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3645 lines
164 KiB
Python
3645 lines
164 KiB
Python
import utils.util_pymilvus as ut
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from utils.util_log import test_log as log
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from common.common_type import CaseLabel, CheckTasks
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from common import common_type as ct
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from common import common_func as cf
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from common.code_mapping import CollectionErrorMessage as clem
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from common.code_mapping import ConnectionErrorMessage as cem
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from base.client_base import TestcaseBase
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from pymilvus.orm.types import CONSISTENCY_STRONG, CONSISTENCY_BOUNDED, CONSISTENCY_EVENTUALLY
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import threading
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from pymilvus import DefaultConfig
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from datetime import datetime
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import time
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import pytest
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import random
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import numpy as np
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import pandas as pd
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pd.set_option("expand_frame_repr", False)
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prefix = "query"
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exp_res = "exp_res"
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count = "count(*)"
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default_term_expr = f'{ct.default_int64_field_name} in [0, 1]'
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default_mix_expr = "int64 >= 0 && varchar >= \"0\""
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default_expr = f'{ct.default_int64_field_name} >= 0'
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default_invalid_expr = "varchar >= 0"
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default_string_term_expr = f'{ct.default_string_field_name} in [\"0\", \"1\"]'
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default_index_params = {"index_type": "IVF_SQ8", "metric_type": "L2", "params": {"nlist": 64}}
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binary_index_params = {"index_type": "BIN_IVF_FLAT", "metric_type": "JACCARD", "params": {"nlist": 64}}
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default_entities = ut.gen_entities(ut.default_nb, is_normal=True)
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default_pos = 5
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json_field = ct.default_json_field_name
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default_int_field_name = ct.default_int64_field_name
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default_float_field_name = "float"
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default_string_field_name = "varchar"
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class TestQueryParams(TestcaseBase):
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"""
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test Query interface
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query(collection_name, expr, output_fields=None, partition_names=None, timeout=None)
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"""
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@pytest.fixture(scope="function", params=[True, False])
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def enable_dynamic_field(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=[True, False])
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def random_primary_key(self, request):
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yield request.param
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@pytest.mark.tags(CaseLabel.L2)
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def test_query_invalid(self):
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"""
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target: test query with invalid term expression
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method: query with invalid term expr
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expected: raise exception
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"""
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collection_w, entities = self.init_collection_general(prefix, insert_data=True, nb=10)[0:2]
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term_expr = f'{default_int_field_name} in {entities[:default_pos]}'
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error = {ct.err_code: 65535, ct.err_msg: "cannot parse expression: int64 in .."}
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collection_w.query(term_expr, check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L0)
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def test_query(self, enable_dynamic_field):
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"""
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target: test query
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method: query with term expr
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expected: verify query result
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"""
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# create collection, insert default_nb, load collection
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collection_w, vectors = self.init_collection_general(prefix, insert_data=True,
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enable_dynamic_field=enable_dynamic_field)[0:2]
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pos = 5
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if enable_dynamic_field:
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int_values = []
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for vector in vectors[0]:
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vector = vector[ct.default_int64_field_name]
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int_values.append(vector)
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res = [{ct.default_int64_field_name: int_values[i]} for i in range(pos)]
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else:
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int_values = vectors[0][ct.default_int64_field_name].values.tolist()
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res = vectors[0].iloc[0:pos, :1].to_dict('records')
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term_expr = f'{ct.default_int64_field_name} in {int_values[:pos]}'
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collection_w.query(term_expr, check_task=CheckTasks.check_query_results, check_items={exp_res: res})
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@pytest.mark.tags(CaseLabel.L1)
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def test_query_no_collection(self):
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"""
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target: test the scenario which query the non-exist collection
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method: 1. create collection
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2. drop collection
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3. query the dropped collection
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expected: raise exception and report the error
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"""
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# 1. initialize without data
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collection_w = self.init_collection_general(prefix)[0]
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# 2. Drop collection
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log.info("test_query_no_collection: drop collection %s" %
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collection_w.name)
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collection_w.drop()
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# 3. Search without collection
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log.info("test_query_no_collection: query without collection ")
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collection_w.query(default_term_expr,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": "collection not found"})
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@pytest.mark.tags(CaseLabel.L2)
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def test_query_empty_collection(self):
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"""
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target: test query empty collection
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method: query on an empty collection
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expected: empty result
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"""
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c_name = cf.gen_unique_str(prefix)
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collection_w = self.init_collection_wrap(name=c_name)
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collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
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collection_w.load()
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res, _ = collection_w.query(default_term_expr)
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assert len(res) == 0
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@pytest.mark.tags(CaseLabel.L0)
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def test_query_auto_id_collection(self):
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"""
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target: test query with auto_id=True collection
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method: test query with auto id
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expected: query result is correct
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"""
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self._connect()
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df = cf.gen_default_dataframe_data()
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df[ct.default_int64_field_name] = None
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insert_res, _, = self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
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primary_field=ct.default_int64_field_name,
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auto_id=True)
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assert self.collection_wrap.num_entities == ct.default_nb
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ids = insert_res[1].primary_keys
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pos = 5
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res = df.iloc[:pos, :1].to_dict('records')
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self.collection_wrap.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
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self.collection_wrap.load()
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# query with all primary keys
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term_expr_1 = f'{ct.default_int64_field_name} in {ids[:pos]}'
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for i in range(5):
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res[i][ct.default_int64_field_name] = ids[i]
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self.collection_wrap.query(term_expr_1, check_task=CheckTasks.check_query_results, check_items={exp_res: res})
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# query with part primary keys
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term_expr_2 = f'{ct.default_int64_field_name} in {[ids[0], 0]}'
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self.collection_wrap.query(term_expr_2, check_task=CheckTasks.check_query_results,
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check_items={exp_res: res[:1]})
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("dup_times", [1, 2, 3])
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@pytest.mark.parametrize("dim", [8, 128])
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def test_query_with_dup_primary_key(self, dim, dup_times):
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"""
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target: test query with duplicate primary key
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method: 1.insert same data twice
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2.search
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expected: query results are de-duplicated
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"""
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nb = ct.default_nb
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collection_w, insert_data, _, _ = self.init_collection_general(prefix, True, nb, dim=dim)[0:4]
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# insert dup data multi times
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for i in range(dup_times):
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collection_w.insert(insert_data[0])
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# query
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res, _ = collection_w.query(default_term_expr)
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# assert that query results are de-duplicated
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res = [m["int64"] for m in res]
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assert sorted(list(set(res))) == sorted(res)
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@pytest.mark.tags(CaseLabel.L2)
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def test_query_auto_id_not_existed_primary_values(self):
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"""
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target: test query on auto_id true collection
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method: 1.create auto_id true collection
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2.query with not existed primary keys
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expected: query result is empty
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"""
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schema = cf.gen_default_collection_schema(auto_id=True)
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collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix), schema=schema)
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df = cf.gen_default_dataframe_data(ct.default_nb)
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df.drop(ct.default_int64_field_name, axis=1, inplace=True)
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mutation_res, _ = collection_w.insert(data=df)
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assert collection_w.num_entities == ct.default_nb
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collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
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collection_w.load()
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term_expr = f'{ct.default_int64_field_name} in [0, 1, 2]'
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res, _ = collection_w.query(term_expr)
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assert len(res) == 0
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@pytest.mark.tags(CaseLabel.L2)
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def test_query_expr_none(self):
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"""
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target: test query with none expr
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method: query with expr None
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expected: raise exception
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"""
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collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
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error = {ct.err_code: 0, ct.err_msg: "The type of expr must be string"}
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collection_w.query(None, check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L2)
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def test_query_non_string_expr(self):
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"""
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target: test query with non-string expr
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method: query with non-string expr, eg 1, [] ..
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expected: raise exception
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"""
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collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
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exprs = [1, 2., [], {}, ()]
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error = {ct.err_code: 0, ct.err_msg: "The type of expr must be string"}
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for expr in exprs:
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collection_w.query(expr, check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L2)
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def test_query_expr_invalid_string(self):
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"""
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target: test query with invalid expr
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method: query with invalid string expr
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expected: raise exception
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"""
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collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
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error = {ct.err_code: 65535, ct.err_msg: "cannot parse expression: 12-s, error: field s not exist"}
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exprs = ["12-s", "中文", "a", " "]
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for expr in exprs:
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collection_w.query(expr, check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.skip(reason="repeat with test_query, waiting for other expr")
