Merge branch 'master' into prevent-dup-dimensions

Author: Mark Kuhn

aws_embedded_metrics/logger/metrics_context.py

@@ -58,11 +58,11 @@ def put_metric(self, key: str, value: float, unit: str = None) -> None:
             self.metrics[key] = Metric(value, unit)
 
     @staticmethod
-    def validate_dimension_set(dimension_set: Dict[str, str]) -> None:
+    def validate_dimension_set(dimensions: Dict[str, str]) -> None:
         """
         Validates dimension set length is not more than MAX_DIMENSION_SET_SIZE
         """
-        if len(dimension_set) > MAX_DIMENSION_SET_SIZE:
+        if len(dimensions) > MAX_DIMENSION_SET_SIZE:
             raise DimensionSetExceededError(
                 f"Maximum number of dimensions per dimension set allowed are {MAX_DIMENSION_SET_SIZE}")
 
@@ -87,7 +87,7 @@ def put_dimensions(self, dimension_set: Dict[str, str]) -> None:
 
         self.dimensions.append(dimension_set)
 
-    def set_dimensions(self, dimension_sets: List[Dict[str, str]]) -> None:
+    def set_dimensions(self, dimensionSets: List[Dict[str, str]]) -> None:
         """
         Overwrite all dimensions.
         ```
@@ -98,10 +98,10 @@ def set_dimensions(self, dimension_sets: List[Dict[str, str]]) -> None:
         """
         self.should_use_default_dimensions = False
 
-        for dimension_set in dimension_sets:
-            self.validate_dimension_set(dimension_set)
+        for dimensionSet in dimensionSets:
+            self.validate_dimension_set(dimensionSet)
 
-        self.dimensions = dimension_sets
+        self.dimensions = dimensionSets
 
     def set_default_dimensions(self, default_dimensions: Dict) -> None:
         """

aws_embedded_metrics/serializers/log_serializer.py

@@ -31,10 +31,6 @@ def serialize(context: MetricsContext) -> List[str]:
         dimensions_properties: Dict[str, str] = {}
 
         for dimension_set in context.get_dimensions():
-
-            # Stringify numerical values and strip non-ascii characters from dimension set values
-            dimension_set = {k: str(v).encode('ascii', 'ignore').decode('ascii') for k, v in dimension_set.items()}
-
             keys = list(dimension_set.keys())
             if len(keys) > MAX_DIMENSION_SET_SIZE:
                 err_msg = (f"Maximum number of dimensions per dimension set allowed are {MAX_DIMENSION_SET_SIZE}. "

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name="aws-embedded-metrics",
-    version="2.0.1",
+    version="2.0.0",
     author="Amazon Web Services",
     author_email="[email protected]",
     description="AWS Embedded Metrics Package",

tests/serializer/test_log_serializer.py

@@ -32,56 +32,6 @@ def test_serialize_dimensions():
     assert_json_equality(result_json, expected)
 
 
-def test_serialize_dimensions_with_numeric_value():
-    # arrange
-    expected_key = fake.word()
-    value = fake.random.randrange(0, 100)
-    expected_value = str(value)
-
-    dimension = {}
-    dimension[expected_key] = value
-
-    expected_dimension = {}
-    expected_dimension[expected_key] = expected_value
-
-    expected = {**get_empty_payload(), **expected_dimension}
-    expected["_aws"]["CloudWatchMetrics"][0]["Dimensions"].append([expected_key])
-
-    context = get_context()
-    context.put_dimensions(dimension)
-
-    # act
-    result_json = serializer.serialize(context)[0]
-
-    # assert
-    assert_json_equality(result_json, expected)
-
-
-def test_serialize_dimenions_with_non_ascii_values():
-    # arrange
-    expected_key = fake.word()
-    value = "asciié🤔"
-    expected_value = "ascii"
-
-    dimension = {}
-    dimension[expected_key] = value
-
-    expected_dimension = {}
-    expected_dimension[expected_key] = expected_value
-
-    expected = {**get_empty_payload(), **expected_dimension}
-    expected["_aws"]["CloudWatchMetrics"][0]["Dimensions"].append([expected_key])
-
-    context = get_context()
-    context.put_dimensions(dimension)
-
-    # act
-    result_json = serializer.serialize(context)[0]
-
-    # assert
-    assert_json_equality(result_json, expected)
-
-
 def test_serialize_properties():
     # arrange
     expected_key = fake.word()