Source code for airflow.models

# -*- coding: utf-8 -*-
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# 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
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# KIND, either express or implied.  See the License for the
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# under the License.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

from builtins import ImportError as BuiltinImportError, bytes, object, str
from collections import defaultdict, namedtuple, OrderedDict
import copy
from typing import Iterable

from future.standard_library import install_aliases

from airflow.models.base import Base, ID_LEN

    # Fix Python > 3.7 deprecation
    from import Hashable
except BuiltinImportError:
    # Preserve Python < 3.3 compatibility
    from collections import Hashable
from datetime import timedelta

import dill
import functools
import getpass
import imp
import importlib
import zipfile
import jinja2
import json
import logging
import os
import pendulum
import pickle
import re
import signal
import sys
import time
import textwrap
import traceback
import warnings
import hashlib

from datetime import datetime
from urllib.parse import quote

from sqlalchemy import (
    Boolean, Column, DateTime, Float, ForeignKey, Index,
    Integer, LargeBinary, PickleType, String, Text, UniqueConstraint, and_,
    func, or_
from sqlalchemy.ext.declarative import declared_attr
from sqlalchemy.orm import reconstructor, relationship, synonym

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

from airflow import settings, utils
from airflow.executors import GetDefaultExecutor, LocalExecutor
from airflow import configuration
from airflow.exceptions import (
    AirflowDagCycleException, AirflowException, AirflowSkipException, AirflowTaskTimeout,
from airflow.dag.base_dag import BaseDag, BaseDagBag
from airflow.lineage import apply_lineage, prepare_lineage
from airflow.models.dagpickle import DagPickle
from airflow.models.errors import ImportError  # noqa: F401
from airflow.models.slamiss import SlaMiss  # noqa: F401
from airflow.models.kubernetes import KubeWorkerIdentifier, KubeResourceVersion  # noqa: F401
from airflow.models.log import Log
from airflow.models.taskfail import TaskFail
from airflow.models.taskreschedule import TaskReschedule
from airflow.ti_deps.deps.not_in_retry_period_dep import NotInRetryPeriodDep
from airflow.ti_deps.deps.prev_dagrun_dep import PrevDagrunDep
from airflow.ti_deps.deps.trigger_rule_dep import TriggerRuleDep

from airflow.ti_deps.dep_context import DepContext, QUEUE_DEPS, RUN_DEPS
from airflow.utils import timezone
from airflow.utils.dag_processing import list_py_file_paths
from airflow.utils.dates import cron_presets, date_range as utils_date_range
from airflow.utils.db import provide_session
from airflow.utils.decorators import apply_defaults
from import send_email
from airflow.utils.helpers import (
    as_tuple, is_container, validate_key, pprinttable)
from airflow.utils.operator_resources import Resources
from airflow.utils.state import State
from airflow.utils.sqlalchemy import UtcDateTime, Interval
from airflow.utils.timeout import timeout
from airflow.utils.trigger_rule import TriggerRule
from airflow.utils.weight_rule import WeightRule
from import get_hostname
from airflow.utils.log.logging_mixin import LoggingMixin


[docs]XCOM_RETURN_KEY = 'return_value'
[docs]Stats = settings.Stats
[docs]class InvalidFernetToken(Exception): # If Fernet isn't loaded we need a valid exception class to catch. If it is # loaded this will get reset to the actual class once get_fernet() is called pass
[docs]class NullFernet(object): """ A "Null" encryptor class that doesn't encrypt or decrypt but that presents a similar interface to Fernet. The purpose of this is to make the rest of the code not have to know the difference, and to only display the message once, not 20 times when `airflow initdb` is ran. """
[docs] is_encrypted = False
[docs] def decrpyt(self, b): return b
[docs] def encrypt(self, b): return b
[docs]_fernet = None
[docs]def get_fernet(): """ Deferred load of Fernet key. This function could fail either because Cryptography is not installed or because the Fernet key is invalid. :return: Fernet object :raises: airflow.exceptions.AirflowException if there's a problem trying to load Fernet """ global _fernet log = LoggingMixin().log if _fernet: return _fernet try: from cryptography.fernet import Fernet, MultiFernet, InvalidToken global InvalidFernetToken InvalidFernetToken = InvalidToken except BuiltinImportError: log.warning( "cryptography not found - values will not be stored encrypted." ) _fernet = NullFernet() return _fernet try: fernet_key = configuration.conf.get('core', 'FERNET_KEY') if not fernet_key: log.warning( "empty cryptography key - values will not be stored encrypted." ) _fernet = NullFernet() else: _fernet = MultiFernet([ Fernet(fernet_part.encode('utf-8')) for fernet_part in fernet_key.split(',') ]) _fernet.is_encrypted = True except (ValueError, TypeError) as ve: raise AirflowException("Could not create Fernet object: {}".format(ve)) return _fernet
# Used by DAG context_managers
[docs]def clear_task_instances(tis, session, activate_dag_runs=True, dag=None, ): """ Clears a set of task instances, but makes sure the running ones get killed. :param tis: a list of task instances :param session: current session :param activate_dag_runs: flag to check for active dag run :param dag: DAG object """ job_ids = [] for ti in tis: if ti.state == State.RUNNING: if ti.job_id: ti.state = State.SHUTDOWN job_ids.append(ti.job_id) else: task_id = ti.task_id if dag and dag.has_task(task_id): task = dag.get_task(task_id) task_retries = task.retries ti.max_tries = ti.try_number + task_retries - 1 else: # Ignore errors when updating max_tries if dag is None or # task not found in dag since database records could be # outdated. We make max_tries the maximum value of its # original max_tries or the current task try number. ti.max_tries = max(ti.max_tries, ti.try_number - 1) ti.state = State.NONE session.merge(ti) if job_ids: from import BaseJob as BJ for job in session.query(BJ).filter( job.state = State.SHUTDOWN if activate_dag_runs and tis: drs = session.query(DagRun).filter( DagRun.dag_id.in_({ti.dag_id for ti in tis}), DagRun.execution_date.in_({ti.execution_date for ti in tis}), ).all() for dr in drs: dr.state = State.RUNNING dr.start_date = timezone.utcnow()
[docs]def get_last_dagrun(dag_id, session, include_externally_triggered=False): """ Returns the last dag run for a dag, None if there was none. Last dag run can be any type of run eg. scheduled or backfilled. Overridden DagRuns are ignored. """ DR = DagRun query = session.query(DR).filter(DR.dag_id == dag_id) if not include_externally_triggered: query = query.filter(DR.external_trigger == False) # noqa query = query.order_by(DR.execution_date.desc()) return query.first()
[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. """ # static class variables to detetct dag cycle
[docs] CYCLE_NEW = 0
[docs] CYCLE_DONE = 2
def __init__( self, dag_folder=None, executor=None, include_examples=configuration.conf.getboolean('core', 'LOAD_EXAMPLES'), safe_mode=configuration.conf.getboolean('core', 'DAG_DISCOVERY_SAFE_MODE')): # do not use default arg in signature, to fix import cycle on plugin load if executor is None: executor = GetDefaultExecutor() dag_folder = dag_folder or settings.DAGS_FOLDER"Filling up the DagBag from %s", dag_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.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): """ Gets the DAG out of the dictionary, and refreshes it if expired """ # If asking for a known subdag, we want to refresh the parent 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 # 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 ) ): # Reprocess source file found_dags = self.process_file( filepath=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. """ 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 and os.path.isfile(filepath): 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(configuration.conf.getint('core', "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 """ 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 = configuration.getboolean('core', '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) Stats.incr('zombies_killed') 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: {dag.dag_id}'.format(**locals())) # 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=configuration.conf.getboolean('core', 'LOAD_EXAMPLES'), safe_mode=configuration.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. """ start_dttm = timezone.utcnow() 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") 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) td = timezone.utcnow() - ts td = td.total_seconds() + ( float(td.microseconds) / 1000000) stats.append(FileLoadStat( filepath.replace(dag_folder, ''), td, len(found_dags), sum([len(dag.tasks) for dag in found_dags]), str([dag.dag_id for dag in found_dags]), )) except Exception as e: self.log.exception(e) Stats.gauge( 'collect_dags', (timezone.utcnow() - start_dttm).total_seconds(), 1) Stats.gauge( 'dagbag_size', len(self.dags), 1) Stats.gauge( 'dagbag_import_errors', len(self.import_errors), 1) self.dagbag_stats = sorted( stats, key=lambda x: x.duration, reverse=True)
[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),
[docs]class User(Base):
[docs] __tablename__ = "users"
[docs] id = Column(Integer, primary_key=True)
[docs] username = Column(String(ID_LEN), unique=True)
[docs] email = Column(String(500))
[docs] superuser = Column(Boolean(), default=False)
[docs] def __repr__(self): return self.username
[docs] def get_id(self): return str(
[docs] def is_superuser(self): return self.superuser
[docs]class TaskInstance(Base, LoggingMixin): """ Task instances store the state of a task instance. This table is the authority and single source of truth around what tasks have run and the state they are in. The SqlAlchemy model doesn't have a SqlAlchemy foreign key to the task or dag model deliberately to have more control over transactions. Database transactions on this table should insure double triggers and any confusion around what task instances are or aren't ready to run even while multiple schedulers may be firing task instances. """
[docs] __tablename__ = "task_instance"
[docs] task_id = Column(String(ID_LEN), primary_key=True)
[docs] dag_id = Column(String(ID_LEN), primary_key=True)
[docs] execution_date = Column(UtcDateTime, primary_key=True)
[docs] start_date = Column(UtcDateTime)
[docs] end_date = Column(UtcDateTime)
[docs] duration = Column(Float)
[docs] state = Column(String(20))
[docs] _try_number = Column('try_number', Integer, default=0)
[docs] max_tries = Column(Integer)
[docs] hostname = Column(String(1000))
[docs] unixname = Column(String(1000))
[docs] job_id = Column(Integer)
[docs] pool = Column(String(50))
[docs] queue = Column(String(50))
[docs] priority_weight = Column(Integer)
[docs] operator = Column(String(1000))
[docs] queued_dttm = Column(UtcDateTime)
[docs] pid = Column(Integer)
[docs] executor_config = Column(PickleType(pickler=dill))
[docs] __table_args__ = ( Index('ti_dag_state', dag_id, state), Index('ti_dag_date', dag_id, execution_date), Index('ti_state', state), Index('ti_state_lkp', dag_id, task_id, execution_date, state), Index('ti_pool', pool, state, priority_weight), Index('ti_job_id', job_id),
) def __init__(self, task, execution_date, state=None): self.dag_id = task.dag_id self.task_id = task.task_id self.task = task self._log = logging.getLogger("airflow.task") # make sure we have a localized execution_date stored in UTC if execution_date and not timezone.is_localized(execution_date): self.log.warning("execution date %s has no timezone information. Using " "default from dag or system", execution_date) if self.task.has_dag(): execution_date = timezone.make_aware(execution_date, self.task.dag.timezone) else: execution_date = timezone.make_aware(execution_date) execution_date = timezone.convert_to_utc(execution_date) self.execution_date = execution_date self.queue = task.queue self.pool = task.pool self.priority_weight = task.priority_weight_total self.try_number = 0 self.max_tries = self.task.retries self.unixname = getpass.getuser() self.run_as_user = task.run_as_user if state: self.state = state self.hostname = '' self.executor_config = task.executor_config self.init_on_load() # Is this TaskInstance being currently running within `airflow run --raw`. # Not persisted to the database so only valid for the current process self.raw = False @reconstructor
[docs] def init_on_load(self): """ Initialize the attributes that aren't stored in the DB. """ self.test_mode = False # can be changed when calling 'run'
[docs] def try_number(self): """ Return the try number that this task number will be when it is actually run. If the TI is currently running, this will match the column in the databse, in all othercases this will be incremenetd """ # This is designed so that task logs end up in the right file. if self.state == State.RUNNING: return self._try_number return self._try_number + 1
@try_number.setter def try_number(self, value): self._try_number = value @property
[docs] def next_try_number(self): return self._try_number + 1
[docs] def command( self, mark_success=False, ignore_all_deps=False, ignore_depends_on_past=False, ignore_task_deps=False, ignore_ti_state=False, local=False, pickle_id=None, raw=False, job_id=None, pool=None, cfg_path=None): """ Returns a command that can be executed anywhere where airflow is installed. This command is part of the message sent to executors by the orchestrator. """ return " ".join(self.command_as_list( mark_success=mark_success, ignore_all_deps=ignore_all_deps, ignore_depends_on_past=ignore_depends_on_past, ignore_task_deps=ignore_task_deps, ignore_ti_state=ignore_ti_state, local=local, pickle_id=pickle_id, raw=raw, job_id=job_id, pool=pool, cfg_path=cfg_path))
[docs] def command_as_list( self, mark_success=False, ignore_all_deps=False, ignore_task_deps=False, ignore_depends_on_past=False, ignore_ti_state=False, local=False, pickle_id=None, raw=False, job_id=None, pool=None, cfg_path=None): """ Returns a command that can be executed anywhere where airflow is installed. This command is part of the message sent to executors by the orchestrator. """ dag = self.task.dag should_pass_filepath = not pickle_id and dag if should_pass_filepath and dag.full_filepath != dag.filepath: path = "DAGS_FOLDER/{}".format(dag.filepath) elif should_pass_filepath and dag.full_filepath: path = dag.full_filepath else: path = None return TaskInstance.generate_command( self.dag_id, self.task_id, self.execution_date, mark_success=mark_success, ignore_all_deps=ignore_all_deps, ignore_task_deps=ignore_task_deps, ignore_depends_on_past=ignore_depends_on_past, ignore_ti_state=ignore_ti_state, local=local, pickle_id=pickle_id, file_path=path, raw=raw, job_id=job_id, pool=pool, cfg_path=cfg_path)
[docs] def generate_command(dag_id, task_id, execution_date, mark_success=False, ignore_all_deps=False, ignore_depends_on_past=False, ignore_task_deps=False, ignore_ti_state=False, local=False, pickle_id=None, file_path=None, raw=False, job_id=None, pool=None, cfg_path=None ): """ Generates the shell command required to execute this task instance. :param dag_id: DAG ID :type dag_id: unicode :param task_id: Task ID :type task_id: unicode :param execution_date: Execution date for the task :type execution_date: datetime :param mark_success: Whether to mark the task as successful :type mark_success: bool :param ignore_all_deps: Ignore all ignorable dependencies. Overrides the other ignore_* parameters. :type ignore_all_deps: bool :param ignore_depends_on_past: Ignore depends_on_past parameter of DAGs (e.g. for Backfills) :type ignore_depends_on_past: bool :param ignore_task_deps: Ignore task-specific dependencies such as depends_on_past and trigger rule :type ignore_task_deps: bool :param ignore_ti_state: Ignore the task instance's previous failure/success :type ignore_ti_state: bool :param local: Whether to run the task locally :type local: bool :param pickle_id: If the DAG was serialized to the DB, the ID associated with the pickled DAG :type pickle_id: unicode :param file_path: path to the file containing the DAG definition :param raw: raw mode (needs more details) :param job_id: job ID (needs more details) :param pool: the Airflow pool that the task should run in :type pool: unicode :param cfg_path: the Path to the configuration file :type cfg_path: basestring :return: shell command that can be used to run the task instance """ iso = execution_date.isoformat() cmd = ["airflow", "run", str(dag_id), str(task_id), str(iso)] cmd.extend(["--mark_success"]) if mark_success else None cmd.extend(["--pickle", str(pickle_id)]) if pickle_id else None cmd.extend(["--job_id", str(job_id)]) if job_id else None cmd.extend(["-A"]) if ignore_all_deps else None cmd.extend(["-i"]) if ignore_task_deps else None cmd.extend(["-I"]) if ignore_depends_on_past else None cmd.extend(["--force"]) if ignore_ti_state else None cmd.extend(["--local"]) if local else None cmd.extend(["--pool", pool]) if pool else None cmd.extend(["--raw"]) if raw else None cmd.extend(["-sd", file_path]) if file_path else None cmd.extend(["--cfg_path", cfg_path]) if cfg_path else None return cmd
[docs] def log_filepath(self): iso = self.execution_date.isoformat() log = os.path.expanduser(configuration.conf.get('core', 'BASE_LOG_FOLDER')) return ( "{log}/{self.dag_id}/{self.task_id}/{iso}.log".format(**locals()))
[docs] def log_url(self): iso = quote(self.execution_date.isoformat()) BASE_URL = configuration.conf.get('webserver', 'BASE_URL') if settings.RBAC: return BASE_URL + ( "/log?" "execution_date={iso}" "&task_id={self.task_id}" "&dag_id={self.dag_id}" ).format(**locals()) else: return BASE_URL + ( "/admin/airflow/log" "?dag_id={self.dag_id}" "&task_id={self.task_id}" "&execution_date={iso}" ).format(**locals())
