import random import numpy import pandas as pd import pytest from pymilvus import DataType from base.client_base import TestcaseBase from common import common_func as cf from common import common_type as ct from common.common_type import CaseLabel, CheckTasks from utils.util_pymilvus import * from utils.util_log import test_log as log prefix = "collection" exp_name = "name" exp_schema = "schema" exp_num = "num_entities" exp_primary = "primary" exp_shards_num = "shards_num" default_term_expr = f'{ct.default_int64_field_name} in [0, 1]' default_schema = cf.gen_default_collection_schema() default_binary_schema = cf.gen_default_binary_collection_schema() default_shards_num = 1 uid_count = "collection_count" tag = "collection_count_tag" uid_stats = "get_collection_stats" uid_create = "create_collection" uid_describe = "describe_collection" uid_drop = "drop_collection" uid_has = "has_collection" uid_list = "list_collections" uid_load = "load_collection" partition1 = 'partition1' partition2 = 'partition2' field_name = default_float_vec_field_name default_single_query = { "data": gen_vectors(1, default_dim), "anns_field": default_float_vec_field_name, "param": {"metric_type": "L2", "params": {"nprobe": 10}}, "limit": default_top_k, } default_index_params = {"index_type": "IVF_SQ8", "metric_type": "L2", "params": {"nlist": 64}} default_binary_index_params = {"index_type": "BIN_IVF_FLAT", "metric_type": "JACCARD", "params": {"nlist": 64}} default_nq = ct.default_nq default_search_exp = "int64 >= 0" default_limit = ct.default_limit vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)] default_search_field = ct.default_float_vec_field_name default_search_params = ct.default_search_params max_vector_field_num = ct.max_vector_field_num SPARSE_FLOAT_VECTOR_data_type = DataType.SPARSE_FLOAT_VECTOR class TestCollectionParams(TestcaseBase): """ Test case of collection interface """ @pytest.fixture(scope="function", params=cf.gen_all_type_fields()) def get_unsupported_primary_field(self, request): if request.param.dtype == DataType.INT64 or request.param.dtype == DataType.VARCHAR: pytest.skip("int64 type is valid primary key") yield request.param @pytest.fixture(scope="function", params=ct.invalid_dims) def get_invalid_dim(self, request): yield request.param @pytest.mark.tags(CaseLabel.L2) def test_collection_invalid_schema_type(self): """ target: test collection with an invalid schema type method: create collection with non-CollectionSchema type schema expected: raise exception """ self._connect() c_name = cf.gen_unique_str(prefix) field, _ = self.field_schema_wrap.init_field_schema(name="field_name", dtype=DataType.INT64, is_primary=True) error = {ct.err_code: 0, ct.err_msg: "Schema type must be schema.CollectionSchema"} self.collection_wrap.init_collection(c_name, schema=field, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L2) def test_collection_none_schema(self): """ target: test collection with none schema method: create collection with none schema expected: raise exception """ self._connect() c_name = cf.gen_unique_str(prefix) error = {ct.err_code: 999, ct.err_msg: f"Collection '{c_name}' not exist, or you can pass in schema to create one."} self.collection_wrap.init_collection(c_name, schema=None, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L2) def test_collection_multi_float_vectors(self): """ target: test collection with multi float vectors method: create collection with two float-vec fields expected: Collection created successfully """ # 1. connect self._connect() # 2. create collection with multiple vectors c_name = cf.gen_unique_str(prefix) fields = [cf.gen_int64_field(is_primary=True), cf.gen_float_field(), cf.gen_float_vec_field(dim=default_dim), cf.gen_float_vec_field(name="tmp", dim=default_dim)] schema = cf.gen_collection_schema(fields=fields) self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.check_collection_property, check_items={exp_name: c_name, exp_schema: schema}) @pytest.mark.tags(CaseLabel.L1) def test_collection_mix_vectors(self): """ target: test collection with mix vectors method: create with float and binary vec expected: Collection created successfully """ self._connect() c_name = cf.gen_unique_str(prefix) fields = [cf.gen_int64_field(is_primary=True), cf.gen_float_vec_field(), cf.gen_binary_vec_field()] schema = cf.gen_collection_schema(fields=fields, auto_id=True) self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.check_collection_property, check_items={exp_name: c_name, exp_schema: schema}) class TestCollectionDataframe(TestcaseBase): """ ****************************************************************** The following cases are used to test construct_from_dataframe ****************************************************************** """ @pytest.mark.tags(CaseLabel.L0) def test_construct_from_dataframe(self): """ target: test collection with dataframe data method: create collection and insert with dataframe expected: collection num entities equal to nb """ self._connect() c_name = cf.gen_unique_str(prefix) df = cf.gen_default_dataframe_data(ct.default_nb) self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name, check_task=CheckTasks.check_collection_property, check_items={exp_name: c_name, exp_schema: default_schema}) # flush assert self.collection_wrap.num_entities == ct.default_nb @pytest.mark.tags(CaseLabel.L0) def test_construct_from_binary_dataframe(self): """ target: test binary collection with dataframe method: create binary collection with dataframe expected: collection num entities equal to nb """ self._connect() c_name = cf.gen_unique_str(prefix) df, _ = cf.gen_default_binary_dataframe_data(nb=ct.default_nb) self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name, check_task=CheckTasks.check_collection_property, check_items={exp_name: c_name, exp_schema: default_binary_schema}) assert self.collection_wrap.num_entities == ct.default_nb @pytest.mark.tags(CaseLabel.L2) def test_construct_from_none_dataframe(self): """ target: test create collection by empty dataframe method: invalid dataframe type create collection expected: raise exception """ self._connect() c_name = cf.gen_unique_str(prefix) error = {ct.err_code: 999, ct.err_msg: "Data type must be pandas.DataFrame"} self.collection_wrap.construct_from_dataframe(c_name, None, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L2) def test_construct_from_dataframe_only_column(self): """ target: test collection with dataframe only columns method: dataframe only has columns expected: raise exception """ self._connect() c_name = cf.gen_unique_str(prefix) df = pd.DataFrame(columns=[ct.default_int64_field_name, ct.default_float_vec_field_name]) error = {ct.err_code: 0, ct.err_msg: "Cannot infer schema from empty dataframe"} self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L2) def test_construct_from_inconsistent_dataframe(self): """ target: test collection with data inconsistent method: create and insert with inconsistent data expected: raise exception """ self._connect() c_name = cf.gen_unique_str(prefix) # one field different type df mix_data = [(1, 2., [0.1, 0.2]), (2, 3., 4)] df = pd.DataFrame(data=mix_data, columns=list("ABC")) error = {ct.err_code: 1, ct.err_msg: "The Input data type is inconsistent with defined schema, " "{C} field should be a FLOAT_VECTOR, but got a {} instead."} self.collection_wrap.construct_from_dataframe(c_name, df, primary_field='A', check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L2) def test_construct_from_non_dataframe(self): """ target: test create collection by invalid dataframe method: non-dataframe type create collection expected: raise exception """ self._connect() c_name = cf.gen_unique_str(prefix) error = {ct.err_code: 0, ct.err_msg: "Data type must be pandas.DataFrame."} df = cf.gen_default_list_data(nb=10) self.collection_wrap.construct_from_dataframe(c_name, df, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L2) def test_construct_from_data_type_dataframe(self): """ target: test collection with invalid dataframe method: create with invalid dataframe expected: raise exception """ self._connect() c_name = cf.gen_unique_str(prefix) df = pd.DataFrame({"date": pd.date_range('20210101', periods=3), ct.default_int64_field_name: [1, 2, 3]}) error = {ct.err_code: 0, ct.err_msg: "Cannot infer schema from empty dataframe."} self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L2) def test_construct_from_invalid_field_name(self): """ target: test collection with invalid field name method: create with invalid field name dataframe expected: raise exception """ self._connect() c_name = cf.gen_unique_str(prefix) df = pd.DataFrame({'%$#': cf.gen_vectors(3, 2), ct.default_int64_field_name: [1, 2, 3]}) error = {ct.err_code: 1, ct.err_msg: "Invalid field name"} self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L2) def test_construct_none_primary_field(self): """ target: test collection with none primary field method: primary_field is none expected: raise exception """ self._connect() c_name = cf.gen_unique_str(prefix) df = cf.gen_default_dataframe_data(ct.default_nb) error = {ct.err_code: 0, ct.err_msg: "Schema must have a primary key field."} self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=None, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L2) def test_construct_not_existed_primary_field(self): """ target: test collection with not existed primary field method: primary field not existed expected: raise exception """ self._connect() c_name = cf.gen_unique_str(prefix) df = cf.gen_default_dataframe_data(ct.default_nb) error = {ct.err_code: 0, ct.err_msg: "Primary field must in dataframe."