def get_single_payload(
self,
query_obj: QueryObject,
force_cached: Optional[bool] = False,
) -> Dict[str, Any]:
"""Return results payload for a single quey"""
if self.result_type == utils.ChartDataResultType.QUERY:
return {
"query": self.datasource.get_query_str(query_obj.to_dict()),
"language": self.datasource.query_language,
}
if self.result_type == utils.ChartDataResultType.SAMPLES:
row_limit = query_obj.row_limit or math.inf
query_obj = copy.copy(query_obj)
query_obj.is_timeseries = False
query_obj.orderby = []
query_obj.groupby = []
query_obj.metrics = []
query_obj.post_processing = []
query_obj.row_limit = min(row_limit, config["SAMPLES_ROW_LIMIT"])
query_obj.row_offset = 0
query_obj.columns = [
o.column_name for o in self.datasource.columns
]
payload = self.get_df_payload(query_obj, force_cached=force_cached)
df = payload["df"]
status = payload["status"]
if status != utils.QueryStatus.FAILED:
payload["colnames"] = list(df.columns)
payload["coltypes"] = utils.extract_dataframe_dtypes(df)
payload["data"] = self.get_data(df)
del payload["df"]
filters = query_obj.filter
filter_columns = cast(List[str], [flt.get("col") for flt in filters])
columns = set(self.datasource.column_names)
applied_time_columns, rejected_time_columns = utils.get_time_filter_status(
self.datasource, query_obj.applied_time_extras)
payload["applied_filters"] = [{
"column": col
} for col in filter_columns if col in columns] + applied_time_columns
payload["rejected_filters"] = [{
"reason": "not_in_datasource",
"column": col
} for col in filter_columns if col not in columns
] + rejected_time_columns
if (self.result_type == utils.ChartDataResultType.RESULTS
and status != utils.QueryStatus.FAILED):
return {"data": payload["data"]}
return payload
def _get_samples(query_context: QueryContext,
query_obj: QueryObject,
force_cached: bool = False) -> Dict[str, Any]:
datasource = _get_datasource(query_context, query_obj)
query_obj = copy.copy(query_obj)
query_obj.is_timeseries = False
query_obj.orderby = []
query_obj.metrics = None
query_obj.post_processing = []
query_obj.columns = [o.column_name for o in datasource.columns]
query_obj.from_dttm = None
query_obj.to_dttm = None
return _get_full(query_context, query_obj, force_cached)
def get_single_payload(self, query_obj: QueryObject) -> Dict[str, Any]:
"""Returns a payload of metadata and data"""
if self.result_type == utils.ChartDataResultType.QUERY:
return {
"query": self.datasource.get_query_str(query_obj.to_dict()),
"language": self.datasource.query_language,
}
if self.result_type == utils.ChartDataResultType.SAMPLES:
row_limit = query_obj.row_limit or math.inf
query_obj = copy.copy(query_obj)
query_obj.orderby = []
query_obj.groupby = []
query_obj.metrics = []
query_obj.post_processing = []
query_obj.row_limit = min(row_limit, config["SAMPLES_ROW_LIMIT"])
query_obj.row_offset = 0
query_obj.columns = [
o.column_name for o in self.datasource.columns
]
payload = self.get_df_payload(query_obj)
# TODO: implement
payload["annotation_data"] = []
df = payload["df"]
status = payload["status"]
if status != utils.QueryStatus.FAILED:
payload["data"] = self.get_data(df)
del payload["df"]
filters = query_obj.filter
filter_columns = cast(List[str], [flt.get("col") for flt in filters])
columns = set(self.datasource.column_names)
applied_time_columns, rejected_time_columns = utils.get_time_filter_status(
self.datasource, query_obj.applied_time_extras)
payload["applied_filters"] = [{
"column": col
} for col in filter_columns if col in columns] + applied_time_columns
payload["rejected_filters"] = [{
"reason": "not_in_datasource",
"column": col
} for col in filter_columns if col not in columns
] + rejected_time_columns
if self.result_type == utils.ChartDataResultType.RESULTS:
return {"data": payload["data"]}
return payload
def _get_samples(
query_context: QueryContext, query_obj: QueryObject, force_cached: bool = False
) -> Dict[str, Any]:
datasource = _get_datasource(query_context, query_obj)
query_obj = copy.copy(query_obj)
query_obj.is_timeseries = False
query_obj.orderby = []
query_obj.metrics = None
query_obj.post_processing = []
qry_obj_cols = []
for o in datasource.columns:
if isinstance(o, dict):
qry_obj_cols.append(o.get("column_name"))
else:
qry_obj_cols.append(o.column_name)
query_obj.columns = qry_obj_cols
query_obj.from_dttm = None
query_obj.to_dttm = None
return _get_full(query_context, query_obj, force_cached)
def _get_drill_detail(
query_context: QueryContext, query_obj: QueryObject, force_cached: bool = False
) -> Dict[str, Any]:
# todo(yongjie): Remove this function,
# when determining whether samples should be applied to the time filter.
datasource = _get_datasource(query_context, query_obj)
query_obj = copy.copy(query_obj)
query_obj.is_timeseries = False
query_obj.orderby = []
query_obj.metrics = None
query_obj.post_processing = []
qry_obj_cols = []
for o in datasource.columns:
if isinstance(o, dict):
qry_obj_cols.append(o.get("column_name"))
else:
qry_obj_cols.append(o.column_name)
query_obj.columns = qry_obj_cols
return _get_full(query_context, query_obj, force_cached)
