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Source code for dagster._core.execution.context.output

import warnings
from typing import (
    TYPE_CHECKING,
    Any,
    ContextManager,
    Iterator,
    List,
    Mapping,
    Optional,
    Sequence,
    Union,
    cast,
)

import dagster._check as check
from dagster._annotations import public
from dagster._core.definitions.asset_layer import AssetOutputInfo
from dagster._core.definitions.events import (
    AssetKey,
    AssetMaterialization,
    AssetObservation,
    CoercibleToAssetKey,
)
from dagster._core.definitions.metadata import (
    ArbitraryMetadataMapping,
    MetadataValue,
    RawMetadataValue,
)
from dagster._core.definitions.partition_key_range import PartitionKeyRange
from dagster._core.definitions.time_window_partitions import TimeWindow
from dagster._core.errors import DagsterInvalidMetadata, DagsterInvariantViolationError
from dagster._core.execution.plan.utils import build_resources_for_manager

if TYPE_CHECKING:
    from dagster._core.definitions import JobDefinition, PartitionsDefinition
    from dagster._core.definitions.op_definition import OpDefinition
    from dagster._core.definitions.resource_definition import Resources
    from dagster._core.events import DagsterEvent
    from dagster._core.execution.context.system import StepExecutionContext
    from dagster._core.execution.plan.outputs import StepOutputHandle
    from dagster._core.execution.plan.plan import ExecutionPlan
    from dagster._core.log_manager import DagsterLogManager
    from dagster._core.system_config.objects import ResolvedRunConfig
    from dagster._core.types.dagster_type import DagsterType

RUN_ID_PLACEHOLDER = "__EPHEMERAL_RUN_ID"


[docs]class OutputContext: """The context object that is available to the `handle_output` method of an :py:class:`IOManager`. Users should not instantiate this object directly. To construct an `OutputContext` for testing an IO Manager's `handle_output` method, use :py:func:`dagster.build_output_context`. Example: .. code-block:: python from dagster import IOManager, OutputContext class MyIOManager(IOManager): def handle_output(self, context: OutputContext, obj): ... """ _step_key: Optional[str] _name: Optional[str] _job_name: Optional[str] _run_id: Optional[str] _metadata: ArbitraryMetadataMapping _user_generated_metadata: Mapping[str, MetadataValue] _mapping_key: Optional[str] _config: object _op_def: Optional["OpDefinition"] _dagster_type: Optional["DagsterType"] _log: Optional["DagsterLogManager"] _version: Optional[str] _resource_config: Optional[Mapping[str, object]] _step_context: Optional["StepExecutionContext"] _asset_info: Optional[AssetOutputInfo] _warn_on_step_context_use: bool _resources: Optional["Resources"] _resources_cm: Optional[ContextManager["Resources"]] _resources_contain_cm: Optional[bool] _cm_scope_entered: Optional[bool] _events: List["DagsterEvent"] _user_events: List[Union[AssetMaterialization, AssetObservation]] def __init__( self, step_key: Optional[str] = None, name: Optional[str] = None, job_name: Optional[str] = None, run_id: Optional[str] = None, metadata: Optional[ArbitraryMetadataMapping] = None, mapping_key: Optional[str] = None, config: object = None, dagster_type: Optional["DagsterType"] = None, log_manager: Optional["DagsterLogManager"] = None, version: Optional[str] = None, resource_config: Optional[Mapping[str, object]] = None, resources: Optional[Union["Resources", Mapping[str, object]]] = None, step_context: Optional["StepExecutionContext"] = None, op_def: Optional["OpDefinition"] = None, asset_info: Optional[AssetOutputInfo] = None, warn_on_step_context_use: bool = False, partition_key: Optional[str] = None, ): from dagster._core.definitions.resource_definition import IContainsGenerator, Resources from dagster._core.execution.build_resources import build_resources self._step_key = step_key self._name = name self._job_name = job_name self._run_id = run_id self._metadata = metadata or {} self._mapping_key = mapping_key self._config = config self._op_def = op_def self._dagster_type = dagster_type self._log = log_manager self._version = version self._resource_config = resource_config self._step_context = step_context self._asset_info = asset_info self._warn_on_step_context_use = warn_on_step_context_use if self._step_context and self._step_context.has_partition_key: self._partition_key: Optional[str] = self._step_context.partition_key else: self._partition_key = partition_key if isinstance(resources, Resources): self._resources_cm = None self._resources = resources else: self._resources_cm = build_resources( check.opt_mapping_param(resources, "resources", key_type=str) ) self._resources = self._resources_cm.