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 _get_timeseries_orderby(self, timeseries_limit_metric, metrics_dict, cols):
if utils.is_adhoc_metric(timeseries_limit_metric):
ob = self.adhoc_metric_to_sqla(timeseries_limit_metric, cols)
elif timeseries_limit_metric in metrics_dict:
timeseries_limit_metric = metrics_dict.get(timeseries_limit_metric)
ob = timeseries_limit_metric.get_sqla_col()
else:
raise Exception(
_("Metric '%(metric)s' does not exist", metric=timeseries_limit_metric)
)
return ob
def _get_timeseries_orderby(self, timeseries_limit_metric, metrics_dict, cols):
if utils.is_adhoc_metric(timeseries_limit_metric):
ob = self.adhoc_metric_to_sqla(timeseries_limit_metric, cols)
elif timeseries_limit_metric in metrics_dict:
timeseries_limit_metric = metrics_dict.get(
timeseries_limit_metric,
)
ob = timeseries_limit_metric.get_sqla_col()
else:
raise Exception(_("Metric '{}' is not valid".format(timeseries_limit_metric)))
return ob
def _get_timeseries_orderby(
self,
timeseries_limit_metric: Metric,
metrics_dict: Dict[str, SqlMetric],
cols: Dict[str, Column],
) -> Optional[Column]:
if utils.is_adhoc_metric(timeseries_limit_metric):
assert isinstance(timeseries_limit_metric, dict)
ob = self.adhoc_metric_to_sqla(timeseries_limit_metric, cols)
elif (isinstance(timeseries_limit_metric, str)
and timeseries_limit_metric in metrics_dict):
ob = metrics_dict[timeseries_limit_metric].get_sqla_col()
else:
raise Exception(
_("Metric '%(metric)s' does not exist",
metric=timeseries_limit_metric))
return ob
def get_sqla_query( # sqla
self,
groupby,
metrics,
granularity,
from_dttm,
to_dttm,
filter=None, # noqa
is_timeseries=True,
timeseries_limit=15,
timeseries_limit_metric=None,
row_limit=None,
inner_from_dttm=None,
inner_to_dttm=None,
orderby=None,
extras=None,
columns=None,
order_desc=True,
prequeries=None,
is_prequery=False,
):
"""Querying any sqla table from this common interface"""
template_kwargs = {
'from_dttm': from_dttm,
'groupby': groupby,
'metrics': metrics,
'row_limit': row_limit,
'to_dttm': to_dttm,
'filter': filter,
'columns': {col.column_name: col
for col in self.columns},
}
template_kwargs.update(self.template_params_dict)
template_processor = self.get_template_processor(**template_kwargs)
db_engine_spec = self.database.db_engine_spec
orderby = orderby or []
# For backward compatibility
if granularity not in self.dttm_cols:
granularity = self.main_dttm_col
# Database spec supports join-free timeslot grouping
time_groupby_inline = db_engine_spec.time_groupby_inline
cols = {col.column_name: col for col in self.columns}
metrics_dict = {m.metric_name: m for m in self.metrics}
if not granularity and is_timeseries:
raise Exception(
_('Datetime column not provided as part table configuration '
'and is required by this type of chart'))
if not groupby and not metrics and not columns:
raise Exception(_('Empty query?'))
metrics_exprs = []
for m in metrics:
if utils.is_adhoc_metric(m):
metrics_exprs.append(self.adhoc_metric_to_sqla(m, cols))
elif m in metrics_dict:
metrics_exprs.append(metrics_dict.get(m).get_sqla_col())
else:
raise Exception(_("Metric '{}' is not valid".format(m)))
if metrics_exprs:
main_metric_expr = metrics_exprs[0]
else:
main_metric_expr = literal_column('COUNT(*)').label(
db_engine_spec.make_label_compatible('count'))
select_exprs = []
groupby_exprs = []
if groupby:
select_exprs = []
inner_select_exprs = []
inner_groupby_exprs = []
for s in groupby:
col = cols[s]
outer = col.get_sqla_col()
inner = col.get_sqla_col(col.column_name + '__')
groupby_exprs.append(outer)
select_exprs.append(outer)
inner_groupby_exprs.append(inner)
inner_select_exprs.append(inner)
elif columns:
for s in columns:
select_exprs.append(cols[s].get_sqla_col())
metrics_exprs = []
if granularity:
dttm_col = cols[granularity]
time_grain = extras.get('time_grain_sqla')
time_filters = []
if is_timeseries:
timestamp = dttm_col.get_timestamp_expression(time_grain)
select_exprs += [timestamp]
groupby_exprs += [timestamp]
# Use main dttm column to support index with secondary dttm columns
if db_engine_spec.time_secondary_columns and \
self.main_dttm_col in self.dttm_cols and \
self.main_dttm_col != dttm_col.column_name:
time_filters.append(cols[self.main_dttm_col].get_time_filter(
from_dttm, to_dttm))
time_filters.append(dttm_col.get_time_filter(from_dttm, to_dttm))
select_exprs += metrics_exprs
qry = sa.select(select_exprs)
tbl = self.get_from_clause(template_processor)
if not columns:
qry = qry.group_by(*groupby_exprs)
where_clause_and = []
having_clause_and = []
for flt in filter:
if not all([flt.get(s) for s in ['col', 'op']]):
continue
col = flt['col']
op = flt['op']
col_obj = cols.get(col)
if col_obj:
is_list_target = op in ('in', 'not in')
eq = self.filter_values_handler(
flt.get('val'),
target_column_is_numeric=col_obj.is_num,
is_list_target=is_list_target)
if op in ('in', 'not in'):
cond = col_obj.get_sqla_col().in_(eq)
if '<NULL>' in eq:
cond = or_(cond,
col_obj.get_sqla_col() == None) # noqa
if op == 'not in':
cond = ~cond
