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Source code for dagster._core.definitions.run_status_sensor_definition

import functools
import logging
import os
from contextlib import ExitStack
from datetime import datetime
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    Iterator,
    Mapping,
    NamedTuple,
    Optional,
    Sequence,
    Set,
    Union,
    cast,
    overload,
)

from typing_extensions import TypeAlias

import dagster._check as check
from dagster._annotations import deprecated_param, experimental_param, public
from dagster._core.definitions.graph_definition import GraphDefinition
from dagster._core.definitions.instigation_logger import InstigationLogger
from dagster._core.definitions.job_definition import JobDefinition
from dagster._core.definitions.repository_definition import RepositoryDefinition
from dagster._core.definitions.resource_annotation import get_resource_args
from dagster._core.definitions.scoped_resources_builder import Resources, ScopedResourcesBuilder
from dagster._core.definitions.sensor_definition import (
    DagsterRunReaction,
    DefaultSensorStatus,
    RawSensorEvaluationFunctionReturn,
    RunRequest,
    SensorDefinition,
    SensorEvaluationContext,
    SensorResult,
    SensorType,
    SkipReason,
    get_context_param_name,
    get_or_create_sensor_context,
    validate_and_get_resource_dict,
)
from dagster._core.definitions.target import ExecutableDefinition
from dagster._core.definitions.unresolved_asset_job_definition import UnresolvedAssetJobDefinition
from dagster._core.errors import (
    DagsterInvalidDefinitionError,
    DagsterInvariantViolationError,
    RunStatusSensorExecutionError,
    user_code_error_boundary,
)
from dagster._core.event_api import RunStatusChangeEventType, RunStatusChangeRecordsFilter
from dagster._core.events import PIPELINE_RUN_STATUS_TO_EVENT_TYPE, DagsterEvent, DagsterEventType
from dagster._core.instance import DagsterInstance
from dagster._core.storage.dagster_run import DagsterRun, DagsterRunStatus, RunsFilter
from dagster._serdes import serialize_value, whitelist_for_serdes
from dagster._serdes.errors import DeserializationError
from dagster._serdes.serdes import deserialize_value
from dagster._seven import JSONDecodeError
from dagster._time import datetime_from_timestamp, parse_time_string
from dagster._utils.error import serializable_error_info_from_exc_info
from dagster._utils.warnings import normalize_renamed_param

if TYPE_CHECKING:
    from dagster._core.definitions.resource_definition import ResourceDefinition
    from dagster._core.definitions.selector import (
        CodeLocationSelector,
        JobSelector,
        RepositorySelector,
    )

RunStatusSensorEvaluationFunction: TypeAlias = Union[
    Callable[..., RawSensorEvaluationFunctionReturn],
    Callable[..., RawSensorEvaluationFunctionReturn],
]
RunFailureSensorEvaluationFn: TypeAlias = Union[
    Callable[..., RawSensorEvaluationFunctionReturn],
    Callable[..., RawSensorEvaluationFunctionReturn],
]


def _get_run_status_sensor_fetch_limit(monitor_all_code_locations: bool) -> int:
    if monitor_all_code_locations:
        # No need to overfetch if we are going to process everything
        return _get_run_status_sensor_process_limit()

    # Otherwise, fetch more than we are planning to process, under the assumption
    # that some will be filtered out
    return int(os.getenv("DAGSTER_RUN_STATUS_SENSOR_FETCH_LIMIT", "25"))


def _get_run_status_sensor_process_limit() -> int:
    return int(os.getenv("DAGSTER_RUN_STATUS_SENSOR_PROCESS_LIMIT", "5"))


@whitelist_for_serdes(old_storage_names={"PipelineSensorCursor"})
