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bb4446e5af
related issue: https://github.com/milvus-io/milvus/issues/44425 pr: #44801 split insert.py into a few files: upsert.py, insert.py, partial_upsert.py ... add test for allow insert auto id --------- Signed-off-by: yanliang567 <yanliang.qiao@zilliz.com>
824 lines
43 KiB
Python
824 lines
43 KiB
Python
import pytest
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import numpy as np
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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_insert"
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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_dynamic_field_name = "field_new"
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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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default_int32_field_name = ct.default_int32_field_name
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default_int32_value = ct.default_int32_value
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class TestMilvusClientUpsertInvalid(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.L2)
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def test_milvus_client_upsert_column_data(self):
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"""
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target: test insert column data
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method: create connection, collection, insert and search
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expected: raise error
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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)
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# 2. insert
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vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nb)]
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data = [[i for i in range(default_nb)], vectors]
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error = {ct.err_code: 999,
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ct.err_msg: "The Input data type is inconsistent with defined schema, please check it."}
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self.upsert(client, collection_name, data,
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check_task=CheckTasks.err_res, check_items=error)
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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_upsert_empty_collection_name(self):
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"""
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target: test high level api: client.create_collection
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method: create collection with invalid primary field
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expected: Raise exception
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"""
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client = self._client()
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collection_name = ""
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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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error = {ct.err_code: 1, ct.err_msg: f"`collection_name` value {collection_name} is illegal"}
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self.upsert(client, collection_name, rows,
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("collection_name", ["12-s", "12 s", "(mn)", "中文", "%$#"])
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def test_milvus_client_upsert_invalid_collection_name(self, collection_name):
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"""
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target: test high level api: client.create_collection
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method: create collection with invalid primary field
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expected: Raise exception
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"""
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client = self._client()
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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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error = {ct.err_code: 1100, ct.err_msg: f"Invalid collection name: {collection_name}. the first character of a "
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f"collection name must be an underscore or letter: invalid parameter"}
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self.upsert(client, collection_name, rows,
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_upsert_collection_name_over_max_length(self):
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"""
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target: test high level api: client.create_collection
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method: create collection with invalid primary field
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expected: Raise exception
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"""
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client = self._client()
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collection_name = "a".join("a" for i in range(256))
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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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error = {ct.err_code: 1100, ct.err_msg: f"the length of a collection name must be less than 255 characters"}
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self.upsert(client, collection_name, rows,
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_upsert_not_exist_collection_name(self):
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"""
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target: test high level api: client.create_collection
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method: create collection with invalid primary field
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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("insert_not_exist")
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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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error = {ct.err_code: 100, ct.err_msg: f"can't find collection[database=default][collection={collection_name}]"}
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self.upsert(client, collection_name, rows,
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("data", ["12-s", "12 s", "(mn)", "中文", "%$#", " "])
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def test_milvus_client_upsert_data_invalid_type(self, data):
