Support 30 dimensions per dimension set

aws_embedded_metrics/constants.py

@@ -12,6 +12,6 @@
 # limitations under the License.
 
 DEFAULT_NAMESPACE = "aws-embedded-metrics"
-MAX_DIMENSIONS = 9
+MAX_DIMENSION_SET_SIZE = 30
 MAX_METRICS_PER_EVENT = 100
 MAX_DATAPOINTS_PER_METRIC = 100

aws_embedded_metrics/exceptions.py

@@ -0,0 +1,17 @@
+# Copyright 2022 Amazon.com, Inc. or its affiliates.
+# Licensed under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+class DimensionSetExceededError(Exception):
+    def __init__(self, message: str) -> None:
+        # Call the base class constructor with the parameters it needs
+        super().__init__(message)

aws_embedded_metrics/logger/metrics_context.py

@@ -14,6 +14,8 @@
 
 from aws_embedded_metrics import constants, utils
 from aws_embedded_metrics.config import get_config
+from aws_embedded_metrics.constants import MAX_DIMENSION_SET_SIZE
+from aws_embedded_metrics.exceptions import DimensionSetExceededError
 from aws_embedded_metrics.logger.metric import Metric
 from typing import List, Dict, Any
 
@@ -55,6 +57,15 @@ def put_metric(self, key: str, value: float, unit: str = None) -> None:
         else:
             self.metrics[key] = Metric(value, unit)
 
+    @staticmethod
+    def validate_dimension_set(dimensions: Dict[str, str]) -> None:
+        """
+        Validates dimension set length is not more than 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}")
+
     def put_dimensions(self, dimensions: Dict[str, str]) -> None:
         """
         Adds dimensions to the context.
@@ -66,6 +77,8 @@ def put_dimensions(self, dimensions: Dict[str, str]) -> None:
             # TODO add ability to define failure strategy
             return
 
+        self.validate_dimension_set(dimensions)
+
         self.dimensions.append(dimensions)
 
     def set_dimensions(self, dimensionSets: List[Dict[str, str]]) -> None:
@@ -78,6 +91,10 @@ def set_dimensions(self, dimensionSets: List[Dict[str, str]]) -> None:
         ```
         """
         self.should_use_default_dimensions = False
+
+        for dimensionSet in dimensionSets:
+            self.validate_dimension_set(dimensionSet)
+
         self.dimensions = dimensionSets
 
     def set_default_dimensions(self, default_dimensions: Dict) -> None:

aws_embedded_metrics/serializers/log_serializer.py

@@ -15,8 +15,9 @@
 from aws_embedded_metrics.logger.metrics_context import MetricsContext
 from aws_embedded_metrics.serializers import Serializer
 from aws_embedded_metrics.constants import (
-    MAX_DIMENSIONS, MAX_METRICS_PER_EVENT, MAX_DATAPOINTS_PER_METRIC
+    MAX_DIMENSION_SET_SIZE, MAX_METRICS_PER_EVENT, MAX_DATAPOINTS_PER_METRIC
 )
+from aws_embedded_metrics.exceptions import DimensionSetExceededError
 import json
 from typing import Any, Dict, List
 
@@ -31,7 +32,11 @@ def serialize(context: MetricsContext) -> List[str]:
 
         for dimension_set in context.get_dimensions():
             keys = list(dimension_set.keys())
-            dimension_keys.append(keys[0:MAX_DIMENSIONS])
+            if len(keys) > MAX_DIMENSION_SET_SIZE:
+                err_msg = (f"Maximum number of dimensions per dimension set allowed are {MAX_DIMENSION_SET_SIZE}. "
+                           f"Account for default dimensions if not using set_dimensions.")
+                raise DimensionSetExceededError(err_msg)
+            dimension_keys.append(keys)
             dimensions_properties = {**dimensions_properties, **dimension_set}
 
         def create_body() -> Dict[str, Any]:

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name="aws-embedded-metrics",
-    version="1.0.8",
+    version="2.0.0",
     author="Amazon Web Services",
     author_email="jarnance@amazon.com",
     description="AWS Embedded Metrics Package",

tests/logger/test_metrics_context.py

@@ -1,8 +1,10 @@
 from aws_embedded_metrics import config
 from aws_embedded_metrics.logger.metrics_context import MetricsContext
 from aws_embedded_metrics.constants import DEFAULT_NAMESPACE
+from aws_embedded_metrics.exceptions import DimensionSetExceededError
 from importlib import reload
 from faker import Faker
+import pytest
 
 fake = Faker()
 
