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milvus/tests/python_client/testcases/test_search.py
binbin 617e029dd3
Add test cases for search without flush (#17844) 
Signed-off-by: Binbin Lv <binbin.lv@zilliz.com>
2022-06-28 20:00:24 +08:00

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import multiprocessing
import numbers
import pytest
from time import sleep
from base.client_base import TestcaseBase
from utils.util_log import test_log as log
from common import common_func as cf
from common import common_type as ct
from common.common_type import CaseLabel, CheckTasks
from utils.util_pymilvus import *
from common.constants import *
from pymilvus.orm.types import CONSISTENCY_STRONG, CONSISTENCY_BOUNDED, CONSISTENCY_SESSION, CONSISTENCY_EVENTUALLY
prefix = "search_collection"
search_num = 10
max_dim = ct.max_dim
epsilon = ct.epsilon
gracefulTime = ct.gracefulTime
default_nb = ct.default_nb
default_nb_medium = ct.default_nb_medium
default_nq = ct.default_nq
default_dim = ct.default_dim
default_limit = ct.default_limit
default_search_exp = "int64 >= 0"
default_search_string_exp = "varchar >= \"0\""
default_search_mix_exp = "int64 >= 0 && varchar >= \"0\""
default_invaild_string_exp = "varchar >= 0"
perfix_expr = 'varchar like "0%"'
default_search_field = ct.default_float_vec_field_name
default_search_params = ct.default_search_params
default_int64_field_name = ct.default_int64_field_name
default_float_field_name = ct.default_float_field_name
default_bool_field_name = ct.default_bool_field_name
default_string_field_name = ct.default_string_field_name
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
uid = "test_search"
nq = 1
epsilon = 0.001
field_name = default_float_vec_field_name
binary_field_name = default_binary_vec_field_name
search_param = {"nprobe": 1}
entity = gen_entities(1, is_normal=True)
entities = gen_entities(default_nb, is_normal=True)
raw_vectors, binary_entities = gen_binary_entities(default_nb)
default_query, _ = gen_search_vectors_params(field_name, entities, default_top_k, nq)
index_name1=cf.gen_unique_str("float")
index_name2=cf.gen_unique_str("varhar")
class TestCollectionSearchInvalid(TestcaseBase):
""" Test case of search interface """
@pytest.fixture(scope="function", params=ct.get_invalid_vectors)
def get_invalid_vectors(self, request):
yield request.param
@pytest.fixture(scope="function", params=ct.get_invalid_strs)
def get_invalid_fields_type(self, request):
if isinstance(request.param, str):
pytest.skip("string is valid type for field")
yield request.param
@pytest.fixture(scope="function", params=ct.get_invalid_strs)
def get_invalid_fields_value(self, request):
if not isinstance(request.param, str):
pytest.skip("field value only support string")
if request.param == "":
pytest.skip("empty field is valid")
yield request.param
@pytest.fixture(scope="function", params=ct.get_invalid_strs)
def get_invalid_metric_type(self, request):
yield request.param
@pytest.fixture(scope="function", params=ct.get_invalid_ints)
def get_invalid_limit(self, request):
if isinstance(request.param, int) and request.param >= 0:
pytest.skip("positive int is valid type for limit")
yield request.param
@pytest.fixture(scope="function", params=ct.get_invalid_strs)
def get_invalid_expr_type(self, request):
if isinstance(request.param, str):
pytest.skip("string is valid type for expr")
if request.param is None:
pytest.skip("None is valid for expr")
yield request.param
@pytest.fixture(scope="function", params=ct.get_invalid_strs)
def get_invalid_expr_value(self, request):
if not isinstance(request.param, str):
pytest.skip("expression value only support string")
if request.param == "":
pytest.skip("empty field is valid")
yield request.param
@pytest.fixture(scope="function", params=ct.get_invalid_strs)
def get_invalid_expr_bool_value(self, request):
yield request.param
@pytest.fixture(scope="function", params=ct.get_invalid_strs)
def get_invalid_partition(self, request):
if request.param == []:
pytest.skip("empty is valid for partition")
if request.param is None:
pytest.skip("None is valid for partition")
yield request.param
@pytest.fixture(scope="function", params=ct.get_invalid_strs)
def get_invalid_output_fields(self, request):
if request.param == []:
pytest.skip("empty is valid for output_fields")
if request.param is None:
pytest.skip("None is valid for output_fields")
yield request.param
@pytest.fixture(scope="function", params=ct.get_invalid_ints)
def get_invalid_travel_timestamp(self, request):
if request.param == 9999999999:
pytest.skip("9999999999 is valid for travel timestamp")
yield request.param
@pytest.fixture(scope="function", params=ct.get_invalid_ints)
def get_invalid_guarantee_timestamp(self, request):
if request.param == 9999999999:
pytest.skip("9999999999 is valid for guarantee_timestamp")
yield request.param
"""
******************************************************************
# The followings are invalid cases
******************************************************************
"""
@pytest.mark.tags(CaseLabel.L1)
def test_search_no_connection(self):
"""
target: test search without connection
method: create and delete connection, then search
expected: raise exception and report the error
"""
# 1. initialize with data
collection_w = self.init_collection_general(prefix)[0]
# 2. remove connection
log.info("test_search_no_connection: removing connection")
self.connection_wrap.remove_connection(alias='default')
log.info("test_search_no_connection: removed connection")
# 3. search without connection
log.info("test_search_no_connection: searching without connection")
collection_w.search(vectors[:default_nq], default_search_field,
default_search_params, default_limit,
default_search_exp,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": "should create connect first"})
@pytest.mark.tags(CaseLabel.L1)
def test_search_no_collection(self):
"""
target: test the scenario which search the non-exist collection
method: 1. create collection
2. drop collection
3. search the dropped collection
expected: raise exception and report the error
"""
# 1. initialize without data
collection_w = self.init_collection_general(prefix)[0]
# 2. Drop collection
collection_w.drop()
# 3. Search without collection
log.info("test_search_no_collection: Searching without collection ")
collection_w.search(vectors, default_search_field,
default_search_params, default_limit, default_search_exp,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": "collection %s doesn't exist!" % collection_w.name})
@pytest.mark.tags(CaseLabel.L2)
def test_search_param_missing(self):
"""
target: test search with incomplete parameters
method: search with incomplete parameters
expected: raise exception and report the error
"""
# 1. initialize without data
collection_w = self.init_collection_general(prefix)[0]
# 2. search collection with missing parameters
log.info("test_search_param_missing: Searching collection %s "
"with missing parameters" % collection_w.name)
try:
collection_w.search()
except TypeError as e:
assert "missing 4 required positional arguments: 'data', " \
"'anns_field', 'param', and 'limit'" in str(e)
@pytest.mark.tags(CaseLabel.L2)
def test_search_param_invalid_vectors(self, get_invalid_vectors):
"""
target: test search with invalid parameter values
method: search with invalid data
expected: raise exception and report the error
"""
# 1. initialize with data
collection_w = self.init_collection_general(prefix)[0]
# 2. search with invalid field
invalid_vectors = get_invalid_vectors
log.info("test_search_param_invalid_vectors: searching with "
"invalid vectors: {}".format(invalid_vectors))
collection_w.search(invalid_vectors, default_search_field, default_search_params,
default_limit, default_search_exp,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": "`search_data` value {} is illegal".format(invalid_vectors)})
@pytest.mark.tags(CaseLabel.L2)
def test_search_param_invalid_dim(self):
"""
target: test search with invalid parameter values
method: search with invalid dim
expected: raise exception and report the error
"""
# 1. initialize with data
collection_w = self.init_collection_general(prefix, True)[0]
# 2. search with invalid dim
log.info("test_search_param_invalid_dim: searching with invalid dim")
wrong_dim = 129
vectors = [[random.random() for _ in range(wrong_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.err_res,
check_items={"err_code": 1,
"err_msg": "The dimension of query entities "
"is different from schema"})
@pytest.mark.tags(CaseLabel.L2)
def test_search_param_invalid_field_type(self, get_invalid_fields_type):
"""
target: test search with invalid parameter type
method: search with invalid field type
expected: raise exception and report the error
"""
# 1. initialize with data
collection_w = self.init_collection_general(prefix)[0]
# 2. search with invalid field
invalid_search_field = get_invalid_fields_type
log.info("test_search_param_invalid_field_type: searching with "
"invalid field: %s" % invalid_search_field)
collection_w.search(vectors[:default_nq], invalid_search_field, default_search_params,
default_limit, default_search_exp,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": "`anns_field` value {} is illegal".format(invalid_search_field)})
@pytest.mark.tags(CaseLabel.L2)
def test_search_param_invalid_field_value(self, get_invalid_fields_value):
"""
target: test search with invalid parameter values
method: search with invalid field value
expected: raise exception and report the error
"""
# 1. initialize with data
collection_w = self.init_collection_general(prefix)[0]
