ref(explore): Drop the argument from the trace metrics score formulas

src/sentry/search/eap/trace_metrics/formulas.py

@@ -122,8 +122,6 @@ def per_minute(args: ResolvedArguments, settings: ResolverSettings) -> Column.Bi
 
 WEB_VITALS = ["lcp", "fcp", "cls", "ttfb", "inp"]
 
-TOTAL_SCORE_ARGUMENT = "measurements.score.total"
-
 WEB_VITAL_SCORE_METRICS: dict[str, TraceMetric] = {
     vital: TraceMetric(
         metric_name=f"browser.web_vital.{vital}.score",
@@ -275,13 +273,6 @@ def opportunity_score(_: ResolvedArguments, settings: ResolverSettings) -> Colum
     return operate_multiple_columns(weighted_scores, Column.BinaryFormula.OP_ADD)
 
 
-def _total_score_argument() -> ValueArgumentDefinition:
-    return ValueArgumentDefinition(
-        argument_types={"string"},
-        validator=literal_validator([TOTAL_SCORE_ARGUMENT]),
-    )
-
-
 def _score_metrics(_: ResolvedArguments) -> frozenset[TraceMetric]:
     return frozenset(WEB_VITAL_SCORE_METRICS.values())
 
@@ -391,7 +382,7 @@ def if_combinator(formula: FormulaDefinition) -> FormulaDefinition:
     "performance_score": MultiTraceMetricFormulaDefinition(
         default_search_type="percentage",
         infer_search_type_from_arguments=False,
-        arguments=[_total_score_argument()],
+        arguments=[],
         formula_resolver=performance_score,
         trace_metrics_resolver=_score_metrics,
         processor=none_if_zero_processor,
@@ -401,7 +392,7 @@ def if_combinator(formula: FormulaDefinition) -> FormulaDefinition:
     "opportunity_score": MultiTraceMetricFormulaDefinition(
         default_search_type="percentage",
         infer_search_type_from_arguments=False,
-        arguments=[_total_score_argument()],
+        arguments=[],
         formula_resolver=opportunity_score,
         trace_metrics_resolver=_score_metrics,
         is_aggregate=True,

tests/snuba/api/endpoints/test_organization_events_timeseries_trace_metrics.py

@@ -555,7 +555,7 @@ def test_web_vital_total_performance_score(self) -> None:
                 "start": self.start,
                 "end": self.end,
                 "interval": "1h",
-                "yAxis": "performance_score(measurements.score.total)",
+                "yAxis": "performance_score()",
                 "project": self.project.id,
                 "dataset": "tracemetrics",
             },
@@ -564,7 +564,7 @@ def test_web_vital_total_performance_score(self) -> None:
         assert response.status_code == 200, response.content
         assert len(response.data["timeSeries"]) == 1
         timeseries = response.data["timeSeries"][0]
-        assert timeseries["yAxis"] == "performance_score(measurements.score.total)"
+        assert timeseries["yAxis"] == "performance_score()"
         # only the vitals with data in each bucket contribute their weight (LCP 0.30, CLS 0.15)
         assert [bucket["value"] for bucket in timeseries["values"]] == pytest.approx(
             [(1.0 * 0.30 + 0.5 * 0.15) / 0.45, 0.5, 1.0]
@@ -593,9 +593,9 @@ def test_web_vital_total_opportunity_score_top_events(self) -> None:
                 "start": self.start,
                 "end": self.end,
                 "interval": "1h",
-                "yAxis": "opportunity_score(measurements.score.total)",
+                "yAxis": "opportunity_score()",
                 "groupBy": ["transaction"],
-                "orderby": "-opportunity_score(measurements.score.total)",
+                "orderby": "-opportunity_score()",
                 "project": self.project.id,
                 "dataset": "tracemetrics",
                 "excludeOther": 0,

tests/snuba/api/endpoints/test_organization_events_trace_metrics.py

@@ -1042,7 +1042,7 @@ def test_web_vital_total_performance_score(self) -> None:
 
