test: refactor checker to using milvus client (#45524)

/kind improvement

Signed-off-by: zhuwenxing <wenxing.zhu@zilliz.com>

tests/python_client/chaos/constants.py

@@ -22,6 +22,7 @@ RELEASE_NAME = 'test-allstandalone-pod-kill-19-25-26'
 WAIT_PER_OP = 10  # time to wait in seconds between operations
 CHAOS_DURATION = 120  # chaos duration time in seconds
 DEFAULT_INDEX_PARAM = {"index_type": "HNSW", "metric_type": "L2", "params": {"M": 48, "efConstruction": 500}}
+DEFAULT_EMB_LIST_INDEX_PARAM = {"index_type": "HNSW", "metric_type": "MAX_SIM_COSINE", "params": {"M": 16, "efConstruction": 200}}
 DEFAULT_SEARCH_PARAM = {"metric_type": "L2", "params": {"ef": 64}}
 DEFAULT_INT8_INDEX_PARAM = {"index_type": "HNSW", "metric_type": "L2", "params": {"M": 48, "efConstruction": 500}}
 DEFAULT_INT8_SEARCH_PARAM = {"metric_type": "L2", "params": {"ef": 64}}

tests/python_client/chaos/testcases/test_single_request_operation.py

@@ -24,6 +24,7 @@ from chaos.checker import (CollectionCreateChecker,
                            AlterCollectionChecker,
                            AddFieldChecker,
                            CollectionRenameChecker,
+                           TensorSearchChecker,
                            Op,
                            EventRecords,
                            ResultAnalyzer
@@ -83,6 +84,7 @@ class TestOperations(TestBase):
         checkers = {
             Op.create: CollectionCreateChecker(collection_name=c_name),
             Op.insert: InsertChecker(collection_name=c_name),
+            Op.tensor_search :TensorSearchChecker(collection_name=c_name),
             Op.upsert: UpsertChecker(collection_name=c_name),
             Op.partial_update: PartialUpdateChecker(collection_name=c_name), 
             Op.flush: FlushChecker(collection_name=c_name),

tests/python_client/common/common_func.py

@@ -836,33 +836,49 @@ def gen_default_collection_schema(description=ct.default_desc, primary_field=ct.
 
 
 def gen_all_datatype_collection_schema(description=ct.default_desc, primary_field=ct.default_int64_field_name,
-                                       auto_id=False, dim=ct.default_dim, enable_dynamic_field=True, nullable=True,**kwargs):
+                                       auto_id=False, dim=ct.default_dim, enable_dynamic_field=True, nullable=True,
+                                       enable_struct_array_field=True, **kwargs):
     analyzer_params = {
         "tokenizer": "standard",
     }
-    fields = [
-        gen_int64_field(),
-        gen_float_field(nullable=nullable),
-        gen_string_field(nullable=nullable),
-        gen_string_field(name="document", max_length=2000, enable_analyzer=True, enable_match=True, nullable=nullable),
-        gen_string_field(name="text", max_length=2000, enable_analyzer=True, enable_match=True,
-                         analyzer_params=analyzer_params),
-        gen_json_field(nullable=nullable),
-        gen_geometry_field(nullable=nullable),
-        gen_array_field(name="array_int", element_type=DataType.INT64),
-        gen_array_field(name="array_float", element_type=DataType.FLOAT),
-        gen_array_field(name="array_varchar", element_type=DataType.VARCHAR, max_length=200),
-        gen_array_field(name="array_bool", element_type=DataType.BOOL),
-        gen_float_vec_field(dim=dim),
-        gen_int8_vec_field(name="image_emb", dim=dim),
-        gen_float_vec_field(name="text_sparse_emb", vector_data_type=DataType.SPARSE_FLOAT_VECTOR),
-        gen_float_vec_field(name="voice_emb", dim=dim),
-        # gen_timestamptz_field(name="timestamptz", nullable=nullable),
-    ]
 
