jenwitteng commented on issue #37114:
URL: https://github.com/apache/superset/issues/37114#issuecomment-3748538046
**Case /r/n**
writing cache
> query_dict={'apply_fetch_values_predicate': False, 'columns':
[{'timeGrain': 'P1M', 'columnType': 'BASE_AXIS', 'sqlExpression':
'booking_datetime_cast', 'label': 'booking_datetime_cast', 'expressionType':
'SQL'}], 'extras': {'time_grain_sqla': 'P1M', 'having': '', 'where': ''},
'filter': [{'col': 'whitelabel_name', 'op': 'IN', 'val': ['RMNectar']}, {'col':
'booking_datetime_cast', 'op': 'TEMPORAL_RANGE', 'val':
'DATEADD(DATETIME("now"), -7, day) : now'}], 'from_dttm':
datetime.datetime(2026, 1, 6, 9, 1, 55), 'granularity': None,
'inner_from_dttm': None, 'inner_to_dttm': None, 'is_rowcount': False,
'is_timeseries': False, 'metrics': [{'aggregate': None, 'column': None,
'datasourceWarning': False, 'expressionType': 'SQL', 'hasCustomLabel': True,
'label': '< 16% Spread', 'optionName': 'metric_d7ocbaa3aq_k1i0msjgad',
'sqlExpression': "SUM(CASE \r\n WHEN cast(original_total_spread_percentage
as float) < 0.16\r\n THEN CASE\r\n WHEN tbl_source = 'PPA' THEN
hotel_no
_of_room\r\n WHEN tbl_source = 'TR' THEN 1\r\n END\r\n
END)"}, {'aggregate': None, 'column': None, 'datasourceWarning': False,
'expressionType': 'SQL', 'hasCustomLabel': True, 'label': '16% - 20% Spread',
'optionName': 'metric_wqmh9wx1z2_ymlki2up3dn', 'sqlExpression': "SUM(CASE \r\n
WHEN cast(original_total_spread_percentage as float) >= 0.16\r\n AND
cast(original_total_spread_percentage as float) < 0.2\r\n THEN CASE\r\n
WHEN tbl_source = 'PPA' THEN hotel_no_of_room\r\n WHEN
tbl_source = 'TR' THEN 1\r\n END\r\n END)"}, {'aggregate': None,
'column': None, 'datasourceWarning': False, 'expressionType': 'SQL',
'hasCustomLabel': True, 'label': '20% - 25% Spread', 'optionName':
'metric_5egn3fc8zja_1b16x3da4md', 'sqlExpression': "SUM(CASE \r\n WHEN
cast(original_total_spread_percentage as float) >= 0.20\r\n AND
cast(original_total_spread_percentage as float) < 0.25\r\n THEN CASE\r\n
WH
EN tbl_source = 'PPA' THEN hotel_no_of_room\r\n WHEN tbl_source =
'TR' THEN 1\r\n END\r\n END)"}, {'aggregate': None, 'column':
None, 'datasourceWarning': False, 'expressionType': 'SQL', 'hasCustomLabel':
True, 'label': '25% - 30% Spread', 'optionName':
'metric_loo72lfax59_rta69r6ml5', 'sqlExpression': "SUM(CASE \r\n WHEN
cast(original_total_spread_percentage as float) >= 0.25\r\n AND
cast(original_total_spread_percentage as float) < 0.30\r\n THEN CASE\r\n
WHEN tbl_source = 'PPA' THEN hotel_no_of_room\r\n WHEN
tbl_source = 'TR' THEN 1\r\n END\r\n END)"}, {'aggregate': None,
'column': None, 'datasourceWarning': False, 'expressionType': 'SQL',
'hasCustomLabel': True, 'label': '>= 30% Spread', 'optionName':
'metric_46nrvjqbi3t_cnxe56gcyl8', 'sqlExpression': "SUM(CASE \r\n WHEN
cast(original_total_spread_percentage as float) >= 0.30\r\n THEN CASE\r\n
WHEN tbl_source = 'PPA' THEN hotel_no_o
f_room\r\n WHEN tbl_source = 'TR' THEN 1\r\n END\r\n
END)"}], 'order_desc': True, 'orderby': [({'aggregate': None, 'column': None,
'datasourceWarning': False, 'expressionType': 'SQL', 'hasCustomLabel': True,
'label': '< 16% Spread', 'optionName': 'metric_d7ocbaa3aq_k1i0msjgad',
'sqlExpression': "SUM(CASE \r\n WHEN cast(original_total_spread_percentage
as float) < 0.16\r\n THEN CASE\r\n WHEN tbl_source = 'PPA' THEN
hotel_no_of_room\r\n WHEN tbl_source = 'TR' THEN 1\r\n
END\r\n END)"}, False)], 'row_limit': 10000, 'row_offset': 0,
'series_columns': [], 'series_limit': 0, 'series_limit_metric': None,
'to_dttm': datetime.datetime(2026, 1, 13, 9, 1, 55), 'time_shift': None,
'conditional_formatting': None, 'comments': None}
reading cache
> query_dict={'apply_fetch_values_predicate': False, 'columns':
