mirror of https://github.com/milvus-io/milvus.git
371 lines
18 KiB
Python
371 lines
18 KiB
Python
import pytest
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from base.client_v2_base import TestMilvusClientV2Base
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from utils.util_log import test_log as log
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from common import common_func as cf
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from common import common_type as ct
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from common.common_type import CaseLabel, CheckTasks
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from utils.util_pymilvus import *
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prefix = "client_delete"
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epsilon = ct.epsilon
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default_nb = ct.default_nb
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default_nb_medium = ct.default_nb_medium
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default_nq = ct.default_nq
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default_dim = ct.default_dim
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default_limit = ct.default_limit
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default_search_exp = "id >= 0"
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exp_res = "exp_res"
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default_search_string_exp = "varchar >= \"0\""
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default_search_mix_exp = "int64 >= 0 && varchar >= \"0\""
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default_invaild_string_exp = "varchar >= 0"
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default_json_search_exp = "json_field[\"number\"] >= 0"
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perfix_expr = 'varchar like "0%"'
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default_search_field = ct.default_float_vec_field_name
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default_search_params = ct.default_search_params
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default_primary_key_field_name = "id"
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default_vector_field_name = "vector"
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default_float_field_name = ct.default_float_field_name
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default_bool_field_name = ct.default_bool_field_name
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default_string_field_name = ct.default_string_field_name
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default_int32_array_field_name = ct.default_int32_array_field_name
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default_string_array_field_name = ct.default_string_array_field_name
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class TestMilvusClientDeleteInvalid(TestMilvusClientV2Base):
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""" Test case of search interface """
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@pytest.fixture(scope="function", params=[False, True])
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def auto_id(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=["COSINE", "L2"])
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def metric_type(self, request):
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yield request.param
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"""
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******************************************************************
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# The following are invalid base cases
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******************************************************************
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"""
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_delete_with_filters_and_ids(self):
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"""
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target: test delete (high level api) with ids and filters
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method: create connection, collection, insert, delete, and search
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expected: raise exception
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"""
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client = self._client()
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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# 2. insert
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default_nb = 1000
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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pks = self.insert(client, collection_name, rows)[0]
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# 3. delete
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delete_num = 3
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self.delete(client, collection_name, ids=[i for i in range(delete_num)], filter=f"id < {delete_num}",
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": "Ambiguous filter parameter, "
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"only one deletion condition can be specified."})
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.skip(reason="pymilvus issue 1869")
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def test_milvus_client_delete_with_invalid_id_type(self):
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"""
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target: test delete (high level api)
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method: create connection, collection, insert delete, and search
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expected: search/query successfully without deleted data
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"""
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client = self._client()
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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# 2. delete
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self.delete(client, collection_name, ids=0,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": "expr cannot be empty"})
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@pytest.mark.tags(CaseLabel.L2)
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def test_milvus_client_delete_with_not_all_required_params(self):
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"""
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target: test delete (high level api)
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method: create connection, collection, insert delete, and search
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expected: search/query successfully without deleted data
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"""
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client = self._client()
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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# 2. delete
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self.delete(client, collection_name,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 999,
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"err_msg": "The type of expr must be string ,but <class 'NoneType'> is given."})
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class TestMilvusClientDeleteValid(TestMilvusClientV2Base):
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""" Test case of search interface """
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@pytest.fixture(scope="function", params=[False, True])
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def auto_id(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=["COSINE", "L2"])
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def metric_type(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=["INVERTED"])
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def supported_varchar_scalar_index(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=["DOUBLE", "VARCHAR", "json", "bool"])
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def supported_json_cast_type(self, request):
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yield request.param
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"""
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******************************************************************
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# The following are valid base cases
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******************************************************************
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"""
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_delete_with_ids(self):
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"""
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target: test delete (high level api)
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method: create connection, collection, insert delete, and search
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expected: search/query successfully without deleted data
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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# 2. insert
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default_nb = 1000
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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pks = self.insert(client, collection_name, rows)[0]
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# 3. delete
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delete_num = 3
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self.delete(client, collection_name, ids=[i for i in range(delete_num)])
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# 4. search
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vectors_to_search = rng.random((1, default_dim))
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insert_ids = [i for i in range(default_nb)]
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for insert_id in range(delete_num):
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if insert_id in insert_ids:
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insert_ids.remove(insert_id)
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limit = default_nb - delete_num
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self.search(client, collection_name, vectors_to_search, limit=default_nb,
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": len(vectors_to_search),
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"pk_name": default_primary_key_field_name,
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"ids": insert_ids,
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"limit": limit})
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# 5. query
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self.query(client, collection_name, filter=default_search_exp,
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check_task=CheckTasks.check_query_results,
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check_items={exp_res: rows[delete_num:],
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"with_vec": True,
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"pk_name": default_primary_key_field_name})
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_delete_with_filters(self):
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"""
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target: test delete (high level api)
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method: create connection, collection, insert delete, and search
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expected: search/query successfully without deleted data
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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# 2. insert
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default_nb = 1000
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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pks = self.insert(client, collection_name, rows)[0]
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# 3. delete
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delete_num = 3
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self.delete(client, collection_name, filter=f"id < {delete_num}")
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# 4. search
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vectors_to_search = rng.random((1, default_dim))
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insert_ids = [i for i in range(default_nb)]
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for insert_id in range(delete_num):
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if insert_id in insert_ids:
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insert_ids.remove(insert_id)
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limit = default_nb - delete_num