__enter__() self._resources_contain_cm = isinstance(self._resources, IContainsGenerator) self._cm_scope_entered = False self._events = [] self._user_events = [] self._user_generated_metadata = {} def __enter__(self): if self._resources_cm: self._cm_scope_entered = True return self def __exit__(self, *exc): if self._resources_cm: self._resources_cm.__exit__(*exc) def __del__(self): if ( hasattr(self, "_resources_cm") and self._resources_cm and self._resources_contain_cm and not self._cm_scope_entered ): self._resources_cm.__exit__(None, None, None) @public @property def step_key(self) -> str: """The step_key for the compute step that produced the output.""" if self._step_key is None: raise DagsterInvariantViolationError( "Attempting to access step_key, " "but it was not provided when constructing the OutputContext" ) return self._step_key @public @property def name(self) -> str: """The name of the output that produced the output.""" if self._name is None: raise DagsterInvariantViolationError( "Attempting to access name, " "but it was not provided when constructing the OutputContext" ) return self._name @property def job_name(self) -> str: if self._job_name is None: raise DagsterInvariantViolationError( "Attempting to access pipeline_name, " "but it was not provided when constructing the OutputContext" ) return self._job_name @public @property def run_id(self) -> str: """The id of the run that produced the output.""" if self._run_id is None: raise DagsterInvariantViolationError( "Attempting to access run_id, " "but it was not provided when constructing the OutputContext" ) return self._run_id @public @property def metadata(self) -> Optional[ArbitraryMetadataMapping]: """A dict of the metadata that is assigned to the OutputDefinition that produced the output. """ return self._metadata @public @property def mapping_key(self) -> Optional[str]: """The key that identifies a unique mapped output. None for regular outputs.""" return self._mapping_key @public @property def config(self) -> Any: """The configuration for the output.""" return self._config @public @property def op_def(self) -> "OpDefinition": """The definition of the op that produced the output.""" from dagster._core.definitions import OpDefinition if self._op_def is None: raise DagsterInvariantViolationError( "Attempting to access op_def, " "but it was not provided when constructing the OutputContext" ) return cast(OpDefinition, self._op_def) @public @property def dagster_type(self) -> "DagsterType": """The type of this output.""" if self._dagster_type is None: raise DagsterInvariantViolationError( "Attempting to access dagster_type, " "but it was not provided when constructing the OutputContext" ) return self._dagster_type @public @property def log(self) -> "DagsterLogManager": """The log manager to use for this output.""" if self._log is None: raise DagsterInvariantViolationError( "Attempting to access log, " "but it was not provided when constructing the OutputContext" ) return self._log @public @property def version(self) -> Optional[str]: """(Experimental) The version of the output.""" return self._version @public @property def resource_config(self) -> Optional[Mapping[str, object]]: """The config associated with the resource that initializes the InputManager.""" return self._resource_config @public @property def resources(self) -> Any: """The resources required by the output manager, specified by the `required_resource_keys` parameter. """ if self._resources is None: raise DagsterInvariantViolationError( "Attempting to access resources, " "but it was not provided when constructing the OutputContext" ) if self._resources_cm and self._resources_contain_cm and not self._cm_scope_entered: 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_output_context(...) as context:`" ) return self._resources @property def asset_info(self) -> Optional[AssetOutputInfo]: """(Experimental) Asset info corresponding to the output.""" return self._asset_info @public @property def has_asset_key(self) -> bool: """Returns True if an asset is being stored, otherwise returns False. A return value of False indicates that an output from an op is being stored. """ return self._asset_info is not None @public @property def asset_key(self) -> AssetKey: """The ``AssetKey`` of the asset that is being stored as an output.""" if self._asset_info is None: raise DagsterInvariantViolationError( "Attempting to access asset_key, " "but it was not provided when constructing the OutputContext" ) return self._asset_info.key @public @property def asset_partitions_def(self) -> "PartitionsDefinition": """The PartitionsDefinition on the asset corresponding to this output.""" asset_key = self.asset_key result = self.step_context.job_def.asset_layer.partitions_def_for_asset(asset_key) if result is None: raise