[docs] def mark_success_url(self): iso = quote(self.execution_date.isoformat()) BASE_URL = configuration.conf.get('webserver', 'BASE_URL') if settings.RBAC: return BASE_URL + ( "/success" "?task_id={self.task_id}" "&dag_id={self.dag_id}" "&execution_date={iso}" "&upstream=false" "&downstream=false" ).format(**locals()) else: return BASE_URL + ( "/admin/airflow/success" "?task_id={self.task_id}" "&dag_id={self.dag_id}" "&execution_date={iso}" "&upstream=false" "&downstream=false" ).format(**locals())
[docs] def current_state(self, session=None): """ Get the very latest state from the database, if a session is passed, we use and looking up the state becomes part of the session, otherwise a new session is used. """ TI = TaskInstance ti = session.query(TI).filter( TI.dag_id == self.dag_id, TI.task_id == self.task_id, TI.execution_date == self.execution_date, ).all() if ti: state = ti[0].state else: state = None return state
[docs] def error(self, session=None): """ Forces the task instance's state to FAILED in the database. """ self.log.error("Recording the task instance as FAILED") self.state = State.FAILED session.merge(self) session.commit()
[docs] def refresh_from_db(self, session=None, lock_for_update=False): """ Refreshes the task instance from the database based on the primary key :param lock_for_update: if True, indicates that the database should lock the TaskInstance (issuing a FOR UPDATE clause) until the session is committed. """ TI = TaskInstance qry = session.query(TI).filter( TI.dag_id == self.dag_id, TI.task_id == self.task_id, TI.execution_date == self.execution_date) if lock_for_update: ti = qry.with_for_update().first() else: ti = qry.first() if ti: self.state = ti.state self.start_date = ti.start_date self.end_date = ti.end_date # Get the raw value of try_number column, don't read through the # accessor here otherwise it will be incremeneted by one already. self.try_number = ti._try_number self.max_tries = ti.max_tries self.hostname = ti.hostname = self.executor_config = ti.executor_config else: self.state = None
[docs] def clear_xcom_data(self, session=None): """ Clears all XCom data from the database for the task instance """ session.query(XCom).filter( XCom.dag_id == self.dag_id, XCom.task_id == self.task_id, XCom.execution_date == self.execution_date ).delete() session.commit()
[docs] def key(self): """ Returns a tuple that identifies the task instance uniquely """ return self.dag_id, self.task_id, self.execution_date, self.try_number
[docs] def set_state(self, state, session=None): self.state = state self.start_date = timezone.utcnow() self.end_date = timezone.utcnow() session.merge(self) session.commit()
[docs] def is_premature(self): """ Returns whether a task is in UP_FOR_RETRY state and its retry interval has elapsed. """ # is the task still in the retry waiting period? return self.state == State.UP_FOR_RETRY and not self.ready_for_retry()
[docs] def are_dependents_done(self, session=None): """ Checks whether the dependents of this task instance have all succeeded. This is meant to be used by wait_for_downstream. This is useful when you do not want to start processing the next schedule of a task until the dependents are done. For instance, if the task DROPs and recreates a table. """ task = self.task if not task.downstream_task_ids: return True ti = session.query(func.count(TaskInstance.task_id)).filter( TaskInstance.dag_id == self.dag_id, TaskInstance.task_id.in_(task.downstream_task_ids), TaskInstance.execution_date == self.execution_date, TaskInstance.state == State.SUCCESS, ) count = ti[0][0] return count == len(task.downstream_task_ids)
[docs] def _get_previous_ti(self, session=None): dag = self.task.dag if dag: dr = self.get_dagrun(session=session) # LEGACY: most likely running from unit tests if not dr: # Means that this TI is NOT being run from a DR, but from a catchup previous_scheduled_date = dag.previous_schedule(self.execution_date) if not previous_scheduled_date: return None return TaskInstance(task=self.task, execution_date=previous_scheduled_date) dr.dag = dag if dag.catchup: last_dagrun = dr.get_previous_scheduled_dagrun(session=session) else: last_dagrun = dr.get_previous_dagrun(session=session) if last_dagrun: return last_dagrun.get_task_instance(self.task_id, session=session) return None
[docs] def previous_ti(self): """The task instance for the task that ran before this task instance.""" return self._get_previous_ti()
[docs] def are_dependencies_met( self, dep_context=None, session=None, verbose=False): """ Returns whether or not all the conditions are met for this task instance to be run given the context for the dependencies (e.g. a task instance being force run from the UI will ignore some dependencies). :param dep_context: The execution context that determines the dependencies that should be evaluated. :type dep_context: DepContext :param session: database session :type session: sqlalchemy.orm.session.Session :param verbose: whether log details on failed dependencies on info or debug log level :type verbose: bool """ dep_context = dep_context or DepContext() failed = False verbose_aware_logger = if verbose else self.log.debug for dep_status in self.get_failed_dep_statuses( dep_context=dep_context, session=session): failed = True verbose_aware_logger( "Dependencies not met for %s, dependency '%s' FAILED: %s", self, dep_status.dep_name, dep_status.reason ) if failed: return False verbose_aware_logger("Dependencies all met for %s", self) return True
[docs] def get_failed_dep_statuses( self, dep_context=None, session=None): dep_context = dep_context or DepContext() for dep in dep_context.deps | self.task.deps: for dep_status in dep.get_dep_statuses( self, session, dep_context): self.log.debug( "%s dependency '%s' PASSED: %s, %s", self, dep_status.dep_name, dep_status.passed, dep_status.reason ) if not dep_status.passed: yield dep_status
[docs] def __repr__(self): return ( "<TaskInstance: {ti.dag_id}.{ti.task_id} " "{ti.execution_date} [{ti.state}]>" ).format(ti=self)
[docs] def next_retry_datetime(self): """ Get datetime of the next retry if the task instance fails. For exponential backoff, retry_delay is used as base and will be converted to seconds. """ delay = self.task.retry_delay if self.task.retry_exponential_backoff: min_backoff = int(delay.total_seconds() * (2 ** (self.try_number - 2))) # deterministic per task instance hash = int(hashlib.sha1("{}#{}#{}#{}".format(self.dag_id, self.task_id, self.execution_date, self.try_number) .encode('utf-8')).hexdigest(), 16) # between 0.5 * delay * (2^retry_number) and 1.0 * delay * (2^retry_number) modded_hash = min_backoff + hash % min_backoff # timedelta has a maximum representable value. The exponentiation # here means this value can be exceeded after a certain number # of tries (around 50 if the initial delay is 1s, even fewer if # the delay is larger). Cap the value here before creating a # timedelta object so the operation doesn't fail. delay_backoff_in_seconds = min( modded_hash, timedelta.max.total_seconds() - 1 ) delay = timedelta(seconds=delay_backoff_in_seconds) if self.task.max_retry_delay: delay = min(self.task.max_retry_delay, delay) return self.end_date + delay
[docs] def ready_for_retry(self): """ Checks on whether the task instance is in the right state and timeframe to be retried. """ return (self.state == State.UP_FOR_RETRY and self.next_retry_datetime() < timezone.utcnow())
[docs] def pool_full(self, session): """ Returns a boolean as to whether the slot pool has room for this task to run """ if not self.task.pool: return False pool = ( session .query(Pool) .filter(Pool.pool == self.task.pool) .first() ) if not pool: return False open_slots = pool.open_slots(session=session) return open_slots <= 0
[docs] def get_dagrun(self, session): """ Returns the DagRun for this TaskInstance :param session: :return: DagRun """ dr = session.query(DagRun).filter( DagRun.dag_id == self.dag_id, DagRun.execution_date == self.execution_date ).first() return dr
[docs] def _check_and_change_state_before_execution( self, verbose=True, ignore_all_deps=False, ignore_depends_on_past=False, ignore_task_deps=False, ignore_ti_state=False, mark_success=False, test_mode=False, job_id=None, pool=None, session=None): """ Checks dependencies and then sets state to RUNNING if they are met. Returns True if and only if state is set to RUNNING, which implies that task should be executed, in preparation for _run_raw_task :param verbose: whether to turn on more verbose logging :type verbose: bool :param ignore_all_deps: Ignore all of the non-critical dependencies, just runs :type ignore_all_deps: bool :param ignore_depends_on_past: Ignore depends_on_past DAG attribute :type ignore_depends_on_past: bool :param ignore_task_deps: Don't check the dependencies of this TI's task :type ignore_task_deps: bool :param ignore_ti_state: Disregards previous task instance state :type ignore_ti_state: bool :param mark_success: Don't run the task, mark its state as success :type mark_success: bool :param test_mode: Doesn't record success or failure in the DB :type test_mode: bool :param pool: specifies the pool to use to run the task instance :type pool: str :return: whether the state was changed to running or not :rtype: bool """ task = self.task self.pool = pool or task.pool self.test_mode = test_mode self.refresh_from_db(session=session, lock_for_update=True) self.job_id = job_id self.hostname = get_hostname() self.operator = task.__class__.__name__ if not ignore_all_deps and not ignore_ti_state and self.state == State.SUCCESS: Stats.incr('previously_succeeded', 1, 1) queue_dep_context = DepContext( deps=QUEUE_DEPS, ignore_all_deps=ignore_all_deps, ignore_ti_state=ignore_ti_state, ignore_depends_on_past=ignore_depends_on_past, ignore_task_deps=ignore_task_deps) if not self.are_dependencies_met( dep_context=queue_dep_context, session=session, verbose=True): session.commit() return False # TODO: Logging needs cleanup, not clear what is being printed hr = "\n" + ("-" * 80) # Line break # For reporting purposes, we report based on 1-indexed, # not 0-indexed lists (i.e. Attempt 1 instead of # Attempt 0 for the first attempt). # Set the task start date. In case it was re-scheduled use the initial # start date that is recorded in task_reschedule table self.start_date = timezone.utcnow() task_reschedules = TaskReschedule.find_for_task_instance(self, session) if task_reschedules: self.start_date = task_reschedules[0].start_date dep_context = DepContext( deps=RUN_DEPS - QUEUE_DEPS, ignore_all_deps=ignore_all_deps, ignore_depends_on_past=ignore_depends_on_past, ignore_task_deps=ignore_task_deps, ignore_ti_state=ignore_ti_state) runnable = self.are_dependencies_met( dep_context=dep_context, session=session, verbose=True) if not runnable and not mark_success: # FIXME: we might have hit concurrency limits, which means we probably # have been running prematurely. This should be handled in the # scheduling mechanism. self.state = State.NONE self.log.warning(hr) self.log.warning( "FIXME: Rescheduling due to concurrency limits reached at task runtime. Attempt %s of " "%s. State set to NONE.", self.try_number, self.max_tries + 1 ) self.log.warning(hr) self.queued_dttm = timezone.utcnow()"Queuing into pool %s", self.pool) session.merge(self) session.commit() return False # Another worker might have started running this task instance while # the current worker process was blocked on refresh_from_db if self.state == State.RUNNING: self.log.warning("Task Instance already running %s", self) session.commit() return False # print status message"Starting attempt %s of %s", self.try_number, self.max_tries + 1) self._try_number += 1 if not test_mode: session.add(Log(State.RUNNING, self)) self.state = State.RUNNING = os.getpid() self.end_date = None if not test_mode: session.merge(self) session.commit() # Closing all pooled connections to prevent # "max number of connections reached" settings.engine.dispose() if verbose: if mark_success:"Marking success for %s on %s", self.task, self.execution_date) else:"Executing %s on %s", self.task, self.execution_date) return True
[docs] def _run_raw_task( self, mark_success=False, test_mode=False, job_id=None, pool=None, session=None): """ Immediately runs the task (without checking or changing db state before execution) and then sets the appropriate final state after completion and runs any post-execute callbacks. Meant to be called only after another function changes the state to running. :param mark_success: Don't run the task, mark its state as success :type mark_success: bool :param test_mode: Doesn't record success or failure in the DB :type test_mode: bool :param pool: specifies the pool to use to run the task instance :type pool: str """ task = self.task self.pool = pool or task.pool self.test_mode = test_mode self.refresh_from_db(session=session) self.job_id = job_id self.hostname = get_hostname() self.operator = task.__class__.__name__ context = {} actual_start_date = timezone.utcnow() try: if not mark_success: context = self.get_template_context() task_copy = copy.copy(task) self.task = task_copy def signal_handler(signum, frame): self.log.error("Received SIGTERM. Terminating subprocesses.") task_copy.on_kill() raise AirflowException("Task received SIGTERM signal") signal.signal(signal.SIGTERM, signal_handler) # Don't clear Xcom until the task is certain to execute self.clear_xcom_data() start_time = time.time() self.render_templates() task_copy.pre_execute(context=context) # If a timeout is specified for the task, make it fail # if it goes beyond result = None if task_copy.execution_timeout: try: with timeout(int( task_copy.execution_timeout.total_seconds())): result = task_copy.execute(context=context) except AirflowTaskTimeout: task_copy.on_kill() raise else: result = task_copy.execute(context=context) # If the task returns a result, push an XCom containing it if result is not None: self.xcom_push(key=XCOM_RETURN_KEY, value=result) # TODO remove deprecated behavior in Airflow 2.0 try: task_copy.post_execute(context=context, result=result) except TypeError as e: if 'unexpected keyword argument' in str(e): warnings.warn( 'BaseOperator.post_execute() now takes two ' 'arguments, `context` and `result`, but "{}" only ' 'expected one. This behavior is deprecated and ' 'will be removed in a future version of ' 'Airflow.'.format(self.task_id), category=DeprecationWarning) task_copy.post_execute(context=context) else: raise end_time = time.time() duration = end_time - start_time Stats.timing( 'dag.{dag_id}.{task_id}.duration'.format( dag_id=task_copy.dag_id, task_id=task_copy.task_id), duration) Stats.incr('operator_successes_{}'.format( self.task.__class__.__name__), 1, 1) Stats.incr('ti_successes') self.refresh_from_db(lock_for_update=True) self.state = State.SUCCESS except AirflowSkipException: self.refresh_from_db(lock_for_update=True) self.state = State.SKIPPED except AirflowRescheduleException as reschedule_exception: self.refresh_from_db() self._handle_reschedule(actual_start_date, reschedule_exception, test_mode, context) return except AirflowException as e: self.refresh_from_db() # for case when task is marked as success/failed externally # current behavior doesn't hit the success callback if self.state in {State.SUCCESS, State.FAILED}: return else: self.handle_failure(e, test_mode, context) raise except (Exception, KeyboardInterrupt) as e: self.handle_failure(e, test_mode, context) raise # Success callback try: if task.on_success_callback: task.on_success_callback(context) except Exception as e3: self.log.error("Failed when executing success callback") self.log.exception(e3) # Recording SUCCESS self.end_date = timezone.utcnow() self.set_duration() if not test_mode: session.add(Log(self.state, self)) session.merge(self) session.commit()
[docs] def run( self, verbose=True, ignore_all_deps=False, ignore_depends_on_past=False, ignore_task_deps=False, ignore_ti_state=False, mark_success=False, test_mode=False, job_id=None, pool=None, session=None): res = self._check_and_change_state_before_execution( verbose=verbose, ignore_all_deps=ignore_all_deps, ignore_depends_on_past=ignore_depends_on_past, ignore_task_deps=ignore_task_deps, ignore_ti_state=ignore_ti_state, mark_success=mark_success, test_mode=test_mode, job_id=job_id, pool=pool, session=session) if res: self._run_raw_task( mark_success=mark_success, test_mode=test_mode, job_id=job_id, pool=pool, session=session)
[docs] def dry_run(self): task = self.task task_copy = copy.copy(task) self.task = task_copy self.render_templates() task_copy.dry_run()
[docs] def _handle_reschedule(self, actual_start_date, reschedule_exception, test_mode=False, context=None, session=None): # Don't record reschedule request in test mode if test_mode: return self.end_date = timezone.utcnow() self.set_duration() # Log reschedule request session.add(TaskReschedule(self.task, self.execution_date, self._try_number, actual_start_date, self.end_date, reschedule_exception.reschedule_date)) # set state self.state = State.UP_FOR_RESCHEDULE # Decrement try_number so subsequent runs will use the same try number and write # to same log file. self._try_number -= 1 session.merge(self) session.commit()'Rescheduling task, marking task as UP_FOR_RESCHEDULE')