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self.search(client, collection_name, vectors_to_search, limit=default_nb,
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": len(vectors_to_search),
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"ids": insert_ids,
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"pk_name": default_primary_key_field_name,
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"limit": limit})
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# 5. query
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self.query(client, collection_name, filter=default_search_exp,
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check_task=CheckTasks.check_query_results,
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check_items={exp_res: rows[delete_num:],
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"with_vec": True,
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"pk_name": default_primary_key_field_name})
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("add_field", [True, False])
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def test_milvus_client_delete_with_filters_partition(self, add_field):
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"""
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target: test delete (high level api)
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method: create connection, collection, insert delete, and search
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expected: search/query successfully without deleted data
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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# 2. insert
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default_nb = 1000
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rng = np.random.default_rng(seed=19530)
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rows = [
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{
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default_primary_key_field_name: i,
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default_vector_field_name: list(rng.random((1, default_dim))[0]),
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default_float_field_name: i * 1.0,
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default_string_field_name: str(i),
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**({"field_new": "default"} if add_field else {})
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}
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for i in range(default_nb)
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]
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if add_field:
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self.add_collection_field(client, collection_name, field_name="field_new", data_type=DataType.VARCHAR,
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nullable=True, max_length=64)
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pks = self.insert(client, collection_name, rows)[0]
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# 3. get partition lists
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partition_names = self.list_partitions(client, collection_name)
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# 4. delete
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delete_num = 3
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filter = f"id < {delete_num} "
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if add_field:
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filter += "and field_new == 'default'"
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self.delete(client, collection_name, filter=filter, partition_names=partition_names)
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# 5. search
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vectors_to_search = rng.random((1, default_dim))
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insert_ids = [i for i in range(default_nb)]
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for insert_id in range(delete_num):
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if insert_id in insert_ids:
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insert_ids.remove(insert_id)
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limit = default_nb - delete_num
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self.search(client, collection_name, vectors_to_search, limit=default_nb,
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": len(vectors_to_search),
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"ids": insert_ids,
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"pk_name": default_primary_key_field_name,
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"limit": limit})
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# 6. query
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self.query(client, collection_name, filter=default_search_exp,
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check_task=CheckTasks.check_query_results,
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check_items={exp_res: rows[delete_num:],
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"with_vec": True,
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"pk_name": default_primary_key_field_name})
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("enable_dynamic_field", [True, False])
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@pytest.mark.parametrize("is_flush", [True, False])
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@pytest.mark.parametrize("is_release", [True, False])
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def test_milvus_client_delete_with_filters_json_path_index(self, enable_dynamic_field, supported_varchar_scalar_index,
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supported_json_cast_type, is_flush, is_release):
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"""
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target: test delete after json path index created
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method: create connection, collection, index, insert, delete, and search
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Step: 1. create schema
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2. prepare index_params with vector and all the json path index params
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3. create collection with the above schema and index params
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4. insert
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5. flush if specified
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6. release collection if specified
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7. load collection if specified
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8. delete with expression on json path
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9. search and query to check that the deleted entities not searched
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expected: Delete and search/query successfully
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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json_field_name = "my_json"
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schema = self.create_schema(client, enable_dynamic_field=enable_dynamic_field)[0]
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schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
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schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
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schema.add_field(default_float_field_name, DataType.FLOAT)
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schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
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if not enable_dynamic_field:
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schema.add_field(json_field_name, DataType.JSON)
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(field_name=default_vector_field_name, index_type="AUTOINDEX", metric_type="L2")
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index_params.add_index(field_name=json_field_name, index_type=supported_varchar_scalar_index,
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params={"json_cast_type": supported_json_cast_type, "json_path": f"{json_field_name}['a']['b']"})
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index_params.add_index(field_name=json_field_name,
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index_type=supported_varchar_scalar_index,
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params={"json_cast_type": supported_json_cast_type,
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"json_path": f"{json_field_name}['a']"})
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index_params.add_index(field_name=json_field_name,
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index_type=supported_varchar_scalar_index,
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params={"json_cast_type": supported_json_cast_type,
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"json_path": f"{json_field_name}"})
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index_params.add_index(field_name=json_field_name,
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index_type=supported_varchar_scalar_index,
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params={"json_cast_type": supported_json_cast_type,
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"json_path": f"{json_field_name}['a'][0]['b']"})
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index_params.add_index(field_name=json_field_name,
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index_type=supported_varchar_scalar_index,
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params={"json_cast_type": supported_json_cast_type,
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"json_path": f"{json_field_name}['a'][0]"})
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self.create_collection(client, collection_name, schema=schema,
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index_params=index_params, metric_type="L2")
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# 2. insert
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default_nb = 1000
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i),
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json_field_name: {'a': {'b': i}}} for i in range(default_nb)]
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pks = self.insert(client, collection_name, rows)[0]
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if is_flush:
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self.flush(client, collection_name)
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if is_release:
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self.release_collection(client, collection_name)
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self.load_collection(client, collection_name)
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# 3. delete
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delete_num = 3
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self.delete(client, collection_name, filter=f"{json_field_name}['a']['b'] < {delete_num}")
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# 4. search
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vectors_to_search = rng.random((1, default_dim))
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insert_ids = [i for i in range(default_nb)]
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for insert_id in range(delete_num):
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if insert_id in insert_ids:
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insert_ids.remove(insert_id)
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limit = default_nb - delete_num
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self.search(client, collection_name, vectors_to_search, limit=default_nb,
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": len(vectors_to_search),
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"ids": insert_ids,
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"pk_name": default_primary_key_field_name,
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"limit": limit})
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# 5. query
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self.query(client, collection_name, filter=default_search_exp,
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check_task=CheckTasks.check_query_results,
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check_items={exp_res: rows[delete_num:],
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"with_vec": True,
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"pk_name": default_primary_key_field_name})
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self.drop_collection(client, collection_name)
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