} self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=c_name, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L2) def test_construct_with_none_auto_id(self): """ target: test construct with non-int64 as primary field method: non-int64 as primary field expected: raise exception """ self._connect() c_name = cf.gen_unique_str(prefix) df = cf.gen_default_dataframe_data(ct.default_nb) error = {ct.err_code: 0, ct.err_msg: "Param auto_id must be bool type"} self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name, auto_id=None, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L1) def test_construct_auto_id_true_insert(self): """ target: test construct with true auto_id method: auto_id=True and insert values expected: raise exception """ self._connect() c_name = cf.gen_unique_str(prefix) df = cf.gen_default_dataframe_data(nb=100) error = {ct.err_code: 0, ct.err_msg: "Auto_id is True, primary field should not have data."} self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name, auto_id=True, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L1) def test_construct_auto_id_true_no_insert(self): """ target: test construct with true auto_id method: auto_id=True and not insert ids(primary fields all values are None) expected: verify num entities """ self._connect() c_name = cf.gen_unique_str(prefix) df = cf.gen_default_dataframe_data() # df.drop(ct.default_int64_field_name, axis=1, inplace=True) df[ct.default_int64_field_name] = None self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name, auto_id=True) assert self.collection_wrap.num_entities == ct.default_nb @pytest.mark.tags(CaseLabel.L2) def test_construct_none_value_auto_id_true(self): """ target: test construct with none value, auto_id method: df primary field with none value, auto_id=true expected: todo """ self._connect() nb = 100 df = cf.gen_default_dataframe_data(nb) df.iloc[:, 0] = numpy.NaN res, _ = self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df, primary_field=ct.default_int64_field_name, auto_id=True) mutation_res = res[1] assert cf._check_primary_keys(mutation_res.primary_keys, 100) assert self.collection_wrap.num_entities == nb @pytest.mark.tags(CaseLabel.L1) def test_construct_auto_id_false(self): """ target: test construct with false auto_id method: auto_id=False, primary_field correct expected: verify auto_id """ self._connect() c_name = cf.gen_unique_str(prefix) df = cf.gen_default_dataframe_data(ct.default_nb) self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name, auto_id=False) assert not self.collection_wrap.schema.auto_id assert self.collection_wrap.num_entities == ct.default_nb @pytest.mark.tags(CaseLabel.L2) def test_construct_none_value_auto_id_false(self): """ target: test construct with none value, auto_id method: df primary field with none value, auto_id=false expected: raise exception """ self._connect() nb = 100 df = cf.gen_default_dataframe_data(nb) df.iloc[:, 0] = numpy.NaN error = {ct.err_code: 0, ct.err_msg: "Primary key type must be DataType.INT64"} self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df, primary_field=ct.default_int64_field_name, auto_id=False, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L1) def test_construct_auto_id_false_same_values(self): """ target: test construct with false auto_id and same value method: auto_id=False, primary field same values expected: verify num entities """ self._connect() nb = 100 df = cf.gen_default_dataframe_data(nb) df.iloc[1:, 0] = 1 res, _ = self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df, primary_field=ct.default_int64_field_name, auto_id=False) collection_w = res[0] collection_w.flush() assert collection_w.num_entities == nb mutation_res = res[1] assert mutation_res.primary_keys == df[ct.default_int64_field_name].values.tolist() @pytest.mark.tags(CaseLabel.L1) def test_construct_auto_id_false_negative_values(self): """ target: test construct with negative values method: auto_id=False, primary field values is negative expected: verify num entities """ self._connect() nb = 100 df = cf.gen_default_dataframe_data(nb) new_values = pd.Series(data=[i for i in range(0, -nb, -1)]) df[ct.default_int64_field_name] = new_values self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df, primary_field=ct.default_int64_field_name, auto_id=False) assert self.collection_wrap.num_entities == nb @pytest.mark.tags(CaseLabel.L1) def test_construct_from_dataframe_dup_name(self): """ target: test collection with dup name and insert dataframe method: create collection with dup name, none schema, dataframe expected: two collection object is correct """ self._connect() c_name = cf.gen_unique_str(prefix) collection_w = self.init_collection_wrap(name=c_name, primary_field=ct.default_int64_field_name, check_task=CheckTasks.check_collection_property, check_items={exp_name: c_name, exp_schema: default_schema}) df = cf.gen_default_dataframe_data(ct.default_nb) self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name, check_task=CheckTasks.check_collection_property, check_items={exp_name: c_name, exp_schema: default_schema}) # flush assert collection_w.num_entities == ct.default_nb assert collection_w.num_entities == self.collection_wrap.num_entities class TestLoadCollection(TestcaseBase): """ ****************************************************************** The following cases are used to test `collection.load()` function ****************************************************************** """ @pytest.mark.tags(CaseLabel.ClusterOnly) def test_load_replica_change(self): """ target: test load replica change method: 1.load with replica 1 2.load with a new replica number 3.release collection 4.load with a new replica 5.create index is a must because get_query_segment_info could only return indexed and loaded segment expected: The second time successfully loaded with a new replica number """ # create, insert collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix)) df = cf.gen_default_dataframe_data() insert_res, _ = collection_w.insert(df) assert collection_w.num_entities == ct.default_nb collection_w.create_index(ct.default_float_vec_field_name, ct.default_index) collection_w.load(replica_number=1) for seg in self.utility_wrap.get_query_segment_info(collection_w.name)[0]: assert len(seg.nodeIds) == 1 collection_w.query(expr=f"{ct.default_int64_field_name} in [0]") loading_progress, _ = self.utility_wrap.loading_progress(collection_w.name) assert loading_progress == {'loading_progress': '100%'} # verify load different replicas thrown an exception collection_w.load(replica_number=2) one_replica, _ = collection_w.get_replicas() assert len(one_replica.groups) == 2 collection_w.release() collection_w.load(replica_number=2) # replicas is not yet reflected in loading progress loading_progress, _ = self.utility_wrap.loading_progress(collection_w.name) assert loading_progress == {'loading_progress': '100%'} two_replicas, _ = collection_w.get_replicas() assert len(two_replicas.groups) == 2 collection_w.query(expr=f"{ct.default_int64_field_name} in [0]", check_task=CheckTasks.check_query_results, check_items={'exp_res': [{'int64': 0}]}) # verify loaded segments included 2 replicas and twice num entities seg_info = self.utility_wrap.get_query_segment_info(collection_w.name)[0] num_entities = 0 for seg in seg_info: assert len(seg.nodeIds) == 2 num_entities += seg.num_rows assert num_entities == ct.default_nb @pytest.mark.tags(CaseLabel.ClusterOnly) def test_load_replica_multi(self): """ target: test load with multiple replicas method: 1.create collection with one shard 2.insert multiple segments 3.load with multiple replicas 4.query and search expected: Query and search successfully """ # create, insert collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix), shards_num=1) tmp_nb = 1000 replica_number = 2 for i in range(replica_number): df = cf.gen_default_dataframe_data(nb=tmp_nb, start=i * tmp_nb) insert_res, _ = collection_w.insert(df) assert collection_w.num_entities == (i + 1) * tmp_nb collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index) collection_w.load(replica_number=replica_number) replicas = collection_w.get_replicas()[0] assert len(replicas.groups) == replica_number for seg in self.utility_wrap.get_query_segment_info(collection_w.name)[0]: assert len(seg.nodeIds) == replica_number query_res, _ = collection_w.query(expr=f"{ct.default_int64_field_name} in [0, {tmp_nb}]") assert len(query_res) == 2 search_res, _ = collection_w.search(vectors, default_search_field, default_search_params, default_limit) assert len(search_res[0]) == ct.default_limit @pytest.mark.tags(CaseLabel.ClusterOnly) def test_load_replica_partitions(self): """ target: test load replica with partitions method: 1.Create collection and one partition 2.Insert data into collection and partition 3.Load multi replicas with partition 4.Query expected: Verify query result """ collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix)) df_1 = cf.gen_default_dataframe_data(nb=ct.default_nb) df_2 = cf.gen_default_dataframe_data(nb=ct.default_nb, start=ct.default_nb) collection_w.insert(df_1) partition_w = self.init_partition_wrap(collection_w, ct.default_tag) partition_w.insert(df_2) assert collection_w.num_entities == ct.default_nb * 2 collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index) collection_w.load([partition_w.name], replica_number=2) for seg in