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https://gitee.com/milvus-io/milvus.git
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1165 lines
60 KiB
Python
1165 lines
60 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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epsilon = ct.epsilon
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default_nb = ct.default_nb
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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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class TestCollectionSearch(TestcaseBase):
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""" Test case of search interface """
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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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log.info("Test case of search interface: test_search_no_connection")
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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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vectors = [[random.random() for _ in range(default_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,
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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, "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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log.info("Test case of search interface: test_search_no_collection "
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"(searching the non-exist collection)")
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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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vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
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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_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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log.info("Test case of search interface: test_search_release_collection")
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# 1. initialize without data
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collection_w = self.init_collection_general(prefix, True, 10, 1)[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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vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
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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.L1)
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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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log.info("Test case of search interface: test_search_release_partition")
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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, 1000, 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 = 1000
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vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
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collection_w.search(vectors, default_search_field,
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default_search_params, limit, default_search_exp,
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[par_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": "partition has been released"})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_with_empty_collection(self):
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"""
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target: test search with empty connection
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method: search the empty collection
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expected: raise exception and report the error
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"""
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log.info("Test case of search interface: test_search_with_empty_collection")
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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 without data before load
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log.info("test_search_with_empty_collection: Searching empty collection %s"
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% collection_w.name)
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vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
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err_msg = "collection" + collection_w.name + "was not loaded into memory"
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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, timeout=1,
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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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# 3. search collection without data after load
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collection_w.load()
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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,
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check_task=CheckTasks.check_search_results,
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check_items={"nq": default_nq,
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"limit": 0})
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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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log.info("Test case of search interface: test_search_param_missing")
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# 1. initialize without data
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collection_w = self.init_collection_general(prefix, True)[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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@pytest.mark.xfail(reason="issue 6257")
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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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log.info("Test case of search interface: test_search_param_invalid_dim")
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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": "UnexpectedError"})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_param_invalid_metric_type(self):
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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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log.info("Test case of search interface: test_search_param_invalid_metric_type")
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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 metric_type
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log.info("test_search_param_invalid_metric_type: searching with invalid metric_type")
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vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
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search_params = {"metric_type": "L10", "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.L1)