class RunStatusSensorCursor(
    NamedTuple(
        "_RunStatusSensorCursor",
        [
            ("record_id", int),
            # deprecated arg, used as a record cursor for the run-sharded sqlite implementation to
            # filter records based on the update timestamp of the run.  When populated, the record
            # id is ignored (since it maybe run-scoped).
            ("update_timestamp", Optional[str]),
            # debug arg, used to quickly inspect the last processed timestamp from the run status
            # sensor's serialized state
            ("record_timestamp", Optional[str]),
        ],
    )
):
    def __new__(cls, record_id, update_timestamp=None, record_timestamp=None):
        return super(RunStatusSensorCursor, cls).__new__(
            cls,
            record_id=check.int_param(record_id, "record_id"),
            update_timestamp=check.opt_str_param(update_timestamp, "update_timestamp"),
            record_timestamp=check.opt_str_param(record_timestamp, "record_timestamp"),
        )

    @staticmethod
    def is_valid(json_str: str) -> bool:
        try:
            obj = deserialize_value(json_str, RunStatusSensorCursor)
            return isinstance(obj, RunStatusSensorCursor)
        except (JSONDecodeError, DeserializationError):
            return False

    def to_json(self) -> str:
        return serialize_value(cast(NamedTuple, self))

    @staticmethod
    def from_json(json_str: str) -> "RunStatusSensorCursor":
        return deserialize_value(json_str, RunStatusSensorCursor)


[docs] class RunStatusSensorContext: """The ``context`` object available to a decorated function of ``run_status_sensor``.""" def __init__( self, sensor_name, dagster_run, dagster_event, instance, context: Optional[ SensorEvaluationContext ] = None, # deprecated arg, but we need to keep it for backcompat resource_defs: Optional[Mapping[str, "ResourceDefinition"]] = None, logger: Optional[logging.Logger] = None, partition_key: Optional[str] = None, repository_def: Optional[RepositoryDefinition] = None, _resources: Optional[Resources] = None, _cm_scope_entered: bool = False, ) -> None: self._exit_stack = ExitStack() self._sensor_name = check.str_param(sensor_name, "sensor_name") self._dagster_run = check.inst_param(dagster_run, "dagster_run", DagsterRun) self._dagster_event = check.inst_param(dagster_event, "dagster_event", DagsterEvent) self._instance = check.inst_param(instance, "instance", DagsterInstance) self._logger: Optional[logging.Logger] = logger or (context.log if context else None) self._partition_key = check.opt_str_param(partition_key, "partition_key") self._repository_def = check.opt_inst_param( repository_def, "repository_def", RepositoryDefinition ) # Wait to set resources unless they're accessed self._resource_defs = resource_defs self._resources = _resources self._cm_scope_entered = _cm_scope_entered def for_run_failure(self) -> "RunFailureSensorContext": """Converts RunStatusSensorContext to RunFailureSensorContext.""" return RunFailureSensorContext( sensor_name=self._sensor_name, dagster_run=self._dagster_run, dagster_event=self._dagster_event, instance=self._instance, logger=self._logger, partition_key=self._partition_key, resource_defs=self._resource_defs, repository_def=self._repository_def, _resources=self._resources, _cm_scope_entered=self._cm_scope_entered, ) @property def resource_defs(self) -> Optional[Mapping[str, "ResourceDefinition"]]: return self._resource_defs @property def repository_def(self) -> Optional[RepositoryDefinition]: """Optional[RepositoryDefinition]: The RepositoryDefinition that this sensor resides in.""" return self._repository_def @property def resources(self) -> Resources: from dagster._core.definitions.scoped_resources_builder import IContainsGenerator from dagster._core.execution.build_resources import build_resources if not self._resources: """ This is similar to what we do in e.g. the op context - we set up a resource building context manager, and immediately enter it. This is so that in cases where a user is not using any context-manager based resources, they don't need to enter this SensorEvaluationContext themselves. For example: my_sensor(build_sensor_context(resources={"my_resource": my_non_cm_resource}) will work ok, but for a CM resource we must do with build_sensor_context(resources={"my_resource": my_cm_resource}) as context: my_sensor(context) """ instance = self.instance if self._instance else None resources_cm = build_resources(resources=self._resource_defs or {}, instance=instance) self._resources = self._exit_stack.enter_context(resources_cm) if isinstance(self._resources, IContainsGenerator) and not self._cm_scope_entered: self._exit_stack.close() raise DagsterInvariantViolationError( "At least one provided resource is a generator, but attempting to access" " resources outside of context manager scope. You can use the following syntax" " to open a context manager: `with build_schedule_context(...) as context:`" ) return self._resources @public @property def sensor_name(self) -> str: """The name of the sensor.""" return self._sensor_name @public @property def dagster_run(self) -> DagsterRun: """The run of the job.""" return self._dagster_run @public @property def dagster_event(self) -> DagsterEvent: """The event associated with the job run status.""" return self._dagster_event @public @property def instance(self) -> DagsterInstance: """The current instance.""" return self._instance @public @property def log(self) -> logging.Logger: """The logger for the current sensor evaluation.""" if not self._logger: self._logger = InstigationLogger() return self._logger @public @property def partition_key(self) -> Optional[str]: """Optional[str]: The partition key of the relevant run.""" return self._partition_key def __enter__(self) -> "RunStatusSensorContext": self._cm_scope_entered = True return self def __exit__(self, *exc) -> None: self._exit_stack.close() self._logger = None def merge_resources(self, resources_dict: Mapping[str, Any]) -> "RunStatusSensorContext": """Merge the specified resources into this context. This method is intended to be used by the Dagster framework, and should not be called by user code. Args: resources_dict (Mapping[str, Any]): The resources to replace in the context. """ check.invariant( self._resources is None, "Cannot merge resources in context that has been initialized.", ) from dagster._core.execution.build_resources import wrap_resources_for_execution return RunStatusSensorContext( sensor_name=self._sensor_name, dagster_run=self._dagster_run, dagster_event=self._dagster_event, instance=self._instance, logger=self._logger, partition_key=self._partition_key, resource_defs={ **(self._resource_defs or {}), **wrap_resources_for_execution(resources_dict), }, repository_def=self._repository_def, )
[docs] class RunFailureSensorContext(RunStatusSensorContext): """The ``context`` object available to a decorated function of ``run_failure_sensor``. Attributes: sensor_name (str): the name of the sensor. dagster_run (DagsterRun): the failed run. """ @public @property def failure_event(self) -> DagsterEvent: """The run failure event. If the run failed because of an error inside a step, get_step_failure_events will have more details on the step failure. """ return self.dagster_event
[docs] @public def get_step_failure_events(self) -> Sequence[DagsterEvent]: """The step failure event for each step in the run that failed. Examples: .. code-block:: python error_strings_by_step_key = { # includes the stack trace event.step_key: event.event_specific_data.error.to_string() for event in context.get_step_failure_events() } """ records = self.instance.get_records_for_run( run_id=self.dagster_run.run_id, of_type=DagsterEventType.STEP_FAILURE ).records return [cast(DagsterEvent, record.event_log_entry.dagster_event) for record in records]
[docs] @experimental_param(param="repository_def") def build_run_status_sensor_context( sensor_name: str, dagster_event: DagsterEvent, dagster_instance: DagsterInstance, dagster_run: DagsterRun, context: Optional[SensorEvaluationContext] = None, resources: Optional[Mapping[str, object]] = None, partition_key: Optional[str] = None, *, repository_def: Optional[RepositoryDefinition] = None, ) -> RunStatusSensorContext: """Builds run status sensor context from provided parameters. This function can be used to provide the context argument when directly invoking a function decorated with `@run_status_sensor` or `@run_failure_sensor`, such as when writing unit tests. Args: sensor_name (str): The name of the sensor the context is being constructed for. dagster_event (DagsterEvent): A DagsterEvent with the same event type as the one that triggers the run_status_sensor dagster_instance (DagsterInstance): The dagster instance configured for the context. dagster_run (DagsterRun): DagsterRun object from running a job resources (Optional[Mapping[str, object]]): A dictionary of resources to be made available to the sensor. repository_def (Optional[RepositoryDefinition]): The repository that the sensor belongs to. Examples: .. code-block:: python instance = DagsterInstance.ephemeral() result = my_job.execute_in_process(instance=instance) dagster_run = result.dagster_run dagster_event = result.get_job_success_event() # or get_job_failure_event() context = build_run_status_sensor_context( sensor_name="run_status_sensor_to_invoke", dagster_instance=instance, dagster_run=dagster_run, dagster_event=dagster_event, ) run_status_sensor_to_invoke(context) """ from dagster._core.execution.build_resources import wrap_resources_for_execution return RunStatusSensorContext( sensor_name=sensor_name, instance=dagster_instance, dagster_run=dagster_run, dagster_event=dagster_event, resource_defs=wrap_resources_for_execution(resources), logger=context.log if context else None, partition_key=partition_key, repository_def=repository_def, )
@overload def run_failure_sensor( name: RunFailureSensorEvaluationFn, ) -> SensorDefinition: ... @overload def run_failure_sensor( name: Optional[str] = None, minimum_interval_seconds: Optional[int] = None, description: Optional[str] = None, monitored_jobs: Optional[ Sequence[ Union[ JobDefinition, GraphDefinition, UnresolvedAssetJobDefinition, "RepositorySelector", "JobSelector", "CodeLocationSelector", ] ] ] = None, job_selection: Optional[ Sequence[ Union[ JobDefinition, GraphDefinition, UnresolvedAssetJobDefinition, "RepositorySelector", "JobSelector", "CodeLocationSelector", ] ] ] = None, monitor_all_code_locations: bool = False, default_status: DefaultSensorStatus = DefaultSensorStatus.STOPPED, request_job: Optional[ExecutableDefinition] = None, request_jobs: Optional[Sequence[ExecutableDefinition]] = None, monitor_all_repositories: bool = False, tags: Optional[Mapping[str, str]] = None, metadata: Optional[Mapping[str, object]] = None, ) -> Callable[ [RunFailureSensorEvaluationFn], SensorDefinition, ]: ...
