Source code for airflow.models.taskinstance

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import contextlib
import hashlib
import logging
import math
import os
import pickle
import signal
import warnings
from collections import defaultdict
from datetime import datetime, timedelta
from tempfile import NamedTemporaryFile
from typing import IO, Any, Dict, Iterable, List, NamedTuple, Optional, Tuple, Union
from urllib.parse import quote

import dill
import jinja2
import lazy_object_proxy
import pendulum
from jinja2 import TemplateAssertionError, UndefinedError
from sqlalchemy import Column, Float, Index, Integer, PickleType, String, and_, func, or_
from sqlalchemy.exc import OperationalError
from sqlalchemy.orm import reconstructor, relationship
from sqlalchemy.orm.session import Session
from sqlalchemy.sql.elements import BooleanClauseList

from airflow import settings
from airflow.configuration import conf
from airflow.exceptions import (
from airflow.models.base import COLLATION_ARGS, ID_LEN, Base
from airflow.models.log import Log
from airflow.models.taskfail import TaskFail
from airflow.models.taskreschedule import TaskReschedule
from airflow.models.variable import Variable
from airflow.models.xcom import XCOM_RETURN_KEY, XCom
from airflow.plugins_manager import integrate_macros_plugins
from airflow.sentry import Sentry
from airflow.stats import Stats
from airflow.ti_deps.dep_context import DepContext
from airflow.ti_deps.dependencies_deps import REQUEUEABLE_DEPS, RUNNING_DEPS
from airflow.typing_compat import Literal
from airflow.utils import timezone
from import send_email
from airflow.utils.helpers import is_container
from airflow.utils.log.logging_mixin import LoggingMixin
from import get_hostname
from airflow.utils.operator_helpers import context_to_airflow_vars
from airflow.utils.platform import getuser
from airflow.utils.session import provide_session
from airflow.utils.sqlalchemy import UtcDateTime, with_row_locks
from airflow.utils.state import State
from airflow.utils.timeout import timeout

    from kubernetes.client.api_client import ApiClient

    from airflow.kubernetes.kube_config import KubeConfig
    from airflow.kubernetes.pod_generator import PodGenerator
except ImportError:
[docs] ApiClient = None
[docs]TR = TaskReschedule
[docs]Context = Dict[str, Any]
[docs]_CURRENT_CONTEXT: List[Context] = []
[docs]log = logging.getLogger(__name__)
[docs]def set_current_context(context: Context): """ Sets the current execution context to the provided context object. This method should be called once per Task execution, before calling operator.execute. """ _CURRENT_CONTEXT.append(context) try: yield context finally: expected_state = _CURRENT_CONTEXT.pop() if expected_state != context: log.warning( "Current context is not equal to the state at context stack. Expected=%s, got=%s", context, expected_state,
[docs]def load_error_file(fd: IO[bytes]) -> Optional[Union[str, Exception]]: """Load and return error from error file""", os.SEEK_SET) data = if not data: return None try: return pickle.loads(data) except Exception: # pylint: disable=broad-except return "Failed to load task run error"
[docs]def set_error_file(error_file: str, error: Union[str, Exception]) -> None: """Write error into error file by path""" with open(error_file, "wb") as fd: try: pickle.dump(error, fd) except Exception: # pylint: disable=broad-except # local class objects cannot be pickled, so we fallback # to store the string representation instead pickle.dump(str(error), fd)
[docs]def clear_task_instances( tis, session, activate_dag_runs=None, dag=None, dag_run_state: Union[str, Literal[False]] = State.RUNNING, ): """ 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 dag_run_state: state to set DagRun to. If set to False, dagrun state will not be changed. :param dag: DAG object :param activate_dag_runs: Deprecated parameter, do not pass """ job_ids = [] task_id_by_key = defaultdict(lambda: defaultdict(lambda: defaultdict(set))) 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) ti.refresh_from_task(task) 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 last attempted try number. ti.max_tries = max(ti.max_tries, ti.prev_attempted_tries) ti.state = State.NONE session.merge(ti) task_id_by_key[ti.dag_id][ti.execution_date][ti.try_number].add(ti.task_id) if task_id_by_key: # Clear all reschedules related to the ti to clear # This is an optimization for the common case where all tis are for a small number # of dag_id, execution_date and try_number. Use a nested dict of dag_id, # execution_date, try_number and task_id to construct the where clause in a # hierarchical manner. This speeds up the delete statement by more than 40x for # large number of tis (50k+). conditions = or_( and_( TR.dag_id == dag_id, or_( and_( TR.execution_date == execution_date, or_( and_(TR.try_number == try_number, TR.task_id.in_(task_ids)) for try_number, task_ids in task_tries.items() ), ) for execution_date, task_tries in dates.items() ), ) for dag_id, dates in task_id_by_key.items() ) delete_qry = TR.__table__.delete().where(conditions) session.execute(delete_qry) if job_ids: from import BaseJob for job in session.query(BaseJob).filter( # noqa job.state = State.SHUTDOWN if activate_dag_runs is not None: warnings.warn( "`activate_dag_runs` parameter to clear_task_instances function is deprecated. " "Please use `dag_run_state`", DeprecationWarning, stacklevel=2, ) if not activate_dag_runs: dag_run_state = False if dag_run_state is not False and tis: from airflow.models.dagrun import DagRun # Avoid circular import dates_by_dag_id = defaultdict(set) for instance in tis: dates_by_dag_id[instance.dag_id].add(instance.execution_date) drs = ( session.query(DagRun) .filter( or_( and_(DagRun.dag_id == dag_id, DagRun.execution_date.in_(dates)) for dag_id, dates in dates_by_dag_id.items() ) ) .all() ) for dr in drs: dr.state = dag_run_state dr.start_date = timezone.utcnow()
[docs]class TaskInstanceKey(NamedTuple): """Key used to identify task instance."""
