fix(tests): telemetry tests take delta for metrics to isolate data to each test

tests/integration/telemetry/collectors/base.py

@@ -6,6 +6,7 @@
 
 """Shared helpers for telemetry test collectors."""
 
+import os
 import time
 from collections.abc import Iterable
 from dataclasses import dataclass
@@ -130,6 +131,53 @@ class BaseTelemetryCollector:
     across both library-client and server modes.
     """
 
+    # Default delay in seconds if OTEL_METRIC_EXPORT_INTERVAL is not set
+    _DEFAULT_BASELINE_STABILIZATION_DELAY = 0.2
+
+    def __init__(self):
+        self._metric_baseline: dict[tuple[str, str], float] = {}
+
+    @classmethod
+    def _get_baseline_stabilization_delay(cls) -> float:
+        """Get baseline stabilization delay from OTEL_METRIC_EXPORT_INTERVAL.
+
+        Adds 1.5x buffer for CI environments.
+        """
+        interval_ms = os.environ.get("OTEL_METRIC_EXPORT_INTERVAL")
+        if interval_ms:
+            try:
+                delay = float(interval_ms) / 1000.0
+            except (ValueError, TypeError):
+                delay = cls._DEFAULT_BASELINE_STABILIZATION_DELAY
+        else:
+            delay = cls._DEFAULT_BASELINE_STABILIZATION_DELAY
+
+        if os.environ.get("CI"):
+            delay *= 1.5
+
+        return delay
+
+    def _get_metric_key(self, metric: MetricStub) -> tuple[str, str]:
+        """Generate a stable key for a metric based on name and attributes."""
+        attrs = metric.attributes or {}
+        attr_key = ",".join(f"{k}={v}" for k, v in sorted(attrs.items()))
+        return (metric.name, attr_key)
+
+    def _compute_metric_delta(self, metric: MetricStub) -> int | float | None:
+        """Compute delta value for a metric from baseline.
+
+        Returns:
+            Delta value if metric was in baseline, absolute value if new, None if unchanged.
+        """
+        metric_key = self._get_metric_key(metric)
+
+        if metric_key in self._metric_baseline:
+            baseline_value = self._metric_baseline[metric_key]
+            delta = metric.value - baseline_value
+            return delta if delta > 0 else None
+        else:
+            return metric.value
+
     def get_spans(
         self,
         expected_count: int | None = None,
@@ -170,41 +218,92 @@ class BaseTelemetryCollector:
         poll_interval: float = 0.05,
         expect_model_id: str | None = None,
     ) -> dict[str, MetricStub]:
-        """Get metrics with polling until metrics are available or timeout is reached."""
+        """Poll until expected metrics are available or timeout is reached.
 
-        # metrics need to be collected since get requests delete stored metrics
+        Returns metrics with delta values computed from baseline.
+        """
         deadline = time.time() + timeout
         min_count = expected_count if expected_count is not None else 1
         accumulated_metrics = {}
-        count_metrics_with_model_id = 0
+        seen_metric_names_with_model_id = set()
 
         while time.time() < deadline:
             current_metrics = self._snapshot_metrics()
             if current_metrics:
                 for metric in current_metrics:
-                    metric_name = metric.name
+                    delta_value = self._compute_metric_delta(metric)
-                    if metric_name not in accumulated_metrics:
+                    if delta_value is None:
-                        accumulated_metrics[metric_name] = metric
+                        continue
-                        if (
-                            expect_model_id
-                            and metric.attributes
-                            and metric.attributes.get("model_id") == expect_model_id
-                        ):
-                            count_metrics_with_model_id += 1
-                    else:
-                        accumulated_metrics[metric_name] = metric
 