# 2. search with invalid field
invalid_search_field = get_invalid_fields_value
log.info("test_search_param_invalid_field_value: searching with "
"invalid field: %s" % invalid_search_field)
collection_w.search(vectors[:default_nq], invalid_search_field, default_search_params,
default_limit, default_search_exp,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": "Field %s doesn't exist in schema"
% invalid_search_field})
@pytest.mark.tags(CaseLabel.L2)
def test_search_param_invalid_metric_type(self, get_invalid_metric_type):
"""
target: test search with invalid parameter values
method: search with invalid metric type
expected: raise exception and report the error
"""
# 1. initialize with data
collection_w = self.init_collection_general(prefix, True, 10)[0]
# 2. search with invalid metric_type
log.info("test_search_param_invalid_metric_type: searching with invalid metric_type")
invalid_metric = get_invalid_metric_type
search_params = {"metric_type": invalid_metric, "params": {"nprobe": 10}}
collection_w.search(vectors[:default_nq], default_search_field, search_params,
default_limit, default_search_exp,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": "metric type not found"})
@pytest.mark.tags(CaseLabel.L2)
@pytest.mark.parametrize("index, params",
zip(ct.all_index_types[:9],
ct.default_index_params[:9]))
def test_search_invalid_params_type(self, index, params):
"""
target: test search with invalid search params
method: test search with invalid params type
expected: raise exception and report the error
"""
if index == "FLAT":
pytest.skip("skip in FLAT index")
# 1. initialize with data
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, 5000,
is_index=True)[0:4]
# 2. create index and load
default_index = {"index_type": index, "params": params, "metric_type": "L2"}
collection_w.create_index("float_vector", default_index)
collection_w.load()
# 3. search
invalid_search_params = cf.gen_invaild_search_params_type()
message = "Search params check failed"
for invalid_search_param in invalid_search_params:
if index == invalid_search_param["index_type"]:
search_params = {"metric_type": "L2", "params": invalid_search_param["search_params"]}
collection_w.search(vectors[:default_nq], default_search_field,
search_params, default_limit,
default_search_exp,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": message})
@pytest.mark.tags(CaseLabel.L2)
def test_search_param_invalid_limit_type(self, get_invalid_limit):
"""
target: test search with invalid limit type
method: search with invalid limit type
expected: raise exception and report the error
"""
# 1. initialize with data
collection_w = self.init_collection_general(prefix)[0]
# 2. search with invalid field
invalid_limit = get_invalid_limit
log.info("test_search_param_invalid_limit_type: searching with "
"invalid limit: %s" % invalid_limit)
collection_w.search(vectors[:default_nq], default_search_field, default_search_params,
invalid_limit, default_search_exp,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": "`limit` value %s is illegal" % invalid_limit})
@pytest.mark.tags(CaseLabel.L2)
@pytest.mark.parametrize("limit", [0, 16385])
def test_search_param_invalid_limit_value(self, limit):
"""
target: test search with invalid limit value
method: search with invalid limit: 0 and maximum
expected: raise exception and report the error
"""
# 1. initialize with data
collection_w = self.init_collection_general(prefix)[0]
# 2. search with invalid limit (topK)
log.info("test_search_param_invalid_limit_value: searching with "
"invalid limit (topK) = %s" % limit)
err_msg = "limit %d is too large!" % limit
if limit == 0:
err_msg = "`limit` value 0 is illegal"
collection_w.search(vectors[:default_nq], default_search_field, default_search_params,
limit, default_search_exp,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": err_msg})
@pytest.mark.tags(CaseLabel.L2)
def test_search_param_invalid_expr_type(self, get_invalid_expr_type):
"""
target: test search with invalid parameter type
method: search with invalid search expressions
expected: raise exception and report the error
"""
# 1. initialize with data
collection_w = self.init_collection_general(prefix)[0]
# 2 search with invalid expr
invalid_search_expr = get_invalid_expr_type
log.info("test_search_param_invalid_expr_type: searching with "
"invalid expr: {}".format(invalid_search_expr))
collection_w.search(vectors[:default_nq], default_search_field,
default_search_params, default_limit, invalid_search_expr,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": "The type of expr must be string ,"
"but {} is given".format(type(invalid_search_expr))})
@pytest.mark.tags(CaseLabel.L2)
def test_search_param_invalid_expr_value(self, get_invalid_expr_value):
"""
target: test search with invalid parameter values
method: search with invalid search expressions
expected: raise exception and report the error
"""
# 1. initialize with data
collection_w = self.init_collection_general(prefix)[0]
# 2 search with invalid expr
invalid_search_expr = get_invalid_expr_value
log.info("test_search_param_invalid_expr_value: searching with "
"invalid expr: %s" % invalid_search_expr)
collection_w.search(vectors[:default_nq], default_search_field,
default_search_params, default_limit, invalid_search_expr,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": "invalid expression %s"
% invalid_search_expr})
@pytest.mark.tags(CaseLabel.L2)
def test_search_param_invalid_expr_bool(self, get_invalid_expr_bool_value):
"""
target: test search with invalid parameter values
method: search with invalid bool search expressions
expected: raise exception and report the error
"""
# 1. initialize with data
collection_w = self.init_collection_general(prefix, True, is_all_data_type=True)[0]
# 2 search with invalid bool expr
invalid_search_expr_bool = f"{default_bool_field_name} == {get_invalid_expr_bool_value}"
log.info("test_search_param_invalid_expr_bool: searching with "
"invalid expr: %s" % invalid_search_expr_bool)
collection_w.search(vectors[:default_nq], default_search_field,
default_search_params, default_limit, invalid_search_expr_bool,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": "failed to create query plan"})
@pytest.mark.tags(CaseLabel.L2)
def test_search_partition_invalid_type(self, get_invalid_partition):
"""
target: test search invalid partition
method: search with invalid partition type
expected: raise exception and report the error
"""
# 1. initialize with data
collection_w = self.init_collection_general(prefix)[0]
# 2. search the invalid partition
partition_name = get_invalid_partition
err_msg = "`partition_name_array` value {} is illegal".format(partition_name)
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": err_msg})
@pytest.mark.tags(CaseLabel.L2)
def test_search_with_output_fields_invalid_type(self, get_invalid_output_fields):
"""
target: test search with output fields
method: search with invalid output_field
expected: raise exception and report the error
"""
# 1. initialize with data
collection_w = self.init_collection_general(prefix)[0]
# 2. search
log.info("test_search_with_output_fields_invalid_type: Searching collection %s" % collection_w.name)
output_fields = get_invalid_output_fields
err_msg = "`output_fields` value {} is illegal".format(output_fields)
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={ct.err_code: 1,
ct.err_msg: err_msg})
@pytest.mark.tags(CaseLabel.L1)
def test_search_release_collection(self):
"""
target: test the scenario which search the released collection
method: 1. create collection
2. release collection
3. search the released collection
expected: raise exception and report the error
"""
# 1. initialize without data
collection_w = self.init_collection_general(prefix, True, 10)[0]
# 2. release collection
collection_w.release()
# 3. Search the released collection
log.info("test_search_release_collection: Searching without collection ")
collection_w.search(vectors, default_search_field,
default_search_params, default_limit, default_search_exp,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": "collection %s was not loaded "
"into memory" % collection_w.name})
@pytest.mark.tags(CaseLabel.L2)
def test_search_release_partition(self):
"""
target: test the scenario which search the released collection
method: 1. create collection
2. release partition
3. search the released partition
expected: raise exception and report the error
"""
# 1. initialize with data
partition_num = 1
collection_w = self.init_collection_general(prefix, True, 10, partition_num, is_index=True)[0]
par = collection_w.partitions
par_name = par[partition_num].name
par[partition_num].load()
# 2. release partition
par[partition_num].release()
# 3. Search the released partition
log.info("test_search_release_partition: Searching the released partition")
limit = 10
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.skip("enable this later using session/strong consistency")
@pytest.mark.tags(CaseLabel.L1)
def test_search_with_empty_collection(self):
"""
target: test search with empty connection
method: 1. search the empty collection before load
2. search the empty collection after load
3. search collection with data inserted but not load again
expected: 1. raise exception if not loaded
2. return topk=0 if loaded
3. return topk successfully
"""
# 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})
# 4. search with data inserted but not load again
data = cf.gen_default_dataframe_data(nb=2000)
insert_res = collection_w.insert(data)[0]
