mirror of
https://gitee.com/milvus-io/milvus.git
synced 2024-12-04 04:49:08 +08:00
998 lines
53 KiB
Python
998 lines
53 KiB
Python
import multiprocessing
|
|
import numbers
|
|
import random
|
|
import numpy
|
|
import threading
|
|
import pytest
|
|
import pandas as pd
|
|
import decimal
|
|
from decimal import Decimal, getcontext
|
|
from time import sleep
|
|
import heapq
|
|
|
|
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
|
|
from base.high_level_api_wrapper import HighLevelApiWrapper
|
|
client_w = HighLevelApiWrapper()
|
|
|
|
prefix = "milvus_client_api_insert"
|
|
epsilon = ct.epsilon
|
|
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 = "id >= 0"
|
|
exp_res = "exp_res"
|
|
default_search_string_exp = "varchar >= \"0\""
|
|
default_search_mix_exp = "int64 >= 0 && varchar >= \"0\""
|
|
default_invaild_string_exp = "varchar >= 0"
|
|
default_json_search_exp = "json_field[\"number\"] >= 0"
|
|
perfix_expr = 'varchar like "0%"'
|
|
default_search_field = ct.default_float_vec_field_name
|
|
default_search_params = ct.default_search_params
|
|
default_primary_key_field_name = "id"
|
|
default_vector_field_name = "vector"
|
|
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
|
|
default_int32_array_field_name = ct.default_int32_array_field_name
|
|
default_string_array_field_name = ct.default_string_array_field_name
|
|
|
|
|
|
class TestMilvusClientInsertInvalid(TestcaseBase):
|
|
""" Test case of search interface """
|
|
|
|
@pytest.fixture(scope="function", params=[False, True])
|
|
def auto_id(self, request):
|
|
yield request.param
|
|
|
|
@pytest.fixture(scope="function", params=["COSINE", "L2"])
|
|
def metric_type(self, request):
|
|
yield request.param
|
|
|
|
"""
|
|
******************************************************************
|
|
# The following are invalid base cases
|
|
******************************************************************
|
|
"""
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.xfail(reason="pymilvus issue 1883")
|
|
def test_milvus_client_insert_column_data(self):
|
|
"""
|
|
target: test insert column data
|
|
method: create connection, collection, insert and search
|
|
expected: raise error
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim)
|
|
# 2. insert
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nb)]
|
|
data = [[i for i in range(default_nb)], vectors]
|
|
error = {ct.err_code: 1, ct.err_msg: "Unexpected error, message=<'list' object has no attribute 'items'"}
|
|
client_w.insert(client, collection_name, data,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
client_w.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_insert_empty_collection_name(self):
|
|
"""
|
|
target: test high level api: client.create_collection
|
|
method: create collection with invalid primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = ""
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 1, ct.err_msg: f"`collection_name` value {collection_name} is illegal"}
|
|
client_w.insert(client, collection_name, rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("collection_name", ["12-s", "12 s", "(mn)", "中文", "%$#"])
|
|
def test_milvus_client_insert_invalid_collection_name(self, collection_name):
|
|
"""
|
|
target: test high level api: client.create_collection
|
|
method: create collection with invalid primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 1100, ct.err_msg: f"Invalid collection name: {collection_name}. the first character of a "
|
|
f"collection name must be an underscore or letter: invalid parameter"}
|
|
client_w.insert(client, collection_name, rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_insert_collection_name_over_max_length(self):
|
|
"""
|
|
target: test high level api: client.create_collection
|
|
method: create collection with invalid primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = "a".join("a" for i in range(256))
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 1100, ct.err_msg: f"invalid dimension: {collection_name}. "
|
|
f"the length of a collection name must be less than 255 characters: "
|
|
f"invalid parameter"}
|
|
client_w.insert(client, collection_name, rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_insert_not_exist_collection_name(self):
|
|
"""
|
|
target: test high level api: client.create_collection
|
|
method: create collection with invalid primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str("insert_not_exist")
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 100, ct.err_msg: f"can't find collection collection not found"
|
|
f"[database=default][collection={collection_name}]"}
|
|
client_w.insert(client, collection_name, rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.xfail(reason="pymilvus issue 1894")
|
|
@pytest.mark.parametrize("data", ["12-s", "12 s", "(mn)", "中文", "%$#", " "])
|
|
def test_milvus_client_insert_data_invalid_type(self, data):
|
|
"""
|
|
target: test high level api: client.create_collection
|
|
method: create collection with invalid primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. insert
