[Bug]: flaky test case `test_search_group_size_default` in ci

[zhuwenxing]

Is there an existing issue for this?

• [X] I have searched the existing issues

Environment

- Milvus version:
- Deployment mode(standalone or cluster):
- MQ type(rocksmq, pulsar or kafka):    
- SDK version(e.g. pymilvus v2.0.0rc2):
- OS(Ubuntu or CentOS): 
- CPU/Memory: 
- GPU: 
- Others:

Current Behavior

pytest : test] self = <test_search.TestSearchGroupBy object at 0x7fe84181ff70>

[pytest : test] 

[pytest : test]     @pytest.mark.tags(CaseLabel.L0)

[pytest : test]     def test_search_group_size_default(self):

[pytest : test]         """

[pytest : test]         target: test search group by

[pytest : test]         method: 1. create a collection with 3 different float vectors

[pytest : test]                 2. build index with 3 different index types and metrics

[pytest : test]                 2. search on 3 different float vector fields with group by varchar field with group size

[pytest : test]                 verify results entity = limit * group_size  and group size is full if group_strict_size is True

[pytest : test]                 verify results group counts = limit if group_strict_size is False

[pytest : test]         """

[pytest : test]         self._connect()

[pytest : test]         dense_types = ["FLOAT16_VECTOR", "FLOAT_VECTOR", "BFLOAT16_VECTOR"]

[pytest : test]         dims = [16, 128, 64]

[pytest : test]         index_types = ["FLAT", "IVF_SQ8", "HNSW"]

[pytest : test]         metrics = ct.float_metrics

[pytest : test]         fields = [cf.gen_int64_field(is_primary=True), cf.gen_string_field()]

[pytest : test]         for i in range(len(dense_types)):

[pytest : test]             fields.append(cf.gen_float_vec_field(name=dense_types[i],

[pytest : test]                                                  vector_data_type=dense_types[i], dim=dims[i]))

[pytest : test]         schema = cf.gen_collection_schema(fields, auto_id=True)

[pytest : test]         collection_w = self.init_collection_wrap(name=prefix, schema=schema)

[pytest : test]     

[pytest : test]         # insert with the same values for scalar fields

[pytest : test]         nb = 100

[pytest : test]         for _ in range(100):

[pytest : test]             string_values = pd.Series(data=[str(i) for i in range(nb)], dtype="string")

[pytest : test]             data = [string_values]

[pytest : test]             for i in range(len(dense_types)):

[pytest : test]                 data.append(cf.gen_vectors(dim=dims[i], nb=nb, vector_data_type=dense_types[i]))

[pytest : test]             collection_w.insert(data)

[pytest : test]     

[pytest : test]         collection_w.flush()

[pytest : test]         for i in range(len(dense_types)):

[pytest : test]             _index_params = {"index_type": index_types[i], "metric_type": metrics[i],

[pytest : test]                              "params": cf.get_index_params_params(index_types[i])}

[pytest : test]             collection_w.create_index(dense_types[i], _index_params)

[pytest : test]         collection_w.load()

[pytest : test]     

[pytest : test]         nq = 2

[pytest : test]         limit = 50

[pytest : test]         group_size = 5

[pytest : test]         for j in range(len(dense_types)):

[pytest : test]             search_vectors = cf.gen_vectors(nq, dim=dims[j], vector_data_type=dense_types[j])

[pytest : test]             search_params = {"params": cf.get_search_params_params(index_types[j])}

[pytest : test]             # when group_strict_size=true, it shall return results with entities = limit * group_size

[pytest : test]             res1 = collection_w.search(data=search_vectors, anns_field=dense_types[j],

[pytest : test]                                        param=search_params, limit=limit, # consistency_level=CONSISTENCY_STRONG,

[pytest : test]                                        group_by_field=ct.default_string_field_name,

[pytest : test]                                        group_size=group_size, group_strict_size=True,

[pytest : test]                                        output_fields=[ct.default_string_field_name])[0]

[pytest : test]             for i in range(nq):

[pytest : test]                 for l in range(limit):

[pytest : test]                     group_values = []

[pytest : test]                     for k in range(10):

[pytest : test]                         group_values.append(res1[i][l].fields.get(ct.default_string_field_name))

[pytest : test]                     assert len(set(group_values)) == 1

[pytest : test]                 assert len(res1[i]) == limit * group_size

[pytest : test]     

[pytest : test]             # when group_strict_size=false, it shall return results with group counts = limit

[pytest : test]             res1 = collection_w.search(data=search_vectors, anns_field=dense_types[j],

[pytest : test]                                        param=search_params, limit=limit, # consistency_level=CONSISTENCY_STRONG,

[pytest : test]                                        group_by_field=ct.default_string_field_name,

[pytest : test]                                        group_size=group_size, group_strict_size=False,

[pytest : test]                                        output_fields=[ct.default_string_field_name])[0]

[pytest : test]             for i in range(nq):

[pytest : test]                 group_values = []

[pytest : test]                 for l in range(len(res1[i])):

[pytest : test]                     group_values.append(res1[i][l].fields.get(ct.default_string_field_name))

[pytest : test]                 assert len(set(group_values)) == limit

[pytest : test]     

[pytest : test]         # hybrid search group by

[pytest : test]         req_list = []

[pytest : test]         for j in range(len(dense_types)):

[pytest : test]             search_params = {

[pytest : test]                 "data": cf.gen_vectors(nq, dim=dims[j], vector_data_type=dense_types[j]),

[pytest : test]                 "anns_field": dense_types[j],

[pytest : test]                 "param": {"params": cf.get_search_params_params(index_types[j])},

[pytest : test]                 "limit": limit,

[pytest : test]                 "expr": "int64 > 0"}

[pytest : test]             req = AnnSearchRequest(**search_params)

[pytest : test]             req_list.append(req)

[pytest : test]         # 4. hybrid search group by

[pytest : test]         import numpy as np

[pytest : test]         rank_scorers = ["max", "avg", "sum"]

[pytest : test]         for scorer in rank_scorers:

[pytest : test]             res = collection_w.hybrid_search(req_list, WeightedRanker(0.3, 0.3, 0.3), limit=limit,

[pytest : test]                                              group_by_field=ct.default_string_field_name,

[pytest : test]                                              group_size=group_size, rank_group_scorer=scorer,

[pytest : test]                                              output_fields=[ct.default_string_field_name])[0]

[pytest : test]             for i in range(nq):

[pytest : test]                 group_values = []

[pytest : test]                 for l in range(len(res[i])):

[pytest : test]                     group_values.append(res[i][l].fields.get(ct.default_string_field_name))

[pytest : test] >               assert len(set(group_values)) == limit

[pytest : test] E               AssertionError: assert 36 == 50

[pytest : test] E                +  where 36 = len({'11', '13', '17', '18', '19', '2', ...})

[pytest : test] E                +    where {'11', '13', '17', '18', '19', '2', ...} = set(['42', '74', '47', '53', '85', '93', ...])

Expected Behavior

No response

Steps To Reproduce

No response

Milvus Log

failed job: https://jenkins.milvus.io:18080/blue/organizations/jenkins/Milvus%20HA%20CI/detail/PR-36381/5/pipeline/94

Anything else?

No response

[zhuwenxing]

It appears to be a case with a relatively high failure probability, failing in both branches on this case.