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"""
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target: test high level api: client.create_collection
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method: create collection with invalid primary field
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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_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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error = {ct.err_code: 1, ct.err_msg: f"wrong type of argument 'data',expected 'Dict' or list of 'Dict'"}
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self.upsert(client, collection_name, data,
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_upsert_data_empty(self):
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"""
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target: test high level api: client.create_collection
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method: create collection with invalid primary field
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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_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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error = {ct.err_code: 1, ct.err_msg: f"wrong type of argument 'data',expected 'Dict' or list of 'Dict'"}
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self.upsert(client, collection_name, data="",
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_upsert_data_vector_field_missing(self):
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"""
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target: test high level api: client.create_collection
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method: create collection with invalid primary field
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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_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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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i,
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(10)]
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error = {ct.err_code: 1,
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ct.err_msg: "Insert missed an field `vector` to collection without set nullable==true or set default_value"}
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self.upsert(client, collection_name, data=rows,
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_upsert_data_id_field_missing(self):
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"""
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target: test high level api: client.create_collection
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method: create collection with invalid primary field
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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_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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rng = np.random.default_rng(seed=19530)
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rows = [{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(20)]
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error = {ct.err_code: 1,
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ct.err_msg: f"Insert missed an field `id` to collection without set nullable==true or set default_value"}
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self.upsert(client, collection_name, data=rows,
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_upsert_data_extra_field(self):
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"""
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target: test milvus client: insert extra field than schema
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method: insert extra field than schema when enable_dynamic_field is False
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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_collection_name_by_testcase_name()
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# 1. create collection
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dim = 32
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self.create_collection(client, collection_name, dim, enable_dynamic_field=False)
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# 2. insert
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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, dim))[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(10)]
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error = {ct.err_code: 1,
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ct.err_msg: f"Attempt to insert an unexpected field `float` to collection without enabling dynamic field"}
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self.upsert(client, collection_name, data=rows,
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_upsert_data_dim_not_match(self):
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"""
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target: test milvus client: insert extra field than schema
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method: insert extra field than schema when enable_dynamic_field is False
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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_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. insert
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rng = np.random.default_rng(seed=19530)
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rows = [
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{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim + 1))[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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error = {ct.err_code: 65536, ct.err_msg: f"of float data should divide the dim({default_dim})"}
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self.upsert(client, collection_name, data=rows,
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_upsert_not_matched_data(self):
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"""
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target: test milvus client: insert not matched data then defined
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method: insert string to int primary field
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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_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. insert
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rng = np.random.default_rng(seed=19530)
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rows = [