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@pytest.mark.parametrize("limit", [0, 16385])
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def test_search_param_invalid_limit(self, limit):
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"""
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target: test search with invalid parameter values
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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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log.info("Test case of search interface: test_search_param_invalid_limit")
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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 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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vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
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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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@pytest.mark.xfail(reason="issue 6259")
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@pytest.mark.parametrize("invalid_search_field",
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["", " ", "float-vector"])
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def test_search_param_invalid_field(self, invalid_search_field):
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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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log.info("Test case of search interface: test_search_param_invalid_field")
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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 field
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log.info("test_search_param_invalid_field: searching with "
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"invalid field: %s" % invalid_search_field)
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vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
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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": "invalid search field"})
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("invalid_search_expr",
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[" ", "int63 >= 0"])
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def test_search_param_invalid_expr(self, invalid_search_expr):
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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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log.info("Test case of search interface: test_search_param_invalid_expr")
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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 expr
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log.info("test_search_param_invalid_expr: searching with "
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"invalid expr: %s" % invalid_search_expr)
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vectors = [[random.random() for _ in range(default_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, 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.L1)
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def test_search_partition_deleted(self):
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"""
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target: test search deleted partition
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method: 1. search the collection
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2. delete a partition
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3. search the deleted partition
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expected: raise exception and report the error
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"""
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log.info("test_search_partition_deleted: test search after partition deletion")
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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, 1000, partition_num)[0]
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# 2. delete partitions
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log.info("test_search_partition_deleted: deleting a partition")
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par = collection_w.partitions
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deleted_par_name = par[partition_num].name
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collection_w.drop_partition(deleted_par_name)
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log.info("test_search_partition_deleted: deleted a partition")
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collection_w.load()
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# 3. search after delete partitions
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log.info("test_search_partition_deleted: searching deleted partition")
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vectors = [[random.random() for _ in range(default_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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[deleted_par_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": "PartitonName: %s not found" % deleted_par_name})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_index_partition_not_existed(self):
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"""
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target: test search not existed partition
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method: search with not existed partition
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expected: raise exception and report the error
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"""
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log.info("Test case of search interface: test_search_index_partition_not_existed")
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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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vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
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# 2. create index
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default_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
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collection_w.create_index("float_vector", default_index)
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# 3. search the non exist partition
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partition_name = "search_non-exist"
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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": "PartitonName: %s not found" % partition_name})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_param_invalid_binary(self):
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"""
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target: test search within binary data (invalid parameter)
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method: search with wrong metric type
