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* starter make file * yea * move messaging to config * Delete Makefile * remove * checkout * check for db generic errors * checkout bad files * add proper message * add docs for new permissions codes * Update superset/errors.py Co-authored-by: AAfghahi <48933336+AAfghahi@users.noreply.github.com> * Update superset/errors.py Co-authored-by: AAfghahi <48933336+AAfghahi@users.noreply.github.com> * rename var * starter regex * fix * fix * fix linting * update test * yerp * fixed test * added regex * Apply suggestions from code review Co-authored-by: Beto Dealmeida <roberto@dealmeida.net> * address comments * update docs * prettier * fux * add space * Update errors.py * Update types.ts Co-authored-by: AAfghahi <48933336+AAfghahi@users.noreply.github.com> Co-authored-by: Beto Dealmeida <roberto@dealmeida.net>
242 lines
9.1 KiB
Python
242 lines
9.1 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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import sys
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import unittest.mock as mock
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from pandas import DataFrame
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from sqlalchemy import column
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from superset.db_engine_specs.base import BaseEngineSpec
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from superset.db_engine_specs.bigquery import BigQueryEngineSpec
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from superset.errors import ErrorLevel, SupersetError, SupersetErrorType
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from tests.db_engine_specs.base_tests import TestDbEngineSpec
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class TestBigQueryDbEngineSpec(TestDbEngineSpec):
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def test_bigquery_sqla_column_label(self):
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"""
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DB Eng Specs (bigquery): Test column label
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"""
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test_cases = {
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"Col": "Col",
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"SUM(x)": "SUM_x__5f110",
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"SUM[x]": "SUM_x__7ebe1",
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"12345_col": "_12345_col_8d390",
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}
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for original, expected in test_cases.items():
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actual = BigQueryEngineSpec.make_label_compatible(column(original).name)
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self.assertEqual(actual, expected)
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def test_convert_dttm(self):
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"""
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DB Eng Specs (bigquery): Test conversion to date time
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"""
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dttm = self.get_dttm()
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test_cases = {
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"DATE": "CAST('2019-01-02' AS DATE)",
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"DATETIME": "CAST('2019-01-02T03:04:05.678900' AS DATETIME)",
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"TIMESTAMP": "CAST('2019-01-02T03:04:05.678900' AS TIMESTAMP)",
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"TIME": "CAST('03:04:05.678900' AS TIME)",
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"UNKNOWNTYPE": None,
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}
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for target_type, expected in test_cases.items():
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actual = BigQueryEngineSpec.convert_dttm(target_type, dttm)
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self.assertEqual(actual, expected)
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def test_timegrain_expressions(self):
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"""
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DB Eng Specs (bigquery): Test time grain expressions
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"""
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col = column("temporal")
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test_cases = {
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"DATE": "DATE_TRUNC(temporal, HOUR)",
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"TIME": "TIME_TRUNC(temporal, HOUR)",
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"DATETIME": "DATETIME_TRUNC(temporal, HOUR)",
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"TIMESTAMP": "TIMESTAMP_TRUNC(temporal, HOUR)",
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}
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for type_, expected in test_cases.items():
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actual = BigQueryEngineSpec.get_timestamp_expr(
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col=col, pdf=None, time_grain="PT1H", type_=type_
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)
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self.assertEqual(str(actual), expected)
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def test_custom_minute_timegrain_expressions(self):
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"""
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DB Eng Specs (bigquery): Test time grain expressions
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"""
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col = column("temporal")
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test_cases = {
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"DATE": "CAST(TIMESTAMP_SECONDS("
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"5*60 * DIV(UNIX_SECONDS(CAST(temporal AS TIMESTAMP)), 5*60)"
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") AS DATE)",
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"DATETIME": "CAST(TIMESTAMP_SECONDS("
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"5*60 * DIV(UNIX_SECONDS(CAST(temporal AS TIMESTAMP)), 5*60)"
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") AS DATETIME)",
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"TIMESTAMP": "CAST(TIMESTAMP_SECONDS("
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"5*60 * DIV(UNIX_SECONDS(CAST(temporal AS TIMESTAMP)), 5*60)"
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") AS TIMESTAMP)",
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}
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for type_, expected in test_cases.items():
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actual = BigQueryEngineSpec.get_timestamp_expr(
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col=col, pdf=None, time_grain="PT5M", type_=type_
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)
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assert str(actual) == expected
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def test_fetch_data(self):
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"""
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DB Eng Specs (bigquery): Test fetch data
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"""
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# Mock a google.cloud.bigquery.table.Row
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class Row(object):
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def __init__(self, value):
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self._value = value
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def values(self):
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return self._value
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data1 = [(1, "foo")]
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with mock.patch.object(BaseEngineSpec, "fetch_data", return_value=data1):
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result = BigQueryEngineSpec.fetch_data(None, 0)
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self.assertEqual(result, data1)
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data2 = [Row(1), Row(2)]
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with mock.patch.object(BaseEngineSpec, "fetch_data", return_value=data2):
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result = BigQueryEngineSpec.fetch_data(None, 0)
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self.assertEqual(result, [1, 2])
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def test_extra_table_metadata(self):
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"""
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DB Eng Specs (bigquery): Test extra table metadata
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"""