where_clause_and.append(cond)
else:
if col_obj.is_num:
eq = utils.string_to_num(flt['val'])
if op == '==':
where_clause_and.append(col_obj.get_sqla_col() == eq)
elif op == '!=':
where_clause_and.append(col_obj.get_sqla_col() != eq)
elif op == '>':
where_clause_and.append(col_obj.get_sqla_col() > eq)
elif op == '<':
where_clause_and.append(col_obj.get_sqla_col() < eq)
elif op == '>=':
where_clause_and.append(col_obj.get_sqla_col() >= eq)
elif op == '<=':
where_clause_and.append(col_obj.get_sqla_col() <= eq)
elif op == 'LIKE':
where_clause_and.append(
col_obj.get_sqla_col().like(eq))
elif op == 'IS NULL':
where_clause_and.append(
col_obj.get_sqla_col() == None) # noqa
elif op == 'IS NOT NULL':
where_clause_and.append(
col_obj.get_sqla_col() != None) # noqa
if extras:
where = extras.get('where')
if where:
where = template_processor.process_template(where)
where_clause_and += [sa.text('({})'.format(where))]
having = extras.get('having')
if having:
having = template_processor.process_template(having)
having_clause_and += [sa.text('({})'.format(having))]
if granularity:
qry = qry.where(and_(*(time_filters + where_clause_and)))
else:
qry = qry.where(and_(*where_clause_and))
qry = qry.having(and_(*having_clause_and))
if not orderby and not columns:
orderby = [(main_metric_expr, not order_desc)]
for col, ascending in orderby:
direction = asc if ascending else desc
if utils.is_adhoc_metric(col):
col = self.adhoc_metric_to_sqla(col, cols)
qry = qry.order_by(direction(col))
if row_limit:
qry = qry.limit(row_limit)
if is_timeseries and \
timeseries_limit and groupby and not time_groupby_inline:
if self.database.db_engine_spec.inner_joins:
# some sql dialects require for order by expressions
# to also be in the select clause -- others, e.g. vertica,
# require a unique inner alias
inner_main_metric_expr = main_metric_expr.label('mme_inner__')
inner_select_exprs += [inner_main_metric_expr]
subq = select(inner_select_exprs)
subq = subq.select_from(tbl)
inner_time_filter = dttm_col.get_time_filter(
inner_from_dttm or from_dttm,
inner_to_dttm or to_dttm,
)
subq = subq.where(
and_(*(where_clause_and + [inner_time_filter])))
subq = subq.group_by(*inner_groupby_exprs)
ob = inner_main_metric_expr
if timeseries_limit_metric:
if utils.is_adhoc_metric(timeseries_limit_metric):
ob = self.adhoc_metric_to_sqla(timeseries_limit_metric,
cols)
elif timeseries_limit_metric in metrics_dict:
timeseries_limit_metric = metrics_dict.get(
timeseries_limit_metric, )
ob = timeseries_limit_metric.get_sqla_col()
else:
raise Exception(_(
"Metric '{}' is not valid".format(m)))
direction = desc if order_desc else asc
subq = subq.order_by(direction(ob))
subq = subq.limit(timeseries_limit)
on_clause = []
for i, gb in enumerate(groupby):
on_clause.append(groupby_exprs[i] == column(gb + '__'))
tbl = tbl.join(subq.alias(), and_(*on_clause))
else:
# run subquery to get top groups
subquery_obj = {
'prequeries': prequeries,
'is_prequery': True,
'is_timeseries': False,
'row_limit': timeseries_limit,
'groupby': groupby,
'metrics': metrics,
'granularity': granularity,
'from_dttm': inner_from_dttm or from_dttm,
'to_dttm': inner_to_dttm or to_dttm,
'filter': filter,
'orderby': orderby,
'extras': extras,
'columns': columns,
'order_desc': True,
}
result = self.query(subquery_obj)
cols = {col.column_name: col for col in self.columns}
dimensions = [
c for c in result.df.columns
if c not in metrics and c in cols
]
top_groups = self._get_top_groups(result.df, dimensions)
qry = qry.where(top_groups)
return qry.select_from(tbl)
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 get_sqla_query( # sqla
self,
groupby,
metrics,
granularity,
from_dttm,
to_dttm,
filter=None, # noqa
is_timeseries=True,
timeseries_limit=15,
timeseries_limit_metric=None,
row_limit=None,
inner_from_dttm=None,
inner_to_dttm=None,
orderby=None,
extras=None,
columns=None,
order_desc=True,
):
"""Querying any sqla table from this common interface"""
template_kwargs = {
"from_dttm": from_dttm,
"groupby": groupby,
"metrics": metrics,
"row_limit": row_limit,
"to_dttm": to_dttm,
"filter": filter,
"columns": {col.column_name: col
for col in self.columns},
}
template_kwargs.update(self.template_params_dict)
extra_cache_keys: List[Any] = []
template_kwargs["extra_cache_keys"] = extra_cache_keys
template_processor = self.get_template_processor(**template_kwargs)
db_engine_spec = self.database.db_engine_spec
prequeries: List[str] = []
orderby = orderby or []
# For backward compatibility
if granularity not in self.dttm_cols:
granularity = self.main_dttm_col
# Database spec supports join-free timeslot grouping
time_groupby_inline = db_engine_spec.time_groupby_inline
cols = {col.column_name: col for col in self.columns}
metrics_dict = {m.metric_name: m for m in self.metrics}
if not granularity and is_timeseries:
raise Exception(
_("Datetime column not provided as part table configuration "
"and is required by this type of chart"))
if not groupby and not metrics and not columns:
raise Exception(_("Empty query?"))