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{default_primary_key_field_name: str(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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error = {ct.err_code: 1,
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ct.err_msg: "The Input data type is inconsistent with defined schema, {id} field should be a int64"}
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self.upsert(client, collection_name, data=rows,
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("partition_name", ["12 s", "(mn)", "中文", "%$#", " "])
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def test_milvus_client_upsert_invalid_partition_name(self, partition_name):
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"""
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target: test milvus client: insert extra field than schema
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method: insert extra field than schema when enable_dynamic_field is False
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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_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. insert
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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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error = {ct.err_code: 65535, ct.err_msg: f"Invalid partition name: {partition_name}"}
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if partition_name == " ":
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error = {ct.err_code: 1, ct.err_msg: f"Invalid partition name: . Partition name should not be empty."}
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self.upsert(client, collection_name, data=rows, partition_name=partition_name,
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_upsert_not_exist_partition_name(self):
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"""
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target: test milvus client: insert extra field than schema
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method: insert extra field than schema when enable_dynamic_field is False
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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_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. insert
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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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partition_name = cf.gen_unique_str("partition_not_exist")
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error = {ct.err_code: 200, ct.err_msg: f"partition not found[partition={partition_name}]"}
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self.upsert(client, collection_name, data=rows, partition_name=partition_name,
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L2)
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def test_milvus_client_upsert_collection_partition_not_match(self):
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"""
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target: test milvus client: insert extra field than schema
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method: insert extra field than schema when enable_dynamic_field is False
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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_collection_name_by_testcase_name()
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another_collection_name = cf.gen_unique_str(prefix + "another")
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partition_name = cf.gen_unique_str("partition")
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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self.create_collection(client, another_collection_name, default_dim)
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self.create_partition(client, another_collection_name, partition_name)
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# 2. insert
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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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error = {ct.err_code: 200, ct.err_msg: f"partition not found[partition={partition_name}]"}
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self.upsert(client, collection_name, data=rows, partition_name=partition_name,
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("nullable", [True, False])
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def test_milvus_client_insert_array_element_null(self, nullable):
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"""
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target: test search with null expression on each key of json
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method: create connection, collection, insert 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_collection_name_by_testcase_name()
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dim = 5
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# 1. create collection
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nullable_field_name = "nullable_field"
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schema = self.create_schema(client, enable_dynamic_field=False)[0]
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schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
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auto_id=False)
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schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
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schema.add_field(nullable_field_name, DataType.ARRAY, element_type=DataType.INT64, max_capacity=12,
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max_length=64, nullable=nullable)
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(default_vector_field_name, metric_type="COSINE")
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self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
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# 2. insert
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vectors = cf.gen_vectors(default_nb, dim)
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rows = [{default_primary_key_field_name: str(i), default_vector_field_name: vectors[i],
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nullable_field_name: [None, 2, 3]} for i in range(default_nb)]
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error = {ct.err_code: 1,
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ct.err_msg: "The Input data type is inconsistent with defined schema, {nullable_field} field "
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"should be a array, but got a {<class 'list'>} instead."}
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self.insert(client, collection_name, rows,