[docs] def handle_failure(self, error, test_mode=False, context=None, session=None): self.log.exception(error) task = self.task self.end_date = timezone.utcnow() self.set_duration() Stats.incr('operator_failures_{}'.format(task.__class__.__name__), 1, 1) Stats.incr('ti_failures') if not test_mode: session.add(Log(State.FAILED, self)) # Log failure duration session.add(TaskFail(task, self.execution_date, self.start_date, self.end_date)) if context is not None: context['exception'] = error # Let's go deeper try: # Since this function is called only when the TI state is running, # try_number contains the current try_number (not the next). We # only mark task instance as FAILED if the next task instance # try_number exceeds the max_tries. if self.is_eligible_to_retry(): self.state = State.UP_FOR_RETRY'Marking task as UP_FOR_RETRY') if task.email_on_retry and self.email_alert(error) else: self.state = State.FAILED if task.retries:'All retries failed; marking task as FAILED') else:'Marking task as FAILED.') if task.email_on_failure and self.email_alert(error) except Exception as e2: self.log.error('Failed to send email to: %s', self.log.exception(e2) # Handling callbacks pessimistically try: if self.state == State.UP_FOR_RETRY and task.on_retry_callback: task.on_retry_callback(context) if self.state == State.FAILED and task.on_failure_callback: task.on_failure_callback(context) except Exception as e3: self.log.error("Failed at executing callback") self.log.exception(e3) if not test_mode: session.merge(self) session.commit()
[docs] def is_eligible_to_retry(self): """Is task instance is eligible for retry""" return self.task.retries and self.try_number <= self.max_tries
[docs] def get_template_context(self, session=None): task = self.task from airflow import macros tables = None if 'tables' in task.params: tables = task.params['tables'] params = {} run_id = '' dag_run = None if hasattr(task, 'dag'): if task.dag.params: params.update(task.dag.params) dag_run = ( session.query(DagRun) .filter_by( dag_id=task.dag.dag_id, execution_date=self.execution_date) .first() ) run_id = dag_run.run_id if dag_run else None session.expunge_all() session.commit() ds = self.execution_date.strftime('%Y-%m-%d') ts = self.execution_date.isoformat() yesterday_ds = (self.execution_date - timedelta(1)).strftime('%Y-%m-%d') tomorrow_ds = (self.execution_date + timedelta(1)).strftime('%Y-%m-%d') # For manually triggered dagruns that aren't run on a schedule, next/previous # schedule dates don't make sense, and should be set to execution date for # consistency with how execution_date is set for manually triggered tasks, i.e. # triggered_date == execution_date. if dag_run and dag_run.external_trigger: prev_execution_date = self.execution_date next_execution_date = self.execution_date else: prev_execution_date = task.dag.previous_schedule(self.execution_date) next_execution_date = task.dag.following_schedule(self.execution_date) next_ds = None next_ds_nodash = None if next_execution_date: next_ds = next_execution_date.strftime('%Y-%m-%d') next_ds_nodash = next_ds.replace('-', '') prev_ds = None prev_ds_nodash = None if prev_execution_date: prev_ds = prev_execution_date.strftime('%Y-%m-%d') prev_ds_nodash = prev_ds.replace('-', '') ds_nodash = ds.replace('-', '') ts_nodash = self.execution_date.strftime('%Y%m%dT%H%M%S') ts_nodash_with_tz = ts.replace('-', '').replace(':', '') yesterday_ds_nodash = yesterday_ds.replace('-', '') tomorrow_ds_nodash = tomorrow_ds.replace('-', '') ti_key_str = "{task.dag_id}__{task.task_id}__{ds_nodash}" ti_key_str = ti_key_str.format(**locals()) if task.params: params.update(task.params) if configuration.getboolean('core', 'dag_run_conf_overrides_params'): self.overwrite_params_with_dag_run_conf(params=params, dag_run=dag_run) class VariableAccessor: """ Wrapper around Variable. This way you can get variables in templates by using {var.value.your_variable_name}. """ def __init__(self): self.var = None def __getattr__(self, item): self.var = Variable.get(item) return self.var def __repr__(self): return str(self.var) class VariableJsonAccessor: """ Wrapper around deserialized Variables. This way you can get variables in templates by using {var.json.your_variable_name}. """ def __init__(self): self.var = None def __getattr__(self, item): self.var = Variable.get(item, deserialize_json=True) return self.var def __repr__(self): return str(self.var) return { 'dag': task.dag, 'ds': ds, 'next_ds': next_ds, 'next_ds_nodash': next_ds_nodash, 'prev_ds': prev_ds, 'prev_ds_nodash': prev_ds_nodash, 'ds_nodash': ds_nodash, 'ts': ts, 'ts_nodash': ts_nodash, 'ts_nodash_with_tz': ts_nodash_with_tz, 'yesterday_ds': yesterday_ds, 'yesterday_ds_nodash': yesterday_ds_nodash, 'tomorrow_ds': tomorrow_ds, 'tomorrow_ds_nodash': tomorrow_ds_nodash, 'END_DATE': ds, 'end_date': ds, 'dag_run': dag_run, 'run_id': run_id, 'execution_date': self.execution_date, 'prev_execution_date': prev_execution_date, 'next_execution_date': next_execution_date, 'latest_date': ds, 'macros': macros, 'params': params, 'tables': tables, 'task': task, 'task_instance': self, 'ti': self, 'task_instance_key_str': ti_key_str, 'conf': configuration, 'test_mode': self.test_mode, 'var': { 'value': VariableAccessor(), 'json': VariableJsonAccessor() }, 'inlets': task.inlets, 'outlets': task.outlets,
[docs] def overwrite_params_with_dag_run_conf(self, params, dag_run): if dag_run and dag_run.conf: params.update(dag_run.conf)
[docs] def render_templates(self): task = self.task jinja_context = self.get_template_context() if hasattr(self, 'task') and hasattr(self.task, 'dag'): if self.task.dag.user_defined_macros: jinja_context.update( self.task.dag.user_defined_macros) rt = self.task.render_template # shortcut to method for attr in task.__class__.template_fields: content = getattr(task, attr) if content: rendered_content = rt(attr, content, jinja_context) setattr(task, attr, rendered_content)
[docs] def email_alert(self, exception): exception_html = str(exception).replace('\n', '<br>') jinja_context = self.get_template_context() # This function is called after changing the state # from State.RUNNING so need to subtract 1 from self.try_number. jinja_context.update(dict( exception=exception, exception_html=exception_html, try_number=self.try_number - 1, max_tries=self.max_tries)) jinja_env = self.task.get_template_env() default_subject = 'Airflow alert: {{ti}}' # For reporting purposes, we report based on 1-indexed, # not 0-indexed lists (i.e. Try 1 instead of # Try 0 for the first attempt). default_html_content = ( 'Try {{try_number}} out of {{max_tries + 1}}<br>' 'Exception:<br>{{exception_html}}<br>' 'Log: <a href="{{ti.log_url}}">Link</a><br>' 'Host: {{ti.hostname}}<br>' 'Log file: {{ti.log_filepath}}<br>' 'Mark success: <a href="{{ti.mark_success_url}}">Link</a><br>' ) def render(key, content): if configuration.has_option('email', key): path = configuration.get('email', key) with open(path) as f: content = return jinja_env.from_string(content).render(**jinja_context) subject = render('subject_template', default_subject) html_content = render('html_content_template', default_html_content) send_email(, subject, html_content)
[docs] def set_duration(self): if self.end_date and self.start_date: self.duration = (self.end_date - self.start_date).total_seconds() else: self.duration = None
[docs] def xcom_push( self, key, value, execution_date=None): """ Make an XCom available for tasks to pull. :param key: A key for the XCom :type key: str :param value: A value for the XCom. The value is pickled and stored in the database. :type value: any pickleable object :param execution_date: if provided, the XCom will not be visible until this date. This can be used, for example, to send a message to a task on a future date without it being immediately visible. :type execution_date: datetime """ if execution_date and execution_date < self.execution_date: raise ValueError( 'execution_date can not be in the past (current ' 'execution_date is {}; received {})'.format( self.execution_date, execution_date)) XCom.set( key=key, value=value, task_id=self.task_id, dag_id=self.dag_id, execution_date=execution_date or self.execution_date)
[docs] def xcom_pull( self, task_ids=None, dag_id=None, key=XCOM_RETURN_KEY, include_prior_dates=False): """ Pull XComs that optionally meet certain criteria. The default value for `key` limits the search to XComs that were returned by other tasks (as opposed to those that were pushed manually). To remove this filter, pass key=None (or any desired value). If a single task_id string is provided, the result is the value of the most recent matching XCom from that task_id. If multiple task_ids are provided, a tuple of matching values is returned. None is returned whenever no matches are found. :param key: A key for the XCom. If provided, only XComs with matching keys will be returned. The default key is 'return_value', also available as a constant XCOM_RETURN_KEY. This key is automatically given to XComs returned by tasks (as opposed to being pushed manually). To remove the filter, pass key=None. :type key: str :param task_ids: Only XComs from tasks with matching ids will be pulled. Can pass None to remove the filter. :type task_ids: str or iterable of strings (representing task_ids) :param dag_id: If provided, only pulls XComs from this DAG. If None (default), the DAG of the calling task is used. :type dag_id: str :param include_prior_dates: If False, only XComs from the current execution_date are returned. If True, XComs from previous dates are returned as well. :type include_prior_dates: bool """ if dag_id is None: dag_id = self.dag_id pull_fn = functools.partial( XCom.get_one, execution_date=self.execution_date, key=key, dag_id=dag_id, include_prior_dates=include_prior_dates) if is_container(task_ids): return tuple(pull_fn(task_id=t) for t in task_ids) else: return pull_fn(task_id=task_ids)
[docs] def get_num_running_task_instances(self, session): TI = TaskInstance return session.query(TI).filter( TI.dag_id == self.dag_id, TI.task_id == self.task_id, TI.state == State.RUNNING
[docs] def init_run_context(self, raw=False): """ Sets the log context. """ self.raw = raw self._set_context(self)
[docs]@functools.total_ordering class BaseOperator(LoggingMixin): """ Abstract base class for all operators. Since operators create objects that become nodes in the dag, BaseOperator contains many recursive methods for dag crawling behavior. To derive this class, you are expected to override the constructor as well as the 'execute' method. Operators derived from this class should perform or trigger certain tasks synchronously (wait for completion). Example of operators could be an operator that runs a Pig job (PigOperator), a sensor operator that waits for a partition to land in Hive (HiveSensorOperator), or one that moves data from Hive to MySQL (Hive2MySqlOperator). Instances of these operators (tasks) target specific operations, running specific scripts, functions or data transfers. This class is abstract and shouldn't be instantiated. Instantiating a class derived from this one results in the creation of a task object, which ultimately becomes a node in DAG objects. Task dependencies should be set by using the set_upstream and/or set_downstream methods. :param task_id: a unique, meaningful id for the task :type task_id: str :param owner: the owner of the task, using the unix username is recommended :type owner: str :param retries: the number of retries that should be performed before failing the task :type retries: int :param retry_delay: delay between retries :type retry_delay: datetime.timedelta :param retry_exponential_backoff: allow progressive longer waits between retries by using exponential backoff algorithm on retry delay (delay will be converted into seconds) :type retry_exponential_backoff: bool :param max_retry_delay: maximum delay interval between retries :type max_retry_delay: datetime.timedelta :param start_date: The ``start_date`` for the task, determines the ``execution_date`` for the first task instance. The best practice is to have the start_date rounded to your DAG's ``schedule_interval``. Daily jobs have their start_date some day at 00:00:00, hourly jobs have their start_date at 00:00 of a specific hour. Note that Airflow simply looks at the latest ``execution_date`` and adds the ``schedule_interval`` to determine the next ``execution_date``. It is also very important to note that different tasks' dependencies need to line up in time. If task A depends on task B and their start_date are offset in a way that their execution_date don't line up, A's dependencies will never be met. If you are looking to delay a task, for example running a daily task at 2AM, look into the ``TimeSensor`` and ``TimeDeltaSensor``. We advise against using dynamic ``start_date`` and recommend using fixed ones. Read the FAQ entry about start_date for more information. :type start_date: datetime.datetime :param end_date: if specified, the scheduler won't go beyond this date :type end_date: datetime.datetime :param depends_on_past: when set to true, task instances will run sequentially while relying on the previous task's schedule to succeed. The task instance for the start_date is allowed to run. :type depends_on_past: bool :param wait_for_downstream: when set to true, an instance of task X will wait for tasks immediately downstream of the previous instance of task X to finish successfully before it runs. This is useful if the different instances of a task X alter the same asset, and this asset is used by tasks downstream of task X. Note that depends_on_past is forced to True wherever wait_for_downstream is used. :type wait_for_downstream: bool :param queue: which queue to target when running this job. Not all executors implement queue management, the CeleryExecutor does support targeting specific queues. :type queue: str :param dag: a reference to the dag the task is attached to (if any) :type dag: airflow.models.DAG :param priority_weight: priority weight of this task against other task. This allows the executor to trigger higher priority tasks before others when things get backed up. :type priority_weight: int :param weight_rule: weighting method used for the effective total priority weight of the task. Options are: ``{ downstream | upstream | absolute }`` default is ``downstream`` When set to ``downstream`` the effective weight of the task is the aggregate sum of all downstream descendants. As a result, upstream tasks will have higher weight and will be scheduled more aggressively when using positive weight values. This is useful when you have multiple dag run instances and desire to have all upstream tasks to complete for all runs before each dag can continue processing downstream tasks. When set to ``upstream`` the effective weight is the aggregate sum of all upstream ancestors. This is the opposite where downtream tasks have higher weight and will be scheduled more aggressively when using positive weight values. This is useful when you have multiple dag run instances and prefer to have each dag complete before starting upstream tasks of other dags. When set to ``absolute``, the effective weight is the exact ``priority_weight`` specified without additional weighting. You may want to do this when you know exactly what priority weight each task should have. Additionally, when set to ``absolute``, there is bonus effect of significantly speeding up the task creation process as for very large DAGS. Options can be set as string or using the constants defined in the static class ``airflow.utils.WeightRule`` :type weight_rule: str :param pool: the slot pool this task should run in, slot pools are a way to limit concurrency for certain tasks :type pool: str :param sla: time by which the job is expected to succeed. Note that this represents the ``timedelta`` after the period is closed. For example if you set an SLA of 1 hour, the scheduler would send an email soon after 1:00AM on the ``2016-01-02`` if the ``2016-01-01`` instance has not succeeded yet. The scheduler pays special attention for jobs with an SLA and sends alert emails for sla misses. SLA misses are also recorded in the database for future reference. All tasks that share the same SLA time get bundled in a single email, sent soon after that time. SLA notification are sent once and only once for each task instance. :type sla: datetime.timedelta :param execution_timeout: max time allowed for the execution of this task instance, if it goes beyond it will raise and fail. :type execution_timeout: datetime.timedelta :param on_failure_callback: a function to be called when a task instance of this task fails. a context dictionary is passed as a single parameter to this function. Context contains references to related objects to the task instance and is documented under the macros section of the API. :type on_failure_callback: callable :param on_retry_callback: much like the ``on_failure_callback`` except that it is executed when retries occur. :type on_retry_callback: callable :param on_success_callback: much like the ``on_failure_callback`` except that it is executed when the task succeeds. :type on_success_callback: callable :param trigger_rule: defines the rule by which dependencies are applied for the task to get triggered. Options are: ``{ all_success | all_failed | all_done | one_success | one_failed | none_failed | none_skipped | dummy}`` default is ``all_success``. Options can be set as string or using the constants defined in the static class ``airflow.utils.TriggerRule`` :type trigger_rule: str :param resources: A map of resource parameter names (the argument names of the Resources constructor) to their values. :type resources: dict :param run_as_user: unix username to impersonate while running the task :type run_as_user: str :param task_concurrency: When set, a task will be able to limit the concurrent runs across execution_dates :type task_concurrency: int :param executor_config: Additional task-level configuration parameters that are interpreted by a specific executor. Parameters are namespaced by the name of executor. **Example**: to run this task in a specific docker container through the KubernetesExecutor :: MyOperator(..., executor_config={ "KubernetesExecutor": {"image": "myCustomDockerImage"} } ) :type executor_config: dict """ # For derived classes to define which fields will get jinjaified
[docs] template_fields = [] # type: Iterable[str]
# Defines which files extensions to look for in the templated fields
[docs] template_ext = [] # type: Iterable[str]
# Defines the color in the UI
[docs] ui_color = '#fff'
[docs] ui_fgcolor = '#000'
