Source code for airflow.models.baseoperator

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"""
Base operator for all operators.
"""
import copy
import functools
import logging
import sys
import warnings

from abc import ABCMeta, abstractmethod
from datetime import datetime, timedelta
from typing import Any, Callable, ClassVar, Dict, FrozenSet, Iterable, List, Optional, Set, Type, Union


import attr
from cached_property import cached_property

import jinja2
import six

from airflow import settings
from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.lineage import prepare_lineage, apply_lineage, DataSet
from airflow.models.dag import DAG
from airflow.models.pool import Pool
from airflow.models.taskinstance import TaskInstance, clear_task_instances
from airflow.models.xcom import XCOM_RETURN_KEY
from airflow.ti_deps.deps.not_in_retry_period_dep import NotInRetryPeriodDep
from airflow.ti_deps.deps.not_previously_skipped_dep import NotPreviouslySkippedDep
from airflow.ti_deps.deps.prev_dagrun_dep import PrevDagrunDep
from airflow.ti_deps.deps.trigger_rule_dep import TriggerRuleDep
from airflow.utils import timezone
from airflow.utils.db import provide_session
from airflow.utils.decorators import apply_defaults
from airflow.utils.helpers import validate_key
from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.operator_resources import Resources
from airflow.utils.trigger_rule import TriggerRule
from airflow.utils.weight_rule import WeightRule