|
|
error = {ct.err_code: 1, ct.err_msg: f"None rows, please provide valid row data."}
|
|
client_w.insert(client, collection_name, data,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.xfail(reason="pymilvus issue 1895")
|
|
def test_milvus_client_insert_data_empty(self):
|
|
"""
|
|
target: test high level api: client.create_collection
|
|
method: create collection with invalid primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. insert
|
|
error = {ct.err_code: 1, ct.err_msg: f"None rows, please provide valid row data."}
|
|
client_w.insert(client, collection_name, data= "")
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_insert_data_vector_field_missing(self):
|
|
"""
|
|
target: test high level api: client.create_collection
|
|
method: create collection with invalid primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i,
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 1, ct.err_msg: f"float vector field 'vector' is illegal, array type mismatch: "
|
|
f"invalid parameter[expected=need float vector][actual=got nil]"}
|
|
client_w.insert(client, collection_name, data=rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_insert_data_id_field_missing(self):
|
|
"""
|
|
target: test high level api: client.create_collection
|
|
method: create collection with invalid primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 1, ct.err_msg: f"currently not support vector field as PrimaryField: invalid parameter"}
|
|
client_w.insert(client, collection_name, data=rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_insert_data_extra_field(self):
|
|
"""
|
|
target: test milvus client: insert extra field than schema
|
|
method: insert extra field than schema when enable_dynamic_field is False
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, enable_dynamic_field=False)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 1, ct.err_msg: f"Attempt to insert an unexpected field "
|
|
f"to collection without enabling dynamic field"}
|
|
client_w.insert(client, collection_name, data= rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_insert_data_dim_not_match(self):
|
|
"""
|
|
target: test milvus client: insert extra field than schema
|
|
method: insert extra field than schema when enable_dynamic_field is False
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim+1))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 65536, ct.err_msg: f"of float data should divide the dim({default_dim})"}
|
|
client_w.insert(client, collection_name, data= rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_insert_not_matched_data(self):
|
|
"""
|
|
target: test milvus client: insert not matched data then defined
|
|
method: insert string to int primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 1, ct.err_msg: f"The Input data type is inconsistent with defined schema, "
|
|
f"please check it."}
|
|
client_w.insert(client, collection_name, data= rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("partition_name", ["12-s", "12 s", "(mn)", "中文", "%$#", " "])
|
|
def test_milvus_client_insert_invalid_partition_name(self, partition_name):
|
|
"""
|
|
target: test milvus client: insert extra field than schema
|
|
method: insert extra field than schema when enable_dynamic_field is False
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 65535, ct.err_msg: f"Invalid partition name: {partition_name}. The first character of "
|
|
f"a partition name must be an underscore or letter."}
|
|
client_w.insert(client, collection_name, data= rows, partition_name=partition_name,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_insert_not_exist_partition_name(self):
|
|
"""
|
|
target: test milvus client: insert extra field than schema
|
|
method: insert extra field than schema when enable_dynamic_field is False
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
partition_name = cf.gen_unique_str("partition_not_exist")
|
|
error = {ct.err_code: 200, ct.err_msg: f"partition not found[partition={partition_name}]"}
|
|
client_w.insert(client, collection_name, data= rows, partition_name=partition_name,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_insert_collection_partition_not_match(self):
|
|
"""
|
|
target: test milvus client: insert extra field than schema
|
|
method: insert extra field than schema when enable_dynamic_field is False
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
another_collection_name = cf.gen_unique_str(prefix + "another")
|
|
partition_name = cf.gen_unique_str("partition")
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim)
|
|
client_w.create_collection(client, another_collection_name, default_dim)
|
|
client_w.create_partition(client, another_collection_name, partition_name)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 200, ct.err_msg: f"partition not found[partition={partition_name}]"}
|
|
client_w.insert(client, collection_name, data= rows, partition_name=partition_name,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
|
|
class TestMilvusClientInsertValid(TestcaseBase):
|
|
