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https://gitee.com/milvus-io/milvus.git
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1829 lines
93 KiB
Python
1829 lines
93 KiB
Python
import threading
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import time
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import pytest
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import random
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import numpy as np
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from base.client_base import TestcaseBase
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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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prefix = "search_collection"
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search_num = 10
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max_dim = ct.max_dim
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epsilon = ct.epsilon
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gracefulTime = ct.gracefulTime
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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 = "int64 >= 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_int64_field_name = ct.default_int64_field_name
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default_float_field_name = ct.default_float_field_name
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vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
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class TestCollectionSearchInvalid(TestcaseBase):
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""" Test case of search interface """
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@pytest.fixture(scope="function", params=ct.get_invalid_vectors)
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def get_invalid_vectors(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=ct.get_invalid_strs)
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def get_invalid_fields_type(self, request):
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if isinstance(request.param, str):
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pytest.skip("string is valid type for field")
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yield request.param
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@pytest.fixture(scope="function", params=ct.get_invalid_strs)
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def get_invalid_fields_value(self, request):
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if not isinstance(request.param, str):
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pytest.skip("field value only support string")
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if request.param == "":
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pytest.skip("empty field is valid")
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yield request.param
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@pytest.fixture(scope="function", params=ct.get_invalid_strs)
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def get_invalid_metric_type(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=ct.get_invalid_ints)
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def get_invalid_limit(self, request):
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if isinstance(request.param, int) and request.param >= 0:
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pytest.skip("positive int is valid type for limit")
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yield request.param
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@pytest.fixture(scope="function", params=ct.get_invalid_strs)
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def get_invalid_expr_type(self, request):
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if isinstance(request.param, str):
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pytest.skip("string is valid type for expr")
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if request.param == None:
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pytest.skip("None is valid for expr")
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yield request.param
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@pytest.fixture(scope="function", params=ct.get_invalid_strs)
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def get_invalid_expr_value(self, request):
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if not isinstance(request.param, str):
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pytest.skip("expression value only support string")
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if request.param == "":
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pytest.skip("empty field is valid")
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yield request.param
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@pytest.fixture(scope="function", params=ct.get_invalid_strs)
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def get_invalid_partition(self, request):
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if request.param == []:
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pytest.skip("empty is valid for partition")
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if request.param == None:
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pytest.skip("None is valid for partition")
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yield request.param
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@pytest.fixture(scope="function", params=ct.get_invalid_strs)
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def get_invalid_output_fields(self, request):
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if request.param == []:
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pytest.skip("empty is valid for output_fields")
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if request.param == None:
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pytest.skip("None is valid for output_fields")
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yield request.param
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"""
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******************************************************************
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# The followings are invalid cases
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******************************************************************
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"""
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_no_connection(self):
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"""
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target: test search without connection
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method: create and delete connection, then search
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expected: raise exception and report the error
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"""
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# 1. initialize with data
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collection_w = self.init_collection_general(prefix)[0]
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# 2. remove connection
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log.info("test_search_no_connection: removing connection")
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self.connection_wrap.remove_connection(alias='default')
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log.info("test_search_no_connection: removed connection")
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# 3. search without connection
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log.info("test_search_no_connection: searching without connection")
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collection_w.search(vectors[:default_nq], default_search_field,
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default_search_params, default_limit,
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default_search_exp,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": "should create connect first"})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_no_collection(self):
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"""
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target: test the scenario which search the non-exist collection
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method: 1. create collection
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2. drop collection
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3. search the dropped collection
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expected: raise exception and report the error
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"""
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# 1. initialize without data
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collection_w = self.init_collection_general(prefix)[0]
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# 2. Drop collection
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collection_w.drop()
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# 3. Search without collection
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log.info("test_search_no_collection: Searching without collection ")
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collection_w.search(vectors, default_search_field,
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default_search_params, default_limit, default_search_exp,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": "collection %s doesn't exist!" % collection_w.name})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_param_missing(self):
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"""
