"""A ``Pipeline`` is a collection of ``Node`` objects which can be executed as
a Directed Acyclic Graph, sequentially or in parallel. The ``Pipeline`` class
offers quick access to input dependencies,
produced outputs and execution order.
"""
from __future__ import annotations
import json
from collections import Counter, defaultdict
from graphlib import CycleError, TopologicalSorter
from itertools import chain
from typing import TYPE_CHECKING, Any
import kedro
from kedro.pipeline.node import Node, _to_list
from .transcoding import _strip_transcoding
if TYPE_CHECKING:
from collections.abc import Iterable
def __getattr__(name: str) -> Any:
if name == "TRANSCODING_SEPARATOR":
import warnings
from kedro.pipeline.transcoding import TRANSCODING_SEPARATOR
warnings.warn(
f"{name!r} has been moved to 'kedro.pipeline.transcoding', "
f"and the alias will be removed in Kedro 0.20.0",
kedro.KedroDeprecationWarning,
stacklevel=2,
)
return TRANSCODING_SEPARATOR
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
class OutputNotUniqueError(Exception):
"""Raised when two or more nodes that are part of the same pipeline
produce outputs with the same name.
"""
pass
class ConfirmNotUniqueError(Exception):
"""Raised when two or more nodes that are part of the same pipeline
attempt to confirm the same dataset.
"""
pass
[docs]
class Pipeline:
"""A ``Pipeline`` defined as a collection of ``Node`` objects. This class
treats nodes as part of a graph representation and provides inputs,
outputs and execution order.
"""
[docs]
def __init__(
self,
nodes: Iterable[Node | Pipeline],
*,
tags: str | Iterable[str] | None = None,
):
"""Initialise ``Pipeline`` with a list of ``Node`` instances.
Args:
nodes: The iterable of nodes the ``Pipeline`` will be made of. If you
provide pipelines among the list of nodes, those pipelines will
be expanded and all their nodes will become part of this
new pipeline.
tags: Optional set of tags to be applied to all the pipeline nodes.
Raises:
ValueError:
When an empty list of nodes is provided, or when not all
nodes have unique names.
CircularDependencyError:
When visiting all the nodes is not
possible due to the existence of a circular dependency.
OutputNotUniqueError:
When multiple ``Node`` instances produce the same output.
ConfirmNotUniqueError:
When multiple ``Node`` instances attempt to confirm the same
dataset.
Example:
::
>>> from kedro.pipeline import Pipeline
>>> from kedro.pipeline import node
>>>
>>> # In the following scenario first_ds and second_ds
>>> # are datasets provided by io. Pipeline will pass these
>>> # datasets to first_node function and provides the result
>>> # to the second_node as input.
>>>
>>> def first_node(first_ds, second_ds):
>>> return dict(third_ds=first_ds+second_ds)
>>>
>>> def second_node(third_ds):
>>> return third_ds
>>>
>>> pipeline = Pipeline([
>>> node(first_node, ['first_ds', 'second_ds'], ['third_ds']),
>>> node(second_node, dict(third_ds='third_ds'), 'fourth_ds')])
>>>
>>> pipeline.describe()
>>>
"""
if nodes is None:
raise ValueError(
"'nodes' argument of 'Pipeline' is None. It must be an "
"iterable of nodes and/or pipelines instead."