""" Test case of search interface """
|
|
|
|
@pytest.fixture(scope="function", params=[False, True])
|
|
def auto_id(self, request):
|
|
yield request.param
|
|
|
|
@pytest.fixture(scope="function", params=["COSINE", "L2"])
|
|
def metric_type(self, request):
|
|
yield request.param
|
|
|
|
"""
|
|
******************************************************************
|
|
# The following are valid base cases
|
|
******************************************************************
|
|
"""
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
def test_milvus_client_insert_default(self):
|
|
"""
|
|
target: test search (high level api) normal case
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
collections = client_w.list_collections(client)[0]
|
|
assert collection_name in collections
|
|
client_w.describe_collection(client, collection_name,
|
|
check_task=CheckTasks.check_describe_collection_property,
|
|
check_items={"collection_name": collection_name,
|
|
"dim": default_dim,
|
|
"consistency_level": 0})
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
results = client_w.insert(client, collection_name, rows)[0]
|
|
assert results['insert_count'] == default_nb
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
insert_ids = [i for i in range(default_nb)]
|
|
client_w.search(client, collection_name, vectors_to_search,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"limit": default_limit})
|
|
# 4. query
|
|
client_w.query(client, collection_name, filter=default_search_exp,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: rows,
|
|
"with_vec": True,
|
|
"primary_field": default_primary_key_field_name})
|
|
client_w.release_collection(client, collection_name)
|
|
client_w.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_insert_different_fields(self):
|
|
"""
|
|
target: test search (high level api) normal case
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
collections = client_w.list_collections(client)[0]
|
|
assert collection_name in collections
|
|
client_w.describe_collection(client, collection_name,
|
|
check_task=CheckTasks.check_describe_collection_property,
|
|
check_items={"collection_name": collection_name,
|
|
"dim": default_dim,
|
|
"consistency_level": 0})
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
results = client_w.insert(client, collection_name, rows)[0]
|
|
assert results['insert_count'] == default_nb
|
|
# 3. insert diff fields
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, "new_diff_str_field": str(i)} for i in range(default_nb)]
|
|
results = client_w.insert(client, collection_name, rows)[0]
|
|
assert results['insert_count'] == default_nb
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
insert_ids = [i for i in range(default_nb)]
|
|
client_w.search(client, collection_name, vectors_to_search,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"limit": default_limit})
|
|
client_w.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_insert_empty_data(self):
|
|
"""
|
|
target: test search (high level api) normal case
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. insert
|
|
rows = []
|
|
results = client_w.insert(client, collection_name, rows)[0]
|
|
assert results['insert_count'] == 0
|
|
# 3. search
|
|
rng = np.random.default_rng(seed=19530)
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
client_w.search(client, collection_name, vectors_to_search,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": [],
|
|
"limit": 0})
|
|
client_w.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_insert_partition(self):
|
|
"""
|
|
target: test fast create collection normal case
|
|
method: create collection
|
|
expected: create collection with default schema, index, and load successfully
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
partition_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. create partition
|
|
client_w.create_partition(client, collection_name, partition_name)
|
|
partitions = client_w.list_partitions(client, collection_name)[0]
|
|
assert partition_name in partitions
|
|
index = client_w.list_indexes(client, collection_name)[0]
|
|
assert index == ['vector']
|
|
# load_state = client_w.get_load_state(collection_name)[0]
|
|
# 3. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
results = client_w.insert(client, collection_name, rows, partition_name=partition_name)[0]
|
|
assert results['insert_count'] == default_nb
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
insert_ids = [i for i in range(default_nb)]
|
|
client_w.search(client, collection_name, vectors_to_search,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"limit": default_limit})
|
|
# partition_number = client_w.get_partition_stats(client, collection_name, "_default")[0]
|
|
# assert partition_number == default_nb
|
|
# partition_number = client_w.get_partition_stats(client, collection_name, partition_name)[0]
|
|
# assert partition_number[0]['value'] == 0
|
|
if client_w.has_partition(client, collection_name, partition_name)[0]:
|
|
client_w.release_partitions(client, collection_name, partition_name)
