(superset) 03/04: Improvements

[be...@apache.org]

This is an automated email from the ASF dual-hosted git repository.

betodealmeida pushed a commit to branch single-sv
in repository https://gitbox.apache.org/repos/asf/superset.git

commit 9a22cd49828ac313a921d847ff151a858de7963f
Author: Beto Dealmeida <ro...@dealmeida.net>
AuthorDate: Wed May 13 09:15:51 2026 -0400

    Improvements
---
 superset/semantic_layers/cache.py              | 10 +++----
 tests/unit_tests/semantic_layers/cache_test.py | 38 ++++++++++++++++++++++++++
 2 files changed, 43 insertions(+), 5 deletions(-)

diff --git a/superset/semantic_layers/cache.py b/superset/semantic_layers/cache.py
index ef5cdc68eb3..a57db368c3f 100644
--- a/superset/semantic_layers/cache.py
+++ b/superset/semantic_layers/cache.py
@@ -376,6 +376,8 @@ def _projection_allowed(
         return False
     if entry.group_limit_key:
         return False
+    if query.group_limit is not None:
+        return False
     # Cached HAVING dropped sub-aggregate rows; the rolled-up totals would be
     # off. Conservative: skip the projection path when cached has any HAVING.
     if any(f.type == PredicateType.HAVING for f in entry.filters):
@@ -623,17 +625,15 @@ def _apply_post_processing(
         aggregates = {
             m.name: {
                 "column": m.name,
-                "operator": _AGGREGATION_TO_PANDAS[
-                    # Guarded by ``_projection_allowed`` — non-None and additive.
-                    m.aggregation  # type: ignore[index]
-                ],
+                "operator": _AGGREGATION_TO_PANDAS[m.aggregation],
             }
             for m in query.metrics
         }
         df = aggregate(df, groupby=groupby, aggregates=aggregates)
-        df = _apply_order(df, query.order)
         note_def = "Served from semantic view smart cache (re-aggregated locally)"
 
+    df = _apply_order(df, query.order)
+
     if query.limit is not None:
         df = df.head(query.limit)
 
diff --git a/tests/unit_tests/semantic_layers/cache_test.py b/tests/unit_tests/semantic_layers/cache_test.py
index c3634511cd5..6a43168d8d6 100644
--- a/tests/unit_tests/semantic_layers/cache_test.py
+++ b/tests/unit_tests/semantic_layers/cache_test.py
@@ -27,6 +27,7 @@ from superset_core.semantic_layers.types import (
     AggregationType,
     Dimension,
     Filter,
+    GroupLimit,
     Metric,
     Operator,
     OrderDirection,
@@ -445,6 +446,7 @@ def _projection_query(
     new_filters: set[Filter] | None = None,
     new_limit: int | None = None,
     new_order: Any = None,
+    new_group_limit: GroupLimit | None = None,
 ) -> tuple[CachedEntry, SemanticQuery]:
     cached_q = SemanticQuery(
         metrics=metrics,
@@ -458,6 +460,7 @@ def _projection_query(
         filters=new_filters,
         limit=new_limit,
         order=new_order,
+        group_limit=new_group_limit,
     )
     return entry_from(cached_q), new_q
 
@@ -594,6 +597,24 @@ def test_projection_with_order_and_limit() -> None:
     assert df["sum_x"].tolist() == [100]
 
 
+def test_apply_post_processing_sorts_before_limit_for_non_projection() -> None:
+    cached_df = pd.DataFrame({"a": ["x", "y", "z"], "x": [1.0, 100.0, 50.0]})
+    cached = SemanticResult(
+        requests=[],
+        results=pa.Table.from_pandas(cached_df, preserve_index=False),
+    )
+    new_q = SemanticQuery(
+        metrics=[M_X],
+        dimensions=[COL_A],
+        order=[(M_X, OrderDirection.DESC)],
+        limit=2,
+    )
+
+    out = _apply_post_processing(cached, new_q, set(), False)
+    df = out.results.to_pandas()
+    assert df["x"].tolist() == [100.0, 50.0]
+
+
 def test_projection_rejected_when_metric_aggregation_unknown() -> None:
     entry, new_q = _projection_query(
         metrics=[M_UNKNOWN],
@@ -637,6 +658,23 @@ def test_projection_rejected_when_cached_has_having() -> None:
     assert ok is False
 
 
+def test_projection_rejected_when_new_query_has_group_limit() -> None:
+    group_limit = GroupLimit(
+        dimensions=[COL_A],
+        top=2,
+        metric=M_SUM,
+        direction=OrderDirection.DESC,
+    )
+    entry, new_q = _projection_query(
+        metrics=[M_SUM],
+        new_dimensions=[COL_A],
+        cached_dimensions=[COL_A, COL_B],
+        new_group_limit=group_limit,
+    )
+    ok, _, _ = can_satisfy(entry, new_q)
+    assert ok is False
+
+
 def test_projection_rejected_when_order_references_dropped_dim() -> None:
     entry, new_q = _projection_query(
         metrics=[M_SUM],