# Previous workaround - verbose and creates multiple EMF objects
metrics1 = EphemeralMetrics()
metrics1.add_dimension(name="environment", value="prod")
metrics1.add_dimension(name="region", value="us-east-1")
metrics1.add_metric(name="SuccessfulRequests", unit=MetricUnit.Count, value=100)

metrics2 = EphemeralMetrics()
metrics2.add_dimension(name="environment", value="prod")
metrics2.add_metric(name="SuccessfulRequests", unit=MetricUnit.Count, value=100)

metrics3 = EphemeralMetrics()
metrics3.add_dimension(name="region", value="us-east-1")
metrics3.add_metric(name="SuccessfulRequests", unit=MetricUnit.Count, value=100)

After:
Users can now easily create multiple dimension sets within a single Metrics instance, enabling CloudWatch to automatically aggregate metrics across different dimension combinations. This creates cleaner code and more efficient metric ingestion:

from aws_lambda_powertools import Metrics
from aws_lambda_powertools.metrics import MetricUnit

metrics = Metrics(namespace="ServerlessAirline", service="booking")

@metrics.log_metrics
def lambda_handler(event, context):
    # Create dimension sets for different aggregation levels
    metrics.add_dimensions({"environment": "prod", "region": "us-east-1"})
    metrics.add_dimensions({"environment": "prod"})
    metrics.add_dimensions({"region": "us-east-1"})
    
    # Single metric value published with all dimension sets
    metrics.add_metric(name="SuccessfulRequests", unit=MetricUnit.Count, value=100)

EMF Output:
{
  "_aws": {
    "CloudWatchMetrics": [{
      "Namespace": "ServerlessAirline",
      "Dimensions": [
        ["service"],
        ["environment", "region"],
        ["environment"],
        ["region"]
      ],
      "Metrics": [{"Name": "SuccessfulRequests", "Unit": "Count"}]
    }]
  },
  "service": "booking",
  "environment": "prod",
  "region": "us-east-1",
  "SuccessfulRequests": 100.0
}

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