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def test_query_expr_term(self):
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"""
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target: test query with TermExpr
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method: query with TermExpr
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expected: query result is correct
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"""
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collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
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res = vectors[0].iloc[:2, :1].to_dict('records')
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collection_w.query(default_term_expr, check_task=CheckTasks.check_query_results, check_items={exp_res: res})
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@pytest.mark.tags(CaseLabel.L2)
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def test_query_expr_not_existed_field(self):
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"""
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target: test query with not existed field
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method: query by term expr with fake field
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expected: raise exception
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"""
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collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix))
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term_expr = 'field in [1, 2]'
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error = {ct.err_code: 65535,
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ct.err_msg: "cannot parse expression: field in [1, 2], error: field field not exist"}
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collection_w.query(term_expr, check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L2)
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def test_query_expr_non_primary_fields(self):
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"""
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target: test query on non-primary non-vector fields
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method: query on non-primary non-vector fields
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expected: verify query result
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"""
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self._connect()
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# construct dataframe and inert data
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df = pd.DataFrame({
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ct.default_int64_field_name: pd.Series(data=[i for i in range(ct.default_nb)]),
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ct.default_int32_field_name: pd.Series(data=[np.int32(i) for i in range(ct.default_nb)], dtype="int32"),
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ct.default_int16_field_name: pd.Series(data=[np.int16(i) for i in range(ct.default_nb)], dtype="int16"),
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ct.default_float_field_name: pd.Series(data=[np.float32(i) for i in range(ct.default_nb)], dtype="float32"),
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ct.default_double_field_name: pd.Series(data=[np.double(i) for i in range(ct.default_nb)], dtype="double"),
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ct.default_string_field_name: pd.Series(data=[str(i) for i in range(ct.default_nb)], dtype="string"),
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ct.default_float_vec_field_name: cf.gen_vectors(ct.default_nb, ct.default_dim)
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})
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self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
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primary_field=ct.default_int64_field_name)
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assert self.collection_wrap.num_entities == ct.default_nb
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self.collection_wrap.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
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self.collection_wrap.load()
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# query by non_primary non_vector scalar field
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non_primary_field = [ct.default_int32_field_name, ct.default_int16_field_name,
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ct.default_float_field_name, ct.default_double_field_name, ct.default_string_field_name]
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# exp res: first two rows and all fields expect last vec field
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res = df.iloc[:2, :].to_dict('records')
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for field in non_primary_field:
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filter_values = df[field].tolist()[:2]
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if field is not ct.default_string_field_name:
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term_expr = f'{field} in {filter_values}'
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else:
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term_expr = f'{field} in {filter_values}'
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term_expr = term_expr.replace("'", "\"")
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log.info(res)
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self.collection_wrap.query(term_expr, output_fields=["*"],
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check_task=CheckTasks.check_query_results,
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check_items={exp_res: res, "with_vec": True})
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@pytest.mark.tags(CaseLabel.L2)
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def test_query_expr_by_bool_field(self):
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"""
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target: test query by bool field and output bool field
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method: 1.create and insert with [int64, float, bool, float_vec] fields
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2.query by bool field, and output all int64, bool fields
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expected: verify query result and output fields
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"""
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self._connect()
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df = cf.gen_default_dataframe_data()
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bool_values = pd.Series(data=[True if i % 2 == 0 else False for i in range(ct.default_nb)], dtype="bool")
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df.insert(2, ct.default_bool_field_name, bool_values)
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self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
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primary_field=ct.default_int64_field_name)
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assert self.collection_wrap.num_entities == ct.default_nb
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self.collection_wrap.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
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self.collection_wrap.load()
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# output bool field
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res, _ = self.collection_wrap.query(default_term_expr, output_fields=[ct.default_bool_field_name])
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assert set(res[0].keys()) == {ct.default_int64_field_name, ct.default_bool_field_name}
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# not support filter bool field with expr 'bool in [0/ 1]'
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not_support_expr = f'{ct.default_bool_field_name} in [0]'
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error = {ct.err_code: 65535,
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ct.err_msg: "cannot parse expression: bool in [0], error: value '0' in list cannot be casted to Bool"}
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self.collection_wrap.query(not_support_expr, output_fields=[ct.default_bool_field_name],
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check_task=CheckTasks.err_res, check_items=error)
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# filter bool field by bool term expr
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for bool_value in [True, False]:
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exprs = [f'{ct.default_bool_field_name} in [{bool_value}]',
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f'{ct.default_bool_field_name} == {bool_value}']
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for expr in exprs:
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res, _ = self.collection_wrap.query(expr, output_fields=[ct.default_bool_field_name])
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assert len(res) == ct.default_nb / 2
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for _r in res:
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assert _r[ct.default_bool_field_name] == bool_value
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@pytest.mark.tags(CaseLabel.L2)
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def test_query_expr_by_int64(self):
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"""
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target: test query through int64 field and output int64 field
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method: use int64 as query expr parameter
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expected: verify query output number
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"""
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self._connect()
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df = cf.gen_default_dataframe_data(nb=ct.default_nb*10)
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self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
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primary_field=ct.default_int64_field_name)
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assert self.collection_wrap.num_entities == ct.default_nb * 10
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self.collection_wrap.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
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self.collection_wrap.load()
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# filter on int64 fields
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expr_list = [f'{ct.default_int64_field_name} > 8192 && {ct.default_int64_field_name} < 8194',
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f'{ct.default_int64_field_name} > 16384 && {ct.default_int64_field_name} < 16386']
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for expr in expr_list:
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res, _ = self.collection_wrap.query(expr, output_fields=[ct.default_int64_field_name])
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assert len(res) == 1
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@pytest.mark.tags(CaseLabel.L1)
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def test_query_expr_by_int8_field(self):
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"""
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target: test query by int8 field
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method: 1.create and insert with [int64, float, int8, float_vec] fields
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2.query by int8 field, and output all scalar fields
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expected: verify query result
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"""
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self._connect()
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# construct collection from dataFrame according to [int64, float, int8, float_vec]
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df = cf.gen_default_dataframe_data()
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int8_values = pd.Series(data=[np.int8(i) for i in range(ct.default_nb)], dtype="int8")
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df.insert(2, ct.default_int8_field_name, int8_values)
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self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
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primary_field=ct.default_int64_field_name)
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assert self.collection_wrap.num_entities == ct.default_nb
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# query expression
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term_expr = f'{ct.default_int8_field_name} in {[0]}'
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# expected query result
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res = []
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# int8 range [-128, 127] so when nb=1200, there are many repeated int8 values equal to 0
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for i in range(0, ct.default_nb, 256):
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res.extend(df.iloc[i:i + 1, :-2].to_dict('records'))
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self.collection_wrap.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
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self.collection_wrap.load()
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self.collection_wrap.query(term_expr, output_fields=["float", "int64", "int8", "varchar"],
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check_task=CheckTasks.check_query_results, check_items={exp_res: res})
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@pytest.fixture(scope="function", params=cf.gen_normal_expressions())
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def get_normal_expr(self, request):
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if request.param == "":
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pytest.skip("query with "" expr is invalid")
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yield request.param
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_with_expression(self, get_normal_expr, enable_dynamic_field):
|
|
"""
|
|
target: test query with different expr
|
|
method: query with different boolean expr
|
|
expected: verify query result
|
|
"""
|
|
# 1. initialize with data
|
|
nb = 1000
|
|
collection_w, _vectors, _, insert_ids = self.init_collection_general(prefix, True, nb,
|
|
enable_dynamic_field=enable_dynamic_field)[0:4]
|
|
|
|
# filter result with expression in collection
|
|
_vectors = _vectors[0]
|
|
expr = get_normal_expr
|
|
expression = expr.replace("&&", "and").replace("||", "or")
|
|
filter_ids = []
|
|
for i, _id in enumerate(insert_ids):
|
|
if enable_dynamic_field:
|
|
int64 = _vectors[i][ct.default_int64_field_name]
|
|
float = _vectors[i][ct.default_float_field_name]
|
|
else:
|
|
int64 = _vectors.int64[i]
|
|
float = _vectors.float[i]
|
|
if not expression or eval(expression):
|
|
filter_ids.append(_id)
|
|
|
|
# query and verify result
|
|
res = collection_w.query(expr=expression)[0]
|
|
query_ids = set(map(lambda x: x[ct.default_int64_field_name], res))
|
|
assert query_ids == set(filter_ids)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_expr_wrong_term_keyword(self):
|
|
"""
|
|
target: test query with wrong term expr keyword
|
|
method: query with wrong keyword term expr
|
|
expected: raise exception
|
|
"""
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
expr_1 = f'{ct.default_int64_field_name} inn [1, 2]'
|
|
error_1 = {ct.err_code: 65535, ct.err_msg: "cannot parse expression: int64 inn [1, 2], "
|
|
"error: invalid expression: int64 inn [1, 2]"}
|
|
collection_w.query(expr_1, check_task=CheckTasks.err_res, check_items=error_1)
|
|
|
|
expr_3 = f'{ct.default_int64_field_name} in not [1, 2]'
|
|
error_3 = {ct.err_code: 65535, ct.err_msg: "cannot parse expression: int64 in not [1, 2], "
|
|
"error: line 1:9 no viable alternative at input 'innot'"}
|
|
collection_w.query(expr_3, check_task=CheckTasks.err_res, check_items=error_3)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("field", [ct.default_int64_field_name, ct.default_float_field_name])
|
|
def test_query_expr_not_in_term(self, field):
|
|
"""
|
|
target: test query with `not in` expr
|
|
method: query with not in expr
|
|
expected: verify query result
|
|
"""
|
|
self._connect()
|
|
df = cf.gen_default_dataframe_data()
|
|
self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
|
|
primary_field=ct.default_int64_field_name)
|
|
assert self.collection_wrap.num_entities == ct.default_nb
|
|
self.collection_wrap.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
self.collection_wrap.load()
|
|
values = df[field].tolist()
|
|
pos = 100
|
|
term_expr = f'{field} not in {values[pos:]}'
|
|
res = df.iloc[:pos, :3].to_dict('records')
|
|
self.collection_wrap.query(term_expr, output_fields=["float", "int64", "varchar"],
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("pos", [0, ct.default_nb])
|
|
def test_query_expr_not_in_empty_and_all(self, pos):
|
|
"""
|
|
target: test query with `not in` expr
|
|
method: query with `not in` expr for (non)empty collection
|
|
expected: verify query result
|
|
"""
|
|
self._connect()
|
|
df = cf.gen_default_dataframe_data()
|
|
self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
|
|
primary_field=ct.default_int64_field_name)
|
|
assert self.collection_wrap.num_entities == ct.default_nb
|
|
self.collection_wrap.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
self.collection_wrap.load()
|
|
int64_values = df[ct.default_int64_field_name].tolist()
|
|
term_expr = f'{ct.default_int64_field_name} not in {int64_values[pos:]}'
|
|
res = df.iloc[:pos, :1].to_dict('records')
|
|
self.collection_wrap.query(term_expr, check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_expr_random_values(self):
|
|
"""
|
|
target: test query with random filter values
|
|
method: query with random filter values, like [0, 2, 4, 3]
|
|
expected: correct query result
|
|
"""
|
|
self._connect()
|
|
df = cf.gen_default_dataframe_data(nb=100)
|
|
log.debug(df.head(5))
|
|
self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
|
|
primary_field=ct.default_int64_field_name)
|
|
assert self.collection_wrap.num_entities == 100
|
|
self.collection_wrap.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
self.collection_wrap.load()
|
|
|
|