|
|
client_w.drop_partition(client, collection_name, partition_name)
|
|
if client_w.has_collection(client, collection_name)[0]:
|
|
client_w.drop_collection(client, collection_name)
|
|
|
|
|
|
class TestMilvusClientUpsertInvalid(TestcaseBase):
|
|
""" Test case of search interface """
|
|
|
|
@pytest.fixture(scope="function", params=[False, True])
|
|
def auto_id(self, request):
|
|
yield request.param
|
|
|
|
@pytest.fixture(scope="function", params=["COSINE", "L2"])
|
|
def metric_type(self, request):
|
|
yield request.param
|
|
|
|
"""
|
|
******************************************************************
|
|
# The following are invalid base cases
|
|
******************************************************************
|
|
"""
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.xfail(reason="pymilvus issue 1883")
|
|
def test_milvus_client_upsert_column_data(self):
|
|
"""
|
|
target: test insert column data
|
|
method: create connection, collection, insert and search
|
|
expected: raise error
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim)
|
|
# 2. insert
|
|
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nb)]
|
|
data = [[i for i in range(default_nb)], vectors]
|
|
error = {ct.err_code: 1, ct.err_msg: "Unexpected error, message=<'list' object has no attribute 'items'"}
|
|
client_w.upsert(client, collection_name, data,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
client_w.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_upsert_empty_collection_name(self):
|
|
"""
|
|
target: test high level api: client.create_collection
|
|
method: create collection with invalid primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = ""
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 1, ct.err_msg: f"`collection_name` value {collection_name} is illegal"}
|
|
client_w.upsert(client, collection_name, rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("collection_name", ["12-s", "12 s", "(mn)", "中文", "%$#"])
|
|
def test_milvus_client_upsert_invalid_collection_name(self, collection_name):
|
|
"""
|
|
target: test high level api: client.create_collection
|
|
method: create collection with invalid primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 1100, ct.err_msg: f"Invalid collection name: {collection_name}. the first character of a "
|
|
f"collection name must be an underscore or letter: invalid parameter"}
|
|
client_w.upsert(client, collection_name, rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_upsert_collection_name_over_max_length(self):
|
|
"""
|
|
target: test high level api: client.create_collection
|
|
method: create collection with invalid primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = "a".join("a" for i in range(256))
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 1100, ct.err_msg: f"invalid dimension: {collection_name}. "
|
|
f"the length of a collection name must be less than 255 characters: "
|
|
f"invalid parameter"}
|
|
client_w.upsert(client, collection_name, rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_upsert_not_exist_collection_name(self):
|
|
"""
|
|
target: test high level api: client.create_collection
|
|
method: create collection with invalid primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str("insert_not_exist")
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 100, ct.err_msg: f"can't find collection collection not found"
|
|
f"[database=default][collection={collection_name}]"}
|
|
client_w.upsert(client, collection_name, rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.xfail(reason="pymilvus issue 1894")
|
|
@pytest.mark.parametrize("data", ["12-s", "12 s", "(mn)", "中文", "%$#", " "])
|
|
def test_milvus_client_upsert_data_invalid_type(self, data):
|
|
"""
|
|
target: test high level api: client.create_collection
|
|
method: create collection with invalid primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. insert
|
|
error = {ct.err_code: 1, ct.err_msg: f"None rows, please provide valid row data."}
|
|
client_w.upsert(client, collection_name, data,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.xfail(reason="pymilvus issue 1895")
|
|
def test_milvus_client_upsert_data_empty(self):
|
|
"""
|
|
target: test high level api: client.create_collection
|
|
method: create collection with invalid primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. insert
|
|
error = {ct.err_code: 1, ct.err_msg: f"None rows, please provide valid row data."}
|
|
client_w.upsert(client, collection_name, data= "")
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_upsert_data_vector_field_missing(self):
|
|
"""
|
|
target: test high level api: client.create_collection
|
|
method: create collection with invalid primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i,
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 1, ct.err_msg: f"float vector field 'vector' is illegal, array type mismatch: "
|
|
f"invalid parameter[expected=need float vector][actual=got nil]"}
|
|
client_w.upsert(client, collection_name, data=rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_upsert_data_id_field_missing(self):