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target: test search with incomplete parameters
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method: search with incomplete parameters
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expected: raise exception and report the error
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"""
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# 1. initialize without data
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collection_w = self.init_collection_general(prefix)[0]
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# 2. search collection with missing parameters
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log.info("test_search_param_missing: Searching collection %s "
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"with missing parameters" % collection_w.name)
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try:
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collection_w.search()
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except TypeError as e:
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assert "missing 4 required positional arguments: 'data', " \
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"'anns_field', 'param', and 'limit'" in str(e)
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_param_invalid_vectors(self, get_invalid_vectors):
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"""
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target: test search with invalid parameter values
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method: search with invalid data
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expected: raise exception and report the error
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"""
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# 1. initialize with data
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collection_w = self.init_collection_general(prefix)[0]
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# 2. search with invalid field
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invalid_vectors = get_invalid_vectors
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log.info("test_search_param_invalid_vectors: searching with "
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"invalid vectors: {}".format(invalid_vectors))
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collection_w.search(invalid_vectors, default_search_field, default_search_params,
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default_limit, default_search_exp,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": "`search_data` value {} is illegal".format(invalid_vectors)})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_param_invalid_dim(self):
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"""
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target: test search with invalid parameter values
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method: search with invalid dim
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expected: raise exception and report the error
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"""
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# 1. initialize with data
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collection_w = self.init_collection_general(prefix, True)[0]
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# 2. search with invalid dim
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log.info("test_search_param_invalid_dim: searching with invalid dim")
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wrong_dim = 129
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vectors = [[random.random() for _ in range(wrong_dim)] for _ in range(default_nq)]
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collection_w.search(vectors[:default_nq], default_search_field,
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default_search_params, default_limit, default_search_exp,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": "The dimension of query entities "
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"is different from schema"})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_param_invalid_field_type(self, get_invalid_fields_type):
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"""
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target: test search with invalid parameter type
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method: search with invalid field
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expected: raise exception and report the error
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"""
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# 1. initialize with data
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collection_w = self.init_collection_general(prefix)[0]
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# 2. search with invalid field
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invalid_search_field = get_invalid_fields_type
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log.info("test_search_param_invalid_field_type: searching with "
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"invalid field: %s" % invalid_search_field)
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collection_w.search(vectors[:default_nq], invalid_search_field, default_search_params,
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default_limit, default_search_exp,
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check_task=CheckTasks.err_res,
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check_items=
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{"err_code": 1,
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"err_msg": "`anns_field` value {} is illegal".format(invalid_search_field)})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_param_invalid_field_value(self, get_invalid_fields_value):
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"""
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target: test search with invalid parameter values
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method: search with invalid field
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expected: raise exception and report the error
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"""
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# 1. initialize with data
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collection_w = self.init_collection_general(prefix)[0]
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# 2. search with invalid field
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invalid_search_field = get_invalid_fields_value
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log.info("test_search_param_invalid_field_value: searching with "
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"invalid field: %s" % invalid_search_field)
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collection_w.search(vectors[:default_nq], invalid_search_field, default_search_params,
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default_limit, default_search_exp,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": "Field %s doesn't exist in schema"
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% invalid_search_field})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_param_invalid_metric_type(self, get_invalid_metric_type):
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"""
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target: test search with invalid parameter values
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method: search with invalid metric type
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expected: raise exception and report the error
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"""
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# 1. initialize with data
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collection_w = self.init_collection_general(prefix, True, 10)[0]
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# 2. search with invalid metric_type
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log.info("test_search_param_invalid_metric_type: searching with invalid metric_type")
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invalid_metric = get_invalid_metric_type
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search_params = {"metric_type": invalid_metric, "params": {"nprobe": 10}}
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collection_w.search(vectors[:default_nq], default_search_field, search_params,
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default_limit, default_search_exp,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": "metric type not found"})
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.xfail(reason="issue 6727")
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@pytest.mark.parametrize("index, params",
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zip(ct.all_index_types[:9],
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ct.default_index_params[:9]))
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def test_search_invalid_params_type(self, index, params):
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"""
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target: test search with invalid search params
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method: test search with invalid params type
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expected: raise exception and report the error
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"""
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if index == "FLAT":
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pytest.skip("skip in FLAT index")