# Using bounded staleness, maybe we cannot search the "inserted" requests,
# since the search requests arrived query nodes earlier than query nodes consume the insert requests.
collection_w.search(vectors[:default_nq], default_search_field, default_search_params,
default_limit, default_search_exp,
guarantee_timestamp=insert_res.timestamp,
check_task=CheckTasks.check_search_results,
check_items={"nq": default_nq,
"ids": insert_res.primary_keys,
"limit": default_limit})
@pytest.mark.tags(CaseLabel.L2)
def test_search_with_empty_collection_with_partition(self):
"""
target: test search with empty collection
method: 1. collection an empty collection with partitions
2. load
3. search
expected: return 0 result
"""
# 1. initialize without data
collection_w = self.init_collection_general(prefix, partition_num=1)[0]
par = collection_w.partitions
# 2. 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})
# 2. search a partition without data after load
collection_w.search(vectors[:default_nq], default_search_field, default_search_params,
default_limit, default_search_exp,
[par[1].name],
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. create a collection with partitions
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, index, params):
"""
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,
is_index=True)[0:4]
# 2. create different index
if params.get("m"):
if (default_dim % params["m"]) != 0:
params["m"] = default_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, default_search_field,
default_search_params, default_limit,
default_search_exp,
check_task=CheckTasks.check_search_results,
check_items={"nq": default_nq,
"ids": insert_ids,
"limit": default_limit})
@pytest.mark.tags(CaseLabel.L2)
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.L2)
@pytest.mark.xfail(reason="issue 15407")
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)
@pytest.mark.xfail(reason="issue 15407")
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.L2)
def test_search_with_output_fields_not_exist(self):
"""
target: test search with output fields
method: search with non-exist output_field
expected: raise exception
"""
# 1. initialize with data
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True)[0:4]
# 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_invalid_wildcard: 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"})
@pytest.mark.tags(CaseLabel.L2)
def test_search_param_invalid_travel_timestamp(self, get_invalid_travel_timestamp):
"""
target: test search with invalid travel timestamp
method: search with invalid travel timestamp
expected: raise exception and report the error
"""
# 1. initialize with data
collection_w = self.init_collection_general(prefix, True, 10)[0]
# 2. search with invalid travel timestamp
log.info("test_search_param_invalid_travel_timestamp: searching with invalid travel timestamp")
invalid_travel_time = get_invalid_travel_timestamp
collection_w.search(vectors[:default_nq], default_search_field, default_search_params,
default_limit, default_search_exp,
travel_timestamp=invalid_travel_time,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": "`travel_timestamp` value %s is illegal" % invalid_travel_time})
@pytest.mark.tags(CaseLabel.L2)
def test_search_param_invalid_guarantee_timestamp(self, get_invalid_guarantee_timestamp):
"""
target: test search with invalid guarantee timestamp
method: search with invalid guarantee timestamp
expected: raise exception and report the error
"""
# 1. initialize with data
collection_w = self.init_collection_general(prefix, True, 10)[0]
# 2. search with invalid travel timestamp
log.info("test_search_param_invalid_guarantee_timestamp: searching with invalid guarantee timestamp")
invalid_guarantee_time = get_invalid_guarantee_timestamp
collection_w.search(vectors[:default_nq], default_search_field, default_search_params,
default_limit, default_search_exp,
guarantee_timestamp=invalid_guarantee_time,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": "`guarantee_timestamp` value %s is illegal"
% invalid_guarantee_time})
@pytest.mark.tags(CaseLabel.L2)
@pytest.mark.parametrize("round_decimal", [7, -2, 999, 1.0, None, [1], "string", {}])
def test_search_invalid_round_decimal(self, round_decimal):
"""
target: test search with invalid round decimal
method: search with invalid round decimal
expected: raise exception and report the error
"""
# 1. initialize with data
collection_w = self.init_collection_general(prefix, True, nb=10)[0]
# 2. search
log.info("test_search_invalid_round_decimal: Searching collection %s" % collection_w.name)
collection_w.search(vectors[:default_nq], default_search_field,
default_search_params, default_limit,
default_search_exp, round_decimal=round_decimal,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": f"`round_decimal` value {round_decimal} is illegal"})
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
@pytest.fixture(scope="function", params=["JACCARD", "HAMMING", "TANIMOTO"])
def metrics(self, request):
yield request.param
@pytest.fixture(scope="function", params=[False, True])
def is_flush(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, is_flush):
"""
target: test search normal case
method: create connection, collection, insert and search
expected: 1. search returned with 0 before travel timestamp
2. search successfully with limit(topK) after travel timestamp
"""
# 1. initialize with data
collection_w, _, _, insert_ids, time_stamp = \
self.init_collection_general(prefix, True, auto_id=auto_id, dim=dim, is_flush=is_flush)[0:5]
# 2. search before insert time_stamp
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,
travel_timestamp=time_stamp - 1,
check_task=CheckTasks.check_search_results,
check_items={"nq": nq,
"ids": [],
"limit": 0})
# 3. search after insert time_stamp
collection_w.search(vectors[:nq], default_search_field,
default_search_params, default_limit,
default_search_exp,
travel_timestamp=time_stamp,
guarantee_timestamp=0,
check_task=CheckTasks.check_search_results,
check_items={"nq": nq,
"ids": insert_ids,
"limit": default_limit})
@pytest.mark.tags(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)[0:4]
# get vectors that inserted into collection
vectors = np.array(_vectors[0]).tolist()
vectors = [vectors[i][-1] for i in range(nq)]
log.info("test_search_with_hit_vectors: searching collection %s" % collection_w.name)
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})
log.info("test_search_with_hit_vectors: checking the distance of top 1")
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)
@pytest.mark.parametrize("dup_times", [1, 2, 3])
def test_search_with_dup_primary_key(self, dim, auto_id, _async, dup_times):
"""
target: test search with duplicate primary key
method: 1.insert same data twice
2.search
expected: search results are de-duplicated
"""
# initialize with data
nb = ct.default_nb
nq = ct.default_nq
collection_w, insert_data, _, insert_ids = self.init_collection_general(prefix, True, nb,
auto_id=auto_id,
dim=dim)[0:4]
# insert dup data multi times
for i in range(dup_times):
insert_res, _ = collection_w.insert(insert_data[0])
insert_ids.extend(insert_res.primary_keys)
# search
vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
search_res, _ = 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})
if _async:
search_res.done()
search_res = search_res.result()
# assert that search results are de-duplicated
for hits in search_res:
ids = hits.ids
assert sorted(list(set(ids))) == sorted(ids)
@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)
def test_search_with_ndarray(self, dim, auto_id, _async):
"""
target: test search with ndarray
method: search using ndarray data
expected: search successfully
"""
# 1. initialize without data
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True,
auto_id=auto_id,
dim=dim)[0:4]
# 2. search collection without data
log.info("test_search_with_ndarray: Searching collection %s "
"using ndarray" % collection_w.name)
vectors = np.random.randn(default_nq, dim)
collection_w.search(vectors, default_search_field, default_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.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)[0:4]
# 2. search
log.info("test_search_normal_default_params: 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,
travel_timestamp=0,
check_task=CheckTasks.check_search_results,
check_items={"nq": default_nq,
"ids": insert_ids,
"limit": default_limit,
"_async": _async})
@pytest.mark.tags(CaseLabel.L1)
@pytest.mark.xfail(reason="issue #13611")
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)[0:4]
# 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
print(deleted_entity_num)
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.L1)
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, time_stamp = self.init_collection_general(prefix, True, nb,
1, auto_id=auto_id,
dim=dim)[0:5]
# 2. release collection
log.info("test_search_collection_after_release_load: releasing collection %s" % collection_w.name)
collection_w.release()
log.info("test_search_collection_after_release_load: released collection %s" % collection_w.name)
# 3. Search the pre-released collection after load
log.info("test_search_collection_after_release_load: loading collection %s" % collection_w.name)
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,
travel_timestamp=time_stamp,
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_load_flush_load(self, nb, nq, dim, auto_id, _async):
"""
target: test search when load before flush
method: 1. insert data and load
2. flush, and load
3. search the collection
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
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.skip("enable this later using session/strong consistency")
@pytest.mark.tags(CaseLabel.L1)
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
4. Use guarantee_timestamp to guarantee data consistency
expected: new data should be searched
"""
# 1. initialize with data
limit = 1000
nb_old = 500
collection_w, _, _, insert_ids, time_stamp = self.init_collection_general(prefix, True, nb_old,
auto_id=auto_id,
dim=dim)[0:5]
# 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,
travel_timestamp=time_stamp + 1,
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, time_stamp = cf.insert_data(collection_w, nb_new,
auto_id=auto_id, dim=dim,
insert_offset=nb_old)
insert_ids.extend(insert_ids_new)