# base list which includes all the attrs that don't need deep copy.
[docs] _base_operator_shallow_copy_attrs = ('user_defined_macros', 'user_defined_filters', 'params', '_log',)
# each operator should override this class attr for shallow copy attrs.
[docs] shallow_copy_attrs = () # type: Iterable[str]
@apply_defaults def __init__( self, task_id, owner=configuration.conf.get('operators', 'DEFAULT_OWNER'), email=None, email_on_retry=True, email_on_failure=True, retries=0, retry_delay=timedelta(seconds=300), retry_exponential_backoff=False, max_retry_delay=None, start_date=None, end_date=None, schedule_interval=None, # not hooked as of now depends_on_past=False, wait_for_downstream=False, dag=None, params=None, default_args=None, priority_weight=1, weight_rule=WeightRule.DOWNSTREAM, queue=configuration.conf.get('celery', 'default_queue'), pool=None, sla=None, execution_timeout=None, on_failure_callback=None, on_success_callback=None, on_retry_callback=None, trigger_rule=TriggerRule.ALL_SUCCESS, resources=None, run_as_user=None, task_concurrency=None, executor_config=None, inlets=None, outlets=None, *args, **kwargs): if args or kwargs: # TODO remove *args and **kwargs in Airflow 2.0 warnings.warn( 'Invalid arguments were passed to {c} (task_id: {t}). ' 'Support for passing such arguments will be dropped in ' 'Airflow 2.0. Invalid arguments were:' '\n*args: {a}\n**kwargs: {k}'.format( c=self.__class__.__name__, a=args, k=kwargs, t=task_id), category=PendingDeprecationWarning, stacklevel=3 ) validate_key(task_id) self.task_id = task_id self.owner = owner = email self.email_on_retry = email_on_retry self.email_on_failure = email_on_failure self.start_date = start_date if start_date and not isinstance(start_date, datetime): self.log.warning("start_date for %s isn't datetime.datetime", self) elif start_date: self.start_date = timezone.convert_to_utc(start_date) self.end_date = end_date if end_date: self.end_date = timezone.convert_to_utc(end_date) if not TriggerRule.is_valid(trigger_rule): raise AirflowException( "The trigger_rule must be one of {all_triggers}," "'{d}.{t}'; received '{tr}'." .format(all_triggers=TriggerRule.all_triggers(), d=dag.dag_id if dag else "", t=task_id, tr=trigger_rule)) self.trigger_rule = trigger_rule self.depends_on_past = depends_on_past self.wait_for_downstream = wait_for_downstream if wait_for_downstream: self.depends_on_past = True if schedule_interval: self.log.warning( "schedule_interval is used for %s, though it has " "been deprecated as a task parameter, you need to " "specify it as a DAG parameter instead", self ) self._schedule_interval = schedule_interval self.retries = retries self.queue = queue self.pool = pool self.sla = sla self.execution_timeout = execution_timeout self.on_failure_callback = on_failure_callback self.on_success_callback = on_success_callback self.on_retry_callback = on_retry_callback if isinstance(retry_delay, timedelta): self.retry_delay = retry_delay else: self.log.debug("Retry_delay isn't timedelta object, assuming secs") self.retry_delay = timedelta(seconds=retry_delay) self.retry_exponential_backoff = retry_exponential_backoff self.max_retry_delay = max_retry_delay self.params = params or {} # Available in templates! self.priority_weight = priority_weight if not WeightRule.is_valid(weight_rule): raise AirflowException( "The weight_rule must be one of {all_weight_rules}," "'{d}.{t}'; received '{tr}'." .format(all_weight_rules=WeightRule.all_weight_rules, d=dag.dag_id if dag else "", t=task_id, tr=weight_rule)) self.weight_rule = weight_rule self.resources = Resources(**(resources or {})) self.run_as_user = run_as_user self.task_concurrency = task_concurrency self.executor_config = executor_config or {} # Private attributes self._upstream_task_ids = set() self._downstream_task_ids = set() if not dag and _CONTEXT_MANAGER_DAG: dag = _CONTEXT_MANAGER_DAG if dag: self.dag = dag self._log = logging.getLogger("airflow.task.operators") # lineage self.inlets = [] self.outlets = [] self.lineage_data = None self._inlets = { "auto": False, "task_ids": [], "datasets": [], } self._outlets = { "datasets": [], } if inlets: self._inlets.update(inlets) if outlets: self._outlets.update(outlets) self._comps = { 'task_id', 'dag_id', 'owner', 'email', 'email_on_retry', 'retry_delay', 'retry_exponential_backoff', 'max_retry_delay', 'start_date', 'schedule_interval', 'depends_on_past', 'wait_for_downstream', 'priority_weight', 'sla', 'execution_timeout', 'on_failure_callback', 'on_success_callback', 'on_retry_callback', }
[docs] def __eq__(self, other): if (type(self) == type(other) and self.task_id == other.task_id): return all(self.__dict__.get(c, None) == other.__dict__.get(c, None) for c in self._comps) return False
[docs] def __ne__(self, other): return not self == other
[docs] def __lt__(self, other): return self.task_id < other.task_id
[docs] def __hash__(self): hash_components = [type(self)] for c in self._comps: val = getattr(self, c, None) try: hash(val) hash_components.append(val) except TypeError: hash_components.append(repr(val)) return hash(tuple(hash_components))
# Composing Operators -----------------------------------------------
[docs] def __rshift__(self, other): """ Implements Self >> Other == self.set_downstream(other) If "Other" is a DAG, the DAG is assigned to the Operator. """ if isinstance(other, DAG): # if this dag is already assigned, do nothing # otherwise, do normal dag assignment if not (self.has_dag() and self.dag is other): self.dag = other else: self.set_downstream(other) return other
[docs] def __lshift__(self, other): """ Implements Self << Other == self.set_upstream(other) If "Other" is a DAG, the DAG is assigned to the Operator. """ if isinstance(other, DAG): # if this dag is already assigned, do nothing # otherwise, do normal dag assignment if not (self.has_dag() and self.dag is other): self.dag = other else: self.set_upstream(other) return other
[docs] def __rrshift__(self, other): """ Called for [DAG] >> [Operator] because DAGs don't have __rshift__ operators. """ self.__lshift__(other) return self
[docs] def __rlshift__(self, other): """ Called for [DAG] << [Operator] because DAGs don't have __lshift__ operators. """ self.__rshift__(other) return self
# /Composing Operators --------------------------------------------- @property
[docs] def dag(self): """ Returns the Operator's DAG if set, otherwise raises an error """ if self.has_dag(): return self._dag else: raise AirflowException( 'Operator {} has not been assigned to a DAG yet'.format(self))
@dag.setter def dag(self, dag): """ Operators can be assigned to one DAG, one time. Repeat assignments to that same DAG are ok. """ if not isinstance(dag, DAG): raise TypeError( 'Expected DAG; received {}'.format(dag.__class__.__name__)) elif self.has_dag() and self.dag is not dag: raise AirflowException( "The DAG assigned to {} can not be changed.".format(self)) elif self.task_id not in dag.task_dict: dag.add_task(self) self._dag = dag
[docs] def has_dag(self): """ Returns True if the Operator has been assigned to a DAG. """ return getattr(self, '_dag', None) is not None
[docs] def dag_id(self): if self.has_dag(): return self.dag.dag_id else: return 'adhoc_' + self.owner
[docs] def deps(self): """ Returns the list of dependencies for the operator. These differ from execution context dependencies in that they are specific to tasks and can be extended/overridden by subclasses. """ return { NotInRetryPeriodDep(), PrevDagrunDep(), TriggerRuleDep(),
} @property
[docs] def schedule_interval(self): """ The schedule interval of the DAG always wins over individual tasks so that tasks within a DAG always line up. The task still needs a schedule_interval as it may not be attached to a DAG. """ if self.has_dag(): return self.dag._schedule_interval else: return self._schedule_interval
[docs] def priority_weight_total(self): if self.weight_rule == WeightRule.ABSOLUTE: return self.priority_weight elif self.weight_rule == WeightRule.DOWNSTREAM: upstream = False elif self.weight_rule == WeightRule.UPSTREAM: upstream = True else: upstream = False return self.priority_weight + sum( map(lambda task_id: self._dag.task_dict[task_id].priority_weight, self.get_flat_relative_ids(upstream=upstream))
) @prepare_lineage
[docs] def pre_execute(self, context): """ This hook is triggered right before self.execute() is called. """ pass
[docs] def execute(self, context): """ This is the main method to derive when creating an operator. Context is the same dictionary used as when rendering jinja templates. Refer to get_template_context for more context. """ raise NotImplementedError()
[docs] def post_execute(self, context, result=None): """ This hook is triggered right after self.execute() is called. It is passed the execution context and any results returned by the operator. """ pass
[docs] def on_kill(self): """ Override this method to cleanup subprocesses when a task instance gets killed. Any use of the threading, subprocess or multiprocessing module within an operator needs to be cleaned up or it will leave ghost processes behind. """ pass
[docs] def __deepcopy__(self, memo): """ Hack sorting double chained task lists by task_id to avoid hitting max_depth on deepcopy operations. """ sys.setrecursionlimit(5000) # TODO fix this in a better way cls = self.__class__ result = cls.__new__(cls) memo[id(self)] = result shallow_copy = cls.shallow_copy_attrs + cls._base_operator_shallow_copy_attrs for k, v in list(self.__dict__.items()): if k not in shallow_copy: setattr(result, k, copy.deepcopy(v, memo)) else: setattr(result, k, copy.copy(v)) return result
[docs] def __getstate__(self): state = dict(self.__dict__) del state['_log'] return state
[docs] def __setstate__(self, state): self.__dict__ = state self._log = logging.getLogger("airflow.task.operators")
[docs] def render_template_from_field(self, attr, content, context, jinja_env): """ Renders a template from a field. If the field is a string, it will simply render the string and return the result. If it is a collection or nested set of collections, it will traverse the structure and render all elements in it. If the field has another type, it will return it as it is. """ rt = self.render_template if isinstance(content, six.string_types): result = jinja_env.from_string(content).render(**context) elif isinstance(content, (list, tuple)): result = [rt(attr, e, context) for e in content] elif isinstance(content, dict): result = { k: rt("{}[{}]".format(attr, k), v, context) for k, v in list(content.items())} else: result = content return result
[docs] def render_template(self, attr, content, context): """ Renders a template either from a file or directly in a field, and returns the rendered result. """ jinja_env = self.get_template_env() exts = self.__class__.template_ext if ( isinstance(content, six.string_types) and any([content.endswith(ext) for ext in exts])): return jinja_env.get_template(content).render(**context) else: return self.render_template_from_field(attr, content, context, jinja_env)
[docs] def get_template_env(self): return self.dag.get_template_env() \ if hasattr(self, 'dag') \ else jinja2.Environment(cache_size=0)
[docs] def prepare_template(self): """ Hook that is triggered after the templated fields get replaced by their content. If you need your operator to alter the content of the file before the template is rendered, it should override this method to do so. """ pass
[docs] def resolve_template_files(self): # Getting the content of files for template_field / template_ext for attr in self.template_fields: content = getattr(self, attr) if content is None: continue elif isinstance(content, six.string_types) and \ any([content.endswith(ext) for ext in self.template_ext]): env = self.get_template_env() try: setattr(self, attr, env.loader.get_source(env, content)[0]) except Exception as e: self.log.exception(e) elif isinstance(content, list): env = self.dag.get_template_env() for i in range(len(content)): if isinstance(content[i], six.string_types) and \ any([content[i].endswith(ext) for ext in self.template_ext]): try: content[i] = env.loader.get_source(env, content[i])[0] except Exception as e: self.log.exception(e) self.prepare_template()
[docs] def upstream_list(self): """@property: list of tasks directly upstream""" return [self.dag.get_task(tid) for tid in self._upstream_task_ids]
[docs] def upstream_task_ids(self): return self._upstream_task_ids
[docs] def downstream_list(self): """@property: list of tasks directly downstream""" return [self.dag.get_task(tid) for tid in self._downstream_task_ids]
[docs] def downstream_task_ids(self): return self._downstream_task_ids
[docs] def clear(self, start_date=None, end_date=None, upstream=False, downstream=False, session=None): """ Clears the state of task instances associated with the task, following the parameters specified. """ TI = TaskInstance qry = session.query(TI).filter(TI.dag_id == self.dag_id) if start_date: qry = qry.filter(TI.execution_date >= start_date) if end_date: qry = qry.filter(TI.execution_date <= end_date) tasks = [self.task_id] if upstream: tasks += [ t.task_id for t in self.get_flat_relatives(upstream=True)] if downstream: tasks += [ t.task_id for t in self.get_flat_relatives(upstream=False)] qry = qry.filter(TI.task_id.in_(tasks)) count = qry.count() clear_task_instances(qry.all(), session, dag=self.dag) session.commit() return count
[docs] def get_task_instances(self, session, start_date=None, end_date=None): """ Get a set of task instance related to this task for a specific date range. """ TI = TaskInstance end_date = end_date or timezone.utcnow() return session.query(TI).filter( TI.dag_id == self.dag_id, TI.task_id == self.task_id, TI.execution_date >= start_date, TI.execution_date <= end_date, ).order_by(TI.execution_date).all()
[docs] def get_flat_relative_ids(self, upstream=False, found_descendants=None): """ Get a flat list of relatives' ids, either upstream or downstream. """ if not found_descendants: found_descendants = set() relative_ids = self.get_direct_relative_ids(upstream) for relative_id in relative_ids: if relative_id not in found_descendants: found_descendants.add(relative_id) relative_task = self._dag.task_dict[relative_id] relative_task.get_flat_relative_ids(upstream, found_descendants) return found_descendants
[docs] def get_flat_relatives(self, upstream=False): """ Get a flat list of relatives, either upstream or downstream. """ return list(map(lambda task_id: self._dag.task_dict[task_id], self.get_flat_relative_ids(upstream)))
[docs] def run( self, start_date=None, end_date=None, ignore_first_depends_on_past=False, ignore_ti_state=False, mark_success=False): """ Run a set of task instances for a date range. """ start_date = start_date or self.start_date end_date = end_date or self.end_date or timezone.utcnow() for dt in self.dag.date_range(start_date, end_date=end_date): TaskInstance(self, dt).run( mark_success=mark_success, ignore_depends_on_past=( dt == start_date and ignore_first_depends_on_past), ignore_ti_state=ignore_ti_state)
[docs] def dry_run(self):'Dry run') for attr in self.template_fields: content = getattr(self, attr) if content and isinstance(content, six.string_types):'Rendering template for %s', attr)
[docs] def get_direct_relative_ids(self, upstream=False): """ Get the direct relative ids to the current task, upstream or downstream. """ if upstream: return self._upstream_task_ids else: return self._downstream_task_ids
[docs] def get_direct_relatives(self, upstream=False): """ Get the direct relatives to the current task, upstream or downstream. """ if upstream: return self.upstream_list else: return self.downstream_list
[docs] def __repr__(self): return "<Task({self.__class__.__name__}): {self.task_id}>".format( self=self)
[docs] def task_type(self): return self.__class__.__name__
[docs] def add_only_new(self, item_set, item): if item in item_set: self.log.warning( 'Dependency {self}, {item} already registered' ''.format(**locals())) else: item_set.add(item)
[docs] def _set_relatives(self, task_or_task_list, upstream=False): try: task_list = list(task_or_task_list) except TypeError: task_list = [task_or_task_list] for t in task_list: if not isinstance(t, BaseOperator): raise AirflowException( "Relationships can only be set between " "Operators; received {}".format(t.__class__.__name__)) # relationships can only be set if the tasks share a single DAG. Tasks # without a DAG are assigned to that DAG. dags = {t._dag.dag_id: t._dag for t in [self] + task_list if t.has_dag()} if len(dags) > 1: raise AirflowException( 'Tried to set relationships between tasks in ' 'more than one DAG: {}'.format(dags.values())) elif len(dags) == 1: dag = dags.popitem()[1] else: raise AirflowException( "Tried to create relationships between tasks that don't have " "DAGs yet. Set the DAG for at least one " "task and try again: {}".format([self] + task_list)) if dag and not self.has_dag(): self.dag = dag for task in task_list: if dag and not task.has_dag(): task.dag = dag if upstream: task.add_only_new(task._downstream_task_ids, self.task_id) self.add_only_new(self._upstream_task_ids, task.task_id) else: self.add_only_new(self._downstream_task_ids, task.task_id) task.add_only_new(task._upstream_task_ids, self.task_id)
[docs] def set_downstream(self, task_or_task_list): """ Set a task or a task list to be directly downstream from the current task. """ self._set_relatives(task_or_task_list, upstream=False)
[docs] def set_upstream(self, task_or_task_list): """ Set a task or a task list to be directly upstream from the current task. """ self._set_relatives(task_or_task_list, upstream=True)
[docs] def xcom_push( self, context, key, value, execution_date=None): """ See TaskInstance.xcom_push() """ context['ti'].xcom_push( key=key, value=value, execution_date=execution_date)
[docs] def xcom_pull( self, context, task_ids=None, dag_id=None, key=XCOM_RETURN_KEY, include_prior_dates=None): """ See TaskInstance.xcom_pull() """ return context['ti'].xcom_pull( key=key, task_ids=task_ids, dag_id=dag_id, include_prior_dates=include_prior_dates)
[docs]class DagModel(Base):
[docs] __tablename__ = "dag"
""" These items are stored in the database for state related information """
[docs] dag_id = Column(String(ID_LEN), primary_key=True)