)
nodes_list = list(nodes) # in case it's a generator
_validate_duplicate_nodes(nodes_list)
nodes_chain = list(
chain.from_iterable(
[[n] if isinstance(n, Node) else n.nodes for n in nodes_list]
)
)
_validate_transcoded_inputs_outputs(nodes_chain)
_tags = set(_to_list(tags))
if _tags:
tagged_nodes = [n.tag(_tags) for n in nodes_chain]
else:
tagged_nodes = nodes_chain
self._nodes_by_name = {node.name: node for node in tagged_nodes}
_validate_unique_outputs(tagged_nodes)
_validate_unique_confirms(tagged_nodes)
# input -> nodes with input
self._nodes_by_input: dict[str, set[Node]] = defaultdict(set)
for node in tagged_nodes:
for input_ in node.inputs:
self._nodes_by_input[_strip_transcoding(input_)].add(node)
# output -> node with output
self._nodes_by_output: dict[str, Node] = {}
for node in tagged_nodes:
for output in node.outputs:
self._nodes_by_output[_strip_transcoding(output)] = node
self._nodes = tagged_nodes
self._toposorter = TopologicalSorter(self.node_dependencies)
# test for circular dependencies without executing the toposort for efficiency
try:
self._toposorter.prepare()
except CycleError as exc:
loop = list(set(exc.args[1]))
message = f"Circular dependencies exist among the following {len(loop)} item(s): {loop}"
raise CircularDependencyError(message) from exc
self._toposorted_nodes: list[Node] = []
self._toposorted_groups: list[list[Node]] = []
def __repr__(self) -> str: # pragma: no cover
"""Pipeline ([node1, ..., node10 ...], name='pipeline_name')"""
max_nodes_to_display = 10
nodes_reprs = [repr(node) for node in self.nodes[:max_nodes_to_display]]
if len(self.nodes) > max_nodes_to_display:
nodes_reprs.append("...")
sep = ",\n"
nodes_reprs_str = f"[\n{sep.join(nodes_reprs)}\n]" if nodes_reprs else "[]"
constructor_repr = f"({nodes_reprs_str})"
return f"{self.__class__.__name__}{constructor_repr}"
def __add__(self, other: Any) -> Pipeline:
if not isinstance(other, Pipeline):
return NotImplemented
return Pipeline(set(self._nodes + other._nodes))
def __radd__(self, other: Any) -> Pipeline:
if isinstance(other, int) and other == 0:
return self
return self.__add__(other)
def __sub__(self, other: Any) -> Pipeline:
if not isinstance(other, Pipeline):
return NotImplemented
return Pipeline(set(self._nodes) - set(other._nodes))
def __and__(self, other: Any) -> Pipeline:
if not isinstance(other, Pipeline):
return NotImplemented
return Pipeline(set(self._nodes) & set(other._nodes))
def __or__(self, other: Any) -> Pipeline:
if not isinstance(other, Pipeline):
return NotImplemented
return Pipeline(set(self._nodes + other._nodes))
[docs]
def all_outputs(self) -> set[str]:
"""All outputs of all nodes in the pipeline.
Returns:
All node outputs.
"""
return set.union(set(), *(node.outputs for node in self._nodes))
def _remove_intermediates(self, datasets: set[str]) -> set[str]:
intermediate = {_strip_transcoding(i) for i in self.all_inputs()} & {
_strip_transcoding(o) for o in self.all_outputs()
}
return {d for d in datasets if _strip_transcoding(d) not in intermediate}
[docs]
def outputs(self) -> set[str]:
"""The names of outputs produced when the whole pipeline is run.
Does not include intermediate outputs that are consumed by
other pipeline nodes. Resolves transcoded names where necessary.
Returns:
The set of final pipeline outputs.
"""
return self._remove_intermediates(self.all_outputs())
[docs]
def datasets(self) -> set[str]:
"""The names of all datasets used by the ``Pipeline``,
including inputs and outputs.
Returns:
The set of all pipeline datasets.
"""
return self.all_outputs() | self.all_inputs()
def _transcode_compatible_names(self) -> set[str]:
return {_strip_transcoding(ds) for ds in self.datasets()}
[docs]
def describe(self, names_only: bool = True) -> str:
"""Obtain the order of execution and expected free input variables in
a loggable pre-formatted string. The order of nodes matches the order
of execution given by the topological sort.
Args:
names_only: The flag to describe names_only pipeline with just
node names.
Example:
::
>>> pipeline = Pipeline([ ... ])
>>>
>>> logger = logging.getLogger(__name__)
>>>
>>> logger.info(pipeline.describe())
After invocation the following will be printed as an info level log
statement:
::
#### Pipeline execution order ####
Inputs: C, D
func1([C]) -> [A]
func2([D]) -> [B]
func3([A, D]) -> [E]
Outputs: B, E
##################################
Returns:
The pipeline description as a formatted string.
