Keep unnamed Rill timeseries expression dimensions addressable

sidemantic/adapters/rill.py

@@ -359,9 +359,21 @@ def _parse_dimension(
         lookup_key_column = dim_def.get("lookup_key_column")
 
         # Derive name following Rill's rules: name -> column -> dimension_<i>.
+        # When an unnamed dimension's SQL expression *is* the timeseries column
+        # (e.g. `dimensions: [{expression: order_date}]` alongside
+        # `timeseries: order_date`), name it after the timeseries column rather
+        # than the positional `dimension_<i>` fallback. Otherwise the generated
+        # time dimension stays addressable only as `dimension_<i>`, the later
+        # auto-create check sees the column already present and skips it, and
+        # `default_time_dimension` (set to the timeseries column) resolves to no
+        # dimension -- causing validate_model to reject the model.
         name = dim_def.get("name")
         if not name:
-            name = column or lookup_key_column or f"dimension_{index}"
+            sql_expr = expression or column or lookup_key_column
+            if timeseries_column and sql_expr == timeseries_column:
+                name = timeseries_column
+            else:
+                name = column or lookup_key_column or f"dimension_{index}"
 
         # `label` is the deprecated alias for `display_name`.
         label = dim_def.get("display_name") or dim_def.get("label")

tests/adapters/rill/test_modern_features.py

@@ -691,5 +691,60 @@ def test_time_comparison_type_records_metadata():
     assert m.meta["rill_type"] == "time_comparison"
 
 
+def test_unnamed_timeseries_expression_dimension_stays_addressable():
+    """An unnamed expression dimension that IS the timeseries keeps the timeseries name.
+
+    Regression: with `timeseries: order_date` and an unnamed expression dimension
+    `{expression: order_date}`, the name fallback produced `dimension_0`. The
+    auto-create check then saw `sql == timeseries_column` and skipped creating an
+    `order_date` dimension, while `default_time_dimension` stayed `order_date` ->
+    validate_model rejected the model because the default time dimension referenced
+    no dimension. The dimension must be addressable under the timeseries name so the
+    default time dimension resolves.
+    """
+    graph = _parse_inline(
+        {
+            "type": "metrics_view",
+            "name": "test",
+            "model": "m",
+            "timeseries": "order_date",
+            "smallest_time_grain": "day",
+            "dimensions": [{"expression": "order_date"}],
+        }
+    )
+    model = graph.models["test"]
+
+    # The unnamed expression dimension is named after the timeseries column, and
+    # exactly one time dimension exists (no duplicate dimension_0 + order_date).
+    time_dim = model.get_dimension("order_date")
+    assert time_dim is not None
+    assert time_dim.sql == "order_date"
+    assert time_dim.type == "time"
+    assert not any(d.name == "dimension_0" for d in model.dimensions)
+
+    # default_time_dimension now resolves, so the model validates.
+    assert model.default_time_dimension == "order_date"
+    assert validate_model(model) == []
+
+
+def test_unnamed_timeseries_expression_dimension_importable_via_semantic_layer():
+    """The CLI-first path (add_model -> validate_model) accepts the model."""
+    graph = _parse_inline(
+        {
+            "type": "metrics_view",
+            "name": "orders",
+            "model": "orders_tbl",
+            "timeseries": "order_date",
+            "smallest_time_grain": "day",
+            "dimensions": [{"expression": "order_date"}],
+            "measures": [{"name": "revenue", "expression": "SUM(amount)"}],
+        }
+    )
+    layer = SemanticLayer()
+    # add_model runs validate_model and raises on failure.
+    layer.add_model(graph.models["orders"])
+    assert layer.get_model("orders").default_time_dimension == "order_date"
+
+
 if __name__ == "__main__":
     pytest.main([__file__, "-v"])