[docs] dag_id: str
[docs] task_id: str
[docs] execution_date: datetime
[docs] try_number: int
[docs] def primary(self) -> Tuple[str, str, datetime]: """Return task instance primary key part of the key""" return self.dag_id, self.task_id, self.execution_date
[docs] def reduced(self) -> 'TaskInstanceKey': """Remake the key by subtracting 1 from try number to match in memory information""" return TaskInstanceKey(self.dag_id, self.task_id, self.execution_date, max(1, self.try_number - 1))
[docs] def with_try_number(self, try_number: int) -> 'TaskInstanceKey': """Returns TaskInstanceKey with provided ``try_number``""" return TaskInstanceKey(self.dag_id, self.task_id, self.execution_date, try_number)
[docs]class TaskInstance(Base, LoggingMixin): # pylint: disable=R0902,R0904 """ 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, **COLLATION_ARGS), primary_key=True)
[docs] dag_id = Column(String(ID_LEN, **COLLATION_ARGS), 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(256), nullable=False)
[docs] pool_slots = Column(Integer, default=1, nullable=False)
[docs] queue = Column(String(256))
[docs] priority_weight = Column(Integer)
[docs] operator = Column(String(1000))
[docs] queued_dttm = Column(UtcDateTime)
[docs] queued_by_job_id = Column(Integer)
[docs] pid = Column(Integer)
[docs] executor_config = Column(PickleType(pickler=dill))
[docs] external_executor_id = Column(String(ID_LEN, **COLLATION_ARGS))
# If adding new fields here then remember to add them to # refresh_from_db() or they won't display in the UI correctly
[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),
[docs] dag_model = relationship( "DagModel", primaryjoin="TaskInstance.dag_id == DagModel.dag_id", foreign_keys=dag_id, uselist=False, innerjoin=True,
) def __init__(self, task, execution_date: datetime, state: Optional[str] = None): super().__init__() self.dag_id = task.dag_id self.task_id = task.task_id self.task = task self.refresh_from_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.try_number = 0 self.unixname = getuser() if state: self.state = state self.hostname = '' self.init_on_load() # Is this TaskInstance being currently running within `airflow tasks run --raw`. # Not persisted to the database so only valid for the current process self.raw = False # can be changed when calling 'run' self.test_mode = 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 TaskInstance is currently running, this will match the column in the database, in all other cases this will be incremented. """ # This is designed so that task logs end up in the right file. # TODO: whether we need sensing here or not (in sensor and task_instance state machine) if self.state in 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 prev_attempted_tries(self): """ Based on this instance's try_number, this will calculate the number of previously attempted tries, defaulting to 0. """ # Expose this for the Task Tries and Gantt graph views. # Using `try_number` throws off the counts for non-running tasks. # Also useful in error logging contexts to get # the try number for the last try that was attempted. # return self._try_number
[docs] def next_try_number(self): """Setting Next Try Number""" return self._try_number + 1
[docs] def command_as_list( # pylint: disable=too-many-arguments 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 = f"DAGS_FOLDER/{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,
) @staticmethod
[docs] def generate_command( dag_id: str, # pylint: disable=too-many-arguments task_id: str, execution_date: datetime, mark_success: bool = False, ignore_all_deps: bool = False, ignore_depends_on_past: bool = False, ignore_task_deps: bool = False, ignore_ti_state: bool = False, local: bool = False, pickle_id: Optional[int] = None, file_path: Optional[str] = None, raw: bool = False, job_id: Optional[str] = None, pool: Optional[str] = None, cfg_path: Optional[str] = None, ) -> List[str]: """ Generates the shell command required to execute this task instance. :param dag_id: DAG ID :type dag_id: str :param task_id: Task ID :type task_id: str :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: Optional[int] :param file_path: path to the file containing the DAG definition :type file_path: Optional[str] :param raw: raw mode (needs more details) :type raw: Optional[bool] :param job_id: job ID (needs more details) :type job_id: Optional[int] :param pool: the Airflow pool that the task should run in :type pool: Optional[str] :param cfg_path: the Path to the configuration file :type cfg_path: Optional[str] :return: shell command that can be used to run the task instance :rtype: list[str] """ iso = execution_date.isoformat() cmd = ["airflow", "tasks", "run", dag_id, task_id, iso] if mark_success: cmd.extend(["--mark-success"]) if pickle_id: cmd.extend(["--pickle", str(pickle_id)]) if job_id: cmd.extend(["--job-id", str(job_id)]) if ignore_all_deps: cmd.extend(["--ignore-all-dependencies"]) if ignore_task_deps: cmd.extend(["--ignore-dependencies"]) if ignore_depends_on_past: cmd.extend(["--ignore-depends-on-past"]) if ignore_ti_state: cmd.extend(["--force"]) if local: cmd.extend(["--local"]) if pool: cmd.extend(["--pool", pool]) if raw: cmd.extend(["--raw"]) if file_path: cmd.extend(["--subdir", file_path]) if cfg_path: cmd.extend(["--cfg-path", cfg_path]) return cmd
[docs] def log_filepath(self): """Filepath for TaskInstance""" iso = self.execution_date.isoformat() the_log = os.path.expanduser(conf.get('logging', 'BASE_LOG_FOLDER')) return f"{the_log}/{self.dag_id}/{self.task_id}/{iso}.log"
[docs] def log_url(self): """Log URL for TaskInstance""" iso = quote(self.execution_date.isoformat()) base_url = conf.get('webserver', 'BASE_URL') return base_url + f"/log?execution_date={iso}&task_id={self.task_id}&dag_id={self.dag_id}"