# random_values = [random.randint(0, ct.default_nb) for _ in range(4)]
|
|
random_values = [0, 2, 4, 3]
|
|
term_expr = f'{ct.default_int64_field_name} in {random_values}'
|
|
res = df.iloc[random_values, :1].to_dict('records')
|
|
self.collection_wrap.query(term_expr, check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_expr_not_in_random(self):
|
|
"""
|
|
target: test query with fixed filter values
|
|
method: query with fixed filter values
|
|
expected: correct query result
|
|
"""
|
|
self._connect()
|
|
df = cf.gen_default_dataframe_data(nb=50)
|
|
log.debug(df.head(5))
|
|
self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
|
|
primary_field=ct.default_int64_field_name)
|
|
assert self.collection_wrap.num_entities == 50
|
|
self.collection_wrap.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
self.collection_wrap.load()
|
|
|
|
random_values = [i for i in range(10, 50)]
|
|
log.debug(f'random values: {random_values}')
|
|
random.shuffle(random_values)
|
|
term_expr = f'{ct.default_int64_field_name} not in {random_values}'
|
|
res = df.iloc[:10, :1].to_dict('records')
|
|
self.collection_wrap.query(term_expr, check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_expr_non_array_term(self):
|
|
"""
|
|
target: test query with non-array term expr
|
|
method: query with non-array term expr
|
|
expected: raise exception
|
|
"""
|
|
exprs = [f'{ct.default_int64_field_name} in 1',
|
|
f'{ct.default_int64_field_name} in "in"',
|
|
f'{ct.default_int64_field_name} in (mn)']
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
error = {ct.err_code: 65535, ct.err_msg: "cannot parse expression: int64 in 1, "
|
|
"error: line 1:9 no viable alternative at input 'in1'"}
|
|
for expr in exprs:
|
|
collection_w.query(expr, check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_expr_empty_term_array(self):
|
|
"""
|
|
target: test query with empty array term expr
|
|
method: query with empty term expr
|
|
expected: empty result
|
|
"""
|
|
term_expr = f'{ct.default_int64_field_name} in []'
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
res, _ = collection_w.query(term_expr)
|
|
assert len(res) == 0
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_expr_inconsistent_mix_term_array(self):
|
|
"""
|
|
target: test query with term expr that field and array are inconsistent or mix type
|
|
method: 1.query with int field and float values
|
|
2.query with term expr that has int and float type value
|
|
expected: raise exception
|
|
"""
|
|
collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix))
|
|
int_values = [[1., 2.], [1, 2.]]
|
|
error = {ct.err_code: 65535,
|
|
ct.err_msg: "cannot parse expression: int64 in [1.0, 2.0], error: value '1.0' "
|
|
"in list cannot be casted to Int64"}
|
|
for values in int_values:
|
|
term_expr = f'{ct.default_int64_field_name} in {values}'
|
|
collection_w.query(term_expr, check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_expr_non_constant_array_term(self):
|
|
"""
|
|
target: test query with non-constant array term expr
|
|
method: query with non-constant array expr
|
|
expected: raise exception
|
|
"""
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
constants = [[1], (), {}]
|
|
error = {ct.err_code: 65535,
|
|
ct.err_msg: "cannot parse expression: int64 in [[1]], error: value '[1]' in "
|
|
"list cannot be casted to Int64"}
|
|
for constant in constants:
|
|
term_expr = f'{ct.default_int64_field_name} in [{constant}]'
|
|
collection_w.query(term_expr, check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("expr_prefix", ["json_contains", "JSON_CONTAINS",
|
|
"array_contains", "ARRAY_CONTAINS"])
|
|
def test_query_expr_json_contains(self, enable_dynamic_field, expr_prefix):
|
|
"""
|
|
target: test query with expression using json_contains
|
|
method: query with expression using json_contains
|
|
expected: succeed
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, enable_dynamic_field=enable_dynamic_field)[0]
|
|
|
|
# 2. insert data
|
|
array = cf.gen_default_rows_data()
|
|
limit = 99
|
|
for i in range(ct.default_nb):
|
|
array[i][json_field] = {"number": i,
|
|
"list": [m for m in range(i, i + limit)]}
|
|
|
|
collection_w.insert(array)
|
|
|
|
# 3. query
|
|
collection_w.load()
|
|
expression = f"{expr_prefix}({json_field}['list'], 1000)"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == limit
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("expr_prefix", ["json_contains", "JSON_CONTAINS"])
|
|
def test_query_expr_list_json_contains(self, expr_prefix):
|
|
"""
|
|
target: test query with expression using json_contains
|
|
method: query with expression using json_contains
|
|
expected: succeed
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, enable_dynamic_field=True)[0]
|
|
|
|
# 2. insert data
|
|
limit = ct.default_nb // 4
|
|
array = []
|
|
for i in range(ct.default_nb):
|
|
data = {
|
|
ct.default_int64_field_name: i,
|
|
ct.default_json_field_name: [str(m) for m in range(i, i + limit)],
|
|
ct.default_float_vec_field_name: cf.gen_vectors(1, ct.default_dim)[0]
|
|
}
|
|
array.append(data)
|
|
collection_w.insert(array)
|
|
|
|
# 3. query
|
|
collection_w.load()
|
|
expression = f"{expr_prefix}({json_field}, '1000')"
|
|
res = collection_w.query(expression, output_fields=["count(*)"])[0]
|
|
assert res[0]["count(*)"] == limit
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("expr_prefix", ["json_contains", "JSON_CONTAINS"])
|
|
def test_query_expr_json_contains_combined_with_normal(self, enable_dynamic_field, expr_prefix):
|
|
"""
|
|
target: test query with expression using json_contains
|
|
method: query with expression using json_contains
|
|
expected: succeed
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, enable_dynamic_field=enable_dynamic_field)[0]
|
|
|
|
# 2. insert data
|
|
array = cf.gen_default_rows_data()
|
|
limit = ct.default_nb // 3
|
|
for i in range(ct.default_nb):
|
|
array[i][ct.default_json_field_name] = {"number": i, "list": [m for m in range(i, i + limit)]}
|
|
|
|
collection_w.insert(array)
|
|
|
|
# 3. query
|
|
collection_w.load()
|
|
tar = 1000
|
|
expression = f"{expr_prefix}({json_field}['list'], {tar}) && float > {tar - limit // 2}"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == limit // 2
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("expr_prefix", ["json_contains_all", "JSON_CONTAINS_ALL",
|
|
"array_contains_all", "ARRAY_CONTAINS_ALL"])
|
|
def test_query_expr_all_datatype_json_contains_all(self, enable_dynamic_field, expr_prefix):
|
|
"""
|
|
target: test query with expression using json_contains
|
|
method: query with expression using json_contains
|
|
expected: succeed
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, enable_dynamic_field=enable_dynamic_field)[0]
|
|
|
|
# 2. insert data
|
|
array = cf.gen_default_rows_data()
|
|
limit = 10
|
|
for i in range(ct.default_nb):
|
|
content = {
|
|
# test for int
|
|
"listInt": [m for m in range(i, i + limit)],
|
|
# test for string
|
|
"listStr": [str(m) for m in range(i, i + limit)],
|
|
# test for float
|
|
"listFlt": [m * 1.0 for m in range(i, i + limit)],
|
|
# test for bool
|
|
"listBool": [bool(i % 2)],
|
|
# test for list
|
|
"listList": [[i, str(i + 1)], [i * 1.0, i + 1]],
|
|
# test for mixed data
|
|
"listMix": [i, i * 1.1, str(i), bool(i % 2), [i, str(i)]]
|
|
}
|
|
array[i][ct.default_json_field_name] = content
|
|
|
|
collection_w.insert(array)
|
|
|
|
# 3. query
|
|
collection_w.load()
|
|
# test for int
|
|
_id = random.randint(limit, ct.default_nb - limit)
|
|
ids = [i for i in range(_id, _id + limit)]
|
|
expression = f"{expr_prefix}({json_field}['listInt'], {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == 1
|
|
|
|
# test for string
|
|
ids = [str(_id), str(_id + 1), str(_id + 2)]
|
|
expression = f"{expr_prefix}({json_field}['listStr'], {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == limit - len(ids) + 1
|
|
|
|
# test for float
|
|
ids = [_id * 1.0]
|
|
expression = f"{expr_prefix}({json_field}['listFlt'], {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == limit
|
|
|
|
# test for bool
|
|
ids = [True]
|
|
expression = f"{expr_prefix}({json_field}['listBool'], {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == ct.default_nb // 2
|
|
|
|
# test for list
|
|
ids = [[_id, str(_id + 1)]]
|
|
expression = f"{expr_prefix}({json_field}['listList'], {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == 1
|
|
|
|
# test for mixed data
|
|
ids = [[_id, str(_id)], bool(_id % 2)]
|
|
expression = f"{expr_prefix}({json_field}['listMix'], {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == 1
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("expr_prefix", ["json_contains_all", "JSON_CONTAINS_ALL"])
|
|
def test_query_expr_list_all_datatype_json_contains_all(self, expr_prefix):
|
|
"""
|
|
target: test query with expression using json_contains_all
|
|
method: query with expression using json_contains_all
|
|
expected: succeed
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, enable_dynamic_field=True)[0]
|
|
|
|
# 2. insert data
|
|
array = cf.gen_default_rows_data(with_json=False)
|
|
limit = 10
|
|
for i in range(ct.default_nb):
|
|
array[i]["listInt"] = [m for m in range(i, i + limit)] # test for int
|
|
array[i]["listStr"] = [str(m) for m in range(i, i + limit)] # test for string
|
|
array[i]["listFlt"] = [m * 1.0 for m in range(i, i + limit)] # test for float
|
|
array[i]["listBool"] = [bool(i % 2)] # test for bool
|
|
array[i]["listList"] = [[i, str(i + 1)], [i * 1.0, i + 1]] # test for list
|
|
array[i]["listMix"] = [i, i * 1.1, str(i), bool(i % 2), [i, str(i)]] # test for mixed data
|
|
|
|
collection_w.insert(array)
|
|
|
|
# 3. query
|
|
collection_w.load()
|
|
|
|
# test for int
|
|
_id = random.randint(limit, ct.default_nb - limit)
|
|
ids = [i for i in range(_id, _id + limit)]
|
|
expression = f"{expr_prefix}(listInt, {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == 1
|
|
|
|
# test for string
|
|
ids = [str(_id), str(_id + 1), str(_id + 2)]
|
|
expression = f"{expr_prefix}(listStr, {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == limit - len(ids) + 1
|
|
|
|
# test for float
|
|
ids = [_id * 1.0]
|
|
expression = f"{expr_prefix}(listFlt, {ids})"
|
|
res = collection_w.query(expression, output_fields=["count(*)"])[0]
|
|
assert res[0]["count(*)"] == limit
|
|
|
|
# test for bool
|
|
ids = [True]
|
|
expression = f"{expr_prefix}(listBool, {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == ct.default_nb // 2
|
|
|
|
# test for list
|
|
ids = [[_id, str(_id + 1)]]
|
|
expression = f"{expr_prefix}(listList, {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == 1
|
|
|
|
# test for mixed data
|
|
ids = [[_id, str(_id)], bool(_id % 2)]
|
|
expression = f"{expr_prefix}(listMix, {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == 1
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("expr_prefix", ["json_contains_any", "JSON_CONTAINS_ANY"])
|
|
def test_query_expr_all_datatype_json_contains_any(self, enable_dynamic_field, expr_prefix):
|
|
"""
|
|
target: test query with expression using json_contains
|
|
method: query with expression using json_contains
|
|
expected: succeed
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, enable_dynamic_field=enable_dynamic_field)[0]
|
|
|
|
# 2. insert data
|
|
array = cf.gen_default_rows_data()
|
|
limit = 10
|
|
for i in range(ct.default_nb):
|
|
content = {
|
|
# test for int
|
|
"listInt": [m for m in range(i, i + limit)],
|
|
# test for string
|
|
"listStr": [str(m) for m in range(i, i + limit)],
|
|
# test for float
|
|
"listFlt": [m * 1.0 for m in range(i, i + limit)],
|
|
# test for bool
|
|
"listBool": [bool(i % 2)],
|
|
# test for list
|
|
"listList": [[i, str(i + 1)], [i * 1.0, i + 1]],
|
|
# test for mixed data
|
|
"listMix": [i, i * 1.1, str(i), bool(i % 2), [i, str(i)]]
|
|
}
|
|
array[i][ct.default_json_field_name] = content
|
|
|
|
collection_w.insert(array)
|
|
|
|
# 3. query
|
|
collection_w.load()
|
|
|
|
# test for int
|
|
_id = random.randint(limit, ct.default_nb - limit)
|
|
ids = [i for i in range(_id, _id + limit)]
|
|
expression = f"{expr_prefix}({json_field}['listInt'], {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == 2 * limit - 1
|
|
|
|
# test for string
|
|
ids = [str(_id), str(_id + 1), str(_id + 2)]
|
|
expression = f"{expr_prefix}({json_field}['listStr'], {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == limit + len(ids) - 1
|
|
|
|
# test for float
|
|
ids = [_id * 1.0]
|
|
expression = f"{expr_prefix}({json_field}['listFlt'], {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == limit
|
|
|
|
# test for bool
|
|
ids = [True]
|
|
expression = f"{expr_prefix}({json_field}['listBool'], {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == ct.default_nb // 2
|
|
|
|
# test for list
|
|
ids = [[_id, str(_id + 1)]]
|
|
expression = f"{expr_prefix}({json_field}['listList'], {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == 1
|
|
|
|
# test for mixed data
|
|
ids = [_id, bool(_id % 2)]
|
|
expression = f"{expr_prefix}({json_field}['listMix'], {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == ct.default_nb // 2
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("expr_prefix", ["json_contains_any", "JSON_CONTAINS_ANY",
|
|
"array_contains_any", "ARRAY_CONTAINS_ANY"])
|
|
def test_query_expr_list_all_datatype_json_contains_any(self, expr_prefix):
|
|
"""
|
|
target: test query with expression using json_contains_any
|
|
method: query with expression using json_contains_any
|
|
expected: succeed
|
|
"""
|
|
# 1. initialize with data
|
|
nb = ct.default_nb
|
|
pk_field = ct.default_int64_field_name
|
|
collection_w = self.init_collection_general(prefix, enable_dynamic_field=True)[0]
|
|
|
|
# 2. insert data
|
|
array = cf.gen_default_rows_data(with_json=False)
|
|
limit = random.randint(10, 20)
|
|
int_data = [[m for m in range(i, i + limit)] for i in range(nb)]
|
|
str_data = [[str(m) for m in range(i, i + limit)] for i in range(nb)]
|
|
flt_data = [[m * 1.0 for m in range(i, i + limit)] for i in range(nb)]
|
|
bool_data = [[bool(i % 2)] for i in range(nb)]
|
|
list_data = [[[i, str(i + 1)], [i * 1.0, i + 1]] for i in range(nb)]
|
|
mix_data = [[i, i * 1.1, str(i), bool(i % 2), [i, str(i)]] for i in range(nb)]
|
|
for i in range(nb):
|
|
array[i]["listInt"] = int_data[i] # test for int
|
|
array[i]["listStr"] = str_data[i] # test for string
|
|
array[i]["listFlt"] = flt_data[i] # test for float
|
|
array[i]["listBool"] = bool_data[i] # test for bool
|
|
array[i]["listList"] = list_data[i] # test for list
|
|
array[i]["listMix"] = mix_data[i] # test for mixed data
|
|
|
|
collection_w.insert(array)
|
|
|
|
# 3. query
|
|
collection_w.load()
|
|
|
|
_id = random.randint(limit, nb - limit)
|
|
# test for int
|
|
ids = [i for i in range(_id, _id + limit)]
|
|
expression = f"{expr_prefix}(listInt, {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert [entity[pk_field] for entity in res] == cf.assert_json_contains(expression, int_data)
|
|
|
|
# test for string
|
|
ids = [str(_id), str(_id + 1), str(_id + 2)]
|
|
expression = f"{expr_prefix}(listStr, {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert [entity[pk_field] for entity in res] == cf.assert_json_contains(expression, str_data)
|
|
|
|
# test for float
|
|
ids = [_id * 1.0]
|
|
expression = f"{expr_prefix}(listFlt, {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert [entity[pk_field] for entity in res] == cf.assert_json_contains(expression, flt_data)
|
|
|
|
# test for bool
|
|
ids = [True]
|
|
expression = f"{expr_prefix}(listBool, {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert [entity[pk_field] for entity in res] == cf.assert_json_contains(expression, bool_data)
|
|
|
|
# test for list
|
|
ids = [[_id, str(_id + 1)]]
|
|
expression = f"{expr_prefix}(listList, {ids})"
|
|
res = collection_w.query(expression, output_fields=["count(*)"])[0]
|
|
assert res[0]["count(*)"] == 1
|
|
|
|
# test for mixed data
|
|
ids = [str(_id)]
|
|
expression = f"{expr_prefix}(listMix, {ids})"
|
|
res = collection_w.query(expression, output_fields=["count(*)"])[0]
|
|
assert res[0]["count(*)"] == 1
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("expr_prefix", ["json_contains_any", "json_contains_all"])
|
|
def test_query_expr_json_contains_list_in_list(self, expr_prefix, enable_dynamic_field):
|
|
"""
|
|
target: test query with expression using json_contains_any
|
|
method: query with expression using json_contains_any
|
|
expected: succeed
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, enable_dynamic_field=enable_dynamic_field)[0]
|
|
|
|
# 2. insert data
|
|
array = cf.gen_default_rows_data()
|
|
for i in range(ct.default_nb):
|
|
array[i][json_field] = {"list": [[i, i + 1], [i, i + 2], [i, i + 3]]}
|
|
|
|
collection_w.insert(array)
|
|
|
|
# 3. query
|
|
collection_w.load()
|
|
_id = random.randint(3, ct.default_nb-3)
|
|
ids = [[_id, _id + 1]]
|
|
expression = f"{expr_prefix}({json_field}['list'], {ids})"
|
|
res = collection_w.query(expression)[0]
|
|
assert len(res) == 1
|
|
|
|
ids = [[_id + 4, _id], [_id]]
|
|
expression = f"{expr_prefix}({json_field}['list'], {ids})"
|
|
collection_w.query(expression, check_task=CheckTasks.check_query_empty)
|
|
|
|
@pytest.fixture(scope="function", params=ct.get_invalid_strs)
|
|
def get_not_list(self, request):
|
|
if request.param == [1, "2", 3]:
|
|
pytest.skip('[1, "2", 3] is valid type for list')
|
|
yield request.param
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("expr_prefix", ["json_contains_any", "JSON_CONTAINS_ANY",
|
|
"json_contains_all", "JSON_CONTAINS_ALL"])
|
|
def test_query_expr_json_contains_invalid_type(self, expr_prefix, enable_dynamic_field, get_not_list):
|
|
"""
|
|
target: test query with expression using json_contains_any
|
|
method: query with expression using json_contains_any
|
|
expected: succeed
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, enable_dynamic_field=enable_dynamic_field)[0]
|
|
|
|
# 2. insert data
|
|
array = cf.gen_default_rows_data()
|
|
for i in range(ct.default_nb):
|
|
array[i][json_field] = {"number": i,
|
|
"list": [m for m in range(i, i + 10)]}
|
|
|
|
collection_w.insert(array)
|
|
|
|
# 3. query
|
|
collection_w.load()
|
|
expression = f"{expr_prefix}({json_field}['list'], {get_not_list})"
|
|
error = {ct.err_code: 65535, ct.err_msg: f"cannot parse expression: {expression}, "
|
|
f"error: contains_any operation element must be an array"}
|
|
collection_w.query(expression, check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("expr_prefix", ["json_contains", "JSON_CONTAINS"])
|
|
def test_query_expr_json_contains_pagination(self, enable_dynamic_field, expr_prefix):
|
|
"""
|
|
target: test query with expression using json_contains
|
|
method: query with expression using json_contains
|
|
expected: succeed
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, enable_dynamic_field=enable_dynamic_field)[0]
|
|
|
|
# 2. insert data
|
|
array = cf.gen_default_rows_data()