[docs] @deprecated_param( param="job_selection", breaking_version="2.0", additional_warn_text="Use `monitored_jobs` instead.", ) @deprecated_param( param="monitor_all_repositories", breaking_version="2.0", additional_warn_text="Use `monitor_all_code_locations` instead.", ) def run_failure_sensor( name: Optional[Union[RunFailureSensorEvaluationFn, str]] = None, minimum_interval_seconds: Optional[int] = None, description: Optional[str] = None, monitored_jobs: Optional[ Sequence[ Union[ JobDefinition, GraphDefinition, UnresolvedAssetJobDefinition, "RepositorySelector", "JobSelector", "CodeLocationSelector", ] ] ] = None, job_selection: Optional[ Sequence[ Union[ JobDefinition, GraphDefinition, UnresolvedAssetJobDefinition, "RepositorySelector", "JobSelector", "CodeLocationSelector", ] ] ] = None, monitor_all_code_locations: Optional[bool] = None, default_status: DefaultSensorStatus = DefaultSensorStatus.STOPPED, request_job: Optional[ExecutableDefinition] = None, request_jobs: Optional[Sequence[ExecutableDefinition]] = None, monitor_all_repositories: Optional[bool] = None, tags: Optional[Mapping[str, str]] = None, metadata: Optional[Mapping[str, object]] = None, ) -> Union[ SensorDefinition, Callable[ [RunFailureSensorEvaluationFn], SensorDefinition, ], ]: """Creates a sensor that reacts to job failure events, where the decorated function will be run when a run fails. Takes a :py:class:`~dagster.RunFailureSensorContext`. Args: name (Optional[str]): The name of the job failure sensor. Defaults to the name of the decorated function. minimum_interval_seconds (Optional[int]): The minimum number of seconds that will elapse between sensor evaluations. description (Optional[str]): A human-readable description of the sensor. monitored_jobs (Optional[List[Union[JobDefinition, GraphDefinition, UnresolvedAssetJobDefinition, RepositorySelector, JobSelector, CodeLocationSelector]]]): The jobs in the current repository that will be monitored by this failure sensor. Defaults to None, which means the alert will be sent when any job in the current repository fails. monitor_all_code_locations (bool): If set to True, the sensor will monitor all runs in the Dagster deployment. If set to True, an error will be raised if you also specify monitored_jobs or job_selection. Defaults to False. job_selection (Optional[List[Union[JobDefinition, GraphDefinition, RepositorySelector, JobSelector, CodeLocationSelector]]]): (deprecated in favor of monitored_jobs) The jobs in the current repository that will be monitored by this failure sensor. Defaults to None, which means the alert will be sent when any job in the repository fails. 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. request_job (Optional[Union[GraphDefinition, JobDefinition, UnresolvedAssetJob]]): The job a RunRequest should execute if yielded from the sensor. request_jobs (Optional[Sequence[Union[GraphDefinition, JobDefinition, UnresolvedAssetJob]]]): (experimental) A list of jobs to be executed if RunRequests are yielded from the sensor. monitor_all_repositories (bool): (deprecated in favor of monitor_all_code_locations) If set to True, the sensor will monitor all runs in the Dagster instance. If set to True, an error will be raised if you also specify monitored_jobs or job_selection. Defaults to False. 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. metadata (Optional[Mapping[str, object]]): A set of metadata entries that annotate the sensor. Values will be normalized to typed `MetadataValue` objects. """ def inner( fn: RunFailureSensorEvaluationFn, ) -> SensorDefinition: check.callable_param(fn, "fn") if name is None or callable(name): sensor_name = fn.__name__ else: sensor_name = name jobs = monitored_jobs if monitored_jobs else job_selection monitor_all = normalize_renamed_param( monitor_all_code_locations, "monitor_all_code_locations", monitor_all_repositories, "monitor_all_repositories", ) @run_status_sensor( run_status=DagsterRunStatus.FAILURE, name=sensor_name, minimum_interval_seconds=minimum_interval_seconds, description=description, monitored_jobs=jobs, monitor_all_code_locations=monitor_all, default_status=default_status, request_job=request_job, request_jobs=request_jobs, tags=tags, metadata=metadata, ) @functools.wraps(fn) def _run_failure_sensor(*args, **kwargs) -> Any: args_modified = [ arg.for_run_failure() if isinstance(arg, RunStatusSensorContext) else arg for arg in args ] kwargs_modified = { k: v.for_run_failure() if isinstance(v, RunStatusSensorContext) else v for k, v in kwargs.items() } return fn(*args_modified, **kwargs_modified) return _run_failure_sensor # This case is for when decorator is used bare, without arguments if callable(name): return inner(name) return inner