[docs] def mark_success_url(self): """URL to mark TI success""" iso = quote(self.execution_date.isoformat()) base_url = conf.get('webserver', 'BASE_URL') return base_url + ( "/success"
f"?task_id={self.task_id}" f"&dag_id={self.dag_id}" f"&execution_date={iso}" "&upstream=false" "&downstream=false" ) @provide_session
[docs] def current_state(self, session=None) -> str: """ 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. :param session: SQLAlchemy ORM Session :type session: Session """ ti = ( session.query(TaskInstance) .filter( TaskInstance.dag_id == self.dag_id, TaskInstance.task_id == self.task_id, TaskInstance.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. :param session: SQLAlchemy ORM Session :type session: Session """ 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) -> None: """ Refreshes the task instance from the database based on the primary key :param session: SQLAlchemy ORM Session :type session: Session :param lock_for_update: if True, indicates that the database should lock the TaskInstance (issuing a FOR UPDATE clause) until the session is committed. :type lock_for_update: bool """ self.log.debug("Refreshing TaskInstance %s from DB", self) qry = session.query(TaskInstance).filter( TaskInstance.dag_id == self.dag_id, TaskInstance.task_id == self.task_id, TaskInstance.execution_date == self.execution_date, ) if lock_for_update: ti = qry.with_for_update().first() else: ti = qry.first() if ti: # Fields ordered per model definition self.start_date = ti.start_date self.end_date = ti.end_date self.duration = ti.duration self.state = ti.state # Get the raw value of try_number column, don't read through the # accessor here otherwise it will be incremented by one already. self.try_number = ti._try_number # noqa pylint: disable=protected-access self.max_tries = ti.max_tries self.hostname = ti.hostname self.unixname = ti.unixname self.job_id = ti.job_id self.pool = ti.pool self.pool_slots = ti.pool_slots or 1 self.queue = ti.queue self.priority_weight = ti.priority_weight self.operator = ti.operator self.queued_dttm = ti.queued_dttm = else: self.state = None self.log.debug("Refreshed TaskInstance %s", self)
[docs] def refresh_from_task(self, task, pool_override=None): """ Copy common attributes from the given task. :param task: The task object to copy from :type task: airflow.models.BaseOperator :param pool_override: Use the pool_override instead of task's pool :type pool_override: str """ self.queue = task.queue self.pool = pool_override or task.pool self.pool_slots = task.pool_slots self.priority_weight = task.priority_weight_total self.run_as_user = task.run_as_user self.max_tries = task.retries self.executor_config = task.executor_config self.operator = task.task_type
[docs] def clear_xcom_data(self, session=None): """ Clears all XCom data from the database for the task instance :param session: SQLAlchemy ORM Session :type session: Session """ self.log.debug("Clearing XCom data") 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() self.log.debug("XCom data cleared")
[docs] def key(self) -> TaskInstanceKey: """Returns a tuple that identifies the task instance uniquely""" return TaskInstanceKey(self.dag_id, self.task_id, self.execution_date, self.try_number)
[docs] def set_state(self, state: str, session=None): """ Set TaskInstance state. :param state: State to set for the TI :type state: str :param session: SQLAlchemy ORM Session :type session: Session """ current_time = timezone.utcnow() self.log.debug("Setting task state for %s to %s", self, state) self.state = state self.start_date = self.start_date or current_time if self.state in State.finished or self.state == State.UP_FOR_RETRY: self.end_date = self.end_date or current_time self.duration = (self.end_date - self.start_date).total_seconds() session.merge(self)
[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 immediate dependents of this task instance have succeeded or have been skipped. 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. :param session: SQLAlchemy ORM Session :type session: Session """ 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.in_([State.SKIPPED, State.SUCCESS]), ) count = ti[0][0] return count == len(task.downstream_task_ids)
[docs] def get_previous_ti( self, state: Optional[str] = None, session: Session = None ) -> Optional['TaskInstance']: """ The task instance for the task that ran before this task instance. :param state: If passed, it only take into account instances of a specific state. :param session: SQLAlchemy ORM Session """ 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 TaskInstance 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 # We always ignore schedule in dagrun lookup when `state` is given or `schedule_interval is None`. # For legacy reasons, when `catchup=True`, we use `get_previous_scheduled_dagrun` unless # `ignore_schedule` is `True`. ignore_schedule = state is not None or dag.schedule_interval is None if dag.catchup is True and not ignore_schedule: last_dagrun = dr.get_previous_scheduled_dagrun(session=session) else: last_dagrun = dr.get_previous_dagrun(session=session, state=state) if last_dagrun: return last_dagrun.get_task_instance(self.task_id, session=session) return None
[docs] def previous_ti(self): """ This attribute is deprecated. Please use `airflow.models.taskinstance.TaskInstance.get_previous_ti` method. """ warnings.warn( """ This attribute is deprecated. Please use `airflow.models.taskinstance.TaskInstance.get_previous_ti` method. """, DeprecationWarning, stacklevel=2, ) return self.get_previous_ti()