-    schema, _ = ApiCollectionSchemaWrapper().init_collection_schema(fields=fields, description=description,
-                                                                    primary_field=primary_field, auto_id=auto_id,
-                                                                    enable_dynamic_field=enable_dynamic_field, **kwargs)
+    # Create schema using MilvusClient
+    schema = MilvusClient.create_schema(
+        auto_id=auto_id,
+        enable_dynamic_field=enable_dynamic_field,
+        description=description,
+        **kwargs
+    )
+
+    # Add all fields using schema.add_field()
+    schema.add_field(primary_field, DataType.INT64, is_primary=True)
+    schema.add_field(ct.default_float_field_name, DataType.FLOAT, nullable=nullable)
+    schema.add_field(ct.default_string_field_name, DataType.VARCHAR, max_length=ct.default_max_length, nullable=nullable)
+    schema.add_field("document", DataType.VARCHAR, max_length=2000, enable_analyzer=True, enable_match=True, nullable=nullable)
+    schema.add_field("text", DataType.VARCHAR, max_length=2000, enable_analyzer=True, enable_match=True,
+                    analyzer_params=analyzer_params)
+    schema.add_field(ct.default_json_field_name, DataType.JSON, nullable=nullable)
+    schema.add_field(ct.default_geometry_field_name, DataType.GEOMETRY, nullable=nullable)
+    schema.add_field("array_int", DataType.ARRAY, element_type=DataType.INT64, max_capacity=ct.default_max_capacity)
+    schema.add_field("array_float", DataType.ARRAY, element_type=DataType.FLOAT, max_capacity=ct.default_max_capacity)
+    schema.add_field("array_varchar", DataType.ARRAY, element_type=DataType.VARCHAR, max_length=200, max_capacity=ct.default_max_capacity)
+    schema.add_field("array_bool", DataType.ARRAY, element_type=DataType.BOOL, max_capacity=ct.default_max_capacity)
+    schema.add_field(ct.default_float_vec_field_name, DataType.FLOAT_VECTOR, dim=dim)
+    schema.add_field("image_emb", DataType.INT8_VECTOR, dim=dim)
+    schema.add_field("text_sparse_emb", DataType.SPARSE_FLOAT_VECTOR)
+    # schema.add_field("voice_emb", DataType.FLOAT_VECTOR, dim=dim)
+    # schema.add_field("timestamptz", DataType.TIMESTAMPTZ, nullable=nullable)
+
+    # Add struct array field
+    if enable_struct_array_field:
+        struct_schema = MilvusClient.create_struct_field_schema()
+        struct_schema.add_field("name", DataType.VARCHAR, max_length=200)
+        struct_schema.add_field("age", DataType.INT64)
+        struct_schema.add_field("float_vector", DataType.FLOAT_VECTOR, dim=dim)
+        schema.add_field("array_struct", datatype=DataType.ARRAY, element_type=DataType.STRUCT,
+                        struct_schema=struct_schema, max_capacity=10)
+
+    # Add BM25 function
     bm25_function = Function(
         name=f"text",
         function_type=FunctionType.BM25,
@@ -871,6 +887,7 @@ def gen_all_datatype_collection_schema(description=ct.default_desc, primary_fiel
         params={},
     )
     schema.add_function(bm25_function)
+
     return schema
 
 
@@ -1779,16 +1796,44 @@ def get_column_data_by_schema(nb=ct.default_nb, schema=None, skip_vectors=False,
     return data
 
 
+def convert_orm_schema_to_dict_schema(orm_schema):
+    """
+    Convert ORM CollectionSchema object to dict format (same as describe_collection output).
+
+    Args:
+        orm_schema: CollectionSchema object from pymilvus.orm
+
+    Returns:
+        dict: Schema in dict format compatible with MilvusClient describe_collection output
+    """
+    # Use the built-in to_dict() method which already provides the right structure
+    schema_dict = orm_schema.to_dict()
+
+    # to_dict() already includes:
+    # - auto_id
+    # - description
+    # - fields (with each field's to_dict())
+    # - enable_dynamic_field
+    # - functions (if present)
+    # - struct_fields (if present)
+
+    return schema_dict
+
+
 def gen_row_data_by_schema(nb=ct.default_nb, schema=None, start=0, random_pk=False, skip_field_names=[], desired_field_names=[]):
     """
     Generates row data based on the given schema.
 
     Args:
         nb (int): Number of rows to generate. Defaults to ct.default_nb.
-        schema (Schema): Collection schema or collection info. If None, uses default schema.
+        schema (Schema): Collection schema or collection info. Can be:
+                        - dict (from client.describe_collection())
+                        - CollectionSchema object (from ORM)
+                        - None (uses default schema)
         start (int): Starting value for primary key fields. Defaults to 0.
         random_pk (bool, optional): Whether to generate random primary key values (default: False)
         skip_field_names(list, optional): whether to skip some field to gen data manually (default: [])
+        desired_field_names(list, optional): only generate data for specified field names (default: [])
 
