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MetricDescription.md

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MetricDescription

Properties

Name Type Description Notes
aggregations List[str] The types of aggregations that can be applied to a series of values. For example, `P25` is 25th percentile. Note that not all aggregations are available for metrics. For example, `Trace count` has only `SUM` as an aggregation whereas `Call Count` has two aggregations, `SUM` and `PER_SECOND`.
default_aggregation str The preselected aggregation for a metric. For example, for `Call latency` the default aggregation is `MEAN`. [optional]
description str A description of the metric. For example, for `Call count` metric, the description would be something like `Number of received calls` [optional]
formatter str * `NUMBER`: Generic number * `BYTES`: Number of bytes * `KILO_BYTES`: Number of kilobytes * `MEGA_BYTES`: Number of megabytes * `PERCENTAGE`: Percentage in scale [0,1] * `PERCENTAGE_100`: Percentage in scale [0,100] * `PERCENTAGE_NO_CAPPING`: Percentage in scale [0,1] but value could exceed 1 for example when metric is aggregated * `PERCENTAGE_100_NO_CAPPING`: Percentage in scale [0,100] but value could exceed 100 for example when metric is aggregated * `LATENCY`: Time in milliseconds, with value of 0 should not be considered a a strict 0, but considered as < 1ms * `NANOS`: Time in nanoseconds * `MILLIS`: Time in milliseconds * `MICROS`: Time in microseconds * `SECONDS`: Time in seconds * `RATE`: Number of occurrences per second * `BYTE_RATE`: Number of bytes per second * `UNDEFINED`: Metric value unit is not known
label str The name of the metric. For example, `Call count`, `Erroneous calls`, `Service count` etc.
metric_id str The unique id of the metric. For example, `calls`, `erroneousCalls`, `latency` etc.

Example

from instana_client.models.metric_description import MetricDescription

# TODO update the JSON string below
json = "{}"
# create an instance of MetricDescription from a JSON string
metric_description_instance = MetricDescription.from_json(json)
# print the JSON string representation of the object
print(MetricDescription.to_json())

# convert the object into a dict
metric_description_dict = metric_description_instance.to_dict()
# create an instance of MetricDescription from a dict
metric_description_from_dict = MetricDescription.from_dict(metric_description_dict)

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