DagsterInvariantViolationError( f"Attempting to access partitions def for asset {asset_key}, but it is not" " partitioned" ) return result @property def step_context(self) -> "StepExecutionContext": if self._warn_on_step_context_use: warnings.warn( "You are using InputContext.upstream_output.step_context" "This use on upstream_output is deprecated and will fail in the future" "Try to obtain what you need directly from InputContext" "For more details: https://github.com/dagster-io/dagster/issues/7900" ) if self._step_context is None: raise DagsterInvariantViolationError( "Attempting to access step_context, " "but it was not provided when constructing the OutputContext" ) return self._step_context @public @property def has_partition_key(self) -> bool: """Whether the current run is a partitioned run.""" if self._warn_on_step_context_use: warnings.warn( "You are using InputContext.upstream_output.has_partition_key" "This use on upstream_output is deprecated and will fail in the future" "Try to obtain what you need directly from InputContext" "For more details: https://github.com/dagster-io/dagster/issues/7900" ) return self._partition_key is not None @public @property def partition_key(self) -> str: """The partition key for the current run. Raises an error if the current run is not a partitioned run. """ if self._warn_on_step_context_use: warnings.warn( "You are using InputContext.upstream_output.partition_key" "This use on upstream_output is deprecated and will fail in the future" "Try to obtain what you need directly from InputContext" "For more details: https://github.com/dagster-io/dagster/issues/7900" ) if self._partition_key is None: check.failed( "Tried to access partition_key on a non-partitioned run.", ) return self._partition_key @public @property def has_asset_partitions(self) -> bool: """Returns True if the asset being stored is partitioned.""" if self._warn_on_step_context_use: warnings.warn( "You are using InputContext.upstream_output.has_asset_partitions" "This use on upstream_output is deprecated and will fail in the future" "Try to obtain what you need directly from InputContext" "For more details: https://github.com/dagster-io/dagster/issues/7900" ) if self._step_context is not None: return self._step_context.has_asset_partitions_for_output(self.name) else: return False @public @property def asset_partition_key(self) -> str: """The partition key for output asset. Raises an error if the output asset has no partitioning, or if the run covers a partition range for the output asset. """ if self._warn_on_step_context_use: warnings.warn( "You are using InputContext.upstream_output.asset_partition_key" "This use on upstream_output is deprecated and will fail in the future" "Try to obtain what you need directly from InputContext" "For more details: https://github.com/dagster-io/dagster/issues/7900" ) return self.step_context.asset_partition_key_for_output(self.name) @public @property def asset_partition_key_range(self) -> PartitionKeyRange: """The partition key range for output asset. Raises an error if the output asset has no partitioning. """ if self._warn_on_step_context_use: warnings.warn( "You are using InputContext.upstream_output.asset_partition_key_range" "This use on upstream_output is deprecated and will fail in the future" "Try to obtain what you need directly from InputContext" "For more details: https://github.com/dagster-io/dagster/issues/7900" ) return self.step_context.asset_partition_key_range_for_output(self.name) @public @property def asset_partition_keys(self) -> Sequence[str]: """The partition keys for the output asset. Raises an error if the output asset has no partitioning. """ if self._warn_on_step_context_use: warnings.warn( "You are using InputContext.upstream_output.asset_partition_keys" "This use on upstream_output is deprecated and will fail in the future" "Try to obtain what you need directly from InputContext" "For more details: https://github.com/dagster-io/dagster/issues/7900" ) return self.asset_partitions_def.get_partition_keys_in_range( self.step_context.asset_partition_key_range_for_output(self.name), dynamic_partitions_store=self.step_context.instance, ) @public @property def asset_partitions_time_window(self) -> TimeWindow: """The time window for the partitions of the output asset. Raises an error if either of the following are true: - The output asset has no partitioning. - The output asset is not partitioned with a TimeWindowPartitionsDefinition or a MultiPartitionsDefinition with one time-partitioned dimension. """ if self._warn_on_step_context_use: warnings.warn( "You are using InputContext.upstream_output.asset_partitions_time_window" "This use on upstream_output is deprecated and will fail in the future" "Try to obtain what you need directly from