-            # Check if we have enough metrics
+                    metric_with_delta = MetricStub(
-            if len(accumulated_metrics) >= min_count:
+                        name=metric.name,
-                if not expect_model_id:
+                        value=delta_value,
-                    return accumulated_metrics
+                        attributes=metric.attributes,
-                if count_metrics_with_model_id >= min_count:
+                    )
+
+                    self._accumulate_metric(
+                        accumulated_metrics,
+                        metric_with_delta,
+                        expect_model_id,
+                        seen_metric_names_with_model_id,
+                    )
+
+            if self._has_enough_metrics(
+                accumulated_metrics, seen_metric_names_with_model_id, min_count, expect_model_id
+            ):
                 return accumulated_metrics
 
             time.sleep(poll_interval)
 
         return accumulated_metrics
 
+    def _accumulate_metric(
+        self,
+        accumulated: dict[str, MetricStub],
+        metric: MetricStub,
+        expect_model_id: str | None,
+        seen_with_model_id: set[str],
+    ) -> None:
+        """Accumulate a metric, preferring those matching expected model_id."""
+        metric_name = metric.name
+        matches_model_id = (
+            expect_model_id and metric.attributes and metric.attributes.get("model_id") == expect_model_id
+        )
+
+        if metric_name not in accumulated:
+            accumulated[metric_name] = metric
+            if matches_model_id:
+                seen_with_model_id.add(metric_name)
+            return
+
+        existing = accumulated[metric_name]
+        existing_matches = (
+            expect_model_id and existing.attributes and existing.attributes.get("model_id") == expect_model_id
+        )
+
+        if matches_model_id and not existing_matches:
+            accumulated[metric_name] = metric
+            seen_with_model_id.add(metric_name)
+        elif matches_model_id == existing_matches:
+            if metric.value > existing.value:
+                accumulated[metric_name] = metric
+            if matches_model_id:
+                seen_with_model_id.add(metric_name)
+
+    def _has_enough_metrics(
+        self,
+        accumulated: dict[str, MetricStub],
+        seen_with_model_id: set[str],
+        min_count: int,
+        expect_model_id: str | None,
+    ) -> bool:
+        """Check if we have collected enough metrics."""
+        if len(accumulated) < min_count:
+            return False
+        if not expect_model_id:
+            return True
+        return len(seen_with_model_id) >= min_count
+
     @staticmethod
     def _convert_attributes_to_dict(attrs: Any) -> dict[str, Any]:
         """Convert various attribute types to a consistent dictionary format.
@@ -289,10 +388,8 @@ class BaseTelemetryCollector:
         if not (metric.data.data_points and len(metric.data.data_points) > 0):
             return None
 
-        # Get the value from the first data point
         data_point = metric.data.data_points[0]
 
-        # Handle different metric types
         if hasattr(data_point, "value"):
             # Counter or Gauge
             value = data_point.value
@@ -302,7 +399,6 @@ class BaseTelemetryCollector:
         else:
             return None
 
-        # Extract attributes if available
         attributes = {}
         if hasattr(data_point, "attributes"):
             attrs = data_point.attributes
@@ -318,47 +414,85 @@ class BaseTelemetryCollector:
         )
 
     @staticmethod
-    def _create_metric_stub_from_protobuf(metric: Any) -> MetricStub | None:
+    def _create_metric_stubs_from_protobuf(metric: Any) -> list[MetricStub]:
-        """Create MetricStub from protobuf metric object.
+        """Create list of MetricStub objects from protobuf metric object.
 
-        Protobuf metrics have a different structure than OpenTelemetry metrics.
+        Protobuf metrics can have sum, gauge, or histogram data. Each metric can have
-        They can have sum, gauge, or histogram data.
+        multiple data points with different attributes, so we return one MetricStub
+        per data point.
+
+        Returns:
+            List of MetricStub objects, one per data point in the metric.
         """
         if not hasattr(metric, "name"):
-            return None
+            return []
+
+        metric_stubs = []
 
-        # Try to extract value from different metric types
         for metric_type in ["sum", "gauge", "histogram"]:
-            if hasattr(metric, metric_type):
+            if not hasattr(metric, metric_type):
+                continue
+
             metric_data = getattr(metric, metric_type)
-                if metric_data and hasattr(metric_data, "data_points"):
+            if not metric_data or not hasattr(metric_data, "data_points"):
+                continue
+
             data_points = metric_data.data_points
-                    if data_points and len(data_points) > 0:
+            if not data_points:
-                        data_point = data_points[0]
+                continue
 