@@ -42,14 +44,14 @@ def test_put_dimension_adds_to_dimensions():
     # arrange
     context = MetricsContext()
 
-    dimension_key = fake.word()
-    dimension_value = fake.word()
+    dimensions_to_add = 30
+    dimension_set = generate_dimension_set(dimensions_to_add)
 
     # act
-    context.put_dimensions({dimension_key: dimension_value})
+    context.put_dimensions(dimension_set)
 
     # assert
-    assert context.dimensions == [{dimension_key: dimension_value}]
+    assert context.dimensions == [dimension_set]
 
 
 def test_get_dimensions_returns_only_custom_dimensions_if_no_default_dimensions_not_set():
@@ -264,3 +266,32 @@ def test_create_copy_with_context_does_not_repeat_dimensions():
 
     # assert
     assert len(new_context.get_dimensions()) == 1
+
+
+def test_cannot_set_more_than_30_dimensions():
+    context = MetricsContext()
+    dimensions_to_add = 32
+    dimension_set = generate_dimension_set(dimensions_to_add)
+
+    with pytest.raises(DimensionSetExceededError):
+        context.set_dimensions([dimension_set])
+
+
+def test_cannot_put_more_than_30_dimensions():
+    context = MetricsContext()
+    dimensions_to_add = 32
+    dimension_set = generate_dimension_set(dimensions_to_add)
+
+    with pytest.raises(DimensionSetExceededError):
+        context.put_dimensions(dimension_set)
+
+
+# Test utility method
+
+
+def generate_dimension_set(dimensions_to_add):
+    dimension_set = {}
+    for i in range(0, dimensions_to_add):
+        dimension_set[f"{i}"] = fake.word()
+
+    return dimension_set

tests/serializer/test_log_serializer.py

@@ -1,9 +1,11 @@
 from aws_embedded_metrics.config import get_config
+from aws_embedded_metrics.exceptions import DimensionSetExceededError
 from aws_embedded_metrics.logger.metrics_context import MetricsContext
 from aws_embedded_metrics.serializers.log_serializer import LogSerializer
 from collections import Counter
 from faker import Faker
 import json
+import pytest
 
 fake = Faker()
 
@@ -30,37 +32,6 @@ def test_serialize_dimensions():
     assert_json_equality(result_json, expected)
 
 
-def test_cannot_serialize_more_than_9_dimensions():
-    # arrange
-    dimensions = {}
-    dimension_pointers = []
-    allowed_dimensions = 9
-    dimensions_to_add = 15
-
-    for i in range(0, dimensions_to_add):
-        print(i)
-        expected_key = f"{i}"
-        expected_value = fake.word()
-        dimensions[expected_key] = expected_value
-        dimension_pointers.append(expected_key)
-
-    expected_dimensions_pointers = dimension_pointers[0:allowed_dimensions]
-
-    expected = {**get_empty_payload(), **dimensions}
-    expected["_aws"]["CloudWatchMetrics"][0]["Dimensions"].append(
-        expected_dimensions_pointers
-    )
-
-    context = get_context()
-    context.put_dimensions(dimensions)
-
-    # act
-    result_json = serializer.serialize(context)[0]
-
-    # assert
-    assert_json_equality(result_json, expected)
-
-
 def test_serialize_properties():
     # arrange
     expected_key = fake.word()
@@ -79,6 +50,25 @@ def test_serialize_properties():
     assert_json_equality(result_json, expected)
 
 
+def test_default_and_custom_dimensions_combined_limit_exceeded():
+    # While serializing default dimensions are added to the custom dimension set,
+    # and the combined size of the dimension set should not be more than 30
+    dimensions = {}
+    default_dimension_key = fake.word()
+    default_dimension_value = fake.word()
+    custom_dimensions_to_add = 30
+
+    for i in range(0, custom_dimensions_to_add):
+        dimensions[f"{i}"] = fake.word()
+
+    context = get_context()
+    context.set_default_dimensions({default_dimension_key: default_dimension_value})
+    context.put_dimensions(dimensions)
+
+    with pytest.raises(DimensionSetExceededError):
+        serializer.serialize(context)
+
+
 def test_serialize_metrics():
     # arrange
     expected_key = fake.word()