# A DAG can be paused from the UI / DB # Set this default value of is_paused based on a configuration value!
[docs] is_paused_at_creation = configuration.conf\ .getboolean('core', 'dags_are_paused_at_creation')
[docs] is_paused = Column(Boolean, default=is_paused_at_creation)
# Whether the DAG is a subdag
[docs] is_subdag = Column(Boolean, default=False)
# Whether that DAG was seen on the last DagBag load
[docs] is_active = Column(Boolean, default=False)
# Last time the scheduler started
[docs] last_scheduler_run = Column(UtcDateTime)
# Last time this DAG was pickled
[docs] last_pickled = Column(UtcDateTime)
# Time when the DAG last received a refresh signal # (e.g. the DAG's "refresh" button was clicked in the web UI)
[docs] last_expired = Column(UtcDateTime)
# Whether (one of) the scheduler is scheduling this DAG at the moment
[docs] scheduler_lock = Column(Boolean)
# Foreign key to the latest pickle_id
[docs] pickle_id = Column(Integer)
# The location of the file containing the DAG object
[docs] fileloc = Column(String(2000))
# String representing the owners
[docs] owners = Column(String(2000))
# Description of the dag
[docs] description = Column(Text)
# Default view of the inside the webserver
[docs] default_view = Column(String(25))
# Schedule interval
[docs] schedule_interval = Column(Interval)
[docs] def __repr__(self): return "<DAG: {self.dag_id}>".format(self=self)
[docs] def timezone(self): return settings.TIMEZONE
@staticmethod @provide_session
[docs] def get_dagmodel(dag_id, session=None): return session.query(DagModel).filter(DagModel.dag_id == dag_id).first()
@classmethod @provide_session
[docs] def get_current(cls, dag_id, session=None): return session.query(cls).filter(cls.dag_id == dag_id).first()
[docs] def get_default_view(self): if self.default_view is None: return configuration.conf.get('webserver', 'dag_default_view').lower() else: return self.default_view
[docs] def get_last_dagrun(self, session=None, include_externally_triggered=False): return get_last_dagrun(self.dag_id, session=session, include_externally_triggered=include_externally_triggered)
[docs] def safe_dag_id(self): return self.dag_id.replace('.', '__dot__')
[docs] def get_dag(self): return DagBag(dag_folder=self.fileloc).get_dag(self.dag_id)
[docs] def create_dagrun(self, run_id, state, execution_date, start_date=None, external_trigger=False, conf=None, session=None): """ Creates a dag run from this dag including the tasks associated with this dag. Returns the dag run. :param run_id: defines the the run id for this dag run :type run_id: str :param execution_date: the execution date of this dag run :type execution_date: datetime.datetime :param state: the state of the dag run :type state: airflow.utils.state.State :param start_date: the date this dag run should be evaluated :type start_date: datetime.datetime :param external_trigger: whether this dag run is externally triggered :type external_trigger: bool :param session: database session :type session: sqlalchemy.orm.session.Session """ return self.get_dag().create_dagrun(run_id=run_id, state=state, execution_date=execution_date, start_date=start_date, external_trigger=external_trigger, conf=conf, session=session)
[docs]@functools.total_ordering class DAG(BaseDag, LoggingMixin): """ A dag (directed acyclic graph) is a collection of tasks with directional dependencies. A dag also has a schedule, a start end an end date (optional). For each schedule, (say daily or hourly), the DAG needs to run each individual tasks as their dependencies are met. Certain tasks have the property of depending on their own past, meaning that they can't run until their previous schedule (and upstream tasks) are completed. DAGs essentially act as namespaces for tasks. A task_id can only be added once to a DAG. :param dag_id: The id of the DAG :type dag_id: str :param description: The description for the DAG to e.g. be shown on the webserver :type description: str :param schedule_interval: Defines how often that DAG runs, this timedelta object gets added to your latest task instance's execution_date to figure out the next schedule :type schedule_interval: datetime.timedelta or dateutil.relativedelta.relativedelta or str that acts as a cron expression :param start_date: The timestamp from which the scheduler will attempt to backfill :type start_date: datetime.datetime :param end_date: A date beyond which your DAG won't run, leave to None for open ended scheduling :type end_date: datetime.datetime :param template_searchpath: This list of folders (non relative) defines where jinja will look for your templates. Order matters. Note that jinja/airflow includes the path of your DAG file by default :type template_searchpath: str or list[str] :param user_defined_macros: a dictionary of macros that will be exposed in your jinja templates. For example, passing ``dict(foo='bar')`` to this argument allows you to ``{{ foo }}`` in all jinja templates related to this DAG. Note that you can pass any type of object here. :type user_defined_macros: dict :param user_defined_filters: a dictionary of filters that will be exposed in your jinja templates. For example, passing ``dict(hello=lambda name: 'Hello %s' % name)`` to this argument allows you to ``{{ 'world' | hello }}`` in all jinja templates related to this DAG. :type user_defined_filters: dict :param default_args: A dictionary of default parameters to be used as constructor keyword parameters when initialising operators. Note that operators have the same hook, and precede those defined here, meaning that if your dict contains `'depends_on_past': True` here and `'depends_on_past': False` in the operator's call `default_args`, the actual value will be `False`. :type default_args: dict :param params: a dictionary of DAG level parameters that are made accessible in templates, namespaced under `params`. These params can be overridden at the task level. :type params: dict :param concurrency: the number of task instances allowed to run concurrently :type concurrency: int :param max_active_runs: maximum number of active DAG runs, beyond this number of DAG runs in a running state, the scheduler won't create new active DAG runs :type max_active_runs: int :param dagrun_timeout: specify how long a DagRun should be up before timing out / failing, so that new DagRuns can be created :type dagrun_timeout: datetime.timedelta :param sla_miss_callback: specify a function to call when reporting SLA timeouts. :type sla_miss_callback: types.FunctionType :param default_view: Specify DAG default view (tree, graph, duration, gantt, landing_times) :type default_view: str :param orientation: Specify DAG orientation in graph view (LR, TB, RL, BT) :type orientation: str :param catchup: Perform scheduler catchup (or only run latest)? Defaults to True :type catchup: bool :param on_failure_callback: A function to be called when a DagRun of this dag fails. A context dictionary is passed as a single parameter to this function. :type on_failure_callback: callable :param on_success_callback: Much like the ``on_failure_callback`` except that it is executed when the dag succeeds. :type on_success_callback: callable """ def __init__( self, dag_id, description='', schedule_interval=timedelta(days=1), start_date=None, end_date=None, full_filepath=None, template_searchpath=None, user_defined_macros=None, user_defined_filters=None, default_args=None, concurrency=configuration.conf.getint('core', 'dag_concurrency'), max_active_runs=configuration.conf.getint( 'core', 'max_active_runs_per_dag'), dagrun_timeout=None, sla_miss_callback=None, default_view=None, orientation=configuration.conf.get('webserver', 'dag_orientation'), catchup=configuration.conf.getboolean('scheduler', 'catchup_by_default'), on_success_callback=None, on_failure_callback=None, doc_md=None, params=None): self.user_defined_macros = user_defined_macros self.user_defined_filters = user_defined_filters self.default_args = default_args or {} self.params = params or {} # merging potentially conflicting default_args['params'] into params if 'params' in self.default_args: self.params.update(self.default_args['params']) del self.default_args['params'] validate_key(dag_id) # Properties from BaseDag self._dag_id = dag_id self._full_filepath = full_filepath if full_filepath else '' self._concurrency = concurrency self._pickle_id = None self._description = description # set file location to caller source path self.fileloc = sys._getframe().f_back.f_code.co_filename self.task_dict = dict() # set timezone if start_date and start_date.tzinfo: self.timezone = start_date.tzinfo elif 'start_date' in self.default_args and self.default_args['start_date']: if isinstance(self.default_args['start_date'], six.string_types): self.default_args['start_date'] = ( timezone.parse(self.default_args['start_date']) ) self.timezone = self.default_args['start_date'].tzinfo if not hasattr(self, 'timezone') or not self.timezone: self.timezone = settings.TIMEZONE self.start_date = timezone.convert_to_utc(start_date) self.end_date = timezone.convert_to_utc(end_date) # also convert tasks if 'start_date' in self.default_args: self.default_args['start_date'] = ( timezone.convert_to_utc(self.default_args['start_date']) ) if 'end_date' in self.default_args: self.default_args['end_date'] = ( timezone.convert_to_utc(self.default_args['end_date']) ) self.schedule_interval = schedule_interval if isinstance(schedule_interval, Hashable) and schedule_interval in cron_presets: self._schedule_interval = cron_presets.get(schedule_interval) elif schedule_interval == '@once': self._schedule_interval = None else: self._schedule_interval = schedule_interval if isinstance(template_searchpath, six.string_types): template_searchpath = [template_searchpath] self.template_searchpath = template_searchpath self.parent_dag = None # Gets set when DAGs are loaded self.last_loaded = timezone.utcnow() self.safe_dag_id = dag_id.replace('.', '__dot__') self.max_active_runs = max_active_runs self.dagrun_timeout = dagrun_timeout self.sla_miss_callback = sla_miss_callback self._default_view = default_view self.orientation = orientation self.catchup = catchup self.is_subdag = False # DagBag.bag_dag() will set this to True if appropriate self.partial = False self.on_success_callback = on_success_callback self.on_failure_callback = on_failure_callback self.doc_md = doc_md self._old_context_manager_dags = [] self._comps = { 'dag_id', 'task_ids', 'parent_dag', 'start_date', 'schedule_interval', 'full_filepath', 'template_searchpath', 'last_loaded', }
[docs] def __repr__(self): return "<DAG: {self.dag_id}>".format(self=self)
[docs] def __eq__(self, other): if (type(self) == type(other) and self.dag_id == other.dag_id): # Use getattr() instead of __dict__ as __dict__ doesn't return # correct values for properties. return all(getattr(self, c, None) == getattr(other, c, None) for c in self._comps) return False
[docs] def __ne__(self, other): return not self == other
[docs] def __lt__(self, other): return self.dag_id < other.dag_id
[docs] def __hash__(self): hash_components = [type(self)] for c in self._comps: # task_ids returns a list and lists can't be hashed if c == 'task_ids': val = tuple(self.task_dict.keys()) else: val = getattr(self, c, None) try: hash(val) hash_components.append(val) except TypeError: hash_components.append(repr(val)) return hash(tuple(hash_components))
# Context Manager -----------------------------------------------
[docs] def __enter__(self): global _CONTEXT_MANAGER_DAG self._old_context_manager_dags.append(_CONTEXT_MANAGER_DAG) _CONTEXT_MANAGER_DAG = self return self
[docs] def __exit__(self, _type, _value, _tb): global _CONTEXT_MANAGER_DAG _CONTEXT_MANAGER_DAG = self._old_context_manager_dags.pop()
# /Context Manager ----------------------------------------------
[docs] def get_default_view(self): """This is only there for backward compatible jinja2 templates""" if self._default_view is None: return configuration.conf.get('webserver', 'dag_default_view').lower() else: return self._default_view
[docs] def date_range(self, start_date, num=None, end_date=timezone.utcnow()): if num: end_date = None return utils_date_range( start_date=start_date, end_date=end_date, num=num, delta=self._schedule_interval)
[docs] def is_fixed_time_schedule(self): """ Figures out if the DAG schedule has a fixed time (e.g. 3 AM). :return: True if the schedule has a fixed time, False if not. """ now = cron = croniter(self._schedule_interval, now) start = cron.get_next(datetime) cron_next = cron.get_next(datetime) if cron_next.minute == start.minute and cron_next.hour == start.hour: return True return False
[docs] def following_schedule(self, dttm): """ Calculates the following schedule for this dag in UTC. :param dttm: utc datetime :return: utc datetime """ if isinstance(self._schedule_interval, six.string_types): # we don't want to rely on the transitions created by # croniter as they are not always correct dttm = pendulum.instance(dttm) naive = timezone.make_naive(dttm, self.timezone) cron = croniter(self._schedule_interval, naive) # We assume that DST transitions happen on the minute/hour if not self.is_fixed_time_schedule(): # relative offset (eg. every 5 minutes) delta = cron.get_next(datetime) - naive following = dttm.in_timezone(self.timezone).add_timedelta(delta) else: # absolute (e.g. 3 AM) naive = cron.get_next(datetime) tz = pendulum.timezone( following = timezone.make_aware(naive, tz) return timezone.convert_to_utc(following) elif self._schedule_interval is not None: return dttm + self._schedule_interval
[docs] def previous_schedule(self, dttm): """ Calculates the previous schedule for this dag in UTC :param dttm: utc datetime :return: utc datetime """ if isinstance(self._schedule_interval, six.string_types): # we don't want to rely on the transitions created by # croniter as they are not always correct dttm = pendulum.instance(dttm) naive = timezone.make_naive(dttm, self.timezone) cron = croniter(self._schedule_interval, naive) # We assume that DST transitions happen on the minute/hour if not self.is_fixed_time_schedule(): # relative offset (eg. every 5 minutes) delta = naive - cron.get_prev(datetime) previous = dttm.in_timezone(self.timezone).subtract_timedelta(delta) else: # absolute (e.g. 3 AM) naive = cron.get_prev(datetime) tz = pendulum.timezone( previous = timezone.make_aware(naive, tz) return timezone.convert_to_utc(previous) elif self._schedule_interval is not None: return dttm - self._schedule_interval
[docs] def get_run_dates(self, start_date, end_date=None): """ Returns a list of dates between the interval received as parameter using this dag's schedule interval. Returned dates can be used for execution dates. :param start_date: the start date of the interval :type start_date: datetime :param end_date: the end date of the interval, defaults to timezone.utcnow() :type end_date: datetime :return: a list of dates within the interval following the dag's schedule :rtype: list """ run_dates = [] using_start_date = start_date using_end_date = end_date # dates for dag runs using_start_date = using_start_date or min([t.start_date for t in self.tasks]) using_end_date = using_end_date or timezone.utcnow() # next run date for a subdag isn't relevant (schedule_interval for subdags # is ignored) so we use the dag run's start date in the case of a subdag next_run_date = (self.normalize_schedule(using_start_date) if not self.is_subdag else using_start_date) while next_run_date and next_run_date <= using_end_date: run_dates.append(next_run_date) next_run_date = self.following_schedule(next_run_date) return run_dates