metrics_exprs = []
for m in metrics:
if utils.is_adhoc_metric(m):
metrics_exprs.append(self.adhoc_metric_to_sqla(m, cols))
elif m in metrics_dict:
metrics_exprs.append(metrics_dict.get(m).get_sqla_col())
else:
raise Exception(
_("Metric '%(metric)s' does not exist", metric=m))
if metrics_exprs:
main_metric_expr = metrics_exprs[0]
else:
main_metric_expr, label = literal_column("COUNT(*)"), "ccount"
main_metric_expr = self.make_sqla_column_compatible(
main_metric_expr, label)
select_exprs = []
groupby_exprs_sans_timestamp = OrderedDict()
if groupby:
select_exprs = []
for s in groupby:
if s in cols:
outer = cols[s].get_sqla_col()
else:
outer = literal_column(f"({s})")
outer = self.make_sqla_column_compatible(outer, s)
groupby_exprs_sans_timestamp[outer.name] = outer
select_exprs.append(outer)
elif columns:
for s in columns:
select_exprs.append(
cols[s].get_sqla_col() if s in cols else self.
make_sqla_column_compatible(literal_column(s)))
metrics_exprs = []
groupby_exprs_with_timestamp = OrderedDict(
groupby_exprs_sans_timestamp.items())
if granularity:
dttm_col = cols[granularity]
time_grain = extras.get("time_grain_sqla")
time_filters = []
if is_timeseries:
timestamp = dttm_col.get_timestamp_expression(time_grain)
select_exprs += [timestamp]
groupby_exprs_with_timestamp[timestamp.name] = timestamp
# Use main dttm column to support index with secondary dttm columns
if (db_engine_spec.time_secondary_columns
and self.main_dttm_col in self.dttm_cols
and self.main_dttm_col != dttm_col.column_name):
time_filters.append(cols[self.main_dttm_col].get_time_filter(
from_dttm, to_dttm))
time_filters.append(dttm_col.get_time_filter(from_dttm, to_dttm))
select_exprs += metrics_exprs
labels_expected = [c._df_label_expected for c in select_exprs]
select_exprs = db_engine_spec.make_select_compatible(
groupby_exprs_with_timestamp.values(), select_exprs)
qry = sa.select(select_exprs)
tbl = self.get_from_clause(template_processor)
if not columns:
qry = qry.group_by(*groupby_exprs_with_timestamp.values())
where_clause_and = []
having_clause_and = []
for flt in filter:
if not all([flt.get(s) for s in ["col", "op"]]):
continue
col = flt["col"]
op = flt["op"]
col_obj = cols.get(col)
if col_obj:
is_list_target = op in ("in", "not in")
eq = self.filter_values_handler(
flt.get("val"),
target_column_is_numeric=col_obj.is_num,
is_list_target=is_list_target,
)
if op in ("in", "not in"):
cond = col_obj.get_sqla_col().in_(eq)
if "<NULL>" in eq:
cond = or_(cond,
col_obj.get_sqla_col() == None) # noqa
if op == "not in":
cond = ~cond
where_clause_and.append(cond)
else:
if col_obj.is_num:
eq = utils.string_to_num(flt["val"])
if op == "==":
where_clause_and.append(col_obj.get_sqla_col() == eq)
elif op == "!=":
where_clause_and.append(col_obj.get_sqla_col() != eq)
elif op == ">":
where_clause_and.append(col_obj.get_sqla_col() > eq)
elif op == "<":
where_clause_and.append(col_obj.get_sqla_col() < eq)
elif op == ">=":
where_clause_and.append(col_obj.get_sqla_col() >= eq)
elif op == "<=":
where_clause_and.append(col_obj.get_sqla_col() <= eq)
elif op == "LIKE":
where_clause_and.append(
col_obj.get_sqla_col().like(eq))
elif op == "IS NULL":
where_clause_and.append(
col_obj.get_sqla_col() == None) # noqa
elif op == "IS NOT NULL":
where_clause_and.append(
col_obj.get_sqla_col() != None) # noqa
if extras:
where = extras.get("where")
if where:
where = template_processor.process_template(where)
where_clause_and += [sa.text("({})".format(where))]
having = extras.get("having")
if having:
having = template_processor.process_template(having)
having_clause_and += [sa.text("({})".format(having))]
if granularity:
qry = qry.where(and_(*(time_filters + where_clause_and)))
else:
qry = qry.where(and_(*where_clause_and))
qry = qry.having(and_(*having_clause_and))
if not orderby and not columns:
orderby = [(main_metric_expr, not order_desc)]
for col, ascending in orderby:
direction = asc if ascending else desc
if utils.is_adhoc_metric(col):
col = self.adhoc_metric_to_sqla(col, cols)
qry = qry.order_by(direction(col))
if row_limit:
qry = qry.limit(row_limit)
if is_timeseries and timeseries_limit and groupby and not time_groupby_inline:
if self.database.db_engine_spec.allows_joins:
# some sql dialects require for order by expressions
# to also be in the select clause -- others, e.g. vertica,
# require a unique inner alias
inner_main_metric_expr = self.make_sqla_column_compatible(
main_metric_expr, "mme_inner__")
inner_groupby_exprs = []
inner_select_exprs = []
for gby_name, gby_obj in groupby_exprs_sans_timestamp.items():
inner = self.make_sqla_column_compatible(
gby_obj, gby_name + "__")
inner_groupby_exprs.append(inner)
inner_select_exprs.append(inner)
inner_select_exprs += [inner_main_metric_expr]
subq = select(inner_select_exprs).select_from(tbl)
inner_time_filter = dttm_col.get_time_filter(
inner_from_dttm or from_dttm, inner_to_dttm or to_dttm)
subq = subq.where(
and_(*(where_clause_and + [inner_time_filter])))
subq = subq.group_by(*inner_groupby_exprs)
ob = inner_main_metric_expr
if timeseries_limit_metric:
ob = self._get_timeseries_orderby(timeseries_limit_metric,