-                        # Extract attributes first (needed for all metric types)
+            for data_point in data_points:
-                        attributes = (
+                attributes = attributes_to_dict(data_point.attributes) if hasattr(data_point, "attributes") else {}
-                            attributes_to_dict(data_point.attributes) if hasattr(data_point, "attributes") else {}
-                        )
 
-                        # Extract value based on metric type
+                value = BaseTelemetryCollector._extract_data_point_value(data_point, metric_type)
-                        if metric_type == "sum":
+                if value is None:
-                            value = data_point.as_int
+                    continue
-                        elif metric_type == "gauge":
-                            value = data_point.as_double
-                        else:  # histogram
-                            value = data_point.sum
 
-                        return MetricStub(
+                metric_stubs.append(
+                    MetricStub(
                         name=metric.name,
                         value=value,
                         attributes=attributes,
                     )
+                )
+
+            # Only process one metric type per metric
+            break
+
+        return metric_stubs
+
+    @staticmethod
+    def _extract_data_point_value(data_point: Any, metric_type: str) -> float | int | None:
+        """Extract value from a protobuf metric data point based on metric type."""
+        if metric_type == "sum":
+            if hasattr(data_point, "as_int"):
+                return data_point.as_int
+            if hasattr(data_point, "as_double"):
+                return data_point.as_double
+        elif metric_type == "gauge":
+            if hasattr(data_point, "as_double"):
+                return data_point.as_double
+        elif metric_type == "histogram":
+            # Histograms use sum field which represents cumulative sum of all recorded values
+            if hasattr(data_point, "sum"):
+                return data_point.sum
+
         return None
 
     def clear(self) -> None:
+        """Clear telemetry data and establish baseline for metric delta computation."""
+        self._metric_baseline.clear()
+
         self._clear_impl()
 
+        delay = self._get_baseline_stabilization_delay()
+        time.sleep(delay)
+        baseline_metrics = self._snapshot_metrics()
+        if baseline_metrics:
+            for metric in baseline_metrics:
+                metric_key = self._get_metric_key(metric)
+                self._metric_baseline[metric_key] = metric.value
+
     def _snapshot_spans(self) -> tuple[SpanStub, ...]:  # pragma: no cover - interface hook
         raise NotImplementedError
 

tests/integration/telemetry/collectors/in_memory.py

@@ -28,6 +28,7 @@ class InMemoryTelemetryCollector(BaseTelemetryCollector):
     """
 
     def __init__(self, span_exporter: InMemorySpanExporter, metric_reader: InMemoryMetricReader) -> None:
+        super().__init__()
         self._span_exporter = span_exporter
         self._metric_reader = metric_reader
 

tests/integration/telemetry/collectors/otlp.py

@@ -21,6 +21,7 @@ from .base import BaseTelemetryCollector, MetricStub, SpanStub, attributes_to_di
 
 class OtlpHttpTestCollector(BaseTelemetryCollector):
     def __init__(self) -> None:
+        super().__init__()
         self._spans: list[SpanStub] = []
         self._metrics: list[MetricStub] = []
         self._lock = threading.Lock()
@@ -60,9 +61,9 @@ class OtlpHttpTestCollector(BaseTelemetryCollector):
         for resource_metrics in request.resource_metrics:
             for scope_metrics in resource_metrics.scope_metrics:
                 for metric in scope_metrics.metrics:
-                    metric_stub = self._create_metric_stub_from_protobuf(metric)
+                    # Handle multiple data points per metric (e.g., different attribute sets)
-                    if metric_stub:
+                    metric_stubs = self._create_metric_stubs_from_protobuf(metric)
-                        new_metrics.append(metric_stub)
+                    new_metrics.extend(metric_stubs)
 
         if not new_metrics:
             return