         response = self.do_request(
             {
-                "field": ["performance_score(measurements.score.total)"],
+                "field": ["performance_score()"],
                 "project": self.project.id,
                 "dataset": self.dataset,
             }
@@ -1054,10 +1054,13 @@ def test_web_vital_total_performance_score(self) -> None:
         assert len(data) == 1
         # only LCP (weight 0.30) and CLS (weight 0.15) have data, so the other weights drop out
         expected = (1.0 * 0.30 + 0.5 * 0.15) / (0.30 + 0.15)
-        assert data[0]["performance_score(measurements.score.total)"] == pytest.approx(expected)
-        assert meta["fields"]["performance_score(measurements.score.total)"] == "percentage"
+        assert data[0]["performance_score()"] == pytest.approx(expected)
+        assert meta["fields"]["performance_score()"] == "percentage"
+
+    def test_web_vital_total_performance_score_legacy_argument(self) -> None:
+        # Queries saved before the argument was dropped still resolve, and the argument is ignored
+        self.store_eap_items([self._create_web_vital_score("lcp", 0.5)])
 
-    def test_web_vital_total_performance_score_without_data(self) -> None:
         response = self.do_request(
             {
                 "field": ["performance_score(measurements.score.total)"],
@@ -1069,7 +1072,21 @@ def test_web_vital_total_performance_score_without_data(self) -> None:
         assert response.status_code == 200, response.content
         data = response.data["data"]
         assert len(data) == 1
-        assert data[0]["performance_score(measurements.score.total)"] is None
+        assert data[0]["performance_score(measurements.score.total)"] == pytest.approx(0.5)
+
+    def test_web_vital_total_performance_score_without_data(self) -> None:
+        response = self.do_request(
+            {
+                "field": ["performance_score()"],
+                "project": self.project.id,
+                "dataset": self.dataset,
+            }
+        )
+
+        assert response.status_code == 200, response.content
+        data = response.data["data"]
+        assert len(data) == 1
+        assert data[0]["performance_score()"] is None
 
     def test_web_vital_total_opportunity_score(self) -> None:
         self.store_eap_items(
@@ -1082,8 +1099,8 @@ def test_web_vital_total_opportunity_score(self) -> None:
 
         response = self.do_request(
             {
-                "field": ["transaction", "opportunity_score(measurements.score.total)"],
-                "orderby": "-opportunity_score(measurements.score.total)",
+                "field": ["transaction", "opportunity_score()"],
+                "orderby": "-opportunity_score()",
                 "project": self.project.id,
                 "dataset": self.dataset,
             }
@@ -1095,9 +1112,9 @@ def test_web_vital_total_opportunity_score(self) -> None:
         assert data[0]["transaction"] == "/slow"
         # (2 scores - 1.0 total ratio) / 3 scores overall; LCP is the only vital with data, so
         # it carries the full weight
-        assert data[0]["opportunity_score(measurements.score.total)"] == pytest.approx(1 / 3)
+        assert data[0]["opportunity_score()"] == pytest.approx(1 / 3)
         assert data[1]["transaction"] == "/fast"
-        assert data[1]["opportunity_score(measurements.score.total)"] == pytest.approx(0)
+        assert data[1]["opportunity_score()"] == pytest.approx(0)
 
     def test_web_vital_scores_alongside_metric_aggregates(self) -> None:
         # Regression guard: the query level metric filter must include the metrics the score
@@ -1115,7 +1132,7 @@ def test_web_vital_scores_alongside_metric_aggregates(self) -> None:
                 "field": [
                     "p75(value,browser.web_vital.cls,distribution,-)",
                     "avg(value,browser.web_vital.lcp.score,distribution,ratio)",
-                    "performance_score(measurements.score.total)",
+                    "performance_score()",
                 ],
                 "project": self.project.id,
                 "dataset": self.dataset,
@@ -1130,4 +1147,4 @@ def test_web_vital_scores_alongside_metric_aggregates(self) -> None:
             "avg(value,browser.web_vital.lcp.score,distribution,ratio)"
         ] == pytest.approx(0.5)
         expected = (0.5 * 0.30 + 0.25 * 0.15) / (0.30 + 0.15)
-        assert data[0]["performance_score(measurements.score.total)"] == pytest.approx(expected)
+        assert data[0]["performance_score()"] == pytest.approx(expected)