diff --git a/src/migrations/versions/d0d1d2df4540_migration.py b/src/migrations/versions/d0d1d2df4540_migration.py
new file mode 100644
index 000000000..e156a30f2
--- /dev/null
+++ b/src/migrations/versions/d0d1d2df4540_migration.py
@@ -0,0 +1,31 @@
+"""Migration
+
+Revision ID: d0d1d2df4540
+Revises: da0a9c9f1bb4
+Create Date: 2024-09-17 17:03:02.000258
+
+"""
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision = "d0d1d2df4540"
+down_revision = "da0a9c9f1bb4"
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table("grouping_records", schema=None) as batch_op:
+        batch_op.alter_column("message", existing_type=sa.VARCHAR(), nullable=True)
+
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table("grouping_records", schema=None) as batch_op:
+        batch_op.alter_column("message", existing_type=sa.VARCHAR(), nullable=False)
+
+    # ### end Alembic commands ###
diff --git a/src/seer/db.py b/src/seer/db.py
index a66ecddf8..0b6e63ef3 100644
--- a/src/seer/db.py
+++ b/src/seer/db.py
@@ -256,7 +256,7 @@ class DbGroupingRecord(Base):
         server_default=text("nextval('grouping_records_id_seq')"),
     )
     project_id: Mapped[int] = mapped_column(BigInteger, primary_key=True, nullable=False)
-    message: Mapped[str] = mapped_column(String, nullable=False)
+    message: Mapped[Optional[str]] = mapped_column(String, nullable=True)
     error_type: Mapped[str] = mapped_column(String, nullable=True)
     stacktrace_embedding: Mapped[Vector] = mapped_column(Vector(768), nullable=False)
     hash: Mapped[str] = mapped_column(String(32), nullable=False)
diff --git a/src/seer/grouping/grouping.py b/src/seer/grouping/grouping.py
index 56115c7d3..472262e3c 100644
--- a/src/seer/grouping/grouping.py
+++ b/src/seer/grouping/grouping.py
@@ -424,46 +424,50 @@ def insert_batch_grouping_records(
         )
         with Session() as session:
             for i, entry in enumerate(data.data):
-                embedding = embeddings[i].astype("float32")
-                nearest_neighbor = self.query_nearest_k_neighbors(
-                    session,
-                    embedding,
-                    entry.project_id,
-                    entry.hash,
-                    data.threshold,
-                    data.k,
-                    data.hnsw_candidates,
-                    data.hnsw_distance,
-                    data.use_reranking,
-                )
-
-                if nearest_neighbor:
-                    neighbor, distance = nearest_neighbor[0][0], nearest_neighbor[0][1]
-                    message_similarity_score = difflib.SequenceMatcher(
-                        None, entry.message, neighbor.message
-                    ).ratio()
-                    response = GroupingResponse(
-                        parent_hash=neighbor.hash,
-                        stacktrace_distance=distance,
-                        message_distance=1.0 - message_similarity_score,
-                        should_group=True,
-                    )
-                    groups_with_neighbor[str(entry.group_id)] = response
-                else:
-                    insert_stmt = insert(DbGroupingRecord).values(
-                        project_id=entry.project_id,
-                        message=entry.message,
-                        error_type=entry.exception_type,
-                        hash=entry.hash,
-                        stacktrace_embedding=embedding,
+                with sentry_sdk.start_span(
+                    op="seer.grouping", description="insert single grouping record"
+                ) as span:
+                    span.set_data("stacktrace_len", len(data.stacktrace_list[i]))
+                    embedding = embeddings[i].astype("float32")
+                    nearest_neighbor = self.query_nearest_k_neighbors(
+                        session,
+                        embedding,
+                        entry.project_id,
+                        entry.hash,
+                        data.threshold,
+                        data.k,
+                        data.hnsw_candidates,
+                        data.hnsw_distance,
+                        data.use_reranking,
                     )
 
-                    session.execute(
-                        insert_stmt.on_conflict_do_nothing(
-                            index_elements=(DbGroupingRecord.project_id, DbGroupingRecord.hash)
+                    if nearest_neighbor:
+                        neighbor, distance = nearest_neighbor[0][0], nearest_neighbor[0][1]
+                        message_similarity_score = difflib.SequenceMatcher(
+                            None, entry.message, neighbor.message
+                        ).ratio()
+                        response = GroupingResponse(
+                            parent_hash=neighbor.hash,
+                            stacktrace_distance=distance,
+                            message_distance=1.0 - message_similarity_score,
+                            should_group=True,
                         )
-                    )
-                    session.commit()
+                        groups_with_neighbor[str(entry.group_id)] = response
+                    else:
+                        insert_stmt = insert(DbGroupingRecord).values(
+                            project_id=entry.project_id,
+                            message=entry.message,
+                            error_type=entry.exception_type,
+                            hash=entry.hash,
+                            stacktrace_embedding=embedding,
+                        )
+
+                        session.execute(
+                            insert_stmt.on_conflict_do_nothing(
+                                index_elements=(DbGroupingRecord.project_id, DbGroupingRecord.hash)
+                            )
+                        )
+                        session.commit()
 
         return groups_with_neighbor