InputContext" "For more details: https://github.com/dagster-io/dagster/issues/7900" ) return self.step_context.asset_partitions_time_window_for_output(self.name) def get_run_scoped_output_identifier(self) -> Sequence[str]: """Utility method to get a collection of identifiers that as a whole represent a unique step output. The unique identifier collection consists of - ``run_id``: the id of the run which generates the output. Note: This method also handles the re-execution memoization logic. If the step that generates the output is skipped in the re-execution, the ``run_id`` will be the id of its parent run. - ``step_key``: the key for a compute step. - ``name``: the name of the output. (default: 'result'). Returns: Sequence[str, ...]: A list of identifiers, i.e. run id, step key, and output name """ warnings.warn( "`OutputContext.get_run_scoped_output_identifier` is deprecated. Use " "`OutputContext.get_identifier` instead." ) # if run_id is None and this is a re-execution, it means we failed to find its source run id check.invariant( self.run_id is not None, "Unable to find the run scoped output identifier: run_id is None on OutputContext.", ) check.invariant( self.step_key is not None, "Unable to find the run scoped output identifier: step_key is None on OutputContext.", ) check.invariant( self.name is not None, "Unable to find the run scoped output identifier: name is None on OutputContext.", ) run_id = cast(str, self.run_id) step_key = cast(str, self.step_key) name = cast(str, self.name) if self.mapping_key: return [run_id, step_key, name, self.mapping_key] return [run_id, step_key, name]
[docs] @public def get_identifier(self) -> Sequence[str]: """Utility method to get a collection of identifiers that as a whole represent a unique step output. If not using memoization, the unique identifier collection consists of - ``run_id``: the id of the run which generates the output. Note: This method also handles the re-execution memoization logic. If the step that generates the output is skipped in the re-execution, the ``run_id`` will be the id of its parent run. - ``step_key``: the key for a compute step. - ``name``: the name of the output. (default: 'result'). If using memoization, the ``version`` corresponding to the step output is used in place of the ``run_id``. Returns: Sequence[str, ...]: A list of identifiers, i.e. (run_id or version), step_key, and output_name """ version = self.version step_key = self.step_key name = self.name if version is not None: check.invariant( self.mapping_key is None, f"Mapping key and version both provided for output '{name}' of step" f" '{step_key}'. Dynamic mapping is not supported when using versioning.", ) identifier = ["versioned_outputs", version, step_key, name] else: run_id = self.run_id identifier = [run_id, step_key, name] if self.mapping_key: identifier.append(self.mapping_key) return identifier
def get_output_identifier(self) -> Sequence[str]: warnings.warn( "`OutputContext.get_output_identifier` is deprecated. Use " "`OutputContext.get_identifier` instead." ) return self.get_identifier()
[docs] @public def get_asset_identifier(self) -> Sequence[str]: """The sequence of strings making up the AssetKey for the asset being stored as an output. If the asset is partitioned, the identifier contains the partition key as the final element in the sequence. For example, for the asset key ``AssetKey(["foo", "bar", "baz"])`` materialized with partition key "2023-06-01", ``get_asset_identifier`` will return ``["foo", "bar", "baz", "2023-06-01"]``. """ if self.asset_key is not None: if self.has_asset_partitions: return [*self.asset_key.path, self.asset_partition_key] else: return self.asset_key.path else: check.failed("Can't get asset output identifier for an output with no asset key")
def get_asset_output_identifier(self) -> Sequence[str]: warnings.warn( "`OutputContext.get_asset_output_identifier` is deprecated. Use " "`OutputContext.get_asset_identifier` instead." ) return self.get_asset_identifier()
[docs] @public def log_event(self, event: Union[AssetObservation, AssetMaterialization]) -> None: """Log an AssetMaterialization or AssetObservation from within the body of an io manager's `handle_output` method. Events logged with this method will appear in the event log. Args: event (Union[AssetMaterialization, AssetObservation]): The event to log. Examples: .. code-block:: python from dagster import IOManager, AssetMaterialization class MyIOManager(IOManager): def handle_output(self, context, obj): context.log_event(AssetMaterialization("foo")) """ from dagster._core.events import DagsterEvent if isinstance(event, (AssetMaterialization)): if self._step_context: self._events.append(DagsterEvent.asset_materialization(self._step_context, event)) self._user_events.append(event) elif isinstance(event, AssetObservation): if self._step_context: self._events.append(DagsterEvent.asset_observation(self._step_context, event)) self._user_events.append(event) else: check.failed(f"Unexpected event {event}")