|
|
limit = ct.default_nb // 3
|
|
for i in range(ct.default_nb):
|
|
array[i][json_field] = {"number": i,
|
|
"list": [m for m in range(i, i + limit)]}
|
|
|
|
collection_w.insert(array)
|
|
|
|
# 3. query
|
|
collection_w.load()
|
|
expression = f"{expr_prefix}({json_field}['list'], 1000)"
|
|
offset = random.randint(1, limit)
|
|
res = collection_w.query(expression, limit=limit, offset=offset)[0]
|
|
assert len(res) == limit - offset
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("array_length", ["ARRAY_LENGTH", "array_length"])
|
|
@pytest.mark.parametrize("op", ["==", "!="])
|
|
def test_query_expr_array_length(self, array_length, op, enable_dynamic_field):
|
|
"""
|
|
target: test query with expression using array_length
|
|
method: query with expression using array_length
|
|
array_length only support == , !=
|
|
expected: succeed
|
|
"""
|
|
# 1. create a collection
|
|
schema = cf.gen_array_collection_schema()
|
|
collection_w = self.init_collection_wrap(schema=schema, enable_dynamic_field=enable_dynamic_field)
|
|
|
|
# 2. insert data
|
|
data = cf.gen_array_dataframe_data()
|
|
length = []
|
|
for i in range(ct.default_nb):
|
|
ran_int = random.randint(50, 53)
|
|
length.append(ran_int)
|
|
|
|
data[ct.default_float_array_field_name] = \
|
|
[[np.float32(j) for j in range(length[i])] for i in range(ct.default_nb)]
|
|
collection_w.insert(data)
|
|
|
|
# 3. load and query
|
|
collection_w.create_index(ct.default_float_vec_field_name, ct.default_flat_index)
|
|
collection_w.load()
|
|
expression = f"{array_length}({ct.default_float_array_field_name}) {op} 51"
|
|
res = collection_w.query(expression)[0]
|
|
|
|
# 4. check
|
|
expression = expression.replace(f"{array_length}(float_array)", "array_length")
|
|
filter_ids = []
|
|
for i in range(ct.default_nb):
|
|
array_length = length[i]
|
|
if not expression or eval(expression):
|
|
filter_ids.append(i)
|
|
assert len(res) == len(filter_ids)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("op", [">", "<=", "+ 1 =="])
|
|
def test_query_expr_invalid_array_length(self, op):
|
|
"""
|
|
target: test query with expression using array_length
|
|
method: query with expression using array_length
|
|
array_length only support == , !=
|
|
expected: raise error
|
|
"""
|
|
# 1. create a collection
|
|
schema = cf.gen_array_collection_schema()
|
|
collection_w = self.init_collection_wrap(schema=schema)
|
|
|
|
# 2. insert data
|
|
data = cf.gen_array_dataframe_data()
|
|
collection_w.insert(data)
|
|
|
|
# 3. load and query
|
|
collection_w.create_index(ct.default_float_vec_field_name, ct.default_flat_index)
|
|
collection_w.load()
|
|
expression = f"array_length({ct.default_float_array_field_name}) {op} 51"
|
|
collection_w.query(expression, check_task=CheckTasks.err_res,
|
|
check_items={ct.err_code: 65535,
|
|
ct.err_msg: "cannot parse expression: %s, error %s "
|
|
"is not supported" % (expression, op)})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_expr_empty_without_limit(self):
|
|
"""
|
|
target: test query with empty expression and no limit
|
|
method: query empty expression without setting limit
|
|
expected: raise error
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
|
|
# 2. query with no limit and no offset
|
|
error = {ct.err_code: 1, ct.err_msg: "empty expression should be used with limit"}
|
|
collection_w.query("", check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
# 3. query with offset but no limit
|
|
collection_w.query("", offset=1, check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_empty(self):
|
|
"""
|
|
target: test query empty
|
|
method: query empty
|
|
expected: return error
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
|
|
# 2. query
|
|
try:
|
|
collection_w.query()
|
|
except TypeError as e:
|
|
assert "missing 1 required positional argument: 'expr'" in str(e)
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
@pytest.mark.parametrize("limit", [10, 100, 1000])
|
|
@pytest.mark.parametrize("auto_id", [True, False])
|
|
def test_query_expr_empty(self, auto_id, limit):
|
|
"""
|
|
target: test query with empty expression
|
|
method: query empty expression with a limit
|
|
expected: return topK results by order
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, auto_id=auto_id)[0:4]
|
|
exp_ids, res = insert_ids[:limit], []
|
|
for ids in exp_ids:
|
|
res.append({ct.default_int64_field_name: ids})
|
|
|
|
# 2. query with limit
|
|
collection_w.query("", limit=limit, check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_expr_empty_pk_string(self):
|
|
"""
|
|
target: test query with empty expression
|
|
method: query empty expression with a limit
|
|
expected: return topK results by order
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = \
|
|
self.init_collection_general(prefix, True, primary_field=ct.default_string_field_name)[0:4]
|
|
# string field is sorted by lexicographical order
|
|
exp_ids, res = ['0', '1', '10', '100', '1000', '1001', '1002', '1003', '1004', '1005'], []
|
|
for ids in exp_ids:
|
|
res.append({ct.default_string_field_name: ids})
|
|
|
|
# 2. query with limit
|
|
collection_w.query("", limit=ct.default_limit,
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
# 2. query with limit + offset
|
|
res = res[5:]
|
|
collection_w.query("", limit=5, offset=5,
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("offset", [100, 1000])
|
|
@pytest.mark.parametrize("limit", [100, 1000])
|
|
@pytest.mark.parametrize("auto_id", [True, False])
|
|
def test_query_expr_empty_with_pagination(self, auto_id, limit, offset):
|
|
"""
|
|
target: test query with empty expression
|
|
method: query empty expression with a limit
|
|
expected: return topK results by order
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, auto_id=auto_id)[0:4]
|
|
exp_ids, res = insert_ids[:limit + offset][offset:], []
|
|
for ids in exp_ids:
|
|
res.append({ct.default_int64_field_name: ids})
|
|
|
|
# 2. query with limit and offset
|
|
collection_w.query("", limit=limit, offset=offset,
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("offset", [100, 1000])
|
|
@pytest.mark.parametrize("limit", [100, 1000])
|
|
def test_query_expr_empty_with_random_pk(self, limit, offset):
|
|
"""
|
|
target: test query with empty expression
|
|
method: create a collection using random pk, query empty expression with a limit
|
|
expected: return topK results by order
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, with_json=False)[0]
|
|
|
|
# 2. generate unordered pk array and insert
|
|
unordered_ids = [i for i in range(ct.default_nb)]
|
|
random.shuffle(unordered_ids)
|
|
float_value = [np.float32(i) for i in unordered_ids]
|
|
string_value = [str(i) for i in unordered_ids]
|
|
vector_value = cf.gen_vectors(nb=ct.default_nb, dim=ct.default_dim)
|
|
collection_w.insert([unordered_ids, float_value, string_value, vector_value])
|
|
collection_w.load()
|
|
|
|
# 3. query with empty expr and check the result
|
|
exp_ids, res = sorted(unordered_ids)[:limit], []
|
|
for ids in exp_ids:
|
|
res.append({ct.default_int64_field_name: ids, ct.default_string_field_name: str(ids)})
|
|
|
|
collection_w.query("", limit=limit, output_fields=[ct.default_string_field_name],
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
# 4. query with pagination
|
|
exp_ids, res = sorted(unordered_ids)[:limit + offset][offset:], []
|
|
for ids in exp_ids:
|
|
res.append({ct.default_int64_field_name: ids, ct.default_string_field_name: str(ids)})
|
|
|
|
collection_w.query("", limit=limit, offset=offset, output_fields=[ct.default_string_field_name],
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
def test_query_expr_with_limit_offset_out_of_range(self):
|
|
"""
|
|
target: test query with empty expression
|
|
method: query empty expression with limit and offset out of range
|
|
expected: raise error
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
|
|
# 2. query with limit > 16384
|
|
error = {ct.err_code: 1,
|
|
ct.err_msg: "invalid max query result window, (offset+limit) should be in range [1, 16384]"}
|
|
collection_w.query("", limit=16385, check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
# 3. query with offset + limit > 16384
|
|
collection_w.query("", limit=1, offset=16384, check_task=CheckTasks.err_res, check_items=error)
|
|
collection_w.query("", limit=16384, offset=1, check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
# 4. query with limit < 0
|
|
error = {ct.err_code: 1,
|
|
ct.err_msg: "invalid max query result window, offset [-1] is invalid, should be gte than 0"}
|
|
collection_w.query("", limit=2, offset=-1,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("expression", cf.gen_integer_overflow_expressions())
|
|
def test_query_expr_out_of_range(self, expression):
|
|
"""
|
|
target: test query expression out of range
|
|
method: query empty expression with limit and offset out of range
|
|
expected:
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, is_all_data_type=True)[0]
|
|
start = ct.default_nb // 2
|
|
_vectors = cf.gen_dataframe_all_data_type(start=start)
|
|
|
|
# increase the value to cover the int range
|
|
_vectors["int16"] = \
|
|
pd.Series(data=[np.int16(i*40) for i in range(start, start + ct.default_nb)], dtype="int16")
|
|
_vectors["int32"] = \
|
|
pd.Series(data=[np.int32(i*2200000) for i in range(start, start + ct.default_nb)], dtype="int32")
|
|
insert_ids = collection_w.insert(_vectors)[0].primary_keys
|
|
|
|
# filter result with expression in collection
|
|
expression = expression.replace("&&", "and").replace("||", "or")
|
|
filter_ids = []
|
|
for i, _id in enumerate(insert_ids):
|
|
int8 = _vectors.int8[i]
|
|
int16 = _vectors.int16[i]
|
|
int32 = _vectors.int32[i]
|
|
if not expression or eval(expression):
|
|
filter_ids.append(_id)
|
|
|
|
# query
|
|
collection_w.load()
|
|
res = collection_w.query(expression, output_fields=["int8"])[0]
|
|
assert len(res) == len(filter_ids)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_output_field_none_or_empty(self, enable_dynamic_field):
|
|
"""
|
|
target: test query with none and empty output field
|
|
method: query with output field=None, field=[]
|
|
expected: return primary field
|
|
"""
|
|
collection_w = self.init_collection_general(prefix, insert_data=True,
|
|
enable_dynamic_field=enable_dynamic_field)[0]
|
|
for fields in [None, []]:
|
|
res, _ = collection_w.query(default_term_expr, output_fields=fields)
|
|
assert res[0].keys() == {ct.default_int64_field_name}
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
def test_query_output_one_field(self, enable_dynamic_field):
|
|
"""
|
|
target: test query with output one field
|
|
method: query with output one field
|
|
expected: return one field
|
|
"""
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True,
|
|
enable_dynamic_field=enable_dynamic_field)[0:2]
|
|
res, _ = collection_w.query(default_term_expr, output_fields=[ct.default_float_field_name])
|
|
assert set(res[0].keys()) == {ct.default_int64_field_name, ct.default_float_field_name}
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.xfail(reason="issue 30437")
|
|
def test_query_output_all_fields(self, enable_dynamic_field, random_primary_key):
|
|
"""
|
|
target: test query with none output field
|
|
method: query with output field=None
|
|
expected: return all fields
|
|
"""
|
|
enable_dynamic_field = False
|
|
# 1. initialize with data
|
|
collection_w, df, _, insert_ids = \
|
|
self.init_collection_general(prefix, True, nb=10, is_all_data_type=True,
|
|
enable_dynamic_field=enable_dynamic_field,
|
|
random_primary_key=random_primary_key)[0:4]
|
|
all_fields = [ct.default_int64_field_name, ct.default_int32_field_name, ct.default_int16_field_name,
|
|
ct.default_int8_field_name, ct.default_bool_field_name, ct.default_float_field_name,
|
|
ct.default_double_field_name, ct.default_string_field_name, ct.default_json_field_name,
|
|
ct.default_float_vec_field_name, ct.default_float16_vec_field_name,
|
|
ct.default_bfloat16_vec_field_name]
|
|
if enable_dynamic_field:
|
|
res = df[0][:2]
|
|
else:
|
|
res = []
|
|
for id in range(2):
|
|
num = df[0][df[0][ct.default_int64_field_name] == id].index.to_list()[0]
|
|
res.append(df[0].iloc[num].to_dict())
|
|
log.info(res)
|
|
collection_w.load()
|
|
actual_res, _ = collection_w.query(default_term_expr, output_fields=all_fields,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: res, "with_vec": True})
|
|
assert set(actual_res[0].keys()) == set(all_fields)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_output_float_vec_field(self):
|
|
"""
|
|
target: test query with vec output field
|
|
method: specify vec field as output field
|
|
expected: return primary field and vec field
|
|
"""
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
df = cf.gen_default_dataframe_data()
|
|
collection_w.insert(df)
|
|
assert collection_w.num_entities == ct.default_nb
|
|
fields = [[ct.default_float_vec_field_name],
|
|
[ct.default_int64_field_name, ct.default_float_vec_field_name]]
|
|
res = df.loc[:1, [ct.default_int64_field_name, ct.default_float_vec_field_name]].to_dict('records')
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
for output_fields in fields:
|
|
collection_w.query(default_term_expr, output_fields=output_fields,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: res, "with_vec": True})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("wildcard_output_fields", [["*"], ["*", default_float_field_name],
|
|
["*", default_int_field_name]])
|
|
def test_query_output_field_wildcard(self, wildcard_output_fields):
|
|
"""
|
|
target: test query with output fields using wildcard
|
|
method: query with one output_field (wildcard)
|
|
expected: query success
|
|
"""
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
df = cf.gen_default_dataframe_data()
|
|
collection_w.insert(df)
|
|
assert collection_w.num_entities == ct.default_nb
|
|
output_fields = cf.get_wildcard_output_field_names(collection_w, wildcard_output_fields)
|
|
output_fields.append(default_int_field_name)
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
with_vec = True if ct.default_float_vec_field_name in output_fields else False
|
|
actual_res = collection_w.query(default_term_expr, output_fields=wildcard_output_fields)[0]
|
|
assert set(actual_res[0].keys()) == set(output_fields)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.skip(reason="https://github.com/milvus-io/milvus/issues/12680")
|
|
@pytest.mark.parametrize("vec_fields", [[cf.gen_float_vec_field(name="float_vector1")]])
|
|
def test_query_output_multi_float_vec_field(self, vec_fields):
|
|
"""
|
|
target: test query and output multi float vec fields
|
|
method: a.specify multi vec field as output
|
|
b.specify output_fields with wildcard %
|
|
expected: verify query result
|
|
"""
|
|
# init collection with two float vector fields
|
|
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
|
|
|
|
# query with two vec output_fields
|
|
output_fields = [ct.default_int64_field_name, ct.default_float_vec_field_name]
|
|
for vec_field in vec_fields:
|
|
output_fields.append(vec_field.name)
|
|
res = df.loc[:1, output_fields].to_dict('records')
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
collection_w.query(default_term_expr, output_fields=output_fields,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: res, "with_vec": True})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.skip(reason="https://github.com/milvus-io/milvus/issues/12680")
|
|
@pytest.mark.parametrize("vec_fields", [[cf.gen_binary_vec_field()],
|
|
[cf.gen_binary_vec_field(), cf.gen_binary_vec_field("binary_vec1")]])
|
|
def test_query_output_mix_float_binary_field(self, vec_fields):
|
|
"""
|
|
target: test query and output mix float and binary vec fields
|
|
method: a.specify mix vec field as output
|
|
b.specify output_fields with wildcard %
|
|
expected: output binary vector and float vec
|
|
"""
|
|
# init collection with two float vector fields
|
|
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
|
|
|
|
# query with two vec output_fields
|
|
output_fields = [ct.default_int64_field_name, ct.default_float_vec_field_name]
|
|
for vec_field in vec_fields:
|
|
output_fields.append(vec_field.name)
|
|
res = df.loc[:1, output_fields].to_dict('records')
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
collection_w.query(default_term_expr, output_fields=output_fields,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: res, "with_vec": True})
|
|
|
|
# query with wildcard %
|
|
collection_w.query(default_term_expr, output_fields=["*"],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: res, "with_vec": True})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_output_binary_vec_field(self):
|
|
"""
|
|
target: test query with binary vec output field
|
|
method: specify binary vec field as output field
|
|
expected: return primary field and binary vec field
|
|
"""
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True, is_binary=True)[0:2]
|
|
fields = [[ct.default_binary_vec_field_name],
|
|
[ct.default_int64_field_name, ct.default_binary_vec_field_name]]
|
|
for output_fields in fields:
|
|
res, _ = collection_w.query(default_term_expr, output_fields=output_fields)
|
|
assert res[0].keys() == set(fields[-1])
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_output_primary_field(self):
|
|
"""
|
|
target: test query with output field only primary field
|
|
method: specify int64 primary field as output field
|
|
expected: return int64 field
|
|
"""
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
res, _ = collection_w.query(default_term_expr, output_fields=[ct.default_int64_field_name])
|
|
assert res[0].keys() == {ct.default_int64_field_name}
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_output_not_existed_field(self):
|
|
"""
|
|
target: test query output not existed field
|
|
method: query with not existed output field
|
|
expected: raise exception
|
|
"""
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
error = {ct.err_code: 65535, ct.err_msg: 'field int not exist'}
|
|
output_fields = [["int"], [ct.default_int64_field_name, "int"]]
|
|
for fields in output_fields:
|
|
collection_w.query(default_term_expr, output_fields=fields,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.xfail(reason="exception not MilvusException")
|
|
def test_query_invalid_output_fields(self):
|
|
"""
|
|
target: test query with invalid output fields
|
|
method: query with invalid field fields
|
|
expected: raise exception
|
|
"""
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
output_fields = ["12-s", 1, [1, "2", 3], (1,), {1: 1}]
|
|
error = {ct.err_code: 0, ct.err_msg: f'Invalid query format. \'output_fields\' must be a list'}
|
|
for fields in output_fields:
|
|
collection_w.query(default_term_expr, output_fields=fields, check_task=CheckTasks.err_res,
|
|
check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
@pytest.mark.xfail(reason="issue 24637")
|
|
def test_query_output_fields_simple_wildcard(self):