[docs] class RunStatusSensorDefinition(SensorDefinition): """Define a sensor that reacts to a given status of job execution, where the decorated function will be evaluated when a run is at the given status. Args: name (str): The name of the sensor. Defaults to the name of the decorated function. run_status (DagsterRunStatus): The status of a run which will be monitored by the sensor. run_status_sensor_fn (Callable[[RunStatusSensorContext], Union[SkipReason, DagsterRunReaction]]): The core evaluation function for the sensor. Takes a :py:class:`~dagster.RunStatusSensorContext`. minimum_interval_seconds (Optional[int]): The minimum number of seconds that will elapse between sensor evaluations. description (Optional[str]): A human-readable description of the sensor. monitored_jobs (Optional[List[Union[JobDefinition, GraphDefinition, UnresolvedAssetJobDefinition, JobSelector, RepositorySelector, CodeLocationSelector]]]): The jobs in the current repository that will be monitored by this sensor. Defaults to None, which means the alert will be sent when any job in the repository fails. monitor_all_code_locations (bool): If set to True, the sensor will monitor all runs in the Dagster deployment. If set to True, an error will be raised if you also specify monitored_jobs or job_selection. Defaults to False. 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. request_job (Optional[Union[GraphDefinition, JobDefinition]]): The job a RunRequest should execute if yielded from the sensor. 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. metadata (Optional[Mapping[str, object]]): A set of metadata entries that annotate the sensor. Values will be normalized to typed `MetadataValue` objects. request_jobs (Optional[Sequence[Union[GraphDefinition, JobDefinition]]]): (experimental) A list of jobs to be executed if RunRequests are yielded from the sensor. """ def __init__( self, name: str, run_status: DagsterRunStatus, run_status_sensor_fn: RunStatusSensorEvaluationFunction, minimum_interval_seconds: Optional[int] = None, description: Optional[str] = None, monitored_jobs: Optional[ Sequence[ Union[ JobDefinition, GraphDefinition, UnresolvedAssetJobDefinition, "RepositorySelector", "JobSelector", "CodeLocationSelector", ] ] ] = None, monitor_all_code_locations: Optional[bool] = None, default_status: DefaultSensorStatus = DefaultSensorStatus.STOPPED, request_job: Optional[ExecutableDefinition] = None, request_jobs: Optional[Sequence[ExecutableDefinition]] = None, tags: Optional[Mapping[str, str]] = None, metadata: Optional[Mapping[str, object]] = None, required_resource_keys: Optional[Set[str]] = None, ): from dagster._core.definitions.selector import ( CodeLocationSelector, JobSelector, RepositorySelector, ) check.str_param(name, "name") check.inst_param(run_status, "run_status", DagsterRunStatus) check.callable_param(run_status_sensor_fn, "run_status_sensor_fn") check.opt_int_param(minimum_interval_seconds, "minimum_interval_seconds") check.opt_str_param(description, "description") check.opt_list_param( monitored_jobs, "monitored_jobs", ( JobDefinition, GraphDefinition, UnresolvedAssetJobDefinition, RepositorySelector, JobSelector, CodeLocationSelector, ), ) check.inst_param(default_status, "default_status", DefaultSensorStatus) monitor_all_code_locations = check.opt_bool_param( monitor_all_code_locations, "monitor_all_code_locations", default=False ) resource_arg_names: Set[str] = {arg.name for arg in get_resource_args(run_status_sensor_fn)} combined_required_resource_keys = ( check.opt_set_param(required_resource_keys, "required_resource_keys", of_type=str) | resource_arg_names ) # coerce CodeLocationSelectors to RepositorySelectors with repo name "__repository__" monitored_jobs = [ job.to_repository_selector() if isinstance(job, CodeLocationSelector) else job for job in (monitored_jobs or []) ] self._run_status_sensor_fn = check.callable_param( run_status_sensor_fn, "run_status_sensor_fn" ) event_type = PIPELINE_RUN_STATUS_TO_EVENT_TYPE[run_status] # split monitored_jobs into external repos, external jobs, and jobs in the current repo other_repos = ( [x for x in monitored_jobs if isinstance(x, RepositorySelector)] if monitored_jobs else [] ) other_repo_jobs = ( [x for x in monitored_jobs if isinstance(x, JobSelector)] if monitored_jobs else [] ) current_repo_jobs = ( [x for x in monitored_jobs if not isinstance(x, (JobSelector, RepositorySelector))] if