:mod:`airflow.models.baseoperator`
==================================
.. py:module:: airflow.models.baseoperator
Module Contents
---------------
.. py:class:: BaseOperator(task_id, owner=configuration.conf.get('operators', 'DEFAULT_OWNER'), email=None, email_on_retry=True, email_on_failure=True, retries=0, retry_delay=timedelta(seconds=300), retry_exponential_backoff=False, max_retry_delay=None, start_date=None, end_date=None, schedule_interval=None, depends_on_past=False, wait_for_downstream=False, dag=None, params=None, default_args=None, priority_weight=1, weight_rule=WeightRule.DOWNSTREAM, queue=configuration.conf.get('celery', 'default_queue'), pool=Pool.DEFAULT_POOL_NAME, sla=None, execution_timeout=None, on_failure_callback=None, on_success_callback=None, on_retry_callback=None, trigger_rule=TriggerRule.ALL_SUCCESS, resources=None, run_as_user=None, task_concurrency=None, executor_config=None, do_xcom_push=True, inlets=None, outlets=None, *args, **kwargs)
Bases: :class:`airflow.utils.log.logging_mixin.LoggingMixin`
Abstract base class for all operators. Since operators create objects that
become nodes in the dag, BaseOperator contains many recursive methods for
dag crawling behavior. To derive this class, you are expected to override
the constructor as well as the 'execute' method.
Operators derived from this class should perform or trigger certain tasks
synchronously (wait for completion). Example of operators could be an
operator that runs a Pig job (PigOperator), a sensor operator that
waits for a partition to land in Hive (HiveSensorOperator), or one that
moves data from Hive to MySQL (Hive2MySqlOperator). Instances of these
operators (tasks) target specific operations, running specific scripts,
functions or data transfers.
This class is abstract and shouldn't be instantiated. Instantiating a
class derived from this one results in the creation of a task object,
which ultimately becomes a node in DAG objects. Task dependencies should
be set by using the set_upstream and/or set_downstream methods.
:param task_id: a unique, meaningful id for the task
:type task_id: str
:param owner: the owner of the task, using the unix username is recommended
:type owner: str
:param retries: the number of retries that should be performed before
failing the task
:type retries: int
:param retry_delay: delay between retries
:type retry_delay: datetime.timedelta
:param retry_exponential_backoff: allow progressive longer waits between
retries by using exponential backoff algorithm on retry delay (delay
will be converted into seconds)
:type retry_exponential_backoff: bool
:param max_retry_delay: maximum delay interval between retries
:type max_retry_delay: datetime.timedelta
:param start_date: The ``start_date`` for the task, determines
the ``execution_date`` for the first task instance. The best practice
is to have the start_date rounded
to your DAG's ``schedule_interval``. Daily jobs have their start_date
some day at 00:00:00, hourly jobs have their start_date at 00:00
of a specific hour. Note that Airflow simply looks at the latest
``execution_date`` and adds the ``schedule_interval`` to determine
the next ``execution_date``. It is also very important
to note that different tasks' dependencies
need to line up in time. If task A depends on task B and their
start_date are offset in a way that their execution_date don't line
up, A's dependencies will never be met. If you are looking to delay
a task, for example running a daily task at 2AM, look into the
``TimeSensor`` and ``TimeDeltaSensor``. We advise against using
dynamic ``start_date`` and recommend using fixed ones. Read the
FAQ entry about start_date for more information.
:type start_date: datetime.datetime
:param end_date: if specified, the scheduler won't go beyond this date
:type end_date: datetime.datetime
:param depends_on_past: when set to true, task instances will run
sequentially while relying on the previous task's schedule to
succeed. The task instance for the start_date is allowed to run.
:type depends_on_past: bool
:param wait_for_downstream: when set to true, an instance of task
X will wait for tasks immediately downstream of the previous instance
of task X to finish successfully before it runs. This is useful if the
different instances of a task X alter the same asset, and this asset
is used by tasks downstream of task X. Note that depends_on_past
is forced to True wherever wait_for_downstream is used.
:type wait_for_downstream: bool
:param queue: which queue to target when running this job. Not
all executors implement queue management, the CeleryExecutor
does support targeting specific queues.
:type queue: str
:param dag: a reference to the dag the task is attached to (if any)
:type dag: airflow.models.DAG
:param priority_weight: priority weight of this task against other task.