[docs] def normalize_schedule(self, dttm): """ Returns dttm + interval unless dttm is first interval then it returns dttm """ following = self.following_schedule(dttm) # in case of @once if not following: return dttm if self.previous_schedule(following) != dttm: return following return dttm
[docs] def get_last_dagrun(self, session=None, include_externally_triggered=False): return get_last_dagrun(self.dag_id, session=session, include_externally_triggered=include_externally_triggered)
[docs] def dag_id(self): return self._dag_id
@dag_id.setter def dag_id(self, value): self._dag_id = value @property
[docs] def full_filepath(self): return self._full_filepath
@full_filepath.setter def full_filepath(self, value): self._full_filepath = value @property
[docs] def concurrency(self): return self._concurrency
@concurrency.setter def concurrency(self, value): self._concurrency = value @property
[docs] def description(self): return self._description
[docs] def pickle_id(self): return self._pickle_id
@pickle_id.setter def pickle_id(self, value): self._pickle_id = value @property
[docs] def tasks(self): return list(self.task_dict.values())
@tasks.setter def tasks(self, val): raise AttributeError( 'DAG.tasks can not be modified. Use dag.add_task() instead.') @property
[docs] def task_ids(self): return list(self.task_dict.keys())
[docs] def filepath(self): """ File location of where the dag object is instantiated """ fn = self.full_filepath.replace(settings.DAGS_FOLDER + '/', '') fn = fn.replace(os.path.dirname(__file__) + '/', '') return fn
[docs] def folder(self): """ Folder location of where the dag object is instantiated """ return os.path.dirname(self.full_filepath)
[docs] def owner(self): return ", ".join(list(set([t.owner for t in self.tasks])))
[docs] def _get_concurrency_reached(self, session=None): TI = TaskInstance qry = session.query(func.count(TI.task_id)).filter( TI.dag_id == self.dag_id, TI.state == State.RUNNING, ) return qry.scalar() >= self.concurrency
[docs] def concurrency_reached(self): """ Returns a boolean indicating whether the concurrency limit for this DAG has been reached """ return self._get_concurrency_reached()
[docs] def _get_is_paused(self, session=None): qry = session.query(DagModel).filter( DagModel.dag_id == self.dag_id) return qry.value('is_paused')
[docs] def is_paused(self): """ Returns a boolean indicating whether this DAG is paused """ return self._get_is_paused()
[docs] def handle_callback(self, dagrun, success=True, reason=None, session=None): """ Triggers the appropriate callback depending on the value of success, namely the on_failure_callback or on_success_callback. This method gets the context of a single TaskInstance part of this DagRun and passes that to the callable along with a 'reason', primarily to differentiate DagRun failures. .. note: The logs end up in ``$AIRFLOW_HOME/logs/scheduler/latest/PROJECT/`` :param dagrun: DagRun object :param success: Flag to specify if failure or success callback should be called :param reason: Completion reason :param session: Database session """ callback = self.on_success_callback if success else self.on_failure_callback if callback:'Executing dag callback function: {}'.format(callback)) tis = dagrun.get_task_instances(session=session) ti = tis[-1] # get first TaskInstance of DagRun ti.task = self.get_task(ti.task_id) context = ti.get_template_context(session=session) context.update({'reason': reason}) callback(context)
[docs] def get_active_runs(self, session=None): """ Returns a list of dag run execution dates currently running :param session: :return: List of execution dates """ runs = DagRun.find(dag_id=self.dag_id, state=State.RUNNING) active_dates = [] for run in runs: active_dates.append(run.execution_date) return active_dates
[docs] def get_num_active_runs(self, external_trigger=None, session=None): """ Returns the number of active "running" dag runs :param external_trigger: True for externally triggered active dag runs :type external_trigger: bool :param session: :return: number greater than 0 for active dag runs """ query = (session .query(DagRun) .filter(DagRun.dag_id == self.dag_id) .filter(DagRun.state == State.RUNNING)) if external_trigger is not None: query = query.filter(DagRun.external_trigger == external_trigger) return query.count()
[docs] def get_dagrun(self, execution_date, session=None): """ Returns the dag run for a given execution date if it exists, otherwise none. :param execution_date: The execution date of the DagRun to find. :param session: :return: The DagRun if found, otherwise None. """ dagrun = ( session.query(DagRun) .filter( DagRun.dag_id == self.dag_id, DagRun.execution_date == execution_date) .first()) return dagrun
[docs] def _get_latest_execution_date(self, session=None): return session.query(func.max(DagRun.execution_date)).filter( DagRun.dag_id == self.dag_id
).scalar() @property
[docs] def latest_execution_date(self): """ Returns the latest date for which at least one dag run exists """ return self._get_latest_execution_date()
[docs] def subdags(self): """ Returns a list of the subdag objects associated to this DAG """ # Check SubDag for class but don't check class directly from airflow.operators.subdag_operator import SubDagOperator subdag_lst = [] for task in self.tasks: if (isinstance(task, SubDagOperator) or # TODO remove in Airflow 2.0 type(task).__name__ == 'SubDagOperator'): subdag_lst.append(task.subdag) subdag_lst += task.subdag.subdags return subdag_lst
[docs] def resolve_template_files(self): for t in self.tasks: t.resolve_template_files()
[docs] def get_template_env(self): """ Returns a jinja2 Environment while taking into account the DAGs template_searchpath, user_defined_macros and user_defined_filters """ searchpath = [self.folder] if self.template_searchpath: searchpath += self.template_searchpath env = jinja2.Environment( loader=jinja2.FileSystemLoader(searchpath), extensions=[""], cache_size=0) if self.user_defined_macros: env.globals.update(self.user_defined_macros) if self.user_defined_filters: env.filters.update(self.user_defined_filters) return env
[docs] def set_dependency(self, upstream_task_id, downstream_task_id): """ Simple utility method to set dependency between two tasks that already have been added to the DAG using add_task() """ self.get_task(upstream_task_id).set_downstream( self.get_task(downstream_task_id))
[docs] def get_task_instances( self, session, start_date=None, end_date=None, state=None): TI = TaskInstance if not start_date: start_date = (timezone.utcnow() - timedelta(30)).date() start_date = timezone.make_aware( datetime.combine(start_date, datetime.min.time())) end_date = end_date or timezone.utcnow() tis = session.query(TI).filter( TI.dag_id == self.dag_id, TI.execution_date >= start_date, TI.execution_date <= end_date, TI.task_id.in_([t.task_id for t in self.tasks]), ) if state: tis = tis.filter(TI.state == state) tis = tis.order_by(TI.execution_date).all() return tis
[docs] def roots(self): return [t for t in self.tasks if not t.downstream_list]
[docs] def topological_sort(self): """ Sorts tasks in topographical order, such that a task comes after any of its upstream dependencies. Heavily inspired by: :return: list of tasks in topological order """ # convert into an OrderedDict to speedup lookup while keeping order the same graph_unsorted = OrderedDict((task.task_id, task) for task in self.tasks) graph_sorted = [] # special case if len(self.tasks) == 0: return tuple(graph_sorted) # Run until the unsorted graph is empty. while graph_unsorted: # Go through each of the node/edges pairs in the unsorted # graph. If a set of edges doesn't contain any nodes that # haven't been resolved, that is, that are still in the # unsorted graph, remove the pair from the unsorted graph, # and append it to the sorted graph. Note here that by using # using the items() method for iterating, a copy of the # unsorted graph is used, allowing us to modify the unsorted # graph as we move through it. We also keep a flag for # checking that that graph is acyclic, which is true if any # nodes are resolved during each pass through the graph. If # not, we need to bail out as the graph therefore can't be # sorted. acyclic = False for node in list(graph_unsorted.values()): for edge in node.upstream_list: if edge.task_id in graph_unsorted: break # no edges in upstream tasks else: acyclic = True del graph_unsorted[node.task_id] graph_sorted.append(node) if not acyclic: raise AirflowException("A cyclic dependency occurred in dag: {}" .format(self.dag_id)) return tuple(graph_sorted)
[docs] def set_dag_runs_state( self, state=State.RUNNING, session=None, start_date=None, end_date=None, ): query = session.query(DagRun).filter_by(dag_id=self.dag_id) if start_date: query = query.filter(DagRun.execution_date >= start_date) if end_date: query = query.filter(DagRun.execution_date <= end_date) drs = query.all() dirty_ids = [] for dr in drs: dr.state = state dirty_ids.append(dr.dag_id)
[docs] def clear( self, start_date=None, end_date=None, only_failed=False, only_running=False, confirm_prompt=False, include_subdags=True, include_parentdag=True, reset_dag_runs=True, dry_run=False, session=None, get_tis=False, ): """ Clears a set of task instances associated with the current dag for a specified date range. """ TI = TaskInstance tis = session.query(TI) if include_subdags: # Crafting the right filter for dag_id and task_ids combo conditions = [] for dag in self.subdags + [self]: conditions.append( & TI.task_id.in_(dag.task_ids) ) tis = tis.filter(or_(*conditions)) else: tis = session.query(TI).filter(TI.dag_id == self.dag_id) tis = tis.filter(TI.task_id.in_(self.task_ids)) if include_parentdag and self.is_subdag: p_dag = self.parent_dag.sub_dag( task_regex=self.dag_id.split('.')[1], include_upstream=False, include_downstream=True) tis = tis.union(p_dag.clear( start_date=start_date, end_date=end_date, only_failed=only_failed, only_running=only_running, confirm_prompt=confirm_prompt, include_subdags=include_subdags, include_parentdag=False, reset_dag_runs=reset_dag_runs, get_tis=True, session=session, )) if start_date: tis = tis.filter(TI.execution_date >= start_date) if end_date: tis = tis.filter(TI.execution_date <= end_date) if only_failed: tis = tis.filter(or_( TI.state == State.FAILED, TI.state == State.UPSTREAM_FAILED)) if only_running: tis = tis.filter(TI.state == State.RUNNING) if get_tis: return tis if dry_run: tis = tis.all() session.expunge_all() return tis count = tis.count() do_it = True if count == 0: return 0 if confirm_prompt: ti_list = "\n".join([str(t) for t in tis]) question = ( "You are about to delete these {count} tasks:\n" "{ti_list}\n\n" "Are you sure? (yes/no): ").format(**locals()) do_it = utils.helpers.ask_yesno(question) if do_it: clear_task_instances(tis.all(), session, dag=self, ) if reset_dag_runs: self.set_dag_runs_state(session=session, start_date=start_date, end_date=end_date, ) else: count = 0 print("Bail. Nothing was cleared.") session.commit() return count
[docs] def clear_dags( cls, dags, start_date=None, end_date=None, only_failed=False, only_running=False, confirm_prompt=False, include_subdags=True, include_parentdag=False, reset_dag_runs=True, dry_run=False, ): all_tis = [] for dag in dags: tis = dag.clear( start_date=start_date, end_date=end_date, only_failed=only_failed, only_running=only_running, confirm_prompt=False, include_subdags=include_subdags, include_parentdag=include_parentdag, reset_dag_runs=reset_dag_runs, dry_run=True) all_tis.extend(tis) if dry_run: return all_tis count = len(all_tis) do_it = True if count == 0: print("Nothing to clear.") return 0 if confirm_prompt: ti_list = "\n".join([str(t) for t in all_tis]) question = ( "You are about to delete these {} tasks:\n" "{}\n\n" "Are you sure? (yes/no): ").format(count, ti_list) do_it = utils.helpers.ask_yesno(question) if do_it: for dag in dags: dag.clear(start_date=start_date, end_date=end_date, only_failed=only_failed, only_running=only_running, confirm_prompt=False, include_subdags=include_subdags, reset_dag_runs=reset_dag_runs, dry_run=False, ) else: count = 0 print("Bail. Nothing was cleared.") return count
[docs] def __deepcopy__(self, memo): # Swiwtcharoo to go around deepcopying objects coming through the # backdoor cls = self.__class__ result = cls.__new__(cls) memo[id(self)] = result for k, v in list(self.__dict__.items()): if k not in ('user_defined_macros', 'user_defined_filters', 'params'): setattr(result, k, copy.deepcopy(v, memo)) result.user_defined_macros = self.user_defined_macros result.user_defined_filters = self.user_defined_filters result.params = self.params return result
[docs] def sub_dag(self, task_regex, include_downstream=False, include_upstream=True): """ Returns a subset of the current dag as a deep copy of the current dag based on a regex that should match one or many tasks, and includes upstream and downstream neighbours based on the flag passed. """ dag = copy.deepcopy(self) regex_match = [ t for t in dag.tasks if re.findall(task_regex, t.task_id)] also_include = [] for t in regex_match: if include_downstream: also_include += t.get_flat_relatives(upstream=False) if include_upstream: also_include += t.get_flat_relatives(upstream=True) # Compiling the unique list of tasks that made the cut dag.task_dict = {t.task_id: t for t in regex_match + also_include} for t in dag.tasks: # Removing upstream/downstream references to tasks that did not # made the cut t._upstream_task_ids = t._upstream_task_ids.intersection(dag.task_dict.keys()) t._downstream_task_ids = t._downstream_task_ids.intersection( dag.task_dict.keys()) if len(dag.tasks) < len(self.tasks): dag.partial = True return dag
[docs] def has_task(self, task_id): return task_id in (t.task_id for t in self.tasks)
[docs] def get_task(self, task_id): if task_id in self.task_dict: return self.task_dict[task_id] raise AirflowException("Task {task_id} not found".format(**locals()))
[docs] def pickle_info(self, session=None): d = {} d['is_picklable'] = True try: dttm = timezone.utcnow() pickled = pickle.dumps(self) d['pickle_len'] = len(pickled) d['pickling_duration'] = "{}".format(timezone.utcnow() - dttm) except Exception as e: self.log.debug(e) d['is_picklable'] = False d['stacktrace'] = traceback.format_exc() return d
[docs] def pickle(self, session=None): dag = session.query( DagModel).filter(DagModel.dag_id == self.dag_id).first() dp = None if dag and dag.pickle_id: dp = session.query(DagPickle).filter( == dag.pickle_id).first() if not dp or dp.pickle != self: dp = DagPickle(dag=self) session.add(dp) self.last_pickled = timezone.utcnow() session.commit() self.pickle_id = return dp