|
|
"""
|
|
target: test high level api: client.create_collection
|
|
method: create collection with invalid primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 1, ct.err_msg: f"currently not support vector field as PrimaryField: invalid parameter"}
|
|
client_w.upsert(client, collection_name, data= rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_upsert_data_extra_field(self):
|
|
"""
|
|
target: test milvus client: insert extra field than schema
|
|
method: insert extra field than schema when enable_dynamic_field is False
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, enable_dynamic_field=False)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 1, ct.err_msg: f"Attempt to insert an unexpected field "
|
|
f"to collection without enabling dynamic field"}
|
|
client_w.upsert(client, collection_name, data= rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_upsert_data_dim_not_match(self):
|
|
"""
|
|
target: test milvus client: insert extra field than schema
|
|
method: insert extra field than schema when enable_dynamic_field is False
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim+1))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 65536, ct.err_msg: f"of float data should divide the dim({default_dim})"}
|
|
client_w.upsert(client, collection_name, data= rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_upsert_not_matched_data(self):
|
|
"""
|
|
target: test milvus client: insert not matched data then defined
|
|
method: insert string to int primary field
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 1, ct.err_msg: f"The Input data type is inconsistent with defined schema, "
|
|
f"please check it."}
|
|
client_w.upsert(client, collection_name, data= rows,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("partition_name", ["12-s", "12 s", "(mn)", "中文", "%$#", " "])
|
|
def test_milvus_client_upsert_invalid_partition_name(self, partition_name):
|
|
"""
|
|
target: test milvus client: insert extra field than schema
|
|
method: insert extra field than schema when enable_dynamic_field is False
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 65535, ct.err_msg: f"Invalid partition name: {partition_name}. The first character of "
|
|
f"a partition name must be an underscore or letter."}
|
|
client_w.upsert(client, collection_name, data= rows, partition_name=partition_name,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_upsert_not_exist_partition_name(self):
|
|
"""
|
|
target: test milvus client: insert extra field than schema
|
|
method: insert extra field than schema when enable_dynamic_field is False
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
partition_name = cf.gen_unique_str("partition_not_exist")
|
|
error = {ct.err_code: 200, ct.err_msg: f"partition not found[partition={partition_name}]"}
|
|
client_w.upsert(client, collection_name, data= rows, partition_name=partition_name,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_upsert_collection_partition_not_match(self):
|
|
"""
|
|
target: test milvus client: insert extra field than schema
|
|
method: insert extra field than schema when enable_dynamic_field is False
|
|
expected: Raise exception
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
another_collection_name = cf.gen_unique_str(prefix + "another")
|
|
partition_name = cf.gen_unique_str("partition")
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim)
|
|
client_w.create_collection(client, another_collection_name, default_dim)
|
|
client_w.create_partition(client, another_collection_name, partition_name)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
error = {ct.err_code: 200, ct.err_msg: f"partition not found[partition={partition_name}]"}
|
|
client_w.upsert(client, collection_name, data= rows, partition_name=partition_name,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
|
|
class TestMilvusClientUpsertValid(TestcaseBase):
|
|
""" Test case of search interface """
|
|
|
|
@pytest.fixture(scope="function", params=[False, True])
|
|
def auto_id(self, request):
|
|
yield request.param
|
|
|
|
@pytest.fixture(scope="function", params=["COSINE", "L2"])
|
|
def metric_type(self, request):
|
|
yield request.param
|
|
|
|
"""
|
|
******************************************************************
|
|
# The following are valid base cases
|
|
******************************************************************
|
|
"""
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
def test_milvus_client_upsert_default(self):
|
|
"""
|
|
target: test search (high level api) normal case
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
collections = client_w.list_collections(client)[0]
|
|
assert collection_name in collections
|
|
client_w.describe_collection(client, collection_name,
|
|
check_task=CheckTasks.check_describe_collection_property,
|
|
check_items={"collection_name": collection_name,
|
|
"dim": default_dim,
|
|
"consistency_level": 0})
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
