def test_fetch_data_with_date_dimensions(self, reduce_mock):
database = MagicMock()
database.fetch_dataframes.return_value = []
dimensions = [number_field, date_field, boolean_field, text_field]
fetch_data(database, self.test_queries, dimensions)
database.fetch_dataframes.assert_called_with(
'SELECT * FROM "politics"."politician"',
'SELECT * FROM "politics"."hints"',
parse_dates={'$date': {}})
def test_fetch_data_strips_rows_over_max_result_set_size(
self, reduce_mock):
database = MagicMock(max_result_set_size=2)
database.fetch_dataframes.return_value = [
pd.DataFrame([{
"a": 1.0
}, {
"a": 2.0
}, {
"a": 3.0
}]),
pd.DataFrame([{
"a": 1.0
}]),
]
reduce_mock.side_effect = lambda *args: args[
0] # let the reduce mock pass on the dataframes unchanged
max_rows_returned, result = fetch_data(database, self.test_queries,
self.test_dimensions)
database.fetch_dataframes.assert_called_with(
'SELECT * FROM "politics"."politician"',
'SELECT * FROM "politics"."hints"',
parse_dates={})
assert_frame_equal(result[0], pd.DataFrame([{"a": 1.0}, {"a": 2.0}]))
assert_frame_equal(result[1], pd.DataFrame([{"a": 1.0}]))
self.assertEqual(max_rows_returned, 3)
def fetch(self, hint=None, force_include=()) -> List[str]:
"""
Fetch the data for this query and transform it into the widgets.
:param hint:
For database vendors that support it, add a query hint to collect analytics on the queries triggered by
fireant.
:param force_include:
A list of dimension values to include in the result set. This can be used to avoid having necessary results
cut off due to the pagination. These results will be returned at the head of the results.
:return:
A list of dict (JSON) objects containing the widget configurations.
"""
query = add_hints(self.sql, hint)[0]
dimension = self.dimensions[0]
alias_definition = dimension.definition.as_(
alias_selector(dimension.alias))
dimension_definition = dimension.definition
if self.hint_table:
alias_definition = alias_definition.replace_table(
alias_definition.table, self.hint_table)
dimension_definition = dimension.definition.replace_table(
dimension_definition.table, self.hint_table)
if force_include:
include = self.dataset.database.to_char(dimension_definition).isin(
[str(x) for x in force_include])
# Ensure that these values are included
query = query.orderby(include, order=Order.desc)
# Filter out NULL values from choices
query = query.where(dimension_definition.notnull())
# Order by the dimension definition that the choices are for
query = query.orderby(alias_definition)
max_rows_returned, data = fetch_data(self.dataset.database, [query],
self.dimensions)
if len(data.index.names) > 1:
display_alias = data.index.names[1]
data.reset_index(display_alias, inplace=True)
choices = data[display_alias]
else:
data["display"] = data.index.tolist()
choices = data["display"]
dimension_display = self.dimensions[-1]
choices = choices.map(
lambda raw: display_value(raw, dimension_display) or raw)
return self._transform_for_return(choices,
max_rows_returned=max_rows_returned)
def test_fetch_data(self, reduce_mock):
database = MagicMock(max_result_set_size=1000)
database.fetch_dataframes.return_value = [
self.test_result_a,
self.test_result_b,
]
mocked_result = pd.DataFrame([{"a": 1.0}])
reduce_mock.return_value = mocked_result
max_rows_returned, result = fetch_data(database, self.test_queries,
self.test_dimensions)
pandas.testing.assert_frame_equal(mocked_result, result)
database.fetch_dataframes.assert_called_with(
'SELECT * FROM "politics"."politician"',
'SELECT * FROM "politics"."hints"',
parse_dates={})
reduce_mock.assert_called_once_with(
[self.test_result_a, self.test_result_b], (), self.test_dimensions,
())
def fetch(self, hint=None):
queries = add_hints(self.sql, hint)
max_rows_returned, data = fetch_data(self.dataset.database, queries, self.dimensions)
data = self._get_latest_data_from_df(data)
return self._transform_for_return(data, max_rows_returned=max_rows_returned)