self.utility_wrap.get_query_segment_info(collection_w.name)[0]: assert len(seg.nodeIds) == 2 # default tag query 0 empty collection_w.query(expr=f"{ct.default_int64_field_name} in [0]", partition_names=[ct.default_tag], check_tasks=CheckTasks.check_query_empty) # default query 0 empty collection_w.query(expr=f"{ct.default_int64_field_name} in [2000]", check_task=CheckTasks.check_query_results, check_items={'exp_res': df_2.iloc[:1, :1].to_dict('records')}) error = {ct.err_code: 65538, ct.err_msg: "partition not loaded"} collection_w.query(expr=f"{ct.default_int64_field_name} in [0]", partition_names=[ct.default_partition_name, ct.default_tag], check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L3) def test_load_replica_non_shard_leader(self): """ target: test replica groups which one of QN is not shard leader method: 1.deploy cluster with 5 QNs 2.create collection with 2 shards 3.insert and flush 4.load with 2 replica number 5.insert growing data 6.search and query expected: Verify search and query results """ # create and insert entities collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix), shards_num=2) df = cf.gen_default_dataframe_data() collection_w.insert(df) assert collection_w.num_entities == ct.default_nb collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index) # load with multi replica and insert growing data collection_w.load(replica_number=2) df_growing = cf.gen_default_dataframe_data(100, start=ct.default_nb) collection_w.insert(df_growing) replicas = collection_w.get_replicas()[0] # verify there are 2 groups (2 replicas) assert len(replicas.groups) == 2 log.debug(replicas) all_group_nodes = [] for group in replicas.groups: # verify each group have 3 shards assert len(group.shards) == 2 all_group_nodes.extend(group.group_nodes) # verify all groups has 5 querynodes assert len(all_group_nodes) == 5 # Verify 2 replicas segments loaded seg_info, _ = self.utility_wrap.get_query_segment_info(collection_w.name) for seg in seg_info: assert len(seg.nodeIds) == 2 # verify search successfully res, _ = collection_w.search(vectors, default_search_field, default_search_params, default_limit) assert len(res[0]) == ct.default_limit # verify query sealed and growing data successfully collection_w.query(expr=f"{ct.default_int64_field_name} in [0, {ct.default_nb}]", check_task=CheckTasks.check_query_results, check_items={'exp_res': [{'int64': 0}, {'int64': 3000}]}) @pytest.mark.tags(CaseLabel.L3) def test_load_replica_multiple_shard_leader(self): """ target: test replica groups which one of QN is shard leader of multiple shards method: 1.deploy cluster with 5 QNs 2.create collection with 3 shards 3.insert and flush 4.load with 2 replica number 5.insert growng data 6.search and query expected: Verify search and query results """ # craete and insert collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix), shards_num=3) df = cf.gen_default_dataframe_data() collection_w.insert(df) assert collection_w.num_entities == ct.default_nb collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index) # load with multi replicas and insert growing data collection_w.load(replica_number=2) df_growing = cf.gen_default_dataframe_data(100, start=ct.default_nb) collection_w.insert(df_growing) # verify replica infos replicas, _ = collection_w.get_replicas() log.debug(replicas) assert len(replicas.groups) == 2 all_group_nodes = [] for group in replicas.groups: # verify each group have 3 shards assert len(group.shards) == 3 all_group_nodes.extend(group.group_nodes) # verify all groups has 5 querynodes assert len(all_group_nodes) == 5 # Verify 2 replicas segments loaded seg_info, _ = self.utility_wrap.get_query_segment_info(collection_w.name) for seg in seg_info: assert len(seg.nodeIds) == 2 # Verify search successfully res, _ = collection_w.search(vectors, default_search_field, default_search_params, default_limit) assert len(res[0]) == ct.default_limit # Verify query sealed and growing entities successfully collection_w.query(expr=f"{ct.default_int64_field_name} in [0, {ct.default_nb}]", check_task=CheckTasks.check_query_results, check_items={'exp_res': [{'int64': 0}, {'int64': 3000}]}) @pytest.mark.tags(CaseLabel.L3) def test_load_replica_sq_count_balance(self): """ target: test load with multi replicas, and sq request load balance cross replicas method: 1.Deploy milvus with multi querynodes 2.Insert entities and load with replicas 3.Do query req many times 4.Verify the querynode sq_req_count metrics expected: Infer whether the query request is load balanced. """ from utils.util_k8s import get_metrics_querynode_sq_req_count collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix)) df = cf.gen_default_dataframe_data(nb=5000) mutation_res, _ = collection_w.insert(df) assert collection_w.num_entities == 5000 total_sq_count = 20 collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index) collection_w.load(replica_number=3) for i in range(total_sq_count): ids = [random.randint(0, 100) for _ in range(5)] collection_w.query(f"{ct.default_int64_field_name} in {ids}") replicas, _ = collection_w.get_replicas() log.debug(replicas) sq_req_count = get_metrics_querynode_sq_req_count() for group in replicas.groups: group_nodes = group.group_nodes group_sq_req_count = 0 for node in group_nodes: group_sq_req_count += sq_req_count[node] log.debug(f"Group nodes {group_nodes} with total sq_req_count {group_sq_req_count}") @pytest.mark.tags(CaseLabel.L2) def test_get_collection_replicas_not_loaded(self): """ target: test get replicas of not loaded collection method: not loaded collection and get replicas expected: raise an exception """ # create, insert collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix)) df = cf.gen_default_dataframe_data() insert_res, _ = collection_w.insert(df) assert collection_w.num_entities == ct.default_nb res, _ = collection_w.get_replicas() assert len(res.groups) == 0 @pytest.mark.tags(CaseLabel.L3) def test_count_multi_replicas(self): """ target: test count multi replicas method: 1. load data with multi replicas 2. count expected: verify count """ # create -> insert -> flush collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix)) df = cf.gen_default_dataframe_data() collection_w.insert(df) collection_w.flush() # index -> load replicas collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index) collection_w.load(replica_number=2) # count collection_w.query(expr=f'{ct.default_int64_field_name} >= 0', output_fields=[ct.default_count_output], check_task=CheckTasks.check_query_results, check_items={'exp_res': [{"count(*)": ct.default_nb}]}) class TestReleaseAdvanced(TestcaseBase): @pytest.mark.tags(CaseLabel.L2) def test_release_partition_during_searching(self): """ target: test release partition during searching method: insert entities into partition, flush and load partition, release partition during searching expected: raise exception """ self._connect() partition_num = 1 collection_w = self.init_collection_general(prefix, True, 10, partition_num, is_index=False)[0] collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index) par = collection_w.partitions par_name = par[partition_num].name par[partition_num].load() limit = 10 collection_w.search(vectors, default_search_field, default_search_params, limit, default_search_exp, [par_name]) par[partition_num].release() collection_w.search(vectors, default_search_field, default_search_params, limit, default_search_exp, [par_name], check_task=CheckTasks.err_res, check_items={"err_code": 65535, "err_msg": "collection not loaded"}) @pytest.mark.tags(CaseLabel.L0) def test_release_indexed_collection_during_searching(self): """ target: test release indexed collection during searching method: insert entities into partition, flush and load partition, release collection during searching expected: raise exception """ self._connect() partition_num = 1 collection_w = self.init_collection_general(prefix, True, 10, partition_num, is_index=False)[0] collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index) par = collection_w.partitions par_name = par[partition_num].name par[partition_num].load() limit = 10 collection_w.search(vectors, default_search_field, default_search_params, limit, default_search_exp, [par_name], _async=True) collection_w.release() error = {ct.err_code: 65535, ct.err_msg: "collection not loaded"} collection_w.search(vectors, default_search_field, default_search_params, limit, default_search_exp, [par_name], check_task=CheckTasks.err_res, check_items=error) class TestLoadPartition(TestcaseBase): """ ****************************************************************** The following cases are used to test `load_collection` function ****************************************************************** """ @pytest.fixture( scope="function", params=gen_binary_index() ) def get_binary_index(self, request): log.info(request.param) if request.param["index_type"] in ct.binary_supported_index_types: return request.param else: pytest.skip("Skip index Temporary") @pytest.mark.tags(CaseLabel.L0) @pytest.mark.parametrize('binary_index', gen_binary_index()) @pytest.mark.parametrize('metric_type', ct.binary_metrics) def test_load_partition_after_index_binary(self, binary_index, metric_type): """ target: test load binary_collection, after index created method: insert and create index, load binary_collection with correct params expected: no error raised """ self._connect() partition_num = 1 collection_w = self.init_collection_general(prefix, True, ct.default_nb, partition_num, is_binary=True, is_index=False)[0] # for