import warnings
from typing import (
TYPE_CHECKING,
AbstractSet,
Any,
Callable,
List,
NamedTuple,
Optional,
Type,
TypeVar,
Union,
cast,
)
from dagster import check
from dagster.core.definitions.events import AssetKey, DynamicAssetKey
from dagster.core.definitions.metadata import MetadataEntry, MetadataUserInput, normalize_metadata
from dagster.core.errors import DagsterError, DagsterInvalidDefinitionError
from dagster.core.types.dagster_type import DagsterType, resolve_dagster_type
from dagster.utils.backcompat import experimental_arg_warning
from .inference import InferredOutputProps
from .input import NoValueSentinel
from .utils import DEFAULT_OUTPUT, check_valid_name
if TYPE_CHECKING:
from dagster.core.definitions.partition import PartitionsDefinition
from dagster.core.execution.context.output import OutputContext
TOut = TypeVar("TOut", bound="OutputDefinition")
[docs]class OutputDefinition:
"""Defines an output from a solid's compute function.
Solids can have multiple outputs, in which case outputs cannot be anonymous.
Many solids have only one output, in which case the user can provide a single output definition
that will be given the default name, "result".
Output definitions may be typed using the Dagster type system.
Args:
dagster_type (Optional[Union[Type, DagsterType]]]): The type of this output.
Users should provide the Python type of the objects that they expect the solid to yield
for this output, or a :py:class:`DagsterType` that defines a runtime check that they
want to be run on this output. Defaults to :py:class:`Any`.
name (Optional[str]): Name of the output. (default: "result")
description (Optional[str]): Human-readable description of the output.
is_required (Optional[bool]): Whether the presence of this field is required. (default: True)
io_manager_key (Optional[str]): The resource key of the IOManager used for storing this
output and loading it in downstream steps (default: "io_manager").
metadata (Optional[Dict[str, Any]]): A dict of the metadata for the output.
For example, users can provide a file path if the data object will be stored in a
filesystem, or provide information of a database table when it is going to load the data
into the table.
asset_key (Optional[AssetKey]]): (Experimental) An AssetKey which should be associated
with this OutputDefinition. Used for tracking lineage information through Dagster.
asset_partitions (Optional[Union[Set[str], OutputContext -> Set[str]]]): (Experimental) A
set of partitions of the given asset_key (or a function that produces this list of
partitions from the OutputContext) which should be associated with this OutputDefinition.
"""
def __init__(
self,
dagster_type=None,
name: Optional[str] = None,
description: Optional[str] = None,
is_required: bool = True,
io_manager_key: Optional[str] = None,
metadata: Optional[MetadataUserInput] = None,
asset_key: Optional[Union[AssetKey, DynamicAssetKey]] = None,
asset_partitions: Optional[
Union[AbstractSet[str], Callable[["OutputContext"], AbstractSet[str]]]
] = None,
asset_partitions_def: Optional["PartitionsDefinition"] = None
# make sure new parameters are updated in combine_with_inferred below
):
from dagster.core.definitions.partition import PartitionsDefinition
self._name = check_valid_name(check.opt_str_param(name, "name", DEFAULT_OUTPUT))
self._type_not_set = dagster_type is None
self._dagster_type = resolve_dagster_type(dagster_type)
self._description = check.opt_str_param(description, "description")
self._is_required = check.bool_param(is_required, "is_required")
self._io_manager_key = check.opt_str_param(
io_manager_key,
"io_manager_key",
default="io_manager",
)
self._metadata = check.opt_dict_param(metadata, "metadata", key_type=str)
self._metadata_entries = check.is_list(
normalize_metadata(self._metadata, [], allow_invalid=True), MetadataEntry
)
if asset_key:
experimental_arg_warning("asset_key", "OutputDefinition.__init__")
if callable(asset_key):
warnings.warn(
"Passing a function as the `asset_key` argument to `Out` or `OutputDefinition` is "
"deprecated behavior and will be removed in version 0.15.0."