|
|
"""
|
|
target: test query output_fields with simple wildcard (* and %)
|
|
method: specify output_fields as "*"
|
|
expected: output all scale field; output all fields
|
|
"""
|
|
# init collection with fields: int64, float, float_vec, float_vector1
|
|
# collection_w, df = self.init_multi_fields_collection_wrap(cf.gen_unique_str(prefix))
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
df = vectors[0]
|
|
|
|
# query with wildcard all fields
|
|
res3 = df.iloc[:2].to_dict('records')
|
|
collection_w.query(default_term_expr, output_fields=["*"],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: res3, "with_vec": True})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.xfail(reason="issue 24637")
|
|
def test_query_output_fields_part_scale_wildcard(self):
|
|
"""
|
|
target: test query output_fields with part wildcard
|
|
method: specify output_fields as wildcard and part field
|
|
expected: verify query result
|
|
"""
|
|
# init collection with fields: int64, float, float_vec
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True, is_index=False)[0:2]
|
|
df = vectors[0]
|
|
|
|
# query with output_fields=["*", float_vector)
|
|
res = df.iloc[:2].to_dict('records')
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
collection_w.query(default_term_expr, output_fields=["*", ct.default_float_vec_field_name],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: res, "with_vec": True})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("output_fields", [["*%"], ["**"], ["*", "@"]])
|
|
def test_query_invalid_wildcard(self, output_fields):
|
|
"""
|
|
target: test query with invalid output wildcard
|
|
method: output_fields is invalid output wildcard
|
|
expected: raise exception
|
|
"""
|
|
# init collection with fields: int64, float, float_vec
|
|
collection_w = self.init_collection_general(prefix, insert_data=True, nb=100)[0]
|
|
collection_w.load()
|
|
|
|
# query with invalid output_fields
|
|
error = {ct.err_code: 65535, ct.err_msg: f"field {output_fields[-1]} not exist"}
|
|
collection_w.query(default_term_expr, output_fields=output_fields,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
def test_query_partition(self):
|
|
"""
|
|
target: test query on partition
|
|
method: create a partition and query
|
|
expected: verify query result
|
|
"""
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
partition_w = self.init_partition_wrap(collection_wrap=collection_w)
|
|
df = cf.gen_default_dataframe_data()
|
|
partition_w.insert(df)
|
|
assert collection_w.num_entities == ct.default_nb
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
partition_w.load()
|
|
res = df.iloc[:2, :1].to_dict('records')
|
|
collection_w.query(default_term_expr, partition_names=[partition_w.name],
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_partition_without_loading(self):
|
|
"""
|
|
target: test query on partition without loading
|
|
method: query on partition and no loading
|
|
expected: raise exception
|
|
"""
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
partition_w = self.init_partition_wrap(collection_wrap=collection_w)
|
|
df = cf.gen_default_dataframe_data()
|
|
partition_w.insert(df)
|
|
assert partition_w.num_entities == ct.default_nb
|
|
error = {ct.err_code: 65535, ct.err_msg: "collection not loaded"}
|
|
collection_w.query(default_term_expr, partition_names=[partition_w.name],
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_default_partition(self):
|
|
"""
|
|
target: test query on default partition
|
|
method: query on default partition
|
|
expected: verify query result
|
|
"""
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
res = vectors[0].iloc[:2, :1].to_dict('records')
|
|
collection_w.query(default_term_expr, partition_names=[ct.default_partition_name],
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_empty_partition_names(self):
|
|
"""
|
|
target: test query with empty partition_names
|
|
method: query with partition_names=[]
|
|
expected: query from all partitions
|
|
"""
|
|
# insert [0, half) into partition_w, [half, nb) into _default
|
|
half = ct.default_nb // 2
|
|
collection_w, partition_w, _, _ = self.insert_entities_into_two_partitions_in_half(half)
|
|
|
|
# query from empty partition_names
|
|
term_expr = f'{ct.default_int64_field_name} in [0, {half}, {ct.default_nb}-1]'
|
|
res = [{'int64': 0}, {'int64': half}, {'int64': ct.default_nb - 1}]
|
|
collection_w.query(term_expr, partition_names=[], check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_empty_partition(self):
|
|
"""
|
|
target: test query on empty partition
|
|
method: query on an empty collection
|
|
expected: empty query result
|
|
"""
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
partition_w = self.init_partition_wrap(collection_wrap=collection_w)
|
|
assert partition_w.is_empty
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
partition_w.load()
|
|
res, _ = collection_w.query(default_term_expr, partition_names=[partition_w.name])
|
|
assert len(res) == 0
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_not_existed_partition(self):
|
|
"""
|
|
target: test query on a not existed partition
|
|
method: query on not existed partition
|
|
expected: raise exception
|
|
"""
|
|
collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix))
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
partition_names = cf.gen_unique_str()
|
|
error = {ct.err_code: 65535, ct.err_msg: f'partition name {partition_names} not found'}
|
|
collection_w.query(default_term_expr, partition_names=[partition_names],
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_ignore_growing(self):
|
|
"""
|
|
target: test search ignoring growing segment
|
|
method: 1. create a collection, insert data, create index and load
|
|
2. insert data again
|
|
3. query with param ignore_growing=True
|
|
expected: query successfully
|
|
"""
|
|
# 1. create a collection
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
|
|
# 2. insert data again
|
|
data = cf.gen_default_dataframe_data(start=10000)
|
|
collection_w.insert(data)
|
|
|
|
# 3. query with param ignore_growing=True
|
|
res = collection_w.query('int64 >= 0', ignore_growing=True)[0]
|
|
assert len(res) == ct.default_nb
|
|
for ids in [res[i][default_int_field_name] for i in range(ct.default_nb)]:
|
|
assert ids < 10000
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_ignore_growing_after_upsert(self):
|
|
"""
|
|
target: test query ignoring growing segment after upsert
|
|
method: 1. create a collection, insert data, create index and load
|
|
2. upsert the inserted data
|
|
3. query with param ignore_growing=True
|
|
expected: query successfully
|
|
"""
|
|
# 1. create a collection
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
|
|
# 2. insert data again
|
|
data = cf.gen_default_data_for_upsert()[0]
|
|
collection_w.upsert(data)
|
|
|
|
# 3. query with param ignore_growing=True
|
|
res1 = collection_w.query('int64 >= 0', ignore_growing=True)[0]
|
|
res2 = collection_w.query('int64 >= 0')[0]
|
|
assert len(res1) == 0
|
|
assert len(res2) == ct.default_nb
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("ignore_growing", ct.get_invalid_strs[:8])
|
|
def test_query_invalid_ignore_growing_param(self, ignore_growing):
|
|
"""
|
|
target: test query ignoring growing segment param invalid
|
|
method: 1. create a collection, insert data and load
|
|
2. insert data again
|
|
3. query with ignore_growing type invalid
|
|
expected: raise exception
|
|
"""
|
|
if ignore_growing == 1:
|
|
pytest.skip("number is valid")
|
|
# 1. create a collection
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
|
|
# 2. insert data again
|
|
data = cf.gen_default_dataframe_data(start=10000)
|
|
collection_w.insert(data)
|
|
|
|
# 3. query with param ignore_growing invalid
|
|
error = {ct.err_code: 1, ct.err_msg: "parse search growing failed"}
|
|
collection_w.query('int64 >= 0', ignore_growing=ignore_growing,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.fixture(scope="function", params=[0, 10, 100])
|
|
def offset(self, request):
|
|
yield request.param
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_pagination(self, offset):
|
|
"""
|
|
target: test query pagination
|
|
method: create collection and query with pagination params,
|
|
verify if the result is ordered by primary key
|
|
expected: query successfully and verify query result
|
|
"""
|
|
# create collection, insert default_nb, load collection
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
int_values = vectors[0][ct.default_int64_field_name].values.tolist()
|
|
pos = 10
|
|
term_expr = f'{ct.default_int64_field_name} in {int_values[offset: pos + offset]}'
|
|
res = vectors[0].iloc[offset:pos + offset, :1].to_dict('records')
|
|
query_params = {"offset": offset, "limit": 10}
|
|
query_res = collection_w.query(term_expr, params=query_params,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: res})[0]
|
|
key_res = [item[key] for item in query_res for key in item]
|
|
assert key_res == int_values[offset: pos + offset]
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_binary_pagination(self, offset):
|
|
"""
|
|
target: test query binary pagination
|
|
method: create collection and query with pagination params,
|
|
verify if the result is ordered by primary key
|
|
expected: query successfully and verify query result
|
|
"""
|
|
# create collection, insert default_nb, load collection
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True,
|
|
is_binary=True)[0:2]
|
|
int_values = vectors[0][ct.default_int64_field_name].values.tolist()
|
|
pos = 10
|
|
term_expr = f'{ct.default_int64_field_name} in {int_values[offset: pos + offset]}'
|
|
res = vectors[0].iloc[offset:pos + offset, :1].to_dict('records')
|
|
query_params = {"offset": offset, "limit": 10}
|
|
query_res = collection_w.query(term_expr, params=query_params,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: res})[0]
|
|
key_res = [item[key] for item in query_res for key in item]
|
|
assert key_res == int_values[offset: pos + offset]
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_pagination_with_expression(self, offset, get_normal_expr):
|
|
"""
|
|
target: test query pagination with different expression
|
|
method: query with different expression and verify the result
|
|
expected: query successfully
|
|
"""
|
|
# 1. initialize with data
|
|
nb = 1000
|
|
collection_w, _vectors, _, insert_ids = self.init_collection_general(prefix, True, nb)[0:4]
|
|
|
|
# filter result with expression in collection
|
|
_vectors = _vectors[0]
|
|
expr = get_normal_expr
|
|
expression = expr.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)
|
|
|
|
# query and verify result
|
|
query_params = {"offset": offset, "limit": 10}
|
|
res = collection_w.query(expr=expression, params=query_params)[0]
|
|
key_res = [item[key] for item in res for key in item]
|
|
assert key_res == filter_ids
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_pagination_with_partition(self, offset):
|
|
"""
|
|
target: test query pagination on partition
|
|
method: create a partition and query with different offset
|
|
expected: verify query result
|
|
"""
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
partition_w = self.init_partition_wrap(collection_wrap=collection_w)
|
|
df = cf.gen_default_dataframe_data()
|
|
partition_w.insert(df)
|
|
assert collection_w.num_entities == ct.default_nb
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
partition_w.load()
|
|
res = df.iloc[:2, :1].to_dict('records')
|
|
query_params = {"offset": offset, "limit": 10}
|
|
collection_w.query(default_term_expr, params=query_params, partition_names=[partition_w.name],
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_pagination_with_insert_data(self, offset):
|
|
"""
|
|
target: test query pagination on partition
|
|
method: create a partition and query with pagination
|
|
expected: verify query result
|
|
"""
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
df = cf.gen_default_dataframe_data()
|
|
collection_w.insert(df)
|
|
assert collection_w.num_entities == ct.default_nb
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
res = df.iloc[:2, :1].to_dict('records')
|
|
query_params = {"offset": offset, "limit": 10}
|
|
collection_w.query(default_term_expr, params=query_params,
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_pagination_without_limit(self, offset):
|
|
"""
|
|
target: test query pagination without limit
|
|
method: create collection and query with pagination params(only offset),
|
|
compare the result with query without pagination params
|
|
expected: query successfully
|
|
"""
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
int_values = vectors[0][ct.default_int64_field_name].values.tolist()
|
|
pos = 10
|
|
term_expr = f'{ct.default_int64_field_name} in {int_values[offset: pos + offset]}'
|
|
res = vectors[0].iloc[offset:pos + offset, :1].to_dict('records')
|
|
query_params = {"offset": offset}
|
|
query_res = collection_w.query(term_expr, params=query_params,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: res})[0]
|
|
res = collection_w.query(term_expr,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: res})[0]
|
|
assert query_res == res
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("offset", [3000, 5000])
|
|
def test_query_pagination_with_offset_over_num_entities(self, offset):
|
|
"""
|
|
target: test query pagination with offset over num_entities
|
|
method: query with offset over num_entities
|
|
expected: return an empty list
|
|
"""
|
|
# create collection, insert default_nb, load collection
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
int_values = vectors[0][ct.default_int64_field_name].values.tolist()
|
|
pos = 10
|
|
term_expr = f'{ct.default_int64_field_name} in {int_values[10: pos + 10]}'
|
|
res = collection_w.query(term_expr, offset=offset, limit=10)[0]
|
|
assert len(res) == 0
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("limit", ["12 s", " ", [0, 1], {2}])
|
|
def test_query_pagination_with_invalid_limit_type(self, limit):
|
|
"""
|
|
target: test query pagination with invalid limit type
|
|
method: query with invalid limit tyype
|
|
expected: raise exception
|
|
"""
|
|
# create collection, insert default_nb, load collection
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
int_values = vectors[0][ct.default_int64_field_name].values.tolist()
|
|
pos = 10
|
|
term_expr = f'{ct.default_int64_field_name} in {int_values[10: pos + 10]}'
|
|
collection_w.query(term_expr, offset=10, limit=limit,
|
|
check_task=CheckTasks.err_res,
|
|
check_items={ct.err_code: 1,
|
|
ct.err_msg: "limit [%s] is invalid" % limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("limit", [-1, 67890])
|
|
def test_query_pagination_with_invalid_limit_value(self, limit):
|
|
"""
|
|
target: test query pagination with invalid limit value
|
|
method: query with invalid limit value
|
|
expected: raise exception
|
|
"""
|
|
# create collection, insert default_nb, load collection
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
int_values = vectors[0][ct.default_int64_field_name].values.tolist()
|
|
pos = 10
|
|
term_expr = f'{ct.default_int64_field_name} in {int_values[10: pos + 10]}'
|
|
collection_w.query(term_expr, offset=10, limit=limit,
|
|
check_task=CheckTasks.err_res,
|
|
check_items={ct.err_code: 65535,
|
|
ct.err_msg: f"invalid max query result window, (offset+limit) "
|
|
f"should be in range [1, 16384], but got {limit}"})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("offset", ["12 s", " ", [0, 1], {2}])
|
|
def test_query_pagination_with_invalid_offset_type(self, offset):
|
|
"""
|
|
target: test query pagination with invalid offset type
|
|
method: query with invalid offset type
|
|
expected: raise exception
|
|
"""
|
|
# create collection, insert default_nb, load collection
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
int_values = vectors[0][ct.default_int64_field_name].values.tolist()
|
|
pos = 10
|
|
term_expr = f'{ct.default_int64_field_name} in {int_values[10: pos + 10]}'
|
|
collection_w.query(term_expr, offset=offset, limit=10,
|
|
check_task=CheckTasks.err_res,
|
|
check_items={ct.err_code: 1,
|
|
ct.err_msg: "offset [%s] is invalid" % offset})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("offset", [-1, 67890])
|
|
def test_query_pagination_with_invalid_offset_value(self, offset):
|
|
"""
|
|
target: test query pagination with invalid offset value
|
|
method: query with invalid offset value
|
|
expected: raise exception
|
|
"""
|
|
# create collection, insert default_nb, load collection
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
int_values = vectors[0][ct.default_int64_field_name].values.tolist()
|
|
pos = 10
|
|
term_expr = f'{ct.default_int64_field_name} in {int_values[10: pos + 10]}'
|
|
collection_w.query(term_expr, offset=offset, limit=10,
|
|
check_task=CheckTasks.err_res,
|
|
check_items={ct.err_code: 65535,
|
|
ct.err_msg: f"invalid max query result window, (offset+limit) "
|
|
f"should be in range [1, 16384], but got {offset}"})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_during_upsert(self):
|
|
"""
|
|
target: test query during upsert
|
|
method: 1. create a collection and query
|
|
2. query during upsert
|
|
3. compare two query results
|
|
expected: the two query results is the same
|
|
"""
|
|
upsert_nb = 1000
|
|
expr = f"int64 >= 0 && int64 <= {upsert_nb}"
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
res1 = collection_w.query(expr, output_fields=[default_float_field_name])[0]
|
|
|
|
def do_upsert():
|
|
data = cf.gen_default_data_for_upsert(upsert_nb)[0]
|
|
collection_w.upsert(data=data)
|
|
|
|
t = threading.Thread(target=do_upsert, args=())
|
|
t.start()
|
|
res2 = collection_w.query(expr, output_fields=[default_float_field_name])[0]
|
|
t.join()
|
|
assert [res1[i][default_float_field_name] for i in range(upsert_nb)] == \
|
|
[res2[i][default_float_field_name] for i in range(upsert_nb)]
|
|
|
|
|
|
class TestQueryOperation(TestcaseBase):
|
|
"""
|
|
******************************************************************
|
|
The following cases are used to test query interface operations
|
|
******************************************************************
|
|
"""
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_without_connection(self):
|
|
"""
|
|
target: test query without connection
|
|
method: close connect and query
|
|
expected: raise exception
|
|
"""
|
|
|
|
# init a collection with default connection
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
|
|
# remove default connection
|
|
self.connection_wrap.remove_connection(alias=DefaultConfig.DEFAULT_USING)
|
|
|
|
# list connection to check
|
|
self.connection_wrap.list_connections(check_task=ct.CheckTasks.ccr, check_items={ct.list_content: []})
|
|
|
|
# query after remove default connection
|
|
collection_w.query(default_term_expr, check_task=CheckTasks.err_res,
|
|
