feat(metrics): add default_dimensions to single_metric

aws_lambda_powertools/metrics/base.py

@@ -463,7 +463,11 @@ def add_metric(self, name: str, unit: Union[MetricUnit, str], value: float) -> N
 
 @contextmanager
 def single_metric(
-    name: str, unit: MetricUnit, value: float, namespace: Optional[str] = None
+    name: str,
+    unit: MetricUnit,
+    value: float,
+    namespace: Optional[str] = None,
+    default_dimensions: Optional[Dict[str, str]] = None,
 ) -> Generator[SingleMetric, None, None]:
     """Context manager to simplify creation of a single metric
 
@@ -516,6 +520,11 @@ def single_metric(
     try:
         metric: SingleMetric = SingleMetric(namespace=namespace)
         metric.add_metric(name=name, unit=unit, value=value)
+
+        if default_dimensions:
+            for dim_name, dim_value in default_dimensions.items():
+                metric.add_dimension(name=dim_name, value=dim_value)
+
         yield metric
         metric_set = metric.serialize_metric_set()
     finally:

docs/core/metrics.md

@@ -216,6 +216,20 @@ CloudWatch EMF uses the same dimensions across all your metrics. Use `single_met
     --8<-- "examples/metrics/src/single_metric_output.json"
     ```
 
+By default it will skip all previously defined dimensions including default dimensions. Use `default_dimensions` keyword argument if you want to reuse default dimensions or specify custom dimensions from a dictionary.
+
+=== "single_metric_default_dimensions_inherit.json"
+
+    ```json hl_lines="10 15"
+    --8<-- "examples/metrics/src/single_metric_default_dimensions_inherit.py"
+    ```
+
+=== "single_metric_default_dimensions.py"
+
+    ```python hl_lines="12"
+    --8<-- "examples/metrics/src/single_metric_default_dimensions.py"
+    ```
+
 ### Flushing metrics manually
 
 If you prefer not to use `log_metrics` because you might want to encapsulate additional logic when doing so, you can manually flush and clear metrics as follows:

examples/metrics/src/single_metric_default_dimensions.py

@@ -0,0 +1,14 @@
+import os
+
+from aws_lambda_powertools import single_metric
+from aws_lambda_powertools.metrics import MetricUnit
+from aws_lambda_powertools.utilities.typing import LambdaContext
+
+STAGE = os.getenv("STAGE", "dev")
+
+
+def lambda_handler(event: dict, context: LambdaContext):
+    with single_metric(
+        name="RecordsCount", unit=MetricUnit.Count, value=10, default_dimensions={"environment": STAGE}
+    ) as metric:
+        metric.add_dimension(name="TableName", value="Users")

examples/metrics/src/single_metric_default_dimensions_inherit.py

@@ -0,0 +1,17 @@
+import os
+
+from aws_lambda_powertools import single_metric
+from aws_lambda_powertools.metrics import Metrics, MetricUnit
+from aws_lambda_powertools.utilities.typing import LambdaContext
+
+STAGE = os.getenv("STAGE", "dev")
+
+metrics = Metrics()
+metrics.set_default_dimensions(environment=STAGE)
+
+
+def lambda_handler(event: dict, context: LambdaContext):
+    with single_metric(
+        name="RecordsCount", unit=MetricUnit.Count, value=10, default_dimensions=metrics.default_dimensions
+    ) as metric:
+        metric.add_dimension(name="TableName", value="Users")

tests/functional/test_metrics.py

@@ -156,6 +156,38 @@ def test_single_metric_logs_one_metric_only(capsys, metric, dimension, namespace
     assert expected == output
 
 
+def test_single_metric_default_dimensions(capsys, metric, dimension, namespace):
+    # GIVEN we provide default dimensions
+    # WHEN using single_metric context manager
+    default_dimensions = {dimension["name"]: dimension["value"]}
+    with single_metric(namespace=namespace, default_dimensions=default_dimensions, **metric) as my_metric:
+        my_metric.add_metric(name="second_metric", unit="Count", value=1)
+
+    output = capture_metrics_output(capsys)
+    expected = serialize_single_metric(metric=metric, dimension=dimension, namespace=namespace)
+
+    # THEN we should have default dimension added to the metric
+    remove_timestamp(metrics=[output, expected])
+    assert expected == output
+
+
+def test_single_metric_default_dimensions_inherit(capsys, metric, dimension, namespace):
+    # GIVEN we provide Metrics default dimensions
+    # WHEN using single_metric context manager
+    metrics = Metrics()
+    default_dimensions = {dimension["name"]: dimension["value"]}
+    metrics.set_default_dimensions(**default_dimensions)
+    with single_metric(namespace=namespace, default_dimensions=metrics.default_dimensions, **metric) as my_metric:
+        my_metric.add_metric(name="second_metric", unit="Count", value=1)
+
+    output = capture_metrics_output(capsys)
+    expected = serialize_single_metric(metric=metric, dimension=dimension, namespace=namespace)
+
+    # THEN we should have default dimension added to the metric
+    remove_timestamp(metrics=[output, expected])
+    assert expected == output
+
+
 def test_log_metrics(capsys, metrics, dimensions, namespace):
     # GIVEN Metrics is initialized
     my_metrics = Metrics(namespace=namespace)