# 4. search for new data without load
# Using bounded staleness, maybe we could not search the "inserted" entities,
# since the search requests arrived query nodes earlier than query nodes consume the insert requests.
collection_w.search(vectors[:nq], default_search_field,
default_search_params, limit,
default_search_exp, _async=_async,
guarantee_timestamp=time_stamp,
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)
@pytest.mark.skip(reason="debug")
def test_search_max_dim(self, auto_id, _async):
"""
target: test search with max configuration
method: create connection, collection, insert and search with max dim
expected: search successfully with limit(topK)
"""
# 1. initialize with data
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, 100,
auto_id=auto_id,
dim=max_dim)[0:4]
# 2. search
nq = 2
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, nq,
default_search_exp, _async=_async,
check_task=CheckTasks.check_search_results,
check_items={"nq": nq,
"ids": insert_ids,
"limit": nq,
"_async": _async})
@pytest.mark.tags(CaseLabel.L1)
@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 after different index
method: test search after different index and corresponding search params
expected: search successfully with limit(topK)
"""
# 1. initialize with data
collection_w, _, _, insert_ids, time_stamp = self.init_collection_general(prefix, True, 5000,
partition_num=1,
auto_id=auto_id,
dim=dim, is_index=True)[0:5]
# 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,
travel_timestamp=time_stamp,
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, time_stamp = self.init_collection_general(prefix, True, 5000,
partition_num=1,
auto_id=auto_id,
dim=dim, is_index=True)[0:5]
# 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,
travel_timestamp=time_stamp,
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)[0:4]
# 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, time_stamp = self.init_collection_general(prefix, True, nb,
auto_id=auto_id,
dim=dim)[0:5]
# 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,
travel_timestamp=time_stamp,
check_task=CheckTasks.check_search_results,
check_items={"nq": nq,
"ids": insert_ids,
"limit": default_limit,
"_async": _async})
@pytest.mark.tags(CaseLabel.L1)
@pytest.mark.skip(reason="issue #12680")
# TODO: add one more for binary vectors
# @pytest.mark.parametrize("vec_fields", [[cf.gen_float_vec_field(name="test_vector1")],
# [cf.gen_binary_vec_field(name="test_vector1")],
# [cf.gen_binary_vec_field(), cf.gen_binary_vec_field("test_vector1")]])
def test_search_multiple_vectors_with_one_indexed(self):
"""
target: test indexing on one vector fields when there are multi float vec fields
method: 1. create collection with multiple float vector fields
2. insert data and build index on one of float vector fields
3. load collection and search
expected: load and search successfully
"""
vec_fields = [cf.gen_float_vec_field(name="test_vector1")]
schema = cf.gen_schema_multi_vector_fields(vec_fields)
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix), schema=schema)
df = cf.gen_dataframe_multi_vec_fields(vec_fields=vec_fields)
collection_w.insert(df)
assert collection_w.num_entities == ct.default_nb
_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
res, ch = collection_w.create_index(field_name="test_vector1", index_params=_index)
assert ch is True
collection_w.load()
vectors = [[random.random() for _ in range(default_dim)] for _ in range(2)]
search_params = {"metric_type": "L2", "params": {"nprobe": 16}}
res_1, _ = collection_w.search(data=vectors, anns_field="test_vector1",
param=search_params, limit=1)
@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, time_stamp = self.init_collection_general(prefix, True, nb,
partition_num=1,
auto_id=auto_id,
is_index=True)[0:5]
# 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,
travel_timestamp=time_stamp,
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)[0:4]
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)[0:4]
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)
@pytest.mark.parametrize("index", ["BIN_FLAT", "BIN_IVF_FLAT"])
def test_search_binary_jaccard_flat_index(self, nq, dim, auto_id, _async, index, is_flush):
"""
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, time_stamp = self.init_collection_general(prefix, True, 2,
is_binary=True,
auto_id=auto_id,
dim=dim,
is_index=True,
is_flush=is_flush)[0:5]
# 2. create index
default_index = {"index_type": index, "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,
travel_timestamp=time_stamp,
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.parametrize("index", ["BIN_FLAT", "BIN_IVF_FLAT"])
def test_search_binary_hamming_flat_index(self, nq, dim, auto_id, _async, index, is_flush):
"""
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,
is_flush=is_flush)[0:4]
# 2. create index
default_index = {"index_type": index, "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")
@pytest.mark.parametrize("index", ["BIN_FLAT", "BIN_IVF_FLAT"])
def test_search_binary_tanimoto_flat_index(self, nq, dim, auto_id, _async, index, is_flush):
"""
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,
is_flush=is_flush)[0:4]
log.info("auto_id= %s, _async= %s" % (auto_id, _async))
# 2. create index
default_index = {"index_type": index, "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.L2)
def test_search_binary_without_flush(self, metrics, auto_id):
"""
target: test search without flush for binary data (no index)
method: create connection, collection, insert, load and search
expected: search successfully with limit(topK)
"""
# 1. initialize a collection without data
collection_w = self.init_collection_general(prefix, is_binary=True, auto_id=auto_id)[0]
# 2. insert data
insert_ids = cf.insert_data(collection_w, default_nb, is_binary=True, auto_id=auto_id)[3]
# 3. load data
collection_w.load()
# 4. search
log.info("test_search_binary_without_flush: searching collection %s" % collection_w.name)
binary_vectors = cf.gen_binary_vectors(default_nq, default_dim)[1]
search_params = {"metric_type": metrics, "params": {"nprobe": 10}}
collection_w.search(binary_vectors[:default_nq], "binary_vector",
search_params, default_limit,
default_search_exp,
check_task=CheckTasks.check_search_results,
check_items={"nq": default_nq,
"ids": insert_ids,
"limit": default_limit})
@pytest.mark.tags(CaseLabel.L2)
def test_search_travel_time_without_expression(self, auto_id):
"""
target: test search using travel time without expression
method: 1. create connections,collection
2. first insert, and return with timestamp1
3. second insert, and return with timestamp2
4. search before timestamp1 and timestamp2
expected: 1 data inserted at a timestamp could not be searched before it
2 data inserted at a timestamp could be searched after it
"""
# 1. create connection, collection and insert
nb = 10
collection_w, _, _, insert_ids_1, time_stamp_1 = \
self.init_collection_general(prefix, True, nb, auto_id=auto_id, dim=default_dim)[0:5]
# 2. insert for the second time
log.info("test_search_travel_time_without_expression: inserting for the second time")
_, entities, _, insert_ids_2, time_stamp_2 = cf.insert_data(collection_w, nb, auto_id=auto_id,
dim=default_dim, insert_offset=nb)[0:5]
# 3. extract vectors inserted for the second time
entities_list = np.array(entities[0]).tolist()
vectors = [entities_list[i][-1] for i in range(default_nq)]
# 4. search with insert timestamp1
log.info("test_search_travel_time_without_expression: searching collection %s with time_stamp_1 '%d'"
% (collection_w.name, time_stamp_1))
search_res = collection_w.search(vectors, default_search_field,
default_search_params, default_limit,
travel_timestamp=time_stamp_1,
check_task=CheckTasks.check_search_results,
check_items={"nq": default_nq,
"ids": insert_ids_1,
"limit": default_limit})[0]
log.info("test_search_travel_time_without_expression: checking that data inserted "
"after time_stamp_2 is not searched at time_stamp_1")
for i in range(len(search_res)):
assert insert_ids_2[i] not in search_res[i].ids
# 5. search with insert timestamp2
log.info("test_search_travel_time_without_expression: searching collection %s with time_stamp_2 '%d'"
% (collection_w.name, time_stamp_2))
search_res = collection_w.search(vectors, default_search_field,
