def data_for_slices(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 param in METRIC_FORM_DATA_PARAMS:
for metric in utils.get_iterable(form_data.get(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"))
# pull out all required columns from the form_data
for filter_ in form_data.get("adhoc_filters") or []:
if filter_["clause"] == "WHERE" and filter_.get("subject"):
column_names.add(filter_.get("subject"))
for param in COLUMN_FORM_DATA_PARAMS:
for column in utils.get_iterable(form_data.get(param) or []):
column_names.add(column)
filtered_metrics = [
metric for metric in data["metrics"]
if metric["metric_name"] in metric_names
]
filtered_columns = [
column for column in data["columns"]
if column["column_name"] in column_names
]
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
def test_get_iterable(self):
self.assertListEqual(get_iterable(123), [123])
self.assertListEqual(get_iterable([123]), [123])
self.assertListEqual(get_iterable("foo"), ["foo"])
def data_for_slices(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 param in METRIC_FORM_DATA_PARAMS:
for metric in utils.get_iterable(form_data.get(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
)
column_names.update(
column
for column in utils.get_iterable(form_data.get(param) or [])
for param in COLUMN_FORM_DATA_PARAMS
)
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
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
