mirror of https://github.com/milvus-io/milvus.git
test: Add bulk insert related test cases for default and null support (#36219)
issue: #36129 Signed-off-by: binbin lv <binbin.lv@zilliz.com>pull/36266/head
binbin
GitHub
parent
23b95aeba3
commit
5ca4d5977a
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@ -323,6 +323,7 @@ class ResponseChecker:
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for hits in search_res:
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searched_original_vectors = []
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ids = []
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vector_id = 0
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if enable_milvus_client_api:
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for hit in hits:
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ids.append(hit['id'])
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@ -349,12 +350,13 @@ class ResponseChecker:
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raise Exception("inserted vectors are needed for distance check")
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for id in hits.ids:
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searched_original_vectors.append(check_items["original_vectors"][id])
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cf.compare_distance_vector_and_vector_list(check_items["vector_nq"][i],
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cf.compare_distance_vector_and_vector_list(check_items["vector_nq"][vector_id],
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searched_original_vectors,
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check_items["metric"], hits.distances)
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log.info("search_results_check: Checked the distances for one nq: OK")
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else:
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pass # just check nq and topk, not specific ids need check
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vector_id += 1
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log.info("search_results_check: limit (topK) and "
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"ids searched for %d queries are correct" % len(search_res))
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@ -14,6 +14,7 @@ from sklearn import preprocessing
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from common.common_func import gen_unique_str
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from common.minio_comm import copy_files_to_minio
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from utils.util_log import test_log as log
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import pyarrow as pa
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data_source = "/tmp/bulk_insert_data"
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fake = Faker()
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@ -444,7 +445,7 @@ def gen_json_in_numpy_file(dir, data_field, rows, start=0, force=False):
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return file_name
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def gen_int_or_float_in_numpy_file(dir, data_field, rows, start=0, force=False):
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def gen_int_or_float_in_numpy_file(dir, data_field, rows, start=0, force=False, nullable=False):
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file_name = f"{data_field}.npy"
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file = f"{dir}/{file_name}"
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if not os.path.exists(file) or force:
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@ -459,7 +460,10 @@ def gen_int_or_float_in_numpy_file(dir, data_field, rows, start=0, force=False):
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elif data_field == DataField.pk_field:
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data = [i for i in range(start, start + rows)]
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elif data_field == DataField.int_field:
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if not nullable:
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data = [random.randint(-999999, 9999999) for _ in range(rows)]
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else:
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data = [None for _ in range(rows)]
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arr = np.array(data)
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log.info(f"file_name: {file_name} data type: {arr.dtype} data shape: {arr.shape}")
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np.save(file, arr)
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@ -496,11 +500,14 @@ def gen_data_by_data_field(data_field, rows, start=0, float_vector=True, dim=128
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array_length = random.randint(0, 10)
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schema = kwargs.get("schema", None)
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schema = schema.to_dict() if schema is not None else None
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nullable = False
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if schema is not None:
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fields = schema.get("fields", [])
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for field in fields:
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if data_field == field["name"] and "params" in field:
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if data_field == field["name"]:
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if "params" in field:
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dim = field["params"].get("dim", dim)
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nullable = field.get("nullable", False)
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data = []
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if rows > 0:
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if "vec" in data_field:
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@ -522,37 +529,75 @@ def gen_data_by_data_field(data_field, rows, start=0, float_vector=True, dim=128
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else:
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data = gen_vectors(float_vector=float_vector, rows=rows, dim=dim)
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elif data_field == DataField.float_field:
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if not nullable:
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data = [np.float32(random.random()) for _ in range(rows)]
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else:
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data = [None for _ in range(rows)]
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elif data_field == DataField.double_field:
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if not nullable:
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data = [np.float64(random.random()) for _ in range(rows)]
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else:
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data = [None for _ in range(rows)]
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elif data_field == DataField.pk_field:
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if not nullable:
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data = [np.int64(i) for i in range(start, start + rows)]
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else:
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data = [None for _ in range(start, start + rows)]
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elif data_field == DataField.int_field:
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if not nullable:
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data = [np.int64(random.randint(-999999, 9999999)) for _ in range(rows)]
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else:
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data = [None for _ in range(rows)]
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elif data_field == DataField.string_field:
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if not nullable:
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data = [gen_unique_str(str(i)) for i in range(start, rows + start)]
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else:
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data = [None for _ in range(start, rows + start)]
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elif data_field == DataField.bool_field:
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if not nullable:
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data = [random.choice([True, False]) for i in range(start, rows + start)]
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else:
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data = [None for _ in range(start, rows + start)]
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elif data_field == DataField.json_field:
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if not nullable:
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data = pd.Series([json.dumps({
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gen_unique_str(): random.randint(-999999, 9999999)
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}) for i in range(start, rows + start)], dtype=np.dtype("str"))
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else:
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data = pd.Series([json.dumps({
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gen_unique_str(): None}) for _ in range(start, rows + start)])
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elif data_field == DataField.array_bool_field:
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if not nullable:
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data = pd.Series(
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[np.array([random.choice([True, False]) for _ in range(array_length)], dtype=np.dtype("bool"))
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for i in range(start, rows + start)])
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else:
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data = pd.Series(
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[np.array(None) for i in range(start, rows + start)])
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elif data_field == DataField.array_int_field:
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if not nullable:
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data = pd.Series(
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[np.array([random.randint(-999999, 9999999) for _ in range(array_length)], dtype=np.dtype("int64"))
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for i in range(start, rows + start)])
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else:
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data = pd.Series(
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[np.array(None) for i in range(start, rows + start)])
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elif data_field == DataField.array_float_field:
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if not nullable:
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data = pd.Series(
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[np.array([random.random() for _ in range(array_length)], dtype=np.dtype("float32"))
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for i in range(start, rows + start)])
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else:
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data = pd.Series(
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[np.array(None) for i in range(start, rows + start)])
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elif data_field == DataField.array_string_field:
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if not nullable:
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data = pd.Series(
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[np.array([gen_unique_str(str(i)) for _ in range(array_length)], dtype=np.dtype("str"))
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for i in range(start, rows + start)])
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else:
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data = pd.Series(
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[np.array(None) for i in range(start, rows + start)])
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return data
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@ -627,14 +672,18 @@ def gen_dict_data_by_data_field(data_fields, rows, start=0, float_vector=True, d
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schema = kwargs.get("schema", None)
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schema = schema.to_dict() if schema is not None else None
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data = []
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nullable = False
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for r in range(rows):
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d = {}
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for data_field in data_fields:
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d[data_field] = None
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if schema is not None:
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fields = schema.get("fields", [])
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for field in fields:
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if data_field == field["name"] and "params" in field:
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if data_field == field["name"]:
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if "params" in field:
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dim = field["params"].get("dim", dim)
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nullable = field.get("nullable", False)
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if "vec" in data_field:
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if "float" in data_field:
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@ -651,31 +700,52 @@ def gen_dict_data_by_data_field(data_fields, rows, start=0, float_vector=True, d
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if "fp16" in data_field:
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d[data_field] = gen_fp16_vectors(1, dim, True)[1][0]
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elif data_field == DataField.float_field:
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if not nullable:
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d[data_field] = random.random()
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elif data_field == DataField.double_field:
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if not nullable:
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d[data_field] = random.random()
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elif data_field == DataField.pk_field:
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if not nullable:
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d[data_field] = r+start
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elif data_field == DataField.int_field:
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if not nullable:
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d[data_field] = random.randint(-999999, 9999999)
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elif data_field == DataField.string_field:
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if not nullable:
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d[data_field] = gen_unique_str(str(r + start))
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elif data_field == DataField.bool_field:
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if not nullable:
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d[data_field] = random.choice([True, False])
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elif data_field == DataField.json_field:
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if not nullable:
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d[data_field] = {str(r+start): r+start}
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else:
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d[data_field] = {str(r + start): None}
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elif data_field == DataField.array_bool_field:
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array_length = random.randint(0, 10) if array_length is None else array_length
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if not nullable:
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d[data_field] = [random.choice([True, False]) for _ in range(array_length)]
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else:
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d[data_field] = None
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elif data_field == DataField.array_int_field:
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array_length = random.randint(0, 10) if array_length is None else array_length
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if not nullable:
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d[data_field] = [random.randint(-999999, 9999999) for _ in range(array_length)]
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else:
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d[data_field] = None
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elif data_field == DataField.array_float_field:
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array_length = random.randint(0, 10) if array_length is None else array_length
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if not nullable:
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d[data_field] = [random.random() for _ in range(array_length)]
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else:
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d[data_field] = None
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elif data_field == DataField.array_string_field:
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array_length = random.randint(0, 10) if array_length is None else array_length