default_search_params, default_limit,
travel_timestamp=time_stamp_2,
check_task=CheckTasks.check_search_results,
check_items={"nq": default_nq,
"ids": insert_ids_1 + insert_ids_2,
"limit": default_limit})[0]
log.info("test_search_travel_time_without_expression: checking that data inserted "
"after time_stamp_2 is searched at time_stamp_2")
for i in range(len(search_res)):
assert insert_ids_2[i] in search_res[i].ids
@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)[0:4]
# 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("bool_type", [True, False, "true", "false"])
def test_search_with_expression_bool(self, dim, auto_id, _async, bool_type):
"""
target: test search with different bool expressions
method: search with different bool 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,
is_all_data_type=True,
auto_id=auto_id,
dim=dim)[0:4]
# 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. filter result with expression in collection
filter_ids = []
bool_type_cmp = bool_type
if bool_type == "true":
bool_type_cmp = True
if bool_type == "false":
bool_type_cmp = False
for i, _id in enumerate(insert_ids):
if _vectors[0][f"{default_bool_field_name}"][i] == bool_type_cmp:
filter_ids.append(_id)
# 4. search with different expressions
expression = f"{default_bool_field_name} == {bool_type}"
log.info("test_search_with_expression_bool: searching with bool 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 with auto id
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)[0:4]
# 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_auto_id: 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 all supported data types
method: search using different supported data types
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)[0:4]
# 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)[0:4]
# 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)[0:4]
# 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)[0:4]
# 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)[0:4]
# 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)[0:4]
# 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, time_stamp = self.init_collection_general(prefix, True, nb,
auto_id=auto_id,
dim=dim)[0:5]
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,
travel_timestamp=time_stamp,
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()
@pytest.mark.skip(reason="Not running for now")
@pytest.mark.tags(CaseLabel.L2)
def test_search_insert_in_parallel(self):
"""
target: test search and insert in parallel
method: One process do search while other process do insert
expected: No exception
"""
c_name = cf.gen_unique_str(prefix)
collection_w = self.init_collection_wrap(name=c_name)
default_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
collection_w.create_index(ct.default_float_vec_field_name, default_index)
collection_w.load()
def do_insert():
df = cf.gen_default_dataframe_data(10000)
for i in range(11):
collection_w.insert(df)
log.info(f'Collection num entities is : {collection_w.num_entities}')
def do_search():
while True:
results, _ = collection_w.search(cf.gen_vectors(nq, ct.default_dim), default_search_field,
default_search_params, default_limit, default_search_exp, timeout=30)
ids = []
for res in results:
ids.extend(res.ids)
expr = f'{ct.default_int64_field_name} in {ids}'
collection_w.query(expr, output_fields=[ct.default_int64_field_name, ct.default_float_field_name],
timeout=30)
p_insert = multiprocessing.Process(target=do_insert, args=())
p_search = multiprocessing.Process(target=do_search, args=(), daemon=True)
p_insert.start()
p_search.start()
p_insert.join()
@pytest.mark.tags(CaseLabel.L1)
@pytest.mark.parametrize("round_decimal", [0, 1, 2, 3, 4, 5, 6])
def test_search_round_decimal(self, round_decimal):
"""
target: test search with valid round decimal
method: search with valid round decimal
expected: search successfully
"""
import math
tmp_nb = 500
tmp_nq = 1
tmp_limit = 5
# 1. initialize with data
collection_w = self.init_collection_general(prefix, True, nb=tmp_nb)[0]
# 2. search
log.info("test_search_round_decimal: Searching collection %s" % collection_w.name)
res, _ = collection_w.search(vectors[:tmp_nq], default_search_field,
default_search_params, tmp_limit)
res_round, _ = collection_w.search(vectors[:tmp_nq], default_search_field,
default_search_params, tmp_limit, round_decimal=round_decimal)
abs_tol = pow(10, 1 - round_decimal)
# log.debug(f'abs_tol: {abs_tol}')
for i in range(tmp_limit):
dis_expect = round(res[0][i].distance, round_decimal)
dis_actual = res_round[0][i].distance
# log.debug(f'actual: {dis_actual}, expect: {dis_expect}')
# abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)
assert math.isclose(dis_actual, dis_expect, rel_tol=0, abs_tol=abs_tol)
@pytest.mark.tags(CaseLabel.L1)
def test_search_with_expression_large(self, dim):
"""
target: test search with large expression
method: test search with large expression
expected: searched successfully
"""
# 1. initialize with data
nb = 10000
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True,
nb, dim=dim,
is_index=True)[0:4]
# 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
expression = f"0 < {default_int64_field_name} < 5001"
log.info("test_search_with_expression: searching with expression: %s" % expression)
nums = 5000
vectors = [[random.random() for _ in range(dim)] for _ in range(nums)]
search_res, _ = collection_w.search(vectors, default_search_field,
default_search_params, default_limit, expression,
check_task=CheckTasks.check_search_results,
check_items={
"nq": nums,
"ids": insert_ids,
"limit": default_limit,
})
@pytest.mark.tags(CaseLabel.L1)
def test_search_with_expression_large_two(self, dim):
"""
target: test search with large expression
method: test one of the collection ids to another collection search for it, with the large expression
expected: searched successfully
"""
# 1. initialize with data
nb = 10000
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True,
nb, dim=dim,
is_index=True)[0:4]
# 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()
nums = 5000
vectors = [[random.random() for _ in range(dim)] for _ in range(nums)]
vectors_id = [random.randint(0,nums)for _ in range(nums)]
expression = f"{default_int64_field_name} in {vectors_id}"
search_res, _ = collection_w.search(vectors, default_search_field,
default_search_params, default_limit, expression,
check_task=CheckTasks.check_search_results,
check_items={
"nq": nums,
"ids": insert_ids,
"limit": default_limit,
})
@pytest.mark.tags(CaseLabel.L1)
def test_search_with_consistency_bounded(self, nq, dim, auto_id, _async):
"""
target: test search with different consistency level
method: 1. create a collection
2. insert data
3. search with consistency_level is "bounded"
expected: searched successfully
"""
limit = 1000
nb_old = 500
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb_old,
auto_id=auto_id,
dim=dim)[0:4]
# 2. search for original data 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": nb_old,
"_async": _async,
})
kwargs = {}
consistency_level = kwargs.get("consistency_level", CONSISTENCY_BOUNDED)
kwargs.update({"consistency_level": consistency_level})
nb_new = 400
_, _, _, insert_ids_new, _= cf.insert_data(collection_w, nb_new,
auto_id=auto_id, dim=dim,
insert_offset=nb_old)
insert_ids.extend(insert_ids_new)
collection_w.search(vectors[:nq], default_search_field,
default_search_params, limit,
default_search_exp, _async=_async,
**kwargs,
)
@pytest.mark.tags(CaseLabel.L1)
def test_search_with_consistency_strong(self, nq, dim, auto_id, _async):
"""
target: test search with different consistency level
method: 1. create a collection
2. insert data
3. search with consistency_level is "Strong"
expected: searched successfully
"""
limit = 1000
nb_old = 500
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb_old,
auto_id=auto_id,
dim=dim)[0:4]
# 2. search for original data 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": nb_old,
"_async": _async})
nb_new = 400
_, _, _, insert_ids_new, _ = cf.insert_data(collection_w, nb_new,
auto_id=auto_id, dim=dim,
insert_offset=nb_old)
insert_ids.extend(insert_ids_new)
kwargs = {}
consistency_level = kwargs.get("consistency_level", CONSISTENCY_STRONG)
kwargs.update({"consistency_level": consistency_level})