"""
def set_to_string(set_of_strings: set[str]) -> str:
"""Convert set to a string but return 'None' in case of an empty
set.
"""
return ", ".join(sorted(set_of_strings)) if set_of_strings else "None"
nodes_as_string = "\n".join(
node.name if names_only else str(node) for node in self.nodes
)
str_representation = (
"#### Pipeline execution order ####\n"
"Inputs: {0}\n\n"
"{1}\n\n"
"Outputs: {2}\n"
"##################################"
)
return str_representation.format(
set_to_string(self.inputs()), nodes_as_string, set_to_string(self.outputs())
)
@property
def node_dependencies(self) -> dict[Node, set[Node]]:
"""All dependencies of nodes where the first Node has a direct dependency on
the second Node.
Returns:
Dictionary where keys are nodes and values are sets made up of
their parent nodes. Independent nodes have this as empty sets.
"""
dependencies: dict[Node, set[Node]] = {node: set() for node in self._nodes}
for parent in self._nodes:
for output in parent.outputs:
for child in self._nodes_by_input[_strip_transcoding(output)]:
dependencies[child].add(parent)
return dependencies
@property
def nodes(self) -> list[Node]:
"""Return a list of the pipeline nodes in topological order, i.e. if
node A needs to be run before node B, it will appear earlier in the
list.
Returns:
The list of all pipeline nodes in topological order.
"""
if not self._toposorted_nodes:
self._toposorted_nodes = [n for group in self.grouped_nodes for n in group]
return list(self._toposorted_nodes)
@property
def grouped_nodes(self) -> list[list[Node]]:
"""Return a list of the pipeline nodes in topologically ordered groups,
i.e. if node A needs to be run before node B, it will appear in an
earlier group.
Returns:
The pipeline nodes in topologically ordered groups.
"""
if not self._toposorted_groups:
while self._toposorter:
group = sorted(self._toposorter.get_ready())
self._toposorted_groups.append(group)
self._toposorter.done(*group)
return [list(group) for group in self._toposorted_groups]
[docs]
def only_nodes(self, *node_names: str) -> Pipeline:
"""Create a new ``Pipeline`` which will contain only the specified
nodes by name.
Args:
*node_names: One or more node names. The returned ``Pipeline``
will only contain these nodes.
Raises:
ValueError: When some invalid node name is given.
Returns:
A new ``Pipeline``, containing only ``nodes``.
"""
unregistered_nodes = set(node_names) - set(self._nodes_by_name.keys())
if unregistered_nodes:
# check if unregistered nodes are available under namespace
namespaces = []
for unregistered_node in unregistered_nodes:
namespaces.extend(
[
node_name
for node_name in self._nodes_by_name.keys()
if node_name.endswith(f".{unregistered_node}")
]
)
if namespaces:
raise ValueError(
f"Pipeline does not contain nodes named {list(unregistered_nodes)}. "
f"Did you mean: {namespaces}?"
)
raise ValueError(
f"Pipeline does not contain nodes named {list(unregistered_nodes)}."
)
nodes = [self._nodes_by_name[name] for name in node_names]
return Pipeline(nodes)
[docs]
def only_nodes_with_namespace(self, node_namespace: str) -> Pipeline:
"""Creates a new ``Pipeline`` containing only nodes with the specified
namespace.
Args:
node_namespace: One node namespace.
Raises:
ValueError: When pipeline contains no nodes with the specified namespace.
Returns:
A new ``Pipeline`` containing nodes with the specified namespace.
"""
nodes = [
n
for n in self._nodes
if n.namespace and n.namespace.startswith(node_namespace)
]
if not nodes:
raise ValueError(
f"Pipeline does not contain nodes with namespace '{node_namespace}'"
)
return Pipeline(nodes)
def _get_nodes_with_inputs_transcode_compatible(
self, datasets: set[str]
) -> set[Node]:
"""Retrieves nodes that use the given `datasets` as inputs.
