topological order for deletes

datadog_sync/utils/resource_utils.py

@@ -256,3 +256,56 @@ def init_topological_sorter(
     sorter = TopologicalSorter(graph)
     sorter.prepare()
     return sorter
+
+
+def detect_circular_dependencies(graph: Dict[Tuple[str, str], Set[Tuple[str, str]]]) -> Optional[List[Tuple[str, str]]]:
+    """Detect circular dependencies in a dependency graph.
+
+    Args:
+        graph: Dependency graph to check
+
+    Returns:
+        None if no cycles, or a list showing one cycle path if found
+
+    Example:
+        >>> graph = {("monitors", "A"): {("monitors", "B")}, ("monitors", "B"): {("monitors", "A")}}
+        >>> detect_circular_dependencies(graph)
+        [("monitors", "A"), ("monitors", "B"), ("monitors", "A")]
+    """
+    try:
+        sorter = TopologicalSorter(graph)
+        sorter.prepare()
+        # If prepare() succeeds, no cycles
+        return None
+    except Exception:
+        # Cycle detected - try to find it using DFS
+        visited = set()
+        path = []
+
+        def find_cycle(node):
+            if node in path:
+                # Found cycle!
+                cycle_start = path.index(node)
+                return path[cycle_start:] + [node]
+
+            if node in visited:
+                return None
+
+            visited.add(node)
+            path.append(node)
+
+            for neighbor in graph.get(node, []):
+                cycle = find_cycle(neighbor)
+                if cycle:
+                    return cycle
+
+            path.pop()
+            return None
+
+        for node in graph:
+            if node not in visited:
+                cycle = find_cycle(node)
+                if cycle:
+                    return cycle
+
+        return None

datadog_sync/utils/resources_handler.py

@@ -36,6 +36,7 @@ class ResourcesHandler:
     def __init__(self, config: Configuration) -> None:
         self.config = config
         self.sorter: Optional[TopologicalSorter] = None
+        self.cleanup_sorter: Optional[TopologicalSorter] = None
         self.worker: Optional[Workers] = None
         self._dependency_graph = Optional[Dict[Tuple[str, str], List[Tuple[str, str]]]]
 
@@ -90,12 +91,63 @@ async def apply_resources(self) -> Tuple[int, int]:
             if cleanup_resources:
                 cleanup = _cleanup_prompt(self.config, cleanup_resources)
                 if cleanup:
-                    self.config.logger.info("cleaning up resources...")
-                    await self.worker.init_workers(self._cleanup_worker, None, None)
-                    for i in cleanup_resources:
-                        self.worker.work_queue.put_nowait(i)
-                    await self.worker.schedule_workers()
-                    self.config.logger.info("finished cleaning up resources")
+                    self.config.logger.info("cleaning up resources with dependency ordering...")
+
+                    # Build reverse dependency graph for ordered cleanup
+                    try:
+                        cleanup_graph = self.get_cleanup_dependency_graph(cleanup_resources)
+                        self.config.logger.info(f"Built cleanup dependency graph with {len(cleanup_graph)} resources")
+
+                        # Detect circular dependencies
+                        from datadog_sync.utils.resource_utils import detect_circular_dependencies
+
+                        cycle = detect_circular_dependencies(cleanup_graph)
+                        if cycle:
+                            # Circular dependency detected!
+                            cycle_str = " -> ".join([f"{rt}:{rid}" for rt, rid in cycle])
+                            self.config.logger.error(f"Circular dependency detected in cleanup graph: {cycle_str}")
+                            self.config.logger.error(
+                                "Cannot safely delete resources. Please manually break circular references first."
+                            )
+                            raise ValueError(
+                                f"Circular dependency in cleanup graph: {cycle_str}. "
+                                "Manual intervention required to break the cycle."
+                            )
+
+                        # Initialize cleanup sorter
+                        self.cleanup_sorter = init_topological_sorter(cleanup_graph)
+
+                        # Initialize workers with cleanup sorter stop condition
+                        await self.worker.init_workers(
+                            self._cleanup_worker, lambda: not self.cleanup_sorter.is_active(), None
+                        )
+
+                        # Run cleanup with ordering
+                        if self.config.show_progress_bar:
+                            await self.worker.schedule_workers_with_pbar(
+                                total=len(cleanup_graph), additional_coros=[self.run_cleanup_sorter()]
+                            )
+                        else:
+                            await self.worker.schedule_workers(additional_coros=[self.run_cleanup_sorter()])
+
+                        self.config.logger.info("finished cleaning up resources")
+
+                    except ValueError:
+                        # Circular dependency - already logged, re-raise
+                        raise
+                    except Exception as e:
+                        # Unexpected error building or running cleanup graph
+                        self.config.logger.error(f"Error during ordered cleanup: {str(e)}")
+                        self.config.logger.warning(
+                            "Falling back to unordered cleanup (may fail due to dependency issues)"
+                        )
+
+                        # Fallback to old unordered behavior
+                        await self.worker.init_workers(self._cleanup_worker, None, None)
+                        for i in cleanup_resources:
+                            self.worker.work_queue.put_nowait(i)
+                        await self.worker.schedule_workers()
+                        self.config.logger.info("finished cleaning up resources (unordered fallback)")
 