results = client_w.upsert(client, collection_name, rows)[0]
|
|
assert results['upsert_count'] == default_nb
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
insert_ids = [i for i in range(default_nb)]
|
|
client_w.search(client, collection_name, vectors_to_search,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"limit": default_limit})
|
|
# 4. query
|
|
client_w.query(client, collection_name, filter=default_search_exp,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: rows,
|
|
"with_vec": True,
|
|
"primary_field": default_primary_key_field_name})
|
|
client_w.release_collection(client, collection_name)
|
|
client_w.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_upsert_empty_data(self):
|
|
"""
|
|
target: test search (high level api) normal case
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. insert
|
|
rows = []
|
|
results = client_w.upsert(client, collection_name, rows)[0]
|
|
assert results['upsert_count'] == 0
|
|
# 3. search
|
|
rng = np.random.default_rng(seed=19530)
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
client_w.search(client, collection_name, vectors_to_search,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": [],
|
|
"limit": 0})
|
|
client_w.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_upsert_partition(self):
|
|
"""
|
|
target: test fast create collection normal case
|
|
method: create collection
|
|
expected: create collection with default schema, index, and load successfully
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
partition_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. create partition
|
|
client_w.create_partition(client, collection_name, partition_name)
|
|
partitions = client_w.list_partitions(client, collection_name)[0]
|
|
assert partition_name in partitions
|
|
index = client_w.list_indexes(client, collection_name)[0]
|
|
assert index == ['vector']
|
|
# load_state = client_w.get_load_state(collection_name)[0]
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
# 3. upsert to default partition
|
|
results = client_w.upsert(client, collection_name, rows, partition_name=partitions[0])[0]
|
|
assert results['upsert_count'] == default_nb
|
|
# 4. upsert to non-default partition
|
|
results = client_w.upsert(client, collection_name, rows, partition_name=partition_name)[0]
|
|
assert results['upsert_count'] == default_nb
|
|
# 5. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
insert_ids = [i for i in range(default_nb)]
|
|
client_w.search(client, collection_name, vectors_to_search,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"limit": default_limit})
|
|
# partition_number = client_w.get_partition_stats(client, collection_name, "_default")[0]
|
|
# assert partition_number == default_nb
|
|
# partition_number = client_w.get_partition_stats(client, collection_name, partition_name)[0]
|
|
# assert partition_number[0]['value'] == 0
|
|
if client_w.has_partition(client, collection_name, partition_name)[0]:
|
|
client_w.release_partitions(client, collection_name, partition_name)
|
|
client_w.drop_partition(client, collection_name, partition_name)
|
|
if client_w.has_collection(client, collection_name)[0]:
|
|
client_w.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_insert_upsert(self):
|
|
"""
|
|
target: test fast create collection normal case
|
|
method: create collection
|
|
expected: create collection with default schema, index, and load successfully
|
|
"""
|
|
client = self._connect(enable_milvus_client_api=True)
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
partition_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
client_w.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. create partition
|
|
client_w.create_partition(client, collection_name, partition_name)
|
|
partitions = client_w.list_partitions(client, collection_name)[0]
|
|
assert partition_name in partitions
|
|
index = client_w.list_indexes(client, collection_name)[0]
|
|
assert index == ['vector']
|
|
# load_state = client_w.get_load_state(collection_name)[0]
|
|
# 3. insert and upsert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
results = client_w.insert(client, collection_name, rows, partition_name=partition_name)[0]
|
|
assert results['insert_count'] == default_nb
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, "new_diff_str_field": str(i)} for i in range(default_nb)]
|
|
results = client_w.upsert(client, collection_name, rows, partition_name=partition_name)[0]
|
|
assert results['upsert_count'] == default_nb
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
insert_ids = [i for i in range(default_nb)]
|
|
client_w.search(client, collection_name, vectors_to_search,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"limit": default_limit})
|
|
if client_w.has_partition(client, collection_name, partition_name)[0]:
|
|
client_w.release_partitions(client, collection_name, partition_name)
|
|
client_w.drop_partition(client, collection_name, partition_name)
|
|
if client_w.has_collection(client, collection_name)[0]:
|
|
client_w.drop_collection(client, collection_name)
|