"""``AbstractRunner`` is the base class for all ``Pipeline`` runner
implementations.
"""
import logging
from abc import ABC, abstractmethod
from concurrent.futures import (
ALL_COMPLETED,
Future,
ThreadPoolExecutor,
as_completed,
wait,
)
from typing import Any, Dict, Iterable
from pluggy import PluginManager
from kedro.framework.hooks.manager import _NullPluginManager
from kedro.io import AbstractDataSet, DataCatalog
from kedro.pipeline import Pipeline
from kedro.pipeline.node import Node
[docs]class AbstractRunner(ABC):
"""``AbstractRunner`` is the base class for all ``Pipeline`` runner
implementations.
"""
[docs] def __init__(self, is_async: bool = False):
"""Instantiates the runner classs.
Args:
is_async: If True, the node inputs and outputs are loaded and saved
asynchronously with threads. Defaults to False.
"""
self._is_async = is_async
@property
def _logger(self):
return logging.getLogger(self.__module__)
[docs] def run(
self,
pipeline: Pipeline,
catalog: DataCatalog,
hook_manager: PluginManager = None,
session_id: str = None,
) -> Dict[str, Any]:
"""Run the ``Pipeline`` using the datasets provided by ``catalog``
and save results back to the same objects.
Args:
pipeline: The ``Pipeline`` to run.
catalog: The ``DataCatalog`` from which to fetch data.
hook_manager: The ``PluginManager`` to activate hooks.
session_id: The id of the session.
Raises:
ValueError: Raised when ``Pipeline`` inputs cannot be satisfied.
Returns:
Any node outputs that cannot be processed by the ``DataCatalog``.
These are returned in a dictionary, where the keys are defined
by the node outputs.
"""
hook_manager = hook_manager or _NullPluginManager()
catalog = catalog.shallow_copy()
unsatisfied = pipeline.inputs() - set(catalog.list())
if unsatisfied:
raise ValueError(
f"Pipeline input(s) {unsatisfied} not found in the DataCatalog"
)
free_outputs = pipeline.outputs() - set(catalog.list())
unregistered_ds = pipeline.data_sets() - set(catalog.list())
for ds_name in unregistered_ds:
catalog.add(ds_name, self.create_default_data_set(ds_name))
if self._is_async:
self._logger.info(
"Asynchronous mode is enabled for loading and saving data"
)
self._run(pipeline, catalog, hook_manager, session_id)
self._logger.info("Pipeline execution completed successfully.")
return {ds_name: catalog.load(ds_name) for ds_name in free_outputs}
[docs] def run_only_missing(
self, pipeline: Pipeline, catalog: DataCatalog, hook_manager: PluginManager
) -> Dict[str, Any]:
"""Run only the missing outputs from the ``Pipeline`` using the
datasets provided by ``catalog``, and save results back to the
same objects.
Args:
pipeline: The ``Pipeline`` to run.
catalog: The ``DataCatalog`` from which to fetch data.
hook_manager: The ``PluginManager`` to activate hooks.
Raises:
ValueError: Raised when ``Pipeline`` inputs cannot be
satisfied.
Returns:
Any node outputs that cannot be processed by the
``DataCatalog``. These are returned in a dictionary, where
the keys are defined by the node outputs.
"""
free_outputs = pipeline.outputs() - set(catalog.list())
missing = {ds for ds in catalog.list() if not catalog.exists(ds)}
to_build = free_outputs | missing
to_rerun = pipeline.only_nodes_with_outputs(*to_build) + pipeline.from_inputs(
*to_build
)
# We also need any missing datasets that are required to run the
# `to_rerun` pipeline, including any chains of missing datasets.
unregistered_ds = pipeline.data_sets() - set(catalog.list())
output_to_unregistered = pipeline.only_nodes_with_outputs(*unregistered_ds)
input_from_unregistered = to_rerun.inputs() & unregistered_ds
to_rerun += output_to_unregistered.to_outputs(*input_from_unregistered)
return self.run(to_rerun, catalog, hook_manager)
@abstractmethod # pragma: no cover
def _run(
self,
pipeline: Pipeline,
catalog: DataCatalog,
hook_manager: PluginManager,
session_id: str = None,
) -> None:
"""The abstract interface for running pipelines, assuming that the
inputs have already been checked and normalized by run().
Args:
pipeline: The ``Pipeline`` to run.
catalog: The ``DataCatalog`` from which to fetch data.
hook_manager: The ``PluginManager`` to activate hooks.
session_id: The id of the session.
"""
pass
[docs] @abstractmethod # pragma: no cover
def create_default_data_set(self, ds_name: str) -> AbstractDataSet:
"""Factory method for creating the default dataset for the runner.
Args:
ds_name: Name of the missing dataset.
Returns:
An instance of an implementation of ``AbstractDataSet`` to be
used for all unregistered datasets.
