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airflow.models.mappedoperator

Module Contents

Classes

OperatorPartial	An "intermediate state" returned by BaseOperator.partial().
MappedOperator	Object representing a mapped operator in a DAG.

Functions

validate_mapping_kwargs(op, func, value)
ensure_xcomarg_return_value(arg)

Attributes

ValidationSource

airflow.models.mappedoperator.ValidationSource

airflow.models.mappedoperator.validate_mapping_kwargs(op, func, value)

airflow.models.mappedoperator.ensure_xcomarg_return_value(arg)

class airflow.models.mappedoperator.OperatorPartial

An “intermediate state” returned by BaseOperator.partial().

This only exists at DAG-parsing time; the only intended usage is for the user to call .expand() on it at some point (usually in a method chain) to create a MappedOperator to add into the DAG.

operator_class: type[airflow.models.baseoperator.BaseOperator]

kwargs: dict[str, Any]

params: ParamsDict | dict

__attrs_post_init__()

__repr__()

Return repr(self).

__del__()

expand(**mapped_kwargs)

expand_kwargs(kwargs, *, strict=True)

class airflow.models.mappedoperator.MappedOperator(context=None)

Bases: airflow.models.abstractoperator.AbstractOperator

Object representing a mapped operator in a DAG.

property task_type: str

Implementing Operator.

property operator_name: str

property inherits_from_empty_operator: bool

Implementing Operator.

property roots: Sequence[airflow.models.abstractoperator.AbstractOperator]

Implementing DAGNode.

property leaves: Sequence[airflow.models.abstractoperator.AbstractOperator]

Implementing DAGNode.

property owner: str

property email: None | str | Iterable[str]

property trigger_rule: airflow.utils.trigger_rule.TriggerRule

property depends_on_past: bool

property ignore_first_depends_on_past: bool

property wait_for_downstream: bool

property retries: int | None

property queue: str

property pool: str

property pool_slots: str | None

property execution_timeout: datetime.timedelta | None

property max_retry_delay: datetime.timedelta | None

property retry_delay: datetime.timedelta

property retry_exponential_backoff: bool

property priority_weight: int

property weight_rule: int

property sla: datetime.timedelta | None

property max_active_tis_per_dag: int | None

property resources: Resources | None

property on_execute_callback: TaskStateChangeCallback | None

property on_failure_callback: TaskStateChangeCallback | None

property on_retry_callback: TaskStateChangeCallback | None

property on_success_callback: TaskStateChangeCallback | None

property run_as_user: str | None

property executor_config: dict

property inlets: list[Any]

property outlets: list[Any]

property doc: str | None

property doc_md: str | None

property doc_json: str | None

property doc_yaml: str | None

property doc_rst: str | None

property output: airflow.models.xcom_arg.XComArg

Returns reference to XCom pushed by current operator

operator_class: type[BaseOperator] | dict[str, Any]

expand_input: airflow.models.expandinput.ExpandInput

partial_kwargs: dict[str, Any]

task_id: str

params: ParamsDict | dict

deps: frozenset[airflow.ti_deps.deps.base_ti_dep.BaseTIDep]

operator_extra_links: Collection[airflow.models.baseoperator.BaseOperatorLink]

template_ext: Sequence[str]

template_fields: Collection[str]

template_fields_renderers: dict[str, str]

ui_color: str

ui_fgcolor: str

dag: DAG | None

task_group: TaskGroup | None

start_date: pendulum.DateTime | None

end_date: pendulum.DateTime | None

upstream_task_ids: set[str]

downstream_task_ids: set[str]

subdag: None

HIDE_ATTRS_FROM_UI: ClassVar[frozenset[str]]

__hash__()

Return hash(self).

__repr__()

Return repr(self).

__attrs_post_init__()

classmethod get_serialized_fields()

static deps_for(operator_class)

get_dag()

Implementing Operator.

serialize_for_task_group()

Implementing DAGNode.

unmap(resolve)

Get the “normal” Operator after applying the current mapping.

The resolve argument is only used if operator_class is a real class, i.e. if this operator is not serialized. If operator_class is not a class (i.e. this DAG has been deserialized), this returns a SerializedBaseOperator that “looks like” the actual unmapping result.

If resolve is a two-tuple (context, session), the information is used to resolve the mapped arguments into init arguments. If it is a mapping, no resolving happens, the mapping directly provides those init arguments resolved from mapped kwargs.

prepare_for_execution()

iter_mapped_dependencies()

Upstream dependencies that provide XComs used by this task for task mapping.

get_parse_time_mapped_ti_count()

Number of mapped task instances that can be created on DAG run creation.

This only considers literal mapped arguments, and would return None when any non-literal values are used for mapping.

Raises

• NotFullyPopulated – If non-literal mapped arguments are encountered.

• NotMapped – If the operator is neither mapped, nor has any parent mapped task groups.

Returns

Total number of mapped TIs this task should have.

Return type

int

get_mapped_ti_count(run_id, *, session)

Number of mapped TaskInstances that can be created at run time.

This considers both literal and non-literal mapped arguments, and the result is therefore available when all depended tasks have finished. The return value should be identical to parse_time_mapped_ti_count if all mapped arguments are literal.

Raises

• NotFullyPopulated – If upstream tasks are not all complete yet.

• NotMapped – If the operator is neither mapped, nor has any parent mapped task groups.

Returns

Total number of mapped TIs this task should have.

Return type

int

render_template_fields(context, jinja_env=None)

Template all attributes listed in self.template_fields.

This updates context to reference the map-expanded task and relevant information, without modifying the mapped operator. The expanded task in context is then rendered in-place.

Parameters

• context (airflow.utils.context.Context) – Context dict with values to apply on content.

• jinja_env (jinja2.Environment | None) – Jinja environment to use for rendering.

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