rusackas commented on code in PR #45431:
URL: https://github.com/apache/superset/pull/45431#discussion_r4385328694
##########
superset/connectors/sqla/models.py:
##########
@@ -2593,26 +2593,109 @@ def has_extra_cache_key_calls(self, query_obj:
QueryObjectDict) -> bool: # noqa
templatable_statements.append(extras["where"])
if "having" in extras:
templatable_statements.append(extras["having"])
- if columns := query_obj.get("columns"):
- calculated_columns: dict[str, Any] = {
- c.column_name: c.expression for c in self.columns if
c.expression
- }
- for column_ in columns:
- if utils.is_adhoc_column(column_):
- templatable_statements.append(column_["sqlExpression"])
- elif isinstance(column_, str) and column_ in
calculated_columns:
- templatable_statements.append(calculated_columns[column_])
- if metrics := query_obj.get("metrics"):
- metrics_by_name: dict[str, Any] = {
- m.metric_name: m.expression for m in self.metrics
- }
- for metric in metrics:
- if utils.is_adhoc_metric(metric) and (
- sql := metric.get("sqlExpression")
- ):
- templatable_statements.append(sql)
- elif isinstance(metric, str) and metric in metrics_by_name:
- templatable_statements.append(metrics_by_name[metric])
+ calculated_columns: dict[str, Any] = {
+ c.column_name: c.expression for c in self.columns if c.expression
+ }
+ calculated_columns_by_verbose_name: dict[str, Any] = {
+ c.verbose_name: c.expression
+ for c in self.columns
+ if c.expression and c.verbose_name
+ }
+ metrics_by_name: dict[str, Any] = {
+ m.metric_name: m.expression for m in self.metrics
+ }
+
+ def column_expression(column_: Any) -> Any:
+ """The templatable expression behind an adhoc or calculated
column."""
+ if utils.is_adhoc_column(column_):
+ # ``adhoc_column_to_sqla`` looks the expression up against the
+ # dataset's columns before treating it as SQL of its own, so an
+ # adhoc column that only names a calculated column renders that
+ # column's expression instead of this string. The frontend
+ # builds that shape for an ``x_axis`` column reference.
+ expression = column_["sqlExpression"] or ""
+ return calculated_columns.get(expression.strip(), expression)
+ if isinstance(column_, str):
+ return calculated_columns.get(column_)
+ return None
+
+ def metric_expression(metric: Any) -> Any:
+ """The templatable expression behind an adhoc or saved metric."""
+ if utils.is_adhoc_metric(metric):
+ if sql_expression := metric.get("sqlExpression"):
+ return sql_expression
+ # A SIMPLE adhoc metric has no expression of its own, but
+ # ``adhoc_metric_to_sqla`` wraps its aggregate around the
+ # column's rendered expression, so a macro in a calculated
+ # column reaches the SQL through it.
+ metric_column = metric.get("column") or {}
+ column_name = metric_column.get("column_name")
+ return calculated_columns.get(column_name) if column_name else
None
+ if isinstance(metric, str):
+ return metrics_by_name.get(metric)
+ return None
+
+ def filter_expression(flt: Any) -> Any:
+ """The templatable expression a filter's ``col`` resolves to."""
+ col = flt.get("col")
+ if expression := column_expression(col):
+ return expression
+ if isinstance(col, str):
+ # A filter column that is not a column name is resolved against
+ # the columns' verbose names (right-click "Drill to detail by"
+ # passes those) and then against the saved metrics, for a
+ # filter that becomes a HAVING clause.
+ return calculated_columns_by_verbose_name.get(
+ col
+ ) or metrics_by_name.get(col)
+ return None
+
+ # ``get_sqla_query`` resolves ``groupby`` through the same column
lookup
+ # as ``columns``, and prefers it when both are set, so a calculated
+ # column grouped on is rendered exactly like a selected one.
+ for column_ in [
+ *(query_obj.get("columns") or []),
+ *(query_obj.get("groupby") or []),
+ ]:
+ if expression := column_expression(column_):
+ templatable_statements.append(expression)
+ for metric in query_obj.get("metrics") or []:
+ if expression := metric_expression(metric):
+ templatable_statements.append(expression)
+ # ``get_sqla_query`` renders these through the same template processor
as
+ # the columns and metrics above, so a macro in them must trigger the
+ # extraction too, otherwise two users whose rendered SQL differs share
a
+ # cache key:
+ # - an adhoc metric in ``orderby``, or an ``orderby`` entry naming a
+ # saved metric or calculated column not selected by the query
+ # - ``series_limit_metric`` (and its legacy
``timeseries_limit_metric``
+ # alias), via ``_get_series_orderby``
+ # - a filter whose ``col`` resolves to a calculated column or a saved
+ # metric
+ # - the temporal column named by ``granularity``
+ for order_by in query_obj.get("orderby") or []:
+ entry = order_by[0] if isinstance(order_by, (list, tuple)) else
order_by
+ if expression := metric_expression(entry) or
column_expression(entry):
+ templatable_statements.append(expression)
+ for limit_metric in (
+ query_obj.get("series_limit_metric"),
+ query_obj.get("timeseries_limit_metric"),
+ ):
+ if expression := metric_expression(limit_metric):
+ templatable_statements.append(expression)
+ for flt in query_obj.get("filter") or []:
+ if isinstance(flt, dict) and (expression :=
filter_expression(flt)):
+ templatable_statements.append(expression)
+ if granularity := query_obj.get("granularity"):
+ # The temporal column reaches the select list and the time filter
+ # through ``get_timestamp_expression``, which renders its
+ # expression. ``get_sqla_query`` falls back to ``main_dttm_col``
+ # when ``granularity`` is not one of the temporal columns, so the
+ # column whose expression is rendered is not always the named one.
+ if granularity not in self.dttm_cols:
+ granularity = self.main_dttm_col
+ if granularity and (expression :=
calculated_columns.get(granularity)):
+ templatable_statements.append(expression)
Review Comment:
Picked it up separately, opened #45754 for both of these. Thanks for
tracking them down.
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