If provided a name, but no format, for a transcoded dataset, it
includes all nodes that use inputs with that name, otherwise it
matches to the fully-qualified name only (i.e. name@format).
Raises:
ValueError: if any of the given datasets do not exist in the
``Pipeline`` object
Returns:
Set of ``Nodes`` that use the given datasets as inputs.
"""
missing = sorted(
datasets - self.datasets() - self._transcode_compatible_names()
)
if missing:
raise ValueError(f"Pipeline does not contain datasets named {missing}")
relevant_nodes = set()
for input_ in datasets:
if _strip_transcoding(input_) == input_:
relevant_nodes.update(self._nodes_by_input[_strip_transcoding(input_)])
else:
for node_ in self._nodes_by_input[_strip_transcoding(input_)]:
if input_ in node_.inputs:
relevant_nodes.add(node_)
return relevant_nodes
def _get_nodes_with_outputs_transcode_compatible(
self, datasets: set[str]
) -> set[Node]:
"""Retrieves nodes that output to the given `datasets`.
If provided a name, but no format, for a transcoded dataset, it
includes the node that outputs to that name, otherwise it matches
to the fully-qualified name only (i.e. name@format).
Raises:
ValueError: if any of the given datasets do not exist in the
``Pipeline`` object
Returns:
Set of ``Nodes`` that output to the given datasets.
"""
missing = sorted(
datasets - self.datasets() - self._transcode_compatible_names()
)
if missing:
raise ValueError(f"Pipeline does not contain datasets named {missing}")
relevant_nodes = set()
for output in datasets:
if _strip_transcoding(output) in self._nodes_by_output:
node_with_output = self._nodes_by_output[_strip_transcoding(output)]
if (
_strip_transcoding(output) == output
or output in node_with_output.outputs
):
relevant_nodes.add(node_with_output)
return relevant_nodes
[docs]
def only_nodes_with_outputs(self, *outputs: str) -> Pipeline:
"""Create a new ``Pipeline`` object with the nodes which are directly
required to produce the provided outputs.
If provided a name, but no format, for a transcoded dataset, it
includes all the nodes that output to that name, otherwise it matches
to the fully-qualified name only (i.e. name@format).
Args:
*outputs: A list of outputs which should be the final outputs
of the new ``Pipeline``.
Raises:
ValueError: Raised when any of the given outputs do not exist in the
``Pipeline`` object.
Returns:
A new ``Pipeline`` object, containing a subset of the nodes of the
current one such that only nodes which are directly required to
produce the provided outputs are being copied.
"""
starting = set(outputs)
nodes = self._get_nodes_with_outputs_transcode_compatible(starting)
return Pipeline(nodes)
[docs]
def to_outputs(self, *outputs: str) -> Pipeline:
"""Create a new ``Pipeline`` object with the nodes which are directly
or transitively required to produce the provided outputs.
If provided a name, but no format, for a transcoded dataset, it
includes all the nodes that output to that name, otherwise it matches
to the fully-qualified name only (i.e. name@format).
Args:
*outputs: A list of outputs which should be the final outputs of
the new ``Pipeline``.
Raises:
ValueError: Raised when any of the given outputs do not exist in the
``Pipeline`` object.
Returns:
A new ``Pipeline`` object, containing a subset of the nodes of the
current one such that only nodes which are directly or transitively
required to produce the provided outputs are being copied.
"""
starting = set(outputs)
result: set[Node] = set()
next_nodes = self._get_nodes_with_outputs_transcode_compatible(starting)
while next_nodes:
result |= next_nodes
inputs = set(chain.from_iterable(node.inputs for node in next_nodes))
starting = inputs
next_nodes = {
self._nodes_by_output[_strip_transcoding(output)]
for output in starting
if _strip_transcoding(output) in self._nodes_by_output
}
return Pipeline(result)
[docs]
def from_nodes(self, *node_names: str) -> Pipeline:
"""Create a new ``Pipeline`` object with the nodes which depend
directly or transitively on the provided nodes.