"""
pass
def _suggest_resume_scenario(
self, pipeline: Pipeline, done_nodes: Iterable[Node]
) -> None:
remaining_nodes = set(pipeline.nodes) - set(done_nodes)
postfix = ""
if done_nodes:
node_names = (n.name for n in remaining_nodes)
resume_p = pipeline.only_nodes(*node_names)
start_p = resume_p.only_nodes_with_inputs(*resume_p.inputs())
start_node_names = (n.name for n in start_p.nodes)
postfix += f" --from-nodes \"{','.join(start_node_names)}\""
self._logger.warning(
"There are %d nodes that have not run.\n"
"You can resume the pipeline run by adding the following "
"argument to your previous command:\n%s",
len(remaining_nodes),
postfix,
)
[docs]def run_node(
node: Node,
catalog: DataCatalog,
hook_manager: PluginManager,
is_async: bool = False,
session_id: str = None,
) -> Node:
"""Run a single `Node` with inputs from and outputs to the `catalog`.
Args:
node: The ``Node`` to run.
catalog: A ``DataCatalog`` containing the node's inputs and outputs.
hook_manager: The ``PluginManager`` to activate hooks.
is_async: If True, the node inputs and outputs are loaded and saved
asynchronously with threads. Defaults to False.
session_id: The session id of the pipeline run.
Returns:
The node argument.
"""
if is_async:
node = _run_node_async(node, catalog, hook_manager, session_id)
else:
node = _run_node_sequential(node, catalog, hook_manager, session_id)
for name in node.confirms:
catalog.confirm(name)
return node
def _collect_inputs_from_hook(
node: Node,
catalog: DataCatalog,
inputs: Dict[str, Any],
is_async: bool,
hook_manager: PluginManager,
session_id: str = None,
) -> Dict[str, Any]:
# pylint: disable=too-many-arguments
inputs = inputs.copy() # shallow copy to prevent in-place modification by the hook
hook_response = hook_manager.hook.before_node_run(
node=node,
catalog=catalog,
inputs=inputs,
is_async=is_async,
session_id=session_id,
)
additional_inputs = {}
if (
hook_response is not None
): # all hooks on a _NullPluginManager will return None instead of a list
for response in hook_response:
if response is not None and not isinstance(response, dict):
response_type = type(response).__name__
raise TypeError(
f"`before_node_run` must return either None or a dictionary mapping "
f"dataset names to updated values, got `{response_type}` instead."
)
response = response or {}
additional_inputs.update(response)
return additional_inputs
def _call_node_run(
node: Node,
catalog: DataCatalog,
inputs: Dict[str, Any],
is_async: bool,
hook_manager: PluginManager,
session_id: str = None,
) -> Dict[str, Any]:
# pylint: disable=too-many-arguments
try:
outputs = node.run(inputs)
except Exception as exc:
hook_manager.hook.on_node_error(
error=exc,
node=node,
catalog=catalog,
inputs=inputs,
is_async=is_async,
session_id=session_id,
)
raise exc
hook_manager.hook.after_node_run(
node=node,
catalog=catalog,
inputs=inputs,
outputs=outputs,
is_async=is_async,
session_id=session_id,
)
return outputs
def _run_node_sequential(
node: Node,
catalog: DataCatalog,
hook_manager: PluginManager,
session_id: str = None,
) -> Node:
inputs = {}
for name in node.inputs:
hook_manager.hook.before_dataset_loaded(dataset_name=name)
inputs[name] = catalog.load(name)
hook_manager.hook.after_dataset_loaded(dataset_name=name, data=inputs[name])
is_async = False
additional_inputs = _collect_inputs_from_hook(
node, catalog, inputs, is_async, hook_manager, session_id=session_id
)
inputs.update(additional_inputs)
outputs = _call_node_run(
node, catalog, inputs, is_async, hook_manager, session_id=session_id
)
for name, data in outputs.items():
hook_manager.hook.before_dataset_saved(dataset_name=name, data=data)
catalog.save(name, data)
hook_manager.hook.after_dataset_saved(dataset_name=name, data=data)
return node
def _run_node_async(
node: Node,
catalog: DataCatalog,
hook_manager: PluginManager,
session_id: str = None,
) -> Node:
def _synchronous_dataset_load(dataset_name: str):
"""Minimal wrapper to ensure Hooks are run synchronously
within an asynchronous dataset load."""
hook_manager.hook.before_dataset_loaded(dataset_name=dataset_name)
return_ds = catalog.load(dataset_name)
hook_manager.hook.after_dataset_loaded(
dataset_name=dataset_name, data=return_ds
)
return return_ds
with ThreadPoolExecutor() as pool:
inputs: Dict[str, Future] = {}
for name in node.inputs:
inputs[name] = pool.submit(_synchronous_dataset_load, name)
wait(inputs.values(), return_when=ALL_COMPLETED)
inputs = {key: value.result() for key, value in inputs.items()}
is_async = True
additional_inputs = _collect_inputs_from_hook(
node, catalog, inputs, is_async, hook_manager, session_id=session_id
)
inputs.update(additional_inputs)
outputs = _call_node_run(
node, catalog, inputs, is_async, hook_manager, session_id=session_id
)
save_futures = set()
for name, data in outputs.items():
hook_manager.hook.before_dataset_saved(dataset_name=name, data=data)
save_futures.add(pool.submit(catalog.save, name, data))
for future in as_completed(save_futures):
exception = future.exception()
if exception:
raise exception
hook_manager.hook.after_dataset_saved(
dataset_name=name, data=data # pylint: disable=undefined-loop-variable
)
return node