def send_chart_response(
self,
result: Dict[Any, Any],
form_data: Optional[Dict[str, Any]] = None,
) -> Response:
result_type = result["query_context"].result_type
result_format = result["query_context"].result_format
# Post-process the data so it matches the data presented in the chart.
# This is needed for sending reports based on text charts that do the
# post-processing of data, eg, the pivot table.
if result_type == ChartDataResultType.POST_PROCESSED:
result = apply_post_process(result, form_data)
if result_format == ChartDataResultFormat.CSV:
# Verify user has permission to export CSV file
if not security_manager.can_access("can_csv", "Superset"):
return self.response_403()
# return the first result
data = result["queries"][0]["data"]
return CsvResponse(data, headers=generate_download_headers("csv"))
if result_format == ChartDataResultFormat.JSON:
response_data = simplejson.dumps(
{"result": result["queries"]},
default=json_int_dttm_ser,
ignore_nan=True,
)
resp = make_response(response_data, 200)
resp.headers["Content-Type"] = "application/json; charset=utf-8"
return resp
return self.response_400(
message=f"Unsupported result_format: {result_format}")
def _send_chart_response(
self,
result: Dict[Any, Any],
form_data: Optional[Dict[str, Any]] = None,
datasource: Optional[BaseDatasource] = None,
) -> Response:
result_type = result["query_context"].result_type
result_format = result["query_context"].result_format
# Post-process the data so it matches the data presented in the chart.
# This is needed for sending reports based on text charts that do the
# post-processing of data, eg, the pivot table.
if result_type == ChartDataResultType.POST_PROCESSED:
result = apply_post_process(result, form_data, datasource)
if result_format == ChartDataResultFormat.CSV:
# Verify user has permission to export CSV file
if not security_manager.can_access("can_csv", "Superset"):
return self.response_403()
if not result["queries"]:
return self.response_400(_("Empty query result"))
if len(result["queries"]) == 1:
# return single query results csv format
data = result["queries"][0]["data"]
return CsvResponse(data,
headers=generate_download_headers("csv"))
# return multi-query csv results bundled as a zip file
encoding = current_app.config["CSV_EXPORT"].get(
"encoding", "utf-8")
files = {
f"query_{idx + 1}.csv": result["data"].encode(encoding)
for idx, result in enumerate(result["queries"])
}
return Response(
create_zip(files),
headers=generate_download_headers("zip"),
mimetype="application/zip",
)
if result_format == ChartDataResultFormat.JSON:
response_data = simplejson.dumps(
{"result": result["queries"]},
default=json_int_dttm_ser,
ignore_nan=True,
)
resp = make_response(response_data, 200)
resp.headers["Content-Type"] = "application/json; charset=utf-8"
return resp
return self.response_400(
message=f"Unsupported result_format: {result_format}")
def test_pivot_table():
form_data = {
"adhoc_filters": [],
"columns": ["state"],
"datasource":
"3__table",
"date_format":
"smart_date",
"extra_form_data": {},
"granularity_sqla":
"ds",
"groupby": ["gender"],
"metrics": [{
"aggregate": "SUM",
"column": {
"column_name": "num",
"type": "BIGINT"
},
"expressionType": "SIMPLE",
"label": "Births",
"optionName": "metric_11",
}],
"number_format":
"SMART_NUMBER",
"order_desc":
True,
"pandas_aggfunc":
"sum",
"pivot_margins":
True,
"row_limit":
50000,
"slice_id":
143,
"time_grain_sqla":
"P1D",
"time_range":
"100 years ago : now",
"time_range_endpoints": ["inclusive", "exclusive"],
"url_params": {},
"viz_type":
"pivot_table",
}
result = copy.deepcopy(RESULT)
assert apply_post_process(result, form_data) == {
"query_context":
None,
"queries": [{
"cache_key":
"1bd3ab8c01e98a0e349fb61bc76d9b90",
