Improve resume suggestions (#3719)

* Improve suggestions to resume a failed pipeline

- if dataset (or param) is persistent & shared, don't keep looking for ancestors
- only look for ancestors producing impersistent inputs
- minimize number of suggested nodes (= shorter message for the same pipeline)
- testable logic, tests cases outside of scenarios for sequential runner

- Use _EPHEMERAL attribute
- Move tests to separate file
- Docstring updates

---------

Signed-off-by: Ondrej Zacha <ondrej.zacha@okra.ai>
Co-authored-by: Nok Lam Chan <nok.lam.chan@quantumblack.com>

RELEASE.md

@@ -7,6 +7,7 @@
 * Kedro CLI now provides a better error message when project commands are run outside of a project i.e. `kedro run`.
 * Dropped the dependency on `toposort` in favour of the built-in `graphlib` module.
 * Improve the performance of `Pipeline` object creation and summing.
+* Improve suggestions to resume failed pipeline runs.
 
 ## Bug fixes and other changes
 * Updated `kedro pipeline create` and `kedro pipeline delete` to read the base environment from the project settings.
@@ -18,6 +19,9 @@
 ## Documentation changes
 
 ## Community contributions
+Many thanks to the following Kedroids for contributing PRs to this release:
+
+* [ondrejzacha](https://github.com/ondrejzacha)
 
 # Release 0.19.3
 

kedro/runner/runner.py

@@ -15,7 +15,7 @@
     as_completed,
     wait,
 )
-from typing import Any, Iterable, Iterator
+from typing import Any, Collection, Iterable, Iterator
 
 from more_itertools import interleave
 from pluggy import PluginManager
@@ -198,98 +198,180 @@ def _suggest_resume_scenario(
 
         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())
-
-            # find the nearest persistent ancestors of the nodes in start_p
-            start_p_persistent_ancestors = _find_persistent_ancestors(
-                pipeline, start_p.nodes, catalog
+            start_node_names = _find_nodes_to_resume_from(
+                pipeline=pipeline,
+                unfinished_nodes=remaining_nodes,
+                catalog=catalog,
             )
-
-            start_node_names = (n.name for n in start_p_persistent_ancestors)
-            postfix += f"  --from-nodes \"{','.join(start_node_names)}\""
+            start_nodes_str = ",".join(sorted(start_node_names))
+            postfix += f'  --from-nodes "{start_nodes_str}"'
 
         if not postfix:
             self._logger.warning(
                 "No nodes ran. Repeat the previous command to attempt a new run."
             )
         else:
             self._logger.warning(
-                "There are %d nodes that have not run.\n"
+                f"There are {len(remaining_nodes)} nodes that have not run.\n"
                 "You can resume the pipeline run from the nearest nodes with "
                 "persisted inputs by adding the following "
-                "argument to your previous command:\n%s",
-                len(remaining_nodes),
-                postfix,
+                f"argument to your previous command:\n{postfix}"
             )
 
 
-def _find_persistent_ancestors(
-    pipeline: Pipeline, children: Iterable[Node], catalog: DataCatalog
+def _find_nodes_to_resume_from(
+    pipeline: Pipeline, unfinished_nodes: Collection[Node], catalog: DataCatalog
+) -> set[str]:
+    """Given a collection of unfinished nodes in a pipeline using
+    a certain catalog, find the node names to pass to pipeline.from_nodes()
+    to cover all unfinished nodes, including any additional nodes
+    that should be re-run if their outputs are not persisted.
+
+    Args:
+        pipeline: the ``Pipeline`` to find starting nodes for.
+        unfinished_nodes: collection of ``Node``s that have not finished yet
+        catalog: the ``DataCatalog`` of the run.
+
+    Returns:
+        Set of node names to pass to pipeline.from_nodes() to continue
+        the run.
+
+    """
+    nodes_to_be_run = _find_all_nodes_for_resumed_pipeline(
+        pipeline, unfinished_nodes, catalog
+    )
+
+    # Find which of the remaining nodes would need to run first (in topo sort)
+    persistent_ancestors = _find_initial_node_group(pipeline, nodes_to_be_run)
+
+    return {n.name for n in persistent_ancestors}
+
+
+def _find_all_nodes_for_resumed_pipeline(
+    pipeline: Pipeline, unfinished_nodes: Iterable[Node], catalog: DataCatalog
 ) -> set[Node]:
     """Breadth-first search approach to finding the complete set of
-    persistent ancestors of an iterable of ``Node``s. Persistent
-    ancestors exclusively have persisted ``Dataset``s as inputs.
+    ``Node``s which need to run to cover all unfinished nodes,
+    including any additional nodes that should be re-run if their outputs
+    are not persisted.
 
     Args:
-        pipeline: the ``Pipeline`` to find ancestors in.
-        children: the iterable containing ``Node``s to find ancestors of.
+        pipeline: the ``Pipeline`` to analyze.
+        unfinished_nodes: the iterable of ``Node``s which have not finished yet.
         catalog: the ``DataCatalog`` of the run.
 
     Returns:
-        A set containing first persistent ancestors of the given
-        ``Node``s.
+        A set containing all input unfinished ``Node``s and all remaining
+        ``Node``s that need to run in case their outputs are not persisted.
 