metrics_dict, cols)
direction = desc if order_desc else asc
subq = subq.order_by(direction(ob))
subq = subq.limit(timeseries_limit)
on_clause = []
for gby_name, gby_obj in groupby_exprs_sans_timestamp.items():
# in this case the column name, not the alias, needs to be
# conditionally mutated, as it refers to the column alias in
# the inner query
col_name = db_engine_spec.make_label_compatible(gby_name +
"__")
on_clause.append(gby_obj == column(col_name))
tbl = tbl.join(subq.alias(), and_(*on_clause))
else:
if timeseries_limit_metric:
orderby = [(
self._get_timeseries_orderby(timeseries_limit_metric,
metrics_dict, cols),
False,
)]
# run prequery to get top groups
prequery_obj = {
"is_timeseries": False,
"row_limit": timeseries_limit,
"groupby": groupby,
"metrics": metrics,
"granularity": granularity,
"from_dttm": inner_from_dttm or from_dttm,
"to_dttm": inner_to_dttm or to_dttm,
"filter": filter,
"orderby": orderby,
"extras": extras,
"columns": columns,
"order_desc": True,
}
result = self.query(prequery_obj)
prequeries.append(result.query)
dimensions = [
c for c in result.df.columns
if c not in metrics and c in groupby_exprs_sans_timestamp
]
top_groups = self._get_top_groups(
result.df, dimensions, groupby_exprs_sans_timestamp)
qry = qry.where(top_groups)
return SqlaQuery(
extra_cache_keys=extra_cache_keys,
labels_expected=labels_expected,
sqla_query=qry.select_from(tbl),
prequeries=prequeries,
)
def is_str_or_adhoc(metric: Metric) -> bool:
return isinstance(metric, str) or is_adhoc_metric(metric)
def get_sqla_query( # sqla
self,
metrics: List[Metric],
granularity: str,
from_dttm: Optional[datetime],
to_dttm: Optional[datetime],
columns: Optional[List[str]] = None,
groupby: Optional[List[str]] = None,
filter: Optional[List[Dict[str, Any]]] = None,
is_timeseries: bool = True,
timeseries_limit: int = 15,
timeseries_limit_metric: Optional[Metric] = None,
row_limit: Optional[int] = None,
inner_from_dttm: Optional[datetime] = None,
inner_to_dttm: Optional[datetime] = None,
orderby: Optional[List[Tuple[ColumnElement, bool]]] = None,
extras: Optional[Dict[str, Any]] = None,
order_desc: bool = True,
) -> SqlaQuery:
"""Querying any sqla table from this common interface"""
template_kwargs = {
"from_dttm": from_dttm,
"groupby": groupby,
"metrics": metrics,
"row_limit": row_limit,
"to_dttm": to_dttm,
"filter": filter,
"columns": {col.column_name: col
for col in self.columns},
}
is_sip_38 = is_feature_enabled("SIP_38_VIZ_REARCHITECTURE")
template_kwargs.update(self.template_params_dict)
extra_cache_keys: List[Any] = []
template_kwargs["extra_cache_keys"] = extra_cache_keys
template_processor = self.get_template_processor(**template_kwargs)
db_engine_spec = self.database.db_engine_spec
prequeries: List[str] = []
orderby = orderby or []
# For backward compatibility
if granularity not in self.dttm_cols:
granularity = self.main_dttm_col
# Database spec supports join-free timeslot grouping
time_groupby_inline = db_engine_spec.time_groupby_inline
cols: Dict[str,
Column] = {col.column_name: col
for col in self.columns}
metrics_dict: Dict[str, SqlMetric] = {
m.metric_name: m
for m in self.metrics
}
if not granularity and is_timeseries:
raise Exception(
_("Datetime column not provided as part table configuration "
"and is required by this type of chart"))
if (not metrics and not columns
and (is_sip_38 or (not is_sip_38 and not groupby))):
raise Exception(_("Empty query?"))
metrics_exprs: List[ColumnElement] = []
for m in metrics:
if utils.is_adhoc_metric(m):
assert isinstance(m, dict)
metrics_exprs.append(self.adhoc_metric_to_sqla(m, cols))
elif isinstance(m, str) and m in metrics_dict:
metrics_exprs.append(metrics_dict[m].get_sqla_col())
else:
raise Exception(
_("Metric '%(metric)s' does not exist", metric=m))
if metrics_exprs:
main_metric_expr = metrics_exprs[0]
else:
main_metric_expr, label = literal_column("COUNT(*)"), "ccount"
main_metric_expr = self.make_sqla_column_compatible(
main_metric_expr, label)
select_exprs: List[Column] = []
groupby_exprs_sans_timestamp: OrderedDict = OrderedDict()
if (is_sip_38 and metrics and columns) or (not is_sip_38 and groupby):
# dedup columns while preserving order
columns_ = columns if is_sip_38 else groupby
assert columns_
groupby = list(dict.fromkeys(columns_))
select_exprs = []
for s in groupby:
if s in cols:
outer = cols[s].get_sqla_col()
else:
outer = literal_column(f"({s})")
outer = self.make_sqla_column_compatible(outer, s)
groupby_exprs_sans_timestamp[outer.name] = outer
select_exprs.append(outer)
elif columns:
for s in columns:
select_exprs.append(
cols[s].get_sqla_col() if s in cols else self.
make_sqla_column_compatible(literal_column(s)))
metrics_exprs = []
assert extras is not None
time_range_endpoints = extras.get("time_range_endpoints")
groupby_exprs_with_timestamp = OrderedDict(
groupby_exprs_sans_timestamp.items())
if granularity:
dttm_col = cols[granularity]
time_grain = extras.get("time_grain_sqla")
time_filters = []
if is_timeseries:
timestamp = dttm_col.get_timestamp_expression(time_grain)
select_exprs += [timestamp]