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# 1. initialize with data
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collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, 5000,
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is_index=True)
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# 2. create index and load
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default_index = {"index_type": index, "params": params, "metric_type": "L2"}
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collection_w.create_index("float_vector", default_index)
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collection_w.load()
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# 3. search
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invalid_search_params = cf.gen_invaild_search_params_type()
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for invalid_search_param in invalid_search_params:
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if index == invalid_search_param["index_type"]:
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search_params = {"metric_type": "L2", "params": invalid_search_param["search_params"]}
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collection_w.search(vectors[:default_nq], default_search_field,
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search_params, default_limit,
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default_search_exp,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 0,
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"err_msg": "metric type not found"})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_param_invalid_limit_type(self, get_invalid_limit):
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"""
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target: test search with invalid limit type
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method: search with invalid limit
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expected: raise exception and report the error
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"""
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# 1. initialize with data
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collection_w = self.init_collection_general(prefix)[0]
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# 2. search with invalid field
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invalid_limit = get_invalid_limit
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log.info("test_search_param_invalid_limit_type: searching with "
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"invalid limit: %s" % invalid_limit)
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collection_w.search(vectors[:default_nq], default_search_field, default_search_params,
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invalid_limit, default_search_exp,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": "`limit` value %s is illegal" % invalid_limit})
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("limit", [0, 16385])
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def test_search_param_invalid_limit_value(self, limit):
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"""
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target: test search with invalid limit value
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method: search with invalid limit: 0 and maximum
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expected: raise exception and report the error
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"""
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# 1. initialize with data
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collection_w = self.init_collection_general(prefix)[0]
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# 2. search with invalid limit (topK)
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log.info("test_search_param_invalid_limit: searching with "
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"invalid limit (topK) = %s" % limit)
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err_msg = "limit %d is too large!" % limit
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if limit == 0:
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err_msg = "`limit` value 0 is illegal"
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collection_w.search(vectors[:default_nq], default_search_field, default_search_params,
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limit, default_search_exp,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": err_msg})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_param_invalid_expr_type(self, get_invalid_expr_type):
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"""
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target: test search with invalid parameter type
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method: search with invalid search expressions
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expected: raise exception and report the error
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"""
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# 1. initialize with data
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collection_w = self.init_collection_general(prefix)[0]
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# 2 search with invalid expr
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invalid_search_expr = get_invalid_expr_type
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log.info("test_search_param_invalid_expr_type: searching with "
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"invalid expr: {}".format(invalid_search_expr))
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collection_w.search(vectors[:default_nq], default_search_field,
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default_search_params, default_limit, invalid_search_expr,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": "The type of expr must be string ,"
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"but {} is given".format(type(invalid_search_expr))})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_param_invalid_expr_value(self, get_invalid_expr_value):
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"""
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target: test search with invalid parameter values
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method: search with invalid search expressions
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expected: raise exception and report the error
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"""
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# 1. initialize with data
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collection_w = self.init_collection_general(prefix)[0]
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# 2 search with invalid expr
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invalid_search_expr = get_invalid_expr_value
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log.info("test_search_param_invalid_expr_value: searching with "
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"invalid expr: %s" % invalid_search_expr)
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collection_w.search(vectors[:default_nq], default_search_field,
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default_search_params, default_limit, invalid_search_expr,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": "invalid expression %s"
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% invalid_search_expr})
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@pytest.mark.tags(CaseLabel.L2)
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def test_search_partition_invalid_type(self, get_invalid_partition):
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"""
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target: test search invalid partition
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method: search with invalid partition type
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expected: raise exception and report the error
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"""
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# 1. initialize with data
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collection_w = self.init_collection_general(prefix)[0]
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# 2. search the invalid partition
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partition_name = get_invalid_partition
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err_msg = "`partition_name_array` value {} is illegal".format(partition_name)
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collection_w.search(vectors[:default_nq], default_search_field, default_search_params,
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default_limit, default_search_exp, partition_name,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": err_msg})
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@pytest.mark.tags(CaseLabel.L2)
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def test_search_with_output_fields_invalid_type(self, get_invalid_output_fields):
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"""
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target: test search with output fields
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method: search with invalid output_field
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expected: raise exception and report the error
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"""
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# 1. initialize with data
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collection_w = self.init_collection_general(prefix)[0]
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# 2. search