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database = mock.Mock()
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# Test no indexes
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database.get_indexes = mock.MagicMock(return_value=None)
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result = BigQueryEngineSpec.extra_table_metadata(
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database, "some_table", "some_schema"
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)
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self.assertEqual(result, {})
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index_metadata = [
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{"name": "clustering", "column_names": ["c_col1", "c_col2", "c_col3"],},
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{"name": "partition", "column_names": ["p_col1", "p_col2", "p_col3"],},
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]
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expected_result = {
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"partitions": {"cols": [["p_col1", "p_col2", "p_col3"]]},
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"clustering": {"cols": [["c_col1", "c_col2", "c_col3"]]},
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}
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database.get_indexes = mock.MagicMock(return_value=index_metadata)
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result = BigQueryEngineSpec.extra_table_metadata(
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database, "some_table", "some_schema"
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)
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self.assertEqual(result, expected_result)
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def test_normalize_indexes(self):
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"""
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DB Eng Specs (bigquery): Test extra table metadata
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"""
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indexes = [{"name": "partition", "column_names": [None], "unique": False}]
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normalized_idx = BigQueryEngineSpec.normalize_indexes(indexes)
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self.assertEqual(normalized_idx, [])
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indexes = [{"name": "partition", "column_names": ["dttm"], "unique": False}]
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normalized_idx = BigQueryEngineSpec.normalize_indexes(indexes)
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self.assertEqual(normalized_idx, indexes)
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indexes = [
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{"name": "partition", "column_names": ["dttm", None], "unique": False}
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]
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normalized_idx = BigQueryEngineSpec.normalize_indexes(indexes)
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self.assertEqual(
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normalized_idx,
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[{"name": "partition", "column_names": ["dttm"], "unique": False}],
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)
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def test_df_to_sql(self):
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"""
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DB Eng Specs (bigquery): Test DataFrame to SQL contract
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"""
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# test missing google.oauth2 dependency
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sys.modules["pandas_gbq"] = mock.MagicMock()
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df = DataFrame()
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self.assertRaisesRegexp(
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Exception,
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"Could not import libraries",
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BigQueryEngineSpec.df_to_sql,
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df,
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con="some_connection",
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schema="schema",
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name="name",
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)
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invalid_kwargs = [
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{"name": "some_name"},
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{"schema": "some_schema"},
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{"con": "some_con"},
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{"name": "some_name", "con": "some_con"},
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{"name": "some_name", "schema": "some_schema"},
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{"con": "some_con", "schema": "some_schema"},
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]
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# Test check for missing required kwargs (name, schema, con)
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sys.modules["google.oauth2"] = mock.MagicMock()
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for invalid_kwarg in invalid_kwargs:
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self.assertRaisesRegexp(
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Exception,
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"name, schema and con need to be defined in kwargs",
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BigQueryEngineSpec.df_to_sql,
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df,
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**invalid_kwarg,
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)
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import pandas_gbq
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from google.oauth2 import service_account
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pandas_gbq.to_gbq = mock.Mock()
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service_account.Credentials.from_service_account_info = mock.MagicMock(
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return_value="account_info"
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)
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connection = mock.Mock()
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connection.engine.url.host = "google-host"
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connection.dialect.credentials_info = "secrets"
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BigQueryEngineSpec.df_to_sql(
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df, con=connection, schema="schema", name="name", if_exists="extra_key"
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)
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pandas_gbq.to_gbq.assert_called_with(
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df,
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project_id="google-host",
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destination_table="schema.name",
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credentials="account_info",
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if_exists="extra_key",
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)
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def test_extract_errors(self):
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msg = "403 POST https://bigquery.googleapis.com/bigquery/v2/projects/test-keel-310804/jobs?prettyPrint=false: Access Denied: Project User does not have bigquery.jobs.create permission in project profound-keel-310804"
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result = BigQueryEngineSpec.extract_errors(Exception(msg))
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assert result == [
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SupersetError(
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message="We were unable to connect to your database. Please confirm that your service account has the Viewer and Job User roles on the project.",
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error_type=SupersetErrorType.CONNECTION_DATABASE_PERMISSIONS_ERROR,
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level=ErrorLevel.ERROR,
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extra={
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"engine_name": "Google BigQuery",
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"issue_codes": [{"code": 1017, "message": "",}],
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},
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)
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]
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