| # 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 == [] |