[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 email: the 'to' email address(es) used in email alerts. This can be a single email or multiple ones. Multiple addresses can be specified as a comma or semi-colon separated string or by passing a list of strings. :type email: str or list[str] :param email_on_retry: Indicates whether email alerts should be sent when a task is retried :type email_on_retry: bool :param email_on_failure: Indicates whether email alerts should be sent when a task failed :type email_on_failure: bool :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. Also note that only tasks *immediately* downstream of the previous task instance are waited for; the statuses of any tasks further downstream are ignored. :type wait_for_downstream: bool :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. Set priority_weight as a higher number for more important tasks. :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 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 pool: the slot pool this task should run in, slot pools are a way to limit concurrency for certain tasks :type pool: str :param pool_slots: the number of pool slots this task should use (>= 1) Values less than 1 are not allowed. :type pool_slots: int :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_failed_or_skipped | 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 :param do_xcom_push: if True, an XCom is pushed containing the Operator's result :type do_xcom_push: bool """ # 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' # type str
[docs] ui_fgcolor = '#000' # type str
[docs] pool = "" # type: str
# 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',) # type: Iterable[str]
# each operator should override this class attr for shallow copy attrs.
[docs] shallow_copy_attrs = () # type: Iterable[str]
# Defines the operator level extra links # The _serialized_fields are lazily loaded when get_serialized_fields() method is called
[docs] __serialized_fields = None # type: Optional[FrozenSet[str]]
[docs] _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', 'do_xcom_push',
} @apply_defaults def __init__( self, task_id, # type: str owner=conf.get('operators', 'DEFAULT_OWNER'), # type: str email=None, # type: Optional[Union[str, Iterable[str]]] email_on_retry=True, # type: bool email_on_failure=True, # type: bool retries=conf.getint('core', 'default_task_retries', fallback=0), # type: int retry_delay=timedelta(seconds=300), # type: timedelta retry_exponential_backoff=False, # type: bool max_retry_delay=None, # type: Optional[datetime] start_date=None, # type: Optional[datetime] end_date=None, # type: Optional[datetime] schedule_interval=None, # not hooked as of now depends_on_past=False, # type: bool wait_for_downstream=False, # type: bool dag=None, # type: Optional[DAG] params=None, # type: Optional[Dict] default_args=None, # type: Optional[Dict] priority_weight=1, # type: int weight_rule=WeightRule.DOWNSTREAM, # type: str queue=conf.get('celery', 'default_queue'), # type: str pool=Pool.DEFAULT_POOL_NAME, # type: str pool_slots=1, # type: int sla=None, # type: Optional[timedelta] execution_timeout=None, # type: Optional[timedelta] on_failure_callback=None, # type: Optional[Callable] on_success_callback=None, # type: Optional[Callable] on_retry_callback=None, # type: Optional[Callable] trigger_rule=TriggerRule.ALL_SUCCESS, # type: str resources=None, # type: Optional[Dict] run_as_user=None, # type: Optional[str] task_concurrency=None, # type: Optional[int] executor_config=None, # type: Optional[Dict] do_xcom_push=True, # type: bool inlets=None, # type: Optional[Dict] outlets=None, # type: Optional[Dict] *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 self.email = 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.pool_slots = pool_slots if self.pool_slots < 1: raise AirflowException("pool slots for %s in dag %s cannot be less than 1" % (self.task_id, self.dag_id)) 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) if resources is not None else None self.run_as_user = run_as_user self.task_concurrency = task_concurrency self.executor_config = executor_config or {} self.do_xcom_push = do_xcom_push # Private attributes self._upstream_task_ids = set() # type: Set[str] self._downstream_task_ids = set() # type: Set[str] if not dag and settings.CONTEXT_MANAGER_DAG: dag = settings.CONTEXT_MANAGER_DAG if dag: self.dag = dag # subdag parameter is only set for SubDagOperator. # Setting it to None by default as other Operators do not have that field self.subdag = None # type: Optional[DAG] self._log = logging.getLogger("airflow.task.operators") # lineage self.inlets = [] # type: List[DataSet] self.outlets = [] # type: List[DataSet] self.lineage_data = None self._inlets = { "auto": False, "task_ids": [], "datasets": [], } self._outlets = { "datasets": [], } # type: Dict if inlets: self._inlets.update(inlets) if outlets: self._outlets.update(outlets)
[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 component in self._comps: val = getattr(self, component, 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) elif self.task_id in dag.task_dict and dag.task_dict[self.task_id] is not self: 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
@property
[docs] def dag_id(self): """Returns dag id if it has one or an adhoc + owner""" if self.has_dag(): return self.dag.dag_id else: return 'adhoc_' + self.owner
@property
[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(), NotPreviouslySkippedDep(),
} @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.normalized_schedule_interval else: return self._schedule_interval
@property
[docs] def priority_weight_total(self): """ Total priority weight for the task. It might include all upstream or downstream tasks. depending on the weight rule. - WeightRule.ABSOLUTE - only own weight - WeightRule.DOWNSTREAM - adds priority weight of all downstream tasks - WeightRule.UPSTREAM - adds priority weight of all upstream tasks """ 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))
) @cached_property @cached_property @prepare_lineage
[docs] def pre_execute(self, context):
""" This hook is triggered right before self.execute() is called. """
[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()
@apply_lineage
[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. """
[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. """
[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 # noinspection PyProtectedMember 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_fields(self, context, jinja_env=None): # type: (Dict, Optional[jinja2.Environment]) -> None """ Template all attributes listed in template_fields. Note this operation is irreversible. :param context: Dict with values to apply on content :type context: dict :param jinja_env: Jinja environment :type jinja_env: jinja2.Environment """ if not jinja_env: jinja_env = self.get_template_env() self._do_render_template_fields(self, self.template_fields, context, jinja_env, set())