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if not nullable:
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d[data_field] = [gen_unique_str(str(i)) for i in range(array_length)]
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else:
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d[data_field] = None
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if enable_dynamic_field:
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d[str(r+start)] = r+start
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d["name"] = fake.name()
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@ -685,7 +755,8 @@ def gen_dict_data_by_data_field(data_fields, rows, start=0, float_vector=True, d
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return data
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def gen_new_json_files(float_vector, rows, dim, data_fields, file_nums=1, array_length=None, file_size=None, err_type="", enable_dynamic_field=False, **kwargs):
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def gen_new_json_files(float_vector, rows, dim, data_fields, file_nums=1, array_length=None, file_size=None,
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err_type="", enable_dynamic_field=False, **kwargs):
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schema = kwargs.get("schema", None)
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dir_prefix = f"json-{uuid.uuid4()}"
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data_source_new = f"{data_source}/{dir_prefix}"
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@ -703,7 +774,9 @@ def gen_new_json_files(float_vector, rows, dim, data_fields, file_nums=1, array_
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file_name = f"data-fields-{len(data_fields)}-rows-{rows}-dim-{dim}-file-num-{i}-{int(time.time())}.json"
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file = f"{data_source_new}/{file_name}"
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Path(file).parent.mkdir(parents=True, exist_ok=True)
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data = gen_dict_data_by_data_field(data_fields=data_fields, rows=rows, start=start_uid, float_vector=float_vector, dim=dim, array_length=array_length, enable_dynamic_field=enable_dynamic_field, **kwargs)
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data = gen_dict_data_by_data_field(data_fields=data_fields, rows=rows, start=start_uid,
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float_vector=float_vector, dim=dim, array_length=array_length,
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enable_dynamic_field=enable_dynamic_field, **kwargs)
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# log.info(f"data: {data}")
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with open(file, "w") as f:
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json.dump(data, f)
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@ -742,14 +815,17 @@ def gen_npy_files(float_vector, rows, dim, data_fields, file_size=None, file_num
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json.dump(schema, f)
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files = []
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start_uid = 0
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nullable = False
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if file_nums == 1:
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# gen the numpy file without subfolders if only one set of files
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for data_field in data_fields:
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if schema is not None:
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fields = schema.get("fields", [])
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for field in fields:
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if data_field == field["name"] and "params" in field:
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if data_field == field["name"]:
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if "params" in field:
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dim = field["params"].get("dim", dim)
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nullable = field.get("nullable", False)
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if "vec" in data_field:
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vector_type = "float32"
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if "float" in data_field:
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@ -775,7 +851,7 @@ def gen_npy_files(float_vector, rows, dim, data_fields, file_size=None, file_num
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file_name = gen_json_in_numpy_file(dir=data_source_new, data_field=data_field, rows=rows, force=force)
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else:
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file_name = gen_int_or_float_in_numpy_file(dir=data_source_new, data_field=data_field,
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rows=rows, force=force)
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rows=rows, force=force, nullable=nullable)
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files.append(file_name)
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if enable_dynamic_field and include_meta:
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file_name = gen_dynamic_field_in_numpy_file(dir=data_source_new, rows=rows, force=force)
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@ -827,7 +903,9 @@ def gen_dynamic_field_data_in_parquet_file(rows, start=0):
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return data
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def gen_parquet_files(float_vector, rows, dim, data_fields, file_size=None, row_group_size=None, file_nums=1, array_length=None, err_type="", enable_dynamic_field=False, include_meta=True, sparse_format="doc", **kwargs):
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def gen_parquet_files(float_vector, rows, dim, data_fields, file_size=None, row_group_size=None, file_nums=1,
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array_length=None, err_type="", enable_dynamic_field=False, include_meta=True,
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sparse_format="doc", **kwargs):
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schema = kwargs.get("schema", None)
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u_id = f"parquet-{uuid.uuid4()}"
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data_source_new = f"{data_source}/{u_id}"
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@ -850,7 +928,8 @@ def gen_parquet_files(float_vector, rows, dim, data_fields, file_size=None, row_
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all_field_data = {}
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for data_field in data_fields:
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data = gen_data_by_data_field(data_field=data_field, rows=rows, start=0,
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float_vector=float_vector, dim=dim, array_length=array_length, sparse_format=sparse_format, **kwargs)
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float_vector=float_vector, dim=dim, array_length=array_length,
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sparse_format=sparse_format, **kwargs)
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all_field_data[data_field] = data
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if enable_dynamic_field and include_meta:
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all_field_data["$meta"] = gen_dynamic_field_data_in_parquet_file(rows=rows, start=0)
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@ -1023,8 +1102,10 @@ def prepare_bulk_insert_numpy_files(minio_endpoint="", bucket_name="milvus-bucke
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return files
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def prepare_bulk_insert_parquet_files(minio_endpoint="", bucket_name="milvus-bucket", rows=100, dim=128, array_length=None, file_size=None, row_group_size=None,
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enable_dynamic_field=False, data_fields=[DataField.vec_field], float_vector=True, file_nums=1, force=False, include_meta=True, sparse_format="doc", **kwargs):
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def prepare_bulk_insert_parquet_files(minio_endpoint="", bucket_name="milvus-bucket", rows=100, dim=128, array_length=None,
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file_size=None, row_group_size=None, enable_dynamic_field=False,
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data_fields=[DataField.vec_field], float_vector=True, file_nums=1, force=False,
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include_meta=True, sparse_format="doc", **kwargs):
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"""
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Generate column based files based on params in parquet format and copy them to the minio
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Note: each field in data_fields would be generated one parquet file.