This allows the executor to trigger higher priority tasks before
others when things get backed up. 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 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
.. attribute:: template_fields
:annotation: :Iterable[str] = []
.. attribute:: template_ext
:annotation: :Iterable[str] = []
.. attribute:: ui_color
:annotation: = #fff
.. attribute:: ui_fgcolor
:annotation: = #000
.. attribute:: _base_operator_shallow_copy_attrs
:annotation: = ['user_defined_macros', 'user_defined_filters', 'params', '_log']
.. attribute:: shallow_copy_attrs
:annotation: :Iterable[str] = []
.. attribute:: operator_extra_links
:annotation: :Iterable[BaseOperatorLink] = []
.. attribute:: _comps
.. attribute:: dag
Returns the Operator's DAG if set, otherwise raises an error
.. attribute:: dag_id
.. attribute:: deps
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.
.. attribute:: schedule_interval
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.
.. attribute:: priority_weight_total
.. attribute:: upstream_list
@property: list of tasks directly upstream
.. attribute:: upstream_task_ids
.. attribute:: downstream_list
@property: list of tasks directly downstream
.. attribute:: downstream_task_ids
.. attribute:: task_type
.. method:: __eq__(self, other)
.. method:: __ne__(self, other)
.. method:: __lt__(self, other)
.. method:: __hash__(self)
.. method:: __rshift__(self, other)
Implements Self >> Other == self.set_downstream(other)
If "Other" is a DAG, the DAG is assigned to the Operator.
.. method:: __lshift__(self, other)
Implements Self << Other == self.set_upstream(other)
If "Other" is a DAG, the DAG is assigned to the Operator.
.. method:: __rrshift__(self, other)
Called for [DAG] >> [Operator] because DAGs don't have
__rshift__ operators.
.. method:: __rlshift__(self, other)
Called for [DAG] << [Operator] because DAGs don't have
__lshift__ operators.
.. method:: has_dag(self)
Returns True if the Operator has been assigned to a DAG.
.. method:: operator_extra_link_dict(self)
.. method:: global_operator_extra_link_dict(self)
.. method:: pre_execute(self, context)
This hook is triggered right before self.execute() is called.
.. method:: 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.
.. method:: 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.
.. method:: 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.
.. method:: __deepcopy__(self, memo)
Hack sorting double chained task lists by task_id to avoid hitting
max_depth on deepcopy operations.
.. method:: __getstate__(self)
.. method:: __setstate__(self, state)
.. method:: render_template_from_field(self, attr, content, context, jinja_env)
Renders a template from a field. If the field is a string, it will
simply render the string and return the result. If it is a collection or
nested set of collections, it will traverse the structure and render
all elements in it. If the field has another type, it will return it as it is.
.. method:: render_template(self, attr, content, context)
Renders a template either from a file or directly in a field, and returns
the rendered result.
.. method:: get_template_env(self)
.. method:: 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.
.. method:: resolve_template_files(self)
.. method:: 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.
.. method:: 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.
.. method:: get_flat_relative_ids(self, upstream=False, found_descendants=None)
Get a flat list of relatives' ids, either upstream or downstream.
.. method:: get_flat_relatives(self, upstream=False)
Get a flat list of relatives, either upstream or downstream.
.. method:: 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.
.. method:: dry_run(self)
.. method:: get_direct_relative_ids(self, upstream=False)
Get the direct relative ids to the current task, upstream or
downstream.
.. method:: get_direct_relatives(self, upstream=False)
Get the direct relatives to the current task, upstream or
downstream.
.. method:: __repr__(self)
.. method:: add_only_new(self, item_set, item)
.. method:: _set_relatives(self, task_or_task_list, upstream=False)
.. method:: set_downstream(self, task_or_task_list)
Set a task or a task list to be directly downstream from the current
task.
.. method:: set_upstream(self, task_or_task_list)
Set a task or a task list to be directly upstream from the current
task.
.. method:: xcom_push(self, context, key, value, execution_date=None)
See TaskInstance.xcom_push()
.. method:: xcom_pull(self, context, task_ids=None, dag_id=None, key=XCOM_RETURN_KEY, include_prior_dates=None)
See TaskInstance.xcom_pull()
.. method:: extra_links(self)
.. method:: get_extra_links(self, dttm, link_name)
For an operator, gets the URL that the external links specified in
`extra_links` should point to.
:raise ValueError: The error message of a ValueError will be passed on through to
the fronted to show up as a tooltip on the disabled link
:param dttm: The datetime parsed execution date for the URL being searched for
:param link_name: The name of the link we're looking for the URL for. Should be
one of the options specified in `extra_links`
:return: A URL
.. py:class:: BaseOperatorLink
Abstract base class that defines how we get an operator link.
.. attribute:: __metaclass__
.. attribute:: name
Name of the link. This will be the button name on the task UI.
:return: link name
.. method:: get_link(self, operator, dttm)
Link to external system.
:param operator: airflow operator
:param dttm: datetime
:return: link to external system