feat: kedro-airflow group in memory nodes

kedro-airflow/README.md

@@ -154,6 +154,19 @@ See ["What if I want to use a different Jinja2 template?"](#what-if-i-want-to-us
 The [rich offering](https://airflow.apache.org/docs/apache-airflow-providers/operators-and-hooks-ref/index.html) of operators means that the `kedro-airflow` plugin is providing templates for specific operators.
 The default template provided by `kedro-airflow` uses the `BaseOperator`.
 
+### Can I group nodes together?
+
+When running Kedro nodes using Airflow, MemoryDatasets are often not shared across operators.
+This will cause the DAG run to fail.
+
+MemoryDatasets may be used to provide logical separation between nodes in Kedro, without the overhead of needing to write to disk (and in the case of distributed running needing multiple executors).
+
+Nodes that are connected through MemoryDatasets are grouped together via the `--group-in-memory` flag.
+This preserves the option to have logical separation in Kedro, with little computational overhead.
+
+It is possible to use [task groups](https://docs.astronomer.io/learn/task-groups) by changing the template.
+See ["What if I want to use a different Jinja2 template?"](#what-if-i-want-to-use-a-different-jinja2-template) for instructions on using custom templates.
+
 ## Can I contribute?
 
 Yes! Want to help build Kedro-Airflow? Check out our guide to [contributing](https://github.com/kedro-org/kedro-plugins/blob/main/kedro-airflow/CONTRIBUTING.md).

kedro-airflow/RELEASE.md

@@ -1,4 +1,5 @@
 # Upcoming Release
+* Option to group MemoryDatasets in the same Airflow task (breaking change for custom template via `--jinja-file`).
 
 # Release 0.8.0
 * Added support for Kedro 0.19.x

kedro-airflow/kedro_airflow/airflow_dag_template.j2

@@ -1,4 +1,5 @@
 from __future__ import annotations
+
 from datetime import datetime, timedelta
 from pathlib import Path
 
@@ -16,7 +17,7 @@ class KedroOperator(BaseOperator):
         self,
         package_name: str,
         pipeline_name: str,
-        node_name: str,
+        node_name: str | list[str],
         project_path: str | Path,
         env: str,
         *args, **kwargs
@@ -30,10 +31,10 @@ class KedroOperator(BaseOperator):
 
     def execute(self, context):
         configure_project(self.package_name)
-        with KedroSession.create(project_path=self.project_path,
-                                 env=self.env) as session:
-            session.run(self.pipeline_name, node_names=[self.node_name])
-
+        with KedroSession.create(self.project_path, env=self.env) as session:
+            if isinstance(self.node_name, str):
+                self.node_name = [self.node_name]
+            session.run(self.pipeline_name, node_names=self.node_name)
 
 # Kedro settings required to run your pipeline
 env = "{{ env }}"
@@ -60,17 +61,17 @@ with DAG(
     )
 ) as dag:
     tasks = {
-    {% for node in pipeline.nodes %}        "{{ node.name | safe | slugify }}": KedroOperator(
-            task_id="{{ node.name | safe | slugify  }}",
+    {% for node_name, node_list in nodes.items() %}        "{{ node_name | safe | slugify }}": KedroOperator(
+            task_id="{{ node_name | safe | slugify  }}",
             package_name=package_name,
             pipeline_name=pipeline_name,
-            node_name="{{ node.name | safe }}",
+            node_name={% if node_list | length > 1 %}[{% endif %}{% for node in node_list %}"{{ node.name | safe | slugify }}"{% if not loop.last %}, {% endif %}{% endfor %}{% if node_list | length > 1 %}]{% endif %},
             project_path=project_path,
             env=env,
         ),
 {% endfor %}    }
 
     {% for parent_node, child_nodes in dependencies.items() -%}
-    {% for child in child_nodes %}    tasks["{{ parent_node.name | safe | slugify }}"] >> tasks["{{ child.name | safe | slugify }}"]
+    {% for child in child_nodes %}    tasks["{{ parent_node | safe | slugify }}"] >> tasks["{{ child | safe | slugify }}"]
     {% endfor %}
     {%- endfor %}