monitored_jobs else [] ) def _wrapped_fn( context: SensorEvaluationContext, ) -> Iterator[Union[RunRequest, SkipReason, DagsterRunReaction, SensorResult]]: # initiate the cursor to (most recent event id, current timestamp) when: # * it's the first time starting the sensor # * or, the cursor isn't in valid format (backcompt) if context.cursor is None or not RunStatusSensorCursor.is_valid(context.cursor): most_recent_event_records = context.instance.fetch_run_status_changes( records_filter=event_type, limit=1 ).records most_recent_event_id = ( most_recent_event_records[0].storage_id if len(most_recent_event_records) == 1 else -1 ) record_timestamp = ( datetime_from_timestamp(most_recent_event_records[0].timestamp).isoformat() if len(most_recent_event_records) == 1 else None ) new_cursor = RunStatusSensorCursor( record_id=most_recent_event_id, record_timestamp=record_timestamp ) context.update_cursor(new_cursor.to_json()) yield SkipReason(f"Initiating {name}. Set cursor to {new_cursor}") return sensor_cursor = RunStatusSensorCursor.from_json(context.cursor) process_limit = _get_run_status_sensor_process_limit() fetch_limit = _get_run_status_sensor_fetch_limit( monitor_all_code_locations=cast(bool, monitor_all_code_locations) ) # Fetch events after the cursor id # * we move the cursor forward to the latest visited event's id to avoid revisits # * when the daemon is down, bc we persist the cursor info, we can go back to where we # left and backfill alerts for the qualified events during the downtime if sensor_cursor.update_timestamp and context.instance.event_log_storage.is_run_sharded: # The run status sensor cursor has the timestamp set... and the event log storage # is run sharded. We need to query the index shard by timestamp instead of by # record id (which is reindexed relative to some run sharded query). When we update # the cursor, we should omit the timestamp, since this API only queries the global # index shard instead of the run shard. event_records = context.instance.fetch_run_status_changes( records_filter=RunStatusChangeRecordsFilter( event_type=cast(RunStatusChangeEventType, event_type), after_timestamp=cast( datetime, parse_time_string(sensor_cursor.update_timestamp) ).timestamp(), ), ascending=True, limit=fetch_limit, ).records else: # the cursor storage id is globally unique, either because the event log storage is # not run sharded or because the cursor was set from an event returned from the # index shard. When we update the cursor, we should omit the timestamp, since this # API only queries the global index shard instead of the run shard. event_records = context.instance.fetch_run_status_changes( records_filter=RunStatusChangeRecordsFilter( event_type=cast(RunStatusChangeEventType, event_type), after_storage_id=sensor_cursor.record_id, ), ascending=True, limit=fetch_limit, ).records run_ids_to_fetch = list( set(event_record.event_log_entry.run_id for event_record in event_records) ) run_records = ( { record.dagster_run.run_id: record for record in context.instance.get_run_records( filters=RunsFilter(run_ids=run_ids_to_fetch) ) } if run_ids_to_fetch else {} ) num_processed_runs = 0 for event_record in event_records: event_log_entry = event_record.event_log_entry storage_id = event_record.storage_id record_timestamp = datetime_from_timestamp(event_record.timestamp).isoformat() # skip if we couldn't find the right run if event_log_entry.run_id not in run_records: context.update_cursor( RunStatusSensorCursor( record_id=storage_id, record_timestamp=record_timestamp ).to_json() ) continue dagster_run = run_records[event_log_entry.run_id].dagster_run job_match = False # if monitor_all_code_locations is provided, then we want to run the sensor for all jobs in all code locations if monitor_all_code_locations: job_match = True code_location_name = ( context.code_location_origin.location_name if context.code_location_origin else None ) # check if the run is in the current repository and (if provided) one of jobs specified in monitored_jobs if ( not job_match and # the job has a repository (not manually executed) dagster_run.remote_job_origin and # the job belongs to the current code location dagster_run.remote_job_origin.repository_origin.code_location_origin.location_name == code_location_name and # the job belongs to the current repository dagster_run.remote_job_origin.repository_origin.repository_name == context.repository_name ): if monitored_jobs: if dagster_run.job_name in map(lambda x: x.name, current_repo_jobs): job_match = True else: job_match = True if ( not