# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # isort:skip_file from datetime import datetime import tests.integration_tests.test_app # noqa: F401 from superset.dataframe import df_to_records from superset.db_engine_specs import BaseEngineSpec from superset.result_set import dedup, SupersetResultSet from superset.utils.core import GenericDataType from .base_tests import SupersetTestCase class TestSupersetResultSet(SupersetTestCase): def test_dedup(self): assert dedup(["foo", "bar"]) == ["foo", "bar"] assert dedup(["foo", "bar", "foo", "bar", "Foo"]) == [ "foo", "bar", "foo__1", "bar__1", "Foo", ] assert dedup(["foo", "bar", "bar", "bar", "Bar"]) == [ "foo", "bar", "bar__1", "bar__2", "Bar", ] assert dedup(["foo", "bar", "bar", "bar", "Bar"], case_sensitive=False) == [ "foo", "bar", "bar__1", "bar__2", "Bar__3", ] def test_get_columns_basic(self): data = [("a1", "b1", "c1"), ("a2", "b2", "c2")] cursor_descr = (("a", "string"), ("b", "string"), ("c", "string")) results = SupersetResultSet(data, cursor_descr, BaseEngineSpec) assert results.columns == [ { "is_dttm": False, "type": "STRING", "type_generic": GenericDataType.STRING, "column_name": "a", "name": "a", }, { "is_dttm": False, "type": "STRING", "type_generic": GenericDataType.STRING, "column_name": "b", "name": "b", }, { "is_dttm": False, "type": "STRING", "type_generic": GenericDataType.STRING, "column_name": "c", "name": "c", }, ] def test_get_columns_with_int(self): data = [("a1", 1), ("a2", 2)] cursor_descr = (("a", "string"), ("b", "int")) results = SupersetResultSet(data, cursor_descr, BaseEngineSpec) assert results.columns == [ { "is_dttm": False, "type": "STRING", "type_generic": GenericDataType.STRING, "column_name": "a", "name": "a", }, { "is_dttm": False, "type": "INT", "type_generic": GenericDataType.NUMERIC, "column_name": "b", "name": "b", }, ] def test_get_columns_type_inference(self): data = [ (1.2, 1, "foo", datetime(2018, 10, 19, 23, 39, 16, 660000), True), (3.14, 2, "bar", datetime(2019, 10, 19, 23, 39, 16, 660000), False), ] cursor_descr = (("a", None), ("b", None), ("c", None), ("d", None), ("e", None)) results = SupersetResultSet(data, cursor_descr, BaseEngineSpec) assert results.columns == [ { "is_dttm": False, "type": "FLOAT", "type_generic": GenericDataType.NUMERIC, "column_name": "a", "name": "a", }, { "is_dttm": False, "type": "INT", "type_generic": GenericDataType.NUMERIC, "column_name": "b", "name": "b", }, { "is_dttm": False, "type": "STRING", "type_generic": GenericDataType.STRING, "column_name": "c", "name": "c", }, { "is_dttm": True, "type": "DATETIME", "type_generic": GenericDataType.TEMPORAL, "column_name": "d", "name": "d", }, { "is_dttm": False, "type": "BOOL", "type_generic": GenericDataType.BOOLEAN, "column_name": "e", "name": "e", }, ] def test_is_date(self): data = [("a", 1), ("a", 2)] cursor_descr = (("a", "string"), ("a", "string")) results = SupersetResultSet(data, cursor_descr, BaseEngineSpec) assert results.is_temporal("DATE") is True assert results.is_temporal("DATETIME") is True assert results.is_temporal("TIME") is True assert results.is_temporal("TIMESTAMP") is True assert results.is_temporal("STRING") is False assert results.is_temporal("") is False assert results.is_temporal(None) is False def test_dedup_with_data(self): data = [("a", 1), ("a", 2)] cursor_descr = (("a", "string"), ("a", "string")) results = SupersetResultSet(data, cursor_descr, BaseEngineSpec) column_names = [col["column_name"] for col in results.columns] self.assertListEqual(column_names, ["a", "a__1"]) # noqa: PT009 def test_int64_with_missing_data(self): data = [(None,), (1239162456494753670,), (None,), (None,), (None,), (None,)] cursor_descr = [("user_id", "bigint", None, None, None, None, True)] results = SupersetResultSet(data, cursor_descr, BaseEngineSpec) assert results.columns[0]["type"] == "BIGINT" assert results.columns[0]["type_generic"] == GenericDataType.NUMERIC def test_data_as_list_of_lists(self): data = [[1, "a"], [2, "b"]] cursor_descr = [ ("user_id", "INT", None, None, None, None, True), ("username", "STRING", None, None, None, None, True), ] results = SupersetResultSet(data, cursor_descr, BaseEngineSpec) df = results.to_pandas_df() assert df_to_records(df) == [ {"user_id": 1, "username": "a"}, {"user_id": 2, "username": "b"}, ] def test_nullable_bool(self): data = [(None,), (True,), (None,), (None,), (None,), (None,)] cursor_descr = [("is_test", "bool", None, None, None, None, True)] results = SupersetResultSet(data, cursor_descr, BaseEngineSpec) assert results.columns[0]["type"] == "BOOL" assert results.columns[0]["type_generic"] == GenericDataType.BOOLEAN df = results.to_pandas_df() assert df_to_records(df) == [ {"is_test": None}, {"is_test": True}, {"is_test": None}, {"is_test": None}, {"is_test": None}, {"is_test": None}, ] def test_nested_types(self): data = [ ( 4, [{"table_name": "unicode_test", "database_id": 1}], [1, 2, 3], {"chart_name": "scatter"}, ), ( 3, [{"table_name": "birth_names", "database_id": 1}], [4, 5, 6], {"chart_name": "plot"}, ), ] cursor_descr = [("id",), ("dict_arr",), ("num_arr",), ("map_col",)] results = SupersetResultSet(data, cursor_descr, BaseEngineSpec) assert results.columns[0]["type"] == "INT" assert results.columns[0]["type_generic"] == GenericDataType.NUMERIC assert results.columns[1]["type"] == "STRING" assert results.columns[1]["type_generic"] == GenericDataType.STRING assert results.columns[2]["type"] == "STRING" assert results.columns[2]["type_generic"] == GenericDataType.STRING assert results.columns[3]["type"] == "STRING" assert results.columns[3]["type_generic"] == GenericDataType.STRING df = results.to_pandas_df() assert df_to_records(df) == [ { "id": 4, "dict_arr": '[{"table_name": "unicode_test", "database_id": 1}]', "num_arr": "[1, 2, 3]", "map_col": "{'chart_name': 'scatter'}", }, { "id": 3, "dict_arr": '[{"table_name": "birth_names", "database_id": 1}]', "num_arr": "[4, 5, 6]", "map_col": "{'chart_name': 'plot'}", }, ] def test_single_column_multidim_nested_types(self): data = [ ( [ "test", [ [ "foo", 123456, [ [["test"], 3432546, 7657658766], [["fake"], 656756765, 324324324324], ], ] ], ["test2", 43, 765765765], None, None, ], ) ] cursor_descr = [("metadata",)] results = SupersetResultSet(data, cursor_descr, BaseEngineSpec) assert results.columns[0]["type"] == "STRING" assert results.columns[0]["type_generic"] == GenericDataType.STRING df = results.to_pandas_df() assert df_to_records(df) == [ { "metadata": '["test", [["foo", 123456, [[["test"], 3432546, 7657658766], [["fake"], 656756765, 324324324324]]]], ["test2", 43, 765765765], null, null]' # noqa: E501 } ] def test_nested_list_types(self): data = [([{"TestKey": [123456, "foo"]}],)] cursor_descr = [("metadata",)] results = SupersetResultSet(data, cursor_descr, BaseEngineSpec) assert results.columns[0]["type"] == "STRING" assert results.columns[0]["type_generic"] == GenericDataType.STRING df = results.to_pandas_df() assert df_to_records(df) == [{"metadata": '[{"TestKey": [123456, "foo"]}]'}] def test_empty_datetime(self): data = [(None,)] cursor_descr = [("ds", "timestamp", None, None, None, None, True)] results = SupersetResultSet(data, cursor_descr, BaseEngineSpec) assert results.columns[0]["type"] == "TIMESTAMP" assert results.columns[0]["type_generic"] == GenericDataType.TEMPORAL def test_no_type_coercion(self): data = [("a", 1), ("b", 2)] cursor_descr = [ ("one", "varchar", None, None, None, None, True), ("two", "int", None, None, None, None, True), ] results = SupersetResultSet(data, cursor_descr, BaseEngineSpec) assert results.columns[0]["type"] == "VARCHAR" assert results.columns[0]["type_generic"] == GenericDataType.STRING assert results.columns[1]["type"] == "INT" assert results.columns[1]["type_generic"] == GenericDataType.NUMERIC def test_empty_data(self): data = [] cursor_descr = [ ("emptyone", "varchar", None, None, None, None, True), ("emptytwo", "int", None, None, None, None, True), ] results = SupersetResultSet(data, cursor_descr, BaseEngineSpec) assert results.columns == []