kedro-airflow/kedro_airflow/grouping.py

@@ -0,0 +1,97 @@
+from __future__ import annotations
+
+from collections import defaultdict
+
+from kedro.io import DataCatalog, MemoryDataset
+from kedro.pipeline.node import Node
+from kedro.pipeline.pipeline import Pipeline
+
+
+def _is_memory_dataset(catalog, dataset_name: str) -> bool:
+    if dataset_name == "parameters" or dataset_name.startswith("params:"):
+        return False
+
+    dataset = catalog._datasets.get(dataset_name, None)
+    return dataset is not None and isinstance(dataset, MemoryDataset)
+
+
+def get_memory_datasets(catalog: DataCatalog, pipeline: Pipeline) -> set[str]:
+    """Gather all datasets in the pipeline that are of type MemoryDataset, excluding 'parameters'."""
+    return {
+        dataset_name
+        for dataset_name in pipeline.datasets()
+        if _is_memory_dataset(catalog, dataset_name)
+    }
+
+
+def node_sequence_name(node_sequence: list[Node]) -> str:
+    return "_".join([node.name for node in node_sequence])
+
+
+def group_memory_nodes(catalog: DataCatalog, pipeline: Pipeline):
+    """
+    Nodes that are connected through MemoryDatasets cannot be distributed across
+    multiple machines, e.g. be in different Kubernetes pods. This function
+    groups nodes that are connected through MemoryDatasets in the pipeline
+    together. Essentially, this computes connected components over the graph of
+    nodes connected by MemoryDatasets.
+    """
+    # get all memory datasets in the pipeline
+    memory_datasets = get_memory_datasets(catalog, pipeline)
+
+    # Node sequences
+    node_sequences = []
+
+    # Mapping from dataset name -> node sequence index
+    sequence_map = {}
+    for node in pipeline.nodes:
+        if all(o not in memory_datasets for o in node.inputs + node.outputs):
+            # standalone node
+            node_sequences.append([node])
+        else:
+            if all(i not in memory_datasets for i in node.inputs):
+                # start of a sequence; create a new sequence and store the id
+                node_sequences.append([node])
+                sequence_id = len(node_sequences) - 1
+            else:
+                # continuation of a sequence; retrieve sequence_id
+                sequence_id = None
+                for i in node.inputs:
+                    if i in memory_datasets:
+                        assert sequence_id is None or sequence_id == sequence_map[i]
+                        sequence_id = sequence_map[i]
+
+                # Append to map
+                node_sequences[sequence_id].append(node)
+
+            # map outputs to sequence_id
+            for o in node.outputs:
+                if o in memory_datasets:
+                    sequence_map[o] = sequence_id
+
+    # Named node sequences
+    nodes = {
+        node_sequence_name(node_sequence): node_sequence
+        for node_sequence in node_sequences
+    }
+
+    # Inverted mapping
+    node_mapping = {
+        node.name: sequence_name
+        for sequence_name, node_sequence in nodes.items()
+        for node in node_sequence
+    }
+
+    # Grouped dependencies
+    dependencies = defaultdict(list)
+    for node, parent_nodes in pipeline.node_dependencies.items():
+        for parent in parent_nodes:
+            parent_name = node_mapping[parent.name]
+            node_name = node_mapping[node.name]
+            if parent_name != node_name and (
+                parent_name not in dependencies
+                or node_name not in dependencies[parent_name]
+            ):
+                dependencies[parent_name].append(node_name)
+
+    return nodes, dependencies

kedro-airflow/kedro_airflow/plugin.py

@@ -17,6 +17,8 @@
 from kedro.framework.startup import ProjectMetadata, bootstrap_project
 from slugify import slugify
 
+from kedro_airflow.grouping import group_memory_nodes
+
 PIPELINE_ARG_HELP = """Name of the registered pipeline to convert.
 If not set, the '__default__' pipeline is used. This argument supports
 passing multiple values using `--pipeline [p1] --pipeline [p2]`.
@@ -100,6 +102,14 @@ def _get_pipeline_config(config_airflow: dict, params: dict, pipeline_name: str)
     default=Path(__file__).parent / "airflow_dag_template.j2",
     help="The template file for the generated Airflow dags",
 )
+@click.option(
+    "-g",
+    "--group-in-memory",
+    is_flag=True,
+    default=False,
+    help="Group nodes with at least one MemoryDataset as input/output together, "
+    "as they do not persist between Airflow operators.",
+)
 @click.option(
     "--params",
     type=click.UNPROCESSED,
@@ -114,6 +124,7 @@ def create(  # noqa: PLR0913
     env,
     target_path,
     jinja_file,
+    group_in_memory,
     params,
     convert_all: bool,
 ):
@@ -165,13 +176,20 @@ def create(  # noqa: PLR0913
             else f"{package_name}_{name}_dag.py"
         )
 
-        dependencies = defaultdict(list)
-        for node, parent_nodes in pipeline.node_dependencies.items():
-            for parent in parent_nodes:
-                dependencies[parent].append(node)
+        # group memory nodes
+        if group_in_memory:
+            nodes, dependencies = group_memory_nodes(context.catalog, pipeline)
+        else:
+            nodes = {node.name: [node] for node in pipeline.nodes}
+
+            dependencies = defaultdict(list)
+            for node, parent_nodes in pipeline.node_dependencies.items():
+                for parent in parent_nodes:
+                    dependencies[parent.name].append(node.name)
 
         template.stream(
             dag_name=package_name,
+            nodes=nodes,
             dependencies=dependencies,
             env=env,
             pipeline_name=name,

kedro-airflow/pyproject.toml

@@ -11,7 +11,7 @@ description = "Kedro-Airflow makes it easy to deploy Kedro projects to Airflow"
 requires-python = ">=3.8"
 license = {text = "Apache Software License (Apache 2.0)"}
 dependencies = [
-    "kedro>=0.17.5",
+    "kedro>=0.19.0",
     "python-slugify>=4.0",
     "semver>=2.10",  # Needs to be at least 2.10.0 to make use of `VersionInfo.match`.
 ]