[docs] def previous_ti_success(self) -> Optional['TaskInstance']: """ This attribute is deprecated. Please use `airflow.models.taskinstance.TaskInstance.get_previous_ti` method. """ warnings.warn( """ This attribute is deprecated. Please use `airflow.models.taskinstance.TaskInstance.get_previous_ti` method. """, DeprecationWarning, stacklevel=2, ) return self.get_previous_ti(state=State.SUCCESS)
[docs] def get_previous_execution_date( self, state: Optional[str] = None, session: Session = None, ) -> Optional[pendulum.DateTime]: """ The execution date from property previous_ti_success. :param state: If passed, it only take into account instances of a specific state. :param session: SQLAlchemy ORM Session """ self.log.debug("previous_execution_date was called") prev_ti = self.get_previous_ti(state=state, session=session) return prev_ti and pendulum.instance(prev_ti.execution_date)
[docs] def get_previous_start_date( self, state: Optional[str] = None, session: Session = None ) -> Optional[pendulum.DateTime]: """ The start date from property previous_ti_success. :param state: If passed, it only take into account instances of a specific state. :param session: SQLAlchemy ORM Session """ self.log.debug("previous_start_date was called") prev_ti = self.get_previous_ti(state=state, session=session) # prev_ti may not exist and prev_ti.start_date may be None. return prev_ti and prev_ti.start_date and pendulum.instance(prev_ti.start_date)
[docs] def previous_start_date_success(self) -> Optional[pendulum.DateTime]: """ This attribute is deprecated. Please use `airflow.models.taskinstance.TaskInstance.get_previous_start_date` method. """ warnings.warn( """ This attribute is deprecated. Please use `airflow.models.taskinstance.TaskInstance.get_previous_start_date` method. """, DeprecationWarning, stacklevel=2, ) return self.get_previous_start_date(state=State.SUCCESS)
[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): """Get failed Dependencies""" 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 f"<TaskInstance: {self.dag_id}.{self.task_id} {self.execution_date} [{self.state}]>"
[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: # If the min_backoff calculation is below 1, it will be converted to 0 via int. Thus, # we must round up prior to converting to an int, otherwise a divide by zero error # will occur in the modded_hash calculation. min_backoff = int(math.ceil(delay.total_seconds() * (2 ** (self.try_number - 2)))) # deterministic per task instance ti_hash = int( hashlib.sha1( "{}#{}#{}#{}".format( self.dag_id, self.task_id, self.execution_date, self.try_number # noqa ).encode('utf-8') ).hexdigest(), 16, ) # between 1 and 1.0 * delay * (2^retry_number) modded_hash = min_backoff + ti_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 get_dagrun(self, session: Session = None): """ Returns the DagRun for this TaskInstance :param session: SQLAlchemy ORM Session :return: DagRun """ from airflow.models.dagrun import DagRun # Avoid circular import 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( # pylint: disable=too-many-arguments self, verbose: bool = True, ignore_all_deps: bool = False, ignore_depends_on_past: bool = False, ignore_task_deps: bool = False, ignore_ti_state: bool = False, mark_success: bool = False, test_mode: bool = False, job_id: Optional[str] = None, pool: Optional[str] = None, session=None, ) -> bool: """ 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 TaskInstance'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 job_id: Job (BackfillJob / LocalTaskJob / SchedulerJob) ID :type job_id: str :param pool: specifies the pool to use to run the task instance :type pool: str :param session: SQLAlchemy ORM Session :type session: Session :return: whether the state was changed to running or not :rtype: bool """ task = self.task self.refresh_from_task(task, pool_override=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() if not ignore_all_deps and not ignore_ti_state and self.state == State.SUCCESS: Stats.incr('previously_succeeded', 1, 1) # TODO: Logging needs cleanup, not clear what is being printed hr_line_break = "\n" + ("-" * 80) # Line break if not mark_success: # Firstly find non-runnable and non-requeueable tis. # Since mark_success is not set, we do nothing. non_requeueable_dep_context = DepContext( deps=RUNNING_DEPS - REQUEUEABLE_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=non_requeueable_dep_context, session=session, verbose=True ): session.commit() return False # 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() if self.state == State.UP_FOR_RESCHEDULE: task_reschedule: TR = TR.query_for_task_instance(self, session=session).first() if task_reschedule: self.start_date = task_reschedule.start_date # Secondly we find non-runnable but requeueable tis. We reset its state. # This is because we might have hit concurrency limits, # e.g. because of backfilling. dep_context = DepContext( deps=REQUEUEABLE_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, ) if not self.are_dependencies_met(dep_context=dep_context, session=session, verbose=True): self.state = State.NONE self.log.warning(hr_line_break) self.log.warning( "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_line_break) self.queued_dttm = timezone.utcnow() session.merge(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 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() # type: ignore 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 _date_or_empty(self, attr): if hasattr(self, attr): date = getattr(self, attr) if date: return date.strftime('%Y%m%dT%H%M%S') return ''
@provide_session @Sentry.enrich_errors