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log.info("test_search_with_output_fields_invalid_type: Searching collection %s" % collection_w.name)
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output_fields = get_invalid_output_fields
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err_msg = "`output_fields` value {} is illegal".format(output_fields)
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collection_w.search(vectors[:default_nq], default_search_field,
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default_search_params, default_limit,
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default_search_exp, output_fields=output_fields,
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check_task=CheckTasks.err_res,
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check_items={ct.err_code: 1,
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ct.err_msg: err_msg})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_release_collection(self):
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"""
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target: test the scenario which search the released collection
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method: 1. create collection
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2. release collection
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3. search the released collection
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expected: raise exception and report the error
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"""
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# 1. initialize without data
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collection_w = self.init_collection_general(prefix, True, 10)[0]
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# 2. release collection
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collection_w.release()
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# 3. Search the released collection
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log.info("test_search_release_collection: Searching without collection ")
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collection_w.search(vectors, default_search_field,
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default_search_params, default_limit, default_search_exp,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": "collection %s was not loaded "
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"into memory" % collection_w.name})
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@pytest.mark.tags(CaseLabel.L2)
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def test_search_release_partition(self):
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"""
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target: test the scenario which search the released collection
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method: 1. create collection
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2. release partition
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3. search with specifying the released partition
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expected: raise exception and report the error
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"""
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# 1. initialize with data
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partition_num = 1
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collection_w = self.init_collection_general(prefix, True, 10, partition_num)[0]
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par = collection_w.partitions
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par_name = par[partition_num].name
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# 2. release partition
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conn = self.connection_wrap.get_connection()[0]
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conn.release_partitions(collection_w.name, [par_name])
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# 3. Search the released partition
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log.info("test_search_release_partition: Searching specifying the released partition")
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limit = 10
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collection_w.search(vectors, default_search_field,
|
|
default_search_params, limit, default_search_exp,
|
|
[par_name],
|
|
check_task=CheckTasks.err_res,
|
|
check_items={"err_code": 1,
|
|
"err_msg": "partition has been released"})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_search_with_empty_collection(self):
|
|
"""
|
|
target: test search with empty connection
|
|
method: search the empty collection
|
|
expected: raise exception and report the error
|
|
"""
|
|
# 1. initialize without data
|
|
collection_w = self.init_collection_general(prefix)[0]
|
|
# 2. search collection without data before load
|
|
log.info("test_search_with_empty_collection: Searching empty collection %s"
|
|
% collection_w.name)
|
|
err_msg = "collection" + collection_w.name + "was not loaded into memory"
|
|
collection_w.search(vectors[:default_nq], default_search_field, default_search_params,
|
|
default_limit, default_search_exp, timeout=1,
|
|
check_task=CheckTasks.err_res,
|
|
check_items={"err_code": 1,
|
|
"err_msg": err_msg})
|
|
# 3. search collection without data after load
|
|
collection_w.load()
|
|
collection_w.search(vectors[:default_nq], default_search_field, default_search_params,
|
|
default_limit, default_search_exp,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"ids": [],
|
|
"limit": 0})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_search_partition_deleted(self):
|
|
"""
|
|
target: test search deleted partition
|
|
method: 1. search the collection
|
|
2. delete a partition
|
|
3. search the deleted partition
|
|
expected: raise exception and report the error
|
|
"""
|
|
# 1. initialize with data
|
|
partition_num = 1
|
|
collection_w = self.init_collection_general(prefix, True, 1000, partition_num)[0]
|
|
# 2. delete partitions
|
|
log.info("test_search_partition_deleted: deleting a partition")
|
|
par = collection_w.partitions
|
|
deleted_par_name = par[partition_num].name
|
|
collection_w.drop_partition(deleted_par_name)
|
|
log.info("test_search_partition_deleted: deleted a partition")
|
|
collection_w.load()
|
|
# 3. search after delete partitions
|
|
log.info("test_search_partition_deleted: searching deleted partition")
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, default_limit, default_search_exp,
|
|
[deleted_par_name],
|
|
check_task=CheckTasks.err_res,
|
|
check_items={"err_code": 1,
|
|
"err_msg": "PartitonName: %s not found" % deleted_par_name})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.xfail(reason="issue 6731")
|
|
@pytest.mark.parametrize("index, params",
|
|
zip(ct.all_index_types[:9],
|
|
ct.default_index_params[:9]))
|
|
def test_search_different_index_invalid_params(self, nq, dim, index, params, auto_id, _async):
|
|
"""
|
|
target: test search with different index
|
|
method: test search with different index
|
|
expected: searched successfully
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, 5000,
|
|
partition_num=1,
|
|
auto_id=auto_id,
|
|
dim=dim, is_index=True)
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
# 2. create different index
|
|
if params.get("m"):
|
|
if (dim % params["m"]) != 0:
|
|
params["m"] = dim//4
|
|
log.info("test_search_different_index_invalid_params: Creating index-%s" % index)
|
|
default_index = {"index_type": index, "params": params, "metric_type": "L2"}
|
|
collection_w.create_index("float_vector", default_index)
|
|
log.info("test_search_different_index_invalid_params: Created index-%s" % index)
|
|
collection_w.load()
|
|
# 3. search
|
|
log.info("test_search_different_index_invalid_params: Searching after creating index-%s" % index)
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_search_index_partition_not_existed(self):
|
|
"""
|
|
target: test search not existed partition
|
|
method: search with not existed partition
|
|
expected: raise exception and report the error
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
# 2. create index
|
|
default_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
|
|
collection_w.create_index("float_vector", default_index)
|
|
# 3. search the non exist partition
|
|
partition_name = "search_non_exist"
|
|
collection_w.search(vectors[:default_nq], default_search_field, default_search_params,
|
|
default_limit, default_search_exp, [partition_name],
|
|
check_task=CheckTasks.err_res,
|
|
check_items={"err_code": 1,
|
|
"err_msg": "PartitonName: %s not found" % partition_name})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_search_param_invalid_binary(self):
|
|
"""
|
|
target: test search within binary data (invalid parameter)
|
|
method: search with wrong metric type
|
|
expected: raise exception and report the error
|
|
"""
|
|
# 1. initialize with binary data
|
|
collection_w = self.init_collection_general(prefix, True, is_binary=True)[0]
|
|
# 2. create index
|
|
default_index = {"index_type": "BIN_IVF_FLAT", "params": {"nlist": 128}, "metric_type": "JACCARD"}
|
|
collection_w.create_index("binary_vector", default_index)
|
|
# 3. search with exception
|
|
binary_vectors = cf.gen_binary_vectors(3000, default_dim)[1]
|
|
wrong_search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
|
|
collection_w.search(binary_vectors[:default_nq], "binary_vector", wrong_search_params,
|
|
default_limit, default_search_exp,
|
|
check_task=CheckTasks.err_res,
|
|
check_items={"err_code": 1,
|
|
"err_msg": "unsupported"})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_binary_flat_with_L2(self):
|
|
"""
|
|
target: search binary collection using FlAT with L2