)
else:
check.opt_inst_param(asset_key, "asset_key", AssetKey)
self._asset_key = asset_key
if asset_partitions:
experimental_arg_warning("asset_partitions", "OutputDefinition.__init__")
check.param_invariant(
asset_key is not None,
"asset_partitions",
'Cannot specify "asset_partitions" argument without also specifying "asset_key"',
)
self._asset_partitions_fn: Optional[Callable[["OutputContext"], AbstractSet[str]]]
if callable(asset_partitions):
self._asset_partitions_fn = asset_partitions
elif asset_partitions is not None:
asset_partitions = check.opt_set_param(asset_partitions, "asset_partitions", str)
def _fn(_context: "OutputContext") -> AbstractSet[str]:
return cast(AbstractSet[str], asset_partitions) # mypy bug?
self._asset_partitions_fn = _fn
else:
self._asset_partitions_fn = None
if asset_partitions_def:
experimental_arg_warning("asset_partitions_def", "OutputDefinition.__init__")
self._asset_partitions_def = check.opt_inst_param(
asset_partitions_def, "asset_partition_def", PartitionsDefinition
)
@property
def name(self):
return self._name
@property
def dagster_type(self) -> DagsterType:
return self._dagster_type
@property
def description(self) -> Optional[str]:
return self._description
@property
def is_required(self) -> bool:
return self._is_required
@property
def io_manager_key(self) -> str:
return self._io_manager_key
@property
def optional(self) -> bool:
return not self.is_required
@property
def metadata(self) -> MetadataUserInput:
return self._metadata
@property
def metadata_entries(self) -> List[MetadataEntry]:
return self._metadata_entries
@property
def is_dynamic(self) -> bool:
return False
@property
def is_asset(self) -> bool:
return self._asset_key is not None
@property
def asset_partitions_def(self) -> Optional["PartitionsDefinition"]:
return self._asset_partitions_def
@property
def hardcoded_asset_key(self) -> Optional[AssetKey]:
if not callable(self._asset_key):
return self._asset_key
else:
return None
def get_asset_key(self, context: "OutputContext") -> Optional[AssetKey]:
"""Get the AssetKey associated with this OutputDefinition for the given
:py:class:`OutputContext` (if any).
Args:
context (OutputContext): The OutputContext that this OutputDefinition is being evaluated
in
"""
if callable(self._asset_key):
return self._asset_key(context)
else:
return self.hardcoded_asset_key
def get_asset_partitions(self, context: "OutputContext") -> Optional[AbstractSet[str]]:
"""Get the set of partitions associated with this OutputDefinition for the given
:py:class:`OutputContext` (if any).
Args:
context (OutputContext): The OutputContext that this OutputDefinition is being evaluated
in
"""
if self._asset_partitions_fn is None:
return None
return self._asset_partitions_fn(context)
def mapping_from(self, solid_name: str, output_name: Optional[str] = None) -> "OutputMapping":
"""Create an output mapping from an output of a child solid.
In a CompositeSolidDefinition, you can use this helper function to construct
an :py:class:`OutputMapping` from the output of a child solid.
Args:
solid_name (str): The name of the child solid from which to map this output.
output_name (str): The name of the child solid's output from which to map this output.
Examples:
.. code-block:: python
output_mapping = OutputDefinition(Int).mapping_from('child_solid')
"""
return OutputMapping(self, OutputPointer(solid_name, output_name))
@staticmethod
def create_from_inferred(inferred: InferredOutputProps) -> "OutputDefinition":
return OutputDefinition(
dagster_type=_checked_inferred_type(inferred.annotation),
description=inferred.description,
)
def combine_with_inferred(self: TOut, inferred: InferredOutputProps) -> TOut:
dagster_type = self.dagster_type
if self._type_not_set:
dagster_type = _checked_inferred_type(inferred.annotation)
if self.description is None:
description = inferred.description
else:
description = self.description
return self.__class__(
name=self.name,
dagster_type=dagster_type,
description=description,
is_required=self.is_required,
io_manager_key=self.io_manager_key,
metadata=self._metadata,
asset_key=self._asset_key,
asset_partitions=self._asset_partitions_fn,
asset_partitions_def=self.asset_partitions_def,
)
def _checked_inferred_type(inferred: Any) -> DagsterType:
try:
return resolve_dagster_type(inferred)
except DagsterError as e:
raise DagsterInvalidDefinitionError(
f"Problem using type '{inferred}' from return type annotation, correct the issue "
"or explicitly set the dagster_type on your OutputDefinition."