[docs] def _run_raw_task( self, mark_success: bool = False, test_mode: bool = False, job_id: Optional[str] = None, pool: Optional[str] = None, error_file: Optional[str] = None, session=None, ) -> 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 :param session: SQLAlchemy ORM Session :type session: Session """ task = self.task self.test_mode = test_mode self.refresh_from_task(task, pool_override=pool) self.refresh_from_db(session=session) self.job_id = job_id self.hostname = get_hostname() = os.getpid() session.merge(self) session.commit() actual_start_date = timezone.utcnow() Stats.incr(f'ti.start.{task.dag_id}.{task.task_id}') try: if not mark_success: context = self.get_template_context() self._prepare_and_execute_task_with_callbacks(context, task) self.refresh_from_db(lock_for_update=True) self.state = State.SUCCESS except AirflowSmartSensorException as e: return except AirflowSkipException as e: # Recording SKIP # log only if exception has any arguments to prevent log flooding if e.args: self.refresh_from_db(lock_for_update=True) self.state = State.SKIPPED 'Marking task as SKIPPED. ' 'dag_id=%s, task_id=%s, execution_date=%s, start_date=%s, end_date=%s', self.dag_id, self.task_id, self._date_or_empty('execution_date'), self._date_or_empty('start_date'), self._date_or_empty('end_date'), ) except AirflowRescheduleException as reschedule_exception: self.refresh_from_db() self._handle_reschedule(actual_start_date, reschedule_exception, test_mode) return except AirflowFailException as e: self.refresh_from_db() self.handle_failure(e, test_mode, force_fail=True, error_file=error_file) raise 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, error_file=error_file) raise except (Exception, KeyboardInterrupt) as e: self.handle_failure(e, test_mode, error_file=error_file) raise finally: Stats.incr(f'ti.finish.{task.dag_id}.{task.task_id}.{self.state}') # Recording SUCCESS self.end_date = timezone.utcnow() 'Marking task as SUCCESS. ' 'dag_id=%s, task_id=%s, execution_date=%s, start_date=%s, end_date=%s', self.dag_id, self.task_id, self._date_or_empty('execution_date'), self._date_or_empty('start_date'), self._date_or_empty('end_date'), ) self.set_duration() if not test_mode: session.add(Log(self.state, self)) session.merge(self) session.commit() if not test_mode: self._run_mini_scheduler_on_child_tasks(session)
@provide_session @Sentry.enrich_errors
[docs] def _run_mini_scheduler_on_child_tasks(self, session=None) -> None: if conf.getboolean('scheduler', 'schedule_after_task_execution', fallback=True): from airflow.models.dagrun import DagRun # Avoid circular import try: # Re-select the row with a lock dag_run = with_row_locks( session.query(DagRun).filter_by( dag_id=self.dag_id, execution_date=self.execution_date, ), session=session, ).one() # Get a partial dag with just the specific tasks we want to # examine. In order for dep checks to work correctly, we # include ourself (so TriggerRuleDep can check the state of the # task we just executed) partial_dag = self.task.dag.partial_subset( self.task.downstream_task_ids, include_downstream=False, include_upstream=False, include_direct_upstream=True, ) dag_run.dag = partial_dag info = dag_run.task_instance_scheduling_decisions(session) skippable_task_ids = { task_id for task_id in partial_dag.task_ids if task_id not in self.task.downstream_task_ids } schedulable_tis = [ti for ti in info.schedulable_tis if ti.task_id not in skippable_task_ids] for schedulable_ti in schedulable_tis: if not hasattr(schedulable_ti, "task"): schedulable_ti.task = self.task.dag.get_task(schedulable_ti.task_id) num = dag_run.schedule_tis(schedulable_tis)"%d downstream tasks scheduled from follow-on schedule check", num) session.commit() except OperationalError as e: # Any kind of DB error here is _non fatal_ as this block is just an optimisation. f"Skipping mini scheduling run due to exception: {e.statement}", exc_info=True, ) session.rollback()
[docs] def _prepare_and_execute_task_with_callbacks(self, context, task): """Prepare Task for Execution""" from airflow.models.renderedtifields import RenderedTaskInstanceFields task_copy = task.prepare_for_execution() self.task = task_copy def signal_handler(signum, frame): # pylint: disable=unused-argument 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() with Stats.timer(f'dag.{task_copy.dag_id}.{task_copy.task_id}.duration'): self.render_templates(context=context) RenderedTaskInstanceFields.write(RenderedTaskInstanceFields(ti=self, render_templates=False)) RenderedTaskInstanceFields.delete_old_records(self.task_id, self.dag_id) # Export context to make it available for operators to use. airflow_context_vars = context_to_airflow_vars(context, in_env_var_format=True) "Exporting the following env vars:\n%s", '\n'.join(f"{k}={v}" for k, v in airflow_context_vars.items()), ) os.environ.update(airflow_context_vars) # Run pre_execute callback task_copy.pre_execute(context=context) # Run on_execute callback self._run_execute_callback(context, task) if task_copy.is_smart_sensor_compatible(): # Try to register it in the smart sensor service. registered = False try: registered = task_copy.register_in_sensor_service(self, context) except Exception: # pylint: disable=broad-except self.log.warning( "Failed to register in sensor service." " Continue to run task in non smart sensor mode.", exc_info=True, ) if registered: # Will raise AirflowSmartSensorException to avoid long running execution. self._update_ti_state_for_sensing() # Execute the task with set_current_context(context): result = self._execute_task(context, task_copy) # Run post_execute callback task_copy.post_execute(context=context, result=result) Stats.incr(f'operator_successes_{self.task.task_type}', 1, 1) Stats.incr('ti_successes')
[docs] def _update_ti_state_for_sensing(self, session=None):'Submitting %s to sensor service', self) self.state = State.SENSING self.start_date = timezone.utcnow() session.merge(self) session.commit() # Raise exception for sensing state raise AirflowSmartSensorException("Task successfully registered in smart sensor.")
