import pytest import random import numpy as np import pandas as pd from pymilvus import DefaultConfig from base.client_base import TestcaseBase from common.code_mapping import ConnectionErrorMessage as cem from common.code_mapping import CollectionErrorMessage as clem from common import common_func as cf from common import common_type as ct from common.common_type import CaseLabel, CheckTasks from utils.util_log import test_log as log prefix = "query" exp_res = "exp_res" default_term_expr = f'{ct.default_int64_field_name} in [0, 1]' default_index_params = {"index_type": "IVF_SQ8", "metric_type": "L2", "params": {"nlist": 64}} binary_index_params = {"index_type": "BIN_IVF_FLAT", "metric_type": "JACCARD", "params": {"nlist": 64}} class TestQueryBase(TestcaseBase): """ test Query interface query(collection_name, expr, output_fields=None, partition_names=None, timeout=None) """ @pytest.mark.tags(CaseLabel.L0) def test_query(self): """ target: test query method: query with term expr expected: 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 = 5 term_expr = f'{ct.default_int64_field_name} in {int_values[:pos]}' res = vectors[0].iloc[0:pos, :1].to_dict('records') collection_w.query(term_expr, check_task=CheckTasks.check_query_results, check_items={exp_res: res}) @pytest.mark.tags(CaseLabel.L1) def test_query_empty_collection(self): """ target: test query empty collection method: query on a empty collection expected: empty result """ c_name = cf.gen_unique_str(prefix) collection_w = self.init_collection_wrap(name=c_name) collection_w.load() res, _ = collection_w.query(default_term_expr) assert len(res) == 0 @pytest.mark.tags(CaseLabel.L0) def test_query_auto_id_collection(self): """ target: test query with auto_id=True collection method: test query with auto id expected: query result is correct """ self._connect() df = cf.gen_default_dataframe_data(ct.default_nb) df[ct.default_int64_field_name] = None insert_res, _, = self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df, primary_field=ct.default_int64_field_name, auto_id=True) assert self.collection_wrap.num_entities == ct.default_nb ids = insert_res[1].primary_keys pos = 5 res = df.iloc[:pos, :1].to_dict('records') self.collection_wrap.load() # query with all primary keys term_expr_1 = f'{ct.default_int64_field_name} in {ids[:pos]}' for i in range(5): res[i][ct.default_int64_field_name] = ids[i] self.collection_wrap.query(term_expr_1, check_task=CheckTasks.check_query_results, check_items={exp_res: res}) # query with part primary keys term_expr_2 = f'{ct.default_int64_field_name} in {[ids[0], 0]}' self.collection_wrap.query(term_expr_2, check_task=CheckTasks.check_query_results, check_items={exp_res: res[:1]}) @pytest.mark.tags(CaseLabel.L1) def test_query_auto_id_not_existed_primary_values(self): """ target: test query on auto_id true collection method: 1.create auto_id true collection 2.query with not existed primary keys expected: query result is empty """ schema = cf.gen_default_collection_schema(auto_id=True) collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix), schema=schema) df = cf.gen_default_dataframe_data(ct.default_nb) df.drop(ct.default_int64_field_name, axis=1, inplace=True) mutation_res, _ = collection_w.insert(data=df) assert collection_w.num_entities == ct.default_nb collection_w.load() term_expr = f'{ct.default_int64_field_name} in [0, 1, 2]' res, _ = collection_w.query(term_expr) assert len(res) == 0 @pytest.mark.tags(CaseLabel.L1) def test_query_expr_none(self): """ target: test query with none expr method: query with expr None expected: raise exception """ collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2] error = {ct.err_code: 0, ct.err_msg: "The type of expr must be string"} collection_w.query(None, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L1) def test_query_non_string_expr(self): """ target: test query with non-string expr method: query with non-string expr, eg 1, [] .. expected: raise exception """ collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2] exprs = [1, 2., [], {}, ()] error = {ct.err_code: 0, ct.err_msg: "The type of expr must be string"} for expr in exprs: collection_w.query(expr, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L1) def test_query_expr_invalid_string(self): """ target: test query with invalid expr method: query with invalid string expr expected: raise exception """ collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2] error = {ct.err_code: 1, ct.err_msg: "Invalid expression!"} exprs = ["12-s", "中文", "a", " "] for expr in exprs: collection_w.query(expr, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L1) @pytest.mark.skip(reason="repeat with test_query, waiting for other expr") def test_query_expr_term(self): """ target: test query with TermExpr method: query with TermExpr expected: query result is correct """ 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, check_task=CheckTasks.check_query_results, check_items={exp_res: res}) @pytest.mark.tags(CaseLabel.L1) def test_query_expr_not_existed_field(self): """ target: test query with not existed field method: query by term expr with fake field expected: raise exception """ collection_w = self.init_collection_wrap(cf.gen_unique_str(prefix)) term_expr = 'field in [1, 2]' error = {ct.err_code: 1, ct.err_msg: "fieldName(field) not found"} collection_w.query(term_expr, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L1) def test_query_expr_non_primary_fields(self): """ target: test query on non-primary non-vector fields method: query on non-primary non-vector fields expected: verify query result """ self._connect() # construct dataframe and inert data df = pd.DataFrame({ ct.default_int64_field_name: pd.Series(data=[i for i in range(ct.default_nb)]), ct.default_int32_field_name: pd.Series(data=[np.int32(i) for i in range(ct.default_nb)], dtype="int32"), ct.default_int16_field_name: pd.Series(data=[np.int16(i) for