metric_type in ct.binary_metrics: binary_index["metric_type"] = metric_type if binary_index["index_type"] == "BIN_IVF_FLAT" and metric_type in ct.structure_metrics: error = {ct.err_code: 65535, ct.err_msg: f"metric type {metric_type} not found or not supported, supported: [HAMMING JACCARD]"} collection_w.create_index(ct.default_binary_vec_field_name, binary_index, check_task=CheckTasks.err_res, check_items=error) collection_w.create_index(ct.default_binary_vec_field_name, ct.default_bin_flat_index) else: collection_w.create_index(ct.default_binary_vec_field_name, binary_index) par = collection_w.partitions par[partition_num].load() @pytest.mark.tags(CaseLabel.L2) def test_load_partition_dis_connect(self): """ target: test load partition, without connection method: load partition with correct params, with a disconnected instance expected: load raise exception """ self._connect() collection_w = self.init_collection_wrap() partition_name = cf.gen_unique_str(prefix) description = cf.gen_unique_str("desc_") partition_w = self.init_partition_wrap(collection_w, partition_name, description=description, check_task=CheckTasks.check_partition_property, check_items={"name": partition_name, "description": description, "is_empty": True, "num_entities": 0} ) collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index) partition_w.load() self.connection_wrap.remove_connection(ct.default_alias) res_list, _ = self.connection_wrap.list_connections() assert ct.default_alias not in res_list error = {ct.err_code: 999, ct.err_msg: 'should create connection first.'} partition_w.load(check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L2) def test_release_partition_dis_connect(self): """ target: test release collection, without connection method: release collection with correct params, with a disconnected instance expected: release raise exception """ self._connect() collection_w = self.init_collection_wrap() partition_name = cf.gen_unique_str(prefix) description = cf.gen_unique_str("desc_") partition_w = self.init_partition_wrap(collection_w, partition_name, description=description, check_task=CheckTasks.check_partition_property, check_items={"name": partition_name, "description": description, "is_empty": True, "num_entities": 0} ) collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index) partition_w.load() self.connection_wrap.remove_connection(ct.default_alias) res_list, _ = self.connection_wrap.list_connections() assert ct.default_alias not in res_list error = {ct.err_code: 1, ct.err_msg: 'should create connection first.'} partition_w.release(check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L2) def test_load_partition_not_existed(self): """ target: test load partition for invalid scenario method: load not existed partition expected: raise exception and report the error """ self._connect() collection_w = self.init_collection_wrap() partition_name = cf.gen_unique_str(prefix) description = cf.gen_unique_str("desc_") partition_w = self.init_partition_wrap(collection_w, partition_name, description=description, check_task=CheckTasks.check_partition_property, check_items={"name": partition_name, "description": description, "is_empty": True, "num_entities": 0}) collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index) partition_w.drop() error = {ct.err_code: 200, ct.err_msg: 'partition not found[partition=%s]' % partition_name} partition_w.load(check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L0) def test_release_partition_not_load(self): """ target: test release partition without load method: release partition without load expected: release success """ self._connect() collection_w = self.init_collection_wrap() partition_name = cf.gen_unique_str(prefix) description = cf.gen_unique_str("desc_") partition_w = self.init_partition_wrap(collection_w, partition_name, description=description, check_task=CheckTasks.check_partition_property, check_items={"name": partition_name, "description": description, "is_empty": True, "num_entities": 0}) partition_w.release() @pytest.mark.tags(CaseLabel.L2) def test_load_release_after_drop(self): """ target: test load and release partition after drop method: drop partition and then load and release it expected: raise exception """ self._connect() collection_w = self.init_collection_wrap() partition_name = cf.gen_unique_str(prefix) description = cf.gen_unique_str("desc_") partition_w = self.init_partition_wrap(collection_w, partition_name, description=description, check_task=CheckTasks.check_partition_property, check_items={"name": partition_name, "description": description, "is_empty": True, "num_entities": 0}) partition_w.drop() collection_w.create_index(ct.default_float_vec_field_name) error = {ct.err_code: 200, ct.err_msg: 'partition not found[partition=%s]' % partition_name} partition_w.load(check_task=CheckTasks.err_res, check_items=error) partition_w.release(check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L0) def test_release_partition_after_drop(self): """ target: test release collection after drop method: insert and flush, then release collection after load and drop expected: raise exception """ self._connect() collection_w = self.init_collection_wrap() partition_name = cf.gen_unique_str(prefix) description = cf.gen_unique_str("desc_") partition_w = self.init_partition_wrap(collection_w, partition_name, description=description, check_task=CheckTasks.check_partition_property, check_items={"name": partition_name, "description": description, "is_empty": True, "num_entities": 0}) partition_w.drop() error = {ct.err_code: 200, ct.err_msg: 'partition not found[partition=%s]' % partition_name} partition_w.release(check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L0) def test_load_release_after_collection_drop(self): """ target: test release collection after drop method: insert and flush, then release collection after load and drop expected: raise exception """ self._connect() collection_w = self.init_collection_wrap() name = collection_w.name partition_name = cf.gen_unique_str(prefix) description = cf.gen_unique_str("desc_") partition_w = self.init_partition_wrap(collection_w, partition_name, description=description, check_task=CheckTasks.check_partition_property, check_items={"name": partition_name, "description": description, "is_empty": True, "num_entities": 0}) collection_w.drop() error = {ct.err_code: 0, ct.err_msg: "collection not found"} partition_w.load(check_task=CheckTasks.err_res, check_items=error) partition_w.release(check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L2) def test_load_collection_after_load_loaded_partition(self): """ target: test load partition after load partition method: 1. load partition 2. load the partition again 3. query on the non-loaded partition 4. load collection expected: No exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() partition_w1.load() error = {ct.err_code: 65538, ct.err_msg: 'partition not loaded'} collection_w.query(default_term_expr, partition_names=[partition2], check_task=CheckTasks.err_res, check_items=error) collection_w.load() @pytest.mark.tags(CaseLabel.L2) def test_load_collection_after_load_unloaded_partition(self): """ target: test load partition after load an unloaded partition method: 1. load partition 2. load another partition 3. query on the collection 4. load collection expected: No exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() partition_w2.load() collection_w.query(default_term_expr) collection_w.load() @pytest.mark.tags(CaseLabel.L2) def test_load_collection_after_load_one_partition(self): """ target: test load partition after load partition method: 1. load partition 2. load collection 3. query on the partitions expected: No exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() collection_w.load() collection_w.query(default_term_expr, partition_names=[partition1, partition2]) @pytest.mark.tags(CaseLabel.L0) def test_load_partitions_release_collection(self): """ target: test release collection after load partitions method: 1. load partition 2. release collection 3. query on the partition 4. load partitions 5. query on the collection expected: no exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() collection_w.release() error = {ct.err_code: 65535, ct.err_msg: "collection not loaded"} collection_w.query(default_term_expr, partition_names=[partition1], check_task=CheckTasks.err_res, check_items=error) partition_w1.load() partition_w2.load() collection_w.query(default_term_expr) @pytest.mark.tags(CaseLabel.L2) def test_load_collection_release_collection(self): """ target: test load collection after load partitions method: 1. load partition 2. release collection 3. load collection expected: no exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() collection_w.release() collection_w.load() @pytest.mark.tags(CaseLabel.L2) def test_load_partitions_after_load_release_partition(self): """ target: test load partitions after load and release partition method: 1. load partition 2. release partition 3. query on the partition 4. load partitions(include released partition and non-released partition) 5. query on the collection expected: no exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() partition_w1.release() error = {ct.err_code: 65535, ct.err_msg: 'collection not loaded'} collection_w.query(default_term_expr, partition_names=[partition1], check_task=CheckTasks.err_res, check_items=error) partition_w1.load() partition_w2.load() collection_w.query(default_term_expr) @pytest.mark.tags(CaseLabel.L2) def