     Returns:
         list[dict]: List of dictionaries where each dictionary represents a row,
@@ -1798,6 +1843,7 @@ def gen_row_data_by_schema(nb=ct.default_nb, schema=None, start=0, random_pk=Fal
         - Skips auto_id fields and function output fields.
         - For primary key fields, generates sequential values starting from 'start'.
         - For non-primary fields, generates random data based on field type.
+        - Supports struct array fields in both dict and ORM schema formats.
     """
     # if both skip_field_names and desired_field_names are specified, raise an exception
     if skip_field_names and desired_field_names:
@@ -1806,10 +1852,12 @@ def gen_row_data_by_schema(nb=ct.default_nb, schema=None, start=0, random_pk=Fal
     if schema is None:
         schema = gen_default_collection_schema()
 
-    # ignore auto id field and the fields in function output
-    func_output_fields = []
-    if isinstance(schema, dict):
-        # a dict of collection schema info is usually from client.describe_collection()
+    # Convert ORM schema to dict schema for unified processing
+    if not isinstance(schema, dict):
+        schema = convert_orm_schema_to_dict_schema(schema)
+
+    # Now schema is always a dict after conversion, process it uniformly
+    # Get all fields from schema
     all_fields = schema.get('fields', [])
     fields = []
     for field in all_fields:
@@ -1820,12 +1868,44 @@ def gen_row_data_by_schema(nb=ct.default_nb, schema=None, start=0, random_pk=Fal
         elif not desired_field_names:
             fields.append(field)
 
+    # Get struct_fields from schema
+    struct_fields = schema.get('struct_fields', [])
+    log.debug(f"[gen_row_data_by_schema] struct_fields from schema: {len(struct_fields)} items")
+    if struct_fields:
+        log.debug(f"[gen_row_data_by_schema] First struct_field: {struct_fields[0]}")
+
+    # If struct_fields is not present, extract struct array fields from fields list
+    # This happens when using client.describe_collection()
+    if not struct_fields:
+        struct_fields = []
+        for field in fields:
+            if field.get('type') == DataType.ARRAY and field.get('element_type') == DataType.STRUCT:
+                # Convert field format to struct_field format
+                struct_field_dict = {
+                    'name': field.get('name'),
+                    'max_capacity': field.get('params', {}).get('max_capacity', 100),
+                    'fields': []
+                }
+                # Get struct fields from field - key can be 'struct_fields' or 'struct_schema'
+                struct_field_list = field.get('struct_fields') or field.get('struct_schema')
+                if struct_field_list:
+                    # If it's a dict with 'fields' key, get the fields
+                    if isinstance(struct_field_list, dict) and 'fields' in struct_field_list:
+                        struct_field_dict['fields'] = struct_field_list['fields']
+                    # If it's already a list, use it directly
+                    elif isinstance(struct_field_list, list):
+                        struct_field_dict['fields'] = struct_field_list
+                struct_fields.append(struct_field_dict)
+
+    # Get function output fields to skip
+    func_output_fields = []
     functions = schema.get('functions', [])
     for func in functions:
         output_field_names = func.get('output_field_names', [])
         func_output_fields.extend(output_field_names)
     func_output_fields = list(set(func_output_fields))
 
+    # Filter fields that need data generation
     fields_needs_data = []
     for field in fields:
         field_name = field.get('name', None)
@@ -1833,57 +1913,35 @@ def gen_row_data_by_schema(nb=ct.default_nb, schema=None, start=0, random_pk=Fal
             continue
         if field_name in func_output_fields or field_name in skip_field_names:
             continue
+        # Skip struct array fields as they are handled separately via struct_fields
+        if field.get('type') == DataType.ARRAY and field.get('element_type') == DataType.STRUCT:
+            continue
         fields_needs_data.append(field)
+
+    # Generate data for each row
     data = []
     for i in range(nb):
         tmp = {}
+        # Generate data for regular fields
         for field in fields_needs_data:
             tmp[field.get('name', None)] = gen_data_by_collection_field(field, random_pk=random_pk)
+            # Handle primary key fields specially
             if field.get('is_primary', False) is True and field.get('type', None) == DataType.INT64:
                 tmp[field.get('name', None)] = start
                 start += 1
             if field.get('is_primary', False) is True and field.get('type', None) == DataType.VARCHAR:
                 tmp[field.get('name', None)] = str(start)
                 start += 1
-            data.append(tmp)
-    else:
-        # a schema object is usually form orm schema object
-        all_fields = schema.fields
-        fields = []
-        for field in all_fields:
-            # if desired_field_names is specified, only generate the fields in desired_field_names
-            if field.name in desired_field_names:
-                fields.append(field)
-            # elif desired_field_names is not specified, generate all fields
-            elif not desired_field_names:
-                fields.append(field)
 