check_items={ct.err_code: 0, ct.err_msg: cem.ConnectFirst})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_without_loading(self):
|
|
"""
|
|
target: test query without loading
|
|
method: no loading before query
|
|
expected: raise exception
|
|
"""
|
|
|
|
# init a collection with default connection
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
collection_w = self.init_collection_wrap(name=collection_name)
|
|
|
|
# insert data to collection
|
|
collection_w.insert(data=cf.gen_default_list_data())
|
|
|
|
# check number of entities and that method calls the flush interface
|
|
assert collection_w.num_entities == ct.default_nb
|
|
|
|
# query without load
|
|
collection_w.query(default_term_expr, check_task=CheckTasks.err_res,
|
|
check_items={ct.err_code: 65535,
|
|
ct.err_msg: "collection not loaded"})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("term_expr", [f'{ct.default_int64_field_name} in [0]'])
|
|
def test_query_expr_single_term_array(self, term_expr):
|
|
"""
|
|
target: test query with single array term expr
|
|
method: query with single array value
|
|
expected: query result is one entity
|
|
"""
|
|
|
|
# init a collection and insert data
|
|
collection_w, vectors, binary_raw_vectors = self.init_collection_general(prefix, insert_data=True)[0:3]
|
|
|
|
# query the first row of data
|
|
check_vec = vectors[0].iloc[:, [0]][0:1].to_dict('records')
|
|
collection_w.query(term_expr,
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: check_vec})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("term_expr", [f'{ct.default_int64_field_name} in [0]'])
|
|
def test_query_binary_expr_single_term_array(self, term_expr, check_content):
|
|
"""
|
|
target: test query with single array term expr
|
|
method: query with single array value
|
|
expected: query result is one entity
|
|
"""
|
|
|
|
# init a collection and insert data
|
|
collection_w, vectors, binary_raw_vectors = self.init_collection_general(prefix, insert_data=True,
|
|
is_binary=True)[0:3]
|
|
|
|
# query the first row of data
|
|
check_vec = vectors[0].iloc[:, [0]][0:1].to_dict('records')
|
|
collection_w.query(term_expr,
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: check_vec})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_expr_all_term_array(self):
|
|
"""
|
|
target: test query with all array term expr
|
|
method: query with all array value
|
|
expected: verify query result
|
|
"""
|
|
|
|
# init a collection and insert data
|
|
collection_w, vectors, binary_raw_vectors = \
|
|
self.init_collection_general(prefix, insert_data=True)[0:3]
|
|
|
|
# data preparation
|
|
int_values = vectors[0][ct.default_int64_field_name].values.tolist()
|
|
term_expr = f'{ct.default_int64_field_name} in {int_values}'
|
|
check_vec = vectors[0].iloc[:, [0]][0:len(int_values)].to_dict('records')
|
|
|
|
# query all array value
|
|
collection_w.query(term_expr,
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: check_vec})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_expr_half_term_array(self):
|
|
"""
|
|
target: test query with half array term expr
|
|
method: query with half array value
|
|
expected: verify query result
|
|
"""
|
|
|
|
half = ct.default_nb // 2
|
|
collection_w, partition_w, df_partition, df_default = \
|
|
self.insert_entities_into_two_partitions_in_half(half)
|
|
|
|
int_values = df_default[ct.default_int64_field_name].values.tolist()
|
|
term_expr = f'{ct.default_int64_field_name} in {int_values}'
|
|
res, _ = collection_w.query(term_expr)
|
|
assert len(res) == len(int_values)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_expr_repeated_term_array(self):
|
|
"""
|
|
target: test query with repeated term array on primary field with unique value
|
|
method: query with repeated array value
|
|
expected: return hit entities, no repeated
|
|
"""
|
|
collection_w, vectors, binary_raw_vectors = self.init_collection_general(prefix, insert_data=True)[0:3]
|
|
int_values = [0, 0, 0, 0]
|
|
term_expr = f'{ct.default_int64_field_name} in {int_values}'
|
|
res, _ = collection_w.query(term_expr)
|
|
assert len(res) == 1
|
|
assert res[0][ct.default_int64_field_name] == int_values[0]
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_dup_ids_dup_term_array(self):
|
|
"""
|
|
target: test query on duplicate primary keys with dup term array
|
|
method: 1.create collection and insert dup primary keys
|
|
2.query with dup term array
|
|
expected: todo
|
|
"""
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
df = cf.gen_default_dataframe_data(nb=100)
|
|
df[ct.default_int64_field_name] = 0
|
|
mutation_res, _ = collection_w.insert(df)
|
|
assert mutation_res.primary_keys == df[ct.default_int64_field_name].tolist()
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
term_expr = f'{ct.default_int64_field_name} in {[0, 0, 0]}'
|
|
res = df.iloc[:, :2].to_dict('records')
|
|
collection_w.query(term_expr, output_fields=["*"], check_items=CheckTasks.check_query_results,
|
|
check_task={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
def test_query_after_index(self):
|
|
"""
|
|
target: test query after creating index
|
|
method: 1. indexing
|
|
2. load
|
|
3. query
|
|
expected: query result is correct
|
|
"""
|
|
collection_w, vectors, binary_raw_vectors = self.init_collection_general(prefix, insert_data=True,
|
|
is_index=False)[0:3]
|
|
|
|
default_field_name = ct.default_float_vec_field_name
|
|
collection_w.create_index(default_field_name, default_index_params)
|
|
|
|
collection_w.load()
|
|
|
|
int_values = [0]
|
|
term_expr = f'{ct.default_int64_field_name} in {int_values}'
|
|
check_vec = vectors[0].iloc[:, [0]][0:len(int_values)].to_dict('records')
|
|
collection_w.query(term_expr,
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: check_vec})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_after_search(self):
|
|
"""
|
|
target: test query after search
|
|
method: 1. search
|
|
2. query without load again
|
|
expected: query result is correct
|
|
"""
|
|
|
|
limit = 1000
|
|
nb_old = 500
|
|
collection_w, vectors, binary_raw_vectors, insert_ids = \
|
|
self.init_collection_general(prefix, True, nb_old)[0:4]
|
|
|
|
# 2. search for original data after load
|
|
vectors_s = [[random.random() for _ in range(ct.default_dim)] for _ in range(ct.default_nq)]
|
|
collection_w.search(vectors_s[:ct.default_nq], ct.default_float_vec_field_name,
|
|
ct.default_search_params, limit, "int64 >= 0",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"nq": ct.default_nq, "limit": nb_old, "ids": insert_ids})
|
|
|
|
# check number of entities and that method calls the flush interface
|
|
assert collection_w.num_entities == nb_old
|
|
|
|
term_expr = f'{ct.default_int64_field_name} in [0, 1]'
|
|
check_vec = vectors[0].iloc[:, [0]][0:2].to_dict('records')
|
|
collection_w.query(term_expr,
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: check_vec})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_output_vec_field_after_index(self):
|
|
"""
|
|
target: test query output vec field after index
|
|
method: create index and specify vec field as output field
|
|
expected: return primary field and vec field
|
|
"""
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
df = cf.gen_default_dataframe_data(nb=5000)
|
|
collection_w.insert(df)
|
|
assert collection_w.num_entities == 5000
|
|
fields = [ct.default_int64_field_name, ct.default_float_vec_field_name]
|
|
collection_w.create_index(ct.default_float_vec_field_name, default_index_params)
|
|
assert collection_w.has_index()[0]
|
|
res = df.loc[:1, [ct.default_int64_field_name, ct.default_float_vec_field_name]].to_dict('records')
|
|
collection_w.load()
|
|
collection_w.query(default_term_expr, output_fields=fields,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: res, "with_vec": True})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_output_binary_vec_field_after_index(self):
|
|
"""
|
|
target: test query output vec field after index
|
|
method: create index and specify vec field as output field
|
|
expected: return primary field and vec field
|
|
"""
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True,
|
|
is_binary=True, is_index=False)[0:2]
|
|
fields = [ct.default_int64_field_name, ct.default_binary_vec_field_name]
|
|
collection_w.create_index(ct.default_binary_vec_field_name, binary_index_params)
|
|
assert collection_w.has_index()[0]
|
|
collection_w.load()
|
|
res, _ = collection_w.query(default_term_expr, output_fields=[ct.default_binary_vec_field_name])
|
|
assert res[0].keys() == set(fields)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_partition_repeatedly(self):
|
|
"""
|
|
target: test query repeatedly on partition
|
|
method: query on partition twice
|
|
expected: verify query result
|
|
"""
|
|
|
|
# create connection
|
|
self._connect()
|
|
|
|
# init collection
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
|
|
# init partition
|
|
partition_w = self.init_partition_wrap(collection_wrap=collection_w)
|
|
|
|
# insert data to partition
|
|
df = cf.gen_default_dataframe_data()
|
|
partition_w.insert(df)
|
|
|
|
# check number of entities and that method calls the flush interface
|
|
assert collection_w.num_entities == ct.default_nb
|
|
|
|
# load partition
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
partition_w.load()
|
|
|
|
# query twice
|
|
res_one, _ = collection_w.query(default_term_expr, partition_names=[partition_w.name])
|
|
res_two, _ = collection_w.query(default_term_expr, partition_names=[partition_w.name])
|
|
assert res_one == res_two
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_another_partition(self):
|
|
"""
|
|
target: test query another partition
|
|
method: 1. insert entities into two partitions
|
|
2.query on one partition and query result empty
|
|
expected: query result is empty
|
|
"""
|
|
half = ct.default_nb // 2
|
|
collection_w, partition_w, _, _ = self.insert_entities_into_two_partitions_in_half(half)
|
|
|
|
term_expr = f'{ct.default_int64_field_name} in [{half}]'
|
|
# half entity in _default partition rather than partition_w
|
|
collection_w.query(term_expr, partition_names=[partition_w.name],
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: []})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_multi_partitions_multi_results(self):
|
|
"""
|
|
target: test query on multi partitions and get multi results
|
|
method: 1.insert entities into two partitions
|
|
2.query on two partitions and query multi result
|
|
expected: query results from two partitions
|
|
"""
|
|
half = ct.default_nb // 2
|
|
collection_w, partition_w, _, _ = self.insert_entities_into_two_partitions_in_half(half)
|
|
|
|
term_expr = f'{ct.default_int64_field_name} in [{half - 1}, {half}]'
|
|
# half entity in _default, half-1 entity in partition_w
|
|
res, _ = collection_w.query(term_expr,
|
|
partition_names=[ct.default_partition_name, partition_w.name])
|
|
assert len(res) == 2
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_multi_partitions_single_result(self):
|
|
"""
|
|
target: test query on multi partitions and get single result
|
|
method: 1.insert into two partitions
|
|
2.query on two partitions and query single result
|
|
expected: query from two partitions and get single result
|
|
"""
|
|
half = ct.default_nb // 2
|
|
collection_w, partition_w, df_partition, df_default = \
|
|
self.insert_entities_into_two_partitions_in_half(half)
|
|
|
|
term_expr = f'{ct.default_int64_field_name} in [{half}]'
|
|
# half entity in _default
|
|
res, _ = collection_w.query(term_expr,
|
|
partition_names=[ct.default_partition_name, partition_w.name])
|
|
assert len(res) == 1
|
|
assert res[0][ct.default_int64_field_name] == half
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_growing_segment_data(self):
|
|
"""
|
|
target: test query data in the growing segment
|
|
method: 1. create collection
|
|
2.load collection
|
|
3.insert without flush
|
|
4.query
|
|
expected: Data can be queried
|
|
"""
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
# load collection
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
tmp_nb = 100
|
|
df = cf.gen_default_dataframe_data(tmp_nb)
|
|
collection_w.insert(df)
|
|
|
|
res = df.iloc[1:2, :1].to_dict('records')
|
|
time.sleep(1)
|
|
collection_w.query(f'{ct.default_int64_field_name} in [1]',
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.skip("not support default_value now")
|
|
def test_query_using_all_types_of_default_value(self):
|
|
"""
|
|
target: test create collection with default_value
|
|
method: create a schema with all fields using default value and query
|
|
expected: query results are as expected
|
|
"""
|
|
fields = [
|
|
cf.gen_int64_field(name='pk', is_primary=True),
|
|
cf.gen_float_vec_field(),
|
|
cf.gen_int8_field(default_value=np.int8(8)),
|
|
cf.gen_int16_field(default_value=np.int16(16)),
|
|
cf.gen_int32_field(default_value=np.int32(32)),
|
|
cf.gen_int64_field(default_value=np.int64(64)),
|
|
cf.gen_float_field(default_value=np.float32(3.14)),
|
|
cf.gen_double_field(default_value=np.double(3.1415)),
|
|
cf.gen_bool_field(default_value=False),
|
|
cf.gen_string_field(default_value="abc")
|
|
]
|
|
schema = cf.gen_collection_schema(fields)
|
|
collection_w = self.init_collection_wrap(schema=schema)
|
|
data = [
|
|
[i for i in range(ct.default_nb)],
|
|
cf.gen_vectors(ct.default_nb, ct.default_dim)
|
|
]
|
|
collection_w.insert(data)
|
|
collection_w.create_index(ct.default_float_vec_field_name)
|
|
collection_w.load()
|
|
expr = "pk in [0, 1]"
|
|
res = collection_w.query(expr, output_fields=["*"])[0][0]
|
|
log.info(res)
|
|
assert res[ct.default_int8_field_name] == 8
|
|
assert res[ct.default_int16_field_name] == 16
|
|
assert res[ct.default_int32_field_name] == 32
|
|
assert res[ct.default_int64_field_name] == 64
|
|
assert res[ct.default_float_field_name] == np.float32(3.14)
|
|
assert res[ct.default_double_field_name] == 3.1415
|
|
assert res[ct.default_bool_field_name] is False
|
|
assert res[ct.default_string_field_name] == "abc"
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
def test_query_multi_logical_exprs(self):
|
|
"""
|
|
target: test the scenario which query with many logical expressions
|
|
method: 1. create collection
|
|
3. query the expr that like: int64 == 0 || int64 == 1 ........
|
|
expected: run successfully
|
|
"""
|
|
c_name = cf.gen_unique_str(prefix)
|
|
collection_w = self.init_collection_wrap(name=c_name)
|
|
df = cf.gen_default_dataframe_data()
|
|
collection_w.insert(df)
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
multi_exprs = " || ".join(f'{default_int_field_name} == {i}' for i in range(60))
|
|
_, check_res = collection_w.query(multi_exprs, output_fields=[f'{default_int_field_name}'])
|
|
assert(check_res == True)
|
|
|
|
|
|
class TestQueryString(TestcaseBase):
|
|
"""
|
|
******************************************************************
|
|
The following cases are used to test query with string
|
|
******************************************************************
|
|
"""
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_string_is_not_primary(self):
|
|
"""
|
|
target: test query data with string field is not primary
|
|
method: create collection and insert data
|
|
collection.load()
|
|
query with string expr in string field is not primary
|
|
expected: query successfully
|
|
"""
|
|
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2]
|
|
res = vectors[0].iloc[:2, :3].to_dict('records')
|
|
output_fields = [default_float_field_name, default_string_field_name]
|
|
collection_w.query(default_string_term_expr, output_fields=output_fields,
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("expression", cf.gen_normal_string_expressions([default_string_field_name]))
|
|
def test_query_string_is_primary(self, expression):
|
|
"""
|
|
target: test query with output field only primary field
|
|
method: specify string primary field as output field
|
|
expected: return string primary field
|
|
"""
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True,
|
|
primary_field=ct.default_string_field_name)[0:2]
|
|
res, _ = collection_w.query(expression, output_fields=[ct.default_string_field_name])
|
|
assert res[0].keys() == {ct.default_string_field_name}
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_string_with_mix_expr(self):
|
|
"""
|
|
target: test query data
|
|
method: create collection and insert data
|
|
query with mix expr in string field and int field
|
|
expected: query successfully
|
|
"""
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True,
|
|
primary_field=ct.default_string_field_name)[0:2]
|
|
res = vectors[0].iloc[:, 1:3].to_dict('records')
|
|
output_fields = [default_float_field_name, default_string_field_name]
|
|
collection_w.query(default_mix_expr, output_fields=output_fields,
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("expression", cf.gen_invalid_string_expressions())
|
|
def test_query_with_invalid_string_expr(self, expression):
|
|
"""
|
|
target: test query data
|
|
method: create collection and insert data
|
|
query with invalid expr
|
|
expected: Raise exception
|
|
"""
|
|
collection_w = self.init_collection_general(prefix, insert_data=True)[0]
|
|
collection_w.query(expression, check_task=CheckTasks.err_res,
|
|
check_items={ct.err_code: 1100,
|
|
ct.err_msg: f"failed to create query plan: cannot parse expression: {expression}, "
|
|
f"error: value '1' in list cannot be casted to VarChar: invalid parameter"})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_string_expr_with_binary(self):
|
|
"""
|
|
target: test query string expr with binary
|
|
method: query string expr with binary
|
|
expected: verify query successfully
|
|
"""
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True,
|
|
is_binary=True, is_index=False)[0:2]
|
|
collection_w.create_index(ct.default_binary_vec_field_name, binary_index_params)
|
|
collection_w.load()
|
|
assert collection_w.has_index()[0]
|
|
res, _ = collection_w.query(default_string_term_expr, output_fields=[ct.default_binary_vec_field_name])
|
|
assert len(res) == 2
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_string_expr_with_prefixes(self):
|
|
"""
|
|
target: test query with prefix string expression
|
|
method: specify string is primary field, use prefix string expr
|
|
expected: verify query successfully
|
|
"""
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True,
|
|
primary_field=ct.default_string_field_name)[0:2]
|
|
res = vectors[0].iloc[:1, :3].to_dict('records')
|
|
expression = 'varchar like "0%"'
|
|
output_fields = [default_int_field_name, default_float_field_name, default_string_field_name]
|
|
collection_w.query(expression, output_fields=output_fields,
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_string_with_invalid_prefix_expr(self):
|
|
"""
|
|
target: test query with invalid prefix string expression
|
|
method: specify string primary field, use invalid prefix string expr
|
|
expected: raise error
|
|
"""
|
|
collection_w = self.init_collection_general(prefix, insert_data=True)[0]