groupby_exprs_with_timestamp[timestamp.name] = timestamp
# Use main dttm column to support index with secondary dttm columns.
if (db_engine_spec.time_secondary_columns
and self.main_dttm_col in self.dttm_cols
and self.main_dttm_col != dttm_col.column_name):
time_filters.append(cols[self.main_dttm_col].get_time_filter(
from_dttm, to_dttm, time_range_endpoints))
time_filters.append(
dttm_col.get_time_filter(from_dttm, to_dttm,
time_range_endpoints))
select_exprs += metrics_exprs
labels_expected = [c._df_label_expected for c in select_exprs]
select_exprs = db_engine_spec.make_select_compatible(
groupby_exprs_with_timestamp.values(), select_exprs)
qry = sa.select(select_exprs)
tbl = self.get_from_clause(template_processor)
if (is_sip_38 and metrics) or (not is_sip_38 and not columns):
qry = qry.group_by(*groupby_exprs_with_timestamp.values())
where_clause_and = []
having_clause_and: List = []
for flt in filter: # type: ignore
if not all([flt.get(s) for s in ["col", "op"]]):
continue
col = flt["col"]
op = flt["op"].upper()
col_obj = cols.get(col)
if col_obj:
is_list_target = op in (
utils.FilterOperator.IN.value,
utils.FilterOperator.NOT_IN.value,
)
eq = self.filter_values_handler(
values=flt.get("val"),
target_column_is_numeric=col_obj.is_numeric,
is_list_target=is_list_target,
)
if op in (
utils.FilterOperator.IN.value,
utils.FilterOperator.NOT_IN.value,
):
cond = col_obj.get_sqla_col().in_(eq)
if isinstance(eq, str) and NULL_STRING in eq:
cond = or_(cond, col_obj.get_sqla_col() is None)
if op == utils.FilterOperator.NOT_IN.value:
cond = ~cond
where_clause_and.append(cond)
else:
if col_obj.is_numeric:
eq = utils.cast_to_num(flt["val"])
if op == utils.FilterOperator.EQUALS.value:
where_clause_and.append(col_obj.get_sqla_col() == eq)
elif op == utils.FilterOperator.NOT_EQUALS.value:
where_clause_and.append(col_obj.get_sqla_col() != eq)
elif op == utils.FilterOperator.GREATER_THAN.value:
where_clause_and.append(col_obj.get_sqla_col() > eq)
elif op == utils.FilterOperator.LESS_THAN.value:
where_clause_and.append(col_obj.get_sqla_col() < eq)
elif op == utils.FilterOperator.GREATER_THAN_OR_EQUALS.value:
where_clause_and.append(col_obj.get_sqla_col() >= eq)
elif op == utils.FilterOperator.LESS_THAN_OR_EQUALS.value:
where_clause_and.append(col_obj.get_sqla_col() <= eq)
elif op == utils.FilterOperator.LIKE.value:
where_clause_and.append(
col_obj.get_sqla_col().like(eq))
elif op == utils.FilterOperator.IS_NULL.value:
where_clause_and.append(col_obj.get_sqla_col() == None)
elif op == utils.FilterOperator.IS_NOT_NULL.value:
where_clause_and.append(col_obj.get_sqla_col() != None)
else:
raise Exception(
_("Invalid filter operation type: %(op)s", op=op))
if config["ENABLE_ROW_LEVEL_SECURITY"]:
where_clause_and += self._get_sqla_row_level_filters(
template_processor)
if extras:
where = extras.get("where")
if where:
where = template_processor.process_template(where)
where_clause_and += [sa.text("({})".format(where))]
having = extras.get("having")
if having:
having = template_processor.process_template(having)
having_clause_and += [sa.text("({})".format(having))]
if granularity:
qry = qry.where(and_(*(time_filters + where_clause_and)))
else:
qry = qry.where(and_(*where_clause_and))
qry = qry.having(and_(*having_clause_and))
if not orderby and ((is_sip_38 and metrics) or
(not is_sip_38 and not columns)):
orderby = [(main_metric_expr, not order_desc)]
# To ensure correct handling of the ORDER BY labeling we need to reference the
# metric instance if defined in the SELECT clause.
metrics_exprs_by_label = {m._label: m for m in metrics_exprs}
for col, ascending in orderby:
direction = asc if ascending else desc
if utils.is_adhoc_metric(col):
col = self.adhoc_metric_to_sqla(col, cols)
elif col in cols:
col = cols[col].get_sqla_col()
if isinstance(col, Label) and col._label in metrics_exprs_by_label:
col = metrics_exprs_by_label[col._label]
qry = qry.order_by(direction(col))
if row_limit:
qry = qry.limit(row_limit)
if (is_timeseries and timeseries_limit and not time_groupby_inline
and ((is_sip_38 and columns) or (not is_sip_38 and groupby))):
if self.database.db_engine_spec.allows_joins:
# some sql dialects require for order by expressions
# to also be in the select clause -- others, e.g. vertica,
# require a unique inner alias
inner_main_metric_expr = self.make_sqla_column_compatible(
main_metric_expr, "mme_inner__")
inner_groupby_exprs = []
inner_select_exprs = []
for gby_name, gby_obj in groupby_exprs_sans_timestamp.items():
inner = self.make_sqla_column_compatible(
gby_obj, gby_name + "__")
inner_groupby_exprs.append(inner)
inner_select_exprs.append(inner)
inner_select_exprs += [inner_main_metric_expr]
subq = select(inner_select_exprs).select_from(tbl)
inner_time_filter = dttm_col.get_time_filter(
inner_from_dttm or from_dttm,
inner_to_dttm or to_dttm,
time_range_endpoints,
)
subq = subq.where(
and_(*(where_clause_and + [inner_time_filter])))
subq = subq.group_by(*inner_groupby_exprs)
ob = inner_main_metric_expr
if timeseries_limit_metric:
ob = self._get_timeseries_orderby(timeseries_limit_metric,
metrics_dict, cols)
direction = desc if order_desc else asc
subq = subq.order_by(direction(ob))
subq = subq.limit(timeseries_limit)
on_clause = []