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check_task=CheckTasks.err_res,
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check_items=error)
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class TestMilvusClientUpsertValid(TestMilvusClientV2Base):
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""" Test case of search interface """
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|
|
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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"])
|
|
def metric_type(self, request):
|
|
yield request.param
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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.L0)
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def test_milvus_client_upsert_default(self):
|
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"""
|
|
target: test search (high level api) normal case
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method: create connection, collection, insert and search
|
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expected: 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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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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collections = self.list_collections(client)[0]
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assert collection_name in collections
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self.describe_collection(client, collection_name,
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check_task=CheckTasks.check_describe_collection_property,
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check_items={"collection_name": collection_name,
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"dim": default_dim,
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"consistency_level": 0})
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# 2. insert
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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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results = self.upsert(client, collection_name, rows)[0]
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assert results['upsert_count'] == default_nb
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# 3. 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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self.search(client, collection_name, vectors_to_search,
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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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"limit": default_limit,
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"pk_name": default_primary_key_field_name})
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# 4. 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,
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"with_vec": True,
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"pk_name": default_primary_key_field_name})
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self.release_collection(client, collection_name)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L2)
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def test_milvus_client_upsert_empty_data(self):
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"""
|
|
target: test search (high level api) normal case
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method: create connection, collection, insert and search
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expected: 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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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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# 2. insert
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rows = []
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results = self.upsert(client, collection_name, rows)[0]
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assert results['upsert_count'] == 0
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# 3. search
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rng = np.random.default_rng(seed=19530)
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vectors_to_search = rng.random((1, default_dim))
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self.search(client, collection_name, vectors_to_search,
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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": [],
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"pk_name": default_primary_key_field_name,
|
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"limit": 0})
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self.drop_collection(client, collection_name)
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|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
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def test_milvus_client_upsert_partition(self):
|
|
"""
|
|
target: test fast create collection normal case
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|
method: create collection
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|
expected: create collection with default schema, index, and load successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
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|
partition_name = cf.gen_unique_str(prefix)
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|
# 1. create collection
|
|
self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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|
# 2. create partition
|
|
self.create_partition(client, collection_name, partition_name)
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|
partitions = self.list_partitions(client, collection_name)[0]
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|
assert partition_name in partitions
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index = self.list_indexes(client, collection_name)[0]
|
|
assert index == ['vector']
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# load_state = self.get_load_state(collection_name)[0]
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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]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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|
# 3. upsert to default partition
|
|
results = self.upsert(client, collection_name, rows, partition_name=partitions[0])[0]
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|
assert results['upsert_count'] == default_nb
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|
# 4. upsert to non-default partition
|
|
results = self.upsert(client, collection_name, rows, partition_name=partition_name)[0]
|
|