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expected: raise exception and report the error
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"""
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log.info("test_search_param_invalid_binary: test invalid paramter with binary data")
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# 1. initialize with binary data
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collection_w = self.init_collection_general(prefix, True, is_binary=True)[0]
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# 2. create index
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default_index = {"index_type": "BIN_IVF_FLAT", "params": {"nlist": 128}, "metric_type": "JACCARD"}
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collection_w.create_index("binary_vector", default_index)
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# 3. search with exception
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binary_vectors = cf.gen_binary_vectors(3000, default_dim)[1]
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wrong_search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
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collection_w.search(binary_vectors[:default_nq], "binary_vector", wrong_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": "unsupported"})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_binary_flat_with_L2(self):
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"""
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target: search binary collection using FlAT with L2
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method: search binary collection using FLAT with L2
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expected: raise exception and report error
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"""
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log.info("Test case of search interface: test_search_binary_flat_with_L2")
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# 1. initialize with binary data
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collection_w = self.init_collection_general(prefix, True, is_binary=True)[0]
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# 2. search and assert
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query_raw_vector, binary_vectors = cf.gen_binary_vectors(3000, default_dim)
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search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
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collection_w.search(binary_vectors[:default_nq], "binary_vector",
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search_params, default_limit, "int64 >= 0",
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": "Search failed"})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_output_field_vector(self):
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"""
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target: test search with vector as output field
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method: search with one vector output_field
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expected: raise exception and report the error
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"""
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log.info("Test case of search interface: test_search_output_field_vector")
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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
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log.info("test_search_output_field_vector: Searching collection %s" % collection_w.name)
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vectors = [[random.random() for _ in range(default_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,
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default_search_exp, output_fields=[default_search_field],
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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 doesn't support "
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"vector field as output_fields"})
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"""
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******************************************************************
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# The following are valid base cases
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******************************************************************
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"""
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@pytest.mark.tags(CaseLabel.L0)
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def test_search_normal(self):
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"""
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target: test search normal case
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method: create connection, collection, insert and search
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expected: search successfully with limit(topK)
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"""
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log.info("Test case of search interface: test_search_normal")
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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
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log.info("test_search_normal: searching collection %s" % collection_w.name)
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vectors = [[random.random() for _ in range(default_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,
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default_search_exp,
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check_task=CheckTasks.check_search_results,
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check_items={"nq": default_nq,
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"nb": default_nb,
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"limit": default_limit})
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@pytest.mark.tags(CaseLabel.L1)
|
|
def test_search_with_empty_vectors(self):
|
|
"""
|
|
target: test search with empty query vector
|
|
method: search using empty query vector
|
|
expected: search successfully with 0 results
|
|
"""
|
|
log.info("Test case of search interface: test_search_with_empty_vectors")
|
|
# 1. initialize without data
|
|
collection_w = self.init_collection_general(prefix, True)[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,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": 0})
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
def test_search_before_after_delete(self):
|
|
"""
|
|
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
|
|
"""
|
|
log.info("test_search_before_after_delete: test search after deleting entities")
|
|
# 1. initialize with data
|
|
partition_num = 1
|
|
nb = 1000
|
|
limit = 1000
|
|
collection_w = self.init_collection_general(prefix, True, nb, partition_num)[0]