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@ -748,7 +748,9 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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@pytest.mark.parametrize("entities", [2000])
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@pytest.mark.parametrize("enable_dynamic_field", [True])
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@pytest.mark.parametrize("enable_partition_key", [True, False])
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def test_bulk_insert_all_field_with_new_json_format(self, auto_id, dim, entities, enable_dynamic_field, enable_partition_key):
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@pytest.mark.parametrize("nullable", [True, False])
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def test_bulk_insert_all_field_with_new_json_format(self, auto_id, dim, entities, enable_dynamic_field,
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enable_partition_key, nullable):
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"""
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collection schema 1: [pk, int64, float64, string float_vector]
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data file: vectors.npy and uid.npy,
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@ -757,20 +759,22 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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2. import data
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3. verify
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"""
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if enable_partition_key is True and nullable is True:
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pytest.skip("partition key field not support nullable")
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float_vec_field_dim = dim
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binary_vec_field_dim = ((dim+random.randint(-16, 32)) // 8) * 8
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bf16_vec_field_dim = dim+random.randint(-16, 32)
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fp16_vec_field_dim = dim+random.randint(-16, 32)
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fields = [
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cf.gen_int64_field(name=df.pk_field, is_primary=True, auto_id=auto_id),
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cf.gen_int64_field(name=df.int_field),
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cf.gen_float_field(name=df.float_field),
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cf.gen_string_field(name=df.string_field, is_partition_key=enable_partition_key),
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cf.gen_json_field(name=df.json_field),
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cf.gen_array_field(name=df.array_int_field, element_type=DataType.INT64),
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cf.gen_array_field(name=df.array_float_field, element_type=DataType.FLOAT),
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cf.gen_array_field(name=df.array_string_field, element_type=DataType.VARCHAR, max_length=100),
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cf.gen_array_field(name=df.array_bool_field, element_type=DataType.BOOL),
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cf.gen_int64_field(name=df.int_field, nullable=nullable),
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cf.gen_float_field(name=df.float_field, nullable=nullable),
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cf.gen_string_field(name=df.string_field, is_partition_key=enable_partition_key, nullable=nullable),
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cf.gen_json_field(name=df.json_field, nullable=nullable),
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cf.gen_array_field(name=df.array_int_field, element_type=DataType.INT64, nullable=nullable),
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cf.gen_array_field(name=df.array_float_field, element_type=DataType.FLOAT, nullable=nullable),
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cf.gen_array_field(name=df.array_string_field, element_type=DataType.VARCHAR, max_length=100, nullable=nullable),