test_load_collection_after_load_release_partition(self): """ target: test load collection after load and release partition method: 1. load partition 2. release partition 3. load collection 4. search on the collection expected: no exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() partition_w1.release() collection_w.load() collection_w.search(vectors, default_search_field, default_search_params, default_limit, partition_names=[partition1, partition2]) @pytest.mark.tags(CaseLabel.L2) def test_load_partitions_after_load_partition_release_partitions(self): """ target: test load partitions after load partition and release partitions method: 1. load partition 2. release partitions 3. load partitions 4. query on the partitions expected: no exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() partition_w1.release() partition_w2.release() partition_w1.load() partition_w2.load() collection_w.query(default_term_expr, partition_names=[partition1, partition2]) @pytest.mark.tags(CaseLabel.L2) def test_load_collection_after_load_partition_release_partitions(self): """ target: test load collection after load partition and release partitions method: 1. load partition 2. release partitions 3. query on the partitions 4. load collection 5. query on the partitions expected: no exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() partition_w1.release() partition_w2.release() error = {ct.err_code: 65535, ct.err_msg: 'collection not loaded'} collection_w.query(default_term_expr, partition_names=[partition1, partition2], check_task=CheckTasks.err_res, check_items=error) collection_w.load() collection_w.query(default_term_expr, partition_names=[partition1, partition2]) @pytest.mark.tags(CaseLabel.L2) def test_load_partition_after_load_drop_partition(self): """ target: test load partition after load and drop partition method: 1. load partition 2. drop the loaded partition 3. load the left partition 4. query on the partition expected: no exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() partition_w1.release() partition_w1.drop() partition_w2.load() collection_w.query(default_term_expr, partition_names=[partition2]) @pytest.mark.tags(CaseLabel.L2) def test_load_collection_after_load_drop_partition(self): """ target: test load collection after load and drop partition method: 1. load partition 2. drop the loaded partition 3. query on the partition 4. drop another partition 5. load collection 6. query on the collection expected: no exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() partition_w1.release() partition_w1.drop() error = {ct.err_code: 65535, ct.err_msg: f'partition name {partition1} not found'} collection_w.query(default_term_expr, partition_names=[partition1, partition2], check_task=CheckTasks.err_res, check_items=error) partition_w2.drop() collection_w.load() collection_w.query(default_term_expr) @pytest.mark.tags(CaseLabel.L2) def test_release_load_partition_after_load_drop_partition(self): """ target: test release load partition after load and drop partition method: 1. load partition 2. drop the loaded partition 3. release another partition 4. load the partition expected: no exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() partition_w1.release() partition_w1.drop() partition_w2.release() partition_w2.load() collection_w.query(default_term_expr, partition_names=[partition2]) @pytest.mark.tags(CaseLabel.L2) def test_release_load_collection_after_load_drop_partition(self): """ target: test release load partition after load and drop partition method: 1. load partition 2. drop the loaded partition 3. release another partition 4. load collection expected: no exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() partition_w1.release() partition_w1.drop() partition_w2.release() collection_w.load() collection_w.query(default_term_expr) @pytest.mark.tags(CaseLabel.L2) def test_load_another_partition_after_load_drop_partition(self): """ target: test load another collection after load and drop one partition method: 1. load partition 2. drop the unloaded partition 3. load the partition again 4. query on the partition expected: no exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() partition_w2.drop() partition_w1.load() collection_w.query(default_term_expr, partition_names=[partition1]) @pytest.mark.tags(CaseLabel.L2) def test_release_load_partition_after_load_partition_drop_another(self): """ target: test release load partition after load and drop partition method: 1. load partition 2. drop the unloaded partition 3. release the loaded partition 4. query on the released partition 5. reload the partition 6. query on the partition expected: no exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() partition_w2.drop() partition_w1.release() error = {ct.err_code: 65535, ct.err_msg: 'collection not loaded'} collection_w.query(default_term_expr, partition_names=[partition1], check_task=CheckTasks.err_res, check_items=error) partition_w1.load() collection_w.query(default_term_expr, partition_names=[partition1]) @pytest.mark.tags(CaseLabel.L2) def test_release_load_collection_after_load_partition_drop_another(self): """ target: test release load partition after load and drop partition method: 1. load partition 2. drop the unloaded partition 3. release the loaded partition 4. load collection 5. query on the collection expected: no exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() partition_w2.drop() partition_w1.release() collection_w.load() collection_w.query(default_term_expr) @pytest.mark.tags(CaseLabel.L2) def test_release_unloaded_partition(self): """ target: test load collection after load and drop partition method: 1. load partition 2. release the other partition 3. query on the first partition expected: no exception """ collection_w = self.init_collection_general(prefix, is_index=False)[0] collection_w.create_index(default_search_field) partition_w1 = self.init_partition_wrap(collection_w, partition1) partition_w2 = self.init_partition_wrap(collection_w, partition2) partition_w1.load() partition_w2.release() collection_w.query(default_term_expr, partition_names=[partition1]) class TestCollectionString(TestcaseBase): """ ****************************************************************** The following cases are used to test about string ****************************************************************** """ @pytest.mark.tags(CaseLabel.L1) def test_collection_string_field_is_primary(self): """ target: test create collection with string field method: 1. create collection with string field and vector field 2. set string fields is_primary=True expected: Create collection successfully """ self._connect() c_name = cf.gen_unique_str(prefix) schema = cf.gen_string_pk_default_collection_schema() self.collection_wrap.init_collection(name=c_name, schema=schema, check_task=CheckTasks.check_collection_property, check_items={exp_name: c_name, exp_schema: schema}) @pytest.mark.tags(CaseLabel.L1) def test_collection_with_muti_string_fields(self): """ target: test create collection with muti string fields method: 1. create collection with primary string field and not primary string field 2. string fields is_primary=True expected: Create collection successfully """ self._connect() c_name = cf.gen_unique_str(prefix) int_field = cf.gen_int64_field() vec_field = cf.gen_float_vec_field() string_field_1 = cf.gen_string_field(is_primary=True) string_field_2 = cf.gen_string_field(name=c_name) schema = cf.gen_collection_schema(fields=[int_field, string_field_1, string_field_2, vec_field]) self.collection_wrap.init_collection(name=c_name, schema=schema, check_task=CheckTasks.check_collection_property, check_items={exp_name: c_name, exp_schema: schema}) @pytest.mark.tags(CaseLabel.L1) def test_collection_only_string_field(self): """ target: test create collection with one string field method: create collection with only string field expected: Raise exception """ self._connect() string_field = cf.gen_string_field(is_primary=True) schema = cf.gen_collection_schema([string_field]) error = {ct.err_code: 0, ct.err_msg: "No vector field is found"} self.collection_wrap.init_collection(name=cf.gen_unique_str(prefix), schema=schema, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L1) def test_collection_string_field_with_exceed_max_len(self): """ target: test create collection with string field method: 1. create collection with string field 2. String field max_length exceeds maximum expected: Raise exception """ self._connect() c_name = cf.gen_unique_str(prefix) int_field = cf.gen_int64_field(is_primary=True) vec_field = cf.gen_float_vec_field() max_length = 65535 + 1 string_field = cf.gen_string_field(max_length=max_length) schema = cf.gen_collection_schema([int_field, string_field, vec_field]) error = {ct.err_code: 65535, ct.err_msg: f"the maximum length specified for the field({ct.default_string_field_name}) should be in (0, 65535]"} self.collection_wrap.init_collection(name=c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L1) def test_collection_invalid_string_field_dtype(self): """ target: test create collection with string field method: create collection with string field, the string field datatype is invaild expected: Raise exception """ self._connect() string_field = self.field_schema_wrap.init_field_schema(name="string", dtype=DataType.STRING)[0] int_field = cf.gen_int64_field(is_primary=True) vec_field = cf.gen_float_vec_field() schema = cf.gen_collection_schema(fields=[int_field, string_field, vec_field]) error = {ct.err_code: 0, ct.err_msg: "string data type not supported yet, please use VarChar type instead"} self.collection_wrap.init_collection(name=cf.gen_unique_str(prefix), schema=schema, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L1) def test_collection_string_field_is_primary_and_auto_id(self): """ target: test create collection with string field method: create collection with string field, the string field primary and auto id are true expected: Create collection successfully """ self._connect() int_field = cf.gen_int64_field() vec_field = cf.gen_float_vec_field() string_field = cf.gen_string_field(is_primary=True, auto_id=True) fields = [int_field, string_field, vec_field] schema = self.collection_schema_wrap.init_collection_schema(fields=fields)[0] self.init_collection_wrap(schema=schema, check_task=CheckTasks.check_collection_property, check_items={"schema": schema, "primary": ct.default_string_field_name}) class TestCollectionJSON(TestcaseBase): """ ****************************************************************** The following cases are used to test about json ****************************************************************** """ @pytest.mark.tags(CaseLabel.L1) @pytest.mark.parametrize("auto_id", [True, False]) def test_collection_json_field_as_primary_key(self, auto_id): """ target: test create collection with JSON field as primary key method: 1. create collection with one JSON field, and vector field 2. set json field is_primary=true 3. set auto_id as true expected: Raise exception (not supported) """ self._connect() int_field = cf.gen_int64_field() vec_field = cf.gen_float_vec_field() string_field = cf.gen_string_field() # 1. create json field as primary key through field schema api error = {ct.err_code: 1, ct.err_msg: "Primary key type must be DataType.INT64 or DataType.VARCHAR"} json_field = cf.gen_json_field(is_primary=True, auto_id=auto_id) fields = [int_field, string_field, json_field, vec_field] self.collection_schema_wrap.init_collection_schema(fields=fields, check_task=CheckTasks.err_res, check_items=error) # 2. create json field as primary key through collection schema api json_field = cf.gen_json_field() fields = [int_field, string_field, json_field, vec_field] self.collection_schema_wrap.init_collection_schema(fields=fields, primary_field=ct.default_json_field_name, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.parametrize("primary_field", [ct.default_float_field_name, ct.default_json_field_name]) def test_collection_json_field_partition_key(self, primary_field): """ target: test create collection with multiple JSON fields method: 1. create collection with multiple JSON fields, primary key field and vector field 2. set json field is_primary=false expected: Raise exception """ self._connect() cf.gen_unique_str(prefix) error = {ct.err_code: 1, ct.err_msg: "Primary key type must be DataType.INT64 or DataType.VARCHAR"} cf.gen_json_default_collection_schema(primary_field=primary_field, is_partition_key=True, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L0) @pytest.mark.parametrize("primary_field", [ct.default_int64_field_name, ct.default_string_field_name]) def test_collection_json_field_supported_primary_key(self, primary_field): """ target: test create collection with one JSON field method: 1. create collection with one JSON field, primary key field and vector field 2. set json field is_primary=false expected: Create collection successfully """ self._connect() c_name = cf.gen_unique_str(prefix) schema = cf.gen_json_default_collection_schema(primary_field=primary_field) self.collection_wrap.init_collection(name=c_name, schema=schema, check_task=CheckTasks.check_collection_property, check_items={exp_name: c_name, exp_schema: schema}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.parametrize("primary_field", [ct.default_int64_field_name, ct.default_string_field_name]) def test_collection_multiple_json_fields_supported_primary_key(self, primary_field): """ target: test create collection with multiple JSON fields method: 1. create collection with multiple JSON fields, primary key field and vector field 2. set json field is_primary=false expected: Create collection successfully """ self._connect() c_name = cf.gen_unique_str(prefix) schema = cf.gen_multiple_json_default_collection_schema(primary_field=primary_field) self.collection_wrap.init_collection(name=c_name, schema=schema, check_task=CheckTasks.check_collection_property, check_items={exp_name: c_name, exp_schema: schema}) class TestCollectionARRAY(TestcaseBase): """ ****************************************************************** The following cases are used to test about array ****************************************************************** """ @pytest.mark.tags(CaseLabel.L2) def test_collection_array_field_element_type_not_exist(self): """ target: test create collection with ARRAY field without element type method: create collection with one array field without element type expected: Raise exception """ int_field = cf.gen_int64_field(is_primary=True) vec_field = cf.gen_float_vec_field() array_field = cf.gen_array_field(element_type=None) array_schema = cf.gen_collection_schema([int_field, vec_field, array_field]) self.init_collection_wrap(schema=array_schema, check_task=CheckTasks.err_res, check_items={ct.err_code: 65535, ct.err_msg: "element data type None is not valid"}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.parametrize("element_type", [1001, 'a', [], (), {1}, DataType.BINARY_VECTOR, DataType.FLOAT_VECTOR, DataType.JSON, DataType.ARRAY]) def test_collection_array_field_element_type_invalid(self, element_type): """ target: Create a field with invalid element_type method: Create a field with invalid element_type 1. Type not in DataType: 1, 'a', ... 2. Type in DataType: binary_vector, float_vector, json_field, array_field expected: Raise exception """ int_field = cf.gen_int64_field(is_primary=True) vec_field = cf.gen_float_vec_field() array_field = cf.gen_array_field(element_type=element_type) array_schema = cf.gen_collection_schema([int_field, vec_field, array_field]) error = {ct.err_code: 999, ct.err_msg: f"element type {element_type} is not supported"} if element_type in ['a', {1}]: error = {ct.err_code: 999, ct.err_msg: "Unexpected error"} if element_type in [[], ()]: error = {ct.err_code: 65535, ct.err_msg: "element data type None is not valid"} if element_type in [DataType.BINARY_VECTOR, DataType.FLOAT_VECTOR, DataType.JSON, DataType.ARRAY]: data_type = element_type.name if element_type == DataType.BINARY_VECTOR: data_type = "BinaryVector" if element_type == DataType.FLOAT_VECTOR: data_type = "FloatVector" if element_type == DataType.ARRAY: data_type = "Array" error = {ct.err_code: 999, ct.err_msg: f"element type {data_type} is not supported"} self.init_collection_wrap(schema=array_schema, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.skip("https://github.com/milvus-io/pymilvus/issues/2041") def test_collection_array_field_no_capacity(self): """ target: Create a field without giving max_capacity method: Create a field without giving max_capacity expected: Raise exception """ int_field = cf.gen_int64_field(is_primary=True) vec_field = cf.gen_float_vec_field() array_field = cf.gen_array_field(max_capacity=None) array_schema = cf.gen_collection_schema([int_field, vec_field, array_field]) self.init_collection_wrap(schema=array_schema, check_task=CheckTasks.err_res, check_items={ct.err_code: 65535, ct.err_msg: "the value of max_capacity must be an integer"}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.skip("https://github.com/milvus-io/pymilvus/issues/2041") @pytest.mark.parametrize("max_capacity", [[], 'a', (), -1, 4097]) def test_collection_array_field_invalid_capacity(self, max_capacity): """ target: Create a field with invalid max_capacity method: Create a field with invalid max_capacity 1. Type invalid: [], 'a', () 2. Value invalid: <0, >max_capacity(4096) expected: Raise exception """ int_field = cf.gen_int64_field(is_primary=True) vec_field = cf.gen_float_vec_field() array_field = cf.gen_array_field(max_capacity=max_capacity) array_schema = cf.gen_collection_schema([int_field, vec_field, array_field]) self.init_collection_wrap(schema=array_schema, check_task=CheckTasks.err_res, check_items={ct.err_code: 65535, ct.err_msg: "the maximum capacity specified for a " "Array should be in (0, 4096]"}) @pytest.mark.tags(CaseLabel.L2) def test_collection_string_array_without_max_length(self): """ target: Create string array without giving max length method: Create string array without giving max length expected: Raise exception """ int_field = cf.gen_int64_field(is_primary=True) vec_field = cf.gen_float_vec_field() array_field = cf.gen_array_field(element_type=DataType.VARCHAR) array_schema = cf.gen_collection_schema([int_field, vec_field, array_field]) self.init_collection_wrap(schema=array_schema, check_task=CheckTasks.err_res, check_items={ct.err_code: 65535, ct.err_msg: "type param(max_length) should be specified for the " "field(int_array)"}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.skip("https://github.com/milvus-io/pymilvus/issues/2041") @pytest.mark.parametrize("max_length", [[], 'a', (), -1, 65536]) def test_collection_string_array_max_length_invalid(self, max_length): """ target: Create string array with invalid max length method: Create string array with invalid max length 1. Type invalid: [], 'a', () 2. Value invalid: <0, >max_length(65535) expected: Raise exception """ int_field = cf.gen_int64_field(is_primary=True) vec_field = cf.gen_float_vec_field() array_field = cf.gen_array_field(element_type=DataType.VARCHAR, max_length=max_length) array_schema = cf.gen_collection_schema([int_field, vec_field, array_field]) self.init_collection_wrap(schema=array_schema, check_task=CheckTasks.err_res, check_items={ct.err_code: 65535, ct.err_msg: "the maximum length specified for a VarChar " "should be in (0, 65535]"}) @pytest.mark.tags(CaseLabel.L2) def test_collection_array_field_all_datatype(self): """ target: test create collection with ARRAY field all data type method: 1. Create field respectively: int8, int16, int32, int64, varchar, bool, float, double 2. Insert data respectively: int8, int16, int32, int64, varchar, bool, float, double expected: Raise exception """ # Create field respectively nb = ct.default_nb pk_field = cf.gen_int64_field(is_primary=True) vec_field = cf.gen_float_vec_field() int8_array = cf.gen_array_field(name="int8_array", element_type=DataType.INT8, max_capacity=nb) int16_array = cf.gen_array_field(name="int16_array", element_type=DataType.INT16, max_capacity=nb) int32_array = cf.gen_array_field(name="int32_array", element_type=DataType.INT32, max_capacity=nb) int64_array = cf.gen_array_field(name="int64_array", element_type=DataType.INT64, max_capacity=nb) bool_array = cf.gen_array_field(name="bool_array", element_type=DataType.BOOL, max_capacity=nb) float_array = cf.gen_array_field(name="float_array", element_type=DataType.FLOAT, max_capacity=nb) double_array = cf.gen_array_field(name="double_array", element_type=DataType.DOUBLE, max_capacity=nb) string_array = cf.gen_array_field(name="string_array", element_type=DataType.VARCHAR, max_capacity=nb, max_length=100) array_schema = cf.gen_collection_schema([pk_field, vec_field, int8_array, int16_array, int32_array, int64_array, bool_array, float_array, double_array, string_array]) collection_w = self.init_collection_wrap(schema=array_schema, check_task=CheckTasks.check_collection_property, check_items={exp_schema: array_schema}) # check array in collection.describe() res = collection_w.describe()[0] log.info(res) fields = [ {"field_id": 100, "name": "int64", "description": "", "type": 5, "params": {}, "element_type": 0, "is_primary": True}, {"field_id": 101, "name": "float_vector", "description": "", "type": 101, "params": {"dim": ct.default_dim}, "element_type": 0}, {"field_id": 102, "name": "int8_array", "description": "", "type": 22, "params": {"max_capacity": 2000}, "element_type": 2}, {"field_id": 103, "name": "int16_array", "description": "", "type": 22, "params": {"max_capacity": 2000}, "element_type": 3}, {"field_id": 104, "name": "int32_array", "description": "", "type": 22, "params": {"max_capacity": 2000}, "element_type": 4}, {"field_id": 105, "name": "int64_array", "description": "", "type": 22, "params": {"max_capacity": 2000}, "element_type": 5}, {"field_id": 106, "name": "bool_array", "description": "", "type": 22, "params": {"max_capacity": 2000}, "element_type": 1}, {"field_id": 107, "name": "float_array", "description": "", "type": 22, "params": {"max_capacity": 2000}, "element_type": 10}, {"field_id": 108, "name": "double_array", "description": "", "type": 22, "params": {"max_capacity": 2000}, "element_type": 11}, {"field_id": 109, "name": "string_array", "description": "", "type": 22, "params": {"max_length": 100, "max_capacity": 2000}, "element_type": 21} ] # assert res["fields"] == fields # Insert data respectively nb = 10 pk_values = [i for i in range(nb)] float_vec = cf.gen_vectors(nb, ct.default_dim) int8_values = [[numpy.int8(j) for j in range(nb)] for i in range(nb)] int16_values = [[numpy.int16(j) for j in range(nb)] for i in range(nb)] int32_values = [[numpy.int32(j) for j in range(nb)] for i in range(nb)] int64_values = [[numpy.int64(j) for j in range(nb)] for i in range(nb)] bool_values = [[numpy.bool_(j) for j in range(nb)] for i in range(nb)] float_values = [[numpy.float32(j) for j in range(nb)] for i in range(nb)] double_values = [[numpy.double(j) for j in range(nb)] for i in range(nb)] string_values = [[str(j) for j in range(nb)] for i in range(nb)] data = [pk_values, float_vec, int8_values, int16_values, int32_values, int64_values, bool_values, float_values, double_values, string_values] collection_w.insert(data) # check insert successfully collection_w.flush() collection_w.num_entities == nb class TestCollectionMultipleVectorValid(TestcaseBase): """ ****************************************************************** # The followings are valid cases ****************************************************************** """ @pytest.mark.tags(CaseLabel.L1) @pytest.mark.parametrize("primary_key", [cf.gen_int64_field(is_primary=True), cf.gen_string_field(is_primary=True)]) @pytest.mark.parametrize("auto_id", [True, False]) @pytest.mark.parametrize("shards_num", [1, 3]) def test_create_collection_multiple_vectors_all_supported_field_type(self, primary_key, auto_id, shards_num): """ target: test create collection with multiple vector fields method: create collection with multiple vector fields expected: no exception """ self._connect() c_name = cf.gen_unique_str(prefix) int_fields = [] vector_limit_num = max_vector_field_num - 2 # add multiple vector fields for i in range(vector_limit_num): vector_field_name = cf.gen_unique_str("field_name") field = cf.gen_float_vec_field(name=vector_field_name) int_fields.append(field) # add other vector fields to maximum fields num int_fields.append(cf.gen_int8_field()) int_fields.append(cf.gen_int16_field()) int_fields.append(cf.gen_int32_field()) int_fields.append(cf.gen_float_field()) int_fields.append(cf.gen_double_field()) int_fields.append(cf.gen_string_field(cf.gen_unique_str("vchar_field_name"))) int_fields.append(cf.gen_json_field()) int_fields.append(cf.gen_bool_field()) int_fields.append(cf.gen_array_field()) int_fields.append(cf.gen_binary_vec_field()) int_fields.append(primary_key) schema = cf.gen_collection_schema(fields=int_fields, auto_id=auto_id, shards_num=shards_num) self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.check_collection_property, check_items={exp_name: c_name, exp_schema: schema}) self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.check_collection_property, check_items={exp_name: c_name, exp_schema: schema}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.parametrize("primary_key", [ct.default_int64_field_name, ct.default_string_field_name]) @pytest.mark.parametrize("auto_id", [True, False]) @pytest.mark.parametrize("enable_dynamic_field", [True, False]) def test_create_collection_multiple_vectors_different_dim(self, primary_key, auto_id, enable_dynamic_field): """ target: test create collection with multiple vector fields (different dim) method: create collection with multiple vector fields expected: no exception """ self._connect() c_name = cf.gen_unique_str(prefix) another_dim = ct.min_dim schema = cf.gen_default_collection_schema(primary_field=primary_key, auto_id=auto_id, dim=ct.max_dim, enable_dynamic_field=enable_dynamic_field, multiple_dim_array=[another_dim]) self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.check_collection_property, check_items={exp_name: c_name, exp_schema: schema}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.parametrize("primary_key", [ct.default_int64_field_name, ct.default_string_field_name]) def test_create_collection_multiple_vectors_maximum_dim(self, primary_key): """ target: test create collection with multiple vector fields method: create collection with multiple vector fields expected: no exception """ self._connect() c_name = cf.gen_unique_str(prefix) schema = cf.gen_default_collection_schema(primary_field=primary_key, dim=ct.max_dim, multiple_dim_array=[ct.max_dim]) self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.check_collection_property, check_items={exp_name: c_name, exp_schema: schema}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.parametrize("primary_key", [cf.gen_int64_field(is_primary=True), cf.gen_string_field(is_primary=True)]) @pytest.mark.parametrize("auto_id", [True, False]) @pytest.mark.parametrize("par_key_field", [ct.default_int64_field_name, ct.default_string_field_name]) def test_create_collection_multiple_vectors_partition_key(self, primary_key, auto_id, par_key_field): """ target: test create collection with multiple vector fields method: create collection with multiple vector fields expected: no exception """ self._connect() c_name = cf.gen_unique_str(prefix) int_fields = [] vector_limit_num = max_vector_field_num - 2 # add multiple vector fields for i in range(vector_limit_num): vector_field_name = cf.gen_unique_str("field_name") field = cf.gen_float_vec_field(name=vector_field_name) int_fields.append(field) # add other vector fields to maximum fields num int_fields.append(cf.gen_int8_field()) int_fields.append(cf.gen_int16_field()) int_fields.append(cf.gen_int32_field()) int_fields.append(cf.gen_int64_field(cf.gen_unique_str("int_field_name"), is_partition_key=(par_key_field == ct.default_int64_field_name))) int_fields.append(cf.gen_float_field()) int_fields.append(cf.gen_double_field()) int_fields.append(cf.gen_string_field(cf.gen_unique_str("vchar_field_name"), is_partition_key=(par_key_field == ct.default_string_field_name))) int_fields.append(cf.gen_json_field()) int_fields.append(cf.gen_bool_field()) int_fields.append(cf.gen_array_field()) int_fields.append(cf.gen_binary_vec_field()) int_fields.append(primary_key) schema = cf.gen_collection_schema(fields=int_fields, auto_id=auto_id) collection_w = \ self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.check_collection_property, check_items={exp_name: c_name, exp_schema: schema})[0] assert len(collection_w.partitions) == ct.default_partition_num class TestCollectionMultipleVectorInvalid(TestcaseBase): """ Test case of search interface """ @pytest.fixture(scope="function", params=ct.invalid_dims) def get_invalid_dim(self, request): yield request.param """ ****************************************************************** # The followings are invalid cases ****************************************************************** """ @pytest.mark.tags(CaseLabel.L1) @pytest.mark.parametrize("primary_key", [cf.gen_int64_field(is_primary=True), cf.gen_string_field(is_primary=True)]) def test_create_collection_multiple_vectors_same_vector_field_name(self, primary_key): """ target: test create collection with multiple vector fields method: create collection with multiple vector fields expected: no exception """ self._connect() c_name = cf.gen_unique_str(prefix) int_fields = [] vector_limit_num = max_vector_field_num - 2 # add multiple vector fields for i in range(vector_limit_num): field = cf.gen_float_vec_field() int_fields.append(field) # add other vector fields to maximum fields num int_fields.append(cf.gen_int8_field()) int_fields.append(cf.gen_int16_field()) int_fields.append(cf.gen_int32_field()) int_fields.append(cf.gen_float_field()) int_fields.append(cf.gen_double_field()) int_fields.append(cf.gen_string_field(cf.gen_unique_str("vchar_field_name"))) int_fields.append(cf.gen_json_field()) int_fields.append(cf.gen_bool_field()) int_fields.append(cf.gen_array_field()) int_fields.append(cf.gen_binary_vec_field()) int_fields.append(primary_key) schema = cf.gen_collection_schema(fields=int_fields) error = {ct.err_code: 65535, ct.err_msg: "duplicated field name"} self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L1) @pytest.mark.parametrize("invalid_vector_name", ["12-s", "12 s", "(mn)", "中文", "%$#", "a".join("a" for i in range(256))]) def test_create_collection_multiple_vectors_invalid_part_vector_field_name(self, invalid_vector_name): """ target: test create collection with multiple vector fields method: create collection with multiple vector fields expected: no exception """ self._connect() c_name = cf.gen_unique_str(prefix) int_fields = [] # add multiple vector fields vector_field_1 = cf.gen_float_vec_field(name=invalid_vector_name) int_fields.append(vector_field_1) vector_field_2 = cf.gen_float_vec_field(name="valid_field_name") int_fields.append(vector_field_2) # add other vector fields to maximum fields num int_fields.append(cf.gen_int64_field(is_primary=True)) schema = cf.gen_collection_schema(fields=int_fields) error = {ct.err_code: 1701, ct.err_msg: "Invalid field name: %s" % invalid_vector_name} self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L1) @pytest.mark.parametrize("invalid_vector_name", ["12-s", "12 s", "(mn)", "中文", "%$#", "a".join("a" for i in range(256))]) def test_create_collection_multiple_vectors_invalid_all_vector_field_name(self, invalid_vector_name): """ target: test create collection with multiple vector fields method: create collection with multiple vector fields expected: no exception """ self._connect() c_name = cf.gen_unique_str(prefix) int_fields = [] # add multiple vector fields vector_field_1 = cf.gen_float_vec_field(name=invalid_vector_name) int_fields.append(vector_field_1) vector_field_2 = cf.gen_float_vec_field(name=invalid_vector_name + " ") int_fields.append(vector_field_2) # add other vector fields to maximum fields num int_fields.append(cf.gen_int64_field(is_primary=True)) schema = cf.gen_collection_schema(fields=int_fields) error = {ct.err_code: 1701, ct.err_msg: "Invalid field name: %s" % invalid_vector_name} self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L1) @pytest.mark.skip("issue #37543") def test_create_collection_multiple_vectors_invalid_dim(self, get_invalid_dim): """ target: test create collection with multiple vector fields method: create collection with multiple vector fields expected: no exception """ self._connect() c_name = cf.gen_unique_str(prefix) int_fields = [] # add multiple vector fields vector_field_1 = cf.gen_float_vec_field(dim=get_invalid_dim) int_fields.append(vector_field_1) vector_field_2 = cf.gen_float_vec_field(name="float_vec_field") int_fields.append(vector_field_2) # add other vector fields to maximum fields num int_fields.append(cf.gen_int64_field(is_primary=True)) schema = cf.gen_collection_schema(fields=int_fields) error = {ct.err_code: 65535, ct.err_msg: "invalid dimension"} self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error) class TestCollectionMmap(TestcaseBase): @pytest.mark.tags(CaseLabel.L1) def test_describe_collection_mmap(self): """ target: enable or disable mmap in the collection method: enable or disable mmap in the collection expected: description information contains mmap """ self._connect() c_name = cf.gen_unique_str(prefix) collection_w, _ = self.collection_wrap.init_collection(c_name, schema=default_schema) collection_w.set_properties({'mmap.enabled': True}) pro = collection_w.describe().get("properties") assert "mmap.enabled" in pro.keys() assert pro["mmap.enabled"] == 'True' collection_w.set_properties({'mmap.enabled': False}) pro = collection_w.describe().get("properties") assert pro["mmap.enabled"] == 'False' collection_w.set_properties({'mmap.enabled': True}) pro = collection_w.describe().get("properties") assert pro["mmap.enabled"] == 'True' @pytest.mark.tags(CaseLabel.L1) def test_load_mmap_collection(self): """ target: after loading, enable mmap for the collection method: 1. data preparation and create index 2. load collection 3. enable mmap on collection expected: raise exception """ c_name = cf.gen_unique_str(prefix) collection_w = self.init_collection_wrap(c_name, schema=default_schema) collection_w.insert(cf.gen_default_list_data()) collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index, index_name=ct.default_index_name) collection_w.set_properties({'mmap.enabled': True}) pro = collection_w.describe()[0].get("properties") assert pro["mmap.enabled"] == 'True' collection_w.load() collection_w.set_properties({'mmap.enabled': True}, check_task=CheckTasks.err_res, check_items={ct.err_code: 104, ct.err_msg: f"collection already loaded"}) @pytest.mark.tags(CaseLabel.L2) def test_drop_mmap_collection(self): """ target: set mmap on collection method: 1. set mmap on collection 2. drop collection 3. describe collection expected: description information contains mmap """ self._connect() c_name = "coll_rand" collection_w, _ = self.collection_wrap.init_collection(c_name, schema=default_schema) collection_w.set_properties({'mmap.enabled': True}) collection_w.drop() collection_w, _ = self.collection_wrap.init_collection(c_name, schema=default_schema) pro = collection_w.describe().get("properties") assert "mmap.enabled" not in pro.keys() @pytest.mark.tags(CaseLabel.L2) def test_multiple_collections_enable_mmap(self): """ target: enabling mmap for multiple collections in a single instance method: enabling mmap for multiple collections in a single instance expected: the collection description message for mmap is normal """ self._connect() c_name = "coll_1" c_name2 = "coll_2" c_name3 = "coll_3" collection_w, _ = self.collection_wrap.init_collection(c_name, schema=default_schema) collection_w2, _ = self.collection_wrap.init_collection(c_name2, schema=default_schema) collection_w3, _ = self.collection_wrap.init_collection(c_name3, schema=default_schema) collection_w.set_properties({'mmap.enabled': True}) collection_w2.set_properties({'mmap.enabled': True}) pro = collection_w.describe().get("properties") pro2 = collection_w2.describe().get("properties") assert pro["mmap.enabled"] == 'True' assert pro2["mmap.enabled"] == 'True' collection_w3.set_properties({'mmap.enabled': True}) pro3 = collection_w3.describe().get("properties") assert pro3["mmap.enabled"] == 'True' @pytest.mark.tags(CaseLabel.L2) def test_flush_collection_mmap(self): """ target: after flush, collection enables mmap method: after flush, collection enables mmap expected: the collection description message for mmap is normal """ self._connect() c_name = cf.gen_unique_str(prefix) collection_w, _ = self.collection_wrap.init_collection(c_name, schema=default_schema) collection_w.insert(cf.gen_default_list_data()) collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index, index_name=ct.default_index_name) collection_w.alter_index(ct.default_index_name, {'mmap.enabled': False}) collection_w.flush() collection_w.set_properties({'mmap.enabled': True}) pro = collection_w.describe().get("properties") assert pro["mmap.enabled"] == 'True' collection_w.alter_index(ct.default_index_name, {'mmap.enabled': True}) collection_w.load() vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)] collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, default_search_exp, check_task=CheckTasks.check_search_results, check_items={"nq": default_nq, "limit": default_limit}) @pytest.mark.tags(CaseLabel.L2) def test_enable_mmap_after_drop_collection(self): """ target: enable mmap after deleting a collection method: enable mmap after deleting a collection expected: raise exception """ collection_w = self.init_collection_general(prefix, True, is_binary=True, is_index=False)[0] collection_w.drop() collection_w.set_properties({'mmap.enabled': True}, check_task=CheckTasks.err_res, check_items={ct.err_code: 100, ct.err_msg: f"collection not found"})