def _get_full(
query_context: QueryContext,
query_obj: QueryObject,
force_cached: Optional[bool] = False,
) -> Dict[str, Any]:
datasource = _get_datasource(query_context, query_obj)
result_type = query_obj.result_type or query_context.result_type
payload = query_context.get_df_payload(query_obj, force_cached=force_cached)
applied_template_filters = payload.get("applied_template_filters", [])
df = payload["df"]
status = payload["status"]
if status != QueryStatus.FAILED:
payload["colnames"] = list(df.columns)
payload["indexnames"] = list(df.index)
payload["coltypes"] = extract_dataframe_dtypes(df, datasource)
payload["data"] = query_context.get_data(df)
payload["result_format"] = query_context.result_format
del payload["df"]
filters = query_obj.filter
filter_columns = cast(List[str], [flt.get("col") for flt in filters])
columns = set(datasource.column_names)
applied_time_columns, rejected_time_columns = get_time_filter_status(
datasource, query_obj.applied_time_extras
)
payload["applied_filters"] = [
{"column": get_column_name(col)}
for col in filter_columns
if is_adhoc_column(col) or col in columns or col in applied_template_filters
] + applied_time_columns
payload["rejected_filters"] = [
{"reason": ExtraFiltersReasonType.COL_NOT_IN_DATASOURCE, "column": col}
for col in filter_columns
if not is_adhoc_column(col)
and col not in columns
and col not in applied_template_filters
] + rejected_time_columns
if result_type == ChartDataResultType.RESULTS and status != QueryStatus.FAILED:
return {
"data": payload.get("data"),
"colnames": payload.get("colnames"),
"coltypes": payload.get("coltypes"),
}
return payload
def data_for_slices( # pylint: disable=too-many-locals
self, slices: List[Slice]) -> Dict[str, Any]:
"""
The representation of the datasource containing only the required data
to render the provided slices.
Used to reduce the payload when loading a dashboard.
"""
data = self.data
metric_names = set()
column_names = set()
for slc in slices:
form_data = slc.form_data
# pull out all required metrics from the form_data
for metric_param in METRIC_FORM_DATA_PARAMS:
for metric in utils.get_iterable(
form_data.get(metric_param) or []):
metric_names.add(utils.get_metric_name(metric))
if utils.is_adhoc_metric(metric):
column_names.add((metric.get("column")
or {}).get("column_name"))
# Columns used in query filters
column_names.update(
filter_["subject"]
for filter_ in form_data.get("adhoc_filters") or []
if filter_.get("clause") == "WHERE" and filter_.get("subject"))
# columns used by Filter Box
column_names.update(
filter_config["column"]
for filter_config in form_data.get("filter_configs") or []
if "column" in filter_config)
# for legacy dashboard imports which have the wrong query_context in them
try:
query_context = slc.get_query_context()
except DatasetNotFoundError:
query_context = None
# legacy charts don't have query_context charts
if query_context:
column_names.update([
utils.get_column_name(column)
for query in query_context.queries
for column in query.columns
] or [])
else:
_columns = [
utils.get_column_name(column)
if utils.is_adhoc_column(column) else column
for column_param in COLUMN_FORM_DATA_PARAMS
for column in utils.get_iterable(
form_data.get(column_param) or [])
]
column_names.update(_columns)
filtered_metrics = [
metric for metric in data["metrics"]
if metric["metric_name"] in metric_names
]
filtered_columns: List[Column] = []
column_types: Set[GenericDataType] = set()
for column in data["columns"]:
generic_type = column.get("type_generic")
if generic_type is not None:
column_types.add(generic_type)
if column["column_name"] in column_names:
filtered_columns.append(column)
data["column_types"] = list(column_types)
del data["description"]
data.update({"metrics": filtered_metrics})
data.update({"columns": filtered_columns})
verbose_map = {"__timestamp": "Time"}
verbose_map.update({
metric["metric_name"]: metric["verbose_name"]
or metric["metric_name"]
for metric in filtered_metrics
})
verbose_map.update({
column["column_name"]: column["verbose_name"]
or column["column_name"]
for column in filtered_columns
})
data["verbose_map"] = verbose_map
return data