         # Run pre-apply hooks
         resource_types = set(i[0] for i in self._dependency_graph)
@@ -349,6 +401,10 @@ async def _cleanup_worker(self, q_item: List) -> None:
             await r_class._send_action_metrics("delete", _id, Status.FAILURE.value)
             self.config.logger.error(f"error deleting resource {resource_type} with id {_id}: {str(e)}")
         finally:
+            # Mark as done in cleanup sorter if it exists
+            if hasattr(self, "cleanup_sorter") and self.cleanup_sorter:
+                self.cleanup_sorter.done(q_item)
+
             if not r_class.resource_config.concurrent:
                 r_class.resource_config.async_lock.release()
 
@@ -370,6 +426,30 @@ async def run_sorter(self):
                 await self.worker.work_queue.put(node)
             await asyncio.sleep(0)
 
+    async def run_cleanup_sorter(self):
+        """Mirror of run_sorter() but for cleanup operations.
+
+        Continuously feeds deletion-ready resources to workers in proper order.
+        Resources are only deleted after all their dependents are deleted.
+        """
+        loop = asyncio.get_event_loop()
+
+        while await loop.run_in_executor(None, self.cleanup_sorter.is_active):
+            for node in self.cleanup_sorter.get_ready():
+                resource_type, _id = node
+
+                # Verify resource still exists in destination
+                if _id not in self.config.state.destination[resource_type]:
+                    # Already deleted or doesn't exist
+                    self.config.logger.debug(f"Resource {resource_type}:{_id} already deleted, marking as done")
+                    self.cleanup_sorter.done(node)
+                    continue
+
+                # Add to work queue for deletion
+                await self.worker.work_queue.put(node)
+
+            await asyncio.sleep(0)
+
     def get_dependency_graph(self) -> Tuple[Dict[Tuple[str, str], List[Tuple[str, str]]], Set[Tuple[str, str]]]:
         """Build the dependency graph for all resources.
 
@@ -428,6 +508,160 @@ def _resource_connections(self, resource_type: str, _id: str) -> Tuple[Set[Tuple
 