"cached_dttm":
None,
"cache_timeout":
86400,
"annotation_data": {},
"error":
None,
"is_cached":
None,
"query":
"""SELECT state AS state,
gender AS gender,
sum(num) AS \"Births\"
FROM birth_names
WHERE ds >= TO_TIMESTAMP('1921-07-28 00:00:00.000000', 'YYYY-MM-DD HH24:MI:SS.US')
AND ds < TO_TIMESTAMP('2021-07-28 10:39:44.000000', 'YYYY-MM-DD HH24:MI:SS.US')
GROUP BY state,
gender
LIMIT 50000;
""",
"status":
QueryStatus.SUCCESS,
"stacktrace":
None,
"rowcount":
3,
"colnames": [
"Births CA",
"Births FL",
"Births IL",
"Births MA",
"Births MI",
"Births NJ",
"Births NY",
"Births OH",
"Births PA",
"Births TX",
"Births other",
"Births All",
],
"coltypes": [
GenericDataType.NUMERIC,
GenericDataType.NUMERIC,
GenericDataType.NUMERIC,
GenericDataType.NUMERIC,
GenericDataType.NUMERIC,
GenericDataType.NUMERIC,
GenericDataType.NUMERIC,
GenericDataType.NUMERIC,
GenericDataType.NUMERIC,
GenericDataType.NUMERIC,
GenericDataType.NUMERIC,
GenericDataType.NUMERIC,
],
"data":
"""gender,Births CA,Births FL,Births IL,Births MA,Births MI,Births NJ,Births NY,Births OH,Births PA,Births TX,Births other,Births All
boy,5430796,1968060,2357411,1285126,1938321,1486126,3543961,2376385,2390275,3311985,22044909,48133355
girl,3567754,1312593,1614427,842146,1326229,992702,2280733,1622814,1615383,2313186,15058341,32546308
All,8998550,3280653,3971838,2127272,3264550,2478828,5824694,3999199,4005658,5625171,37103250,80679663
""",
"applied_filters": [],
"rejected_filters": [],
}],
}
def test_pivot_table_v2():
form_data = {
"adhoc_filters": [],
"aggregateFunction":
"Sum as Fraction of Rows",
"colOrder":
"key_a_to_z",
"colTotals":
True,
"combineMetric":
True,
"datasource":
"3__table",
"date_format":
"smart_date",
"extra_form_data": {},
"granularity_sqla":
"ds",
"groupbyColumns": ["state"],
"groupbyRows": ["gender"],
"metrics": [{
"aggregate": "SUM",
"column": {
"column_name": "num",
"type": "BIGINT"
},
"expressionType": "SIMPLE",
"label": "Births",
"optionName": "metric_11",
}],
"metricsLayout":
"ROWS",
"rowOrder":
"key_a_to_z",
"rowTotals":
True,
"row_limit":
50000,
"slice_id":
72,
"time_grain_sqla":
None,
"time_range":
"100 years ago : now",
"time_range_endpoints": ["inclusive", "exclusive"],
"transposePivot":
True,
"url_params": {},
"valueFormat":
"SMART_NUMBER",
"viz_type":
"pivot_table_v2",
}
result = copy.deepcopy(RESULT)
assert apply_post_process(result, form_data) == {
"query_context":
None,
"queries": [{
"cache_key":
"1bd3ab8c01e98a0e349fb61bc76d9b90",
"cached_dttm":
None,
"cache_timeout":
86400,
"annotation_data": {},
"error":
None,
"is_cached":
None,
"query":
"""SELECT state AS state,
gender AS gender,
sum(num) AS \"Births\"
FROM birth_names
WHERE ds >= TO_TIMESTAMP('1921-07-28 00:00:00.000000', 'YYYY-MM-DD HH24:MI:SS.US')
AND ds < TO_TIMESTAMP('2021-07-28 10:39:44.000000', 'YYYY-MM-DD HH24:MI:SS.US')
GROUP BY state,
gender
LIMIT 50000;
""",
"status":
QueryStatus.SUCCESS,
"stacktrace":
None,
"rowcount":
12,
"colnames": ["All Births", "boy Births", "girl Births"],
"coltypes": [
GenericDataType.NUMERIC,
GenericDataType.NUMERIC,
GenericDataType.NUMERIC,
],
"data":
"""state,All Births,boy Births,girl Births
All,1.0,0.5965983645717509,0.40340163542824914
CA,1.0,0.6035190113962805,0.3964809886037195
FL,1.0,0.5998988615985903,0.4001011384014097
IL,1.0,0.5935315085862012,0.40646849141379887
MA,1.0,0.6041192663655611,0.3958807336344389
MI,1.0,0.5937482960898133,0.4062517039101867
NJ,1.0,0.5995276800165239,0.40047231998347604
NY,1.0,0.6084372844307357,0.39156271556926425
OH,1.0,0.5942152416021308,0.40578475839786915
PA,1.0,0.596724682935987,0.40327531706401293
TX,1.0,0.5887794344385264,0.41122056556147357
other,1.0,0.5941503507105172,0.40584964928948275
""",
"applied_filters": [],
"rejected_filters": [],
}],
}