job_match and # the job has a repository (not manually executed) dagster_run.remote_job_origin ): # check if the run is one of the jobs specified by JobSelector or RepositorySelector (ie in another repo) # make a JobSelector for the run in question remote_repository_origin = dagster_run.remote_job_origin.repository_origin run_job_selector = JobSelector( location_name=remote_repository_origin.code_location_origin.location_name, repository_name=remote_repository_origin.repository_name, job_name=dagster_run.job_name, ) if run_job_selector in other_repo_jobs: job_match = True # make a RepositorySelector for the run in question run_repo_selector = RepositorySelector( location_name=remote_repository_origin.code_location_origin.location_name, repository_name=remote_repository_origin.repository_name, ) if run_repo_selector in other_repos: job_match = True if not job_match: # the run in question doesn't match any of the criteria for we advance the cursor and move on context.update_cursor( RunStatusSensorCursor( record_id=storage_id, record_timestamp=record_timestamp ).to_json() ) continue # Stop processing runs once you reach a matching job but have exceeded the limit # (It's fine to keep advancing the cursor for runs that do not match) if num_processed_runs >= process_limit: break num_processed_runs = num_processed_runs + 1 serializable_error = None resource_args_populated = validate_and_get_resource_dict( context.resources, name, resource_arg_names ) try: with RunStatusSensorContext( sensor_name=name, dagster_run=dagster_run, dagster_event=event_log_entry.dagster_event, instance=context.instance, resource_defs=context.resource_defs, logger=context.log, partition_key=dagster_run.tags.get("dagster/partition"), repository_def=context.repository_def, ) as sensor_context, user_code_error_boundary( RunStatusSensorExecutionError, lambda: f'Error occurred during the execution sensor "{name}".', ): context_param_name = get_context_param_name(run_status_sensor_fn) context_param = ( {context_param_name: sensor_context} if context_param_name else {} ) sensor_return = run_status_sensor_fn( **context_param, **resource_args_populated, ) if sensor_return is not None: context.update_cursor( RunStatusSensorCursor( record_id=storage_id, record_timestamp=record_timestamp, ).to_json() ) if isinstance(sensor_return, SensorResult): if sensor_return.cursor: raise DagsterInvariantViolationError( f"Error in run status sensor {name}: Sensor returned a" " SensorResult with a cursor value. The cursor is managed" " by the sensor and should not be modified by a user." ) yield sensor_return elif isinstance( sensor_return, (RunRequest, SkipReason, DagsterRunReaction), ): yield sensor_return else: yield from sensor_return return except RunStatusSensorExecutionError as run_status_sensor_execution_error: # When the user code errors, we report error to the sensor tick not the original run. serializable_error = serializable_error_info_from_exc_info( run_status_sensor_execution_error.original_exc_info ) context.update_cursor( RunStatusSensorCursor( record_id=storage_id, record_timestamp=record_timestamp ).to_json() ) # Yield DagsterRunReaction to indicate the execution success/failure. # The sensor machinery would # * report back to the original run if success # * update cursor and job state yield DagsterRunReaction( dagster_run=dagster_run, run_status=run_status, error=serializable_error, ) super(RunStatusSensorDefinition, self).__init__( name=name, evaluation_fn=_wrapped_fn, minimum_interval_seconds=minimum_interval_seconds, description=description, default_status=default_status, job=request_job, jobs=request_jobs, required_resource_keys=combined_required_resource_keys, tags=tags, metadata=metadata, ) def __call__(self, *args, **kwargs) -> RawSensorEvaluationFunctionReturn: context_param_name = get_context_param_name(self._run_status_sensor_fn) context = get_or_create_sensor_context( self._run_status_sensor_fn, *args, context_type=RunStatusSensorContext, **kwargs, ) context_param = {context_param_name: context} if context_param_name and context else {} resources = validate_and_get_resource_dict( context.resources if context else ScopedResourcesBuilder.build_empty(), self._name, self._required_resource_keys, ) return self._run_status_sensor_fn(**context_param, **resources) @property def sensor_type(self) -> SensorType: return SensorType.RUN_STATUS