[docs] def _execute_task(self, context, task_copy): """Executes Task (optionally with a Timeout) and pushes Xcom results""" # If a timeout is specified for the task, make it fail # if it goes beyond if task_copy.execution_timeout: try: with timeout(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 task_copy.do_xcom_push and result is not None: self.xcom_push(key=XCOM_RETURN_KEY, value=result) return result
[docs] def _run_execute_callback(self, context: Context, task): """Functions that need to be run before a Task is executed""" try: if task.on_execute_callback: task.on_execute_callback(context) except Exception: # pylint: disable=broad-except self.log.exception("Failed when executing execute callback")
[docs] def _run_finished_callback(self, error: Optional[Union[str, Exception]] = None) -> None: """ Call callback defined for finished state change. NOTE: Only invoke this function from caller of self._run_raw_task or """ if self.state == State.FAILED: task = self.task if task.on_failure_callback is not None: context = self.get_template_context() context["exception"] = error task.on_failure_callback(context) elif self.state == State.SUCCESS: task = self.task if task.on_success_callback is not None: context = self.get_template_context() task.on_success_callback(context) elif self.state == State.UP_FOR_RETRY: task = self.task if task.on_retry_callback is not None: context = self.get_template_context() context["exception"] = error task.on_retry_callback(context)
[docs] def run( # pylint: disable=too-many-arguments self, verbose: bool = True, ignore_all_deps: bool = False, ignore_depends_on_past: bool = False, ignore_task_deps: bool = False, ignore_ti_state: bool = False, mark_success: bool = False, test_mode: bool = False, job_id: Optional[str] = None, pool: Optional[str] = None, session=None, ) -> None: """Run TaskInstance""" 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 not res: return try: error_fd = NamedTemporaryFile(delete=True) self._run_raw_task( mark_success=mark_success, test_mode=test_mode, job_id=job_id, pool=pool,, session=session, ) finally: error = None if self.state == State.SUCCESS else load_error_file(error_fd) error_fd.close() self._run_finished_callback(error=error)
[docs] def dry_run(self): """Only Renders Templates for the TI""" task = self.task task_copy = task.prepare_for_execution() self.task = task_copy self.render_templates() task_copy.dry_run()
[docs] def _handle_reschedule(self, actual_start_date, reschedule_exception, test_mode=False, 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: Union[str, Exception], test_mode: Optional[bool] = None, force_fail: bool = False, error_file: Optional[str] = None, session=None, ) -> None: """Handle Failure for the TaskInstance""" if test_mode is None: test_mode = self.test_mode if error: if isinstance(error, Exception): self.log.exception("Task failed with exception") else: self.log.error("%s", error) # external monitoring process provides pickle file so _run_raw_task # can send its runtime errors for access by failure callback if error_file: set_error_file(error_file, error) task = self.task self.end_date = timezone.utcnow() self.set_duration() Stats.incr(f'operator_failures_{task.task_type}', 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)) # Set state correctly and figure out how to log it and decide whether # to email # Note, callback invocation needs to be handled by caller of # _run_raw_task to avoid race conditions which could lead to duplicate # invocations or miss invocation. # Since this function is called only when the TaskInstance 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 ... or if force_fail is truthy if force_fail or not self.is_eligible_to_retry(): self.state = State.FAILED if force_fail: log_message = "Immediate failure requested. Marking task as FAILED." else: log_message = "Marking task as FAILED." email_for_state = task.email_on_failure else: self.state = State.UP_FOR_RETRY log_message = "Marking task as UP_FOR_RETRY." email_for_state = task.email_on_retry '%s dag_id=%s, task_id=%s, execution_date=%s, start_date=%s, end_date=%s', log_message, self.dag_id, self.task_id, self._safe_date('execution_date', '%Y%m%dT%H%M%S'), self._safe_date('start_date', '%Y%m%dT%H%M%S'), self._safe_date('end_date', '%Y%m%dT%H%M%S'), ) if email_for_state and try: self.email_alert(error) except Exception: # pylint: disable=broad-except self.log.exception('Failed to send email to: %s', if not test_mode: session.merge(self) session.commit()
[docs] def handle_failure_with_callback( self, error: Union[str, Exception], test_mode: Optional[bool] = None, force_fail: bool = False, session=None, ) -> None: self.handle_failure(error=error, test_mode=test_mode, force_fail=force_fail, session=session) self._run_finished_callback(error=error)
[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 _safe_date(self, date_attr, fmt): result = getattr(self, date_attr, None) if result is not None: return result.strftime(fmt) return ''
[docs] def get_template_context(self, session=None) -> Context: # pylint: disable=too-many-locals """Return TI Context""" task = self.task from airflow import macros integrate_macros_plugins() params = {} # type: Dict[str, Any] run_id = '' dag_run = None if hasattr(task, 'dag'): if task.dag.params: params.update(task.dag.params) from airflow.models.dagrun import DagRun # Avoid circular import 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('-', '') next_execution_date = pendulum.instance(next_execution_date) 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('-', '') prev_execution_date = pendulum.instance(prev_execution_date) 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 = f"{task.dag_id}__{task.task_id}__{ds_nodash}" if task.params: params.update(task.params) if conf.