collection_w.search(vectors[:nq], default_search_field,
default_search_params, limit,
default_search_exp, _async=_async,
**kwargs,
check_task=CheckTasks.check_search_results,
check_items={"nq": nq,
"ids": insert_ids,
"limit": nb_old + nb_new,
"_async": _async})
@pytest.mark.tags(CaseLabel.L1)
def test_search_with_consistency_eventually(self, nq, dim, auto_id, _async):
"""
target: test search with different consistency level
method: 1. create a collection
2. insert data
3. search with consistency_level is "eventually"
expected: searched successfully
"""
limit = 1000
nb_old = 500
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb_old,
auto_id=auto_id,
dim=dim)[0:4]
# 2. search for original data 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": nb_old,
"_async": _async})
nb_new = 400
_, _, _, insert_ids_new, _= cf.insert_data(collection_w, nb_new,
auto_id=auto_id, dim=dim,
insert_offset=nb_old)
insert_ids.extend(insert_ids_new)
kwargs = {}
consistency_level = kwargs.get("consistency_level", CONSISTENCY_EVENTUALLY)
kwargs.update({"consistency_level": consistency_level})
collection_w.search(vectors[:nq], default_search_field,
default_search_params, limit,
default_search_exp, _async=_async,
**kwargs
)
@pytest.mark.tags(CaseLabel.L1)
def test_search_with_consistency_session(self, nq, dim, auto_id, _async):
"""
target: test search with different consistency level
method: 1. create a collection
2. insert data
3. search with consistency_level is "session"
expected: searched successfully
"""
limit = 1000
nb_old = 500
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb_old,
auto_id=auto_id,
dim=dim)[0:4]
# 2. search for original data 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": nb_old,
"_async": _async})
kwargs = {}
consistency_level = kwargs.get("consistency_level", CONSISTENCY_SESSION)
kwargs.update({"consistency_level": consistency_level})
nb_new = 400
_, _, _, insert_ids_new, _= cf.insert_data(collection_w, nb_new,
auto_id=auto_id, dim=dim,
insert_offset=nb_old)
insert_ids.extend(insert_ids_new)
collection_w.search(vectors[:nq], default_search_field,
default_search_params, limit,
default_search_exp, _async=_async,
**kwargs,
check_task=CheckTasks.check_search_results,
check_items={"nq": nq,
"ids": insert_ids,
"limit": nb_old + nb_new,
"_async": _async})
class TestSearchBase(TestcaseBase):
@pytest.fixture(
scope="function",
params=[1, 10]
)
def get_top_k(self, request):
yield request.param
@pytest.fixture(
scope="function",
params=[1, 10, 1100]
)
def get_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
@pytest.mark.tags(CaseLabel.L2)
def test_search_flat_top_k(self, get_nq):
"""
target: test basic search function, all the search params is correct, change top-k value
method: search with the given vectors, check the result
expected: the length of the result is top_k
"""
top_k = 16385 # max top k is 16384
nq = get_nq
collection_w, data, _, insert_ids = self.init_collection_general(prefix, insert_data=True, nb=nq)[0:4]
collection_w.load()
if top_k <= max_top_k:
res, _ = collection_w.search(vectors[:nq], default_search_field, default_search_params,
top_k)
assert len(res[0]) <= top_k
else:
collection_w.search(vectors[:nq], default_search_field, default_search_params,
top_k,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": "no Available QueryNode result, "
"filter reason limit %s is too large," % top_k})
@pytest.mark.tags(CaseLabel.L2)
@pytest.mark.parametrize("index, params",
zip(ct.all_index_types[:9],
ct.default_index_params[:9]))
def test_search_index_empty_partition(self, index, params):
"""
target: test basic search function, all the search params are correct, test all index params, and build
method: add vectors into collection, search with the given vectors, check the result
expected: the length of the result is top_k, search collection with partition tag return empty
"""
top_k = ct.default_top_k
nq = ct.default_nq
dim = ct.default_dim
# 1. initialize with data
collection_w, _, _, insert_ids, time_stamp = self.init_collection_general(prefix, True, nq,
partition_num=1,
dim=dim, is_index=True)[0:5]
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
# 2. create patition
partition_name = "search_partition_empty"
collection_w.create_partition(partition_name=partition_name, description="search partition empty")
par = collection_w.partitions
collection_w.load()
# 3. 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
default_index = {"index_type": index, "params": params, "metric_type": "L2"}
collection_w.create_index("float_vector", default_index)
collection_w.load()
# 4. search
res, _ = collection_w.search(vectors[:nq], default_search_field,
default_search_params, top_k,
default_search_exp)
assert len(res[0]) <= top_k
collection_w.search(vectors[:nq], default_search_field,
default_search_params, top_k,
default_search_exp, [partition_name],
check_task=CheckTasks.check_search_results,
check_items={"nq": nq,
"ids": [],
"limit": 0})
@pytest.mark.tags(CaseLabel.L2)
@pytest.mark.parametrize("index, params",
zip(ct.all_index_types[:9],
ct.default_index_params[:9]))
def test_search_index_partitions(self, index, params, get_top_k):
"""
target: test basic search function, all the search params are correct, test all index params, and build
method: search collection with the given vectors and tags, check the result
expected: the length of the result is top_k
"""
top_k = get_top_k
nq = ct.default_nq
dim = ct.default_dim
# 1. initialize with data
collection_w, _, _, insert_ids, time_stamp = self.init_collection_general(prefix, True, nq,
partition_num=1,
dim=dim, is_index=True)[0:5]
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
# 2. create patition
partition_name = ct.default_partition_name
par = collection_w.partitions
collection_w.load()
# 3. 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
default_index = {"index_type": index, "params": params, "metric_type": "L2"}
collection_w.create_index("float_vector", default_index)
collection_w.load()
res, _ = collection_w.search(vectors[:nq], default_search_field,
ct.default_search_params, top_k,
default_search_exp, [partition_name])
assert len(res[0]) <= top_k
@pytest.mark.tags(CaseLabel.L2)
def test_search_ip_flat(self, get_top_k):
"""
target: test basic search function, all the search params are correct, change top-k value
method: search with the given vectors, check the result
expected: the length of the result is top_k
"""
top_k = get_top_k
nq = ct.default_nq
dim = ct.default_dim
# 1. initialize with data
collection_w, _, _, insert_ids, time_stamp = self.init_collection_general(prefix, True, nq,
dim=dim, is_index=True)[0:5]
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
# 2. create ip index
default_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "IP"}
collection_w.create_index("float_vector", default_index)
collection_w.load()
res, _ = collection_w.search(vectors[:nq], default_search_field,
ct.default_search_params, top_k,
default_search_exp)
assert len(res[0]) <= top_k
@pytest.mark.tags(CaseLabel.L2)
@pytest.mark.parametrize("index, params",
zip(ct.all_index_types[:9],
ct.default_index_params[:9]))
def test_search_ip_after_index(self, index, params):
"""
target: test basic search function, all the search params are correct, test all index params, and build
method: search with the given vectors, check the result
expected: the length of the result is top_k
"""
top_k = ct.default_top_k
nq = ct.default_nq
dim = ct.default_dim
# 1. initialize with data
collection_w, _, _, insert_ids, time_stamp = self.init_collection_general(prefix, True, nq,
dim=dim, is_index=True)[0:5]
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
# 2. create ip index
default_index = {"index_type": index, "params": params, "metric_type": "IP"}
collection_w.create_index("float_vector", default_index)
collection_w.load()
res, _ = collection_w.search(vectors[:nq], default_search_field,
ct.default_search_params, top_k,
default_search_exp)
assert len(res[0]) <= top_k
@pytest.mark.tags(CaseLabel.L1)
@pytest.mark.parametrize("dim", [2, 8, 128, 768])
@pytest.mark.parametrize("nb", [1, 2, 10, 100])
def test_search_ip_brute_force(self, nb, dim):
"""
target: https://github.com/milvus-io/milvus/issues/17378. Ensure the logic of IP distances won't be changed.
