# -*- coding: utf-8 -*-
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# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
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# with the License. You may obtain a copy of the License at
#
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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.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.
:type wait_for_downstream: bool
:param queue: which queue to target when running this job. Not
all executors implement queue management, the CeleryExecutor
does support targeting specific queues.
:type queue: str
:param dag: a reference to the dag the task is attached to (if any)
:type dag: airflow.models.DAG
:param priority_weight: priority weight of this task against other task.
This allows the executor to trigger higher priority tasks before
others when things get backed up. 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: specifies which task queue to use
: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 sla: time by which the job is expected to succeed. Note that
this represents the ``timedelta`` after the period is closed. For
example if you set an SLA of 1 hour, the scheduler would send an email
soon after 1:00AM on the ``2016-01-02`` if the ``2016-01-01`` instance
has not succeeded yet.
The scheduler pays special attention for jobs with an SLA and
sends alert
emails for sla misses. SLA misses are also recorded in the database
for future reference. All tasks that share the same SLA time
get bundled in a single email, sent soon after that time. SLA
notification are sent once and only once for each task instance.
:type sla: datetime.timedelta
:param execution_timeout: max time allowed for the execution of
this task instance, if it goes beyond it will raise and fail.
:type execution_timeout: datetime.timedelta
:param on_failure_callback: a function to be called when a task instance
of this task fails. a context dictionary is passed as a single
parameter to this function. Context contains references to related
objects to the task instance and is documented under the macros
section of the API.
:type on_failure_callback: callable
:param on_retry_callback: much like the ``on_failure_callback`` except
that it is executed when retries occur.
:type on_retry_callback: callable
:param on_success_callback: much like the ``on_failure_callback`` except
that it is executed when the task succeeds.
:type on_success_callback: callable
:param trigger_rule: defines the rule by which dependencies are applied
for the task to get triggered. Options are:
``{ all_success | all_failed | all_done | one_success |
one_failed | none_failed | none_skipped | dummy}``
default is ``all_success``. Options can be set as string or
using the constants defined in the static class
``airflow.utils.TriggerRule``
:type trigger_rule: str
:param resources: A map of resource parameter names (the argument names of the
Resources constructor) to their values.
:type resources: dict
:param run_as_user: unix username to impersonate while running the task
:type run_as_user: str
:param task_concurrency: When set, a task will be able to limit the concurrent
runs across execution_dates
:type task_concurrency: int
:param executor_config: Additional task-level configuration parameters that are
interpreted by a specific executor. Parameters are namespaced by the name of
executor.
**Example**: to run this task in a specific docker container through
the KubernetesExecutor ::
MyOperator(...,
executor_config={
"KubernetesExecutor":
{"image": "myCustomDockerImage"}
}
)
:type executor_config: dict
: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
# 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
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.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: Iterable[DataSet]
self.outlets = [] # type: Iterable[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)
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(),
}
@property
[docs] def schedule_interval(self):
"""
The schedule interval of the DAG always wins over individual tasks so
that tasks within a DAG always line up. The task still needs a
schedule_interval as it may not be attached to a DAG.
"""
if self.has_dag():
return self.dag._schedule_interval
else:
return self._schedule_interval
@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.
"""
"""
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(TI.task_id.in_(tasks))
count = qry.count()
clear_task_instances(qry.all(), 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
[docs]@attr.s(auto_attribs=True)
class BaseOperatorLink:
"""
Abstract base class that defines how we get an operator link.
"""
[docs] operators = [] # type: ClassVar[List[Type[BaseOperator]]]
"""
This property will be used by Airflow Plugins to find the Operators to which you want
to assign this Operator Link
:return: List of Operator classes used by task for which you want to create extra link
"""
@property
@abstractmethod
# type: () -> str
"""
Name of the link. This will be the button name on the task UI.
:return: link name
"""
@abstractmethod
[docs] def get_link(self, operator, dttm):
# type: (BaseOperator, datetime) -> str
"""
Link to external system.
:param operator: airflow operator
:param dttm: datetime
:return: link to external system
"""