|
|
method: search binary collection using FLAT with L2
|
|
expected: raise exception and report error
|
|
"""
|
|
# 1. initialize with binary data
|
|
collection_w = self.init_collection_general(prefix, True, is_binary=True)[0]
|
|
# 2. search and assert
|
|
query_raw_vector, binary_vectors = cf.gen_binary_vectors(2, default_dim)
|
|
search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
|
|
collection_w.search(binary_vectors[:default_nq], "binary_vector",
|
|
search_params, default_limit, "int64 >= 0",
|
|
check_task=CheckTasks.err_res,
|
|
check_items={"err_code": 1,
|
|
"err_msg": "Search failed"})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_search_with_output_fields_not_exist(self):
|
|
"""
|
|
target: test search with output fields
|
|
method: search with non-exist output_field
|
|
expected: search success
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True)
|
|
# 2. search
|
|
log.info("test_search_with_output_fields_not_exist: Searching collection %s" % collection_w.name)
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, output_fields=["int63"],
|
|
check_task=CheckTasks.err_res,
|
|
check_items={ct.err_code: 1,
|
|
ct.err_msg: 'Field int63 not exist'})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("output_fields", [[default_search_field], ["%"]])
|
|
def test_search_output_field_vector(self, output_fields):
|
|
"""
|
|
target: test search with vector as output field
|
|
method: search with one vector output_field or
|
|
wildcard for vector
|
|
expected: raise exception and report the error
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
# 2. search
|
|
log.info("test_search_output_field_vector: Searching collection %s" % collection_w.name)
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, output_fields=output_fields,
|
|
check_task=CheckTasks.err_res,
|
|
check_items={"err_code": 1,
|
|
"err_msg": "Search doesn't support "
|
|
"vector field as output_fields"})
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("output_fields", [["*%"], ["**"], ["*", "@"]])
|
|
def test_search_output_field_invalid_wildcard(self, output_fields):
|
|
"""
|
|
target: test search with invalid output wildcard
|
|
method: search with invalid output_field wildcard
|
|
expected: raise exception and report the error
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
# 2. search
|
|
log.info("test_search_output_field_vector: Searching collection %s" % collection_w.name)
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, output_fields=output_fields,
|
|
check_task=CheckTasks.err_res,
|
|
check_items={"err_code": 1,
|
|
"err_msg": f"Field {output_fields[-1]} not exist"})
|
|
|
|
class TestCollectionSearch(TestcaseBase):
|
|
""" Test case of search interface """
|
|
|
|
@pytest.fixture(scope="function",
|
|
params=[default_nb, default_nb_medium])
|
|
def nb(self, request):
|
|
yield request.param
|
|
|
|
@pytest.fixture(scope="function", params=[2, 500])
|
|
def nq(self, request):
|
|
yield request.param
|
|
|
|
@pytest.fixture(scope="function", params=[8, 128])
|
|
def dim(self, request):
|
|
yield request.param
|
|
|
|
@pytest.fixture(scope="function", params=[False, True])
|
|
def auto_id(self, request):
|
|
yield request.param
|
|
|
|
@pytest.fixture(scope="function", params=[False, True])
|
|
def _async(self, request):
|
|
yield request.param
|
|
|
|
"""
|
|
******************************************************************
|
|
# The following are valid base cases
|
|
******************************************************************
|
|
"""
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
def test_search_normal(self, nq, dim, auto_id):
|
|
"""
|
|
target: test search normal case
|
|
method: create connection, collection, insert and search
|
|
expected: search successfully with limit(topK)
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = \
|
|
self.init_collection_general(prefix, True, auto_id=auto_id, dim=dim)
|
|
# 2. search
|
|
log.info("test_search_normal: searching collection %s" % collection_w.name)
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit})
|
|
|
|
@pytest.mark.tag(CaseLabel.L0)
|
|
def test_search_with_hit_vectors(self, nq, dim, auto_id):
|
|
"""
|
|
target: test search with vectors in collections
|
|
method: create connections,collection insert and search vectors in collections
|
|
expected: search successfully with limit(topK) and can be hit at top 1 (min distance is 0)
|
|
"""
|
|
collection_w, _vectors, _, insert_ids = \
|
|
self.init_collection_general(prefix, True, auto_id=auto_id, dim=dim)
|
|
# get vectors that inserted into collection
|
|
vectors = np.array(_vectors[0]).tolist()
|
|
vectors = [vectors[i][-1] for i in range(nq)]
|
|
search_res, _ = collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit})
|
|
for hits in search_res:
|
|
# verify that top 1 hit is itself,so min distance is 0
|
|
assert hits.distances[0] == 0.0
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_search_with_empty_vectors(self, dim, auto_id, _async):
|
|
"""
|
|
target: test search with empty query vector
|
|
method: search using empty query vector
|
|
expected: search successfully with 0 results
|
|
"""
|
|
# 1. initialize without data
|
|
collection_w = self.init_collection_general(prefix, True,
|
|
auto_id=auto_id, dim=dim)[0]
|
|
# 2. search collection without data
|
|
log.info("test_search_with_empty_vectors: Searching collection %s "
|
|
"using empty vector" % collection_w.name)
|
|
collection_w.search([], default_search_field, default_search_params,
|
|
default_limit, default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": 0,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("search_params", [{}, {"params": {}}, {"params": {"nprobe": 10}}])
|
|
def test_search_normal_default_params(self, dim, auto_id, search_params, _async):
|
|
"""
|
|
target: test search normal case
|
|
method: create connection, collection, insert and search
|
|
expected: search successfully with limit(topK)
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = \
|
|
self.init_collection_general(prefix, True, auto_id=auto_id, dim=dim)
|
|
# 2. search
|
|
log.info("test_search_normal: searching collection %s" % collection_w.name)
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
search_params, default_limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_search_before_after_delete(self, nq, dim, auto_id, _async):
|
|
"""
|
|
target: test search function before and after deletion
|
|
method: 1. search the collection
|
|
2. delete a partition
|
|
3. search the collection
|
|
expected: the deleted entities should not be searched
|
|
"""
|
|
# 1. initialize with data
|
|
nb = 1000
|
|
limit = 1000
|
|
partition_num = 1
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
partition_num,
|
|
auto_id=auto_id,
|
|
dim=dim)
|
|
# 2. search all the partitions before partition deletion
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
log.info("test_search_before_after_delete: searching before deleting partitions")
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": limit,
|
|
"_async": _async})
|
|
# 3. delete partitions
|
|
log.info("test_search_before_after_delete: deleting a partition")
|
|
par = collection_w.partitions
|
|
deleted_entity_num = par[partition_num].num_entities
|
|
entity_num = nb - deleted_entity_num
|
|
collection_w.drop_partition(par[partition_num].name)
|
|
log.info("test_search_before_after_delete: deleted a partition")
|
|
collection_w.load()
|
|
# 4. search non-deleted part after delete partitions
|
|
log.info("test_search_before_after_delete: searching after deleting partitions")
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids[:entity_num],
|
|
"limit": limit-deleted_entity_num,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_partition_after_release_one(self, nq, dim, auto_id, _async):
|
|
"""
|
|
target: test search function before and after release
|
|
method: 1. search the collection
|
|
2. release a partition
|
|
3. search the collection
|
|
expected: the deleted entities should not be searched
|
|
"""
|
|
# 1. initialize with data
|
|
nb = 1000
|
|
limit = 1000
|
|
partition_num = 1
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
partition_num,
|
|
auto_id=auto_id,
|
|
dim=dim)
|
|
# 2. search all the partitions before partition deletion
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
log.info("test_search_partition_after_release_one: searching before deleting partitions")
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": limit,
|
|
"_async": _async})
|
|
# 3. release one partition
|
|
log.info("test_search_partition_after_release_one: releasing a partition")
|
|
par = collection_w.partitions
|
|
deleted_entity_num = par[partition_num].num_entities
|
|
entity_num = nb - deleted_entity_num
|
|
conn = self.connection_wrap.get_connection()[0]
|
|
conn.release_partitions(collection_w.name, [par[partition_num].name])
|
|
log.info("test_search_partition_after_release_one: released a partition")
|
|
# 4. search collection after release one partition
|
|
log.info("test_search_partition_after_release_one: searching after deleting partitions")
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids[:entity_num],
|
|
"limit": limit - deleted_entity_num,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_partition_after_release_all(self, nq, dim, auto_id, _async):
|
|
"""
|
|
target: test search function before and after release
|
|
method: 1. search the collection
|
|
2. release a partition
|
|
3. search the collection
|
|
expected: the deleted entities should not be searched
|
|
"""
|
|
# 1. initialize with data
|
|
nb = 1000
|
|
limit = 1000
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
1, auto_id=auto_id,
|
|
dim=dim)
|
|
# 2. search all the partitions before partition deletion
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
