# 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 from superset.dataframe import df_to_records from superset.db_engine_specs import BaseEngineSpec from superset.result_set import dedup, SupersetResultSet from .base_tests import SupersetTestCase class TestSupersetResultSet(SupersetTestCase): def test_dedup(self): self.assertEqual(dedup(["foo", "bar"]), ["foo", "bar"]) self.assertEqual( dedup(["foo", "bar", "foo", "bar", "Foo"]), ["foo", "bar", "foo__1", "bar__1", "Foo"], ) self.assertEqual( dedup(["foo", "bar", "bar", "bar", "Bar"]), ["foo", "bar", "bar__1", "bar__2", "Bar"], ) self.assertEqual( 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) self.assertEqual( results.columns, [ {"is_dttm": False, "type": "STRING", "name": "a"}, {"is_dttm": False, "type": "STRING", "name": "b"}, {"is_dttm": False, "type": "STRING", "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) self.assertEqual( results.columns, [ {"is_dttm": False, "type": "STRING", "name": "a"}, {"is_dttm": False, "type": "INT", "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) self.assertEqual( results.columns, [ {"is_dttm": False, "type": "FLOAT", "name": "a"}, {"is_dttm": False, "type": "INT", "name": "b"}, {"is_dttm": False, "type": "STRING", "name": "c"}, {"is_dttm": True, "type": "DATETIME", "name": "d"}, {"is_dttm": False, "type": "BOOL", "name": "e"}, ], ) def test_is_date(self): data = [("a", 1), ("a", 2)] cursor_descr = (("a", "string"), ("a", "string")) results = SupersetResultSet(data, cursor_descr, BaseEngineSpec) self.assertEqual(results.is_temporal("DATE"), True) self.assertEqual(results.is_temporal("DATETIME"), True) self.assertEqual(results.is_temporal("TIME"), True) self.assertEqual(results.is_temporal("TIMESTAMP"), True) self.assertEqual(results.is_temporal("STRING"), False) self.assertEqual(results.is_temporal(""), False) self.assertEqual(results.is_temporal(None), 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["name"] for col in results.columns] self.assertListEqual(column_names, ["a", "a__1"]) 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) self.assertEqual(results.columns[0]["type"], "BIGINT") 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() self.assertEqual( 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) self.assertEqual(results.columns[0]["type"], "BOOL") df = results.to_pandas_df() self.assertEqual( 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) self.assertEqual(results.columns[0]["type"], "INT") self.assertEqual(results.columns[1]["type"], "STRING") self.assertEqual(results.columns[2]["type"], "STRING") self.assertEqual(results.columns[3]["type"], "STRING") df = results.to_pandas_df() self.assertEqual( 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) self.assertEqual(results.columns[0]["type"], "STRING") df = results.to_pandas_df() self.assertEqual( df_to_records(df), [ { "metadata": '["test", [["foo", 123456, [[["test"], 3432546, 7657658766], [["fake"], 656756765, 324324324324]]]], ["test2", 43, 765765765], null, null]' } ], ) def test_nested_list_types(self): data = [([{"TestKey": [123456, "foo"]}],)] cursor_descr = [("metadata",)] results = SupersetResultSet(data, cursor_descr, BaseEngineSpec) self.assertEqual(results.columns[0]["type"], "STRING") df = results.to_pandas_df() self.assertEqual( 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) self.assertEqual(results.columns[0]["type"], "TIMESTAMP") 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) self.assertEqual(results.columns[0]["type"], "VARCHAR") self.assertEqual(results.columns[1]["type"], "INT") 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) self.assertEqual(results.columns, [])