Issue #150/#156 integrate DeepGraphSplitter in AggregatorBatchJobs._c…

…reate_crossbackend_job

and integrate it in test coverage of TestCrossBackendSplitting

src/openeo_aggregator/backend.py

@@ -84,6 +84,7 @@
     streaming_flask_response,
 )
 from openeo_aggregator.constants import (
+    CROSSBACKEND_GRAPH_SPLIT_METHOD,
     JOB_OPTION_FORCE_BACKEND,
     JOB_OPTION_SPLIT_STRATEGY,
     JOB_OPTION_TILE_GRID,
@@ -102,6 +103,7 @@
 from openeo_aggregator.partitionedjobs import PartitionedJob
 from openeo_aggregator.partitionedjobs.crossbackend import (
     CrossBackendJobSplitter,
+    DeepGraphSplitter,
     LoadCollectionGraphSplitter,
 )
 from openeo_aggregator.partitionedjobs.splitting import FlimsySplitter, TileGridSplitter
@@ -806,25 +808,33 @@ def create_job(
         if "process_graph" not in process:
             raise ProcessGraphMissingException()
 
+        # Coverage of messy "split_strategy" job option https://github.com/Open-EO/openeo-aggregator/issues/156
         # TODO: better, more generic/specific job_option(s)?
-        if job_options and (job_options.get(JOB_OPTION_SPLIT_STRATEGY) or job_options.get(JOB_OPTION_TILE_GRID)):
-            if job_options.get(JOB_OPTION_SPLIT_STRATEGY) == "crossbackend":
-                # TODO this is temporary feature flag to trigger "crossbackend" splitting
-                return self._create_crossbackend_job(
-                    user_id=user_id,
-                    process=process,
-                    api_version=api_version,
-                    metadata=metadata,
-                    job_options=job_options,
-                )
-            else:
-                return self._create_partitioned_job(
-                    user_id=user_id,
-                    process=process,
-                    api_version=api_version,
-                    metadata=metadata,
-                    job_options=job_options,
-                )
+        split_strategy = (job_options or {}).get(JOB_OPTION_SPLIT_STRATEGY)
+        # TODO: this job option "tile_grid" is quite generic and not very explicit about being a job splitting approach
+        tile_grid = (job_options or {}).get(JOB_OPTION_TILE_GRID)
+
+        crossbackend_mode = (
+            split_strategy == "crossbackend" or isinstance(split_strategy, dict) and "crossbackend" in split_strategy
+        )
+        spatial_split_mode = tile_grid or split_strategy == "flimsy"
+
+        if crossbackend_mode:
+            return self._create_crossbackend_job(
+                user_id=user_id,
+                process=process,
+                api_version=api_version,
+                metadata=metadata,
+                job_options=job_options,
+            )
+        elif spatial_split_mode:
+            return self._create_partitioned_job(
+                user_id=user_id,
+                process=process,
+                api_version=api_version,
+                metadata=metadata,
+                job_options=job_options,
+            )
         else:
             return self._create_job_standard(
                 user_id=user_id,
@@ -939,16 +949,42 @@ def _create_crossbackend_job(
         if not self.partitioned_job_tracker:
             raise FeatureUnsupportedException(message="Partitioned job tracking is not supported")
 
-        def backend_for_collection(collection_id) -> str:
-            return self._catalog.get_backends_for_collection(cid=collection_id)[0]
+        split_strategy = (job_options or {}).get(JOB_OPTION_SPLIT_STRATEGY)
+        if split_strategy == "crossbackend":
+            graph_split_method = CROSSBACKEND_GRAPH_SPLIT_METHOD.SIMPLE
+        elif isinstance(split_strategy, dict) and isinstance(split_strategy.get("crossbackend"), dict):
+            graph_split_method = split_strategy.get("crossbackend", {}).get(
+                "method", CROSSBACKEND_GRAPH_SPLIT_METHOD.SIMPLE
+            )
+        else:
+            raise ValueError(f"Invalid split strategy {split_strategy!r}")
+
+        _log.info(f"_create_crossbackend_job: {graph_split_method=} from {split_strategy=}")
+        if graph_split_method == CROSSBACKEND_GRAPH_SPLIT_METHOD.SIMPLE:
+
+            def backend_for_collection(collection_id) -> str:
+                return self._catalog.get_backends_for_collection(cid=collection_id)[0]
 
-        splitter = CrossBackendJobSplitter(
-            graph_splitter=LoadCollectionGraphSplitter(
+            graph_splitter = LoadCollectionGraphSplitter(
                 backend_for_collection=backend_for_collection,
                 # TODO: job option for `always_split` feature?
                 always_split=True,
             )
-        )
+        elif graph_split_method == CROSSBACKEND_GRAPH_SPLIT_METHOD.DEEP:
+
+            def supporting_backends(node_id: str, node: dict) -> Union[List[str], None]:
+                if node["process_id"] == "load_collection":
+                    collection_id = node["arguments"]["id"]
+                    return self._catalog.get_backends_for_collection(cid=collection_id)
+
+            graph_splitter = DeepGraphSplitter(
+                supporting_backends=supporting_backends,
+                primary_backend=split_strategy.get("crossbackend", {}).get("primary_backend"),
+            )
+        else:
+            raise ValueError(f"Invalid graph split strategy {graph_split_method!r}")
+
+        splitter = CrossBackendJobSplitter(graph_splitter=graph_splitter)
 
         pjob_id = self.partitioned_job_tracker.create_crossbackend_pjob(
             user_id=user_id, process=process, metadata=metadata, job_options=job_options, splitter=splitter

src/openeo_aggregator/constants.py

@@ -4,3 +4,9 @@
 
 # Experimental feature to force a certain upstream back-end through job options
 JOB_OPTION_FORCE_BACKEND = "_agg_force_backend"
+
+
+class CROSSBACKEND_GRAPH_SPLIT_METHOD:
+    # Poor-man's StrEnum
+    SIMPLE = "simple"
+    DEEP = "deep"

src/openeo_aggregator/partitionedjobs/crossbackend.py

@@ -743,7 +743,7 @@ def next_nodes(node_id: NodeId) -> Iterable[NodeId]:
 
         return up, down
 
-    def produce_split_locations(self, limit: int = 4) -> Iterator[List[NodeId]]:
+    def produce_split_locations(self, limit: int = 10) -> Iterator[List[NodeId]]:
         """
         Produce disjoint subgraphs that can be processed independently.
 
