Source code for dagster._core.definitions.run_request

from enum import Enum
from typing import Any, Mapping, NamedTuple, Optional, Sequence

import dagster._check as check
from dagster._annotations import PublicAttr
from dagster._core.definitions.events import AssetKey
from dagster._core.storage.pipeline_run import DagsterRun, DagsterRunStatus
from dagster._core.storage.tags import PARTITION_NAME_TAG
from dagster._serdes.serdes import register_serdes_enum_fallbacks, whitelist_for_serdes
from dagster._utils.error import SerializableErrorInfo


@whitelist_for_serdes
class InstigatorType(Enum):
    SCHEDULE = "SCHEDULE"
    SENSOR = "SENSOR"


register_serdes_enum_fallbacks({"JobType": InstigatorType})
# for internal backcompat
JobType = InstigatorType


[docs]@whitelist_for_serdes class SkipReason(NamedTuple("_SkipReason", [("skip_message", PublicAttr[Optional[str]])])): """ Represents a skipped evaluation, where no runs are requested. May contain a message to indicate why no runs were requested. Attributes: skip_message (Optional[str]): A message displayed in dagit for why this evaluation resulted in no requested runs. """ def __new__(cls, skip_message: Optional[str] = None): return super(SkipReason, cls).__new__( cls, skip_message=check.opt_str_param(skip_message, "skip_message"), )
[docs]@whitelist_for_serdes class RunRequest( NamedTuple( "_RunRequest", [ ("run_key", PublicAttr[Optional[str]]), ("run_config", PublicAttr[Mapping[str, Any]]), ("tags", PublicAttr[Mapping[str, str]]), ("job_name", PublicAttr[Optional[str]]), ("asset_selection", PublicAttr[Optional[Sequence[AssetKey]]]), ], ) ): """ Represents all the information required to launch a single run. Must be returned by a SensorDefinition or ScheduleDefinition's evaluation function for a run to be launched. To build a run request for a particular partitition, use :py:func:`~JobDefinition.run_request_for_partition`. Attributes: run_key (Optional[str]): A string key to identify this launched run. For sensors, ensures that only one run is created per run key across all sensor evaluations. For schedules, ensures that one run is created per tick, across failure recoveries. Passing in a `None` value means that a run will always be launched per evaluation. run_config (Optional[Dict]): The config that parameterizes the run execution to be launched, as a dict. tags (Optional[Dict[str, str]]): A dictionary of tags (string key-value pairs) to attach to the launched run. job_name (Optional[str]): (Experimental) The name of the job this run request will launch. Required for sensors that target multiple jobs. asset_selection (Optional[Sequence[AssetKey]]): A sequence of AssetKeys that should be launched with this run. """ def __new__( cls, run_key: Optional[str] = None, run_config: Optional[Mapping[str, Any]] = None, tags: Optional[Mapping[str, str]] = None, job_name: Optional[str] = None, asset_selection: Optional[Sequence[AssetKey]] = None, ): return super(RunRequest, cls).__new__( cls, run_key=check.opt_str_param(run_key, "run_key"), run_config=check.opt_mapping_param(run_config, "run_config", key_type=str), tags=check.opt_mapping_param(tags, "tags", key_type=str, value_type=str), job_name=check.opt_str_param(job_name, "job_name"), asset_selection=check.opt_nullable_sequence_param( asset_selection, "asset_selection", of_type=AssetKey ), ) @property def partition_key(self) -> Optional[str]: return self.tags.get(PARTITION_NAME_TAG) def with_replaced_attrs( self, job_name: Optional[str] = None, asset_selection: Optional[Sequence[AssetKey]] = None ) -> "RunRequest": return RunRequest( run_key=self.run_key, run_config=self.run_config, tags=self.tags, job_name=job_name or self.job_name, asset_selection=asset_selection or self.asset_selection, )
@whitelist_for_serdes class PipelineRunReaction( NamedTuple( "_PipelineRunReaction", [ ("pipeline_run", Optional[DagsterRun]), ("error", Optional[SerializableErrorInfo]), ("run_status", Optional[DagsterRunStatus]), ], ) ): """ Represents a request that reacts to an existing pipeline run. If success, it will report logs back to the run. Attributes: pipeline_run (Optional[PipelineRun]): The pipeline run that originates this reaction. error (Optional[SerializableErrorInfo]): user code execution error. run_status: (Optional[PipelineRunStatus]): The run status that triggered the reaction. """ def __new__( cls, pipeline_run: Optional[DagsterRun], error: Optional[SerializableErrorInfo] = None, run_status: Optional[DagsterRunStatus] = None, ): return super(PipelineRunReaction, cls).__new__( cls, pipeline_run=check.opt_inst_param(pipeline_run, "pipeline_run", DagsterRun), error=check.opt_inst_param(error, "error", SerializableErrorInfo), run_status=check.opt_inst_param(run_status, "run_status", DagsterRunStatus), )