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Type: Bug
Resolution: Fixed
Priority: Critical
Fix Version/s: 7.6.0
Affects Version/s: 7.6.0
Component/s: fts
Labels:
Environment:
Couchbase server
Enterprise Edition 7.6.0 build 2029

Triage:
Untriaged
Link to Log File, atop/blg, CBCollectInfo, Core dump:

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https://cb-engineering.s3.amazonaws.com/vector-search-query-failure/collectinfo-2024-01-24T102206-ns_1%40172.23.105.204.zip
https://cb-engineering.s3.amazonaws.com/vector-search-query-failure/collectinfo-2024-01-24T102206-ns_1%40172.23.105.206.zip
https://cb-engineering.s3.amazonaws.com/vector-search-query-failure/collectinfo-2024-01-24T102206-ns_1%40172.23.105.211.zip
https://cb-engineering.s3.amazonaws.com/vector-search-query-failure/collectinfo-2024-01-24T102206-ns_1%40172.23.121.220.zip
https://cb-engineering.s3.amazonaws.com/vector-search-query-failure/collectinfo-2024-01-24T102206-ns_1%40172.23.96.168.zip
https://cb-engineering.s3.amazonaws.com/vector-search-query-failure/collectinfo-2024-01-24T102206-ns_1%40172.23.96.196.zip

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https://cb-engineering.s3.amazonaws.com/vector-search-query-failure/collectinfo-2024-01-24T102206-ns_1%40172.23.105.204.zip https://cb-engineering.s3.amazonaws.com/vector-search-query-failure/collectinfo-2024-01-24T102206-ns_1%40172.23.105.206.zip https://cb-engineering.s3.amazonaws.com/vector-search-query-failure/collectinfo-2024-01-24T102206-ns_1%40172.23.105.211.zip https://cb-engineering.s3.amazonaws.com/vector-search-query-failure/collectinfo-2024-01-24T102206-ns_1%40172.23.121.220.zip https://cb-engineering.s3.amazonaws.com/vector-search-query-failure/collectinfo-2024-01-24T102206-ns_1%40172.23.96.168.zip https://cb-engineering.s3.amazonaws.com/vector-search-query-failure/collectinfo-2024-01-24T102206-ns_1%40172.23.96.196.zip
Story Points:
0
Is this a Regression?:
Yes

Description

1. Created a 6 node cluster.

2. Created a bucket 'b1', scope 's1', collection 'c1'.

3. Loaded 5k docs with vector dimension 150.

4. Loaded 5k docs with vector dimension 130.

5. Created vector indexes with both the dimension. Index 'i0' with dimension 150. Index 'i1' with dimension 130.

6. Wait for indexing to complete.

7. Run both N1QL and FTS queries on index 'i0'. Eg:

N1QL query:

SELECT COUNT(*) FROM `b1`.s1.c1 AS t1 WHERE SEARCH(t1, {'query': {'match_none': {}}, 'explain': True, 'fields': ['*'], 'knn': [{'field': 'vector_data', 'k': 10, 'vector': [1.0, 3.0, 11.0, 110.0, 62.0, 22.0, 4.0, 0.0, 43.0, 21.0, 22.0, 18.0, 6.0, 28.0, 64.0, 9.0, 11.0, 1.0, 0.0, 0.0, 1.0, 40.0, 101.0, 21.0, 20.0, 2.0, 4.0, 2.0, 2.0, 9.0, 18.0, 35.0, 1.0, 1.0, 7.0, 25.0, 108.0, 116.0, 63.0, 2.0, 0.0, 0.0, 11.0, 74.0, 40.0, 101.0, 116.0, 3.0, 33.0, 1.0, 1.0, 11.0, 14.0, 18.0, 116.0, 116.0, 68.0, 12.0, 5.0, 4.0, 2.0, 2.0, 9.0, 102.0, 17.0, 3.0, 10.0, 18.0, 8.0, 15.0, 67.0, 63.0, 15.0, 0.0, 14.0, 116.0, 80.0, 0.0, 2.0, 22.0, 96.0, 37.0, 28.0, 88.0, 43.0, 1.0, 4.0, 18.0, 116.0, 51.0, 5.0, 11.0, 32.0, 14.0, 8.0, 23.0, 44.0, 17.0, 12.0, 9.0, 0.0, 0.0, 19.0, 37.0, 85.0, 18.0, 16.0, 104.0, 22.0, 6.0, 2.0, 26.0, 12.0, 58.0, 67.0, 82.0, 25.0, 12.0, 2.0, 2.0, 25.0, 18.0, 8.0, 2.0, 19.0, 42.0, 48.0, 11.0, 1.0, 3.0, 11.0, 110.0, 62.0, 22.0, 4.0, 0.0, 43.0, 21.0, 22.0, 18.0, 6.0, 28.0, 64.0, 9.0, 11.0, 1.0, 0.0, 0.0, 1.0, 40.0]}]});

FTS query:

{'query': {'match_none': {}}, 'explain': True, 'fields': ['*'], 'knn': [{'field': 'vector_data', 'k': 10, 'vector': [1.0, 3.0, 11.0, 110.0, 62.0, 22.0, 4.0, 0.0, 43.0, 21.0, 22.0, 18.0, 6.0, 28.0, 64.0, 9.0, 11.0, 1.0, 0.0, 0.0, 1.0, 40.0, 101.0, 21.0, 20.0, 2.0, 4.0, 2.0, 2.0, 9.0, 18.0, 35.0, 1.0, 1.0, 7.0, 25.0, 108.0, 116.0, 63.0, 2.0, 0.0, 0.0, 11.0, 74.0, 40.0, 101.0, 116.0, 3.0, 33.0, 1.0, 1.0, 11.0, 14.0, 18.0, 116.0, 116.0, 68.0, 12.0, 5.0, 4.0, 2.0, 2.0, 9.0, 102.0, 17.0, 3.0, 10.0, 18.0, 8.0, 15.0, 67.0, 63.0, 15.0, 0.0, 14.0, 116.0, 80.0, 0.0, 2.0, 22.0, 96.0, 37.0, 28.0, 88.0, 43.0, 1.0, 4.0, 18.0, 116.0, 51.0, 5.0, 11.0, 32.0, 14.0, 8.0, 23.0, 44.0, 17.0, 12.0, 9.0, 0.0, 0.0, 19.0, 37.0, 85.0, 18.0, 16.0, 104.0, 22.0, 6.0, 2.0, 26.0, 12.0, 58.0, 67.0, 82.0, 25.0, 12.0, 2.0, 2.0, 25.0, 18.0, 8.0, 2.0, 19.0, 42.0, 48.0, 11.0, 1.0, 3.0, 11.0, 110.0, 62.0, 22.0, 4.0, 0.0, 43.0, 21.0, 22.0, 18.0, 6.0, 28.0, 64.0, 9.0, 11.0, 1.0, 0.0, 0.0, 1.0, 40.0]}]}

8. Ran 5 such queries on index 'i0' which all returned k results.

9. Now ran 5 N1QL and 5 FTS queries on index 'i1'. Eg:

N1QL:

ELECT COUNT(*) FROM `b1`.s1.c1 AS t1 WHERE SEARCH(t1, {'query': {'match_none': {}}, 'explain': True, 'fields': ['*'], 'knn': [{'field': 'vector_data', 'k': 10, 'vector': [1.0, 3.0, 11.0, 110.0, 62.0, 22.0, 4.0, 0.0, 43.0, 21.0, 22.0, 18.0, 6.0, 28.0, 64.0, 9.0, 11.0, 1.0, 0.0, 0.0, 1.0, 40.0, 101.0, 21.0, 20.0, 2.0, 4.0, 2.0, 2.0, 9.0, 18.0, 35.0, 1.0, 1.0, 7.0, 25.0, 108.0, 116.0, 63.0, 2.0, 0.0, 0.0, 11.0, 74.0, 40.0, 101.0, 116.0, 3.0, 33.0, 1.0, 1.0, 11.0, 14.0, 18.0, 116.0, 116.0, 68.0, 12.0, 5.0, 4.0, 2.0, 2.0, 9.0, 102.0, 17.0, 3.0, 10.0, 18.0, 8.0, 15.0, 67.0, 63.0, 15.0, 0.0, 14.0, 116.0, 80.0, 0.0, 2.0, 22.0, 96.0, 37.0, 28.0, 88.0, 43.0, 1.0, 4.0, 18.0, 116.0, 51.0, 5.0, 11.0, 32.0, 14.0, 8.0, 23.0, 44.0, 17.0, 12.0, 9.0, 0.0, 0.0, 19.0, 37.0, 85.0, 18.0, 16.0, 104.0, 22.0, 6.0, 2.0, 26.0, 12.0, 58.0, 67.0, 82.0, 25.0, 12.0, 2.0, 2.0, 25.0, 18.0, 8.0, 2.0, 19.0, 42.0, 48.0, 11.0, 1.0, 3.0]}]});

FTS query:

{'query': {'match_none': {}}, 'explain': True, 'fields': ['*'], 'knn': [{'field': 'vector_data', 'k': 10, 'vector': [1.0, 3.0, 11.0, 110.0, 62.0, 22.0, 4.0, 0.0, 43.0, 21.0, 22.0, 18.0, 6.0, 28.0, 64.0, 9.0, 11.0, 1.0, 0.0, 0.0, 1.0, 40.0, 101.0, 21.0, 20.0, 2.0, 4.0, 2.0, 2.0, 9.0, 18.0, 35.0, 1.0, 1.0, 7.0, 25.0, 108.0, 116.0, 63.0, 2.0, 0.0, 0.0, 11.0, 74.0, 40.0, 101.0, 116.0, 3.0, 33.0, 1.0, 1.0, 11.0, 14.0, 18.0, 116.0, 116.0, 68.0, 12.0, 5.0, 4.0, 2.0, 2.0, 9.0, 102.0, 17.0, 3.0, 10.0, 18.0, 8.0, 15.0, 67.0, 63.0, 15.0, 0.0, 14.0, 116.0, 80.0, 0.0, 2.0, 22.0, 96.0, 37.0, 28.0, 88.0, 43.0, 1.0, 4.0, 18.0, 116.0, 51.0, 5.0, 11.0, 32.0, 14.0, 8.0, 23.0, 44.0, 17.0, 12.0, 9.0, 0.0, 0.0, 19.0, 37.0, 85.0, 18.0, 16.0, 104.0, 22.0, 6.0, 2.0, 26.0, 12.0, 58.0, 67.0, 82.0, 25.0, 12.0, 2.0, 2.0, 25.0, 18.0, 8.0, 2.0, 19.0, 42.0, 48.0, 11.0, 1.0, 3.0]}]}