         return failed_connections, missing_resources
 
+    def get_cleanup_dependency_graph(
+        self, cleanup_resources: Dict[Tuple[str, str], str | None]
+    ) -> Dict[Tuple[str, str], Set[Tuple[str, str]]]:
+        """Build REVERSE dependency graph for cleanup.
+
+        For deletion, we need to delete dependents BEFORE dependencies.
+        This inverts the normal creation graph.
+
+        Args:
+            cleanup_resources: Resources to be deleted from destination
+
+        Returns:
+            Dict mapping (resource_type, _id) to set of resources that depend on it
+
+        Example:
+            If Dashboard depends on Monitor:
+            Creation graph: {("dashboards", "dash-1"): [("monitors", "mon-1")]}
+            Cleanup graph:  {("monitors", "mon-1"): {("dashboards", "dash-1")}}
+
+            This means: Monitor "mon-1" can only be deleted AFTER Dashboard "dash-1"
+        """
+        reverse_graph = defaultdict(set)
+
+        # Initialize all cleanup resources with empty dependencies
+        for resource_key in cleanup_resources.keys():
+            reverse_graph[resource_key] = set()
+
+        # Build reverse dependencies by scanning destination state
+        for resource_type, _id in cleanup_resources.keys():
+            if resource_type not in self.config.resources:
+                continue
+
+            r_config = self.config.resources[resource_type].resource_config
+            if not r_config.resource_connections:
+                continue
+
+            # Get the actual resource from destination state (already in memory)
+            if _id not in self.config.state.destination[resource_type]:
+                self.config.logger.debug(
+                    f"Resource {resource_type}:{_id} not in destination state, skipping dependency analysis"
+                )
+                continue
+
+            resource = self.config.state.destination[resource_type][_id]
+
+            # For each dependency this resource has
+            for dep_type, attr_paths in r_config.resource_connections.items():
+                for attr_path in attr_paths:
+                    # Find dependency IDs in the resource
+                    dep_ids = self._extract_dependency_ids(resource, attr_path, dep_type)
+
+                    # For each dependency, add THIS resource as a dependent
+                    for dep_id in dep_ids:
+                        dep_key = (dep_type, dep_id)
+                        # Only include if the dependency is also being cleaned up
+                        if dep_key in cleanup_resources:
+                            # This says: "dep_key must be deleted AFTER (resource_type, _id)"
+                            reverse_graph[dep_key].add((resource_type, _id))
+                            self.config.logger.debug(f"Dependency edge: {dep_type}:{dep_id} <- {resource_type}:{_id}")
+
+        return dict(reverse_graph)
+
+    def _extract_dependency_ids(self, resource: Dict, attr_path: str, dep_type: str) -> Set[str]:
+        """Extract dependency IDs from a resource at the given attribute path.
+
+        Args:
+            resource: The resource dict
+            attr_path: Dot-separated path like "widgets.definition.alert_id"
+            dep_type: Type of dependency (for context)
+
+        Returns:
+            Set of dependency IDs found
+        """
+        ids = set()
+
+        def extract_from_obj(obj, path_parts):
+            if not obj or not path_parts:
+                return
+
+            current_key = path_parts[0]
+            remaining = path_parts[1:]
+
+            if isinstance(obj, list):
+                for item in obj:
+                    extract_from_obj(item, path_parts)
+            elif isinstance(obj, dict):
+                if current_key in obj:
+                    if not remaining:
+                        # We're at the target attribute
+                        value = obj[current_key]
+                        if value:
+                            # Handle both lists and single values
+                            values = [value] if not isinstance(value, list) else value
+                            for v in values:
+                                if v:
+                                    # Parse prefixed IDs (e.g., "dashboard:abc-123" → "abc-123")
+                                    parsed_id = self._parse_prefixed_id(str(v), dep_type)
+                                    ids.add(parsed_id)
+                    else:
+                        # Keep traversing
+                        extract_from_obj(obj[current_key], remaining)
+
+        path_parts = attr_path.split(".")
+        extract_from_obj(resource, path_parts)
+        return ids
+
+    def _parse_prefixed_id(self, value: str, expected_resource_type: str) -> str:
+        """Parse IDs that may have resource type prefixes.
+
+        Restriction policies and some other resources use prefixed IDs like
+        "dashboard:abc-123" or "slo:xyz-789". This method strips the prefix
+        to get the actual resource ID that matches the state keys.
+
+        Examples:
+            "dashboard:abc-123" with expected_resource_type="dashboards" → "abc-123"
+            "slo:xyz-789" with expected_resource_type="service_level_objectives" → "xyz-789"
+            "abc-123" with any type → "abc-123" (passthrough)
+
+        Args:
+            value: The ID value, possibly prefixed
+            expected_resource_type: The resource type we expect (e.g., "dashboards")
+
+        Returns:
+            The parsed ID without prefix
+        """
+        # Check if value contains a colon (indicates prefix)
+        if ":" not in value:
+            return value
+
+        # Split on first colon only
+        prefix, actual_id = value.split(":", 1)
+
+        # Map prefix to resource type
+        prefix_to_resource_type = {
+            "dashboard": "dashboards",
+            "slo": "service_level_objectives",
+            "notebook": "notebooks",
+            "monitor": "monitors",
+            "user": "users",
+            "role": "roles",
+            "team": "teams",
+            "security-rule": "security_monitoring_rules",
+        }
+
+        # If prefix matches expected resource type, return just the ID
+        if prefix_to_resource_type.get(prefix) == expected_resource_type:
+            return actual_id
+
+        # If prefix doesn't match, return original value (might be a false positive)
+        self.config.logger.debug(
+            f"ID '{value}' has prefix '{prefix}' but expected resource type '{expected_resource_type}'"
+        )
+        return value
+
 
 def _cleanup_prompt(
     config: Configuration, resources_to_cleanup: Dict[Tuple[str, str], str | None], prompt: bool = True