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)
def run(self, pipeline: Pipeline, catalog: DataCatalog, run_id: str = None) -> Dict[str, Any]:
"""
Run the ``Pipeline`` using the ``DataSet``s provided by ``catalog``.
Parameters
----------
pipeline: Pipeline
The ``Pipeline`` to run
catalog: DataCatalog
The ``DataCatalog`` from which to fetch data.
run_id: str
The id of the run.
Returns
-------
dict
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.
"""
# If missing flag run missing_output pipeline and its child nodes
if self.only_missing:
to_build = {ds for ds in catalog.list() if not catalog.exists(ds)}.intersection(pipeline.data_sets())
pipeline = pipeline.only_nodes_with_outputs(*to_build) + pipeline.from_inputs(*to_build)
return super(DatalabRunner, self).run(pipeline, catalog, run_id)