     """
-    ancestor_nodes_to_run = set()
-    queue, visited = deque(children), set(children)
+    nodes_to_run = set(unfinished_nodes)
+    initial_nodes = _nodes_with_external_inputs(unfinished_nodes)
+
+    queue, visited = deque(initial_nodes), set(initial_nodes)
     while queue:
         current_node = queue.popleft()
-        if _has_persistent_inputs(current_node, catalog):
-            ancestor_nodes_to_run.add(current_node)
-            continue
-        for parent in _enumerate_parents(pipeline, current_node):
-            if parent in visited:
+        nodes_to_run.add(current_node)
+        # Look for parent nodes which produce non-persistent inputs (if those exist)
+        non_persistent_inputs = _enumerate_non_persistent_inputs(current_node, catalog)
+        for node in _enumerate_nodes_with_outputs(pipeline, non_persistent_inputs):
+            if node in visited:
                 continue
-            visited.add(parent)
-            queue.append(parent)
-    return ancestor_nodes_to_run
+            visited.add(node)
+            queue.append(node)
 
+    # Make sure no downstream tasks are skipped
+    nodes_to_run = set(pipeline.from_nodes(*(n.name for n in nodes_to_run)).nodes)
 
-def _enumerate_parents(pipeline: Pipeline, child: Node) -> list[Node]:
-    """For a given ``Node``, returns a list containing the direct parents
-    of that ``Node`` in the given ``Pipeline``.
+    return nodes_to_run
+
+
+def _nodes_with_external_inputs(nodes_of_interest: Iterable[Node]) -> set[Node]:
+    """For given ``Node``s , find their subset which depends on
+    external inputs of the ``Pipeline`` they constitute. External inputs
+    are pipeline inputs not produced by other ``Node``s in the ``Pipeline``.
 
     Args:
-        pipeline: the ``Pipeline`` to search for direct parents in.
-        child: the ``Node`` to find parents of.
+        nodes_of_interest: the ``Node``s to analyze.
 
     Returns:
-        A list of all ``Node``s that are direct parents of ``child``.
+        A set of ``Node``s that depend on external inputs
+        of nodes of interest.
 
     """
-    parent_pipeline = pipeline.only_nodes_with_outputs(*child.inputs)
-    return parent_pipeline.nodes
+    p_nodes_of_interest = Pipeline(nodes_of_interest)
+    p_nodes_with_external_inputs = p_nodes_of_interest.only_nodes_with_inputs(
+        *p_nodes_of_interest.inputs()
+    )
+    return set(p_nodes_with_external_inputs.nodes)
 
 
-def _has_persistent_inputs(node: Node, catalog: DataCatalog) -> bool:
-    """Check if a ``Node`` exclusively has persisted Datasets as inputs.
-    If at least one input is a ``MemoryDataset``, return False.
+def _enumerate_non_persistent_inputs(node: Node, catalog: DataCatalog) -> set[str]:
+    """Enumerate non-persistent input datasets of a ``Node``.
 
     Args:
         node: the ``Node`` to check the inputs of.
         catalog: the ``DataCatalog`` of the run.
 
     Returns:
-        True if the ``Node`` being checked exclusively has inputs that
-        are not ``MemoryDataset``, else False.
+        Set of names of non-persistent inputs of given ``Node``.
 
     """
+    # We use _datasets because they pertain parameter name format
+    catalog_datasets = catalog._datasets
+    non_persistent_inputs: set[str] = set()
     for node_input in node.inputs:
-        if isinstance(catalog._datasets[node_input], MemoryDataset):
-            return False
-    return True
+        if node_input.startswith("params:"):
+            continue
+
+        if (
+            node_input not in catalog_datasets
+            or catalog_datasets[node_input]._EPHEMERAL
+        ):
+            non_persistent_inputs.add(node_input)
+
+    return non_persistent_inputs
+
+
+def _enumerate_nodes_with_outputs(
+    pipeline: Pipeline, outputs: Collection[str]
+) -> list[Node]:
+    """For given outputs, returns a list containing nodes that
+    generate them in the given ``Pipeline``.
+
+    Args:
+        pipeline: the ``Pipeline`` to search for nodes in.
+        outputs: the dataset names to find source nodes for.
+
+    Returns:
+        A list of all ``Node``s that are producing ``outputs``.
+
+    """
+    parent_pipeline = pipeline.only_nodes_with_outputs(*outputs)
+    return parent_pipeline.nodes
+
+
+def _find_initial_node_group(pipeline: Pipeline, nodes: Iterable[Node]) -> list[Node]:
+    """Given a collection of ``Node``s in a ``Pipeline``,
+    find the initial group of ``Node``s to be run (in topological order).
+
+    This can be used to define a sub-pipeline with the smallest possible
+    set of nodes to pass to --from-nodes.
+
+    Args:
+        pipeline: the ``Pipeline`` to search for initial ``Node``s in.
+        nodes: the ``Node``s to find initial group for.
+
+    Returns:
+        A list of initial ``Node``s to run given inputs (in topological order).
+
+    """
+    node_names = set(n.name for n in nodes)
+    if len(node_names) == 0:
+        return []
+    sub_pipeline = pipeline.only_nodes(*node_names)
+    initial_nodes = sub_pipeline.grouped_nodes[0]
+    return initial_nodes
 
 
 def run_node(