-        if hasattr(schema, "functions"):
-            functions = schema.functions
-            for func in functions:
-                output_field_names = func.output_field_names
-                func_output_fields.extend(output_field_names)
-        func_output_fields = list(set(func_output_fields))
+        # Generate data for struct array fields
+        for struct_field in struct_fields:
+            field_name = struct_field.get('name', None)
+            struct_data = gen_struct_array_data(struct_field, start=start, random_pk=random_pk)
+            tmp[field_name] = struct_data
 
-        fields_needs_data = []
-        for field in fields:
-            if field.auto_id:
-                continue
-            if field.name in func_output_fields or field.name in skip_field_names:
-                continue
-            fields_needs_data.append(field)
-        data = []
-        for i in range(nb):
-            tmp = {}
-            for field in fields_needs_data:
-                tmp[field.name] = gen_data_by_collection_field(field, random_pk=random_pk)
-                if field.is_primary is True and field.dtype == DataType.INT64:
-                    tmp[field.name] = start
-                    start += 1
-                if field.is_primary is True and field.dtype == DataType.VARCHAR:
-                    tmp[field.name] = str(start)
-                    start += 1
         data.append(tmp)
+
+    log.debug(f"[gen_row_data_by_schema] Generated {len(data)} rows, first row keys: {list(data[0].keys()) if data else []}")
     return data
 
 
@@ -2035,6 +2093,17 @@ def get_int8_vec_field_name_list(schema=None):
             vec_fields.append(field.name)
     return vec_fields
 
+def get_emb_list_field_name_list(schema=None):
+    vec_fields = []
+    if schema is None:
+        schema = gen_default_collection_schema()
+    struct_fields = schema.struct_fields
+    for struct_field in struct_fields:
+        for field in struct_field.fields:
+            if field.dtype in [DataType.FLOAT_VECTOR]:
+                vec_fields.append(f"{struct_field.name}[{field.name}]")
+    return vec_fields
+
 def get_bm25_vec_field_name_list(schema=None):
     if not hasattr(schema, "functions"):
         return []
@@ -2072,6 +2141,40 @@ def get_dense_anns_field_name_list(schema=None):
             anns_fields.append(item)
     return anns_fields
 
+def get_struct_array_vector_field_list(schema=None):
+    if schema is None:
+        schema = gen_default_collection_schema()
+
+    struct_fields = schema.struct_fields
+    struct_vector_fields = []
+
+    for struct_field in struct_fields:
+            struct_field_name = struct_field.name
+            # Check each sub-field for vector types
+            for sub_field in struct_field.fields:
+                sub_field_name = sub_field.name if hasattr(sub_field, 'name') else sub_field.get('name')
+                sub_field_dtype = sub_field.dtype if hasattr(sub_field, 'dtype') else sub_field.get('type')
+
+                if sub_field_dtype in [DataType.FLOAT_VECTOR, DataType.FLOAT16_VECTOR,
+                                      DataType.BFLOAT16_VECTOR, DataType.INT8_VECTOR,
+                                      DataType.BINARY_VECTOR]:
+                    # Get dimension
+                    if hasattr(sub_field, 'params'):
+                        dim = sub_field.params.get('dim')
+                    else:
+                        dim = sub_field.get('params', {}).get('dim')
+
+                    item = {
+                        "struct_field": struct_field_name,
+                        "vector_field": sub_field_name,
+                        "anns_field": f"{struct_field_name}[{sub_field_name}]",
+                        "dtype": sub_field_dtype,
+                        "dim": dim
+                    }
+                    struct_vector_fields.append(item)
+
+    return struct_vector_fields
+
 
 def gen_varchar_data(length: int, nb: int, text_mode=False):
     if text_mode:
@@ -2080,6 +2183,38 @@ def gen_varchar_data(length: int, nb: int, text_mode=False):
         return ["".join([chr(random.randint(97, 122)) for _ in range(length)]) for _ in range(nb)]
 
