fix: optimize MongoDB aggregation pipeline to prevent memory limit errors

- Move $limit operation early in pipeline for "*" queries to reduce memory usage
- Remove memory-intensive $sort operation for large dataset queries
- Add fallback mechanism for memory limit errors with simple query
- Implement additional safety checks to enforce max_nodes limit
- Improve error handling and logging for memory-related issues

lightrag/kg/mongo_impl.py

@@ -732,24 +732,25 @@ class MongoGraphStorage(BaseGraphStorage):
         node_edges = []
 
         try:
-            pipeline = [
-                {
-                    "$graphLookup": {
-                        "from": self._edge_collection_name,
-                        "startWith": "$_id",
-                        "connectFromField": "target_node_id",
-                        "connectToField": "source_node_id",
-                        "maxDepth": max_depth,
-                        "depthField": "depth",
-                        "as": "connected_edges",
+            # Optimize pipeline to avoid memory issues with large datasets
+            if label == "*":
+                # For getting all nodes, use a simpler pipeline to avoid memory issues
+                pipeline = [
+                    {"$limit": max_nodes},  # Limit early to reduce memory usage
+                    {
+                        "$graphLookup": {
+                            "from": self._edge_collection_name,
+                            "startWith": "$_id",
+                            "connectFromField": "target_node_id",
+                            "connectToField": "source_node_id",
+                            "maxDepth": max_depth,
+                            "depthField": "depth",
+                            "as": "connected_edges",
+                        },
                     },
-                },
-                {"$addFields": {"edge_count": {"$size": "$connected_edges"}}},
-                {"$sort": {"edge_count": -1}},
-                {"$limit": max_nodes},
-            ]
+                ]
 
-            if label == "*":
+                # Check if we need to set truncation flag
                 all_node_count = await self.collection.count_documents({})
                 result.is_truncated = all_node_count > max_nodes
             else:
@@ -759,10 +760,28 @@ class MongoGraphStorage(BaseGraphStorage):
                     logger.warning(f"Starting node with label {label} does not exist!")
                     return result
 
-                # Add starting node to pipeline
-                pipeline.insert(0, {"$match": {"_id": label}})
+                # For specific node queries, use the original pipeline but optimized
+                pipeline = [
+                    {"$match": {"_id": label}},
+                    {
+                        "$graphLookup": {
+                            "from": self._edge_collection_name,
+                            "startWith": "$_id",
+                            "connectFromField": "target_node_id",
+                            "connectToField": "source_node_id",
+                            "maxDepth": max_depth,
+                            "depthField": "depth",
+                            "as": "connected_edges",
+                        },
+                    },
+                    {"$addFields": {"edge_count": {"$size": "$connected_edges"}}},
+                    {"$sort": {"edge_count": -1}},
+                    {"$limit": max_nodes},
+                ]
 
             cursor = await self.collection.aggregate(pipeline, allowDiskUse=True)
+            nodes_processed = 0
+
             async for doc in cursor:
                 # Add the start node
                 node_id = str(doc["_id"])
@@ -786,6 +805,13 @@ class MongoGraphStorage(BaseGraphStorage):
                 if doc.get("connected_edges", []):
                     node_edges.extend(doc.get("connected_edges"))
 
+                nodes_processed += 1
+
+                # Additional safety check to prevent memory issues
+                if nodes_processed >= max_nodes:
+                    result.is_truncated = True
+                    break
+
             for edge in node_edges:
                 if (
                     edge["source_node_id"] not in seen_nodes
@@ -817,11 +843,35 @@ class MongoGraphStorage(BaseGraphStorage):
                     seen_edges.add(edge_id)
 
             logger.info(
-                f"Subgraph query successful | Node count: {len(result.nodes)} | Edge count: {len(result.edges)}"
+                f"Subgraph query successful | Node count: {len(result.nodes)} | Edge count: {len(result.edges)} | Truncated: {result.is_truncated}"
             )
 
         except PyMongoError as e:
-            logger.error(f"MongoDB query failed: {str(e)}")
+            # Handle memory limit errors specifically
+            if "memory limit" in str(e).lower() or "sort exceeded" in str(e).lower():
+                logger.warning(
+                    f"MongoDB memory limit exceeded, falling back to simple query: {str(e)}"
+                )
+                # Fallback to a simple query without complex aggregation
+                try:
+                    simple_cursor = self.collection.find({}).limit(max_nodes)
+                    async for doc in simple_cursor:
+                        node_id = str(doc["_id"])
+                        result.nodes.append(
+                            KnowledgeGraphNode(
+                                id=node_id,
+                                labels=[node_id],
+                                properties={k: v for k, v in doc.items() if k != "_id"},
+                            )
+                        )
+                    result.is_truncated = True
+                    logger.info(
+                        f"Fallback query completed | Node count: {len(result.nodes)}"
+                    )
+                except PyMongoError as fallback_error:
+                    logger.error(f"Fallback query also failed: {str(fallback_error)}")
+            else:
+                logger.error(f"MongoDB query failed: {str(e)}")
 
         return result