assert results['upsert_count'] == default_nb
|
|
# 5. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
insert_ids = [i for i in range(default_nb)]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"pk_name": default_primary_key_field_name})
|
|
# partition_number = self.get_partition_stats(client, collection_name, "_default")[0]
|
|
# assert partition_number == default_nb
|
|
# partition_number = self.get_partition_stats(client, collection_name, partition_name)[0]
|
|
# assert partition_number[0]['value'] == 0
|
|
if self.has_partition(client, collection_name, partition_name)[0]:
|
|
self.release_partitions(client, collection_name, partition_name)
|
|
self.drop_partition(client, collection_name, partition_name)
|
|
if self.has_collection(client, collection_name)[0]:
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_insert_upsert(self):
|
|
"""
|
|
target: test fast create collection normal case
|
|
method: create collection
|
|
expected: create collection with default schema, index, and load successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
partition_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. create partition
|
|
self.create_partition(client, collection_name, partition_name)
|
|
partitions = self.list_partitions(client, collection_name)[0]
|
|
assert partition_name in partitions
|
|
index = self.list_indexes(client, collection_name)[0]
|
|
assert index == ['vector']
|
|
# load_state = self.get_load_state(collection_name)[0]
|
|
# 3. insert and upsert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
results = self.insert(client, collection_name, rows, partition_name=partition_name)[0]
|
|
assert results['insert_count'] == default_nb
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, "new_diff_str_field": str(i)} for i in range(default_nb)]
|
|
results = self.upsert(client, collection_name, rows, partition_name=partition_name)[0]
|
|
assert results['upsert_count'] == default_nb
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
insert_ids = [i for i in range(default_nb)]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"pk_name": default_primary_key_field_name})
|
|
if self.has_partition(client, collection_name, partition_name)[0]:
|
|
self.release_partitions(client, collection_name, partition_name)
|
|
self.drop_partition(client, collection_name, partition_name)
|
|
if self.has_collection(client, collection_name)[0]:
|
|
self.drop_collection(client, collection_name)
|
|
|
|
|
|
|
|
""" Test case of partial update interface """
|
|
@pytest.fixture(scope="function", params=[False, True])
|
|
def auto_id(self, request):
|
|
yield request.param
|
|
|
|
@pytest.fixture(scope="function", params=["COSINE", "L2"])
|
|
def metric_type(self, request):
|
|
yield request.param
|
|
|
|
"""
|
|
******************************************************************
|
|
# The following are invalid base cases
|
|
******************************************************************
|
|
"""
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_partial_update_new_pk_with_missing_field(self):
|
|
"""
|
|
target: Test PU will return error when provided new pk and partial field
|
|
method:
|
|
1. Create a collection
|
|
2. partial upsert a new pk with only partial field
|
|
expected: Step 2 should result fail
|
|
"""
|
|
# step 1: create collection
|
|
client = self._client()
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
schema.add_field(default_int32_field_name, DataType.INT32, nullable=True)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_primary_key_field_name, index_type="AUTOINDEX")
|
|
index_params.add_index(default_vector_field_name, index_type="AUTOINDEX")
|
|
index_params.add_index(default_int32_field_name, index_type="AUTOINDEX")
|
|
collection_name = cf.gen_collection_name_by_testcase_name(module_index=1)
|
|
self.create_collection(client, collection_name, default_dim, schema=schema,
|
|
consistency_level="Strong", index_params=index_params)
|
|
|
|
# step 2: partial upsert a new pk with only partial field
|
|
rows = cf.gen_row_data_by_schema(nb=default_nb, schema=schema,
|
|
desired_field_names=[default_primary_key_field_name, default_int32_field_name])
|
|
error = {ct.err_code: 1100, ct.err_msg:
|
|
f"fieldSchema({default_vector_field_name}) has no corresponding fieldData pass in: invalid parameter"}
|
|
self.upsert(client, collection_name, rows, partial_update=True,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_partial_update_new_field_without_dynamic_field(self):
|
|
"""
|
|
target: Test PU will return error when provided new field without dynamic field
|
|
method:
|
|
1. Create a collection with dynamic field
|
|
2. partial upsert a new field
|
|
expected: Step 2 should result fail
|
|
"""
|
|
# step 1: create collection with dynamic field
|
|
client = self._client()
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_primary_key_field_name, index_type="AUTOINDEX")
|
|
index_params.add_index(default_vector_field_name, index_type="AUTOINDEX")
|
|
collection_name = cf.gen_collection_name_by_testcase_name(module_index=1)
|
|
self.create_collection(client, collection_name, default_dim, schema=schema,
|
|
consistency_level="Strong", index_params=index_params)
|
|
|
|
# step 2: partial upsert a new field
|
|
row = cf.gen_row_data_by_schema(nb=default_nb, schema=schema)
|
|
self.upsert(client, collection_name, row, partial_update=True)
|
|
|
|
new_row = [{default_primary_key_field_name: i, default_int32_field_name: 99} for i in range(default_nb)]
|
|
error = {ct.err_code: 1,
|
|
ct.err_msg: f"Attempt to insert an unexpected field `{default_int32_field_name}` to collection without enabling dynamic field"}
|
|
self.upsert(client, collection_name, new_row, partial_update=True, check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_partial_update_after_release_collection(self):
|
|
"""
|
|
target: test basic function of partial update
|
|
method:
|
|
1. create collection
|
|
2. insert a full row of data using partial update
|
|
3. partial update data
|
|
4. release collection
|
|
5. partial update data
|
|
expected: step 5 should fail
|
|
"""
|
|
# Step 1: create collection
|
|
client = self._client()
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_primary_key_field_name, index_type="AUTOINDEX")
|
|
index_params.add_index(default_vector_field_name, index_type="AUTOINDEX")
|
|
index_params.add_index(default_string_field_name, index_type="AUTOINDEX")
|
|
collection_name = cf.gen_collection_name_by_testcase_name(module_index=1)
|
|
self.create_collection(client, collection_name, default_dim, schema=schema,
|
|
consistency_level="Strong", index_params=index_params)
|
|
|
|
# Step 2: insert a full row of data using partial update
|
|
rows = cf.gen_row_data_by_schema(nb=default_nb, schema=schema)
|
|
self.upsert(client, collection_name, rows, partial_update=True)