method: search with the given vectors, check the result
expected: The inner product of vector themselves should be positive.
"""
top_k = 1
# 1. initialize with data
collection_w, insert_entities, _, insert_ids, _ = self.init_collection_general(prefix, True, nb,
is_binary=False,
dim=dim)[0:5]
insert_vectors = insert_entities[0][default_search_field].tolist()
# 2. load collection.
collection_w.load()
# 3. search and then check if the distances are expected.
res, _ = collection_w.search(insert_vectors[:nb], default_search_field,
ct.default_search_ip_params, top_k,
default_search_exp)
for i, v in enumerate(insert_vectors):
assert len(res[i]) == 1
ref = ip(v, v)
got = res[i][0].distance
assert abs(got - ref) <= epsilon
@pytest.mark.tags(CaseLabel.L2)
@pytest.mark.parametrize("index, params",
zip(ct.all_index_types[:9],
ct.default_index_params[:9]))
def test_search_ip_index_empty_partition(self, index, params):
"""
target: test basic search function, all the search params are correct, test all index params, and build
method: add vectors into collection, search with the given vectors, check the result
expected: the length of the result is top_k, search collection with partition tag return empty
"""
top_k = ct.default_top_k
nq = ct.default_nq
dim = ct.default_dim
# 1. initialize with data
collection_w, _, _, insert_ids, time_stamp = self.init_collection_general(prefix, True, nq,
partition_num=1,
dim=dim, is_index=True)[0:5]
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
# 2. create patition
partition_name = "search_partition_empty"
collection_w.create_partition(partition_name=partition_name, description="search partition empty")
par = collection_w.partitions
collection_w.load()
# 3. create different index
default_index = {"index_type": index, "params": params, "metric_type": "IP"}
collection_w.create_index("float_vector", default_index)
collection_w.load()
# 4. search
res, _ = collection_w.search(vectors[:nq], default_search_field,
default_search_params, top_k,
default_search_exp)
assert len(res[0]) <= top_k
collection_w.search(vectors[:nq], default_search_field,
default_search_params, top_k,
default_search_exp, [partition_name],
check_task=CheckTasks.check_search_results,
check_items={"nq": nq,
"ids": [],
"limit": 0})
@pytest.mark.tags(CaseLabel.L2)
@pytest.mark.parametrize("index, params",
zip(ct.all_index_types[:9],
ct.default_index_params[:9]))
def test_search_ip_index_partitions(self, index, params):
"""
target: test basic search function, all the search params are correct, test all index params, and build
method: search collection with the given vectors and tags, check the result
expected: the length of the result is top_k
"""
top_k = ct.default_top_k
nq = ct.default_nq
dim = ct.default_dim
# 1. initialize with data
collection_w, _, _, insert_ids, time_stamp = self.init_collection_general(prefix, True, nq,
partition_num=1,
dim=dim, is_index=True)[0:5]
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
# 2. create patition
par_name = collection_w.partitions[0].name
collection_w.load()
# 3. create different index
default_index = {"index_type": index, "params": params, "metric_type": "IP"}
collection_w.create_index("float_vector", default_index)
collection_w.load()
# 4. search
res, _ = collection_w.search(vectors[:nq], default_search_field,
default_search_params, top_k,
default_search_exp, [par_name])
assert len(res[0]) <= top_k
@pytest.mark.tags(CaseLabel.L2)
def test_search_without_connect(self):
"""
target: test search vectors without connection
method: use disconnected instance, call search method and check if search successfully
expected: raise exception
"""
self._connect()
collection_w, _, _, insert_ids, time_stamp = self.init_collection_general(prefix, True,
ct.default_nq, is_index=True)[0:5]
vectors = [[random.random() for _ in range(ct.default_dim)] for _ in range(nq)]
collection_w.load()
self.connection_wrap.remove_connection(ct.default_alias)
res_list, _ = self.connection_wrap.list_connections()
assert ct.default_alias not in res_list
res, _ = collection_w.search(vectors[:nq], default_search_field,
ct.default_search_params, ct.default_top_k,
default_search_exp,
check_task=CheckTasks.err_res,
check_items={"err_code": 0,
"err_msg": "'should create connect first.'"})
@pytest.mark.tags(CaseLabel.L2)
# @pytest.mark.timeout(300)
def test_search_concurrent_multithreads_single_connection(self, _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
"""
threads_num = 10
threads = []
collection_w, _, _, insert_ids, time_stamp = self.init_collection_general(prefix, True, ct.default_nb)[0:5]
def search(collection_w):
vectors = [[random.random() for _ in range(ct.default_dim)] for _ in range(nq)]
collection_w.search(vectors[:nq], default_search_field,
default_search_params, default_limit,
default_search_exp, _async=_async,
travel_timestamp=time_stamp,
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()
@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
"""
num = 10
top_k = 10
nq = 20
for i in range(num):
collection = gen_unique_str(uid + str(i))
collection_w, _, _, insert_ids, time_stamp = \
self.init_collection_general(collection, True, ct.default_nb)[0:5]
assert len(insert_ids) == default_nb
vectors = [[random.random() for _ in range(ct.default_dim)] for _ in range(nq)]
collection_w.search(vectors[:nq], default_search_field,
default_search_params, top_k,
default_search_exp,
check_task=CheckTasks.check_search_results,
check_items={"nq": nq,
"ids": insert_ids,
"limit": top_k})
class TestSearchDSL(TestcaseBase):
@pytest.mark.tags(CaseLabel.L0)
def test_query_vector_only(self):
"""
target: test search normal scenario
method: search vector only
expected: search status ok, the length of result
"""
collection_w, _, _, insert_ids, time_stamp = \
self.init_collection_general(prefix, True, ct.default_nb)[0:5]
vectors = [[random.random() for _ in range(ct.default_dim)] for _ in range(nq)]
collection_w.search(vectors[:nq], default_search_field,
default_search_params, ct.default_top_k,
default_search_exp,
check_task=CheckTasks.check_search_results,
check_items={"nq": nq,
"ids": insert_ids,
"limit": ct.default_top_k})
class TestsearchString(TestcaseBase):
"""
******************************************************************
The following cases are used to test search about string
******************************************************************
"""
@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
@pytest.mark.tags(CaseLabel.L2)
def test_search_string_field_not_primary(self, auto_id, _async):
"""
target: test search with string expr and string field is not primary
method: create collection and insert data
create index and collection load
collection search uses string expr in string field, string field is not primary
expected: Search successfully
"""
# 1. initialize with data
collection_w, _, _, insert_ids = \
self.init_collection_general(prefix, True, auto_id=auto_id, dim=default_dim)[0:4]
# 2. search
log.info("test_search_string_field_not_primary: searching collection %s" % collection_w.name)
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
output_fields = [default_string_field_name, default_float_field_name]
collection_w.search(vectors[:default_nq], default_search_field,
default_search_params, default_limit,
default_search_string_exp,
output_fields=output_fields,
_async=_async,
travel_timestamp=0,
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_string_field_is_primary_true(self, dim, _async):
"""
target: test search with string expr and string field is primary
method: create collection and insert data
create index and collection load
collection search uses string expr in string field ,string field is primary
expected: Search successfully
"""
# 1. initialize with data
collection_w, _, _, insert_ids = \
self.init_collection_general(prefix, True, dim=dim, primary_field=ct.default_string_field_name)[0:4]
# 2. search
log.info("test_search_string_field_is_primary_true: searching collection %s" % collection_w.name)
vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
output_fields = [default_string_field_name, default_float_field_name]
collection_w.search(vectors[:default_nq], default_search_field,
