chore(similarity): Add span to individual record insert (#1159)

Add span for individual record insert in bulk record insertion

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