log.info("test_search_partition_after_release_all: searching before deleting partitions")
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": limit,
|
|
"_async": _async})
|
|
# 3. release all partitions
|
|
log.info("test_search_partition_after_release_all: releasing a partition")
|
|
par = collection_w.partitions
|
|
conn = self.connection_wrap.get_connection()[0]
|
|
conn.release_partitions(collection_w.name, [par[0].name, par[1].name])
|
|
log.info("test_search_partition_after_release_all: released a partition")
|
|
# 4. search collection after release all partitions
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": [],
|
|
"limit": 0,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_collection_after_release_load(self, nb, nq, dim, auto_id, _async):
|
|
"""
|
|
target: search the pre-released collection after load
|
|
method: 1. create collection
|
|
2. release collection
|
|
3. load collection
|
|
4. search the pre-released collection
|
|
expected: search successfully
|
|
"""
|
|
# 1. initialize without data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
1, auto_id=auto_id,
|
|
dim=dim)
|
|
# 2. release collection
|
|
collection_w.release()
|
|
# 3. Search the pre-released collection after load
|
|
collection_w.load()
|
|
log.info("test_search_collection_after_release_load: searching after load")
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
collection_w.search(vectors[:nq], default_search_field, default_search_params,
|
|
default_limit, default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.xfail(reason="issue 6997")
|
|
def test_search_partition_after_release_load(self, nb, nq, dim, auto_id, _async):
|
|
"""
|
|
target: search the pre-released collection after load
|
|
method: 1. create collection
|
|
2. release a partition
|
|
3. load partition
|
|
4. search the pre-released partition
|
|
expected: search successfully
|
|
"""
|
|
# 1. initialize without data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
1, auto_id=auto_id,
|
|
dim=dim)
|
|
# 2. release collection
|
|
log.info("test_search_partition_after_release_load: releasing a partition")
|
|
par = collection_w.partitions
|
|
conn = self.connection_wrap.get_connection()[0]
|
|
conn.release_partitions(collection_w.name, [par[1].name])
|
|
log.info("test_search_partition_after_release_load: released a partition")
|
|
# 3. Search the collection after load
|
|
limit = 1000
|
|
collection_w.load()
|
|
log.info("test_search_partition_after_release_load: searching after load")
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
collection_w.search(vectors[:nq], default_search_field, default_search_params,
|
|
limit, default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": limit,
|
|
"_async": _async})
|
|
# 4. Search the pre-released partition after load
|
|
if limit > par[1].num_entities:
|
|
limit_check = par[1].num_entities
|
|
else:
|
|
limit_check = limit
|
|
collection_w.search(vectors[:nq], default_search_field, default_search_params,
|
|
limit, default_search_exp,
|
|
[par[1].name], _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids[par[0].num_entities:],
|
|
"limit": limit_check,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_load_flush_load(self, nb, nq, dim, auto_id, _async):
|
|
"""
|
|
target: test search when load before flush
|
|
method: 1. search the collection
|
|
2. insert data and load
|
|
3. flush, and load
|
|
expected: search success with limit(topK)
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, auto_id=auto_id, dim=dim)[0]
|
|
# 2. insert data
|
|
insert_ids = cf.insert_data(collection_w, nb, auto_id=auto_id, dim=dim)[3]
|
|
# 3. load data
|
|
collection_w.load()
|
|
# 4. flush and load
|
|
collection_w.num_entities
|
|
collection_w.load()
|
|
# 5. search for new data without load
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_new_data(self, nq, dim, auto_id, _async):
|
|
"""
|
|
target: test search new inserted data without load
|
|
method: 1. search the collection
|
|
2. insert new data
|
|
3. search the collection without load again
|
|
expected: new data should be searched
|
|
"""
|
|
# 1. initialize with data
|
|
limit = 1000
|
|
nb_old = 500
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb_old,
|
|
auto_id=auto_id,
|
|
dim=dim)
|
|
# 2. search for original data after load
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
log.info("test_search_new_data: searching for original data after load")
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": nb_old,
|
|
"_async": _async})
|
|
# 3. insert new data
|
|
nb_new = 300
|
|
insert_ids_new = cf.insert_data(collection_w, nb_new,
|
|
auto_id=auto_id, dim=dim)[3]
|
|
insert_ids.extend(insert_ids_new)
|
|
# gracefulTime is default as 1s which allows data
|
|
# could not be searched instantly in gracefulTime
|
|
time.sleep(gracefulTime)
|
|
# 4. search for new data without load
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": nb_old+nb_new,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_max_dim(self, nq, auto_id, _async):
|
|
"""
|
|
target: test search normal case
|
|
method: create connection, collection, insert and search
|
|
expected: search successfully with limit(topK)
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, default_nb,
|
|
auto_id=auto_id,
|
|
dim=max_dim)
|
|
# 2. search
|
|
log.info("test_search_max_dim: searching collection %s" % collection_w.name)
|
|
vectors = [[random.random() for _ in range(max_dim)] for _ in range(nq)]
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, 2,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": 2,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("index, params",
|
|
zip(ct.all_index_types[:9],
|
|
ct.default_index_params[:9]))
|
|
def test_search_after_different_index_with_params(self, dim, index, params, auto_id, _async):
|
|
"""
|
|
target: test search with invalid search params
|
|
method: test search with invalid params type
|
|
expected: raise exception and report the error
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, 5000,
|
|
partition_num=1,
|
|
auto_id=auto_id,
|
|
dim=dim, is_index=True)
|
|
# 2. create index and load
|
|
if params.get("m"):
|
|
if (dim % params["m"]) != 0:
|
|
params["m"] = dim//4
|
|
if params.get("PQM"):
|
|
if (dim % params["PQM"]) != 0:
|
|
params["PQM"] = dim//4
|
|
default_index = {"index_type": index, "params": params, "metric_type": "L2"}
|
|
collection_w.create_index("float_vector", default_index)
|
|
collection_w.load()
|
|
# 3. search
|
|
search_params = cf.gen_search_param(index)
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
|
|
for search_param in search_params:
|
|
log.info("Searching with search params: {}".format(search_param))
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
search_param, default_limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("index, params",
|
|
zip(ct.all_index_types[:9],
|
|
ct.default_index_params[:9]))
|
|
def test_search_after_index_different_metric_type(self, dim, index, params, auto_id, _async):
|
|
"""
|
|
target: test search with different metric type
|
|
method: test search with different metric type
|
|
expected: searched successfully
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, 5000,
|
|
partition_num=1,
|
|
auto_id=auto_id,
|
|
dim=dim, is_index=True)
|
|
# 2. create different index
|
|
if params.get("m"):
|
|
if (dim % params["m"]) != 0:
|
|
params["m"] = dim//4
|
|
if params.get("PQM"):
|
|
if (dim % params["PQM"]) != 0:
|
|
params["PQM"] = dim//4
|
|
log.info("test_search_after_index_different_metric_type: Creating index-%s" % index)
|
|
default_index = {"index_type": index, "params": params, "metric_type": "IP"}
|
|
collection_w.create_index("float_vector", default_index)
|
|
log.info("test_search_after_index_different_metric_type: Created index-%s" % index)
|
|
collection_w.load()
|
|
# 3. search
|
|
search_params = cf.gen_search_param(index, "IP")
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
|
|
for search_param in search_params:
|
|
log.info("Searching with search params: {}".format(search_param))
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
search_param, default_limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_collection_multiple_times(self, nb, nq, dim, auto_id, _async):
|
|
"""
|
|
target: test search for multiple times
|
|
method: search for multiple times
|
|
expected: searched successfully
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
auto_id=auto_id,
|
|
dim=dim)
|
|
# 2. search for multiple times
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
for i in range(search_num):
|
|
log.info("test_search_collection_multiple_times: searching round %d" % (i+1))
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_sync_async_multiple_times(self, nb, nq, dim, auto_id):
|
|
"""
|
|
target: test async search after sync search case
|
|
method: create connection, collection, insert,
|
|
sync search and async search
|
|
expected: search successfully with limit(topK)
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
auto_id=auto_id,
|
|
dim=dim)
|
|
# 2. search
|
|
log.info("test_search_sync_async_multiple_times: searching collection %s" % collection_w.name)
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
for i in range(search_num):
|
|
log.info("test_search_sync_async_multiple_times: searching round %d" % (i + 1))
|
|
for _async in [False, True]:
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_multiple_vectors(self, nb, nq, dim, auto_id, _async):
|
|
"""
|
|
target: test search with multiple vectors
|
|
method: create connection, collection with multiple
|
|
vectors, insert and search
|
|
expected: search successfully with limit(topK)
|
|
"""
|
|
# 1. connect
|
|
self._connect()
|
|
# 2. create collection with multiple vectors
|
|
c_name = cf.gen_unique_str(prefix)
|
|
fields = [cf.gen_int64_field(is_primary=True), cf.gen_float_field(),
|
|