|
|
|
|
# Step 3: partial update data
|
|
new_row = cf.gen_row_data_by_schema(nb=default_nb, schema=schema,
|
|
desired_field_names=[default_primary_key_field_name, default_string_field_name])
|
|
self.upsert(client, collection_name, new_row, partial_update=True)
|
|
|
|
# Step 4: release collection
|
|
self.release_collection(client, collection_name)
|
|
|
|
# Step 5: partial update data
|
|
new_row = cf.gen_row_data_by_schema(nb=default_nb, schema=schema,
|
|
desired_field_names=[default_primary_key_field_name, default_string_field_name])
|
|
error = {ct.err_code: 101,
|
|
ct.err_msg: f"failed to query: collection not loaded"}
|
|
self.upsert(client, collection_name, new_row, partial_update=True,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_partial_update_same_pk_after_delete(self):
|
|
"""
|
|
target: test PU will fail when provided same pk and partial field
|
|
method:
|
|
1. Create a collection with dynamic field
|
|
2. Insert rows
|
|
3. delete the rows
|
|
4. upsert the rows with same pk and partial field
|
|
expected: step 4 should fail
|
|
"""
|
|
# Step 1: create collection
|
|
client = self._client()
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
schema.add_field(default_int32_field_name, DataType.INT32)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_primary_key_field_name, index_type="AUTOINDEX")
|
|
index_params.add_index(default_vector_field_name, index_type="AUTOINDEX")
|
|
index_params.add_index(default_int32_field_name, index_type="AUTOINDEX")
|
|
collection_name = cf.gen_collection_name_by_testcase_name(module_index=1)
|
|
self.create_collection(client, collection_name, default_dim, schema=schema,
|
|
consistency_level="Strong", index_params=index_params)
|
|
|
|
# Step 2: insert rows
|
|
rows = cf.gen_row_data_by_schema(nb=default_nb, schema=schema)
|
|
self.upsert(client, collection_name, rows, partial_update=True)
|
|
|
|
# Step 3: delete the rows
|
|
result = self.delete(client, collection_name, filter=default_search_exp)[0]
|
|
assert result["delete_count"] == default_nb
|
|
result = self.query(client, collection_name, filter=default_search_exp,
|
|
check_task=CheckTasks.check_nothing)[0]
|
|
assert len(result) == 0
|
|
|
|
# Step 4: upsert the rows with same pk and partial field
|
|
new_rows = cf.gen_row_data_by_schema(nb=default_nb, schema=schema,
|
|
desired_field_names=[default_primary_key_field_name, default_vector_field_name])
|
|
error = {ct.err_code: 1100,
|
|
ct.err_msg: f"fieldSchema({default_int32_field_name}) has no corresponding fieldData pass in: invalid parameter"}
|
|
self.upsert(client, collection_name, new_rows, partial_update=True,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_partial_update_pk_in_wrong_partition(self):
|
|
"""
|
|
target: test PU will fail when provided pk in wrong partition
|
|
method:
|
|
1. Create a collection
|
|
2. Create 2 partitions
|
|
3. Insert rows
|
|
4. upsert the rows with pk in wrong partition
|
|
expected: step 4 should fail
|
|
"""
|
|
# Step 1: create collection
|
|
client = self._client()
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
schema.add_field(default_int32_field_name, DataType.INT32)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_primary_key_field_name, index_type="AUTOINDEX")
|
|
index_params.add_index(default_vector_field_name, index_type="AUTOINDEX")
|
|
index_params.add_index(default_int32_field_name, index_type="AUTOINDEX")
|
|
collection_name = cf.gen_collection_name_by_testcase_name(module_index=1)
|
|
self.create_collection(client, collection_name, default_dim, schema=schema,
|
|
consistency_level="Strong", index_params=index_params)
|
|
|
|
# Step 2: Create 2 partitions
|
|
num_of_partitions = 2
|
|
partition_names = []
|
|
for _ in range(num_of_partitions):
|
|
partition_name = cf.gen_unique_str("partition")
|
|
self.create_partition(client, collection_name, partition_name)
|
|
partition_names.append(partition_name)
|
|
|
|
# Step 3: Insert rows
|
|
rows = cf.gen_row_data_by_schema(nb=default_nb, schema=schema)
|
|
gap = default_nb // num_of_partitions
|
|
for i, partition in enumerate(partition_names):
|
|
self.upsert(client, collection_name, rows[i*gap:(i+1)*gap], partition_name=partition, partial_update=True)
|
|
|
|
# Step 4: upsert the rows with pk in wrong partition
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new_rows = cf.gen_row_data_by_schema(nb=gap, schema=schema,
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|
desired_field_names=[default_primary_key_field_name, default_vector_field_name])
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|
error = {ct.err_code: 1100,
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|
ct.err_msg: f"fieldSchema({default_int32_field_name}) has no corresponding fieldData pass in: invalid parameter"}
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|
self.upsert(client, collection_name, new_rows, partition_name=partition_names[-1], partial_update=True,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_partial_update_same_pk_multiple_fields(self):
|
|
"""
|
|
target: Test PU will success and query will success
|
|
method:
|
|
1. Create a collection
|
|
2. Insert rows
|
|
3. Upsert the rows with same pk and different field
|
|
expected: Step 3 should fail
|
|
"""
|
|
# step 1: create collection
|
|
client = self._client()
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
schema.add_field(default_int32_field_name, DataType.INT32, nullable=True)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_primary_key_field_name, index_type="AUTOINDEX")
|
|
index_params.add_index(default_vector_field_name, index_type="AUTOINDEX")
|
|
index_params.add_index(default_int32_field_name, index_type="AUTOINDEX")
|
|
collection_name = cf.gen_collection_name_by_testcase_name(module_index=1)
|
|
self.create_collection(client, collection_name, default_dim, schema=schema,
|
|
consistency_level="Strong", index_params=index_params)
|
|
|
|
# step 2: Insert rows
|
|
rows = cf.gen_row_data_by_schema(nb=default_nb, schema=schema)
|
|
self.upsert(client, collection_name, rows, partial_update=True)
|
|
|
|
# step 3: Upsert the rows with same pk and different field
|
|
new_rows = []
|
|
for i in range(default_nb):
|
|
data = {}
|
|
if i % 2 == 0:
|
|
data[default_int32_field_name] = i + 1000
|
|
data[default_primary_key_field_name] = 0
|
|
else:
|
|
data[default_vector_field_name] = [random.random() for _ in range(default_dim)]
|
|
data[default_primary_key_field_name] = 0
|
|
new_rows.append(data)
|
|
|
|
error = {ct.err_code: 1,
|
|
ct.err_msg: f"The data fields length is inconsistent. previous length is 2000, current length is 1000"}
|
|
self.upsert(client, collection_name, new_rows, partial_update=True,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
self.drop_collection(client, collection_name) |