i in range(ct.default_nb)], dtype="int16"), ct.default_float_field_name: pd.Series(data=[float(i) for i in range(ct.default_nb)], dtype="float32"), ct.default_double_field_name: pd.Series(data=[np.double(i) for i in range(ct.default_nb)], dtype="double"), ct.default_float_vec_field_name: cf.gen_vectors(ct.default_nb, ct.default_dim) }) 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.load() # query by non_primary non_vector scalar field non_primary_field = [ct.default_int32_field_name, ct.default_int16_field_name, ct.default_float_field_name, ct.default_double_field_name] # exp res: first two rows and all fields expect last vec field res = df.iloc[:2, :-1].to_dict('records') for field in non_primary_field: filter_values = df[field].tolist()[:2] term_expr = f'{field} in {filter_values}' self.collection_wrap.query(term_expr, output_fields=["*"], check_task=CheckTasks.check_query_results, check_items={exp_res: res}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.xfail(reason="issue #7521 #7522") def test_query_expr_by_bool_field(self): """ target: test query by bool field and output binary field method: 1.create and insert with [int64, float, bool, float_vec] fields 2.query by bool field, and output all int64, bool fields expected: verify query result and output fields """ self._connect() df = cf.gen_default_dataframe_data() bool_values = pd.Series(data=[True if i % 2 == 0 else False for i in range(ct.default_nb)], dtype="bool") df.insert(2, ct.default_bool_field_name, bool_values) 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.load() term_expr = f'{ct.default_bool_field_name} in [True]' res, _ = self.collection_wrap.query(term_expr, output_fields=[ct.default_bool_field_name]) assert len(res) == ct.default_nb / 2 assert set(res[0].keys()) == set(ct.default_int64_field_name, ct.default_bool_field_name) @pytest.mark.tags(CaseLabel.L2) def test_query_expr_by_int8_field(self): """ target: test query by int8 field method: 1.create and insert with [int64, float, int8, float_vec] fields 2.query by int8 field, and output all scalar fields expected: verify query result """ self._connect() # construct collection from dataFrame according to [int64, float, int8, float_vec] df = cf.gen_default_dataframe_data() int8_values = pd.Series(data=[np.int8(i) for i in range(ct.default_nb)], dtype="int8") df.insert(2, ct.default_int8_field_name, int8_values) 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 # query expression term_expr = f'{ct.default_int8_field_name} in {[0]}' # expected query result res = [] # int8 range [-128, 127] so when nb=1200, there are many repeated int8 values equal to 0 for i in range(0, ct.default_nb, 256): res.extend(df.iloc[i:i + 1, :-1].to_dict('records')) self.collection_wrap.load() self.collection_wrap.query(term_expr, output_fields=["*"], check_task=CheckTasks.check_query_results, check_items={exp_res: res}) @pytest.mark.tags(CaseLabel.L1) 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: 1, ct.err_msg: f'unexpected token Identifier("inn")'} 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: 1, ct.err_msg: 'right operand of the InExpr must be array'} 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.load() values = df[field].tolist() pos = 100 term_expr = f'{field} not in {values[pos:]}' res = df.iloc[:pos, :2].to_dict('records') self.collection_wrap.query(term_expr, output_fields=["*"], check_task=CheckTasks.check_query_results, check_items={exp_res: res}) @pytest.mark.tags(CaseLabel.L2) @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.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.tag(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.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.tag(CaseLabel.L1) 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.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.L1) 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: 1, ct.err_msg: "right operand of the InExpr must be array"} for expr in exprs: collection_w.query(expr, check_task=CheckTasks.err_res, check_items=error) @pytest.mark.tags(CaseLabel.L1) 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.L1) 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: 1, ct.err_msg: "type mismatch"} 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.L1) 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: 1, ct.err_msg: "unsupported leaf node"} 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) def test_query_output_field_none_or_empty(self): """ target: test query with none and empty output field method: query with output field=None, field=[] expected: return primary field """ collection_w, vectors = self.init_collection_general(prefix, insert_data=True)[0:2] for fields in [None, []]: res, _ = collection_w.query(default_term_expr, output_fields=fields) assert list(res[0].keys()) == [ct.default_int64_field_name] @pytest.mark.tags(CaseLabel.L0) def test_query_output_one_field(self): """ 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)[0:2] res, _ = collection_w.query(default_term_expr, output_fields=[ct.default_float_field_name]) assert set(res[0].keys()) == set([ct.default_int64_field_name, ct.default_float_field_name]) @pytest.mark.tags(CaseLabel.L1) def test_query_output_all_fields(self): """ target: test query with none output field method: query with output field=None expected: return all fields """ 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 all_fields = [ct.default_int64_field_name, ct.default_float_field_name, ct.default_float_vec_field_name] res = df.iloc[:2].to_dict('records') 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.L1) 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.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.L1) @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.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.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.