|
|
# 2. search all the partitions before partition deletion
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
log.info("test_search_before_after_delete: searching before deleting partitions")
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, limit, default_search_exp,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": nb,
|
|
"limit": limit})
|
|
# 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
|
|
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[:default_nq], default_search_field,
|
|
default_search_params, limit, default_search_exp,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": nb-deleted_entity_num,
|
|
"limit": limit-deleted_entity_num})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_partition_after_release_one(self):
|
|
"""
|
|
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
|
|
"""
|
|
log.info("test_search_partition_after_release_one: test search after releasing entities")
|
|
# 1. initialize with data
|
|
nb = 1000
|
|
limit = 1000
|
|
partition_num = 1
|
|
collection_w = self.init_collection_general(prefix, True, nb, partition_num)[0]
|
|
# 2. search all the partitions before partition deletion
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
log.info("test_search_partition_after_release_one: searching before deleting partitions")
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, limit, default_search_exp,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": nb,
|
|
"limit": limit})
|
|
# 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
|
|
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[:default_nq], default_search_field,
|
|
default_search_params, limit, default_search_exp,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": nb-deleted_entity_num,
|
|
"limit": limit - deleted_entity_num})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_partition_after_release_all(self):
|
|
"""
|
|
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
|
|
"""
|
|
log.info("test_search_before_after_release: test search after releasing entities")
|
|
# 1. initialize with data
|
|
nb = 1000
|
|
limit = 1000
|
|
collection_w = self.init_collection_general(prefix, True, nb, 1)[0]
|
|
# 2. search all the partitions before partition deletion
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
log.info("test_search_partition_after_release_all: searching before deleting partitions")
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, limit, default_search_exp,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": nb,
|
|
"limit": limit})
|
|
# 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[:default_nq], default_search_field,
|
|
default_search_params, limit, default_search_exp,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"limit": 0})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_collection_after_release_load(self):
|
|
"""
|
|
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
|
|
"""
|
|
log.info("Test case of search interface: test_search_collection_after_release_load")
|
|
# 1. initialize without data
|
|
collection_w = self.init_collection_general(prefix, True, default_nb, 1)[0]
|
|
# 2. release collection
|
|
collection_w.release()
|
|
# 3. Search the pre-released collection after load
|
|
collection_w.load()
|
|
log.info("test_search_partition_after_release_load: searching after load")
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
collection_w.search(vectors[:default_nq], default_search_field, default_search_params,
|
|
default_limit, default_search_exp,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": default_nb,
|
|
"limit": default_limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_partition_after_release_load(self):
|
|
"""
|
|
target: search the pre-released collection after load
|
|
method: 1. create collection
|
|
2. release a partition
|
|
3. load partition
|
|
3. search the pre-released partition
|
|
expected: search successfully
|
|
"""
|
|
log.info("Test case of search interface: test_search_partition_after_release_load")
|
|
# 1. initialize without data
|
|
collection_w = self.init_collection_general(prefix, True, default_nb, 1)[0]
|
|
# 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(default_dim)] for _ in range(default_nq)]
|
|
collection_w.search(vectors[:default_nq], default_search_field, default_search_params,
|
|
limit, default_search_exp,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": default_nb,
|
|
"limit": limit})
|
|
# 4. Search the pre-released partition after load
|
|
collection_w.search(vectors[:default_nq], default_search_field, default_search_params,
|
|
limit, default_search_exp,
|
|
[par[1].name],
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": par[1].num_entities,
|
|
"limit": par[1].num_entities})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_load_flush_load(self):
|
|
"""
|
|
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)
|
|
"""
|
|
log.info("test_search_load_flush_load: search new data without another load")
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix)[0]
|
|
# 2. insert data
|
|
cf.insert_data(collection_w, default_nb)
|
|
# 3. load data
|
|
collection_w.load()
|
|
# 4. flush and load
|
|
collection_w.partitions
|
|
collection_w.load()
|
|
# 5. search for new data without load
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, default_limit, default_search_exp,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": default_nb,
|
|
"limit": default_limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.xfail(reason="issue 5858")
|
|
def test_search_new_data(self):
|
|
"""
|
|
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
|
|
"""
|
|
log.info("test_search_new_data: search new data without another load")
|
|
# 1. initialize with data
|
|
limit = 1000
|
|
nb_old = 500
|
|
collection_w = self.init_collection_general(prefix, True, nb_old)[0]
|
|
# 2. search for original data after load
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
log.info("test_search_new_data: searching for original data after load")
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, limit, default_search_exp,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": nb_old,
|
|
"limit": nb_old})
|
|
# 3. insert new data
|
|
nb_new = 300
|
|
cf.insert_data(collection_w, nb_new, False)
|
|
# 4. search for new data without load
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, limit, default_search_exp,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": nb_old+nb_new,
|
|
"limit": nb_old+nb_new})
|
|
|
|
@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(self, index, params):
|
|
"""
|
|
target: test search with different index
|
|
method: test search with different index
|
|
expected: searched successfully
|
|
"""
|
|
log.info("Test case of search interface: test_search_after_different_index")
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True, partition_num=1)[0]
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
# 2. create different index
|
|
log.info("test_search_after_different_index: 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_after_different_index: Created index-%s" % index)