[docs] def tree_view(self): """ Shows an ascii tree representation of the DAG """ def get_downstream(task, level=0): print((" " * level * 4) + str(task)) level += 1 for t in task.upstream_list: get_downstream(t, level) for t in self.roots: get_downstream(t)
[docs] def add_task(self, task): """ Add a task to the DAG :param task: the task you want to add :type task: task """ if not self.start_date and not task.start_date: raise AirflowException("Task is missing the start_date parameter") # if the task has no start date, assign it the same as the DAG elif not task.start_date: task.start_date = self.start_date # otherwise, the task will start on the later of its own start date and # the DAG's start date elif self.start_date: task.start_date = max(task.start_date, self.start_date) # if the task has no end date, assign it the same as the dag if not task.end_date: task.end_date = self.end_date # otherwise, the task will end on the earlier of its own end date and # the DAG's end date elif task.end_date and self.end_date: task.end_date = min(task.end_date, self.end_date) if task.task_id in self.task_dict: # TODO: raise an error in Airflow 2.0 warnings.warn( 'The requested task could not be added to the DAG because a ' 'task with task_id {} is already in the DAG. Starting in ' 'Airflow 2.0, trying to overwrite a task will raise an ' 'exception.'.format(task.task_id), category=PendingDeprecationWarning) else: self.task_dict[task.task_id] = task task.dag = self self.task_count = len(self.task_dict)
[docs] def add_tasks(self, tasks): """ Add a list of tasks to the DAG :param tasks: a lit of tasks you want to add :type tasks: list of tasks """ for task in tasks: self.add_task(task)
[docs] def run( self, start_date=None, end_date=None, mark_success=False, local=False, executor=None, donot_pickle=configuration.conf.getboolean('core', 'donot_pickle'), ignore_task_deps=False, ignore_first_depends_on_past=False, pool=None, delay_on_limit_secs=1.0, verbose=False, conf=None, rerun_failed_tasks=False, run_backwards=False, ): """ Runs the DAG. :param start_date: the start date of the range to run :type start_date: datetime.datetime :param end_date: the end date of the range to run :type end_date: datetime.datetime :param mark_success: True to mark jobs as succeeded without running them :type mark_success: bool :param local: True to run the tasks using the LocalExecutor :type local: bool :param executor: The executor instance to run the tasks :type executor: airflow.executor.BaseExecutor :param donot_pickle: True to avoid pickling DAG object and send to workers :type donot_pickle: bool :param ignore_task_deps: True to skip upstream tasks :type ignore_task_deps: bool :param ignore_first_depends_on_past: True to ignore depends_on_past dependencies for the first set of tasks only :type ignore_first_depends_on_past: bool :param pool: Resource pool to use :type pool: str :param delay_on_limit_secs: Time in seconds to wait before next attempt to run dag run when max_active_runs limit has been reached :type delay_on_limit_secs: float :param verbose: Make logging output more verbose :type verbose: bool :param conf: user defined dictionary passed from CLI :type conf: dict :param rerun_failed_tasks: :type: bool :param run_backwards: :type: bool """ from import BackfillJob if not executor and local: executor = LocalExecutor() elif not executor: executor = GetDefaultExecutor() job = BackfillJob( self, start_date=start_date, end_date=end_date, mark_success=mark_success, executor=executor, donot_pickle=donot_pickle, ignore_task_deps=ignore_task_deps, ignore_first_depends_on_past=ignore_first_depends_on_past, pool=pool, delay_on_limit_secs=delay_on_limit_secs, verbose=verbose, conf=conf, rerun_failed_tasks=rerun_failed_tasks, run_backwards=run_backwards, )
[docs] def cli(self): """ Exposes a CLI specific to this DAG """ from airflow.bin import cli parser = cli.CLIFactory.get_parser(dag_parser=True) args = parser.parse_args() args.func(args, self)
[docs] def create_dagrun(self, run_id, state, execution_date=None, start_date=None, external_trigger=False, conf=None, session=None): """ Creates a dag run from this dag including the tasks associated with this dag. Returns the dag run. :param run_id: defines the the run id for this dag run :type run_id: str :param execution_date: the execution date of this dag run :type execution_date: datetime.datetime :param state: the state of the dag run :type state: airflow.utils.state.State :param start_date: the date this dag run should be evaluated :type start_date: datetime :param external_trigger: whether this dag run is externally triggered :type external_trigger: bool :param session: database session :type session: sqlalchemy.orm.session.Session """ run = DagRun( dag_id=self.dag_id, run_id=run_id, execution_date=execution_date, start_date=start_date, external_trigger=external_trigger, conf=conf, state=state ) session.add(run) session.commit() run.dag = self # create the associated task instances # state is None at the moment of creation run.verify_integrity(session=session) run.refresh_from_db() return run
[docs] def sync_to_db(self, owner=None, sync_time=None, session=None): """ Save attributes about this DAG to the DB. Note that this method can be called for both DAGs and SubDAGs. A SubDag is actually a SubDagOperator. :param dag: the DAG object to save to the DB :type dag: airflow.models.DAG :param sync_time: The time that the DAG should be marked as sync'ed :type sync_time: datetime :return: None """ if owner is None: owner = self.owner if sync_time is None: sync_time = timezone.utcnow() orm_dag = session.query( DagModel).filter(DagModel.dag_id == self.dag_id).first() if not orm_dag: orm_dag = DagModel(dag_id=self.dag_id)"Creating ORM DAG for %s", self.dag_id) orm_dag.fileloc = self.fileloc orm_dag.is_subdag = self.is_subdag orm_dag.owners = owner orm_dag.is_active = True orm_dag.last_scheduler_run = sync_time orm_dag.default_view = self._default_view orm_dag.description = self.description orm_dag.schedule_interval = self.schedule_interval session.merge(orm_dag) session.commit() for subdag in self.subdags: subdag.sync_to_db(owner=owner, sync_time=sync_time, session=session)
@staticmethod @provide_session
[docs] def deactivate_unknown_dags(active_dag_ids, session=None): """ Given a list of known DAGs, deactivate any other DAGs that are marked as active in the ORM :param active_dag_ids: list of DAG IDs that are active :type active_dag_ids: list[unicode] :return: None """ if len(active_dag_ids) == 0: return for dag in session.query( DagModel).filter(~DagModel.dag_id.in_(active_dag_ids)).all(): dag.is_active = False session.merge(dag) session.commit()
@staticmethod @provide_session
[docs] def deactivate_stale_dags(expiration_date, session=None): """ Deactivate any DAGs that were last touched by the scheduler before the expiration date. These DAGs were likely deleted. :param expiration_date: set inactive DAGs that were touched before this time :type expiration_date: datetime :return: None """ log = LoggingMixin().log for dag in session.query( DagModel).filter(DagModel.last_scheduler_run < expiration_date, DagModel.is_active).all(): "Deactivating DAG ID %s since it was last touched by the scheduler at %s", dag.dag_id, dag.last_scheduler_run.isoformat() ) dag.is_active = False session.merge(dag) session.commit()
@staticmethod @provide_session
[docs] def get_num_task_instances(dag_id, task_ids, states=None, session=None): """ Returns the number of task instances in the given DAG. :param session: ORM session :param dag_id: ID of the DAG to get the task concurrency of :type dag_id: unicode :param task_ids: A list of valid task IDs for the given DAG :type task_ids: list[unicode] :param states: A list of states to filter by if supplied :type states: list[state] :return: The number of running tasks :rtype: int """ qry = session.query(func.count(TaskInstance.task_id)).filter( TaskInstance.dag_id == dag_id, TaskInstance.task_id.in_(task_ids)) if states is not None: if None in states: qry = qry.filter(or_( TaskInstance.state.in_(states), TaskInstance.state.is_(None))) else: qry = qry.filter(TaskInstance.state.in_(states)) return qry.scalar()
[docs] def test_cycle(self): """ Check to see if there are any cycles in the DAG. Returns False if no cycle found, otherwise raises exception. """ # default of int is 0 which corresponds to CYCLE_NEW visit_map = defaultdict(int) for task_id in self.task_dict.keys(): # print('starting %s' % task_id) if visit_map[task_id] == DagBag.CYCLE_NEW: self._test_cycle_helper(visit_map, task_id) return False
[docs] def _test_cycle_helper(self, visit_map, task_id): """ Checks if a cycle exists from the input task using DFS traversal """ # print('Inspecting %s' % task_id) if visit_map[task_id] == DagBag.CYCLE_DONE: return False visit_map[task_id] = DagBag.CYCLE_IN_PROGRESS task = self.task_dict[task_id] for descendant_id in task.get_direct_relative_ids(): if visit_map[descendant_id] == DagBag.CYCLE_IN_PROGRESS: msg = "Cycle detected in DAG. Faulty task: {0} to {1}".format( task_id, descendant_id) raise AirflowDagCycleException(msg) else: self._test_cycle_helper(visit_map, descendant_id) visit_map[task_id] = DagBag.CYCLE_DONE
[docs]class Chart(Base):
[docs] __tablename__ = "chart"
[docs] id = Column(Integer, primary_key=True)
[docs] label = Column(String(200))
[docs] conn_id = Column(String(ID_LEN), nullable=False)
[docs] user_id = Column(Integer(), ForeignKey(''), nullable=True)
[docs] chart_type = Column(String(100), default="line")
[docs] sql_layout = Column(String(50), default="series")
[docs] sql = Column(Text, default="SELECT series, x, y FROM table")
[docs] y_log_scale = Column(Boolean)
[docs] show_datatable = Column(Boolean)
[docs] show_sql = Column(Boolean, default=True)
[docs] height = Column(Integer, default=600)
[docs] default_params = Column(String(5000), default="{}")
[docs] owner = relationship( "User", cascade=False, cascade_backrefs=False, backref='charts')
[docs] x_is_date = Column(Boolean, default=True)
[docs] iteration_no = Column(Integer, default=0)
[docs] last_modified = Column(UtcDateTime, default=timezone.utcnow)
[docs] def __repr__(self): return self.label
[docs]class KnownEventType(Base):
[docs] __tablename__ = "known_event_type"
[docs] id = Column(Integer, primary_key=True)
[docs] know_event_type = Column(String(200))
[docs] def __repr__(self): return self.know_event_type
[docs]class KnownEvent(Base):
[docs] __tablename__ = "known_event"
[docs] id = Column(Integer, primary_key=True)
[docs] label = Column(String(200))
[docs] start_date = Column(UtcDateTime)
[docs] end_date = Column(UtcDateTime)
[docs] user_id = Column(Integer(), ForeignKey(''),)
[docs] known_event_type_id = Column(Integer(), ForeignKey(''),)
[docs] reported_by = relationship( "User", cascade=False, cascade_backrefs=False, backref='known_events')
[docs] event_type = relationship( "KnownEventType", cascade=False, cascade_backrefs=False, backref='known_events')
[docs] description = Column(Text)
[docs] def __repr__(self): return self.label
[docs]class Variable(Base, LoggingMixin):
[docs] __tablename__ = "variable"
[docs] __NO_DEFAULT_SENTINEL = object()
[docs] id = Column(Integer, primary_key=True)
[docs] key = Column(String(ID_LEN), unique=True)
[docs] _val = Column('val', Text)
[docs] is_encrypted = Column(Boolean, unique=False, default=False)
[docs] def __repr__(self): # Hiding the value return '{} : {}'.format(self.key, self._val)
[docs] def get_val(self): log = LoggingMixin().log if self._val and self.is_encrypted: try: fernet = get_fernet() return fernet.decrypt(bytes(self._val, 'utf-8')).decode() except InvalidFernetToken: log.error("Can't decrypt _val for key={}, invalid token " "or value".format(self.key)) return None except Exception: log.error("Can't decrypt _val for key={}, FERNET_KEY " "configuration missing".format(self.key)) return None else: return self._val
[docs] def set_val(self, value): if value: fernet = get_fernet() self._val = fernet.encrypt(bytes(value, 'utf-8')).decode() self.is_encrypted = fernet.is_encrypted
[docs] def val(cls): return synonym('_val', descriptor=property(cls.get_val, cls.set_val))
[docs] def setdefault(cls, key, default, deserialize_json=False): """ Like a Python builtin dict object, setdefault returns the current value for a key, and if it isn't there, stores the default value and returns it. :param key: Dict key for this Variable :type key: str :param default: Default value to set and return if the variable isn't already in the DB :type default: Mixed :param deserialize_json: Store this as a JSON encoded value in the DB and un-encode it when retrieving a value :return: Mixed """ obj = Variable.get(key, default_var=None, deserialize_json=deserialize_json) if obj is None: if default is not None: Variable.set(key, default, serialize_json=deserialize_json) return default else: raise ValueError('Default Value must be set') else: return obj
@classmethod @provide_session
[docs] def get(cls, key, default_var=__NO_DEFAULT_SENTINEL, deserialize_json=False, session=None): obj = session.query(cls).filter(cls.key == key).first() if obj is None: if default_var is not cls.__NO_DEFAULT_SENTINEL: return default_var else: raise KeyError('Variable {} does not exist'.format(key)) else: if deserialize_json: return json.loads(obj.val) else: return obj.val
@classmethod @provide_session
[docs] def set(cls, key, value, serialize_json=False, session=None): if serialize_json: stored_value = json.dumps(value) else: stored_value = str(value) session.query(cls).filter(cls.key == key).delete() session.add(Variable(key=key, val=stored_value)) session.flush()
[docs] def rotate_fernet_key(self): fernet = get_fernet() if self._val and self.is_encrypted: self._val = fernet.rotate(self._val.encode('utf-8')).decode()
[docs]class XCom(Base, LoggingMixin): """ Base class for XCom objects. """
[docs] __tablename__ = "xcom"
[docs] id = Column(Integer, primary_key=True)
[docs] key = Column(String(512))
[docs] value = Column(LargeBinary)
[docs] timestamp = Column( UtcDateTime, default=timezone.utcnow, nullable=False)
[docs] execution_date = Column(UtcDateTime, nullable=False)
# source information
[docs] task_id = Column(String(ID_LEN), nullable=False)
[docs] dag_id = Column(String(ID_LEN), nullable=False)
[docs] __table_args__ = ( Index('idx_xcom_dag_task_date', dag_id, task_id, execution_date, unique=False),
) """ TODO: "pickling" has been deprecated and JSON is preferred. "pickling" will be removed in Airflow 2.0. """ @reconstructor
[docs] def init_on_load(self): enable_pickling = configuration.getboolean('core', 'enable_xcom_pickling') if enable_pickling: self.value = pickle.loads(self.value) else: try: self.value = json.loads(self.value.decode('UTF-8')) except (UnicodeEncodeError, ValueError): # For backward-compatibility. # Preventing errors in webserver # due to XComs mixed with pickled and unpickled. self.value = pickle.loads(self.value)