|
|
expression = 'float like "0%"'
|
|
collection_w.query(expression,
|
|
check_task=CheckTasks.err_res,
|
|
check_items={ct.err_code: 65535,
|
|
ct.err_msg: f"cannot parse expression: {expression}, error: like "
|
|
f"operation on non-string or no-json field is unsupported"})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_compare_two_fields(self):
|
|
"""
|
|
target: test query with bool expression comparing two fields
|
|
method: specify string primary field, compare two fields
|
|
expected: verify query successfully
|
|
"""
|
|
collection_w = self.init_collection_general(prefix, insert_data=True,
|
|
primary_field=ct.default_string_field_name)[0]
|
|
res = []
|
|
expression = 'float > int64'
|
|
output_fields = [default_int_field_name, default_float_field_name, default_string_field_name]
|
|
collection_w.query(expression, output_fields=output_fields,
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_compare_invalid_fields(self):
|
|
"""
|
|
target: test query with
|
|
method: specify string primary field, compare string and int field
|
|
expected: raise error
|
|
"""
|
|
collection_w = self.init_collection_general(prefix, insert_data=True,
|
|
primary_field=ct.default_string_field_name)[0]
|
|
expression = 'varchar == int64'
|
|
collection_w.query(expression, check_task=CheckTasks.err_res,
|
|
check_items={ct.err_code: 1100, ct.err_msg:
|
|
f"failed to create query plan: cannot parse expression: {expression}, "
|
|
f"error: comparisons between VarChar and Int64 are not supported: invalid parameter"})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.xfail(reason="issue 24637")
|
|
def test_query_after_insert_multi_threading(self):
|
|
"""
|
|
target: test data consistency after multi threading insert
|
|
method: multi threads insert, and query, compare queried data with original
|
|
expected: verify data consistency
|
|
"""
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
thread_num = 4
|
|
threads = []
|
|
primary_keys = []
|
|
df_list = []
|
|
|
|
# prepare original data for parallel insert
|
|
for i in range(thread_num):
|
|
df = cf.gen_default_dataframe_data(ct.default_nb, start=i * ct.default_nb)
|
|
df_list.append(df)
|
|
primary_key = df[ct.default_int64_field_name].values.tolist()
|
|
primary_keys.append(primary_key)
|
|
|
|
def insert(thread_i):
|
|
log.debug(f'In thread-{thread_i}')
|
|
mutation_res, _ = collection_w.insert(df_list[thread_i])
|
|
assert mutation_res.insert_count == ct.default_nb
|
|
assert mutation_res.primary_keys == primary_keys[thread_i]
|
|
|
|
for i in range(thread_num):
|
|
x = threading.Thread(target=insert, args=(i,))
|
|
threads.append(x)
|
|
x.start()
|
|
for t in threads:
|
|
t.join()
|
|
assert collection_w.num_entities == ct.default_nb * thread_num
|
|
|
|
# Check data consistency after parallel insert
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
df_dict_list = []
|
|
for df in df_list:
|
|
df_dict_list += df.to_dict('records')
|
|
output_fields = ["*"]
|
|
expression = "int64 >= 0"
|
|
collection_w.query(expression, output_fields=output_fields,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: df_dict_list,
|
|
"primary_field": default_int_field_name,
|
|
"with_vec": True})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_string_field_pk_is_empty(self):
|
|
"""
|
|
target: test query with string expr and string field is primary
|
|
method: create collection , string field is primary
|
|
collection load and insert empty data with string field
|
|
collection query uses string expr in string field
|
|
expected: query successfully
|
|
"""
|
|
# 1. create a collection
|
|
schema = cf.gen_string_pk_default_collection_schema()
|
|
collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix), schema=schema)
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
|
|
nb = 3000
|
|
df = cf.gen_default_list_data(nb)
|
|
df[2] = ["" for _ in range(nb)]
|
|
|
|
collection_w.insert(df)
|
|
assert collection_w.num_entities == nb
|
|
|
|
string_exp = "varchar >= \"\""
|
|
output_fields = [default_int_field_name, default_float_field_name, default_string_field_name]
|
|
res, _ = collection_w.query(string_exp, output_fields=output_fields)
|
|
|
|
assert len(res) == 1
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_string_field_not_primary_is_empty(self):
|
|
"""
|
|
target: test query with string expr and string field is not primary
|
|
method: create collection , string field is primary
|
|
collection load and insert empty data with string field
|
|
collection query uses string expr in string field
|
|
expected: query successfully
|
|
"""
|
|
# 1. create a collection
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=False, is_index=False)[0:2]
|
|
|
|
nb = 3000
|
|
df = cf.gen_default_list_data(nb)
|
|
df[2] = ["" for _ in range(nb)]
|
|
|
|
collection_w.insert(df)
|
|
assert collection_w.num_entities == nb
|
|
|
|
collection_w.create_index(ct.default_float_vec_field_name, default_index_params)
|
|
assert collection_w.has_index()[0]
|
|
collection_w.load()
|
|
|
|
output_fields = [default_int_field_name, default_float_field_name, default_string_field_name]
|
|
|
|
expr = "varchar == \"\""
|
|
res, _ = collection_w.query(expr, output_fields=output_fields)
|
|
|
|
assert len(res) == nb
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_with_create_diskann_index(self):
|
|
"""
|
|
target: test query after create diskann index
|
|
method: create a collection and build diskann index
|
|
expected: verify query result
|
|
"""
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True, is_index=False)[0:2]
|
|
|
|
collection_w.create_index(ct.default_float_vec_field_name, ct.default_diskann_index)
|
|
assert collection_w.has_index()[0]
|
|
|
|
collection_w.load()
|
|
|
|
int_values = [0]
|
|
term_expr = f'{ct.default_int64_field_name} in {int_values}'
|
|
check_vec = vectors[0].iloc[:, [0]][0:len(int_values)].to_dict('records')
|
|
collection_w.query(term_expr,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: check_vec})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_with_create_diskann_with_string_pk(self):
|
|
"""
|
|
target: test query after create diskann index
|
|
method: create a collection with string pk and build diskann index
|
|
expected: verify query result
|
|
"""
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=True,
|
|
primary_field=ct.default_string_field_name,
|
|
is_index=False)[0:2]
|
|
collection_w.create_index(ct.default_float_vec_field_name, ct.default_diskann_index)
|
|
assert collection_w.has_index()[0]
|
|
collection_w.load()
|
|
res = vectors[0].iloc[:, 1:3].to_dict('records')
|
|
output_fields = [default_float_field_name, default_string_field_name]
|
|
collection_w.query(default_mix_expr, output_fields=output_fields,
|
|
check_task=CheckTasks.check_query_results, check_items={exp_res: res})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_with_scalar_field(self):
|
|
"""
|
|
target: test query with Scalar field
|
|
method: create collection , string field is primary
|
|
collection load and insert empty data with string field
|
|
collection query uses string expr in string field
|
|
expected: query successfully
|
|
"""
|
|
# 1. create a collection
|
|
collection_w, vectors = self.init_collection_general(prefix, insert_data=False, is_index=False)[0:2]
|
|
|
|
nb = 3000
|
|
df = cf.gen_default_list_data(nb)
|
|
df[2] = ["" for _ in range(nb)]
|
|
|
|
collection_w.insert(df)
|
|
assert collection_w.num_entities == nb
|
|
|
|
collection_w.create_index(ct.default_float_vec_field_name, default_index_params)
|
|
assert collection_w.has_index()[0]
|
|
index_params = {}
|
|
collection_w.create_index(ct.default_int64_field_name, index_params=index_params)
|
|
|
|
collection_w.load()
|
|
|
|
output_fields = [default_int_field_name, default_float_field_name]
|
|
|
|
expr = "int64 in [2,4,6,8]"
|
|
res, _ = collection_w.query(expr, output_fields=output_fields)
|
|
|
|
assert len(res) == 4
|
|
|
|
|
|
class TestQueryCount(TestcaseBase):
|
|
"""
|
|
test query count(*)
|
|
"""
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("consistency_level", [CONSISTENCY_BOUNDED, CONSISTENCY_STRONG, CONSISTENCY_EVENTUALLY])
|
|
def test_count_consistency_level(self, consistency_level):
|
|
"""
|
|
target: test count(*) with bounded level
|
|
method: 1. create collection with different consistency level
|
|
2. load collection
|
|
3. insert and count
|
|
4. verify count
|
|
expected: expected count
|
|
"""
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix),
|
|
consistency_level=consistency_level)
|
|
# load collection
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
|
|
df = cf.gen_default_dataframe_data()
|
|
collection_w.insert(df)
|
|
|
|
if consistency_level == CONSISTENCY_BOUNDED:
|
|
time.sleep(ct.default_graceful_time)
|
|
elif consistency_level == CONSISTENCY_STRONG:
|
|
pass
|
|
elif consistency_level == CONSISTENCY_EVENTUALLY:
|
|
time.sleep(ct.default_graceful_time)
|
|
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: ct.default_nb}]})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("invalid_output_field", ["count", "count(int64)", "count(**)"])
|
|
def test_count_invalid_output_field(self, invalid_output_field):
|
|
"""
|
|
target: test count with invalid
|
|
method:
|
|
expected:
|
|
"""
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
# load collection
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
|
|
# insert
|
|
df = cf.gen_default_dataframe_data(nb=2)
|
|
insert_res, _ = collection_w.insert(df)
|
|
|
|
collection_w.query(expr=default_term_expr, output_fields=[invalid_output_field],
|
|
check_task=CheckTasks.err_res,
|
|
check_items={"err_code": 1,
|
|
"err_msg": f"field {invalid_output_field} not exist"})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_count_without_loading(self):
|
|
"""
|
|
target: test count without loading
|
|
method: count without loading
|
|
expected: exception
|
|
"""
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
collection_w.query(expr=default_term_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.err_res,
|
|
check_items={"err_code": 65535,
|
|
"err_msg": "collection not loaded"})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_count_duplicate_ids(self):
|
|
"""
|
|
target: test count duplicate ids
|
|
method: 1. insert duplicate ids
|
|
2. count
|
|
3. delete duplicate ids
|
|
4. count
|
|
expected: verify count
|
|
"""
|
|
# create
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
|
|
# insert duplicate ids
|
|
tmp_nb = 100
|
|
df = cf.gen_default_dataframe_data(tmp_nb)
|
|
df[ct.default_int64_field_name] = 0
|
|
collection_w.insert(df)
|
|
|
|
# query count
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: tmp_nb}]}
|
|
)
|
|
|
|
# delete and verify count
|
|
collection_w.delete(default_term_expr)
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: 0}]}
|
|
)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_count_multi_partitions(self):
|
|
"""
|
|
target: test count multi partitions
|
|
method: 1. init partitions: p1, _default
|
|
2. count p1, _default, [p1, _default]
|
|
3. delete _default entities and count _default, [p1, _default]
|
|
4. drop p1 and count p1, [p1, _default]
|
|
expected: verify count
|
|
"""
|
|
half = ct.default_nb // 2
|
|
# insert [0, half) into partition_w, [half, nb) into _default
|
|
collection_w, p1, _, _ = self.insert_entities_into_two_partitions_in_half(half=half)
|
|
|
|
# query count p1, [p1, _default]
|
|
for p_name in [p1.name, ct.default_partition_name]:
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output], partition_names=[p_name],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: half}]})
|
|
|
|
# delete entities from _default
|
|
delete_expr = f"{ct.default_int64_field_name} in {[i for i in range(half, ct.default_nb)]} "
|
|
collection_w.delete(expr=delete_expr)
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
partition_names=[ct.default_partition_name],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: 0}]})
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
partition_names=[p1.name, ct.default_partition_name],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: half}]})
|
|
|
|
# drop p1 partition
|
|
p1.release()
|
|
p1.drop()
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
partition_names=[p1.name],
|
|
check_task=CheckTasks.err_res,
|
|
check_items={"err_code": 65535,
|
|
"err_msg": f'partition name {p1.name} not found'})
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
partition_names=[ct.default_partition_name],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: 0}]})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_count_partition_duplicate(self):
|
|
"""
|
|
target: test count from partitions which have duplicate ids
|
|
method: 1. insert same ids into 2 partitions
|
|
2. count
|
|
3. delete some ids and count
|
|
expected: verify count
|
|
"""
|
|
# init partitions: _default and p1
|
|
p1 = "p1"
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
collection_w.create_partition(p1)
|
|
|
|
df = cf.gen_default_dataframe_data()
|
|
collection_w.insert(df, partition_name=ct.default_partition_name)
|
|
collection_w.insert(df, partition_name=p1)
|
|
|
|
# index and load
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
|
|
# count
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: ct.default_nb * 2}]}
|
|
)
|
|
|
|
# delete some duplicate ids
|
|
delete_res, _ = collection_w.delete(default_term_expr)
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
partition_names=[p1],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: ct.default_nb - delete_res.delete_count}]}
|
|
)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_count_growing_sealed_segment(self):
|
|
"""
|
|
target: test count growing and sealed segment
|
|
method: 1. insert -> index -> load
|
|
2. count
|
|
3. new insert
|
|
4. count
|
|
expected: verify count
|
|
"""
|
|
tmp_nb = 100
|
|
# create -> insert -> index -> load -> count sealed
|
|
collection_w = self.init_collection_general(insert_data=True, nb=tmp_nb)[0]
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: tmp_nb}]}
|
|
)
|
|
|
|
# new insert and growing count
|
|
df = cf.gen_default_dataframe_data(nb=tmp_nb, start=tmp_nb)
|
|
collection_w.insert(df)
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: tmp_nb * 2}]})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_count_during_handoff(self):
|
|
"""
|
|
target: test count during handoff
|
|
method: 1. index -> load
|
|
2. insert
|
|
3. flush while count
|
|
expected: verify count
|
|
"""
|
|
# create -> index -> load
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
|
|
# flush while count
|
|
df = cf.gen_default_dataframe_data()
|
|
collection_w.insert(df)
|
|
|
|
t_flush = threading.Thread(target=collection_w.flush, args=())
|
|
t_count = threading.Thread(target=collection_w.query, args=(default_expr,),
|
|
kwargs={
|
|
"output_fields": [ct.default_count_output],
|
|
"check_task": CheckTasks.check_query_results,
|
|
"check_items": {exp_res: [{count: ct.default_nb}]}
|
|
})
|
|
|
|
t_flush.start()
|
|
t_count.start()
|
|
t_flush.join()
|
|
t_count.join()
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_count_delete_insert_duplicate_ids(self):
|
|
"""
|
|
target: test count after delete and re-insert same entities
|
|
method: 1. insert and delete
|
|
2. count
|
|
3. re-insert deleted ids with different vectors
|
|
4. count
|
|
expected: verify count
|
|
"""
|
|
tmp_nb = 100
|
|
# create -> insert ids [0, default_nb + tmp) -> index -> load
|
|
collection_w = self.init_collection_general(insert_data=True)[0]
|
|
df = cf.gen_default_dataframe_data(nb=tmp_nb, start=ct.default_nb)
|
|
insert_res, _ = collection_w.insert(df)
|
|
|
|
# delete growing and sealed ids -> count
|
|
collection_w.delete(f"{ct.default_int64_field_name} in {[i for i in range(ct.default_nb)]}")
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: tmp_nb}]}
|
|
)
|
|
|
|
# re-insert deleted ids [0, default_nb) with different vectors
|
|
df_same = cf.gen_default_dataframe_data()
|
|
collection_w.insert(df_same)
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: ct.default_nb + tmp_nb}]}
|
|
)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_count_compact_merge(self):
|
|
"""
|
|
target: test count after compact merge segments
|
|
method: 1. init 2 segments with same channel
|
|
2. compact
|
|
3. count
|
|
expected: verify count
|
|
"""
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix), shards_num=1)
|
|
|
|
# init two segments
|
|
tmp_nb = 100
|
|
segment_num = 2
|
|
for i in range(segment_num):
|
|
df = cf.gen_default_dataframe_data(nb=tmp_nb, start=i * tmp_nb)
|
|
collection_w.insert(df)
|
|
collection_w.flush()
|
|
|
|
collection_w.create_index(ct.default_float_vec_field_name, ct.default_index)
|
|
collection_w.compact()
|
|
collection_w.wait_for_compaction_completed()
|
|
|
|
collection_w.load()
|
|
segment_info, _ = self.utility_wrap.get_query_segment_info(collection_w.name)
|
|
assert len(segment_info) == 1
|
|
|
|
# count after compact
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: tmp_nb * segment_num}]})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_count_compact_delete(self):
|
|
"""
|
|
target: test count after delete-compact
|
|
method: 1. init segments
|
|
2. delete half ids and compact
|
|
3. count
|
|
expected: verify count
|
|
"""
|
|
# create -> index -> insert
|
|
collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix), shards_num=1)
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
df = cf.gen_default_dataframe_data()
|
|
insert_res, _ = collection_w.insert(df)
|
|
|
|
# delete half entities, flush
|
|
half_expr = f'{ct.default_int64_field_name} in {[i for i in range(ct.default_nb // 2)]}'
|
|
collection_w.delete(half_expr)
|
|
assert collection_w.num_entities == ct.default_nb
|
|
|
|
# compact
|
|
collection_w.compact()
|
|
collection_w.wait_for_compaction_completed()
|
|
|
|
# load and count
|
|
collection_w.load()
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: ct.default_nb // 2}]}
|
|
)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_count_during_compact(self):
|
|
"""
|
|
target: test count during compact merge many small segments
|
|
method: 1. init many small segments
|
|
2. compact while count
|
|
expected: verify count
|
|
"""
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix), shards_num=1)
|
|
|
|
# init 2 segments
|
|
tmp_nb = 100
|
|
for i in range(10):
|
|
df = cf.gen_default_dataframe_data(tmp_nb, start=i * tmp_nb)
|
|
collection_w.insert(df)
|
|
collection_w.flush()
|
|
|
|
# compact while count
|
|
collection_w.create_index(ct.default_float_vec_field_name, ct.default_index)
|
|
collection_w.load()
|
|
|
|
t_compact = threading.Thread(target=collection_w.compact, args=())
|
|
t_count = threading.Thread(target=collection_w.query, args=(default_expr,),
|
|
kwargs={
|
|
"output_fields": [ct.default_count_output],
|
|
"check_task": CheckTasks.check_query_results,
|
|