|
|
# 3. search
|
|
log.info("test_search_after_different_index: Searching after creating index-%s" % index)
|
|
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,
|
|
"nb": default_nb,
|
|
"limit": default_limit})
|
|
|
|
@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, index, params):
|
|
"""
|
|
target: test search with different metric type
|
|
method: test search with different metric type
|
|
expected: searched successfully
|
|
"""
|
|
log.info("Test case of search interface: test_search_after_index_different_metric_type")
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True, partition_num=1)[0]
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
# 2. create different index
|
|
log.info("test_search_after_different_index: 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_different_index: Created index-%s" % index)
|
|
# 3. search
|
|
log.info("test_search_after_different_index: Searching after creating index-%s" % index)
|
|
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,
|
|
"nb": default_nb,
|
|
"limit": default_limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
def test_search_collection_multiple_times(self):
|
|
"""
|
|
target: test search for multiple times
|
|
method: search for multiple times
|
|
expected: searched successfully
|
|
"""
|
|
log.info("Test case of search interface: test_search_collection_multiple_times")
|
|
# 1. initialize with data
|
|
search_num = 5
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
# 2. search for multiple times
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
for i in range(search_num):
|
|
log.info("test_search_collection_multiple_times: searching round %d" % (i+1))
|
|
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,
|
|
"nb": default_nb,
|
|
"limit": default_limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_search_index_one_partition(self):
|
|
"""
|
|
target: test search from partition
|
|
method: search from one partition
|
|
expected: searched successfully
|
|
"""
|
|
log.info("Test case of search interface: test_search_index_one_partition")
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True, partition_num=1)[0]
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_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 in one partition
|
|
log.info("test_search_index_one_partition: searching (1000 entities) through one partition")
|
|
limit = 1000
|
|
par = collection_w.partitions
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, limit, default_search_exp,
|
|
[par[1].name],
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": par[1].num_entities,
|
|
"limit": par[1].num_entities})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_search_index_partitions(self):
|
|
"""
|
|
target: test search from partitions
|
|
method: search from one partitions
|
|
expected: searched successfully
|
|
"""
|
|
log.info("Test case of search interface: test_search_index_partitions")
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True, partition_num=1)[0]
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_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: searching (1000 entities) through partitions")
|
|
par = collection_w.partitions
|
|
log.info("test_search_index_partitions: partitions: %s" % par)
|
|
limit = 1000
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, limit, default_search_exp,
|
|
[par[0].name, par[1].name],
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": default_nb,
|
|
"limit": limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("partition_names",
|
|
[["(.*)"], ["search(.*)"]])
|
|
def test_search_index_partitions_fuzzy(self, partition_names):
|
|
"""
|
|
target: test search from partitions
|
|
method: search from partitions with fuzzy
|
|
partition name
|
|
expected: searched successfully
|
|
"""
|
|
log.info("Test case of search interface: test_search_index_partitions_fuzzy")
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True, partition_num=1)[0]
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_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
|
|
nb = default_nb
|
|
par = collection_w.partitions
|
|
if partition_names == ["search(.*)"]:
|
|
nb = par[1].num_entities
|
|
limit = par[1].num_entities
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, limit, default_search_exp,
|
|
partition_names,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": nb,
|
|
"limit": limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_index_partition_empty(self):
|
|
"""
|
|
target: test search the empty partition
|
|
method: search from the empty partition
|
|
expected: searched successfully with 0 results
|
|
"""
|
|
log.info("Test case of search interface: test_search_index_partition_empty")
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_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[:default_nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, [partition_name],
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"limit": 0})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_binary_jaccard_flat_index(self):
|
|
"""
|
|
target: search binary_collection, and check the result: distance
|
|
method: compare the return distance value with value computed with L2
|
|
expected: the return distance equals to the computed value
|
|
"""
|
|
log.info("Test case of search interface: test_search_binary_jaccard_flat_index")
|
|
# 1. initialize with binary data
|
|
collection_w, _, binary_raw_vector = \
|
|
self.init_collection_general(prefix, True, 2, is_binary=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)
|
|
# 3. compute the distance
|
|
query_raw_vector, binary_vectors = cf.gen_binary_vectors(3000, default_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[:default_nq], "binary_vector",
|
|
search_params, default_limit, "int64 >= 0")[0]
|
|
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):
|
|
"""
|
|
target: search binary_collection, and check the result: distance
|
|
method: compare the return distance value with value computed with L2
|
|
expected: the return distance equals to the computed value
|
|
"""
|
|
log.info("Test case of search interface: test_search_binary_hamming_flat_index")
|
|
# 1. initialize with binary data
|
|
collection_w, _, binary_raw_vector = \
|
|
self.init_collection_general(prefix, True, 2, is_binary=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
|
|
query_raw_vector, binary_vectors = cf.gen_binary_vectors(3000, default_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[:default_nq], "binary_vector",
|
|
search_params, default_limit, "int64 >= 0")[0]
|
|
assert abs(res[0]._distances[0] - min(distance_0, distance_1)) <= epsilon
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_search_binary_tanimoto_flat_index(self):
|
|
"""
|
|
target: search binary_collection, and check the result: distance
|
|
method: compare the return distance value with value computed with Inner product
|
|
expected: the return distance equals to the computed value
|
|
"""
|
|
# 1. initialize with binary data
|