 
+def gen_struct_array_data(struct_field, start=0, random_pk=False):
+    """
+    Generates struct array data based on the struct field schema.
+
+    Args:
+        struct_field: Either a dict (from dict schema) or StructFieldSchema object (from ORM schema)
+        start: Starting value for primary key fields
+        random_pk: Whether to generate random primary key values
+
+    Returns:
+        List of struct data dictionaries
+    """
+    struct_array_data = []
+
+    # Handle both dict and object formats
+    if isinstance(struct_field, dict):
+        max_capacity = struct_field.get('max_capacity', 100)
+        fields = struct_field.get('fields', [])
+    else:
+        # StructFieldSchema object
+        max_capacity = getattr(struct_field, 'max_capacity', 100) or 100
+        fields = struct_field.fields
+
+    arr_len = random.randint(1, max_capacity)
+    for _ in range(arr_len):
+        struct_data = {}
+        for field in fields:
+            field_name = field.get('name') if isinstance(field, dict) else field.name
+            struct_data[field_name] = gen_data_by_collection_field(field, nb=None, start=start, random_pk=random_pk)
+        struct_array_data.append(struct_data)
+    return struct_array_data
+
 def gen_data_by_collection_field(field, nb=None, start=0, random_pk=False):
     """
     Generates test data for a given collection field based on its data type and properties.
@@ -2105,7 +2240,8 @@ def gen_data_by_collection_field(field, nb=None, start=0, random_pk=False):
         # for v2 client, it accepts a dict of field info
         nullable = field.get('nullable', False)
         data_type = field.get('type', None)
-        enable_analyzer = field.get('params').get("enable_analyzer", False)
+        params = field.get('params', {}) or {}
+        enable_analyzer = params.get("enable_analyzer", False)
         is_primary = field.get('is_primary', False)
     else:
         # for ORM client, it accepts a field object
@@ -2224,9 +2360,16 @@ def gen_data_by_collection_field(field, nb=None, start=0, random_pk=False):
     elif data_type == DataType.ARRAY:
         if isinstance(field, dict):
             max_capacity = field.get('params')['max_capacity']
+            element_type = field.get('element_type')
         else:
             max_capacity = field.params['max_capacity']
             element_type = field.element_type
+
+        # Struct array fields are handled separately in gen_row_data_by_schema
+        # by processing struct_fields, so skip here
+        if element_type == DataType.STRUCT:
+            return None
+
         if element_type == DataType.INT8:
             if nb is None:
                 return [random.randint(-128, 127) for _ in range(max_capacity)] if random.random() < 0.8 or nullable is False else None

tests/python_client/common/common_type.py

@@ -12,6 +12,7 @@ default_dim = 128
 default_nb = 2000
 default_nb_medium = 5000
 default_max_capacity = 100
+default_max_length = 500
 default_top_k = 10
 default_nq = 2
 default_limit = 10

tests/python_client/milvus_client/test_milvus_client_partial_update.py

@@ -111,7 +111,7 @@ class TestMilvusClientPartialUpdateValid(TestMilvusClientV2Base):
         """
         # step 1: create collection with all datatype schema
         client = self._client()
-        schema = cf.gen_all_datatype_collection_schema(dim=default_dim)
+        schema = cf.gen_all_datatype_collection_schema(dim=default_dim, enable_struct_array_field=False)
         index_params = self.prepare_index_params(client)[0]
         text_sparse_emb_field_name = "text_sparse_emb"
 
@@ -201,7 +201,7 @@ class TestMilvusClientPartialUpdateValid(TestMilvusClientV2Base):
         collection_name = cf.gen_collection_name_by_testcase_name()
         
         # Create schema with all data types
-        schema = cf.gen_all_datatype_collection_schema(dim=dim)
+        schema = cf.gen_all_datatype_collection_schema(dim=dim, enable_struct_array_field=False)
 
         # Create index parameters
         index_params = client.prepare_index_params()

tests/python_client/milvus_client/test_milvus_client_struct_array.py

@@ -2659,7 +2659,7 @@ class TestMilvusClientStructArrayCRUD(TestMilvusClientV2Base):
     def test_upsert_struct_array_data(self):
         """
         target: test upsert operation with struct array data
-        method: insert data then upsert with modified struct array
+        method: insert 3000 records, flush 2000, insert 1000 growing, then upsert with modified struct array
         expected: data successfully upserted
         """
         collection_name = cf.gen_unique_str(f"{prefix}_crud")
@@ -2669,25 +2669,50 @@ class TestMilvusClientStructArrayCRUD(TestMilvusClientV2Base):
         # Create collection
         self.create_collection_with_schema(client, collection_name)
 
-        # Initial insert
-        initial_data = [
-            {
-                "id": 1,
+        # Insert 2000 records for flushed data
+        flushed_data = []
+        for i in range(2000):
+            row = {
+                "id": i,
                 "normal_vector": [random.random() for _ in range(default_dim)],
                 "clips": [
                     {
-                        "clip_embedding1": [
-                            random.random() for _ in range(default_dim)
-                        ],
-                        "scalar_field": 100,
-                        "label": "initial",
+                        "clip_embedding1": [random.random() for _ in range(default_dim)],
+                        "scalar_field": i,
+                        "label": f"flushed_{i}",
                     }
                 ],
             }
-        ]
+            flushed_data.append(row)
 
