File size: 3,164 Bytes
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from mypy_boto3_cloudwatch.type_defs import DimensionTypeDef, MetricDataQueryTypeDef
from aws_lambda_powertools.metrics import MetricUnit
from tests.e2e.utils.data_builder.common import build_random_value
def build_metric_query_data(
namespace: str,
metric_name: str,
period: int = 60,
stat: str = "Sum",
dimensions: Optional[List[DimensionTypeDef]] = None,
) -> List[MetricDataQueryTypeDef]:
"""Create input for CloudWatch GetMetricData API call
Parameters
----------
namespace : str
Metric namespace to search for
metric_name : str
Metric name to search for
period : int, optional
Time period in seconds to search metrics, by default 60
stat : str, optional
Aggregate function to use for results, by default "Sum"
dimensions : Optional[List[DimensionTypeDef]], optional
Metric dimensions to search for, by default None
Returns
-------
List[MetricDataQueryTypeDef]
_description_
"""
dimensions = dimensions or []
data_query: List[MetricDataQueryTypeDef] = [
{
"Id": metric_name.lower(),
"MetricStat": {
"Metric": {"Namespace": namespace, "MetricName": metric_name},
"Period": period,
"Stat": stat,
},
"ReturnData": True,
},
]
if dimensions:
data_query[0]["MetricStat"]["Metric"]["Dimensions"] = dimensions
return data_query
def build_add_metric_input(metric_name: str, value: float, unit: str = MetricUnit.Count.value) -> Dict:
"""Create a metric input to be used with Metrics.add_metric()
Parameters
----------
metric_name : str
metric name
value : float
metric value
unit : str, optional
metric unit, by default Count
Returns
-------
Dict
Metric input
"""
return {"name": metric_name, "unit": unit, "value": value}
def build_multiple_add_metric_input(
metric_name: str,
value: float,
unit: str = MetricUnit.Count.value,
quantity: int = 1,
) -> List[Dict]:
"""Create list of metrics input to be used with Metrics.add_metric()
Parameters
----------
metric_name : str
metric name
value : float
metric value
unit : str, optional
metric unit, by default Count
quantity : int, optional
number of metrics to be created, by default 1
Returns
-------
List[Dict]
List of metrics
"""
return [{"name": metric_name, "unit": unit, "value": value} for _ in range(quantity)]
def build_add_dimensions_input(**dimensions) -> List[DimensionTypeDef]:
"""Create dimensions input to be used with either get_metrics or Metrics.add_dimension()
Parameters
----------
dimensions : str
key=value pair as dimension
Returns
-------
List[DimensionTypeDef]
Metric dimension input
"""
return [{"Name": name, "Value": value} for name, value in dimensions.items()]
def build_metric_name() -> str:
return f"test_metric{build_random_value()}"
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