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.L1) 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 list(res[0].keys()) == 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 list(res[0].keys()) == [ct.default_int64_field_name] @pytest.mark.tags(CaseLabel.L1) 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: 1, 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) def test_query_output_fields_simple_wildcard(self): """ target: test query output_fields with simple wildcard (* and %) method: specify output_fields as "*" and "*", "%" 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.load() # query with wildcard scale(*) output_fields = [ct.default_int64_field_name, ct.default_float_field_name] res = df.loc[:1, output_fields].to_dict('records') collection_w.query(default_term_expr, output_fields=["*"], check_task=CheckTasks.check_query_results, check_items={exp_res: res}) # query with wildcard % output_fields2 = [ct.default_int64_field_name, ct.default_float_vec_field_name, ct.another_float_vec_field_name] res2 = df.loc[:1, output_fields2].to_dict('records') collection_w.query(default_term_expr, output_fields=["%"], check_task=CheckTasks.check_query_results, check_items={exp_res: res2, "with_vec": True}) # query with wildcard all fields: vector(%) and scale(*) 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) 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, float_vector1 collection_w, df = self.init_multi_fields_collection_wrap(cf.gen_unique_str(prefix)) # query with output_fields=["*", float_vector) res = df.iloc[:2, :3].to_dict('records') 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}) # query with output_fields=["*", float) res2 = df.iloc[:2, :2].to_dict('records') collection_w.load() collection_w.query(default_term_expr, output_fields=["*", ct.default_float_field_name], check_task=CheckTasks.check_query_results, check_items={exp_res: res2}) @pytest.mark.tags(CaseLabel.L1) def test_query_output_fields_part_vector_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, float_vector1 collection_w, df = self.init_multi_fields_collection_wrap(cf.gen_unique_str(prefix)) collection_w.load() # query with output_fields=["%", float), expected: all fields res = df.iloc[:2].to_dict('records') collection_w.query(default_term_expr, output_fields=["%", ct.default_float_field_name], check_task=CheckTasks.check_query_results, check_items={exp_res: res, "with_vec": True}) # query with output_fields=["%", float_vector), expected: int64, float_vector, float_vector1 output_fields = [ct.default_int64_field_name, ct.default_float_vec_field_name, ct.another_float_vec_field_name] res2 = df.loc[:1, output_fields].to_dict('records') collection_w.query(default_term_expr, output_fields=["%", ct.default_float_vec_field_name], check_task=CheckTasks.check_query_results, check_items={exp_res: res2, "with_vec": True}) @pytest.mark.tags(CaseLabel.L1) @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, float_vector1 collection_w, df = self.init_multi_fields_collection_wrap(cf.gen_unique_str(prefix)) collection_w.load() # query with invalid output_fields error = {ct.err_code: 1, 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(ct.default_nb) partition_w.insert(df) assert collection_w.num_entities == ct.default_nb 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.L1) 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(ct.default_nb) partition_w.insert(df) assert partition_w.num_entities == ct.default_nb error = {ct.err_code: 1, ct.err_msg: f'collection {collection_w.name} was not loaded into memory'} 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(self): """ target: test query on empty partition method: query on a 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 partition_w.load() res, _ = collection_w.query(default_term_expr, partition_names=[partition_w.name]) assert len(res) == 0 @pytest.mark.tags(CaseLabel.L1) 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.load() partition_names = cf.gen_unique_str() error = {ct.err_code: 1, ct.err_msg: f'PartitonName: {partition_names} not found'} collection_w.query(default_term_expr, partition_names=[partition_names], check_task=CheckTasks.err_res, check_items=error) class TestQueryOperation(TestcaseBase): """ ****************************************************************** The following cases are used to test query interface operations ****************************************************************** """ @pytest.mark.tags(CaseLabel.L2) # @pytest.mark.parametrize("collection_name", [cf.gen_unique_str(prefix)]) 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.L1) # @pytest.mark.parametrize("collection_name, data", # [(cf.gen_unique_str(prefix), cf.gen_default_list_data(ct.default_nb))]) 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(ct.default_nb)) # 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: 1, ct.err_msg: clem.CollNotLoaded % collection_name}) @pytest.mark.tags(CaseLabel.L1) @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.L2) 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.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: query after index expected: query result is correct """ collection_w, vectors, binary_raw_vectors = self.init_collection_general(prefix, insert_data=True)[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: query after search 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) # 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.L2) 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)[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] res, _ = collection_w.query(default_term_expr, output_fields=[ct.default_binary_vec_field_name]) assert list(res[0].keys()) == 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(ct.default_nb) 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 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.L2) 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