Args:
*node_names: A list of node_names which should be used as a
starting point of the new ``Pipeline``.
Raises:
ValueError: Raised when any of the given names do not exist in the
``Pipeline`` object.
Returns:
A new ``Pipeline`` object, containing a subset of the nodes of
the current one such that only nodes depending directly or
transitively on the provided nodes are being copied.
"""
res = self.only_nodes(*node_names)
res += self.from_inputs(*map(_strip_transcoding, res.all_outputs()))
return res
[docs]
def to_nodes(self, *node_names: str) -> Pipeline:
"""Create a new ``Pipeline`` object with the nodes required directly
or transitively by the provided nodes.
Args:
*node_names: A list of node_names which should be used as an
end point of the new ``Pipeline``.
Raises:
ValueError: Raised when any of the given names do not exist in the
``Pipeline`` object.
Returns:
A new ``Pipeline`` object, containing a subset of the nodes of the
current one such that only nodes required directly or
transitively by the provided nodes are being copied.
"""
res = self.only_nodes(*node_names)
res += self.to_outputs(*map(_strip_transcoding, res.all_inputs()))
return res
[docs]
def filter( # noqa: PLR0913
self,
tags: Iterable[str] | None = None,
from_nodes: Iterable[str] | None = None,
to_nodes: Iterable[str] | None = None,
node_names: Iterable[str] | None = None,
from_inputs: Iterable[str] | None = None,
to_outputs: Iterable[str] | None = None,
node_namespace: str | None = None,
) -> Pipeline:
"""Creates a new ``Pipeline`` object with the nodes that meet all of the
specified filtering conditions.
The new pipeline object is the intersection of pipelines that meet each
filtering condition. This is distinct from chaining multiple filters together.
Args:
tags: A list of node tags which should be used to lookup
the nodes of the new ``Pipeline``.
from_nodes: A list of node names which should be used as a
starting point of the new ``Pipeline``.
to_nodes: A list of node names which should be used as an
end point of the new ``Pipeline``.
node_names: A list of node names which should be selected for the
new ``Pipeline``.
from_inputs: A list of inputs which should be used as a starting point
of the new ``Pipeline``
to_outputs: A list of outputs which should be the final outputs of
the new ``Pipeline``.
node_namespace: One node namespace which should be used to select
nodes in the new ``Pipeline``.
Returns:
A new ``Pipeline`` object with nodes that meet all of the specified
filtering conditions.
Raises:
ValueError: The filtered ``Pipeline`` has no nodes.
Example:
::
>>> pipeline = Pipeline(
>>> [
>>> node(func, "A", "B", name="node1"),
>>> node(func, "B", "C", name="node2"),
>>> node(func, "C", "D", name="node3"),
>>> ]
>>> )
>>> pipeline.filter(node_names=["node1", "node3"], from_inputs=["A"])
>>> # Gives a new pipeline object containing node1 and node3.
"""
# Use [node_namespace] so only_nodes_with_namespace can follow the same
# *filter_args pattern as the other filtering methods, which all take iterables.
node_namespace_iterable = [node_namespace] if node_namespace else None
filter_methods = {
self.only_nodes_with_tags: tags,
self.from_nodes: from_nodes,
self.to_nodes: to_nodes,
self.only_nodes: node_names,
self.from_inputs: from_inputs,
self.to_outputs: to_outputs,
self.only_nodes_with_namespace: node_namespace_iterable,
}
subset_pipelines = {
filter_method(*filter_args) # type: ignore
for filter_method, filter_args in filter_methods.items()
if filter_args
}
# Intersect all the pipelines subsets. We apply each filter to the original
# pipeline object (self) rather than incrementally chaining filter methods
# together. Hence the order of filtering does not affect the outcome, and the
# resultant pipeline is unambiguously defined.
# If this were not the case then, for example,
# pipeline.filter(node_names=["node1", "node3"], from_inputs=["A"])
# would give different outcomes depending on the order of filter methods:
# only_nodes and then from_inputs would give node1, while only_nodes and then
# from_inputs would give node1 and node3.
filtered_pipeline = Pipeline(self._nodes)
for subset_pipeline in subset_pipelines:
filtered_pipeline &= subset_pipeline
if not filtered_pipeline.nodes:
raise ValueError(
"Pipeline contains no nodes after applying all provided filters"
)
return filtered_pipeline
[docs]
def tag(self, tags: str | Iterable[str]) -> Pipeline:
"""Tags all the nodes in the pipeline.