|
collection_w, _, binary_raw_vector = \
|
|
self.init_collection_general(prefix, True, 2, is_binary=True)
|
|
# 2. create index
|
|
default_index = {"index_type": "BIN_IVF_FLAT", "params": {"nlist": 128}, "metric_type": "TANIMOTO"}
|
|
collection_w.create_index("binary_vector", default_index)
|
|
# 3. compute the distance
|
|
query_raw_vector, binary_vectors = cf.gen_binary_vectors(3000, default_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[:default_nq], "binary_vector",
|
|
search_params, default_limit, "int64 >= 0")[0]
|
|
assert abs(res[0]._distances[0] - min(distance_0, distance_1)) <= epsilon
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("expression, limit",
|
|
zip(cf.gen_normal_expressions(),
|
|
[1000, 999, 898, 997, 2, 3]))
|
|
def test_search_with_expression(self, expression, limit):
|
|
"""
|
|
target: test search with different expressions
|
|
method: test search with different expressions
|
|
expected: searched successfully with correct limit(topK)
|
|
"""
|
|
log.info("Test case of search interface: test_search_with_expression")
|
|
# 1. initialize with data
|
|
nb = 1000
|
|
collection_w = self.init_collection_general(prefix, True, nb)[0]
|
|
# 2. create index
|
|
index_param = {"index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 100}}
|
|
collection_w.create_index("float_vector", index_param)
|
|
# 3. search with different expressions
|
|
log.info("test_search_with_expression: searching with expression: %s" % expression)
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, nb, expression,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": nb,
|
|
"limit": limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_search_with_output_fields_empty(self):
|
|
"""
|
|
target: test search with output fields
|
|
method: search with empty output_field
|
|
expected: search success
|
|
"""
|
|
log.info("Test case of search interface: test_search_with_output_fields_empty")
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
# 2. search
|
|
log.info("test_search_with_output_fields_empty: Searching collection %s" % collection_w.name)
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
res = collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, output_fields=[],
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": default_nb,
|
|
"limit": default_limit})[0]
|
|
assert len(res[0][0].entity._row_data) == 0
|
|
|
|
@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
|
|
"""
|
|
log.info("Test case of search interface: test_search_with_output_fields_not_exist")
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
# 2. search
|
|
log.info("test_search_with_output_fields_not_exist: Searching collection %s" % collection_w.name)
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
res = collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, output_fields=["int63"],
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": default_nb,
|
|
"limit": default_limit})[0]
|
|
assert len(res[0][0].entity._row_data) == 0
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_search_with_output_field(self):
|
|
"""
|
|
target: test search with output fields
|
|
method: search with one output_field
|
|
expected: search success
|
|
"""
|
|
log.info("Test case of search interface: test_search_with_output_field")
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
# 2. search
|
|
log.info("test_search_with_output_field: Searching collection %s" % collection_w.name)
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
res = collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp, output_fields=[default_int64_field_name],
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": default_nb,
|
|
"limit": default_limit})[0]
|
|
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):
|
|
"""
|
|
target: test search with output fields
|
|
method: search with multiple output_field
|
|
expected: search success
|
|
"""
|
|
log.info("Test case of search interface: test_search_with_output_fields")
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True, is_all_data_type=True)[0]
|
|
# 2. search
|
|
log.info("test_search_with_output_fields: Searching collection %s" % collection_w.name)
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
res = collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, default_limit,
|
|
default_search_exp,
|
|
output_fields=[default_int64_field_name,
|
|
default_float_field_name],
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": default_nb,
|
|
"limit": default_limit})[0]
|
|
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_expression_all_data_type(self):
|
|
"""
|
|
target: test search using different supported data type
|
|
method: search using different supported data type
|
|
expected: search success
|
|
"""
|
|
log.info("Test case of search interface: test_search_expression_all_data_type")
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True, is_all_data_type=True)[0]
|
|
# 2. search
|
|
log.info("test_search_expression_all_data_type: Searching collection %s" % collection_w.name)
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
search_exp = "int64 >= 0 && int32 >= 0 && int16 >= 0 " \
|
|
"&& int8 >= 0 && float >= 0 && double >= 0"
|
|
res = collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, default_limit, search_exp,
|
|
output_fields=[default_int64_field_name,
|
|
default_float_field_name],
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": default_nb,
|
|
"limit": default_limit})[0]
|
|
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):
|
|
"""
|
|
target: test search multi collections of L2
|
|
method: add vectors into 10 collections, and search
|
|
expected: search status ok, the length of result
|
|
"""
|
|
log.info("Test case of search interface: test_search_multi_collections")
|
|
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 = self.init_collection_general(prefix, True)[0]
|
|
# 2. search
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
log.info("test_search_multi_collections: searching %s entities (nq = %s) from collection %s" %
|
|
(default_limit, default_nq, collection_w.name))
|
|
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,
|
|
"nb": default_nb,
|
|
"limit": default_limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.timeout(300)
|
|
def test_search_concurrent_multi_threads(self):
|
|
"""
|
|
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
|
|
"""
|
|
log.info("Test case of search interface: test_search_concurrent_multi_threads")
|
|
threads_num = 10
|
|
threads = []
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
|
|
def search(collection_w):
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
|
collection_w.search(vectors[:default_nq], default_search_field,
|
|
default_search_params, default_limit, default_search_exp,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": default_nq,
|
|
"nb": default_nb,
|
|
"limit": default_limit})
|
|
|
|
# 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()
|
|
log.info("test_search_concurrent_multi_threads: searched with %s processes" % threads_num)
|
|
|
|
|
|
|