) from e
class DynamicOutputDefinition(OutputDefinition):
"""
Variant of :py:class:`OutputDefinition <dagster.OutputDefinition>` for an
output that will dynamically alter the graph at runtime.
When using in a composition function such as :py:func:`@pipeline <dagster.pipeline>`,
dynamic outputs must be used with either
* ``map`` - clone downstream solids for each separate :py:class:`DynamicOutput`
* ``collect`` - gather across all :py:class:`DynamicOutput` in to a list
Uses the same constructor as :py:class:`OutputDefinition <dagster.OutputDefinition>`
.. code-block:: python
@solid(
config_schema={
"path": Field(str, default_value=file_relative_path(__file__, "sample"))
},
output_defs=[DynamicOutputDefinition(str)],
)
def files_in_directory(context):
path = context.solid_config["path"]
dirname, _, filenames = next(os.walk(path))
for file in filenames:
yield DynamicOutput(os.path.join(dirname, file), mapping_key=_clean(file))
@pipeline
def process_directory():
files = files_in_directory()
# use map to invoke a solid on each dynamic output
file_results = files.map(process_file)
# use collect to gather the results in to a list
summarize_directory(file_results.collect())
"""
@property
def is_dynamic(self) -> bool:
return True
class OutputPointer(NamedTuple("_OutputPointer", [("solid_name", str), ("output_name", str)])):
def __new__(cls, solid_name: str, output_name: Optional[str] = None):
return super(OutputPointer, cls).__new__(
cls,
check.str_param(solid_name, "solid_name"),
check.opt_str_param(output_name, "output_name", DEFAULT_OUTPUT),
)
[docs]class OutputMapping(
NamedTuple("_OutputMapping", [("definition", OutputDefinition), ("maps_from", OutputPointer)])
):
"""Defines an output mapping for a composite solid.
Args:
definition (OutputDefinition): Defines the output of the composite solid.
solid_name (str): The name of the child solid from which to map the output.
output_name (str): The name of the child solid's output from which to map the output.
"""
def __new__(cls, definition: OutputDefinition, maps_from: OutputPointer):
return super(OutputMapping, cls).__new__(
cls,
check.inst_param(definition, "definition", OutputDefinition),
check.inst_param(maps_from, "maps_from", OutputPointer),
)
[docs]class Out(
NamedTuple(
"_Out",
[
("dagster_type", Union[DagsterType, Type[NoValueSentinel]]),
("description", Optional[str]),
("is_required", bool),
("io_manager_key", str),
("metadata", Optional[MetadataUserInput]),
("asset_key", Optional[Union[AssetKey, DynamicAssetKey]]),
(
"asset_partitions",
Optional[Union[AbstractSet[str], Callable[["OutputContext"], AbstractSet[str]]]],
),
("asset_partitions_def", Optional["PartitionsDefinition"]),
],
)
):
"""
Defines an output from an op's compute function.
Ops can have multiple outputs, in which case outputs cannot be anonymous.
Many ops have only one output, in which case the user can provide a single output definition
that will be given the default name, "result".
Outs may be typed using the Dagster type system.
Args:
dagster_type (Optional[Union[Type, DagsterType]]]):
The type of this output. Should only be set if the correct type can not
be inferred directly from the type signature of the decorated function.
description (Optional[str]): Human-readable description of the output.
is_required (bool): Whether the presence of this field is required. (default: True)
io_manager_key (Optional[str]): The resource key of the output manager used for this output.
(default: "io_manager").
metadata (Optional[Dict[str, Any]]): A dict of the metadata for the output.
For example, users can provide a file path if the data object will be stored in a
filesystem, or provide information of a database table when it is going to load the data
into the table.
asset_key (Optional[AssetKey]): (Experimental) An AssetKey which should be associated
with this Out. Used for tracking lineage information through Dagster.
asset_partitions (Optional[Union[Set[str], OutputContext -> Set[str]]]): (Experimental) A
set of partitions of the given asset_key (or a function that produces this list of
partitions from the OutputContext) which should be associated with this Out.