cf.gen_float_vec_field(dim=dim), cf.gen_float_vec_field(name="tmp", dim=dim)]
|
|
schema = cf.gen_collection_schema(fields=fields, auto_id=auto_id)
|
|
collection_w = self.collection_wrap.init_collection(c_name, schema=schema,
|
|
check_task=CheckTasks.check_collection_property,
|
|
check_items={"name": c_name, "schema": schema})[0]
|
|
# 3. insert
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nb)]
|
|
vectors_tmp = [[random.random() for _ in range(dim)] for _ in range(nb)]
|
|
data = [[i for i in range(nb)], [np.float32(i) for i in range(nb)], vectors, vectors_tmp]
|
|
if auto_id:
|
|
data = [[np.float32(i) for i in range(nb)], vectors, vectors_tmp]
|
|
res = collection_w.insert(data)
|
|
insert_ids = res.primary_keys
|
|
assert collection_w.num_entities == nb
|
|
# 4. load
|
|
collection_w.load()
|
|
# 5. search all the vectors
|
|
log.info("test_search_multiple_vectors: searching collection %s" % collection_w.name)
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})
|
|
collection_w.search(vectors[:nq], "tmp",
|
|
default_search_params, default_limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_search_index_one_partition(self, nb, auto_id, _async):
|
|
"""
|
|
target: test search from partition
|
|
method: search from one partition
|
|
expected: searched successfully
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
partition_num=1,
|
|
auto_id=auto_id,
|
|
is_index=True)
|
|
|
|
# 2. create index
|
|
default_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
|
|
collection_w.create_index("float_vector", default_index)
|
|
collection_w.load()
|
|
# 3. search in one partition
|
|
log.info("test_search_index_one_partition: searching (1000 entities) through one partition")
|
|
limit = 1000
|
|
par = collection_w.partitions
|
|
if limit > par[1].num_entities:
|
|
limit_check = par[1].num_entities
|
|
else:
|
|
limit_check = limit
|
|
search_params = {"metric_type": "L2", "params": {"nprobe": 128}}
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
search_params, limit, default_search_exp,
|
|
[par[1].name], _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"ids": insert_ids[par[0].num_entities:],
|
|
"limit": limit_check,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_index_partitions(self, nb, nq, dim, auto_id, _async):
|
|
"""
|
|
target: test search from partitions
|
|
method: search from partitions
|
|
expected: searched successfully
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
partition_num=1,
|
|
auto_id=auto_id,
|
|
dim=dim,
|
|
is_index=True)
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
# 2. create index
|
|
default_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
|
|
collection_w.create_index("float_vector", default_index)
|
|
collection_w.load()
|
|
# 3. search through partitions
|
|
log.info("test_search_index_partitions: searching (1000 entities) through partitions")
|
|
par = collection_w.partitions
|
|
log.info("test_search_index_partitions: partitions: %s" % par)
|
|
limit = 1000
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, limit, default_search_exp,
|
|
[par[0].name, par[1].name], _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": limit,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("partition_names",
|
|
[["(.*)"], ["search(.*)"]])
|
|
def test_search_index_partitions_fuzzy(self, nb, nq, dim, partition_names, auto_id, _async):
|
|
"""
|
|
target: test search from partitions
|
|
method: search from partitions with fuzzy
|
|
partition name
|
|
expected: searched successfully
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
partition_num=1,
|
|
auto_id=auto_id,
|
|
dim=dim)
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
# 2. create index
|
|
default_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
|
|
collection_w.create_index("float_vector", default_index)
|
|
# 3. search through partitions
|
|
log.info("test_search_index_partitions_fuzzy: searching through partitions")
|
|
limit = 1000
|
|
limit_check = limit
|
|
par = collection_w.partitions
|
|
if partition_names == ["search(.*)"]:
|
|
insert_ids = insert_ids[par[0].num_entities:]
|
|
if limit > par[1].num_entities:
|
|
limit_check = par[1].num_entities
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, limit, default_search_exp,
|
|
partition_names, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": limit_check,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_index_partition_empty(self, nq, dim, auto_id, _async):
|
|
"""
|
|
target: test search the empty partition
|
|
method: search from the empty partition
|
|
expected: searched successfully with 0 results
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True, auto_id=auto_id,
|
|
dim=dim, is_index=True)[0]
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
# 2. create empty partition
|
|
partition_name = "search_partition_empty"
|
|
collection_w.create_partition(partition_name=partition_name, description="search partition empty")
|
|
par = collection_w.partitions
|
|
log.info("test_search_index_partition_empty: partitions: %s" % par)
|
|
collection_w.load()
|
|
# 3. create index
|
|
default_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
|
|
collection_w.create_index("float_vector", default_index)
|
|
# 4. search the empty partition
|
|
log.info("test_search_index_partition_empty: searching %s "
|
|
"entities through empty partition" % default_limit)
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, [partition_name],
|
|
_async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": [],
|
|
"limit": 0,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_binary_jaccard_flat_index(self, nq, dim, auto_id, _async):
|
|
"""
|
|
target: search binary_collection, and check the result: distance
|
|
method: compare the return distance value with value computed with JACCARD
|
|
expected: the return distance equals to the computed value
|
|
"""
|
|
# 1. initialize with binary data
|
|
collection_w, _, binary_raw_vector, insert_ids = self.init_collection_general(prefix, True, 2,
|
|
is_binary=True,
|
|
auto_id=auto_id,
|
|
dim=dim,
|
|
is_index=True)
|
|
# 2. create index
|
|
default_index = {"index_type": "BIN_IVF_FLAT", "params": {"nlist": 128}, "metric_type": "JACCARD"}
|
|
collection_w.create_index("binary_vector", default_index)
|
|
collection_w.load()
|
|
# 3. compute the distance
|
|
query_raw_vector, binary_vectors = cf.gen_binary_vectors(3000, dim)
|
|
distance_0 = cf.jaccard(query_raw_vector[0], binary_raw_vector[0])
|
|
distance_1 = cf.jaccard(query_raw_vector[0], binary_raw_vector[1])
|
|
# 4. search and compare the distance
|
|
search_params = {"metric_type": "JACCARD", "params": {"nprobe": 10}}
|
|
res = collection_w.search(binary_vectors[:nq], "binary_vector",
|
|
search_params, default_limit, "int64 >= 0",
|
|
_async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": 2,
|
|
"_async": _async})[0]
|
|
if _async:
|
|
res.done()
|
|
res = res.result()
|
|
assert abs(res[0]._distances[0] - min(distance_0, distance_1)) <= epsilon
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_binary_hamming_flat_index(self, nq, dim, auto_id, _async):
|
|
"""
|
|
target: search binary_collection, and check the result: distance
|
|
method: compare the return distance value with value computed with HAMMING
|
|
expected: the return distance equals to the computed value
|
|
"""
|
|
# 1. initialize with binary data
|
|
collection_w, _, binary_raw_vector, insert_ids = self.init_collection_general(prefix, True, 2,
|
|
is_binary=True,
|
|
auto_id=auto_id,
|
|
dim=dim,
|
|
is_index=True)
|
|
# 2. create index
|
|
default_index = {"index_type": "BIN_IVF_FLAT", "params": {"nlist": 128}, "metric_type": "HAMMING"}
|
|
collection_w.create_index("binary_vector", default_index)
|
|
# 3. compute the distance
|
|
collection_w.load()
|
|
query_raw_vector, binary_vectors = cf.gen_binary_vectors(3000, dim)
|
|
distance_0 = cf.hamming(query_raw_vector[0], binary_raw_vector[0])
|
|
distance_1 = cf.hamming(query_raw_vector[0], binary_raw_vector[1])
|
|
# 4. search and compare the distance
|
|
search_params = {"metric_type": "HAMMING", "params": {"nprobe": 10}}
|
|
res = collection_w.search(binary_vectors[:nq], "binary_vector",
|
|
search_params, default_limit, "int64 >= 0",
|
|
_async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": 2,
|
|
"_async": _async})[0]
|
|
if _async:
|
|
res.done()
|
|
res = res.result()
|
|
assert abs(res[0]._distances[0] - min(distance_0, distance_1)) <= epsilon
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.xfail(reason="issue 6843")
|
|
def test_search_binary_tanimoto_flat_index(self, nq, dim, auto_id, _async):
|
|
"""
|
|
target: search binary_collection, and check the result: distance
|
|
method: compare the return distance value with value computed with TANIMOTO
|
|
expected: the return distance equals to the computed value
|
|
"""
|
|
# 1. initialize with binary data
|
|
collection_w, _, binary_raw_vector, insert_ids = self.init_collection_general(prefix, True, 2,
|
|
is_binary=True,
|
|
auto_id=auto_id,
|
|
dim=dim,
|
|
is_index=True)
|
|
log.info("auto_id= %s, _async= %s" % (auto_id, _async))
|
|
# 2. create index
|
|
default_index = {"index_type": "BIN_IVF_FLAT", "params": {"nlist": 128}, "metric_type": "TANIMOTO"}
|
|
collection_w.create_index("binary_vector", default_index)
|
|
collection_w.load()
|
|
# 3. compute the distance
|
|
query_raw_vector, binary_vectors = cf.gen_binary_vectors(3000, dim)
|
|
distance_0 = cf.tanimoto(query_raw_vector[0], binary_raw_vector[0])
|
|
distance_1 = cf.tanimoto(query_raw_vector[0], binary_raw_vector[1])
|
|
# 4. search and compare the distance
|
|
search_params = {"metric_type": "TANIMOTO", "params": {"nprobe": 10}}
|
|
res = collection_w.search(binary_vectors[:nq], "binary_vector",
|
|
search_params, default_limit, "int64 >= 0",
|
|
_async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": 2,
|
|
"_async": _async})[0]
|
|
if _async:
|
