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"""``AbstractRunner`` is the base class for all ``Pipeline`` runner
implementations.
"""
import logging
from abc import ABC, abstractmethod
from concurrent.futures import ALL_COMPLETED, ThreadPoolExecutor, as_completed, wait
from typing import Any, Dict, Iterable
from kedro.framework.hooks import get_hook_manager
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, run_id: str = None
) -> Dict[str, Any]:
"""Run the ``Pipeline`` using the ``DataSet``s 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.
run_id: The id of the run.
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.
"""
catalog = catalog.shallow_copy()
unsatisfied = pipeline.inputs() - set(catalog.list())
if unsatisfied:
raise ValueError(
"Pipeline input(s) {} not found in the "
"DataCatalog".format(unsatisfied)
)
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, run_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
) -> Dict[str, Any]:
"""Run only the missing outputs from the ``Pipeline`` using the
``DataSet``s 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.
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 memory data sets that feed into that
# including chains of memory data sets
memory_sets = pipeline.data_sets() - set(catalog.list())
output_to_memory = pipeline.only_nodes_with_outputs(*memory_sets)
input_from_memory = to_rerun.inputs() & memory_sets
to_rerun += output_to_memory.to_outputs(*input_from_memory)
return self.run(to_rerun, catalog)
@abstractmethod # pragma: no cover
def _run(
self, pipeline: Pipeline, catalog: DataCatalog, run_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.
run_id: The id of the run.
"""
pass
[docs] @abstractmethod # pragma: no cover
def create_default_data_set(self, ds_name: str) -> AbstractDataSet:
"""Factory method for creating the default data set for the runner.
Args:
ds_name: Name of the missing data set
Returns:
An instance of an implementation of AbstractDataSet to be
used for all unregistered data sets.
"""
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 += ' --from-nodes "{}"'.format(",".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,
)
def run_node(
node: Node, catalog: DataCatalog, is_async: bool = False, run_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.
is_async: If True, the node inputs and outputs are loaded and saved
asynchronously with threads. Defaults to False.
run_id: The id of the pipeline run
Returns:
The node argument.
"""
if is_async:
node = _run_node_async(node, catalog, run_id)
else:
node = _run_node_sequential(node, catalog, run_id)
for name in node.confirms:
catalog.confirm(name)
return node
def _run_node_sequential(node: Node, catalog: DataCatalog, run_id: str = None) -> Node:
inputs = {name: catalog.load(name) for name in node.inputs}
hook_manager = get_hook_manager()
is_async = False
hook_manager.hook.before_node_run( # pylint: disable=no-member
node=node, catalog=catalog, inputs=inputs, is_async=is_async, run_id=run_id
)
try:
outputs = node.run(inputs)
except Exception as error:
hook_manager.hook.on_node_error( # pylint: disable=no-member
error=error,
node=node,
catalog=catalog,
inputs=inputs,
is_async=is_async,
run_id=run_id,
)
raise error
hook_manager.hook.after_node_run( # pylint: disable=no-member
node=node,
catalog=catalog,
inputs=inputs,
outputs=outputs,
is_async=is_async,
run_id=run_id,
)
for name, data in outputs.items():
catalog.save(name, data)
return node
def _run_node_async(node: Node, catalog: DataCatalog, run_id: str = None) -> Node:
with ThreadPoolExecutor() as pool:
inputs = {
name: pool.submit(catalog.load, name) for name in node.inputs
} # Python dict is thread-safe
wait(inputs.values(), return_when=ALL_COMPLETED)
inputs = {key: value.result() for key, value in inputs.items()}
hook_manager = get_hook_manager()
is_async = True
hook_manager.hook.before_node_run( # pylint: disable=no-member
node=node, catalog=catalog, inputs=inputs, is_async=is_async, run_id=run_id
)
try:
outputs = node.run(inputs)
except Exception as error:
hook_manager.hook.on_node_error( # pylint: disable=no-member
error=error,
node=node,
catalog=catalog,
inputs=inputs,
is_async=is_async,
run_id=run_id,
)
raise error
hook_manager.hook.after_node_run( # pylint: disable=no-member
node=node,
catalog=catalog,
inputs=inputs,
outputs=outputs,
is_async=is_async,
run_id=run_id,
)
save_futures = set()
for name, data in outputs.items():
save_futures.add(pool.submit(catalog.save, name, data))
for future in as_completed(save_futures):
exception = future.exception()
if exception:
raise exception
return node