for gby_name, gby_obj in groupby_exprs_sans_timestamp.items():
# in this case the column name, not the alias, needs to be
# conditionally mutated, as it refers to the column alias in
# the inner query
col_name = db_engine_spec.make_label_compatible(gby_name +
"__")
on_clause.append(gby_obj == column(col_name))
tbl = tbl.join(subq.alias(), and_(*on_clause))
else:
if timeseries_limit_metric:
orderby = [(
self._get_timeseries_orderby(timeseries_limit_metric,
metrics_dict, cols),
False,
)]
# run prequery to get top groups
prequery_obj = {
"is_timeseries": False,
"row_limit": timeseries_limit,
"metrics": metrics,
"granularity": granularity,
"from_dttm": inner_from_dttm or from_dttm,
"to_dttm": inner_to_dttm or to_dttm,
"filter": filter,
"orderby": orderby,
"extras": extras,
"columns": columns,
"order_desc": True,
}
if not is_sip_38:
prequery_obj["groupby"] = groupby
result = self.query(prequery_obj)
prequeries.append(result.query)
dimensions = [
c for c in result.df.columns
if c not in metrics and c in groupby_exprs_sans_timestamp
]
top_groups = self._get_top_groups(
result.df, dimensions, groupby_exprs_sans_timestamp)
qry = qry.where(top_groups)
return SqlaQuery(
extra_cache_keys=extra_cache_keys,
labels_expected=labels_expected,
sqla_query=qry.select_from(tbl),
prequeries=prequeries,
)
def __init__(
self,
datasource: Optional[DatasourceDict] = None,
result_type: Optional[ChartDataResultType] = None,
annotation_layers: Optional[List[Dict[str, Any]]] = None,
applied_time_extras: Optional[Dict[str, str]] = None,
apply_fetch_values_predicate: bool = False,
granularity: Optional[str] = None,
metrics: Optional[List[Union[Dict[str, Any], str]]] = None,
groupby: Optional[List[str]] = None,
filters: Optional[List[Dict[str, Any]]] = None,
time_range: Optional[str] = None,
time_shift: Optional[str] = None,
is_timeseries: Optional[bool] = None,
timeseries_limit: int = 0,
row_limit: Optional[int] = None,
row_offset: Optional[int] = None,
timeseries_limit_metric: Optional[Metric] = None,
order_desc: bool = True,
extras: Optional[Dict[str, Any]] = None,
columns: Optional[List[str]] = None,
orderby: Optional[List[OrderBy]] = None,
post_processing: Optional[List[Optional[Dict[str, Any]]]] = None,
is_rowcount: bool = False,
**kwargs: Any,
):
columns = columns or []
groupby = groupby or []
extras = extras or {}
annotation_layers = annotation_layers or []
self.is_rowcount = is_rowcount
self.datasource = None
if datasource:
self.datasource = ConnectorRegistry.get_datasource(
str(datasource["type"]), int(datasource["id"]), db.session)
self.result_type = result_type
self.apply_fetch_values_predicate = apply_fetch_values_predicate or False
self.annotation_layers = [
layer for layer in annotation_layers
# formula annotations don't affect the payload, hence can be dropped
if layer["annotationType"] != "FORMULA"
]
self.applied_time_extras = applied_time_extras or {}
self.granularity = granularity
self.from_dttm, self.to_dttm = get_since_until(
relative_start=extras.get("relative_start",
config["DEFAULT_RELATIVE_START_TIME"]),
relative_end=extras.get("relative_end",
config["DEFAULT_RELATIVE_END_TIME"]),
time_range=time_range,
time_shift=time_shift,
)
# is_timeseries is True if time column is in either columns or groupby
# (both are dimensions)
self.is_timeseries = (is_timeseries if is_timeseries is not None else
DTTM_ALIAS in columns + groupby)
self.time_range = time_range
self.time_shift = parse_human_timedelta(time_shift)
self.post_processing = [
post_proc for post_proc in post_processing or [] if post_proc
]
# Support metric reference/definition in the format of
# 1. 'metric_name' - name of predefined metric
# 2. { label: 'label_name' } - legacy format for a predefined metric
# 3. { expressionType: 'SIMPLE' | 'SQL', ... } - adhoc metric
self.metrics = metrics and [
x if isinstance(x, str) or is_adhoc_metric(x) else
x["label"] # type: ignore
for x in metrics
]
self.row_limit = config["ROW_LIMIT"] if row_limit is None else row_limit
self.row_offset = row_offset or 0
self.filter = filters or []
self.timeseries_limit = timeseries_limit
self.timeseries_limit_metric = timeseries_limit_metric
self.order_desc = order_desc
self.extras = extras
if config["SIP_15_ENABLED"]:
self.extras["time_range_endpoints"] = get_time_range_endpoints(
form_data=self.extras)
self.columns = columns
self.groupby = groupby or []
self.orderby = orderby or []
# rename deprecated fields
for field in DEPRECATED_FIELDS:
if field.old_name in kwargs:
logger.warning(
"The field `%s` is deprecated, please use `%s` instead.",
field.old_name,
field.new_name,
)
value = kwargs[field.old_name]
if value:
if hasattr(self, field.new_name):
logger.warning(
"The field `%s` is already populated, "
"replacing value with contents from `%s`.",
field.new_name,
field.old_name,
)
setattr(self, field.new_name, value)
# move deprecated extras fields to extras
for field in DEPRECATED_EXTRAS_FIELDS:
if field.old_name in kwargs:
logger.warning(
"The field `%s` is deprecated and should "
"be passed to `extras` via the `%s` property.",
field.old_name,
field.new_name,
)
value = kwargs[field.old_name]
if value:
if hasattr(self.extras, field.new_name):
logger.warning(
"The field `%s` is already populated in "
"`extras`, replacing value with contents "
"from `%s`.",