|
||||
cf.gen_array_field(name=df.array_bool_field, element_type=DataType.BOOL, nullable=nullable),
|
||||
cf.gen_float_vec_field(name=df.float_vec_field, dim=float_vec_field_dim),
|
||||
cf.gen_binary_vec_field(name=df.binary_vec_field, dim=binary_vec_field_dim),
|
||||
cf.gen_bfloat16_vec_field(name=df.bf16_vec_field, dim=bf16_vec_field_dim),
|
||||
|
|
@ -878,10 +882,18 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
|
|||
assert "name" in fields_from_search
|
||||
assert "address" in fields_from_search
|
||||
# query data
|
||||
res, _ = self.collection_wrap.query(expr=f"{df.string_field} >= '0'", output_fields=[df.string_field])
|
||||
if not nullable:
|
||||
expr_field = df.string_field
|
||||
expr = f"{expr_field} >= '0'"
|
||||
else:
|
||||
expr_field = df.pk_field
|
||||
expr = f"{expr_field} >= 0"
|
||||
|
||||
res, _ = self.collection_wrap.query(expr=f"{expr}", output_fields=[expr_field, df.int_field])
|
||||
assert len(res) == entities
|
||||
query_data = [r[df.string_field] for r in res][:len(self.collection_wrap.partitions)]
|
||||
res, _ = self.collection_wrap.query(expr=f"{df.string_field} in {query_data}", output_fields=[df.string_field])
|
||||
log.info(res)
|
||||
query_data = [r[expr_field] for r in res][:len(self.collection_wrap.partitions)]
|
||||
res, _ = self.collection_wrap.query(expr=f"{expr_field} in {query_data}", output_fields=[expr_field])
|
||||
assert len(res) == len(query_data)
|
||||
if enable_partition_key:
|
||||
assert len(self.collection_wrap.partitions) > 1
|
||||
|
|
@ -893,7 +905,8 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
|
|||
@pytest.mark.parametrize("enable_dynamic_field", [True, False])
|
||||
@pytest.mark.parametrize("enable_partition_key", [True, False])
|
||||
@pytest.mark.parametrize("include_meta", [True, False])
|
||||
def test_bulk_insert_all_field_with_numpy(self, auto_id, dim, entities, enable_dynamic_field, enable_partition_key, include_meta):
|
||||
@pytest.mark.parametrize("nullable", [True, False])
|
||||
def test_bulk_insert_all_field_with_numpy(self, auto_id, dim, entities, enable_dynamic_field, enable_partition_key, include_meta, nullable):
|
||||
"""
|
||||
collection schema 1: [pk, int64, float64, string float_vector]
|
||||
data file: vectors.npy and uid.npy,
|
||||
|
|
@ -905,13 +918,15 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
|
|||
"""
|
||||
if enable_dynamic_field is False and include_meta is True:
|
||||
pytest.skip("include_meta only works with enable_dynamic_field")
|
||||
if nullable is True:
|
||||
pytest.skip("issue #36241")
|
||||
float_vec_field_dim = dim
|
||||
binary_vec_field_dim = ((dim+random.randint(-16, 32)) // 8) * 8
|
||||
bf16_vec_field_dim = dim+random.randint(-16, 32)
|
||||
fp16_vec_field_dim = dim+random.randint(-16, 32)
|
||||
fields = [
|
||||
cf.gen_int64_field(name=df.pk_field, is_primary=True, auto_id=auto_id),
|
||||
cf.gen_int64_field(name=df.int_field),
|
||||
cf.gen_int64_field(name=df.int_field, nullable=nullable),
|
||||
cf.gen_float_field(name=df.float_field),
|
||||
cf.gen_string_field(name=df.string_field, is_partition_key=enable_partition_key),
|
||||
cf.gen_json_field(name=df.json_field),
|
||||
|
|
@ -1037,7 +1052,9 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
|
|||
@pytest.mark.parametrize("enable_dynamic_field", [True, False])
|
||||
@pytest.mark.parametrize("enable_partition_key", [True, False])
|
||||
@pytest.mark.parametrize("include_meta", [True, False])
|
||||
def test_bulk_insert_all_field_with_parquet(self, auto_id, dim, entities, enable_dynamic_field, enable_partition_key, include_meta):
|
||||
@pytest.mark.parametrize("nullable", [True, False])
|
||||
def test_bulk_insert_all_field_with_parquet(self, auto_id, dim, entities, enable_dynamic_field,
|
||||
enable_partition_key, include_meta, nullable):
|
||||
"""
|
||||
collection schema 1: [pk, int64, float64, string float_vector]
|
||||
data file: vectors.parquet and uid.parquet,
|
||||
|
|
@ -1048,20 +1065,24 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
|
|||
"""
|
||||
if enable_dynamic_field is False and include_meta is True:
|
||||
pytest.skip("include_meta only works with enable_dynamic_field")
|
||||
if nullable is True:
|
||||
