 'rb') as f: content = if not all([s in content for s in (b'DAG', b'airflow')]): self.file_last_changed[filepath] = file_last_changed_on_disk # Don't want to spam user with skip messages if not self.has_logged: self.has_logged = True "File %s assumed to contain no DAGs. Skipping.", filepath) return found_dags self.log.debug("Importing %s", filepath) org_mod_name, _ = os.path.splitext(os.path.split(filepath)[-1]) mod_name = ('unusual_prefix_' + hashlib.sha1(filepath.encode('utf-8')).hexdigest() + '_' + org_mod_name) if mod_name in sys.modules: del sys.modules[mod_name] with timeout(self.DAGBAG_IMPORT_TIMEOUT): try: m = imp.load_source(mod_name, filepath) mods.append(m) except Exception as e: self.log.exception("Failed to import: %s", filepath) self.import_errors[filepath] = str(e) self.file_last_changed[filepath] = file_last_changed_on_disk else: zip_file = zipfile.ZipFile(filepath) for mod in zip_file.infolist(): head, _ = os.path.split(mod.filename) mod_name, ext = os.path.splitext(mod.filename) if not head and (ext == '.py' or ext == '.pyc'): if mod_name == '__init__': self.log.warning("Found __init__.%s at root of %s", ext, filepath) if safe_mode: with as zf: self.log.debug("Reading %s from %s", mod.filename, filepath) content = if not all([s in content for s in (b'DAG', b'airflow')]): self.file_last_changed[filepath] = ( file_last_changed_on_disk) # todo: create ignore list # Don't want to spam user with skip messages if not self.has_logged: self.has_logged = True "File %s assumed to contain no DAGs. Skipping.", filepath) if mod_name in sys.modules: del sys.modules[mod_name] try: sys.path.insert(0, filepath) m = importlib.import_module(mod_name) mods.append(m) except Exception as e: self.log.exception("Failed to import: %s", filepath) self.import_errors[filepath] = str(e) self.file_last_changed[filepath] = file_last_changed_on_disk for m in mods: for dag in list(m.__dict__.values()): if isinstance(dag, DAG): 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 self.bag_dag(dag, parent_dag=dag, root_dag=dag) if isinstance(dag._schedule_interval, six.string_types): croniter(dag._schedule_interval) 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] = \ "Invalid Cron expression: " + str(cron_e) self.file_last_changed[dag.full_filepath] = \ file_last_changed_on_disk except AirflowDagCycleException as cycle_exception: self.log.exception("Failed to bag_dag: %s", dag.full_filepath) self.import_errors[dag.full_filepath] = str(cycle_exception) self.file_last_changed[dag.full_filepath] = \ file_last_changed_on_disk self.file_last_changed[filepath] = file_last_changed_on_disk return found_dags
[docs] def 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 """ from airflow.models.taskinstance import TaskInstance # Avoid circular import for zombie in zombies: if zombie.dag_id in self.dags: dag = self.dags[zombie.dag_id] if zombie.task_id in dag.task_ids: task = dag.get_task(zombie.task_id) ti = TaskInstance(task, zombie.execution_date) # Get properties needed for failure handling from SimpleTaskInstance. ti.start_date = zombie.start_date ti.end_date = zombie.end_date ti.try_number = zombie.try_number ti.state = zombie.state ti.test_mode = self.UNIT_TEST_MODE ti.handle_failure("{} detected as zombie".format(ti), ti.test_mode, ti.get_template_context())'Marked zombie job %s as %s', ti, ti.state) session.commit()
[docs] def 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 """ dag.test_cycle() # throws if a task cycle is found dag.resolve_template_files() dag.last_loaded = timezone.utcnow() for task in dag.tasks: settings.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(subdag, parent_dag=dag, 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
[docs] def 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. """ if self.store_serialized_dags: return"Filling up the DagBag from %s", dag_folder) dag_folder = dag_folder or self.dag_folder # Used to store stats around DagBag processing stats = [] FileLoadStat = namedtuple( 'FileLoadStat', "file duration dag_num task_num dags") dag_folder = correct_maybe_zipped(dag_folder) dags_by_name = {} for filepath in list_py_file_paths(dag_folder, safe_mode=safe_mode, include_examples=include_examples): try: ts = timezone.utcnow() found_dags = self.process_file( filepath, only_if_updated=only_if_updated, safe_mode=safe_mode) dag_ids = [dag.dag_id for dag in found_dags] dag_id_names = str(dag_ids) td = timezone.utcnow() - ts td = td.total_seconds() + ( float(td.microseconds) / 1000000) dags_by_name[dag_id_names] = dag_ids stats.append(FileLoadStat( filepath.replace(settings.DAGS_FOLDER, ''), td, len(found_dags), sum([len(dag.tasks) for dag in found_dags]), dag_id_names, )) except Exception as e: self.log.exception(e) self.dagbag_stats = sorted( stats, key=lambda x: x.duration, reverse=True) for file_stat in self.dagbag_stats: dag_ids = dags_by_name[file_stat.dags] if file_stat.dag_num >= 1: # if we found multiple dags per file, the stat is 'dag_id1 _ dag_id2' dag_names = '_'.join(dag_ids) Stats.timing('dag.loading-duration.{}'. format(dag_names), file_stat.duration)
[docs] def collect_dags_from_db(self): """Collects DAGs from database.""" from airflow.models.serialized_dag import SerializedDagModel start_dttm = timezone.utcnow()"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) Stats.timing('collect_db_dags', timezone.utcnow() - start_dttm)
[docs] def dagbag_report(self): """Prints a report around DagBag loading stats""" report = textwrap.dedent("""\n ------------------------------------------------------------------- DagBag loading stats for {dag_folder} ------------------------------------------------------------------- Number of DAGs: {dag_num} Total task number: {task_num} DagBag parsing time: {duration} {table} """) stats = self.dagbag_stats return report.format( dag_folder=self.dag_folder, duration=sum([o.duration for o in stats]), dag_num=sum([o.dag_num for o in stats]), task_num=sum([o.task_num for o in stats]), table=pprinttable(stats),

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