-        res, check = self.insert(client, collection_name, initial_data)
+        res, check = self.insert(client, collection_name, flushed_data)
         assert check
+        assert res["insert_count"] == 2000
+
+        # Flush to persist data
+        res, check = self.flush(client, collection_name)
+        assert check
+
+        # Insert 1000 records for growing data
+        growing_data = []
+        for i in range(2000, 3000):
+            row = {
+                "id": i,
+                "normal_vector": [random.random() for _ in range(default_dim)],
+                "clips": [
+                    {
+                        "clip_embedding1": [random.random() for _ in range(default_dim)],
+                        "scalar_field": i,
+                        "label": f"growing_{i}",
+                    }
+                ],
+            }
+            growing_data.append(row)
+
+        res, check = self.insert(client, collection_name, growing_data)
+        assert check
+        assert res["insert_count"] == 1000
+
         # create index and load collection
         index_params = client.prepare_index_params()
         index_params.add_index(
@@ -2707,40 +2732,63 @@ class TestMilvusClientStructArrayCRUD(TestMilvusClientV2Base):
         res, check = self.load_collection(client, collection_name)
         assert check
 
-        # Upsert with modified data
-        upsert_data = [
-            {
-                "id": 1,  # Same ID
+        # Upsert data in both flushed and growing segments
+        upsert_data = []
+        # Upsert 10 records from flushed data
+        for i in range(0, 10):
+            row = {
+                "id": i,
                 "normal_vector": [random.random() for _ in range(default_dim)],
                 "clips": [
                     {
-                        "clip_embedding1": [
-                            random.random() for _ in range(default_dim)
-                        ],
-                        "scalar_field": 200,  # Modified
-                        "label": "updated",  # Modified
+                        "clip_embedding1": [random.random() for _ in range(default_dim)],
+                        "scalar_field": i + 10000,  # Modified
+                        "label": f"updated_flushed_{i}",  # Modified
                     }
                 ],
             }
-        ]
+            upsert_data.append(row)
+
+        # Upsert 10 records from growing data
+        for i in range(2000, 2010):
+            row = {
+                "id": i,
+                "normal_vector": [random.random() for _ in range(default_dim)],
+                "clips": [
+                    {
+                        "clip_embedding1": [random.random() for _ in range(default_dim)],
+                        "scalar_field": i + 10000,  # Modified
+                        "label": f"updated_growing_{i}",  # Modified
+                    }
+                ],
+            }
+            upsert_data.append(row)
 
         res, check = self.upsert(client, collection_name, upsert_data)
         assert check
 
-        # Verify upsert worked
+        # Verify upsert worked for flushed data
         res, check = self.flush(client, collection_name)
         assert check
 
-        results, check = self.query(client, collection_name, filter="id == 1")
+        results, check = self.query(client, collection_name, filter="id < 10")
         assert check
-        assert len(results) == 1
-        assert results[0]["clips"][0]["label"] == "updated"
+        assert len(results) == 10
+        for result in results:
+            assert "updated_flushed" in result["clips"][0]["label"]
+
+        # Verify upsert worked for growing data
+        results, check = self.query(client, collection_name, filter="id >= 2000 and id < 2010")
+        assert check
+        assert len(results) == 10
+        for result in results:
+            assert "updated_growing" in result["clips"][0]["label"]
 
     @pytest.mark.tags(CaseLabel.L0)
     def test_delete_struct_array_data(self):
         """
         target: test delete operation with struct array data
-        method: insert struct array data then delete by ID
+        method: insert 3000 records (2000 flushed + 1000 growing), then delete by ID from both segments
         expected: data successfully deleted
         """
         collection_name = cf.gen_unique_str(f"{prefix}_crud")
@@ -2750,25 +2798,50 @@ class TestMilvusClientStructArrayCRUD(TestMilvusClientV2Base):
         # Create collection and insert data
         self.create_collection_with_schema(client, collection_name)
 
-        data = []
-        for i in range(10):
+        # Insert 2000 records for flushed data
+        flushed_data = []
+        for i in range(2000):
             row = {
                 "id": i,
                 "normal_vector": [random.random() for _ in range(default_dim)],
                 "clips": [
                     {
-                        "clip_embedding1": [
-                            random.random() for _ in range(default_dim)
-                        ],
+                        "clip_embedding1": [random.random() for _ in range(default_dim)],
                         "scalar_field": i,
-                        "label": f"label_{i}",
+                        "label": f"flushed_{i}",
                     }
                 ],
             }
-            data.append(row)
+            flushed_data.append(row)
 