pytest.skip("issue #36252")
|
||||
if enable_partition_key is True and nullable is True:
|
||||
pytest.skip("partition key field not support nullable")
|
||||
float_vec_field_dim = dim
|
||||
binary_vec_field_dim = ((dim+random.randint(-16, 32)) // 8) * 8
|
||||
bf16_vec_field_dim = dim+random.randint(-16, 32)
|
||||
fp16_vec_field_dim = dim+random.randint(-16, 32)
|
||||
fields = [
|
||||
cf.gen_int64_field(name=df.pk_field, is_primary=True, auto_id=auto_id),
|
||||
cf.gen_int64_field(name=df.int_field),
|
||||
cf.gen_float_field(name=df.float_field),
|
||||
cf.gen_string_field(name=df.string_field, is_partition_key=enable_partition_key),
|
||||
cf.gen_json_field(name=df.json_field),
|
||||
cf.gen_array_field(name=df.array_int_field, element_type=DataType.INT64),
|
||||
cf.gen_array_field(name=df.array_float_field, element_type=DataType.FLOAT),
|
||||
cf.gen_array_field(name=df.array_string_field, element_type=DataType.VARCHAR, max_length=100),
|
||||
cf.gen_array_field(name=df.array_bool_field, element_type=DataType.BOOL),
|
||||
cf.gen_int64_field(name=df.int_field, nullable=nullable),
|
||||
cf.gen_float_field(name=df.float_field, nullable=nullable),
|
||||
cf.gen_string_field(name=df.string_field, is_partition_key=enable_partition_key, nullable=nullable),
|
||||
cf.gen_json_field(name=df.json_field, nullable=nullable),
|
||||
cf.gen_array_field(name=df.array_int_field, element_type=DataType.INT64, nullable=nullable),
|
||||
cf.gen_array_field(name=df.array_float_field, element_type=DataType.FLOAT, nullable=nullable),
|
||||
cf.gen_array_field(name=df.array_string_field, element_type=DataType.VARCHAR, max_length=100, nullable=nullable),
|
||||
cf.gen_array_field(name=df.array_bool_field, element_type=DataType.BOOL, nullable=nullable),
|
||||
cf.gen_float_vec_field(name=df.float_vec_field, dim=float_vec_field_dim),
|
||||
cf.gen_binary_vec_field(name=df.binary_vec_field, dim=binary_vec_field_dim),
|
||||
cf.gen_bfloat16_vec_field(name=df.bf16_vec_field, dim=bf16_vec_field_dim),
|
||||
|
|
|
|||
|
|
@ -3804,7 +3804,8 @@ class TestCollectionSearch(TestcaseBase):
|
|||
enable_dynamic_field=enable_dynamic_field)[:2]
|
||||
|
||||
# search with output field vector
|
||||
output_fields = [default_float_field_name, default_string_field_name, default_search_field]
|
||||
output_fields = [default_float_field_name, default_string_field_name,
|
||||
default_json_field_name, default_search_field]
|
||||
original_entities = []
|
||||
if enable_dynamic_field:
|
||||
entities = []
|
||||
|
|
@ -3812,6 +3813,7 @@ class TestCollectionSearch(TestcaseBase):
|
|||
entities.append({default_int64_field_name: vector[default_int64_field_name],
|
||||
default_float_field_name: vector[default_float_field_name],
|
||||
default_string_field_name: vector[default_string_field_name],
|
||||
default_json_field_name: vector[default_json_field_name],
|
||||
default_search_field: vector[default_search_field]})
|
||||
original_entities.append(pd.DataFrame(entities))
|
||||
else:
|
||||
|
|
@ -3824,6 +3826,15 @@ class TestCollectionSearch(TestcaseBase):
|
|||
"limit": default_limit,
|
||||
"original_entities": original_entities,
|
||||
"output_fields": output_fields})
|
||||
if enable_dynamic_field:
|
||||
collection_w.search(vectors[:1], default_search_field,
|
||||
default_search_params, default_limit, default_search_exp,
|
||||
output_fields=["$meta", default_search_field],
|
||||
check_task=CheckTasks.check_search_results,
|
||||
check_items={"nq": 1,
|
||||
"limit": default_limit,
|
||||
"original_entities": original_entities,
|
||||
"output_fields": output_fields})
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L2)
|
||||
def test_search_output_vector_field_and_pk_field(self, enable_dynamic_field):
|
||||
|
|
@ -13432,6 +13443,7 @@ class TestCollectionSearchNoneAndDefaultData(TestcaseBase):
|
|||
check_items={"batch_size": batch_size})
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L1)
|
||||
@pytest.mark.skip(reason="issue #36213")
|
||||
def test_search_normal_none_data_partition_key(self, is_flush, enable_dynamic_field, vector_data_type, null_data_percent):
|
||||
"""
|
||||
target: test search normal case with none data inserted
|
||||
|
|
|
|||
Loading…
Reference in New Issue