|
res.done()
|
|
res = res.result()
|
|
assert abs(res[0]._distances[0] - min(distance_0, distance_1)) <= epsilon
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("expression", cf.gen_normal_expressions())
|
|
def test_search_with_expression(self, dim, expression, _async):
|
|
"""
|
|
target: test search with different expressions
|
|
method: test search with different expressions
|
|
expected: searched successfully with correct limit(topK)
|
|
"""
|
|
# 1. initialize with data
|
|
nb = 1000
|
|
collection_w, _vectors, _, insert_ids = self.init_collection_general(prefix, True,
|
|
nb, dim=dim,
|
|
is_index=True)
|
|
|
|
# filter result with expression in collection
|
|
_vectors = _vectors[0]
|
|
expression = expression.replace("&&", "and").replace("||", "or")
|
|
filter_ids = []
|
|
for i, _id in enumerate(insert_ids):
|
|
int64 = _vectors.int64[i]
|
|
float = _vectors.float[i]
|
|
if not expression or eval(expression):
|
|
filter_ids.append(_id)
|
|
|
|
# 2. create index
|
|
index_param = {"index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 100}}
|
|
collection_w.create_index("float_vector", index_param)
|
|
collection_w.load()
|
|
|
|
# 3. search with expression
|
|
log.info("test_search_with_expression: searching with expression: %s" % expression)
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
|
|
search_res, _ = collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, nb, expression,
|
|
_async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"ids": insert_ids,
|
|
"limit": min(nb, len(filter_ids)),
|
|
"_async": _async})
|
|
if _async:
|
|
search_res.done()
|
|
search_res = search_res.result()
|
|
|
|
filter_ids_set = set(filter_ids)
|
|
for hits in search_res:
|
|
ids = hits.ids
|
|
assert set(ids).issubset(filter_ids_set)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("expression", cf.gen_normal_expressions_field(default_float_field_name))
|
|
def test_search_with_expression_auto_id(self, dim, expression, _async):
|
|
"""
|
|
target: test search with different expressions
|
|
method: test search with different expressions
|
|
expected: searched successfully with correct limit(topK)
|
|
"""
|
|
# 1. initialize with data
|
|
nb = 1000
|
|
collection_w, _vectors, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
auto_id=True,
|
|
dim=dim,
|
|
is_index=True)
|
|
|
|
|
|
# filter result with expression in collection
|
|
_vectors = _vectors[0]
|
|
expression = expression.replace("&&", "and").replace("||", "or")
|
|
filter_ids = []
|
|
for i, _id in enumerate(insert_ids):
|
|
exec(f"{default_float_field_name} = _vectors.{default_float_field_name}[i]")
|
|
if not expression or eval(expression):
|
|
filter_ids.append(_id)
|
|
|
|
# 2. create index
|
|
index_param = {"index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 100}}
|
|
collection_w.create_index("float_vector", index_param)
|
|
collection_w.load()
|
|
|
|
|
|
# 3. search with different expressions
|
|
log.info("test_search_with_expression: searching with expression: %s" % expression)
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
|
|
search_res, _ = collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, nb, expression,
|
|
_async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"ids": insert_ids,
|
|
"limit": min(nb, len(filter_ids)),
|
|
"_async": _async})
|
|
if _async:
|
|
search_res.done()
|
|
search_res = search_res.result()
|
|
|
|
filter_ids_set = set(filter_ids)
|
|
for hits in search_res:
|
|
ids = hits.ids
|
|
assert set(ids).issubset(filter_ids_set)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_expression_all_data_type(self, nb, nq, dim, auto_id, _async):
|
|
"""
|
|
target: test search using different supported data type
|
|
method: search using different supported data type
|
|
expected: search success
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
is_all_data_type=True,
|
|
auto_id=auto_id,
|
|
dim=dim)
|
|
# 2. search
|
|
log.info("test_search_expression_all_data_type: Searching collection %s" % collection_w.name)
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
search_exp = "int64 >= 0 && int32 >= 0 && int16 >= 0 " \
|
|
"&& int8 >= 0 && float >= 0 && double >= 0"
|
|
res = collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
search_exp, _async=_async,
|
|
output_fields=[default_int64_field_name,
|
|
default_float_field_name],
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})[0]
|
|
if _async:
|
|
res.done()
|
|
res = res.result()
|
|
assert len(res[0][0].entity._row_data) != 0
|
|
assert (default_int64_field_name and default_float_field_name) in res[0][0].entity._row_data
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_with_output_fields_empty(self, nb, nq, dim, auto_id, _async):
|
|
"""
|
|
target: test search with output fields
|
|
method: search with empty output_field
|
|
expected: search success
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
auto_id=auto_id,
|
|
dim=dim)
|
|
# 2. search
|
|
log.info("test_search_with_output_fields_empty: Searching collection %s" % collection_w.name)
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
res = collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, _async=_async,
|
|
output_fields=[],
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})[0]
|
|
if _async:
|
|
res.done()
|
|
res = res.result()
|
|
assert len(res[0][0].entity._row_data) == 0
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_search_with_output_field(self, auto_id, _async):
|
|
"""
|
|
target: test search with output fields
|
|
method: search with one output_field
|
|
expected: search success
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True,
|
|
auto_id=auto_id)
|
|
# 2. search
|
|
log.info("test_search_with_output_field: Searching collection %s" % collection_w.name)
|
|
|
|
res = collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, _async=_async,
|
|
output_fields=[default_int64_field_name],
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})[0]
|
|
if _async:
|
|
res.done()
|
|
res = res.result()
|
|
assert len(res[0][0].entity._row_data) != 0
|
|
assert default_int64_field_name in res[0][0].entity._row_data
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_with_output_fields(self, nb, nq, dim, auto_id, _async):
|
|
"""
|
|
target: test search with output fields
|
|
method: search with multiple output_field
|
|
expected: search success
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
is_all_data_type=True,
|
|
auto_id=auto_id,
|
|
dim=dim)
|
|
# 2. search
|
|
log.info("test_search_with_output_fields: Searching collection %s" % collection_w.name)
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
res = collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, _async=_async,
|
|
output_fields=[default_int64_field_name,
|
|
default_float_field_name],
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})[0]
|
|
if _async:
|
|
res.done()
|
|
res = res.result()
|
|
assert len(res[0][0].entity._row_data) != 0
|
|
assert (default_int64_field_name and default_float_field_name) in res[0][0].entity._row_data
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("output_fields", [["*"], ["*", default_float_field_name]])
|
|
def test_search_with_output_field_wildcard(self, output_fields, auto_id, _async):
|
|
"""
|
|
target: test search with output fields using wildcard
|
|
method: search with one output_field (wildcard)
|
|
expected: search success
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True,
|
|
auto_id=auto_id)
|
|
# 2. search
|
|
log.info("test_search_with_output_field_wildcard: Searching collection %s" % collection_w.name)
|
|
|
|
res = collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, _async=_async,
|
|
output_fields=output_fields,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})[0]
|
|
if _async:
|
|
res.done()
|
|
res = res.result()
|
|
assert len(res[0][0].entity._row_data) != 0
|
|
assert (default_int64_field_name and default_float_field_name) in res[0][0].entity._row_data
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_multi_collections(self, nb, nq, dim, auto_id, _async):
|
|
"""
|
|
target: test search multi collections of L2
|
|
method: add vectors into 10 collections, and search
|
|
expected: search status ok, the length of result
|
|
"""
|
|
self._connect()
|
|
collection_num = 10
|
|
for i in range(collection_num):
|
|
# 1. initialize with data
|
|
log.info("test_search_multi_collections: search round %d" % (i + 1))
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
auto_id=auto_id,
|
|
dim=dim)
|
|
# 2. search
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
log.info("test_search_multi_collections: searching %s entities (nq = %s) from collection %s" %
|
|
(default_limit, nq, collection_w.name))
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_concurrent_multi_threads(self, nb, nq, dim, auto_id, _async):
|
|
"""
|
|
target: test concurrent search with multi-processes
|
|
method: search with 10 processes, each process uses dependent connection
|
|
expected: status ok and the returned vectors should be query_records
|
|
"""
|
|
# 1. initialize with data
|
|
threads_num = 10
|
|
threads = []
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
auto_id=auto_id,
|
|
dim=dim)
|
|
|
|
def search(collection_w):
|
|
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
|
collection_w.search(vectors[:nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, _async=_async,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": nq,
|
|
"ids": insert_ids,
|
|
"limit": default_limit,
|
|
"_async": _async})
|
|
|
|
# 2. search with multi-processes
|
|
log.info("test_search_concurrent_multi_threads: searching with %s processes" % threads_num)
|
|
for i in range(threads_num):
|
|
t = threading.Thread(target=search, args=(collection_w,))
|
|
threads.append(t)
|
|
t.start()
|
|
time.sleep(0.2)
|
|
for t in threads:
|
|
t.join()
|
|
|
|
|
|
|
|
|