Args:
tags: The tags to be added to the nodes.
Returns:
New ``Pipeline`` object with nodes tagged.
"""
nodes = [n.tag(tags) for n in self._nodes]
return Pipeline(nodes)
[docs]
def to_json(self) -> str:
"""Return a json representation of the pipeline."""
transformed = [
{
"name": n.name,
"inputs": list(n.inputs),
"outputs": list(n.outputs),
"tags": list(n.tags),
}
for n in self._nodes
]
pipeline_versioned = {
"kedro_version": kedro.__version__,
"pipeline": transformed,
}
return json.dumps(pipeline_versioned)
def _validate_duplicate_nodes(nodes_or_pipes: Iterable[Node | Pipeline]) -> None:
seen_nodes: set[str] = set()
duplicates: dict[Pipeline | None, set[str]] = defaultdict(set)
def _check_node(node_: Node, pipeline_: Pipeline | None = None) -> None:
name = node_.name
if name in seen_nodes:
duplicates[pipeline_].add(name)
else:
seen_nodes.add(name)
for each in nodes_or_pipes:
if isinstance(each, Node):
_check_node(each)
elif isinstance(each, Pipeline):
for node in each.nodes:
_check_node(node, pipeline_=each)
if duplicates:
duplicates_info = ""
for pipeline, names in duplicates.items():
pipe_repr = (
"Free nodes" if pipeline is None else repr(pipeline).replace("\n", "")
)
nodes_repr = "\n".join(f" - {name}" for name in sorted(names))
duplicates_info += f"{pipe_repr}:\n{nodes_repr}\n"
raise ValueError(
f"Pipeline nodes must have unique names. The following node names "
f"appear more than once:\n\n{duplicates_info}\nYou can name your "
f"nodes using the last argument of 'node()'."
)
def _validate_unique_outputs(nodes: list[Node]) -> None:
outputs_chain = chain.from_iterable(node.outputs for node in nodes)
outputs = map(_strip_transcoding, outputs_chain)
duplicates = [key for key, value in Counter(outputs).items() if value > 1]
if duplicates:
raise OutputNotUniqueError(
f"Output(s) {sorted(duplicates)} are returned by more than one nodes. Node "
f"outputs must be unique."
)
def _validate_unique_confirms(nodes: list[Node]) -> None:
confirms_chain = chain.from_iterable(node.confirms for node in nodes)
confirms = map(_strip_transcoding, confirms_chain)
duplicates = [key for key, value in Counter(confirms).items() if value > 1]
if duplicates:
raise ConfirmNotUniqueError(
f"{sorted(duplicates)} datasets are confirmed by more than one node. Node "
f"confirms must be unique."
)
def _validate_transcoded_inputs_outputs(nodes: list[Node]) -> None:
"""Users should not be allowed to refer to a transcoded dataset both
with and without the separator.
"""
all_inputs_outputs = set(
chain(
chain.from_iterable(node.inputs for node in nodes),
chain.from_iterable(node.outputs for node in nodes),
)
)
invalid = set()
for dataset_name in all_inputs_outputs:
name = _strip_transcoding(dataset_name)
if name != dataset_name and name in all_inputs_outputs:
invalid.add(name)
if invalid:
raise ValueError(
f"The following datasets are used with transcoding, but "
f"were referenced without the separator: {', '.join(invalid)}.\n"
f"Please specify a transcoding option or "
f"rename the datasets."
)
class CircularDependencyError(Exception):
"""Raised when it is not possible to provide a topological execution
order for nodes, due to a circular dependency existing in the node
definition.
"""
pass