field.new_name,
field.old_name,
)
self.extras[field.new_name] = value
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
def test_is_adhoc_metric():
assert is_adhoc_metric(STR_METRIC) is False
assert is_adhoc_metric(SIMPLE_SUM_ADHOC_METRIC) is True
assert is_adhoc_metric(SQL_ADHOC_METRIC) is True
def __init__( # pylint: disable=too-many-arguments,too-many-locals
self,
query_context: "QueryContext",
annotation_layers: Optional[List[Dict[str, Any]]] = None,
applied_time_extras: Optional[Dict[str, str]] = None,
apply_fetch_values_predicate: bool = False,
columns: Optional[List[str]] = None,
datasource: Optional[DatasourceDict] = None,
extras: Optional[Dict[str, Any]] = None,
filters: Optional[List[QueryObjectFilterClause]] = None,
granularity: Optional[str] = None,
is_rowcount: bool = False,
is_timeseries: Optional[bool] = None,
metrics: Optional[List[Metric]] = None,
order_desc: bool = True,
orderby: Optional[List[OrderBy]] = None,
post_processing: Optional[List[Optional[Dict[str, Any]]]] = None,
result_type: Optional[ChartDataResultType] = None,
row_limit: Optional[int] = None,
row_offset: Optional[int] = None,
series_columns: Optional[List[str]] = None,
series_limit: int = 0,
series_limit_metric: Optional[Metric] = None,
time_range: Optional[str] = None,
time_shift: Optional[str] = None,
**kwargs: Any,
):
columns = columns or []
extras = extras or {}
annotation_layers = annotation_layers or []
self.time_offsets = kwargs.get("time_offsets", [])
self.inner_from_dttm = kwargs.get("inner_from_dttm")
self.inner_to_dttm = kwargs.get("inner_to_dttm")
if series_columns:
self.series_columns = series_columns
elif is_timeseries and metrics:
self.series_columns = columns
else:
self.series_columns = []
self.is_rowcount = is_rowcount
self.datasource = None
if datasource:
self.datasource = ConnectorRegistry.get_datasource(
str(datasource["type"]), int(datasource["id"]), db.session)
self.result_type = result_type or query_context.result_type
self.apply_fetch_values_predicate = apply_fetch_values_predicate or False
self.annotation_layers = [
layer for layer in annotation_layers
# formula annotations don't affect the payload, hence can be dropped
if layer["annotationType"] != "FORMULA"
]
self.applied_time_extras = applied_time_extras or {}
self.granularity = granularity
self.from_dttm, self.to_dttm = get_since_until(
relative_start=extras.get("relative_start",
config["DEFAULT_RELATIVE_START_TIME"]),
relative_end=extras.get("relative_end",
config["DEFAULT_RELATIVE_END_TIME"]),
time_range=time_range,
time_shift=time_shift,
)
# is_timeseries is True if time column is in either columns or groupby
# (both are dimensions)
self.is_timeseries = (is_timeseries if is_timeseries is not None else
DTTM_ALIAS in columns)
self.time_range = time_range
self.time_shift = parse_human_timedelta(time_shift)
self.post_processing = [
post_proc for post_proc in post_processing or [] if post_proc
]
# Support metric reference/definition in the format of
# 1. 'metric_name' - name of predefined metric
# 2. { label: 'label_name' } - legacy format for a predefined metric
# 3. { expressionType: 'SIMPLE' | 'SQL', ... } - adhoc metric
self.metrics = metrics and [
x if isinstance(x, str) or is_adhoc_metric(x) else
x["label"] # type: ignore
for x in metrics
]
default_row_limit = (config["SAMPLES_ROW_LIMIT"]
if self.result_type == ChartDataResultType.SAMPLES
else config["ROW_LIMIT"])
self.row_limit = apply_max_row_limit(row_limit or default_row_limit)
self.row_offset = row_offset or 0
self.filter = filters or []
self.series_limit = series_limit
self.series_limit_metric = series_limit_metric
self.order_desc = order_desc
self.extras = extras
if config["SIP_15_ENABLED"]:
self.extras["time_range_endpoints"] = get_time_range_endpoints(
form_data=self.extras)
self.columns = columns
self.orderby = orderby or []
self._rename_deprecated_fields(kwargs)
self._move_deprecated_extra_fields(kwargs)
def get_sqla_query( # sqla
self,
groupby, metrics,
granularity,
from_dttm, to_dttm,
filter=None, # noqa
is_timeseries=True,
timeseries_limit=15,
timeseries_limit_metric=None,
row_limit=None,
inner_from_dttm=None,
inner_to_dttm=None,
orderby=None,
extras=None,
columns=None,
order_desc=True,
prequeries=None,
is_prequery=False,
):
"""Querying any sqla table from this common interface"""
template_kwargs = {
'from_dttm': from_dttm,
'groupby': groupby,
'metrics': metrics,
'row_limit': row_limit,
'to_dttm': to_dttm,
'filter': filter,
'columns': {col.column_name: col for col in self.columns},
}
template_kwargs.update(self.template_params_dict)
template_processor = self.get_template_processor(**template_kwargs)
db_engine_spec = self.database.db_engine_spec
orderby = orderby or []
# For backward compatibility
if granularity not in self.dttm_cols:
granularity = self.main_dttm_col
# Database spec supports join-free timeslot grouping
time_groupby_inline = db_engine_spec.time_groupby_inline
cols = {col.column_name: col for col in self.columns}
metrics_dict = {m.metric_name: m for m in self.metrics}
if not granularity and is_timeseries:
raise Exception(_(
'Datetime column not provided as part table configuration '
'and is required by this type of chart'))
if not groupby and not metrics and not columns:
raise Exception(_('Empty query?'))