def consume_events(self) -> Iterator["DagsterEvent"]: """Pops and yields all user-generated events that have been recorded from this context. If consume_events has not yet been called, this will yield all logged events since the call to `handle_output`. If consume_events has been called, it will yield all events since the last time consume_events was called. Designed for internal use. Users should never need to invoke this method. """ events = self._events self._events = [] yield from events def get_logged_events( self, ) -> Sequence[Union[AssetMaterialization, AssetObservation]]: """Retrieve the list of user-generated events that were logged via the context. User-generated events that were yielded will not appear in this list. **Examples:** .. code-block:: python from dagster import IOManager, build_output_context, AssetMaterialization class MyIOManager(IOManager): def handle_output(self, context, obj): ... def test_handle_output(): mgr = MyIOManager() context = build_output_context() mgr.handle_output(context) all_user_events = context.get_logged_events() materializations = [event for event in all_user_events if isinstance(event, AssetMaterialization)] ... """ return self._user_events
[docs] @public def add_output_metadata(self, metadata: Mapping[str, RawMetadataValue]) -> None: """Add a dictionary of metadata to the handled output. Metadata entries added will show up in the HANDLED_OUTPUT and ASSET_MATERIALIZATION events for the run. Args: metadata (Mapping[str, RawMetadataValue]): A metadata dictionary to log Examples: .. code-block:: python from dagster import IOManager class MyIOManager(IOManager): def handle_output(self, context, obj): context.add_output_metadata({"foo": "bar"}) """ from dagster._core.definitions.metadata import normalize_metadata overlapping_labels = set(self._user_generated_metadata.keys()) & metadata.keys() if overlapping_labels: raise DagsterInvalidMetadata( f"Tried to add metadata for key(s) that already have metadata: {overlapping_labels}" ) self._user_generated_metadata = { **self._user_generated_metadata, **normalize_metadata(metadata), }
def get_logged_metadata( self, ) -> Mapping[str, MetadataValue]: """Get the mapping of metadata entries that have been logged for use with this output.""" return self._user_generated_metadata def consume_logged_metadata( self, ) -> Mapping[str, MetadataValue]: """Pops and yields all user-generated metadata entries that have been recorded from this context. If consume_logged_metadata has not yet been called, this will yield all logged events since the call to `handle_output`. If consume_logged_metadata has been called, it will yield all events since the last time consume_logged_metadata_entries was called. Designed for internal use. Users should never need to invoke this method. """ result = self._user_generated_metadata self._user_generated_metadata = {} return result or {}
def get_output_context( execution_plan: "ExecutionPlan", job_def: "JobDefinition", resolved_run_config: "ResolvedRunConfig", step_output_handle: "StepOutputHandle", run_id: Optional[str], log_manager: Optional["DagsterLogManager"], step_context: Optional["StepExecutionContext"], resources: Optional["Resources"], version: Optional[str], warn_on_step_context_use: bool = False, ) -> "OutputContext": """Args: run_id (str): The run ID of the run that produced the output, not necessarily the run that the context will be used in. """ step = execution_plan.get_step_by_key(step_output_handle.step_key) # get config op_config = resolved_run_config.ops[step.node_handle.to_string()] outputs_config = op_config.outputs if outputs_config: output_config = outputs_config.get_output_manager_config(step_output_handle.output_name) else: output_config = None step_output = execution_plan.get_step_output(step_output_handle) output_def = job_def.get_node(step_output.node_handle).output_def_named(step_output.name) io_manager_key = output_def.io_manager_key resource_config = resolved_run_config.resources[io_manager_key].config node_handle = execution_plan.get_step_by_key(step.key).node_handle asset_info = job_def.asset_layer.asset_info_for_output( node_handle=node_handle, output_name=step_output.name ) if asset_info is not None: metadata = job_def.asset_layer.metadata_for_asset(asset_info.key) or output_def.metadata else: metadata = output_def.metadata if step_context: check.invariant( not resources, "Expected either resources or step context to be set, but " "received both. If step context is provided, resources for IO manager will be " "retrieved