kedro-airflow/tests/test_node_grouping.py

@@ -0,0 +1,128 @@
+from __future__ import annotations
+
+from typing import Any
+
+import pytest
+from kedro.io import AbstractDataset, DataCatalog, MemoryDataset
+from kedro.pipeline import node
+from kedro.pipeline.modular_pipeline import pipeline as modular_pipeline
+
+from kedro_airflow.grouping import _is_memory_dataset, group_memory_nodes
+
+
+class TestDataset(AbstractDataset):
+    def _save(self, data) -> None:
+        pass
+
+    def _describe(self) -> dict[str, Any]:
+        return {}
+
+    def _load(self):
+        return []
+
+
+@pytest.mark.parametrize(
+    "memory_nodes,expected_nodes,expected_dependencies",
+    [
+        (
+            ["ds3", "ds6"],
+            [["f1"], ["f2", "f3", "f4", "f6", "f7"], ["f5"]],
+            {"f1": ["f2_f3_f4_f6_f7"], "f2_f3_f4_f6_f7": ["f5"]},
+        ),
+        (
+            ["ds3"],
+            [["f1"], ["f2", "f3", "f4", "f7"], ["f5"], ["f6"]],
+            {"f1": ["f2_f3_f4_f7"], "f2_f3_f4_f7": ["f5", "f6"]},
+        ),
+        (
+            [],
+            [["f1"], ["f2"], ["f3"], ["f4"], ["f5"], ["f6"], ["f7"]],
+            {"f1": ["f2"], "f2": ["f3", "f4", "f5", "f7"], "f4": ["f6", "f7"]},
+        ),
+    ],
+)
+def test_group_memory_nodes(
+    memory_nodes: list[str],
+    expected_nodes: list[list[str]],
+    expected_dependencies: dict[str, list[str]],
+):
+    """Check the grouping of memory nodes."""
+    nodes = [f"ds{i}" for i in range(1, 10)]
+    assert all(node_name in nodes for node_name in memory_nodes)
+
+    mock_catalog = DataCatalog()
+    for dataset_name in nodes:
+        if dataset_name in memory_nodes:
+            dataset = MemoryDataset()
+        else:
+            dataset = TestDataset()
+        mock_catalog.add(dataset_name, dataset)
+
+    def identity_one_to_one(x):
+        return x
+
+    mock_pipeline = modular_pipeline(
+        [
+            node(
+                func=identity_one_to_one,
+                inputs="ds1",
+                outputs="ds2",
+                name="f1",
+            ),
+            node(
+                func=lambda x: (x, x),
+                inputs="ds2",
+                outputs=["ds3", "ds4"],
+                name="f2",
+            ),
+            node(
+                func=identity_one_to_one,
+                inputs="ds3",
+                outputs="ds5",
+                name="f3",
+            ),
+            node(
+                func=identity_one_to_one,
+                inputs="ds3",
+                outputs="ds6",
+                name="f4",
+            ),
+            node(
+                func=identity_one_to_one,
+                inputs="ds4",
+                outputs="ds8",
+                name="f5",
+            ),
+            node(
+                func=identity_one_to_one,
+                inputs="ds6",
+                outputs="ds7",
+                name="f6",
+            ),
+            node(
+                func=lambda x, y: x,
+                inputs=["ds3", "ds6"],
+                outputs="ds9",
+                name="f7",
+            ),
+        ],
+    )
+
+    nodes, dependencies = group_memory_nodes(mock_catalog, mock_pipeline)
+    sequence = [
+        [node_.name for node_ in node_sequence] for node_sequence in nodes.values()
+    ]
+    assert sequence == expected_nodes
+    assert dict(dependencies) == expected_dependencies
+
+
+def test_is_memory_dataset():
+    catalog = DataCatalog()
+    catalog.add("parameters", {"hello": "world"})
+    catalog.add("params:hello", "world")
+    catalog.add("my_dataset", MemoryDataset(True))
+    catalog.add("test_dataset", TestDataset())
+    assert not _is_memory_dataset(catalog, "parameters")
+    assert not _is_memory_dataset(catalog, "params:hello")
+    assert _is_memory_dataset(catalog, "my_dataset")
+    assert not _is_memory_dataset(catalog, "test_dataset")

kedro-airflow/tests/test_plugin.py

@@ -16,6 +16,8 @@
         ("hello_world", "__default__", ["airflow", "create"]),
         # Test execution with alternate pipeline name
         ("hello_world", "ds", ["airflow", "create", "--pipeline", "ds"]),
+        # Test with grouping
+        ("hello_world", "__default__", ["airflow", "create", "--group-in-memory"]),
     ],
 )
 def test_create_airflow_dag(dag_name, pipeline_name, command, cli_runner, metadata):