metrics_exprs = []
for m in metrics:
if utils.is_adhoc_metric(m):
metrics_exprs.append(self.adhoc_metric_to_sqla(m, cols))
elif m in metrics_dict:
metrics_exprs.append(metrics_dict.get(m).get_sqla_col())
else:
raise Exception(_("Metric '{}' is not valid".format(m)))
if metrics_exprs:
main_metric_expr = metrics_exprs[0]
else:
main_metric_expr = literal_column('COUNT(*)').label(
db_engine_spec.make_label_compatible('count'))
select_exprs = []
groupby_exprs = []
if groupby:
select_exprs = []
inner_select_exprs = []
inner_groupby_exprs = []
for s in groupby:
col = cols[s]
outer = col.get_sqla_col()
inner = col.get_sqla_col(col.column_name + '__')
groupby_exprs.append(outer)
select_exprs.append(outer)
inner_groupby_exprs.append(inner)
inner_select_exprs.append(inner)
elif columns:
for s in columns:
select_exprs.append(cols[s].get_sqla_col())
metrics_exprs = []
if granularity:
dttm_col = cols[granularity]
time_grain = extras.get('time_grain_sqla')
time_filters = []
if is_timeseries:
timestamp = dttm_col.get_timestamp_expression(time_grain)
select_exprs += [timestamp]
groupby_exprs += [timestamp]
# Use main dttm column to support index with secondary dttm columns
if db_engine_spec.time_secondary_columns and \
self.main_dttm_col in self.dttm_cols and \
self.main_dttm_col != dttm_col.column_name:
time_filters.append(cols[self.main_dttm_col].
get_time_filter(from_dttm, to_dttm))
time_filters.append(dttm_col.get_time_filter(from_dttm, to_dttm))
select_exprs += metrics_exprs
qry = sa.select(select_exprs)
tbl = self.get_from_clause(template_processor)
if not columns:
qry = qry.group_by(*groupby_exprs)
where_clause_and = []
having_clause_and = []
for flt in filter:
if not all([flt.get(s) for s in ['col', 'op']]):
continue
col = flt['col']
op = flt['op']
col_obj = cols.get(col)
if col_obj:
is_list_target = op in ('in', 'not in')
eq = self.filter_values_handler(
flt.get('val'),
target_column_is_numeric=col_obj.is_num,
is_list_target=is_list_target)
if op in ('in', 'not in'):
cond = col_obj.get_sqla_col().in_(eq)
if '<NULL>' in eq:
cond = or_(cond, col_obj.get_sqla_col() == None) # noqa
if op == 'not in':
cond = ~cond
where_clause_and.append(cond)
else:
if col_obj.is_num:
eq = utils.string_to_num(flt['val'])
if op == '==':
where_clause_and.append(col_obj.get_sqla_col() == eq)
elif op == '!=':
where_clause_and.append(col_obj.get_sqla_col() != eq)
elif op == '>':
where_clause_and.append(col_obj.get_sqla_col() > eq)
elif op == '<':
where_clause_and.append(col_obj.get_sqla_col() < eq)
elif op == '>=':
where_clause_and.append(col_obj.get_sqla_col() >= eq)
elif op == '<=':
where_clause_and.append(col_obj.get_sqla_col() <= eq)
elif op == 'LIKE':
where_clause_and.append(col_obj.get_sqla_col().like(eq))
elif op == 'IS NULL':
where_clause_and.append(col_obj.get_sqla_col() == None) # noqa
elif op == 'IS NOT NULL':
where_clause_and.append(
col_obj.get_sqla_col() != None) # noqa
if extras:
where = extras.get('where')
if where:
where = template_processor.process_template(where)
where_clause_and += [sa.text('({})'.format(where))]
having = extras.get('having')
if having:
having = template_processor.process_template(having)
having_clause_and += [sa.text('({})'.format(having))]
if granularity:
qry = qry.where(and_(*(time_filters + where_clause_and)))
else:
qry = qry.where(and_(*where_clause_and))
qry = qry.having(and_(*having_clause_and))
if not orderby and not columns:
orderby = [(main_metric_expr, not order_desc)]
for col, ascending in orderby:
direction = asc if ascending else desc
if utils.is_adhoc_metric(col):
col = self.adhoc_metric_to_sqla(col, cols)
qry = qry.order_by(direction(col))
if row_limit:
qry = qry.limit(row_limit)
if is_timeseries and \
timeseries_limit and groupby and not time_groupby_inline:
if self.database.db_engine_spec.inner_joins:
# some sql dialects require for order by expressions
# to also be in the select clause -- others, e.g. vertica,
# require a unique inner alias
inner_main_metric_expr = main_metric_expr.label('mme_inner__')
inner_select_exprs += [inner_main_metric_expr]
subq = select(inner_select_exprs)
subq = subq.select_from(tbl)
inner_time_filter = dttm_col.get_time_filter(
inner_from_dttm or from_dttm,
inner_to_dttm or to_dttm,
)
subq = subq.where(and_(*(where_clause_and + [inner_time_filter])))
subq = subq.group_by(*inner_groupby_exprs)
ob = inner_main_metric_expr
if timeseries_limit_metric:
if utils.is_adhoc_metric(timeseries_limit_metric):
ob = self.adhoc_metric_to_sqla(timeseries_limit_metric, cols)
elif timeseries_limit_metric in metrics_dict:
timeseries_limit_metric = metrics_dict.get(
timeseries_limit_metric,
)
ob = timeseries_limit_metric.get_sqla_col()
else:
raise Exception(_("Metric '{}' is not valid".format(m)))
direction = desc if order_desc else asc
subq = subq.order_by(direction(ob))
subq = subq.limit(timeseries_limit)
on_clause = []
for i, gb in enumerate(groupby):
on_clause.append(
groupby_exprs[i] == column(gb + '__'))
tbl = tbl.join(subq.alias(), and_(*on_clause))
else:
# run subquery to get top groups
subquery_obj = {
'prequeries': prequeries,
'is_prequery': True,
'is_timeseries': False,
'row_limit': timeseries_limit,
'groupby': groupby,
'metrics': metrics,
'granularity': granularity,
'from_dttm': inner_from_dttm or from_dttm,
'to_dttm': inner_to_dttm or to_dttm,
'filter': filter,
'orderby': orderby,
'extras': extras,
'columns': columns,
'order_desc': True,
}
result = self.query(subquery_obj)
cols = {col.column_name: col for col in self.columns}
dimensions = [
c for c in result.df.columns
if c not in metrics and c in cols
]
top_groups = self._get_top_groups(result.df, dimensions)
qry = qry.where(top_groups)
return qry.select_from(tbl)