[{'timeGrain': 'P1M', 'columnType': 'BASE_AXIS', 'sqlExpression':
'booking_datetime_cast', 'label': 'booking_datetime_cast', 'expressionType':
'SQL'}], 'extras': {'where': '', 'having': '', 'time_grain_sqla': 'P1M'},
'filter': [{'val': ['RMNectar'], 'op': 'IN', 'col': 'whitelabel_name'}, {'val':
'DATEADD(DATETIME("now"), -7, day) : now', 'op': 'TEMPORAL_RANGE', 'col':
'booking_datetime_cast'}], 'from_dttm': datetime.datetime(2026, 1, 6, 9, 2, 8),
'granularity': None, 'inner_from_dttm': None, 'inner_to_dttm': None,
'is_rowcount': False, 'is_timeseries': False, 'metrics': [{'aggregate': None,
'column': None, 'datasourceWarning': False, 'expressionType': 'SQL',
'hasCustomLabel': True, 'label': '< 16% Spread', 'optionName':
'metric_d7ocbaa3aq_k1i0msjgad', 'sqlExpression': "SUM(CASE \r\n WHEN
cast(original_total_spread_percentage as float) < 0.16\r\n THEN CASE\r\n
WHEN tbl_source = 'PPA' THEN hotel_no_
of_room\r\n WHEN tbl_source = 'TR' THEN 1\r\n END\r\n
END)"}, {'aggregate': None, 'column': None, 'datasourceWarning': False,
'expressionType': 'SQL', 'hasCustomLabel': True, 'label': '16% - 20% Spread',
'optionName': 'metric_wqmh9wx1z2_ymlki2up3dn', 'sqlExpression': "SUM(CASE \r\n
WHEN cast(original_total_spread_percentage as float) >= 0.16\r\n AND
cast(original_total_spread_percentage as float) < 0.2\r\n THEN CASE\r\n
WHEN tbl_source = 'PPA' THEN hotel_no_of_room\r\n WHEN
tbl_source = 'TR' THEN 1\r\n END\r\n END)"}, {'aggregate': None,
'column': None, 'datasourceWarning': False, 'expressionType': 'SQL',
'hasCustomLabel': True, 'label': '20% - 25% Spread', 'optionName':
'metric_5egn3fc8zja_1b16x3da4md', 'sqlExpression': "SUM(CASE \r\n WHEN
cast(original_total_spread_percentage as float) >= 0.20\r\n AND
cast(original_total_spread_percentage as float) < 0.25\r\n THEN CASE\r\n
WHE
N tbl_source = 'PPA' THEN hotel_no_of_room\r\n WHEN tbl_source =
'TR' THEN 1\r\n END\r\n END)"}, {'aggregate': None, 'column':
None, 'datasourceWarning': False, 'expressionType': 'SQL', 'hasCustomLabel':
True, 'label': '25% - 30% Spread', 'optionName':
'metric_loo72lfax59_rta69r6ml5', 'sqlExpression': "SUM(CASE \r\n WHEN
cast(original_total_spread_percentage as float) >= 0.25\r\n AND
cast(original_total_spread_percentage as float) < 0.30\r\n THEN CASE\r\n
WHEN tbl_source = 'PPA' THEN hotel_no_of_room\r\n WHEN
tbl_source = 'TR' THEN 1\r\n END\r\n END)"}, {'aggregate': None,
'column': None, 'datasourceWarning': False, 'expressionType': 'SQL',
'hasCustomLabel': True, 'label': '>= 30% Spread', 'optionName':
'metric_46nrvjqbi3t_cnxe56gcyl8', 'sqlExpression': "SUM(CASE \r\n WHEN
cast(original_total_spread_percentage as float) >= 0.30\r\n THEN CASE\r\n
WHEN tbl_source = 'PPA' THEN hotel_no_of
_room\r\n WHEN tbl_source = 'TR' THEN 1\r\n END\r\n
END)"}], 'order_desc': True, 'orderby': [({'aggregate': None, 'column': None,
'datasourceWarning': False, 'expressionType': 'SQL', 'hasCustomLabel': True,
'label': '< 16% Spread', 'optionName': 'metric_d7ocbaa3aq_k1i0msjgad',
'sqlExpression': "SUM(CASE \n WHEN cast(original_total_spread_percentage
as float) < 0.16\n THEN CASE\n WHEN tbl_source = 'PPA' THEN
hotel_no_of_room\n WHEN tbl_source = 'TR' THEN 1\n END\n
END)"}, False)], 'row_limit': 10000, 'row_offset': 0, 'series_columns': [],
'series_limit': 0, 'series_limit_metric': None, 'to_dttm':
datetime.datetime(2026, 1, 13, 9, 2, 8), 'time_shift': None,
'conditional_formatting': None, 'comments': None}
**case jinja template**
writing cache
> query_dict={'apply_fetch_values_predicate': False, 'columns':
[{'timeGrain': 'P3M', 'columnType': 'BASE_AXIS', 'sqlExpression':
'merge_request_merged_at', 'label': 'merge_request_merged_at',
'expressionType': 'SQL'}], 'extras': {'having': '', 'where': '',
'time_grain_sqla': 'P3M'}, 'filter': [{'val': ['Yes'], 'op': 'IN', 'col':
