feat(insights): Adds spm function to discover metrics and metrics layer datasets (#75661)

Adding `spm` to the metrics + metrics_layer dataset in discover, to be
used for insights based alerts.

Author: edwardgou-sentry

src/sentry/search/events/datasets/metrics.py

@@ -66,6 +66,10 @@ def field_alias_converter(self) -> Mapping[str, Callable[[str], SelectType]]:
 
     def resolve_metric(self, value: str) -> int:
         metric_id = self.builder.resolve_metric_index(constants.METRICS_MAP.get(value, value))
+        if metric_id is None:
+            metric_id = self.builder.resolve_metric_index(
+                constants.SPAN_METRICS_MAP.get(value, value)
+            )
         if metric_id is None:
             # Maybe this is a custom measurment?
             for measurement in self.builder.custom_measurement_map:
@@ -784,6 +788,12 @@ def function_converter(self) -> Mapping[str, fields.MetricsFunction]:
                     optional_args=[fields.IntervalDefault("interval", 1, None)],
                     default_result_type="rate",
                 ),
+                fields.MetricsFunction(
+                    "spm",
+                    snql_distribution=self._resolve_spm,
+                    optional_args=[fields.IntervalDefault("interval", 1, None)],
+                    default_result_type="rate",
+                ),
                 fields.MetricsFunction(
                     "eps",
                     snql_distribution=self._resolve_eps,
@@ -1973,6 +1983,14 @@ def _resolve_epm(
     ) -> SelectType:
         return self._resolve_rate(60, args, alias, extra_condition)
 
+    def _resolve_spm(
+        self,
+        args: Mapping[str, str | Column | SelectType | int | float],
+        alias: str | None = None,
+        extra_condition: Function | None = None,
+    ) -> SelectType:
+        return self._resolve_rate(60, args, alias, extra_condition, "span.self_time")
+
     def _resolve_eps(
         self,
         args: Mapping[str, str | Column | SelectType | int | float],
@@ -1987,12 +2005,13 @@ def _resolve_rate(
         args: Mapping[str, str | Column | SelectType | int | float],
         alias: str | None = None,
         extra_condition: Function | None = None,
+        metric: str | None = "transaction.duration",
     ) -> SelectType:
         base_condition = Function(
             "equals",
             [
                 Column("metric_id"),
-                self.resolve_metric("transaction.duration"),
+                self.resolve_metric(metric),
             ],
         )
         if extra_condition:

src/sentry/search/events/datasets/metrics_layer.py

@@ -12,7 +12,7 @@
 from sentry.search.events.datasets import field_aliases, filter_aliases, function_aliases
 from sentry.search.events.datasets.metrics import MetricsDatasetConfig
 from sentry.search.events.types import SelectType, WhereType
-from sentry.snuba.metrics.naming_layer.mri import SessionMRI, TransactionMRI
+from sentry.snuba.metrics.naming_layer.mri import SessionMRI, SpanMRI, TransactionMRI
 from sentry.utils.numbers import format_grouped_length
 
 
@@ -401,6 +401,20 @@ def function_converter(self) -> Mapping[str, fields.MetricsFunction]:
                     optional_args=[fields.IntervalDefault("interval", 1, None)],
                     default_result_type="rate",
                 ),
+                fields.MetricsFunction(
+                    "spm",
+                    snql_metric_layer=lambda args, alias: Function(
+                        "rate",
+                        [
+                            Column(SpanMRI.SELF_TIME.value),
+                            args["interval"],
+                            60,
+                        ],
+                        alias,
+                    ),
+                    optional_args=[fields.IntervalDefault("interval", 1, None)],
+                    default_result_type="rate",
+                ),
                 fields.MetricsFunction(
                     "eps",
                     snql_metric_layer=lambda args, alias: Function(

tests/snuba/api/endpoints/test_organization_events_stats_mep.py

@@ -78,6 +78,39 @@ def test_throughput_epm_hour_rollup(self):
             for test in zip(event_counts, rows):
                 assert test[1][1][0]["count"] == test[0] / (3600.0 / 60.0)
 
+    @pytest.mark.querybuilder
+    def test_throughput_spm_hour_rollup(self):
+        # Each of these denotes how many events to create in each hour
+        event_counts = [6, 0, 6, 3, 0, 3]
+        for hour, count in enumerate(event_counts):
+            for minute in range(count):
+                self.store_span_metric(
+                    1,
+                    timestamp=self.day_ago + timedelta(hours=hour, minutes=minute),
+                )
+
+        response = self.do_request(
+            data={
+                "start": iso_format(self.day_ago),
+                "end": iso_format(self.day_ago + timedelta(hours=6)),
+                "interval": "1h",
+                "yAxis": "spm()",
+                "project": self.project.id,
+                "dataset": "metrics",
+                **self.additional_params,
+            },
+        )
+
+        assert response.status_code == 200, response.content
+        data = response.data["data"]
+        assert len(data) == 6
+        assert response.data["meta"]["dataset"] == "metrics"
+
+        rows = data[0:6]
+
+        for test in zip(event_counts, rows):
+            assert test[1][1][0]["count"] == test[0] / (3600.0 / 60.0)
+
     def test_throughput_epm_day_rollup(self):
         # Each of these denotes how many events to create in each minute
         event_counts = [6, 0, 6, 3, 0, 3]
@@ -1152,6 +1185,38 @@ def test_gauge_custom_metric(self):
         for (_, value), expected_value in zip(count, [40, 80, 120, 160, 200, 240]):
             assert value[0]["count"] == expected_value  # type: ignore[index]
 
+    @pytest.mark.querybuilder
+    def test_throughput_spm_hour_rollup(self):
+        # Each of these denotes how many events to create in each hour
+        event_counts = [6, 0, 6, 3, 0, 3]
+        for hour, count in enumerate(event_counts):
+            for minute in range(count):
+                self.store_span_metric(
+                    1,
+                    timestamp=self.day_ago + timedelta(hours=hour, minutes=minute),
+                )
+
+        response = self.do_request(
+            data={
+                "start": iso_format(self.day_ago),
+                "end": iso_format(self.day_ago + timedelta(hours=6)),
+                "interval": "1h",
+                "yAxis": "spm()",
+                "project": self.project.id,
+                "dataset": "metrics",
+            },
+        )
+
+        assert response.status_code == 200, response.content
+        data = response.data["data"]
+        assert len(data) == 6
+        assert response.data["meta"]["dataset"] == "metrics"
+
+        rows = data[0:6]
+
+        for test in zip(event_counts, rows):
+            assert test[1][1][0]["count"] == test[0] / (3600.0 / 60.0)
+
 
 class OrganizationEventsStatsMetricsEnhancedPerformanceEndpointTestWithOnDemandWidgets(
     MetricsEnhancedPerformanceTestCase