"check_items": {exp_res: [{count: tmp_nb * 10}]}
|
|
})
|
|
|
|
t_compact.start()
|
|
t_count.start()
|
|
t_count.join()
|
|
t_count.join()
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
def test_count_with_expr(self):
|
|
"""
|
|
target: test count with expr
|
|
method: count with expr
|
|
expected: verify count
|
|
"""
|
|
# create -> insert -> index -> load
|
|
collection_w = self.init_collection_general(insert_data=True)[0]
|
|
|
|
# count with expr
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: ct.default_nb}]})
|
|
|
|
collection_w.query(expr=default_term_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: 2}]})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_count_expr_json(self):
|
|
"""
|
|
target: test query with part json key value
|
|
method: 1. insert data and some entities doesn't have number key
|
|
2. query count with number expr filet
|
|
expected: succeed
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, enable_dynamic_field=True, with_json=True)[0]
|
|
|
|
# 2. insert data
|
|
array = cf.gen_default_rows_data( with_json=False)
|
|
for i in range(ct.default_nb):
|
|
if i % 2 == 0:
|
|
array[i][json_field] = {"string": str(i), "bool": bool(i)}
|
|
else:
|
|
array[i][json_field] = {"string": str(i), "bool": bool(i), "number": i}
|
|
|
|
collection_w.insert(array)
|
|
|
|
# 3. query
|
|
collection_w.load()
|
|
expression = f'{ct.default_json_field_name}["number"] < 100'
|
|
collection_w.query(expression, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: 50}]})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_count_with_pagination_param(self):
|
|
"""
|
|
target: test count with pagination params
|
|
method: count with pagination params: offset, limit
|
|
expected: exception
|
|
"""
|
|
# create -> insert -> index -> load
|
|
collection_w = self.init_collection_general(insert_data=True)[0]
|
|
|
|
# only params offset is not considered pagination
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output], offset=10,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: ct.default_nb}]}
|
|
)
|
|
# count with limit
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output], limit=10,
|
|
check_task=CheckTasks.err_res,
|
|
check_items={ct.err_code: 1, ct.err_msg: "count entities with pagination is not allowed"}
|
|
)
|
|
# count with pagination params
|
|
collection_w.query(default_expr, output_fields=[ct.default_count_output], offset=10, limit=10,
|
|
check_task=CheckTasks.err_res,
|
|
check_items={ct.err_code: 1, ct.err_msg: "count entities with pagination is not allowed"})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_count_alias_insert_delete_drop(self):
|
|
"""
|
|
target: test count after alias insert and load
|
|
method: 1. init collection
|
|
2. alias insert more entities
|
|
3. count and alias count
|
|
expected: verify count
|
|
"""
|
|
# create -> insert -> index -> load
|
|
collection_w = self.init_collection_general(insert_data=True)[0]
|
|
|
|
# create alias
|
|
|
|
alias = cf.gen_unique_str("alias")
|
|
self.utility_wrap.create_alias(collection_w.name, alias)
|
|
collection_w_alias = self.init_collection_wrap(name=alias)
|
|
|
|
# new insert partitions and count
|
|
p_name = cf.gen_unique_str("p_alias")
|
|
collection_w_alias.create_partition(p_name)
|
|
collection_w_alias.insert(cf.gen_default_dataframe_data(start=ct.default_nb), partition_name=p_name)
|
|
collection_w_alias.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: ct.default_nb * 2}]})
|
|
|
|
# release collection and alias drop partition
|
|
collection_w_alias.drop_partition(p_name, check_task=CheckTasks.err_res,
|
|
check_items={ct.err_code: 65535,
|
|
ct.err_msg: "partition cannot be dropped, "
|
|
"partition is loaded, please release it first"})
|
|
self.partition_wrap.init_partition(collection_w_alias.collection, p_name)
|
|
self.partition_wrap.release()
|
|
|
|
collection_w_alias.drop_partition(p_name)
|
|
res, _ = collection_w_alias.has_partition(p_name)
|
|
assert res is False
|
|
collection_w_alias.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: ct.default_nb}]})
|
|
|
|
# alias delete and count
|
|
collection_w_alias.delete(f"{ct.default_int64_field_name} in {[i for i in range(ct.default_nb)]}")
|
|
collection_w_alias.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: 0}]})
|
|
|
|
collection_w_alias.drop(check_task=CheckTasks.err_res,
|
|
check_items={ct.err_code: 1,
|
|
ct.err_msg: "cannot drop the collection via alias"})
|
|
collection_w.drop()
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("is_growing", [True, False])
|
|
def test_count_upsert_growing_sealed(self, is_growing):
|
|
"""
|
|
target: test count after upsert growing
|
|
method: 1. create -> index -> load -> insert -> delete
|
|
2. upsert deleted id and count (+1)
|
|
3. upsert new id and count (+1)
|
|
4. upsert existed id and count (+0)
|
|
expected: verify count
|
|
"""
|
|
if is_growing:
|
|
# create -> index -> load -> insert -> delete
|
|
collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix))
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
collection_w.insert(cf.gen_default_dataframe_data())
|
|
|
|
# delete one entity
|
|
single_expr = f'{ct.default_int64_field_name} in [0]'
|
|
collection_w.delete(single_expr)
|
|
else:
|
|
# create -> insert -> delete -> index -> load
|
|
collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix))
|
|
collection_w.insert(cf.gen_default_dataframe_data())
|
|
|
|
# delete one entity
|
|
single_expr = f'{ct.default_int64_field_name} in [0]'
|
|
collection_w.delete(single_expr)
|
|
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
|
|
# upsert deleted id
|
|
df_zero = cf.gen_default_dataframe_data(nb=1)
|
|
collection_w.upsert(df_zero)
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: ct.default_nb}]})
|
|
|
|
# upsert new id and count
|
|
df_new = cf.gen_default_dataframe_data(nb=1, start=ct.default_nb)
|
|
collection_w.upsert(df_new)
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: ct.default_nb + 1}]})
|
|
|
|
# upsert existed id and count
|
|
df_existed = cf.gen_default_dataframe_data(nb=1, start=10)
|
|
collection_w.upsert(df_existed)
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: ct.default_nb + 1}]})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_count_upsert_duplicate(self):
|
|
"""
|
|
target: test count after upsert duplicate
|
|
method: 1. insert many duplicate ids
|
|
2. upsert id and count
|
|
3. delete id and count
|
|
4. upsert deleted id and count
|
|
expected: verify count
|
|
"""
|
|
# init collection and insert same ids
|
|
tmp_nb = 100
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
df = cf.gen_default_dataframe_data(nb=tmp_nb)
|
|
df[ct.default_int64_field_name] = 0
|
|
collection_w.insert(df)
|
|
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
|
|
# upsert id and count
|
|
df_existed = cf.gen_default_dataframe_data(nb=tmp_nb, start=0)
|
|
collection_w.upsert(df_existed)
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: tmp_nb}]}
|
|
)
|
|
|
|
# delete id and count
|
|
delete_res, _ = collection_w.delete(default_term_expr)
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: tmp_nb - delete_res.delete_count}]})
|
|
|
|
# upsert deleted id and count
|
|
df_deleted = cf.gen_default_dataframe_data(nb=delete_res.delete_count, start=0)
|
|
collection_w.upsert(df_deleted)
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: tmp_nb}]})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_count_rename_collection(self):
|
|
"""
|
|
target: test count after rename collection
|
|
method: 1. create -> insert -> index -> load
|
|
2. rename collection
|
|
3. count
|
|
expected: verify count
|
|
"""
|
|
# create -> insert -> index -> load
|
|
collection_w = self.init_collection_general(insert_data=True)[0]
|
|
new_name = cf.gen_unique_str("new_name")
|
|
self.utility_wrap.rename_collection(collection_w.name, new_name)
|
|
self.collection_wrap.init_collection(new_name)
|
|
self.collection_wrap.query(expr=default_expr, output_fields=[ct.default_count_output],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: ct.default_nb}]})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_count_disable_growing_segments(self):
|
|
"""
|
|
target: test count when disable growing segments
|
|
method: 1. create -> index -> load -> insert
|
|
2. query count with ignore_growing
|
|
expected: verify count 0
|
|
"""
|
|
# create -> index -> load
|
|
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
|
|
collection_w.create_index(ct.default_float_vec_field_name, index_params=ct.default_flat_index)
|
|
collection_w.load()
|
|
|
|
# insert
|
|
collection_w.insert(cf.gen_default_dataframe_data(nb=100))
|
|
collection_w.query(expr=default_expr, output_fields=[ct.default_count_output], ignore_growing=True,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: 0}]})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("expression", cf.gen_normal_expressions())
|
|
def test_count_expressions(self, expression):
|
|
"""
|
|
target: test count with expr
|
|
method: count with expr
|
|
expected: verify count
|
|
"""
|
|
# create -> insert -> index -> load
|
|
collection_w, _vectors, _, insert_ids = self.init_collection_general(insert_data=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)
|
|
res = len(filter_ids)
|
|
|
|
# count with expr
|
|
collection_w.query(expr=expression, output_fields=[count],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: res}]})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("bool_type", [True, False, "true", "false"])
|
|
def test_count_bool_expressions(self, bool_type):
|
|
"""
|
|
target: test count with binary expr
|
|
method: count with binary expr
|
|
expected: verify count
|
|
"""
|
|
# create -> insert -> index -> load
|
|
collection_w, _vectors, _, insert_ids = \
|
|
self.init_collection_general(insert_data=True, is_all_data_type=True)[0:4]
|
|
|
|
# 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"{ct.default_bool_field_name}"][i] == bool_type_cmp:
|
|
filter_ids.append(_id)
|
|
res = len(filter_ids)
|
|
|
|
# count with expr
|
|
expression = f"{ct.default_bool_field_name} == {bool_type}"
|
|
collection_w.query(expr=expression, output_fields=[count],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: res}]})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("expression", cf.gen_normal_expressions_field(default_float_field_name))
|
|
def test_count_expression_auto_field(self, expression):
|
|
"""
|
|
target: test count with expr
|
|
method: count with expr
|
|
expected: verify count
|
|
"""
|
|
# create -> insert -> index -> load
|
|
collection_w, _vectors, _, insert_ids = self.init_collection_general(insert_data=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):
|
|
float = _vectors.float[i]
|
|
if not expression or eval(expression):
|
|
filter_ids.append(_id)
|
|
res = len(filter_ids)
|
|
|
|
# count with expr
|
|
collection_w.query(expr=expression, output_fields=[count],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: res}]})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.skip(reason="issue #25841")
|
|
def test_count_expression_all_datatype(self):
|
|
"""
|
|
target: test count with expr
|
|
method: count with expr
|
|
expected: verify count
|
|
"""
|
|
# create -> insert -> index -> load
|
|
collection_w = self.init_collection_general(insert_data=True, is_all_data_type=True)[0]
|
|
|
|
# count with expr
|
|
expression = "int64 >= 0 && int32 >= 1999 && int16 >= 0 && int8 >= 0 && float <= 1999.0 && double >= 0"
|
|
# expression = "int64 == 1999"
|
|
collection_w.query(expr=expression, output_fields=[count],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: 1}]})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_count_expression_comparative(self):
|
|
"""
|
|
target: test count with expr
|
|
method: count with expr
|
|
expected: verify count
|
|
"""
|
|
# create -> insert -> index -> load
|
|
fields = [cf.gen_int64_field("int64_1"), cf.gen_int64_field("int64_2"),
|
|
cf.gen_float_vec_field()]
|
|
schema = cf.gen_collection_schema(fields=fields, primary_field="int64_1")
|
|
collection_w = self.init_collection_wrap(schema=schema)
|
|
|
|
nb, res = 10, 0
|
|
int_values = [random.randint(0, nb) for _ in range(nb)]
|
|
data = [[i for i in range(nb)], int_values, cf.gen_vectors(nb, ct.default_dim)]
|
|
collection_w.insert(data)
|
|
collection_w.create_index(ct.default_float_vec_field_name)
|
|
collection_w.load()
|
|
|
|
for i in range(nb):
|
|
res = res + 1 if i >= int_values[i] else res
|
|
|
|
# count with expr
|
|
expression = "int64_1 >= int64_2"
|
|
collection_w.query(expr=expression, output_fields=[count],
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: [{count: res}]})
|
|
|
|
|
|
class TestQueryIterator(TestcaseBase):
|
|
"""
|
|
******************************************************************
|
|
The following cases are used to test query iterator
|
|
******************************************************************
|
|
"""
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
def test_query_iterator_normal(self):
|
|
"""
|
|
target: test query iterator normal
|
|
method: 1. query iterator
|
|
2. check the result, expect pk
|
|
expected: query successfully
|
|
"""
|
|
# 1. initialize with data
|
|
batch_size = 100
|
|
collection_w = self.init_collection_general(prefix, True, is_index=False)[0]
|
|
collection_w.create_index(ct.default_float_vec_field_name, {"metric_type": "L2"})
|
|
collection_w.load()
|
|
# 2. search iterator
|
|
expr = "int64 >= 0"
|
|
collection_w.query_iterator(batch_size, expr=expr,
|
|
check_task=CheckTasks.check_query_iterator,
|
|
check_items={"count": ct.default_nb,
|
|
"batch_size": batch_size})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_query_iterator_using_default_batch_size(self):
|
|
"""
|
|
target: test query iterator normal
|
|
method: 1. query iterator
|
|
2. check the result, expect pk
|
|
expected: query successfully
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
# 2. query iterator
|
|
collection_w.query_iterator(check_task=CheckTasks.check_query_iterator,
|
|
check_items={"count": ct.default_nb,
|
|
"batch_size": ct.default_batch_size})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("offset", [500, 1000, 1777])
|
|
def test_query_iterator_with_offset(self, offset):
|
|
"""
|
|
target: test query iterator normal
|
|
method: 1. query iterator
|
|
2. check the result, expect pk
|
|
expected: query successfully
|
|
"""
|
|
# 1. initialize with data
|
|
batch_size = 300
|
|
collection_w = self.init_collection_general(prefix, True, is_index=False)[0]
|
|
collection_w.create_index(ct.default_float_vec_field_name, {"metric_type": "L2"})
|
|
collection_w.load()
|
|
# 2. search iterator
|
|
expr = "int64 >= 0"
|
|
collection_w.query_iterator(batch_size, expr=expr, offset=offset,
|
|
check_task=CheckTasks.check_query_iterator,
|
|
check_items={"count": ct.default_nb - offset,
|
|
"batch_size": batch_size})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("batch_size", [10, 100, 777, 2000])
|
|
def test_query_iterator_with_different_batch_size(self, batch_size):
|
|
"""
|
|
target: test query iterator normal
|
|
method: 1. query iterator
|
|
2. check the result, expect pk
|
|
expected: query successfully
|
|
"""
|
|
# 1. initialize with data
|
|
offset = 500
|
|
collection_w = self.init_collection_general(prefix, True, is_index=False)[0]
|
|
collection_w.create_index(ct.default_float_vec_field_name, {"metric_type": "L2"})
|
|
collection_w.load()
|
|
# 2. search iterator
|
|
expr = "int64 >= 0"
|
|
collection_w.query_iterator(batch_size=batch_size, expr=expr, offset=offset,
|
|
check_task=CheckTasks.check_query_iterator,
|
|
check_items={"count": ct.default_nb - offset,
|
|
"batch_size": batch_size})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("offset", [0, 10, 100, 1000])
|
|
@pytest.mark.parametrize("limit", [0, 100, 1500, 2000, 10000])
|
|
def test_query_iterator_with_different_limit(self, limit, offset):
|
|
"""
|
|
target: test query iterator normal
|
|
method: 1. query iterator
|
|
2. check the result, expect pk
|
|
expected: query successfully
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w = self.init_collection_general(prefix, True)[0]
|
|
# 2. query iterator
|
|
Count = limit if limit + offset <= ct.default_nb else ct.default_nb - offset
|
|
collection_w.query_iterator(limit=limit, expr="", offset=offset,
|
|
check_task=CheckTasks.check_query_iterator,
|
|
check_items={"count": max(Count, 0),
|
|
"batch_size": ct.default_batch_size})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_query_iterator_invalid_batch_size(self):
|
|
"""
|
|
target: test query iterator invalid limit and offset
|
|
method: query iterator using invalid limit and offset
|
|
expected: raise exception
|
|
"""
|
|
# 1. initialize with data
|
|
nb = 17000 # set nb > 16384
|
|
collection_w = self.init_collection_general(prefix, True, nb=nb)[0]
|
|
# 2. search iterator
|
|
expr = "int64 >= 0"
|
|
error = {"err_code": 1, "err_msg": "batch size cannot be less than zero"}
|
|
collection_w.query_iterator(batch_size=-1, expr=expr, check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
@pytest.mark.parametrize("batch_size", [100, 500])
|
|
@pytest.mark.parametrize("auto_id", [True, False])
|
|
def test_query_iterator_empty_expr(self, auto_id, batch_size):
|
|
"""
|
|
target: test query iterator with empty expression
|
|
method: query iterator empty expression with a limit
|
|
expected: return topK results by order
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, auto_id=auto_id)[0:4]
|
|
|
|
# 2. query with limit
|
|
collection_w.query_iterator(batch_size=batch_size,
|
|
check_task=CheckTasks.check_query_iterator,
|
|
check_items={"batch_size": batch_size,
|
|
"count": ct.default_nb,
|
|
"exp_ids": insert_ids})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("offset", [100, 1000])
|
|
@pytest.mark.parametrize("batch_size", [500, 1000])
|
|
def test_query_iterator_expr_empty_with_random_pk_pagination(self, batch_size, offset):
|
|
"""
|
|
target: test query iterator with empty expression
|
|
method: create a collection using random pk, query empty expression with a limit
|
|
expected: return topK results by order
|
|
"""
|
|
# 1. initialize with data
|
|
collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, random_primary_key=True)[0:4]
|
|
|
|
# 2. query with empty expr and check the result
|
|
exp_ids = sorted(insert_ids)
|
|
collection_w.query_iterator(batch_size, output_fields=[ct.default_string_field_name],
|
|
check_task=CheckTasks.check_query_iterator,
|
|
check_items={"batch_size": batch_size, "count": ct.default_nb, "exp_ids": exp_ids})
|
|
|
|
# 3. query with pagination
|
|
exp_ids = sorted(insert_ids)[offset:]
|
|
collection_w.query_iterator(batch_size, offset=offset, output_fields=[ct.default_string_field_name],
|
|
check_task=CheckTasks.check_query_iterator,
|
|
check_items={"batch_size": batch_size, "count": ct.default_nb - offset, "exp_ids": exp_ids})
|