'yes_no_condition'}, {'val': ['No'], 'op': 'IN', 'col': 'is_intern_included'},
{'val': ['No'], 'op': 'IN', 'col': 'is_freezing_period_included'}, {'val':
['No'], 'op': 'IN', 'col': 'is_inactivity_included'}, {'val': [False], 'op':
'IN', 'col': 'true_false_bool_condition'}, {'val': 75, 'op': '==', 'col':
'percentile'}, {'val': ['System owner'], 'op': 'IN', 'col': 'calculated_by'},
{'val': 'datetrunc(dateadd(datetime("today"), -1, quarter), quarter) :
datetime("today")', 'op': 'TEMPORAL_RANGE', 'col': 'merge_request_merged_at'}],
'from_dttm': datetime.datetime(2025, 10, 1, 0, 0), 'granularity': None,
'inner_from_dttm': None, 'inner_to_dttm': None, 'is_rowco
unt': False, 'is_timeseries': False, 'metrics': [{'aggregate': None, 'column':
None, 'datasourceWarning': True, 'expressionType': 'SQL', 'hasCustomLabel':
True, 'label': 'MLTC', 'optionName': 'metric_25y7eu1dboh_x7xp5sc9x2',
'sqlExpression': "{% set no_of_mrs = filter_values('percentile')[0]
%}\n\nAPPROXIMATE_PERCENTILE((office_hour_mltc/(60*9)) USING PARAMETERS
percentile={{ no_of_mrs }}/100)"}], 'order_desc': True, 'orderby':
[({'aggregate': None, 'column': None, 'datasourceWarning': True,
'expressionType': 'SQL', 'hasCustomLabel': True, 'label': 'MLTC', 'optionName':
'metric_25y7eu1dboh_x7xp5sc9x2', 'sqlExpression': "{% set no_of_mrs =
filter_values('percentile')[0]
%}\n\nAPPROXIMATE_PERCENTILE((office_hour_mltc/(60*9)) USING PARAMETERS
percentile={{ no_of_mrs }}/100)"}, False)], 'row_limit': 10000, 'row_offset':
0, 'series_columns': [], 'series_limit': 0, 'series_limit_metric': None,
'to_dttm': datetime.datetime(2026, 1, 14, 0, 0), 'time_shift': None,
'conditional_formatting': N
one, 'comments': None}
reading cache
> query_dict={'apply_fetch_values_predicate': False, 'columns':
[{'timeGrain': 'P3M', 'columnType': 'BASE_AXIS', 'sqlExpression':
'merge_request_merged_at', 'label': 'merge_request_merged_at',
'expressionType': 'SQL'}], 'extras': {'time_grain_sqla': 'P3M', 'where': '',
'having': ''}, 'filter': [{'val': ['Yes'], 'col': 'yes_no_condition', 'op':
'IN'}, {'val': ['No'], 'col': 'is_intern_included', 'op': 'IN'}, {'val':
['No'], 'col': 'is_freezing_period_included', 'op': 'IN'}, {'val': ['No'],
'col': 'is_inactivity_included', 'op': 'IN'}, {'val': [False], 'col':
'true_false_bool_condition', 'op': 'IN'}, {'val': 75, 'col': 'percentile',
'op': '=='}, {'val': ['System owner'], 'col': 'calculated_by', 'op': 'IN'},
{'val': 'datetrunc(dateadd(datetime("today"), -1, quarter), quarter) :
datetime("today")', 'col': 'merge_request_merged_at', 'op': 'TEMPORAL_RANGE'}],
'from_dttm': datetime.datetime(2025, 10, 1, 0, 0), 'granularity': None,
'inner_from_dttm': None, 'inner_to_dttm': None, 'is_rowco
unt': False, 'is_timeseries': False, 'metrics': [{'aggregate': None, 'column':
None, 'datasourceWarning': True, 'expressionType': 'SQL', 'hasCustomLabel':
True, 'label': 'MLTC', 'optionName': 'metric_25y7eu1dboh_x7xp5sc9x2',
'sqlExpression': "{% set no_of_mrs = filter_values('percentile')[0]
%}\n\nAPPROXIMATE_PERCENTILE((office_hour_mltc/(60*9)) USING PARAMETERS
percentile={{ no_of_mrs }}/100)"}], 'order_desc': True, 'orderby':
[({'aggregate': None, 'column': None, 'datasourceWarning': True,
'expressionType': 'SQL', 'hasCustomLabel': True, 'label': 'MLTC', 'optionName':
'metric_25y7eu1dboh_x7xp5sc9x2', 'sqlExpression':
'\n\nAPPROXIMATE_PERCENTILE((office_hour_mltc/(60*9)) USING PARAMETERS
percentile=75/100)'}, False)], 'row_limit': 10000, 'row_offset': 0,
'series_columns': [], 'series_limit': 0, 'series_limit_metric': None,
'to_dttm': datetime.datetime(2026, 1, 14, 0, 0), 'time_shift': None,
'conditional_formatting': None, 'comments': None}
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