[docs] def __repr__(self): return '<XCom "{key}" ({task_id} @ {execution_date})>'.format( key=self.key, task_id=self.task_id, execution_date=self.execution_date)
@classmethod @provide_session
[docs] def set( cls, key, value, execution_date, task_id, dag_id, session=None): """ Store an XCom value. TODO: "pickling" has been deprecated and JSON is preferred. "pickling" will be removed in Airflow 2.0. :return: None """ session.expunge_all() enable_pickling = configuration.getboolean('core', 'enable_xcom_pickling') if enable_pickling: value = pickle.dumps(value) else: try: value = json.dumps(value).encode('UTF-8') except ValueError: log = LoggingMixin().log log.error("Could not serialize the XCOM value into JSON. " "If you are using pickles instead of JSON " "for XCOM, then you need to enable pickle " "support for XCOM in your airflow config.") raise # remove any duplicate XComs session.query(cls).filter( cls.key == key, cls.execution_date == execution_date, cls.task_id == task_id, cls.dag_id == dag_id).delete() session.commit() # insert new XCom session.add(XCom( key=key, value=value, execution_date=execution_date, task_id=task_id, dag_id=dag_id)) session.commit()
@classmethod @provide_session
[docs] def get_one(cls, execution_date, key=None, task_id=None, dag_id=None, include_prior_dates=False, session=None): """ Retrieve an XCom value, optionally meeting certain criteria. TODO: "pickling" has been deprecated and JSON is preferred. "pickling" will be removed in Airflow 2.0. :return: XCom value """ filters = [] if key: filters.append(cls.key == key) if task_id: filters.append(cls.task_id == task_id) if dag_id: filters.append(cls.dag_id == dag_id) if include_prior_dates: filters.append(cls.execution_date <= execution_date) else: filters.append(cls.execution_date == execution_date) query = ( session.query(cls.value).filter(and_(*filters)) .order_by(cls.execution_date.desc(), cls.timestamp.desc())) result = query.first() if result: enable_pickling = configuration.getboolean('core', 'enable_xcom_pickling') if enable_pickling: return pickle.loads(result.value) else: try: return json.loads(result.value.decode('UTF-8')) except ValueError: log = LoggingMixin().log log.error("Could not deserialize the XCOM value from JSON. " "If you are using pickles instead of JSON " "for XCOM, then you need to enable pickle " "support for XCOM in your airflow config.") raise
@classmethod @provide_session
[docs] def get_many(cls, execution_date, key=None, task_ids=None, dag_ids=None, include_prior_dates=False, limit=100, session=None): """ Retrieve an XCom value, optionally meeting certain criteria TODO: "pickling" has been deprecated and JSON is preferred. "pickling" will be removed in Airflow 2.0. """ filters = [] if key: filters.append(cls.key == key) if task_ids: filters.append(cls.task_id.in_(as_tuple(task_ids))) if dag_ids: filters.append(cls.dag_id.in_(as_tuple(dag_ids))) if include_prior_dates: filters.append(cls.execution_date <= execution_date) else: filters.append(cls.execution_date == execution_date) query = ( session.query(cls).filter(and_(*filters)) .order_by(cls.execution_date.desc(), cls.timestamp.desc()) .limit(limit)) results = query.all() return results
@classmethod @provide_session
[docs] def delete(cls, xcoms, session=None): if isinstance(xcoms, XCom): xcoms = [xcoms] for xcom in xcoms: if not isinstance(xcom, XCom): raise TypeError( 'Expected XCom; received {}'.format(xcom.__class__.__name__) ) session.delete(xcom) session.commit()
[docs]class DagRun(Base, LoggingMixin): """ DagRun describes an instance of a Dag. It can be created by the scheduler (for regular runs) or by an external trigger """
[docs] __tablename__ = "dag_run"
[docs] ID_PREFIX = 'scheduled__'
[docs] id = Column(Integer, primary_key=True)
[docs] dag_id = Column(String(ID_LEN))
[docs] execution_date = Column(UtcDateTime, default=timezone.utcnow)
[docs] start_date = Column(UtcDateTime, default=timezone.utcnow)
[docs] end_date = Column(UtcDateTime)
[docs] _state = Column('state', String(50), default=State.RUNNING)
[docs] run_id = Column(String(ID_LEN))
[docs] external_trigger = Column(Boolean, default=True)
[docs] conf = Column(PickleType)
[docs] dag = None
[docs] __table_args__ = ( Index('dag_id_state', dag_id, _state), UniqueConstraint('dag_id', 'execution_date'), UniqueConstraint('dag_id', 'run_id'),
[docs] def __repr__(self): return ( '<DagRun {dag_id} @ {execution_date}: {run_id}, ' 'externally triggered: {external_trigger}>' ).format( dag_id=self.dag_id, execution_date=self.execution_date, run_id=self.run_id, external_trigger=self.external_trigger)
[docs] def get_state(self): return self._state
[docs] def set_state(self, state): if self._state != state: self._state = state self.end_date = timezone.utcnow() if self._state in State.finished() else None
[docs] def state(self): return synonym('_state', descriptor=property(self.get_state, self.set_state))
[docs] def id_for_date(cls, date, prefix=ID_FORMAT_PREFIX): return prefix.format(date.isoformat()[:19])
[docs] def refresh_from_db(self, session=None): """ Reloads the current dagrun from the database :param session: database session """ DR = DagRun exec_date = func.cast(self.execution_date, DateTime) dr = session.query(DR).filter( DR.dag_id == self.dag_id, func.cast(DR.execution_date, DateTime) == exec_date, DR.run_id == self.run_id ).one() = self.state = dr.state
@staticmethod @provide_session
[docs] def find(dag_id=None, run_id=None, execution_date=None, state=None, external_trigger=None, no_backfills=False, session=None): """ Returns a set of dag runs for the given search criteria. :param dag_id: the dag_id to find dag runs for :type dag_id: int, list :param run_id: defines the the run id for this dag run :type run_id: str :param execution_date: the execution date :type execution_date: datetime.datetime :param state: the state of the dag run :type state: airflow.utils.state.State :param external_trigger: whether this dag run is externally triggered :type external_trigger: bool :param no_backfills: return no backfills (True), return all (False). Defaults to False :type no_backfills: bool :param session: database session :type session: sqlalchemy.orm.session.Session """ DR = DagRun qry = session.query(DR) if dag_id: qry = qry.filter(DR.dag_id == dag_id) if run_id: qry = qry.filter(DR.run_id == run_id) if execution_date: if isinstance(execution_date, list): qry = qry.filter(DR.execution_date.in_(execution_date)) else: qry = qry.filter(DR.execution_date == execution_date) if state: qry = qry.filter(DR.state == state) if external_trigger is not None: qry = qry.filter(DR.external_trigger == external_trigger) if no_backfills: # in order to prevent a circular dependency from import BackfillJob qry = qry.filter(DR.run_id.notlike(BackfillJob.ID_PREFIX + '%')) dr = qry.order_by(DR.execution_date).all() return dr
[docs] def get_task_instances(self, state=None, session=None): """ Returns the task instances for this dag run """ TI = TaskInstance tis = session.query(TI).filter( TI.dag_id == self.dag_id, TI.execution_date == self.execution_date, ) if state: if isinstance(state, six.string_types): tis = tis.filter(TI.state == state) else: # this is required to deal with NULL values if None in state: tis = tis.filter( or_(TI.state.in_(state), TI.state.is_(None)) ) else: tis = tis.filter(TI.state.in_(state)) if self.dag and self.dag.partial: tis = tis.filter(TI.task_id.in_(self.dag.task_ids)) return tis.all()
[docs] def get_task_instance(self, task_id, session=None): """ Returns the task instance specified by task_id for this dag run :param task_id: the task id """ TI = TaskInstance ti = session.query(TI).filter( TI.dag_id == self.dag_id, TI.execution_date == self.execution_date, TI.task_id == task_id ).first() return ti
[docs] def get_dag(self): """ Returns the Dag associated with this DagRun. :return: DAG """ if not self.dag: raise AirflowException("The DAG (.dag) for {} needs to be set" .format(self)) return self.dag
[docs] def get_previous_dagrun(self, session=None): """The previous DagRun, if there is one""" return session.query(DagRun).filter( DagRun.dag_id == self.dag_id, DagRun.execution_date < self.execution_date ).order_by( DagRun.execution_date.desc()
).first() @provide_session
[docs] def get_previous_scheduled_dagrun(self, session=None): """The previous, SCHEDULED DagRun, if there is one""" dag = self.get_dag() return session.query(DagRun).filter( DagRun.dag_id == self.dag_id, DagRun.execution_date == dag.previous_schedule(self.execution_date)
).first() @provide_session
[docs] def update_state(self, session=None): """ Determines the overall state of the DagRun based on the state of its TaskInstances. :return: State """ dag = self.get_dag() tis = self.get_task_instances(session=session) self.log.debug("Updating state for %s considering %s task(s)", self, len(tis)) for ti in list(tis): # skip in db? if ti.state == State.REMOVED: tis.remove(ti) else: ti.task = dag.get_task(ti.task_id) # pre-calculate # db is faster start_dttm = timezone.utcnow() unfinished_tasks = self.get_task_instances( state=State.unfinished(), session=session ) none_depends_on_past = all(not t.task.depends_on_past for t in unfinished_tasks) none_task_concurrency = all(t.task.task_concurrency is None for t in unfinished_tasks) # small speed up if unfinished_tasks and none_depends_on_past and none_task_concurrency: # todo: this can actually get pretty slow: one task costs between 0.01-015s no_dependencies_met = True for ut in unfinished_tasks: # We need to flag upstream and check for changes because upstream # failures/re-schedules can result in deadlock false positives old_state = ut.state deps_met = ut.are_dependencies_met( dep_context=DepContext( flag_upstream_failed=True, ignore_in_retry_period=True, ignore_in_reschedule_period=True), session=session) if deps_met or old_state != ut.current_state(session=session): no_dependencies_met = False break duration = (timezone.utcnow() - start_dttm).total_seconds() * 1000 Stats.timing("dagrun.dependency-check.{}".format(self.dag_id), duration) root_ids = [t.task_id for t in dag.roots] roots = [t for t in tis if t.task_id in root_ids] # if all roots finished and at least one failed, the run failed if (not unfinished_tasks and any(r.state in (State.FAILED, State.UPSTREAM_FAILED) for r in roots)):'Marking run %s failed', self) self.set_state(State.FAILED) dag.handle_callback(self, success=False, reason='task_failure', session=session) # if all roots succeeded and no unfinished tasks, the run succeeded elif not unfinished_tasks and all(r.state in (State.SUCCESS, State.SKIPPED) for r in roots):'Marking run %s successful', self) self.set_state(State.SUCCESS) dag.handle_callback(self, success=True, reason='success', session=session) # if *all tasks* are deadlocked, the run failed elif (unfinished_tasks and none_depends_on_past and none_task_concurrency and no_dependencies_met):'Deadlock; marking run %s failed', self) self.set_state(State.FAILED) dag.handle_callback(self, success=False, reason='all_tasks_deadlocked', session=session) # finally, if the roots aren't done, the dag is still running else: self.set_state(State.RUNNING) # todo: determine we want to use with_for_update to make sure to lock the run session.merge(self) session.commit() return self.state
[docs] def verify_integrity(self, session=None): """ Verifies the DagRun by checking for removed tasks or tasks that are not in the database yet. It will set state to removed or add the task if required. """ dag = self.get_dag() tis = self.get_task_instances(session=session) # check for removed or restored tasks task_ids = [] for ti in tis: task_ids.append(ti.task_id) task = None try: task = dag.get_task(ti.task_id) except AirflowException: if ti.state == State.REMOVED: pass # ti has already been removed, just ignore it elif self.state is not State.RUNNING and not dag.partial: self.log.warning("Failed to get task '{}' for dag '{}'. " "Marking it as removed.".format(ti, dag)) Stats.incr( "task_removed_from_dag.{}".format(dag.dag_id), 1, 1) ti.state = State.REMOVED is_task_in_dag = task is not None should_restore_task = is_task_in_dag and ti.state == State.REMOVED if should_restore_task:"Restoring task '{}' which was previously " "removed from DAG '{}'".format(ti, dag)) Stats.incr("task_restored_to_dag.{}".format(dag.dag_id), 1, 1) ti.state = State.NONE # check for missing tasks for task in six.itervalues(dag.task_dict): if task.start_date > self.execution_date and not self.is_backfill: continue if task.task_id not in task_ids: Stats.incr( "task_instance_created-{}".format(task.__class__.__name__), 1, 1) ti = TaskInstance(task, self.execution_date) session.add(ti) session.commit()
[docs] def get_run(session, dag_id, execution_date): """ :param dag_id: DAG ID :type dag_id: unicode :param execution_date: execution date :type execution_date: datetime :return: DagRun corresponding to the given dag_id and execution date if one exists. None otherwise. :rtype: DagRun """ qry = session.query(DagRun).filter( DagRun.dag_id == dag_id, DagRun.external_trigger == False, # noqa DagRun.execution_date == execution_date, ) return qry.first()
[docs] def is_backfill(self): from import BackfillJob return ( self.run_id is not None and self.run_id.startswith(BackfillJob.ID_PREFIX)
) @classmethod @provide_session
[docs] def get_latest_runs(cls, session): """Returns the latest DagRun for each DAG. """ subquery = ( session .query( cls.dag_id, func.max(cls.execution_date).label('execution_date')) .group_by(cls.dag_id) .subquery() ) dagruns = ( session .query(cls) .join(subquery, and_(cls.dag_id == subquery.c.dag_id, cls.execution_date == subquery.c.execution_date)) .all() ) return dagruns
[docs]class Pool(Base):
[docs] __tablename__ = "slot_pool"
[docs] id = Column(Integer, primary_key=True)
[docs] pool = Column(String(50), unique=True)
[docs] slots = Column(Integer, default=0)
[docs] description = Column(Text)
[docs] def __repr__(self): return self.pool
[docs] def to_json(self): return { 'id':, 'pool': self.pool, 'slots': self.slots, 'description': self.description,
} @provide_session
[docs] def used_slots(self, session): """ Returns the number of slots used at the moment """ running = ( session .query(TaskInstance) .filter(TaskInstance.pool == self.pool) .filter(TaskInstance.state == State.RUNNING) .count() ) return running
[docs] def queued_slots(self, session): """ Returns the number of slots used at the moment """ return ( session .query(TaskInstance) .filter(TaskInstance.pool == self.pool) .filter(TaskInstance.state == State.QUEUED)
.count() ) @provide_session
[docs] def open_slots(self, session): """ Returns the number of slots open at the moment """ used_slots = self.used_slots(session=session) queued_slots = self.queued_slots(session=session) return self.slots - used_slots - queued_slots
# To avoid circular import on Python2.7 we need to define this at the _bottom_ from airflow.models.connection import Connection # noqa: E402,F401 from airflow.models.skipmixin import SkipMixin # noqa: F401