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fix(metrics): raise SchemaValidationError for >8 metric dimensions #1240

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Merged
merged 3 commits into from
Jun 7, 2022
Merged

fix(metrics): raise SchemaValidationError for >8 metric dimensions #1240

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0e8a172
fix(metrics): ensure service uses add_dimension
heitorlessa Jun 7, 2022
22b6943
docs(metrics): corrects schema validation data
heitorlessa Jun 7, 2022
31be209
chore(mypy): ignore false positive
heitorlessa Jun 7, 2022
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2 changes: 1 addition & 1 deletion aws_lambda_powertools/metrics/base.py
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Expand Up @@ -178,7 +178,7 @@ def serialize_metric_set(
metadata = self.metadata_set

if self.service and not self.dimension_set.get("service"):
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Not specific from this PR, but won't the condition not self.dimension_set.get("service") blow up if the key is not present? Shouldn't it be something like not self.dimension_set.get("service", "")?

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no, because .get will return None as the default sentinel value, hence why it's suitable within the if condition here

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technically, and self.dimension_set.get("service") is not None would also work here ;-)

>>> a = {}
>>> not a.get("blah")
True
>>> a.get("blah") is None
True

self.dimension_set["service"] = self.service
self.add_dimension(name="service", value=self.service)

if len(metrics) == 0:
raise SchemaValidationError("Must contain at least one metric.")
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10 changes: 5 additions & 5 deletions docs/core/metrics.md
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Expand Up @@ -30,10 +30,10 @@ If you're new to Amazon CloudWatch, there are two terminologies you must be awar

Metric has two global settings that will be used across all metrics emitted:

Setting | Description | Environment variable | Constructor parameter
------------------------------------------------- | ------------------------------------------------- | ------------------------------------------------- | -------------------------------------------------
**Metric namespace** | Logical container where all metrics will be placed e.g. `ServerlessAirline` | `POWERTOOLS_METRICS_NAMESPACE` | `namespace`
**Service** | Optionally, sets **service** metric dimension across all metrics e.g. `payment` | `POWERTOOLS_SERVICE_NAME` | `service`
| Setting | Description | Environment variable | Constructor parameter |
| -------------------- | ------------------------------------------------------------------------------- | ------------------------------ | --------------------- |
| **Metric namespace** | Logical container where all metrics will be placed e.g. `ServerlessAirline` | `POWERTOOLS_METRICS_NAMESPACE` | `namespace` |
| **Service** | Optionally, sets **service** metric dimension across all metrics e.g. `payment` | `POWERTOOLS_SERVICE_NAME` | `service` |

???+ tip
Use your application or main service as the metric namespace to easily group all metrics.
Expand Down Expand Up @@ -191,7 +191,7 @@ This decorator also **validates**, **serializes**, and **flushes** all your metr
???+ tip "Tip: Metric validation"
If metrics are provided, and any of the following criteria are not met, **`SchemaValidationError`** exception will be raised:

* Maximum of 9 dimensions
* Maximum of 8 user-defined dimensions
* Namespace is set, and no more than one
* Metric units must be [supported by CloudWatch](https://docs.aws.amazon.com/AmazonCloudWatch/latest/APIReference/API_MetricDatum.html)

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6 changes: 4 additions & 2 deletions tests/functional/test_metrics.py
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Expand Up @@ -319,9 +319,11 @@ def test_schema_no_metrics(service, namespace):
my_metrics.serialize_metric_set()


def test_exceed_number_of_dimensions(metric, namespace):
def test_exceed_number_of_dimensions(metric, namespace, monkeypatch):
# GIVEN we we have more dimensions than CloudWatch supports
dimensions = [{"name": f"test_{i}", "value": "test"} for i in range(11)]
# and that service dimension is injected like a user-defined dimension (N+1)
monkeypatch.setenv("POWERTOOLS_SERVICE_NAME", "test_service")
dimensions = [{"name": f"test_{i}", "value": "test"} for i in range(9)]

# WHEN we attempt to serialize them into a valid EMF object
# THEN it should fail validation and raise SchemaValidationError
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