@@ -811,49 +811,55 @@ class DeepGraphSplitter(ProcessGraphSplitterInterface):
     More advanced graph splitting (compared to just splitting off `load_collection` nodes)
     """
 
-    def __init__(self, supporting_backends: SupportingBackendsMapper):
+    def __init__(self, supporting_backends: SupportingBackendsMapper, primary_backend: Optional[BackendId] = None):
         self._supporting_backends_mapper = supporting_backends
+        self._primary_backend = primary_backend
 
     def split(self, process_graph: FlatPG) -> _PGSplitResult:
         graph = _GraphViewer.from_flat_graph(
             flat_graph=process_graph, supporting_backends=self._supporting_backends_mapper
         )
 
-        # TODO: make picking "optimal" split location set a bit more deterministic (e.g. sort first)
-        (split_nodes,) = graph.produce_split_locations(limit=1)
-        _log.debug(f"DeepGraphSplitter.split: split nodes: {split_nodes=}")
-
-        secondary_graphs: List[_SubGraphData] = []
-        graph_to_split = graph
-        for split_node_id in split_nodes:
-            up, down = graph_to_split.split_at(split_node_id=split_node_id)
-            # Use upstream graph as secondary graph
-            node_ids = set(nid for nid, _ in up.iter_nodes())
-            backend_candidates = up.get_backend_candidates_for_node_set(node_ids)
-            # TODO: better backend selection?
-            # TODO handle case where backend_candidates is None?
-            backend_id = sorted(backend_candidates)[0]
-            _log.debug(f"DeepGraphSplitter.split: secondary graph: from {split_node_id=}: {backend_id=} {node_ids=}")
-            secondary_graphs.append(
-                _SubGraphData(
-                    split_node=split_node_id,
-                    node_ids=node_ids,
-                    backend_id=backend_id,
+        for split_nodes in graph.produce_split_locations():
+            _log.debug(f"DeepGraphSplitter.split: evaluating split nodes: {split_nodes=}")
+
+            secondary_graphs: List[_SubGraphData] = []
+            graph_to_split = graph
+            for split_node_id in split_nodes:
+                up, down = graph_to_split.split_at(split_node_id=split_node_id)
+                # Use upstream graph as secondary graph
+                node_ids = set(nid for nid, _ in up.iter_nodes())
+                backend_candidates = up.get_backend_candidates_for_node_set(node_ids)
+                # TODO: better backend selection?
+                # TODO handle case where backend_candidates is None?
+                backend_id = sorted(backend_candidates)[0]
+                _log.debug(
+                    f"DeepGraphSplitter.split: secondary graph: from {split_node_id=}: {backend_id=} {node_ids=}"
+                )
+                secondary_graphs.append(
+                    _SubGraphData(
+                        split_node=split_node_id,
+                        node_ids=node_ids,
+                        backend_id=backend_id,
+                    )
                 )
-            )
-
-            # Prepare for next split (if any)
-            graph_to_split = down
 
-        # Remaining graph is primary graph
-        primary_graph = graph_to_split
-        primary_node_ids = set(n for n, _ in primary_graph.iter_nodes())
-        backend_candidates = primary_graph.get_backend_candidates_for_node_set(primary_node_ids)
-        primary_backend_id = sorted(backend_candidates)[0]
-        _log.debug(f"DeepGraphSplitter.split: primary graph: {primary_backend_id=} {primary_node_ids=}")
+                # Prepare for next split (if any)
+                graph_to_split = down
+
+            # Remaining graph is primary graph
+            primary_graph = graph_to_split
+            primary_node_ids = set(n for n, _ in primary_graph.iter_nodes())
+            backend_candidates = primary_graph.get_backend_candidates_for_node_set(primary_node_ids)
+            primary_backend_id = sorted(backend_candidates)[0]
+            _log.debug(f"DeepGraphSplitter.split: primary graph: {primary_backend_id=} {primary_node_ids=}")
+
+            if self._primary_backend is None or primary_backend_id == self._primary_backend:
+                _log.debug(f"DeepGraphSplitter.split: current split matches constraints")
+                return _PGSplitResult(
+                    primary_node_ids=primary_node_ids,
+                    primary_backend_id=primary_backend_id,
+                    secondary_graphs=secondary_graphs,
+                )
 
-        return _PGSplitResult(
-            primary_node_ids=primary_node_ids,
-            primary_backend_id=primary_backend_id,
-            secondary_graphs=secondary_graphs,
-        )
+        raise GraphSplitException("DeepGraphSplitter.split: No matching split found.")

src/openeo_aggregator/testing.py

@@ -103,6 +103,8 @@ def approx_now(abs=10):
 class ApproxStr:
     """Pytest helper in style of `pytest.approx`, but for string checking, based on prefix, body and or suffix"""
 
+    # TODO: port to dirty_equals
+
     def __init__(
         self,
         prefix: Optional[str] = None,