Add metrics instrumentation sqlalchemy

.github/workflows/test.yml

@@ -6,7 +6,7 @@ on:
     - 'release/*'
   pull_request:
 env:
-  CORE_REPO_SHA: d0bb12b34b0c487198c935001636b6163485a50f
+  CORE_REPO_SHA: 2d1f0b9f5fce62549d1338882f37b91b95881c75
 
 jobs:
   build:

instrumentation/opentelemetry-instrumentation-sqlalchemy/tests/test_sqlalchemy_metrics.py

@@ -12,18 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Optional, Sequence
-
 import sqlalchemy
 from sqlalchemy.pool import QueuePool
 
 from opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor
-from opentelemetry.sdk.metrics._internal.point import Metric
-from opentelemetry.sdk.metrics.export import (
-    DataPointT,
-    HistogramDataPoint,
-    NumberDataPoint,
-)
 from opentelemetry.test.test_base import TestBase
 
 
@@ -38,93 +30,6 @@ def tearDown(self):
         super().tearDown()
         SQLAlchemyInstrumentor().uninstrument()
 
-    def get_sorted_metrics(self):
-        resource_metrics = (
-            self.memory_metrics_reader.get_metrics_data().resource_metrics
-        )
-
-        all_metrics = []
-        for metrics in resource_metrics:
-            for scope_metrics in metrics.scope_metrics:
-                all_metrics.extend(scope_metrics.metrics)
-
-        return self.sorted_metrics(all_metrics)
-
-    @staticmethod
-    def sorted_metrics(metrics):
-        """
-        Sorts metrics by metric name.
-        """
-        return sorted(
-            metrics,
-            key=lambda m: m.name,
-        )
-
-    def assert_metric_expected(
-        self,
-        metric: Metric,
-        expected_data_points: Sequence[DataPointT],
-        est_value_delta: Optional[float] = 0,
-    ):
-        self.assertEqual(
-            len(expected_data_points), len(metric.data.data_points)
-        )
-        for expected_data_point in expected_data_points:
-            self.assert_data_point_expected(
-                expected_data_point, metric.data.data_points, est_value_delta
-            )
-
-    # pylint: disable=unidiomatic-typecheck
-    @staticmethod
-    def is_data_points_equal(
-        expected_data_point: DataPointT,
-        data_point: DataPointT,
-        est_value_delta: Optional[float] = 0,
-    ):
-        if type(expected_data_point) != type(data_point) or not isinstance(
-            expected_data_point, (HistogramDataPoint, NumberDataPoint)
-        ):
-            return False
-
-        values_diff = None
-        if isinstance(data_point, HistogramDataPoint):
-            values_diff = abs(expected_data_point.sum - data_point.sum)
-        elif isinstance(data_point, NumberDataPoint):
-            values_diff = abs(expected_data_point.value - data_point.value)
-
-        return (
-            values_diff <= est_value_delta
-            and expected_data_point.attributes == dict(data_point.attributes)
-        )
-
-    def assert_data_point_expected(
-        self,
-        expected_data_point: DataPointT,
-        data_points: Sequence[DataPointT],
-        est_value_delta: Optional[float] = 0,
-    ):
-        is_data_point_exist = False
-        for data_point in data_points:
-            if self.is_data_points_equal(
-                expected_data_point, data_point, est_value_delta
-            ):
-                is_data_point_exist = True
-                break
-
-        self.assertTrue(
-            is_data_point_exist,
-            msg=f"Data point {expected_data_point} does not exist",
-        )
-
-    @staticmethod
-    def create_number_data_point(value, attributes):
-        return NumberDataPoint(
-            value=value,
-            attributes=attributes,
-            start_time_unix_nano=0,
-            time_unix_nano=0,
-        )
-
     def assert_pool_idle_used_expected(self, pool_name, idle, used):
         metrics = self.get_sorted_metrics()
         self.assertEqual(len(metrics), 1)