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.variable_name }}`` or ``{{ var.value.get('variable_name', 'fallback') }}``. """ def __init__(self): self.var = None def __getattr__( self, item: str, ): self.var = Variable.get(item) return self.var def __repr__(self): return str(self.var) @staticmethod def get( item: str, # pylint: disable=protected-access default_var: Any = Variable._Variable__NO_DEFAULT_SENTINEL, # noqa ): """Get Airflow Variable value""" return Variable.get(item, default_var=default_var) class VariableJsonAccessor: """ Wrapper around Variable. This way you can get variables in templates by using ``{{ var.json.variable_name }}`` or ``{{ var.json.get('variable_name', {'fall': 'back'}) }}``. """ def __init__(self): self.var = None def __getattr__( self, item: str, ): self.var = Variable.get(item, deserialize_json=True) return self.var def __repr__(self): return str(self.var) @staticmethod def get( item: str, # pylint: disable=protected-access default_var: Any = Variable._Variable__NO_DEFAULT_SENTINEL, # noqa ): """Get Airflow Variable after deserializing JSON value""" return Variable.get(item, default_var=default_var, deserialize_json=True) return { 'conf': conf, 'dag': task.dag, 'dag_run': dag_run, 'ds': ds, 'ds_nodash': ds_nodash, 'execution_date': pendulum.instance(self.execution_date), 'inlets': task.inlets, 'macros': macros, 'next_ds': next_ds, 'next_ds_nodash': next_ds_nodash, 'next_execution_date': next_execution_date, 'outlets': task.outlets, 'params': params, 'prev_ds': prev_ds, 'prev_ds_nodash': prev_ds_nodash, 'prev_execution_date': prev_execution_date, 'prev_execution_date_success': lazy_object_proxy.Proxy( lambda: self.get_previous_execution_date(state=State.SUCCESS) ), 'prev_start_date_success': lazy_object_proxy.Proxy( lambda: self.get_previous_start_date(state=State.SUCCESS) ), 'run_id': run_id, 'task': task, 'task_instance': self, 'task_instance_key_str': ti_key_str, 'test_mode': self.test_mode, 'ti': self, 'tomorrow_ds': tomorrow_ds, 'tomorrow_ds_nodash': tomorrow_ds_nodash, 'ts': ts, 'ts_nodash': ts_nodash, 'ts_nodash_with_tz': ts_nodash_with_tz, 'var': { 'json': VariableJsonAccessor(), 'value': VariableAccessor(), }, 'yesterday_ds': yesterday_ds, 'yesterday_ds_nodash': yesterday_ds_nodash,
[docs] def get_rendered_template_fields(self): """Fetch rendered template fields from DB""" from airflow.models.renderedtifields import RenderedTaskInstanceFields rendered_task_instance_fields = RenderedTaskInstanceFields.get_templated_fields(self) if rendered_task_instance_fields: for field_name, rendered_value in rendered_task_instance_fields.items(): setattr(self.task, field_name, rendered_value) else: try: self.render_templates() except (TemplateAssertionError, UndefinedError) as e: raise AirflowException( "Webserver does not have access to User-defined Macros or Filters " "when Dag Serialization is enabled. Hence for the task that have not yet " "started running, please use 'airflow tasks render' for debugging the " "rendering of template_fields." ) from e
[docs] def get_rendered_k8s_spec(self): """Fetch rendered template fields from DB""" from airflow.models.renderedtifields import RenderedTaskInstanceFields rendered_k8s_spec = RenderedTaskInstanceFields.get_k8s_pod_yaml(self) if not rendered_k8s_spec: try: rendered_k8s_spec = self.render_k8s_pod_yaml() except (TemplateAssertionError, UndefinedError) as e: raise AirflowException(f"Unable to render a k8s spec for this taskinstance: {e}") from e return rendered_k8s_spec
[docs] def overwrite_params_with_dag_run_conf(self, params, dag_run): """Overwrite Task Params with DagRun.conf""" if dag_run and dag_run.conf: self.log.debug("Updating task params (%s) with DagRun.conf (%s)", params, dag_run.conf) params.update(dag_run.conf)
[docs] def render_templates(self, context: Optional[Context] = None) -> None: """Render templates in the operator fields.""" if not context: context = self.get_template_context() self.task.render_template_fields(context)
[docs] def render_k8s_pod_yaml(self) -> Optional[dict]: """Render k8s pod yaml""" from airflow.kubernetes.kubernetes_helper_functions import create_pod_id # Circular import kube_config = KubeConfig() pod = PodGenerator.construct_pod( dag_id=self.dag_id, task_id=self.task_id, pod_id=create_pod_id(self.dag_id, self.task_id), try_number=self.try_number, kube_image=kube_config.kube_image, date=self.execution_date, args=self.command_as_list(), pod_override_object=PodGenerator.from_obj(self.executor_config), scheduler_job_id="worker-config", namespace=kube_config.executor_namespace, base_worker_pod=PodGenerator.deserialize_model_file(kube_config.pod_template_file), ) settings.pod_mutation_hook(pod) sanitized_pod = ApiClient().sanitize_for_serialization(pod) return sanitized_pod
[docs] def get_email_subject_content(self, exception): """Get the email subject content for exceptions.""" # For a ti from DB (without ti.task), return the default value # Reuse it for smart sensor to send default email alert use_default = not hasattr(self, 'task') exception_html = str(exception).replace('\n', '<br>') 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>' ) default_html_content_err = ( 'Try {{try_number}} out of {{max_tries + 1}}<br>' 'Exception:<br>Failed attempt to attach error logs<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>' ) if use_default: jinja_context = {'ti': self} # 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 = jinja2.Environment( loader=jinja2.FileSystemLoader(os.path.dirname(__file__)), autoescape=True ) subject = jinja_env.from_string(default_subject).render(**jinja_context) html_content = jinja_env.from_string(default_html_content).render(**jinja_context) html_content_err = jinja_env.from_string(default_html_content_err).render(**jinja_context) else: jinja_context = self.get_template_context() 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() def render(key, content): if conf.has_option('email', key): path = conf.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) html_content_err = render('html_content_template', default_html_content_err) return subject, html_content, html_content_err
[docs] def email_alert(self, exception): """Send alert email with exception information.""" subject, html_content, html_content_err = self.get_email_subject_content(exception) try: send_email(, subject, html_content) except Exception: send_email(, subject, html_content_err)