[docs] def _do_render_template_fields(self, parent, template_fields, context, jinja_env, seen_oids): # type: (Any, Iterable[str], Dict, jinja2.Environment, Set) -> None for attr_name in template_fields: content = getattr(parent, attr_name) if content: rendered_content = self.render_template(content, context, jinja_env, seen_oids) setattr(parent, attr_name, rendered_content)
[docs] def render_template(self, content, context, jinja_env=None, seen_oids=None): # type: (Any, Dict, Optional[jinja2.Environment], Optional[Set]) -> Any """ Render a templated string. The content can be a collection holding multiple templated strings and will be templated recursively. :param content: Content to template. Only strings can be templated (may be inside collection). :type content: Any :param context: Dict with values to apply on templated content :type context: dict :param jinja_env: Jinja environment. Can be provided to avoid re-creating Jinja environments during recursion. :type jinja_env: jinja2.Environment :param seen_oids: template fields already rendered (to avoid RecursionError on circular dependencies) :type seen_oids: set :return: Templated content """ if not jinja_env: jinja_env = self.get_template_env() if isinstance(content, six.string_types): if any(content.endswith(ext) for ext in self.template_ext): # Content contains a filepath return jinja_env.get_template(content).render(**context) else: return jinja_env.from_string(content).render(**context) if isinstance(content, tuple): if type(content) is not tuple: # Special case for named tuples return content.__class__( *(self.render_template(element, context, jinja_env) for element in content) ) else: return tuple(self.render_template(element, context, jinja_env) for element in content) elif isinstance(content, list): return [self.render_template(element, context, jinja_env) for element in content] elif isinstance(content, dict): return {key: self.render_template(value, context, jinja_env) for key, value in content.items()} elif isinstance(content, set): return {self.render_template(element, context, jinja_env) for element in content} else: if seen_oids is None: seen_oids = set() self._render_nested_template_fields(content, context, jinja_env, seen_oids) return content
[docs] def _render_nested_template_fields(self, content, context, jinja_env, seen_oids): # type: (Any, Dict, jinja2.Environment, Set) -> None if id(content) not in seen_oids: seen_oids.add(id(content)) try: nested_template_fields = content.template_fields except AttributeError: # content has no inner template fields return self._do_render_template_fields(content, nested_template_fields, context, jinja_env, seen_oids)
[docs] def get_template_env(self): # type: () -> jinja2.Environment """Fetch a Jinja template environment from the DAG or instantiate empty environment if no DAG.""" return self.dag.get_template_env() if self.has_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. """
[docs] def resolve_template_files(self): # Getting the content of files for template_field / template_ext if self.template_ext: for field in self.template_fields: content = getattr(self, field, None) 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, field, 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()
@property
[docs] def upstream_list(self): """@property: list of tasks directly upstream""" return [self.dag.get_task(tid) for tid in self._upstream_task_ids]
@property
[docs] def upstream_task_ids(self): """@property: list of ids of tasks directly upstream""" return self._upstream_task_ids
@property
[docs] def downstream_list(self): """@property: list of tasks directly downstream""" return [self.dag.get_task(tid) for tid in self._downstream_task_ids]
@property
[docs] def downstream_task_ids(self): """@property: list of ids of tasks directly downstream""" return self._downstream_task_ids
@provide_session
[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(TaskInstance.task_id.in_(tasks)) results = qry.all() count = len(results) clear_task_instances(results, session, dag=self.dag) session.commit() return count
@provide_session
[docs] def get_task_instances(self, start_date=None, end_date=None, session=None): """ Get a set of task instance related to this task for a specific date range. """ end_date = end_date or timezone.utcnow() return session.query(TaskInstance)\ .filter(TaskInstance.dag_id == self.dag_id)\ .filter(TaskInstance.task_id == self.task_id)\ .filter(TaskInstance.execution_date >= start_date)\ .filter(TaskInstance.execution_date <= end_date)\ .order_by(TaskInstance.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 execution_date in self.dag.date_range(start_date, end_date=end_date): TaskInstance(self, execution_date).run( mark_success=mark_success, ignore_depends_on_past=( execution_date == start_date and ignore_first_depends_on_past), ignore_ti_state=ignore_ti_state)
[docs] def dry_run(self): """Performs dry run for the operator - just render template fields.""" self.log.info('Dry run') for field in self.template_fields: content = getattr(self, field) if content and isinstance(content, six.string_types): self.log.info('Rendering template for %s', field) self.log.info(content)
[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)
@property
[docs] def task_type(self): """@property: type of the task""" return self.__class__.__name__
[docs] def add_only_new(self, item_set, item): """Adds only new items to item set""" if item in item_set: self.log.warning( 'Dependency %s, %s already registered', self, item) else: item_set.add(item)
[docs] def _set_relatives(self, task_or_task_list, upstream=False): """Sets relatives for the task.""" try: task_list = list(task_or_task_list) except TypeError: task_list = [task_or_task_list] for task in task_list: if not isinstance(task, BaseOperator): raise AirflowException( "Relationships can only be set between " "Operators; received {}".format(task.__class__.__name__)) # relationships can only be set if the tasks share a single DAG. Tasks # without a DAG are assigned to that DAG. dags = { task._dag.dag_id: task._dag # pylint: disable=protected-access for task in [self] + task_list if task.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.get_direct_relative_ids(upstream=False), 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.get_direct_relative_ids(upstream=True), 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)
@cached_property @classmethod
[docs] def get_serialized_fields(cls): """Stringified DAGs and operators contain exactly these fields.""" if not cls.__serialized_fields: cls.__serialized_fields = frozenset( set(vars(BaseOperator(task_id='test')).keys()) - { 'inlets', 'outlets', '_upstream_task_ids', 'default_args', 'dag', '_dag' } | {'_task_type', 'subdag', 'ui_color', 'ui_fgcolor', 'template_fields'}) return cls.__serialized_fields
# type: (BaseOperator, datetime) -> str """ Link to external system. :param operator: airflow operator :param dttm: datetime :return: link to external system """