off of that.", ) resources = build_resources_for_manager(io_manager_key, step_context) return OutputContext( step_key=step_output_handle.step_key, name=step_output_handle.output_name, job_name=job_def.name, run_id=run_id, metadata=metadata, mapping_key=step_output_handle.mapping_key, config=output_config, op_def=job_def.get_node(step.node_handle).definition, # type: ignore # (should be OpDefinition not NodeDefinition) dagster_type=output_def.dagster_type, log_manager=log_manager, version=version, step_context=step_context, resource_config=resource_config, resources=resources, asset_info=asset_info, warn_on_step_context_use=warn_on_step_context_use, ) def step_output_version( job_def: "JobDefinition", execution_plan: "ExecutionPlan", resolved_run_config: "ResolvedRunConfig", step_output_handle: "StepOutputHandle", ) -> Optional[str]: from dagster._core.execution.resolve_versions import resolve_step_output_versions step_output_versions = resolve_step_output_versions( job_def, execution_plan, resolved_run_config ) return ( step_output_versions[step_output_handle] if step_output_handle in step_output_versions else None )
[docs]def build_output_context( step_key: Optional[str] = None, name: Optional[str] = None, metadata: Optional[Mapping[str, RawMetadataValue]] = None, run_id: Optional[str] = None, mapping_key: Optional[str] = None, config: Optional[Any] = None, dagster_type: Optional["DagsterType"] = None, version: Optional[str] = None, resource_config: Optional[Mapping[str, object]] = None, resources: Optional[Mapping[str, object]] = None, op_def: Optional["OpDefinition"] = None, asset_key: Optional[CoercibleToAssetKey] = None, partition_key: Optional[str] = None, ) -> "OutputContext": """Builds output context from provided parameters. ``build_output_context`` can be used as either a function, or a context manager. If resources that are also context managers are provided, then ``build_output_context`` must be used as a context manager. Args: step_key (Optional[str]): The step_key for the compute step that produced the output. name (Optional[str]): The name of the output that produced the output. metadata (Optional[Mapping[str, Any]]): A dict of the metadata that is assigned to the OutputDefinition that produced the output. mapping_key (Optional[str]): The key that identifies a unique mapped output. None for regular outputs. config (Optional[Any]): The configuration for the output. dagster_type (Optional[DagsterType]): The type of this output. version (Optional[str]): (Experimental) The version of the output. resource_config (Optional[Mapping[str, Any]]): The resource config to make available from the input context. This usually corresponds to the config provided to the resource that loads the output manager. resources (Optional[Resources]): The resources to make available from the context. For a given key, you can provide either an actual instance of an object, or a resource definition. op_def (Optional[OpDefinition]): The definition of the op that produced the output. asset_key: Optional[Union[AssetKey, Sequence[str], str]]: The asset key corresponding to the output. partition_key: Optional[str]: String value representing partition key to execute with. Examples: .. code-block:: python build_output_context() with build_output_context(resources={"foo": context_manager_resource}) as context: do_something """ from dagster._core.definitions import OpDefinition from dagster._core.execution.context_creation_job import initialize_console_manager from dagster._core.types.dagster_type import DagsterType step_key = check.opt_str_param(step_key, "step_key") name = check.opt_str_param(name, "name") metadata = check.opt_mapping_param(metadata, "metadata", key_type=str) run_id = check.opt_str_param(run_id, "run_id", default=RUN_ID_PLACEHOLDER) mapping_key = check.opt_str_param(mapping_key, "mapping_key") dagster_type = check.opt_inst_param(dagster_type, "dagster_type", DagsterType) version = check.opt_str_param(version, "version") resource_config = check.opt_mapping_param(resource_config, "resource_config", key_type=str) resources = check.opt_mapping_param(resources, "resources", key_type=str) op_def = check.opt_inst_param(op_def, "op_def", OpDefinition) asset_key = AssetKey.from_coercible(asset_key) if asset_key else None partition_key = check.opt_str_param(partition_key, "partition_key") return OutputContext( step_key=step_key, name=name, job_name=None, run_id=run_id, metadata=metadata, mapping_key=mapping_key, config=config, dagster_type=dagster_type, log_manager=initialize_console_manager(None), version=version, resource_config=resource_config, resources=resources, step_context=None, op_def=op_def, asset_info=AssetOutputInfo(key=asset_key) if asset_key else None, partition_key=partition_key, )