default_search_params, default_limit,
default_search_string_exp,
output_fields=output_fields,
_async=_async,
travel_timestamp=0,
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_string_mix_expr(self, dim, auto_id, _async):
"""
target: test search with mix string and int expr
method: create collection and insert data
create index and collection load
collection search uses mix expr
expected: Search successfully
"""
# 1. initialize with data
collection_w, _, _, insert_ids = \
self.init_collection_general(prefix, True, auto_id=auto_id, dim=dim)[0:4]
# 2. search
log.info("test_search_string_mix_expr: searching collection %s" % collection_w.name)
vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
output_fields = [default_string_field_name, default_float_field_name]
collection_w.search(vectors[:default_nq], default_search_field,
default_search_params, default_limit,
default_search_mix_exp,
output_fields=output_fields,
_async=_async,
travel_timestamp=0,
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_string_with_invalid_expr(self, auto_id):
"""
target: test search data
method: create collection and insert data
create index and collection load
collection search uses invalid string expr
expected: Raise exception
"""
# 1. initialize with data
collection_w, _, _, insert_ids = \
self.init_collection_general(prefix, True, auto_id=auto_id, dim=default_dim)[0:4]
# 2. search
log.info("test_search_string_with_invalid_expr: searching collection %s" % collection_w.name)
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_invaild_string_exp,
check_task=CheckTasks.err_res,
check_items={"err_code": 1,
"err_msg": "failed to create query plan: type mismatch"}
)
@pytest.mark.tags(CaseLabel.L2)
@pytest.mark.parametrize("expression", cf.gen_normal_string_expressions(ct.default_string_field_name))
def test_search_with_different_string_expr(self, dim, expression, _async):
"""
target: test search with different string expressions
method: test search with different string 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)[0:4]
# filter result with expression in collection
_vectors = _vectors[0]
filter_ids = []
expression = expression.replace("&&", "and").replace("||", "or")
for i, _id in enumerate(insert_ids):
int64 = _vectors.int64[i]
varchar = _vectors.varchar[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)
def test_search_string_field_is_primary_binary(self, dim, _async):
"""
target: test search with string expr and string field is primary
method: create collection and insert data
create index and collection load
collection search uses string expr in string field ,string field is primary
expected: Search successfully
"""
# 1. initialize with binary data
collection_w, _, binary_raw_vector, insert_ids = self.init_collection_general(prefix, True, 2,
is_binary=True,
dim=dim,
is_index=True,
primary_field=ct.default_string_field_name)[0:4]
# 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. search with exception
binary_vectors = cf.gen_binary_vectors(3000, dim)[1]
search_params = {"metric_type": "JACCARD", "params": {"nprobe": 10}}
output_fields = [default_string_field_name]
collection_w.search(binary_vectors[:default_nq], "binary_vector", search_params,
default_limit, default_search_string_exp, output_fields=output_fields,
_async=_async,
check_task=CheckTasks.check_search_results,
check_items={"nq": default_nq,
"ids": insert_ids,
"limit": 2,
"_async": _async})
@pytest.mark.tags(CaseLabel.L2)
def test_search_string_field_binary(self, auto_id, dim, _async):
"""
target: test search with string expr and string field is not primary
method: create an binary collection and insert data
create index and collection load
collection search uses string expr in string field, string field is not primary
expected: Search successfully
"""
# 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)[0:4]
# 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()
# 2. search with exception
binary_vectors = cf.gen_binary_vectors(3000, dim)[1]
search_params = {"metric_type": "JACCARD", "params": {"nprobe": 10}}
collection_w.search(binary_vectors[:default_nq], "binary_vector", search_params,
default_limit, default_search_string_exp,
_async=_async,
check_task=CheckTasks.check_search_results,
check_items={"nq": default_nq,
"ids": insert_ids,
"limit": 2,
"_async": _async})
@pytest.mark.tags(CaseLabel.L2)
def test_search_mix_expr_with_binary(self, dim, auto_id, _async):
"""
target: test search with mix string and int expr
method: create an binary collection and insert data
create index and collection load
collection search uses mix expr
expected: Search successfully
"""
# 1. initialize with data
collection_w, _, _, insert_ids = \
self.init_collection_general(prefix, True, auto_id=auto_id, dim=dim, is_binary=True, is_index=True)[0:4]
# 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()
# 2. search
log.info("test_search_mix_expr_with_binary: searching collection %s" % collection_w.name)
binary_vectors = cf.gen_binary_vectors(3000, dim)[1]
search_params = {"metric_type": "JACCARD", "params": {"nprobe": 10}}
output_fields = [default_string_field_name, default_float_field_name]
collection_w.search(binary_vectors[:default_nq], "binary_vector",
search_params, default_limit,
default_search_mix_exp,
output_fields=output_fields,
_async=_async,
travel_timestamp=0,
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_string_field_not_primary_perfix(self, auto_id, _async):
"""
target: test search with string expr and string field is not primary
method: create collection and insert data
create index and collection load
collection search uses string expr in string field, string field is not primary
expected: Search successfully
"""
# 1. initialize with data
collection_w, _, _, insert_ids = \
self.init_collection_general(prefix, True, auto_id=auto_id, dim=default_dim)[0:4]
index_param = {"index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 100}}
collection_w.create_index("float_vector", index_param, index_name="a")
index_param_two ={}
collection_w.create_index("varchar", index_param_two, index_name="b")
collection_w.load()
# 2. search
log.info("test_search_string_field_not_primary: searching collection %s" % collection_w.name)
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
output_fields = [default_float_field_name, default_string_field_name]
collection_w.search(vectors[:default_nq], default_search_field,
default_search_params, default_limit,
perfix_expr,
output_fields=output_fields,
_async=_async,
travel_timestamp=0,
check_task=CheckTasks.check_search_results,
check_items={"nq": default_nq,
"ids": insert_ids,
"limit": 1,
"_async": _async}
)
@pytest.mark.tags(CaseLabel.L1)
def test_search_all_index_with_compare_expr(self, _async):
"""
target: test delete after creating index
method: 1.create collection , insert data, primary_field is string field
2.create string and float index ,delete entities, query
3.search
expected: assert index and deleted id not in search result
"""
# create collection, insert tmp_nb, flush and load
collection_w, vectors, _, insert_ids = self.init_collection_general(prefix, insert_data=True, primary_field=ct.default_string_field_name)[0:4]
# create index
index_params_one = {"index_type": "IVF_SQ8", "metric_type": "L2", "params": {"nlist": 64}}
collection_w.create_index(ct.default_float_vec_field_name, index_params_one, index_name=index_name1)
index_params_two ={}
collection_w.create_index(ct.default_string_field_name, index_params=index_params_two, index_name=index_name2)
assert collection_w.has_index(index_name=index_name2)
collection_w.release()
collection_w.load()
# delete entity
expr = 'float >= int64'
# search with id 0 vectors
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
output_fields = [default_int64_field_name, default_float_field_name, default_string_field_name]
collection_w.search(vectors[:default_nq], default_search_field,
default_search_params, default_limit,
expr,
output_fields=output_fields,
_async=_async,
travel_timestamp=0,
check_task=CheckTasks.check_search_results,
check_items={"nq": default_nq,
"ids": insert_ids,
"limit": default_limit,
"_async": _async}
)
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