[docs] @deprecated_param( param="job_selection", breaking_version="2.0", additional_warn_text="Use `monitored_jobs` instead.", ) @deprecated_param( param="monitor_all_repositories", breaking_version="2.0", additional_warn_text="Use `monitor_all_code_locations` instead.", ) def run_status_sensor( run_status: DagsterRunStatus, name: Optional[str] = None, minimum_interval_seconds: Optional[int] = None, description: Optional[str] = None, monitored_jobs: Optional[ Sequence[ Union[ JobDefinition, GraphDefinition, UnresolvedAssetJobDefinition, "RepositorySelector", "JobSelector", "CodeLocationSelector", ] ] ] = None, job_selection: Optional[ Sequence[ Union[ JobDefinition, GraphDefinition, UnresolvedAssetJobDefinition, "RepositorySelector", "JobSelector", "CodeLocationSelector", ] ] ] = None, monitor_all_code_locations: Optional[bool] = None, default_status: DefaultSensorStatus = DefaultSensorStatus.STOPPED, request_job: Optional[ExecutableDefinition] = None, request_jobs: Optional[Sequence[ExecutableDefinition]] = None, monitor_all_repositories: Optional[bool] = None, tags: Optional[Mapping[str, str]] = None, metadata: Optional[Mapping[str, object]] = None, ) -> Callable[ [RunStatusSensorEvaluationFunction], RunStatusSensorDefinition, ]: """Creates a sensor that reacts to a given status of job execution, where the decorated function will be run when a job is at the given status. Takes a :py:class:`~dagster.RunStatusSensorContext`. Args: run_status (DagsterRunStatus): The status of run execution which will be monitored by the sensor. name (Optional[str]): The name of the sensor. Defaults to the name of the decorated function. minimum_interval_seconds (Optional[int]): The minimum number of seconds that will elapse between sensor evaluations. description (Optional[str]): A human-readable description of the sensor. monitored_jobs (Optional[List[Union[JobDefinition, GraphDefinition, UnresolvedAssetJobDefinition, RepositorySelector, JobSelector, CodeLocationSelector]]]): Jobs in the current code locations that will be monitored by this sensor. Defaults to None, which means the alert will be sent when any job in the code location matches the requested run_status. Jobs in external repositories can be monitored by using RepositorySelector or JobSelector. monitor_all_code_locations (Optional[bool]): If set to True, the sensor will monitor all runs in the Dagster deployment. If set to True, an error will be raised if you also specify monitored_jobs or job_selection. Defaults to False. job_selection (Optional[List[Union[JobDefinition, GraphDefinition, RepositorySelector, JobSelector, CodeLocationSelector]]]): (deprecated in favor of monitored_jobs) Jobs in the current code location that will be monitored by this sensor. Defaults to None, which means the alert will be sent when any job in the code location matches the requested run_status. 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. request_job (Optional[Union[GraphDefinition, JobDefinition, UnresolvedAssetJobDefinition]]): The job that should be executed if a RunRequest is yielded from the sensor. request_jobs (Optional[Sequence[Union[GraphDefinition, JobDefinition, UnresolvedAssetJobDefinition]]]): (experimental) A list of jobs to be executed if RunRequests are yielded from the sensor. monitor_all_repositories (Optional[bool]): (deprecated in favor of monitor_all_code_locations) If set to True, the sensor will monitor all runs in the Dagster instance. If set to True, an error will be raised if you also specify monitored_jobs or job_selection. Defaults to False. 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. metadata (Optional[Mapping[str, object]]): A set of metadata entries that annotate the sensor. Values will be normalized to typed `MetadataValue` objects. """ def inner( fn: RunStatusSensorEvaluationFunction, ) -> RunStatusSensorDefinition: check.callable_param(fn, "fn") sensor_name = name or fn.__name__ jobs = monitored_jobs if monitored_jobs else job_selection monitor_all = normalize_renamed_param( monitor_all_code_locations, "monitor_all_code_locations", monitor_all_repositories, "monitor_all_repositories", ) if jobs and monitor_all: DagsterInvalidDefinitionError( f"Cannot specify both {'monitor_all_code_locations' if monitor_all_code_locations else 'monitor_all_repositories'} and" f" {'monitored_jobs' if monitored_jobs else 'job_selection'}." ) return RunStatusSensorDefinition( name=sensor_name, run_status=run_status, run_status_sensor_fn=fn, minimum_interval_seconds=minimum_interval_seconds, description=description, monitored_jobs=jobs, monitor_all_code_locations=monitor_all, default_status=default_status, request_job=request_job, request_jobs=request_jobs, tags=tags, metadata=metadata, ) return inner