-        res, check = self.insert(client, collection_name, data)
+        res, check = self.insert(client, collection_name, flushed_data)
         assert check
+        assert res["insert_count"] == 2000
+
+        # Flush to persist data
+        res, check = self.flush(client, collection_name)
+        assert check
+
+        # Insert 1000 records for growing data
+        growing_data = []
+        for i in range(2000, 3000):
+            row = {
+                "id": i,
+                "normal_vector": [random.random() for _ in range(default_dim)],
+                "clips": [
+                    {
+                        "clip_embedding1": [random.random() for _ in range(default_dim)],
+                        "scalar_field": i,
+                        "label": f"growing_{i}",
+                    }
+                ],
+            }
+            growing_data.append(row)
+
+        res, check = self.insert(client, collection_name, growing_data)
+        assert check
+        assert res["insert_count"] == 1000
+
         # create index and load collection
         index_params = client.prepare_index_params()
         index_params.add_index(
@@ -2788,9 +2861,14 @@ class TestMilvusClientStructArrayCRUD(TestMilvusClientV2Base):
         res, check = self.load_collection(client, collection_name)
         assert check
 
-        # Delete some records
-        delete_ids = [1, 3, 5]
-        res, check = self.delete(client, collection_name, filter=f"id in {delete_ids}")
+        # Delete some records from flushed segment
+        delete_flushed_ids = [1, 3, 5, 100, 500, 1000]
+        res, check = self.delete(client, collection_name, filter=f"id in {delete_flushed_ids}")
+        assert check
+
+        # Delete some records from growing segment
+        delete_growing_ids = [2001, 2003, 2500, 2999]
+        res, check = self.delete(client, collection_name, filter=f"id in {delete_growing_ids}")
         assert check
 
         # Verify deletion
@@ -2801,14 +2879,21 @@ class TestMilvusClientStructArrayCRUD(TestMilvusClientV2Base):
         assert check
 
         remaining_ids = {result["id"] for result in results}
-        for delete_id in delete_ids:
+        # Verify flushed data deletion
+        for delete_id in delete_flushed_ids:
             assert delete_id not in remaining_ids
+        # Verify growing data deletion
+        for delete_id in delete_growing_ids:
+            assert delete_id not in remaining_ids
+
+        # Verify total count is correct (3000 - 10 deleted)
+        assert len(results) == 2990
 
     @pytest.mark.tags(CaseLabel.L1)
     def test_batch_operations(self):
         """
         target: test batch insert/upsert operations with struct array
-        method: perform large batch operations
+        method: insert 3000 records (2000 flushed + 1000 growing), then perform batch upsert
         expected: all operations successful
         """
         collection_name = cf.gen_unique_str(f"{prefix}_crud")
@@ -2818,42 +2903,77 @@ class TestMilvusClientStructArrayCRUD(TestMilvusClientV2Base):
         # Create collection
         self.create_collection_with_schema(client, collection_name)
 
-        # Large batch insert
-        batch_size = 1000
-        data = []
-        for i in range(batch_size):
+        # Insert 2000 records for flushed data
+        flushed_data = []
+        for i in range(2000):
             row = {
                 "id": i,
                 "normal_vector": [random.random() for _ in range(default_dim)],
                 "clips": [
                     {
-                        "clip_embedding1": [
-                            random.random() for _ in range(default_dim)
-                        ],
+                        "clip_embedding1": [random.random() for _ in range(default_dim)],
                         "scalar_field": i % 100,
-                        "label": f"batch_{i}",
+                        "label": f"flushed_{i}",
                     }
                 ],
             }
-            data.append(row)
+            flushed_data.append(row)
 
-        res, check = self.insert(client, collection_name, data)
+        res, check = self.insert(client, collection_name, flushed_data)
         assert check
-        assert res["insert_count"] == batch_size
+        assert res["insert_count"] == 2000
 