[docs] def set_duration(self) -> None: """Set TI duration""" if self.end_date and self.start_date: self.duration = (self.end_date - self.start_date).total_seconds() else: self.duration = None self.log.debug("Task Duration set to %s", self.duration)
[docs] def xcom_push( self, key: str, value: Any, execution_date: Optional[datetime] = None, session: Session = None, ) -> 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 picklable 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 :param session: Sqlalchemy ORM Session :type session: Session """ 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, session=session,
) @provide_session
[docs] def xcom_pull( # pylint: disable=inconsistent-return-statements self, task_ids: Optional[Union[str, Iterable[str]]] = None, dag_id: Optional[str] = None, key: str = XCOM_RETURN_KEY, include_prior_dates: bool = False, session: Session = None, ) -> Any: """ 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 :param session: Sqlalchemy ORM Session :type session: Session """ if dag_id is None: dag_id = self.dag_id query = XCom.get_many( execution_date=self.execution_date, key=key, dag_ids=dag_id, task_ids=task_ids, include_prior_dates=include_prior_dates, session=session, ) # Since we're only fetching the values field, and not the # whole class, the @recreate annotation does not kick in. # Therefore we need to deserialize the fields by ourselves. if is_container(task_ids): vals_kv = { result.task_id: XCom.deserialize_value(result) for result in query.with_entities(XCom.task_id, XCom.value) } values_ordered_by_id = [vals_kv.get(task_id) for task_id in task_ids] return values_ordered_by_id else: xcom = query.with_entities(XCom.value).first() if xcom: return XCom.deserialize_value(xcom)
[docs] def get_num_running_task_instances(self, session): """Return Number of running TIs from the DB""" # .count() is inefficient return ( session.query(func.count()) .filter( TaskInstance.dag_id == self.dag_id, TaskInstance.task_id == self.task_id, TaskInstance.state == State.RUNNING,
) .scalar() )
[docs] def init_run_context(self, raw=False): """Sets the log context.""" self.raw = raw self._set_context(self)
[docs] def filter_for_tis(tis: Iterable[Union["TaskInstance", TaskInstanceKey]]) -> Optional[BooleanClauseList]: """Returns SQLAlchemy filter to query selected task instances""" if not tis: return None # DictKeys type, (what we often pass here from the scheduler) is not directly indexable :( first = list(tis)[0] dag_id = first.dag_id execution_date = first.execution_date first_task_id = first.task_id # Common path optimisations: when all TIs are for the same dag_id and execution_date, or same dag_id # and task_id -- this can be over 150x for huge numbers of TIs (20k+) if all(t.dag_id == dag_id and t.execution_date == execution_date for t in tis): return and_( TaskInstance.dag_id == dag_id, TaskInstance.execution_date == execution_date, TaskInstance.task_id.in_(t.task_id for t in tis), ) if all(t.dag_id == dag_id and t.task_id == first_task_id for t in tis): return and_( TaskInstance.dag_id == dag_id, TaskInstance.execution_date.in_(t.execution_date for t in tis), TaskInstance.task_id == first_task_id, ) return or_( and_( TaskInstance.dag_id == ti.dag_id, TaskInstance.task_id == ti.task_id, TaskInstance.execution_date == ti.execution_date, ) for ti in tis
) # State of the task instance. # Stores string version of the task state.
[docs]TaskInstanceStateType = Tuple[TaskInstanceKey, str]
[docs]class SimpleTaskInstance: """ Simplified Task Instance. Used to send data between processes via Queues. """ def __init__(self, ti: TaskInstance): self._dag_id: str = ti.dag_id self._task_id: str = ti.task_id self._execution_date: datetime = ti.execution_date self._start_date: datetime = ti.start_date self._end_date: datetime = ti.end_date self._try_number: int = ti.try_number self._state: str = ti.state self._executor_config: Any = ti.executor_config self._run_as_user: Optional[str] = None if hasattr(ti, 'run_as_user'): self._run_as_user = ti.run_as_user self._pool: str = ti.pool self._priority_weight: Optional[int] = None if hasattr(ti, 'priority_weight'): self._priority_weight = ti.priority_weight self._queue: str = ti.queue self._key = ti.key # pylint: disable=missing-docstring @property
[docs] def dag_id(self) -> str: return self._dag_id
[docs] def task_id(self) -> str: return self._task_id
[docs] def execution_date(self) -> datetime: return self._execution_date
[docs] def start_date(self) -> datetime: return self._start_date
[docs] def end_date(self) -> datetime: return self._end_date
[docs] def try_number(self) -> int: return self._try_number
[docs] def state(self) -> str: return self._state
[docs] def pool(self) -> str: return self._pool
[docs] def priority_weight(self) -> Optional[int]: return self._priority_weight
[docs] def queue(self) -> str: return self._queue
[docs] def key(self) -> TaskInstanceKey: return self._key
[docs] def executor_config(self): return self._executor_config
[docs] def construct_task_instance(self, session=None, lock_for_update=False) -> TaskInstance: """ Construct a TaskInstance from the database based on the primary key :param session: DB session. :param lock_for_update: if True, indicates that the database should lock the TaskInstance (issuing a FOR UPDATE clause) until the session is committed. :return: the task instance constructed """ qry = session.query(TaskInstance).filter( TaskInstance.dag_id == self._dag_id, TaskInstance.task_id == self._task_id, TaskInstance.execution_date == self._execution_date, ) if lock_for_update: ti = qry.with_for_update().first() else: ti = qry.first() return ti
[docs]STATICA_HACK = True
globals()['kcah_acitats'[::-1].upper()] = False if STATICA_HACK: # pragma: no cover from airflow.job.base_job import BaseJob from airflow.models.dagrun import DagRun
[docs] TaskInstance.dag_run = relationship(DagRun)
TaskInstance.queued_by_job = relationship(BaseJob)

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