"""
def __new__(
cls,
dagster_type: Union[Type, DagsterType, Type[NoValueSentinel]] = NoValueSentinel,
description: Optional[str] = None,
is_required: bool = True,
io_manager_key: Optional[str] = None,
metadata: Optional[MetadataUserInput] = None,
asset_key: Optional[AssetKey] = None,
asset_partitions: Optional[
Union[AbstractSet[str], Callable[["OutputContext"], AbstractSet[str]]]
] = None,
asset_partitions_def: Optional["PartitionsDefinition"] = None,
# make sure new parameters are updated in combine_with_inferred below
):
if asset_partitions_def:
experimental_arg_warning("assets_definition", "Out.__new__")
return super(Out, cls).__new__(
cls,
dagster_type=NoValueSentinel
if dagster_type is NoValueSentinel
else resolve_dagster_type(dagster_type),
description=description,
is_required=check.bool_param(is_required, "is_required"),
io_manager_key=check.opt_str_param(
io_manager_key, "io_manager_key", default="io_manager"
),
metadata=metadata,
asset_key=asset_key,
asset_partitions=asset_partitions,
asset_partitions_def=asset_partitions_def,
)
@staticmethod
def from_definition(output_def: "OutputDefinition"):
return Out(
dagster_type=output_def.dagster_type,
description=output_def.description,
is_required=output_def.is_required,
io_manager_key=output_def.io_manager_key,
metadata=output_def.metadata,
asset_key=output_def._asset_key, # type: ignore # pylint: disable=protected-access
asset_partitions=output_def._asset_partitions_fn, # pylint: disable=protected-access
asset_partitions_def=output_def.asset_partitions_def, # pylint: disable=protected-access
)
def to_definition(self, annotation_type: type, name: Optional[str]) -> "OutputDefinition":
dagster_type = (
self.dagster_type if self.dagster_type is not NoValueSentinel else annotation_type
)
return OutputDefinition(
dagster_type=dagster_type,
name=name,
description=self.description,
is_required=self.is_required,
io_manager_key=self.io_manager_key,
metadata=self.metadata,
asset_key=self.asset_key,
asset_partitions=self.asset_partitions,
asset_partitions_def=self.asset_partitions_def,
)
[docs]class DynamicOut(Out):
"""
Variant of :py:class:`Out <dagster.Out>` for an output that will dynamically alter the graph at
runtime.
When using in a composition function such as :py:func:`@graph <dagster.graph>`,
dynamic outputs must be used with either
* ``map`` - clone downstream ops for each separate :py:class:`DynamicOut`
* ``collect`` - gather across all :py:class:`DynamicOut` in to a list
Uses the same constructor as :py:class:`Out <dagster.Out>`
.. code-block:: python
@op(
config_schema={
"path": Field(str, default_value=file_relative_path(__file__, "sample"))
},
out=DynamicOut(str),
)
def files_in_directory(context):
path = context.op_config["path"]
dirname, _, filenames = next(os.walk(path))
for file in filenames:
yield DynamicOutput(os.path.join(dirname, file), mapping_key=_clean(file))
@job
def process_directory():
files = files_in_directory()
# use map to invoke an op on each dynamic output
file_results = files.map(process_file)
# use collect to gather the results in to a list
summarize_directory(file_results.collect())
"""
def to_definition(self, annotation_type: type, name: Optional[str]) -> "OutputDefinition":
dagster_type = (
self.dagster_type if self.dagster_type is not NoValueSentinel else annotation_type
)
return DynamicOutputDefinition(
dagster_type=dagster_type,
name=name,
description=self.description,
is_required=self.is_required,
io_manager_key=self.io_manager_key,
metadata=self.metadata,
asset_key=self.asset_key,
asset_partitions=self.asset_partitions,
)
[docs]class GraphOut(NamedTuple("_GraphOut", [("description", Optional[str])])):
"""
Represents information about the outputs that a graph maps.
Args:
description (Optional[str]): Human-readable description of the output.
"""
def __new__(cls, description: Optional[str] = None):
return super(GraphOut, cls).__new__(cls, description=description)
def to_definition(self, name: Optional[str]) -> "OutputDefinition":
return OutputDefinition(name=name, description=self.description)
