from functools import partial
from typing import Any, Mapping, Optional, cast
import dagster._check as check
from dagster._annotations import experimental
from dagster._core.definitions.asset_selection import AssetSelection, CoercibleToAssetSelection
from dagster._core.definitions.run_request import SensorResult
from dagster._core.definitions.sensor_definition import (
DefaultSensorStatus,
SensorDefinition,
SensorEvaluationContext,
SensorType,
)
from dagster._core.definitions.utils import check_valid_name, normalize_tags
def evaluate_automation_conditions(
sensor_def: "AutomationConditionSensorDefinition", context: SensorEvaluationContext
):
from dagster._core.definitions.automation_tick_evaluation_context import (
AutomationTickEvaluationContext,
)
from dagster._daemon.asset_daemon import (
asset_daemon_cursor_from_instigator_serialized_cursor,
asset_daemon_cursor_to_instigator_serialized_cursor,
)
asset_graph = check.not_none(context.repository_def).asset_graph
cursor = asset_daemon_cursor_from_instigator_serialized_cursor(
context.cursor,
asset_graph,
)
run_requests, new_cursor, updated_evaluations = AutomationTickEvaluationContext(
evaluation_id=cursor.evaluation_id,
instance=context.instance,
asset_graph=asset_graph,
cursor=cursor,
materialize_run_tags=context.instance.auto_materialize_run_tags,
observe_run_tags={},
auto_observe_asset_keys=set(),
asset_selection=sensor_def.asset_selection,
logger=context.log,
).evaluate()
return SensorResult(
run_requests=run_requests,
cursor=asset_daemon_cursor_to_instigator_serialized_cursor(new_cursor),
automation_condition_evaluations=updated_evaluations,
)
def not_supported(context) -> None:
raise NotImplementedError(
"Automation policy sensors cannot be evaluated like regular user-space sensors."
)
[docs]
@experimental
class AutomationConditionSensorDefinition(SensorDefinition):
"""Targets a set of assets and repeatedly evaluates all the AutomationConditions on all of
those assets to determine which to request runs for.
Args:
name: The name of the sensor.
asset_selection (Union[str, Sequence[str], Sequence[AssetKey], Sequence[Union[AssetsDefinition, SourceAsset]], AssetSelection]):
The assets to evaluate AutomationConditions of and request runs for.
tags (Optional[Mapping[str, str]]): A set of key-value tags that annotate the sensor and can
be used for searching and filtering in the UI.
run_tags (Optional[Mapping[str, Any]]): Tags that will be automatically attached to runs launched by this sensor.
default_status (DefaultSensorStatus): Whether the sensor starts as running or not. The default
status can be overridden from the Dagster UI or via the GraphQL API.
minimum_interval_seconds (Optional[int]): The frequency at which to try to evaluate the
sensor. The actual interval will be longer if the sensor evaluation takes longer than
the provided interval.
description (Optional[str]): A human-readable description of the sensor.
"""
def __init__(
self,
name: str,
*,
asset_selection: CoercibleToAssetSelection,
tags: Optional[Mapping[str, str]] = None,
run_tags: Optional[Mapping[str, Any]] = None,
default_status: DefaultSensorStatus = DefaultSensorStatus.STOPPED,
minimum_interval_seconds: Optional[int] = None,
description: Optional[str] = None,
**kwargs,
):
self._user_code = kwargs.get("user_code", False)
self._run_tags = normalize_tags(run_tags).tags
super().__init__(
name=check_valid_name(name),
job_name=None,
evaluation_fn=partial(evaluate_automation_conditions, sensor_def=self)
if self._user_code
else not_supported,
minimum_interval_seconds=minimum_interval_seconds,
description=description,
job=None,
jobs=None,
default_status=default_status,
required_resource_keys=None,
asset_selection=asset_selection,
tags=tags,
)
@property
def run_tags(self) -> Mapping[str, str]:
return self._run_tags
@property
def asset_selection(self) -> AssetSelection:
return cast(AssetSelection, super().asset_selection)
@property
def sensor_type(self) -> SensorType:
return SensorType.AUTOMATION if self._user_code else SensorType.AUTO_MATERIALIZE