-        # Batch upsert (update first 100 records)
+        # Flush to persist data
+        res, check = self.flush(client, collection_name)
+        assert check
+
+        # Insert 1000 records for growing data
+        growing_data = []
+        for i in range(2000, 3000):
+            row = {
+                "id": i,
+                "normal_vector": [random.random() for _ in range(default_dim)],
+                "clips": [
+                    {
+                        "clip_embedding1": [random.random() for _ in range(default_dim)],
+                        "scalar_field": i % 100,
+                        "label": f"growing_{i}",
+                    }
+                ],
+            }
+            growing_data.append(row)
+
+        res, check = self.insert(client, collection_name, growing_data)
+        assert check
+        assert res["insert_count"] == 1000
+
+        # Batch upsert (update first 100 flushed records and 50 growing records)
         upsert_data = []
+        # Update first 100 flushed records
         for i in range(100):
             row = {
                 "id": i,
                 "normal_vector": [random.random() for _ in range(default_dim)],
                 "clips": [
                     {
-                        "clip_embedding1": [
-                            random.random() for _ in range(default_dim)
-                        ],
+                        "clip_embedding1": [random.random() for _ in range(default_dim)],
                         "scalar_field": i + 1000,  # Modified
-                        "label": f"upserted_{i}",  # Modified
+                        "label": f"upserted_flushed_{i}",  # Modified
+                    }
+                ],
+            }
+            upsert_data.append(row)
+
+        # Update first 50 growing records
+        for i in range(2000, 2050):
+            row = {
+                "id": i,
+                "normal_vector": [random.random() for _ in range(default_dim)],
+                "clips": [
+                    {
+                        "clip_embedding1": [random.random() for _ in range(default_dim)],
+                        "scalar_field": i + 1000,  # Modified
+                        "label": f"upserted_growing_{i}",  # Modified
                     }
                 ],
             }
@@ -2862,11 +2982,15 @@ class TestMilvusClientStructArrayCRUD(TestMilvusClientV2Base):
         res, check = self.upsert(client, collection_name, upsert_data)
         assert check
 
+        # Verify upsert success with flush
+        res, check = self.flush(client, collection_name)
+        assert check
+
     @pytest.mark.tags(CaseLabel.L1)
     def test_collection_operations(self):
         """
         target: test collection operations (load/release/drop) with struct array
-        method: perform collection management operations
+        method: insert 3000 records (2000 flushed + 1000 growing), then perform collection management operations
         expected: all operations successful
         """
         collection_name = cf.gen_unique_str(f"{prefix}_crud")
@@ -2876,25 +3000,49 @@ class TestMilvusClientStructArrayCRUD(TestMilvusClientV2Base):
         # Create collection with data
         self.create_collection_with_schema(client, collection_name)
 
-        # Insert some data
-        data = [
-            {
-                "id": 1,
+        # Insert 2000 records for flushed data
+        flushed_data = []
+        for i in range(2000):
+            row = {
+                "id": i,
                 "normal_vector": [random.random() for _ in range(default_dim)],
                 "clips": [
                     {
-                        "clip_embedding1": [
-                            random.random() for _ in range(default_dim)
-                        ],
-                        "scalar_field": 100,
-                        "label": "test",
+                        "clip_embedding1": [random.random() for _ in range(default_dim)],
+                        "scalar_field": i,
+                        "label": f"flushed_{i}",
                     }
                 ],
             }
-        ]
+            flushed_data.append(row)
 
-        res, check = self.insert(client, collection_name, data)
+        res, check = self.insert(client, collection_name, flushed_data)
         assert check
+        assert res["insert_count"] == 2000
+
+        # Flush to persist data
+        res, check = self.flush(client, collection_name)
+        assert check
+
+        # Insert 1000 records for growing data
+        growing_data = []
+        for i in range(2000, 3000):
+            row = {
+                "id": i,
+                "normal_vector": [random.random() for _ in range(default_dim)],
+                "clips": [
+                    {
+                        "clip_embedding1": [random.random() for _ in range(default_dim)],
+                        "scalar_field": i,
+                        "label": f"growing_{i}",
+                    }
+                ],
+            }
+            growing_data.append(row)
+
+        res, check = self.insert(client, collection_name, growing_data)
+        assert check
+        assert res["insert_count"] == 1000
 
         # Create index for loading
         index_params = client.prepare_index_params()
@@ -2922,6 +3070,11 @@ class TestMilvusClientStructArrayCRUD(TestMilvusClientV2Base):
         load_state = client.get_load_state(collection_name)
         assert str(load_state["state"]) == "Loaded"
 
+        # Query to verify both flushed and growing data are accessible
+        results, check = self.query(client, collection_name, filter="id >= 0", limit=3000)
+        assert check
+        assert len(results) == 3000
+
         # Release collection
         res, check = self.release_collection(client, collection_name)
         assert check

tests/python_client/pytest.ini

@@ -1,7 +1,7 @@
 [pytest]
 
 
-addopts = --host localhost --html=/tmp/ci_logs/report.html --self-contained-html -v
+addopts = --host 10.104.31.219 --html=/tmp/ci_logs/report.html --self-contained-html -v --log-cli-level  INFO
 #  python3 -W ignore -m pytest
 
 log_format = [%(asctime)s - %(levelname)s - %(name)s]: %(message)s (%(filename)s:%(lineno)s)